Abstracts of the 6th International Electronic Conference on Applied Sciences (Part 1) ^†

Abstract

This collection presents the accepted abstracts for the session on Applied Biosciences and Bioengineering; Nanosciences, Chemistry and Materials Science; Computing and Artificial Intelligence as part of the 6th International Electronic Conference on Applied Sciences.

1. Applied Biosciences and Bioengineering

1.1. Assessing Environmental Iron Exposure in Adolescents Through Hair Biomonitoring in an Urban–Industrial Area of Central Spain

• Antonio Peña-Fernández ¹, Manuel Higueras ², Roberto Valiente Borox ³ and María del Carmen Lobo-Bedmar ⁴

¹ 

Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Ctra. Madrid-Barcelona, Km. 33.600, 28871 Alcalá de Henares, Madrid, Spain

² 

Scientific Computation & Technological Innovation Center (SCoTIC), Universidad de La Rioja, Logroño, Spain

³ 

Department of Geology, Geography and Environment, Universidad de Alcalá, C/Colegios 2, 28801 Alcalá de Henares, Spain

⁴ 

Departamento de Investigación Agroambiental. IMIDRA. Finca el Encín, Crta. Madrid-Barcelona Km, 38.2, 28800 Alcalá de Henares, Madrid, Spain

Iron (Fe) is an essential mineral for human health, but chronic overexposure may induce oxidative stress, particularly in adolescents undergoing physiological and endocrine changes. Scalp hair was collected from 97 adolescents (13–16 years old; 68 girls) living in Alcalá de Henares, an urban–industrial municipality near Madrid, and analysed using ICP-MS (LoD = 1.148 µg/g). Fe was detected in 100% of samples and showed clear sex dependency: significantly higher concentrations were recorded in females than in males [median (range), µg/g: 5.524 (3.167–13.262) vs. 4.464 (2.666–6.173); p = 0.000057]. This effect may reflect hormonal differences, as the endocrine system typically becomes active earlier in females. Nevertheless, potential confounders such as dietary iron intake, supplements, or cosmetic hair treatments could also contribute to variability and warrant further consideration. Hair Fe levels were correlated with matched surface soil samples from the city. Fe was detected in 100% of soils, ranging from 12,588 to 54,169 µg/g (median 26,159 µg/g). A modest but significant correlation was observed between hair and soil concentrations (r = 0.21, p < 0.05), stronger in males (r = 0.43, p < 0.01) and females (r = 0.36, p < 0.01). Fe concentrations did not differ significantly across the four residential areas defined in Alcalá (p = 0.370). These results support the value of hair as a non-invasive biomonitoring matrix for Fe exposure and emphasize the importance of sex-disaggregated and geospatial analyses. Establishing age- and sex-specific reference values could facilitate the inclusion of hair biomonitoring in public health surveillance and preventive strategies.

1.2. Molecular Dynamics Simulations Reveal the Structural Mechanisms Behind the Divergent Cytotoxicity of a Bacterial Amyloid Peptide

• Nikhil Agrawal and Emilio Parisini

• Department of Biotechnology, Latvian Institute of Organic Synthesis, Aizkraukles 21, LV-1006 Riga, Latvia

Molecular dynamics simulations decipher the structure–function relationship of a key bacterial amyloid peptide and its cytotoxic mutants. Experimentally, a Lys17Ala mutation reduces α-helicity and cytotoxicity, while an Asp13Ala mutation reduces helicity but paradoxically enhances toxic activity. To elucidate the underlying atomistic mechanisms, we performed extensive sampling for the wild-type (WT) peptide and both single-point mutants (Ala13, Ala17). Our computational strategy included twenty independent classical MD simulations (4 µs per variant) for robust sampling, supplemented by 400 ns of well-tempered metadynamics per variant to explore free energy landscapes and metastable states exhaustively.

Time-structured Independent Component Analysis (TICA) identified the dominant conformational states, confirming the experimental helical trend: WT > Ala17 > Ala13. Our results reveal that residue 17 is critical for C-terminal helix stabilization via specific intramolecular contacts. Conversely, the Ala13 mutation disrupts a key salt bridge, resulting in a pronounced increase in N-terminal flexibility. We propose that this enhanced conformational freedom and an altered amphipathic profile may promote deeper insertion into and more effective disruption of cell membranes, explaining the elevated cytotoxicity despite lower helicity.

This study provides the crucial mechanistic basis for the mutants’ divergent behaviors, highlighting that while one residue acts as a primary structural stabilizer, the other serves as an electrostatic regulator. These detailed insights are crucial for understanding bacterial amyloid toxicity and can guide the rational development of novel anti-staphylococcal therapies.

1.3. Ultrasound-Assisted Extraction of Bioactive Compounds from a Medicinal Plant: Impact on Phenolic Content and Antioxidant Capacity

• Monssef Merdjemak, Hadjer Mabrouki, Rosa Haouche and Djamal Eddine Akretche

• Laboratory of Hydrometallurgy and Inorganique Molecular Chemistry, Faculty of Chemistry, University of Sciences and Technology Houari Boumediene USTHB, BP 32, El-Alia 16111, Bab-Ezzouar, Algiers, Algeria

Ultrasound-assisted extraction (UAE) is increasingly regarded as a promising green technology, offering significant environmental and economic benefits, including lower energy consumption, shorter extraction time, and reduced solvent usage. These advantages make UAE a sustainable alternative to conventional extraction techniques, especially in the field of natural product research. In the present study, UAE was employed to extract phenolic compounds from Verbascum sinuatum leaves. Several solvents were used to compare their efficiency in extracting bioactive compounds. The total phenolic content (TPC) of each extract was measured using the Folin–Ciocalteu method, while antioxidant activity was evaluated through the DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay. Among all tested solvents, 50% ethanol proved to be the most effective, yielding 105.97 ± 3.97 µg of gallic acid equivalents (GAEs) per mg of dry extract. This extract also demonstrated high antioxidant activity, with an IC50 value of 163.65 ± 0.08 µg/mL, indicating strong free radical scavenging potential. These results suggest that UAE, particularly with hydroalcoholic solvents such as 50% ethanol, is highly efficient for recovering phenolic compounds from plant matrices. These findings are consistent with literature data, highlighting the potential of UAE to produce high-quality antioxidant agents in a short time compared to conventional extraction methods. UAE typically reduces extraction time from several days or hours to tens of minutes compared to maceration, as reported for various Verbascum species.

1.4. A Circular Economy Approach: Transforming Pumpkin (Cucurbita pepo) Peel Waste into a Low-Cost Adsorbent for Water Purification

• Amina Ghedjemis and Samia Mokhtari

• Department of Sciences, Teacher Education College of Setif—Messaoud Zeghar, El Eulma 19600, Setif, Algeria

The widespread contamination of aqueous ecosystems by synthetic dyes, discharged from industries such as textiles and printing, poses a severe environmental and health risk due to their toxicity and recalcitrance. Conventional treatment methods often suffer from high operational costs and the generation of secondary pollutants. Addressing this global challenge, this study explores the valorization of agro-industrial Cucurbita pepo peel waste into low-cost, effective bioadsorbents. Embracing a circular economy model, we transformed this abundant by-product into three distinct materials: raw untreated biomass (CPP-R), and two thermally treated versions produced via pyrolysis at 200 °C (CPP-200) and 400 °C (CPP-400).

Comprehensive physicochemical characterization using FTIR, laser granulometry, and point of zero charge (PZC) analysis was conducted. The results confirmed that thermal treatment significantly modifies the material’s surface chemistry, increasing the presence of key functional groups and altering its porous structure. The materials’ efficacy in removing Methylene Blue (MB) from aqueous solutions was then assessed in a batch system, systematically optimizing parameters such as pH, contact time, and initial concentration.

Our findings reveal that thermal treatment markedly enhances adsorption capacity, with the biochar produced at 400 °C (CPP-400) demonstrating superior performance. Adsorption kinetics were best described by the pseudo-second-order model, indicating that chemisorption is the dominant rate-limiting mechanism. Furthermore, equilibrium data correlated strongly with the Freundlich isotherm, suggesting multilayer adsorption onto a heterogeneous surface with energetically diverse binding sites. This research highlights a viable pathway for converting agricultural by-products into a valuable resource for environmental remediation.

1.5. A Dual Adjunct Modulation to Co-Sensitize Osmertinib-Refractory Small-Cell Transformed EGFR-Mutant Non-Small Cell Lung Cancer to Ferroptosis: Leveraging the Potentially Synergistic Effects of Dauricine and Curcumin

• Mustafa Salman

• School of Medicine, RCSI Medical University of Bahrain (MUB), Royal College of Surgeons in Ireland, Bahrain, Adliya 15503, Bahrain

Introduction: Osmertinib, a third-generation EGFR-TKI inhibitor, is known as the first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC). Nevertheless, acquired resistance occurs with histological small-cell transformation in around 15% of cases. This necessitates the development of adjunctive strategies targeting a therapeutic vulnerability found in such phenotypic neuroendocrine progression; ferroptosis susceptibility. Ferroptosis is marked by increased intracellular free ferrous ion concentration, autocatalytic lipid peroxidation, and subsequent cell death. In such cases, the transformed cells exhibit dysregulated iron metabolism, creating a tumor microenvironment (TME) primed for Dauricine and Curcumin in regulating ferroptosis effectors—showing promise as a polytherapy regimen.

Methods: Articles were searched via PubMed and SCOPUS (2015–2025). Search terms included the following: “SCLC transformation”, “EGFR NSCLC”, “osmertinib resistance”. Final selection was made considering mechanistic implications to propose an integrative thematic model.

Results: Dauricine promotes polyamine catabolism and accumulation of ROS via SAT1 stabilization. Curcumin, a pro-autophagic agent downregulates circFOXP1, liberating miR-520a-5p, which decreases SLC7A11 activity. The net synergistic result is an increase in malondialdehyde, alongside impairment of GPX4-mediated antioxidant defense driven by enhanced ROS and glutathione depletion, overburdening the intrinsic antioxidant buffer capacity via positive feedback. Consequently, lipid peroxidation events propagate within the TME—a crucial hallmark of ferroptosis.

Conclusion: In conclusion, this novel strategy demonstrates therapeutic potential by targeting ferroptosis via metabolic and epigenetic nodes—with both compounds demonstrating overlapping modulatory activity, giving rise to possible synergism. This warrants further experimental validation of combined PK-PD parameters for this resistance phenotype using lung cancer xenografts.

1.6. A Quantum-Based Parallel Model Approach for the Classification of Alzheimer’s Disease Stages: An Application on OASIS-1 and ADNI Datasets

• Emine Akpinar and Murat Oduncuoglu

• Department of Physics, Yildiz Technical University, Istanbul, Turkey

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and behavioral impairments caused by the dysfunction or death of nerve cells, severely affecting daily life activities. Current approaches for early detection rely on biomarkers and neuroimaging techniques. Among these, MRI is widely used for the early diagnosis of AD stages; however, the growing volume of data and aging population is making manual processing and analysis increasingly challenging. Recently, quantum artificial intelligence-based methods have emerged as promising tools for overcoming the limitations of classical approaches and achieving more efficient results, particularly in the early diagnosis and stage classification of AD using MRI data. In this study, we propose a quantum-based parallel model, inspired by classical model parallelism, to classify AD stages with high accuracy. The model was evaluated on two widely used datasets in the literature, OASIS-1 and ADNI. It incorporates two distinct quantum circuits with rotational (U3, RX, RY) and entanglement blocks (CNOT, CY, CCNOT) to exploit quantum advantages. The implementation was carried out on the state-vector simulator default.qubit provided by PennyLane 0.35.1, using 15 epochs and a batch size of eight. The experimental results demonstrate that, for the OASIS-1 dataset, the average training and validation accuracies reached 0.90 and 0.93, with training and validation losses of 1.65 and 1.85, respectively. For the ADNI dataset, the average training/validation accuracies were 0.85/0.81, and the corresponding losses were 1.66/1.86. These findings indicate that the proposed model offers a novel, generalizable, and robust approach for classifying stages of complex diseases such as Alzheimer’s disease.

1.7. Ambient Storage of Bioengineered MSC-Based 3D Constructs

• Natalia Trufanova ¹, Galyna Bozhok ², Daria Cherkashina ¹, Oleh Trufanov ³, Olena Revenko ¹, Oleksandr Pakhomov ² and Oleksandr Petrenko ¹

¹ 

Cryobiochemistry Department, Institute for Problems of Cryobiology and Cryomedicine of National Academy of Sciences of Ukraine, 61015 Kharkiv, Ukraine

² 

Cryoendocrinology Department, Institute for Problems of Cryobiology and Cryomedicine of National Academy of Sciences of Ukraine, 61015 Kharkiv, Ukraine

³ 

Cryomicrobiology Department, Institute for Problems of Cryobiology and Cryomedicine of National Academy of Sciences of Ukraine, 61015 Kharkiv, Ukraine

Introduction: Bioengineered mesenchymal stem cell (MSC)-based 3D constructs hold immense promise for regenerative medicine and biomedical research. Realizing their full potential necessitates effective preservation methods. While cryopreservation is the current gold standard, it presents challenges such as reduced viability and demanding cold chain logistics. This study investigates novel approaches for ambient storage of MSCs in spheroids, alginate microspheres (AMSs), and macroporous scaffolds, aiming to develop effective technology for short-term storage.

Methods: Human adipose tissue-derived MSCs were used. Spheroids were formed using the “hanging drop” method, AMSs via electrospraying, and scaffolds by means of plasma cryogelation before cell seeding. Constructs were cultured for 3 days and then stored in complete medium at 22 °C. Viability/apoptosis (6-CFDA/annexin V-Cy3), metabolic activity (resazurin), differentiation potential, and reactive oxygen species (ROS) levels (DCFH-DA) were assessed before and after storage.

Results and Discussion: Ambient storage of MSCs in suspension resulted in significant viability loss. Cells within all 3D constructs demonstrated preserved viability, metabolic activity, and differentiation ability for up to 7 days of storage. Basal metabolic activity was decreased in spheroids and AMSs, and these constructs maintained unchanged levels of annexin-positive cells and ROS throughout storage. Annexin-positive cell number increased minimally in scaffolds and notably in suspension, with ROS rise in suspension after 7-day storage.

Conclusions: This study demonstrates the feasibility of ambient storage for MSC-based 3D constructs and represents a significant step towards developing a safer, cost-effective, cold chain-independent solution for their short-term storage and transportation.

This study was supported by the National Research Foundation of Ukraine (project № 2021.01/0276).

1.8. Analysis of the Antifungal and Biocontrol Potential of Fungi of the Genus Trichoderma

• Adrianna Kubiak ¹, Agnieszka Wolna-Maruwka ¹, Agnieszka Pilarska ², Alicja Niewiadomska ¹ and Katarzyna Panasiewicz ³

¹ 

Department of Soil Science and Microbiology, Poznań University of Life Sciences, Szydłowska 50, 60-656 Poznań, Poland

² 

Department of Hydraulic and Sanitary Engineering, Poznań University of Life Sciences, Piątkowska 94A, 60-649 Poznań, Poland

³ 

Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland

Modern agriculture’s main challenge is increasing crop production while meeting the requirements of EU programs for sustainable agriculture, environmental protection, and human health. An innovative solution to this problem is the development of biopreparations based on microorganisms, which are responsible for improving the physiological processes of plants. The aim of this research was to select Trichoderma sp. fungi, which will form the basis of biofungicides and biostimulants.

The first step of the research was to isolate Trichoderma spp. from environmental samples and analyze their growth under varying environmental conditions. Next, the ability of fungi to produce metabolites and phytohormones that increase plant biological potential was determined. In addition, the antagonistic properties of Trichoderma sp. against fungal pathogens, which significantly reduce crop yields, were analyzed using a breeding method.

Based on the results, the 40 isolates had a high tolerance level to different temperatures, pH, and salinity of the medium. The results of spectrophotometric analyses showed that fungi produce dehydrogenases, cellulases, proteases, ureases, phosphatases, organic acids, and phytohormones, which are responsible for the transformation of carbon, nitrogen, and phosphorus compounds in the soil, as well as the improvement of physiological processes in crop plants. In addition, Trichoderma sp. showed a high degree of antagonism towards fungal pathogens.

Analyses of the properties of Trichoderma sp. allowed the selection of four isolates that will be tested in field studies for their effects on plant growth, which will allow the construction of biopreparations to assist in the protection and development of crop plants.

1.9. Anti-Inflammatory and Anti-Oxidative Effects of Prosopis africana Leaf Extract in Experimentally Induced Obesity in Rats

• Sophia Uyo Chibuogwu ¹, Ezinne Cynthia Nwani ¹, Chikamara Benedicta Chinagoro ¹ and Christian Chibuogwu ^1,2

¹ 

Department of Biochemistry, University of Nigeria, Nsukka, Enugu, Nigeria

² 

Institute for Drug-Herbal Medicine-Excipient Research and Development, University of Nigeria, Nsukka, Nigeria

Many therapeutic and nutritional benefits have been attributed to the different parts of Prosopis africana in numerous studies. This study investigated the effect of Prosopis africana leaf extract on inflammatory and oxidative stress markers in rats experimentally induced with obesity. Twenty Wistar albino rats (n = 5) were divided into a non-obese control group and three obese groups. The obese groups were first maintained on a high-fat diet for ten weeks before treatment. One of the obese groups served as the untreated control, while the remaining two groups were treated with 200 mg/kg and 400 mg/kg of the P. africana leaf extract (with return to normal diet), respectively. Serum TNF-α, IFN-γ, and MDA levels were measured after 28 days of treatment. The results showed that the untreated obese group had significantly (p < 0.05) elevated levels of TNF-α, IFN-γ, and MDA compared to the non-obese control. In contrast to the untreated obese group, the treatment groups exhibited significantly reduced levels of these parameters, with the most pronounced effect observed at the highest extract dose. This result highlights the potential therapeutic benefits of P. africana leaves in reducing obesity-related inflammatory processes, further supporting the health benefits that have been reported with the use of P. africana leaves.

1.10. Biocatalysis Meets Green Extraction: A Case Study on Origanum dictamnus L.

• Zafeiria Lemoni ¹, Rosa Leka ¹, Theopisti Lymperopoulou ² and Diomi Mamma ¹

¹ 

Biotechnology Laboratory, School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 9 Iroon Polytechniou Str, 15780 Athens, Greece

² 

Processes and Products Quality Control Horizontal Laboratory, School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 9 Iroon Polytechniou Str, 15780 Athens, Greece

Origanum dictamnus L. is a medicinal plant known for its rich content in bioactive compounds. The plant cell wall consists of structural polysaccharides such as cellulose, hemicellulose, pectin, along with lignin, proteins and bioactive compounds. These compounds are trapped within the plant cell wall or free in the cytocol of the plant cell. Enzyme-assisted extraction (EAE) is a green technology that relies on the enzymes ability to selectively degrade the plant cell wall, thereby facilitating the release of the bioactive compounds. In the present study, EAE of bioactive compounds from the leaves of Origanum dictamnus L. was applied using the commercial enzyme preparation Pectinex^® Ultra Color (Novozymes). A Taguchi experimental design was employed to determine the optimal EAE conditions. The variables were enzyme loading (50, 100, and 200 U/mg), solid-to-liquid ratio (1, 4, and 7% w/v), and extraction time (1, 3, and 6 h). The responses were total phenolic content (TPC) and total flavonoid content (TFC). TPC was determined using the Folin–Ciocalteu method and TFC with the aluminum chloride method. Kinetic modelling of the extraction process for the optimum extract was carried out using first-order, second-order, Peleg’s, and power law models. EAE achieved the highest TPC yield 153.4 ± 3.4 mg GAE/g DW and TFC yield 81.3 ± 3.7 mg CAE/g DW at 1% w/v, 200 U/mg, and 1 h, outperforming the conventional ethanol–water extraction up to 20%. These findings highlight EAE as an efficient technique with strong potential for scale-up and integration into industrial processes for the production of natural bioactive-rich extracts from Origanum Dictamnus L.

1.11. BsEndo9: A Thermostable GH9 Endoglucanase with Broad Substrate Specificity from Bacillus safensis

• Konstantinos Evangeliou ¹, Elpida Maragkozoglou ², Georgia Georgakopoulou ¹, Panayiotis D. Glekas ³, Dimitris G. Hatzinikolaou ⁴ and Diomi Mamma ¹

¹ 

Biotechnology Laboratory, Sector of Synthesis and Development of Industrial Processes (IV), School of Chemical Engineering, National Technical University of Athens, 9 Iroon Polytechneiou, Zografou, 15772 Athens, Greece

² 

Biotechnology Department, IFP Energies Nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France

³ 

Systems Biology Program, Centro Nacional de Biotecnologia (CNB-CSIC), C. Darwin, 3, Fuencarral-El Pardo, 28049 Cantoblanco, Madrid, Spain

⁴ 

Enzyme and Microbial Biotechnology Unit, Department of Biology, National and Kapodistrian University of Athens, Zografou Campus, 15784 Athens, Greece

The exploitation of lignocellulosic biomass, driven by cellulolytic enzymes, offers a sustainable route for converting plant-based waste into biofuels, biochemicals, and other value-added products.

BsEndo9 is a novel glycoside hydrolase family 9 (GH9) endoglucanase identified in Bacillus safensis ATHUBA63, a soil-derived strain from the Attica region of Greece. The gene encoding BsEndo9 was amplified via PCR and heterologously expressed in E. coli BL21 (DE3) competent cells. The recombinant enzyme was purified using His-Tag-assisted chromatography and its molecular weight (69 kDa) was confirmed via SDS-PAGE. BsEndo9 exhibited optimal activity at 60 °C and pH 6.0, retaining over 85% of activity after 48 h within the pH range 5.0–8.0. Thermal inactivation studies revealed a half-life of 74.53 min at 60 °C and an inactivation energy (E_(a)d) of 198.51 kJ/mol. Additional thermodynamic parameters (ΔH*, ΔS*, ΔG*) were also determined. Enzyme activity was enhanced by Mg²⁺ and stable in the presence of several metal ions (K⁺, Na⁺, Fe²⁺, Ca²⁺, Ba²⁺, Co²⁺, Mn²⁺, Ni²⁺, Zn²⁺), as well as EDTA and SDS, though inhibited by 5 mM Fe³⁺ and Cu²⁺. Substrate specificity was examined against amorphous cellulose substrates: carboxymethyl cellulose (CMC), β-glucan (barley) and phosphoric acid-swollen cellulose (PASC). Kinetic parameters (K_m and V_max) were determined for carboxymethyl cellulose (CMC). Structural prediction using AlphaFold3 (beta) indicated a modular structure with a GH9 catalytic domain and a CBM3 module, consistent with efficient cellulases.

1.12. Characterization of Pressure–Volume Dynamics in Cuffed Endotracheal Tubes for Effective Airway Pressure Management: A Benchtop Study

• Kieran Chase MacMillan ¹, Patrick Michael Burns ² and Sundeep Singh ¹

¹ 

Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

² 

Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

Cuffed endotracheal tubes (ETTs) are widely used in critical care settings to provide life-saving mechanical ventilation to patients undergoing surgery or experiencing respiratory distress. However, improper inflation and inadequate monitoring of ETT cuff pressure can lead to postoperative complications such as sore throat, tracheal mucosal injury, and ventilator-associated pneumonia. The recommended cuff pressure range to reduce the chances of any complications is 20–30 cmH₂O. This study aims to characterize the inflation characteristics of different sizes of commercially available ETT to improve our understanding of their cuff compliance dynamics. Two benchtop tests were performed: (1) unrestricted inflation to measure intracuff pressure, inflation volume, and cuff diameter of the ETT, and (2) restricted inflation within a rigid tracheal analog to capture the relationship between intracuff pressure and tracheal wall contact pressure. Using volume and pressure data from these tests, cuff compliance was calculated to measure each cuff’s inflation characteristics. In comparing data from both tests, it was found that the cuff compliance is higher for high-volume cuffs than medium-volume cuffs in the unrestricted tests, but is found to be higher for medium-volume cuffs in the restricted tests. Unrestricted data also revealed significant manufacturing differences across cuff sizes, with no uniform pattern between sizes of either cuff type. These findings highlight critical differences in inflation characteristics between ETT types and sizes, underscoring the dangers of generalizing ETT behavior and emphasizing the importance of continuous intracuff pressure monitoring to reduce the risk of complications and improve patient care.

1.13. Concentration-Dependent Anti-Inflammatory Effects of Dimethyl Sulfoxide in Macrophages In Vitro

• Martina Toldo ¹, Eugenio Refrigeri ^1,2, Golnar Eftekhari ¹, Stefano Toldo ¹ and Jazmin Kelly ¹

¹ 

Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA 22903, USA

² 

Unit of Cardiovascular Science Department of Medicine, Campus Bio-Medico University, Via Alvaro del Portillo, 200, 00128 Rome, Italy

Introduction. Dimethyl sulfoxide (DMSO) is a solvent widely used in biomedical research. It is known to interfere with cell signaling, but at a final concentration of 1% or lower is often deemed safe. One of the applications of DMSO is to dissolve hydrophobic compounds. During the characterization of a novel hydrophobic anti-inflammatory compound, we found that dilutions of 4 orders of magnitude of the compound diluted in DMSO, used at a 1% final concentration in cell culture media, strongly inhibited the lipopolysaccharide (LPS) induction of Interleukin (IL)-1b and Tumor Necrosis Factor (TNF)-a secretion without a dose-dependent effect (p < 0.001 for all doses).

Hypothesis. We hypothesized that DMSO could inhibit LPS-dependent cytokine release.

Methods. We stimulated mouse macrophages (J774.1 cells) with LPS (1 µg/mL, 6 h, to activate NF-kB-dependent gene expression) and administered ATP (5 mM, 30 min). LPS promotes TNF-a secretion and pro-IL-1b intracellular production, while ATP induces caspase-1-dependent IL-1 b maturation and release. Cells were exposed to increasing concentrations of DMSO (0.2, 0.5, 1.0%). We collected the cell supernatants and performed ELISA assays to measure IL-1b and TNF-a production.

Results. The 1% dose of DMSO significantly reduced IL-1β secretion (205 ± 12 vs. 709 ± 7 pg/mL; p < 0.0001); 0.5% and 0.2% DMSO had no effect. DMSO at concentrations of 1.0% and 0.5% reduced TNF-α (1348 ± 37 and 2672 ± 25 vs. 3125 ± 27 pg/mL; all p < 0.0001); 0.2% DMSO had no effect on TNF-α release.

Conclusion. DMSO induces a dose-dependent inhibition of IL-1β and TNF-α by reducing LPS/NF-kB-dependent gene expression.

1.14. CRISPR-Engineered Universal CAR T-Cells: A Scalable Solution for Rapid Cancer Treatment

• Sheetal Sandip Buddhadev, Jhanvi Chauhan, Dave Purva, Belim Sahir, Maheta Shivam and Meghnathi Omishgiri

• Faculty of Pharmacy, Noble University, Junagadh 362310, Gujarat, India

CAR T-cell therapy has been a massive win against some of the toughest blood cancers. But there is a real sticking point. Every single treatment is a one-off, handcrafted from a patient’s own cells. That process takes too long, costs far too much, and frankly, depends on the patient having T-cells strong enough for the job after they have already been through punishing therapies.

So, our goal has been to move from making these one-by-one to having them ready on the shelf. The intention with allogeneic therapy is to use cells from healthy donors to build a stockpile of a standardized, reliable treatment. This makes a powerful therapy available to patients in days, not weeks, at a fraction of the cost. Of course, the big question is safety—how do you stop the donor cells from attacking the patient? That is where the elegant part comes in. Using CRISPR gene-editing, we make one tiny, precise tweak: we just snip out the T-cell receptor. By removing that one piece, the new cells no longer see the patient’s body as foreign, solving the graft-versus-host disease problem and making a universal therapy a reality.

This paper involves allogeneic CRISPR-engineered CAR T-cells that provide standardized, affordable, and ready-to-use cancer immunotherapy, overcoming delays, costs, and graft-versus-host limitations.

1.15. Design of Experiment-Based Optimization of Pulsed Field Ablation Electrodes for Treating Cardiac Arrhythmias: A Computational Study

• Angela Torres Navarrete ^1,2 and Sundeep Singh ¹

¹ 

Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

² 

School of Engineering and Sciences, Tecnologico de Monterrey, Jalisco, Mexico

Cardiac arrhythmias, defined as irregular heartbeats caused by disrupted electrical signaling, remain a major clinical challenge worldwide. Pulsed field ablation (PFA) has recently emerged as a promising treatment due to its ability to selectively ablate arrhythmic tissue while minimizing injury to surrounding structures, offering advantages over thermal methods such as radiofrequency and cryoablation. PFA induces irreversible electroporation by delivering short, high-voltage pulses, leading to targeted cell death. Despite its recent FDA approval and encouraging clinical data, standardized electrode and waveform designs are still lacking to ensure consistent outcomes. This study employs a finite element method (FEM) integrated with a design of experiments (DOE) framework to optimize PFA electrode design. The coupled multiphysics model incorporates electrical, thermal, and fluid dynamic equations to capture the complex interactions during treatment. Using a Taguchi DOE approach, parametric studies were conducted to evaluate the effect of three critical variables in a bipolar electrode configuration (contact depth, active electrode length, and electrode diameter) on PFA outcomes. The computational model was validated against previously reported ex vivo data. Analysis of variance (ANOVA) was used to quantify the effect of each design variable on the irreversible ablation volume and maximum cardiac tissue temperature. Furthermore, simplified statistical correlations were developed to predict ablation volume within the studied design space, enabling rapid predictions without reliance on computationally expensive FEM simulations. These findings highlight the importance of computational modeling in advancing the preclinical development of PFA by providing actionable insights into electrode design strategies and their impact on treatment efficacy and safety.

1.16. Effects of Support Stays in a Soft Knee Brace on Muscle Activity During Jogging

• Kodai Kitagawa ¹, Riki Kosaka ¹, Chikamune Wada ² and Hiroaki Yamamoto ³

¹ 

Department of Industrial Systems Engineering, National Institute of Technology, Hachinohe College, Hachinohe, Japan

² 

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

³ 

Department of Physical Therapy, Fukuoka Tenjin Medical Rehabilitation Academy, Japan

Introduction: Soft knee braces can be used to improve stability and lower limb loads in various activities such as jogging. Support stays are installed in soft knee braces to maintain knee posture. However, support stays often make the wearing of soft knee braces uncomfortable. To wear a soft knee brace continuously, it is necessary to minimize the frequency of use of the support stays to maintain comfortability for wearing. Thus, activities that require support stays should be investigated and selected. This study aimed to investigate the effects of support stays on jogging with a soft knee brace. Methods: In the experiment, 10 young males as participants were asked to perform 10 s of jogging on a treadmill (5.7 km/h) with different brace conditions (with and without support stays). Muscle activities of quadriceps femoris and hamstrings during jogging were measured using an electromyograph. Results: The results showed that there were no significant differences in muscle activities between patients with and without support stays. Conclusions: These results indicate possibility that support stays are not necessary in short-term jogging to maintain the comfort of wearing. In the future, the effect of the support stays of soft knee braces should be investigated for more difficult or longer activities. Acknowledgements: The authors received only soft knee braces for the experiments from Nippon Sigmax Co., Ltd.

1.17. Electrochemical Immunosensor for the Determination of Fibronectin as a Breast Cancer Diagnosis Biomarker

• Yuliana García ¹, Matías Regiart ², Francisco G. Ortega ^3,4, Gonzalo R. Tortella ⁵ and Martín A. Fernández Baldo ²

¹ 

Instituto de Química de San Luis, INQUISAL (UNSL—CONICET), Facultad de Química, Bioquímica y Farmacia, Universidad Nacional de San Luis, Av. Ejército de los Andes 950, San Luis D5700BWS, Argentina

² 

Facultad de Química, Bioquímica y Farmacia, Instituto de Química de San Luis, INQUISAL (UNSL—CONICET), Universidad Nacional de San Luis, Av. Ejército de los Andes 950, San Luis D5700BWS, Argentina

³ 

Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, Spain

⁴ 

Center for Genomics and Oncological Research—Centro de Genómica e Investigación Oncológica (GENYO), 18016 Granada, Spain

⁵ 

Centro de Excelencia en Investigación Biotecnológica Aplicada al Medio Ambiente (CIBAMA), Facultad de Ingeniería y Ciencias, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4811230, Chile

Introduction: Cancer is one of the leading causes of death worldwide. Deaths from cancer are mainly due to metastasis. The metastatic process involves the spread of cells from the primary tumor to distant locations, either within the same organ or to other organs. This is generally responsible for patient death as it affects the proper functioning of organs. The present work reports an electrochemical immunosensor based on magnetic nanoparticles (MNPs) that can determine the fibronectin (FN) biomarker in epithelial extracellular vesicle (EpEV) patient samples.

Methods: Our method employs MNPs as an immobilization platform. In this work, we report an electrochemical sandwich-type assay for assessing FN + EpEVs in the early stages of breast cancer. Through the immobilization of the monoclonal anti-FN on NH₂-MNPs, its incubation with EpEVs, and a conjugated antibody labeled with horseradish peroxidase was performed. The amperometric detection of the affinity reaction was performed using disposable screen-printed carbon electrodes (SPCEs) and the hydroquinone (HQ)/H₂O₂ system.

Results: The detection limit for the proposed sensor and the commercial ELISA test were 8 pg mL⁻¹ and 0.1 ng mL⁻¹, and the intra- and inter-assay coefficients of variation were below 3.80% and 6.51%, respectively. Moreover, the total analysis time was around 30 min.

Conclusions: Our immunosensor could be an interesting analytical tool for breast cancer diagnosis and prognosis.

1.18. Engineered Nanostructured Surfaces for Dual Antibacterial and Cell-Guiding Applications in Biomedical Devices

• Paolo Pellegrino ^1,2, Isabella Farella ¹, Valeria De Matteis ^1,3, Mariafrancesca Cascione ^1,2, Stefania Villani ⁴, Matteo Calcagnile ³, Lorenzo Vincenti ^1,2, Fabio Quaranta ¹, Pietro Alifano ³, Antonio Della Torre ¹, Christian Demitri ³ and Rosaria Rinaldi ^1,2

¹ 

Institute for Microelectronics and Microsystems (IMM), CNR, Via Monteroni, 73100 Lecce, Italy

² 

Department of Mathematics and Physics “E. De Giorgi”, University of Salento, Via Monteroni, 73100 Lecce, Italy

³ 

Department of Experimental Medicine, University of Salento, Via Monteroni, 73100 Lecce, Italy

⁴ 

Department of Engineering for Innovation (DII), Campus Ecotekne, University of Salento, Via Monteroni, 73100 Lecce, Italy

The rise of antibiotic-resistant pathogens, including Escherichia coli, presents a pressing global health concern, demanding innovative, non-chemical strategies to combat microbial infections. Among these, nanostructured surfaces inspired by natural bactericidal topographies offer a promising route. In this work, we present a robust approach to fabricate highly controlled nanopatterns—specifically nanogratings and nanopillar arrays—on poly(methyl methacrylate) (PMMA) substrates using Electron Beam Lithography, with pitch sizes varying from 160 to 210 nm.

Following detailed surface characterization, the interaction between these engineered nanotopographies and E. coli was assessed, focusing on bacterial adhesion, viability, and mechanical integrity. Advanced imaging techniques including Atomic Force Microscopy (AFM), Ultra High-Resolution Scanning Electron Microscopy (UHR-SEM), and Focused Ion Beam (FIB) milling enabled the observation of nanoscale morphological changes and structural damage to bacterial membranes, shedding light on the physical mechanisms behind their antibacterial activity.

Beyond antimicrobial functionality, these nanostructured surfaces were evaluated for their compatibility with human cells to explore their potential for biomedical applications. Cell adhesion, proliferation, and alignment were studied to determine cellular responses to the topographic features. The results confirm the feasibility of using a single platform to investigate and modulate both bacterial inhibition and guided cell behavior.

This study highlights a multifunctional surface engineering strategy that integrates bactericidal performance with cell-instructive capabilities, paving the way for next-generation implantable devices with enhanced infection resistance and improved tissue integration.

1.19. Epigenetic Switches for Next-Generation Genomic Intervention: From Synthetic Biology to Personalized Medicine

• Sheetal Sandip Buddhadev, Dave Purva, Jhanvi Chauhan, Belim Sahir, Maheta Shivam and Meghnathi Omishgiri

• Faculty of Pharmacy, Noble University, Junagadh 362310, Gujarat, India

Epigenetic engineering is a novel aspect of genomic control, which makes such regulation with precision, programmability, and reversibility capable of controlling gene expression far beyond the capabilities of other genome methods. The method is essential to the development of personalized medicine, synthetic biology, and functional genomics in that it allows for a change in chromatin states without causing permanent changes to DNA, so it may potentially address irreversible mutagenesis.

Programmable proteins (such as deactivated Cas9 (dCas9), zinc-finger domains, and TALE fusions) are used as epigenetic switches to activate or silence epigenetic marks to create tunable and heritable gene expression. Experimental evidence of Saccharomyces cerevisiae has shown that epigenetic switching provides a selective benefit during unstable environments by swapping cellular identity states as fast as possible by switching the expression state of genes, but also adapts cellular identity in genetic silence by avoiding genetic mutations. Modular CRISPR-dCas9 systems have been used to target methylation and demethylation stability at specific loci within genomic systems (e.g., BACH2, HNF1A, IL6ST, MGAT3) to induce long-lasting transcriptional effects up to 30 days following transfection in mammalian systems. Also, optimization of dCas9-fusion protein expression reduces off-target epigenomic activities, thus increasing specificity and biosafety.

All this evidence shows that combinatorially designed high-resolution epigenetic switches coupled with advances in synthetic biology revolutionize genomic interventions. The modality is a safer, reversible, and more accurate modality for personalized therapies and disease modeling, as well as more flexible and specific synthetic biology applications due to higher functional flexibility and specificity.

1.20. Evaluating the Effectiveness of a Boundary Detection System (BDS) for Indoor Wheelchair Training

• Chi-Chau Chan, King-Pong YU, Kwok-Keung Chan, Fai Poon, Ka-Leung Chan and Wai-Ling Ma

• Community Rehabilitation Service Support Center (CRSSC), Hospital Authority, Hong Kong 999077, China

Powered wheelchair maneuvering skill is essential for independence and safety in people with mobility impairment. However, real-time monitoring of maneuver compliance under training or assessment is challenging for therapists, affecting intervention effectiveness and assessment objectiveness. To overcome this and digitalize users’ performance, rehabilitation engineers at the Hospital Authority Community Rehabilitation Service Support Centre (CRSSC) created a Boundary Detection System (BDS); this study assesses the effectiveness of the BDS, comparing system and manual counting.

BDS utilized wheelchair-clamped webcams recording real-time video of wheels and boundaries. Through computer vision algorithms, wheels (grey) and boundaries (yellow) were separated through color filtering, with contours identified using binary masking. Boundary violations were registered when wheel contours intersected a dilated boundary contour during indoor training within a training area. Subjects were asked to complete clockwise and counterclockwise circles for three laps without cues. Human ethics approval was acquired from the Central Institutional Review Board (Ref. No. KC/KE-23-0216/ER-1).

In total, 13 male and 13 female (mean age = 67.3 ± 10.2) wheelchair users were recruited. The system-detected boundary violations (8.74 ± 7.25) differ significantly (p < 0.001) from the manual counting method (7.22 ± 6.86), representing the high sensitivity of the proposed system. Overall, an average of 2.64 ± 2.48 boundary violations that lasted less than 0.5 s wascaptured by the system and validated by video inspection, which shows the system’s ability to eliminate human error.

This pilot study illustrates the viability of the BDS as a computer vision-based, scalable solution for objective wheelchair training monitoring. Future research will advance algorithmic accuracy and investigate integrations with clinical rehabilitation practice to modernize intervention effectiveness and objectiveness.

1.21. Evaluation of the Antimicrobial and Anti-Inflammatory Activity of Lippia javanica Against Pathogenic Microorganisms

• Patrícia Branco ^1,2, Djanimila Viegas ¹, Vandércia Escovalo ¹ and Elisabete Muchagato Maurício ^1,3

¹ 

BIORG—Bioengineering and Sustainability Research Group, Faculdade de Engenharia, Universidade Lusófona, Av. Campo Grande 376, 1749-024 Lisbon, Portugal

² 

Linking Landscape, Environment, Agriculture and Food (LEAF), Associated Laboratory TERRA, Instituto Superior de Agronomia, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal

³ 

CBIOS—Research Center for Biosciences & Health Technologies, Universidade Lusófona, Campo Grande 376, 1749-024 Lisbon, Portugal

The contamination of cosmetics and herbal products by pathogenic microorganisms presents a significant public health concern, especially in developing regions where quality control may be limited. This study aimed to evaluate the antimicrobial and anti-inflammatory properties of Lippia javanica, a medicinal plant traditionally used in Mozambique, against microbial contaminants commonly found in cosmetic products. Ethanolic extracts of L. javanica were tested for antimicrobial activity using agar well diffusion and broth microdilution methods against reference strains of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans. Minimum inhibitory concentrations (MICs) were determined to evaluate potency. For anti-inflammatory assessment, a COX-2 (cyclooxygenase-2) inhibition screening assay was conducted using a commercial ELISA-based method, which measures the reduction in PGF2α levels in the presence of test extracts.

The results revealed that L. javanica exhibits broad-spectrum antimicrobial activity, with strong inhibitory effects, particularly against S. aureus and E. coli, and MIC values ranging from 6.25 to 50 mg/mL. In the COX-2 inhibition assay, the extracts demonstrated dose-dependent suppression of COX-2 activity, indicating potential anti-inflammatory effects. The reduction in PGF2α production suggests the presence of bioactive compounds that may interfere with prostaglandin synthesis pathways.

These findings provide scientific support for the traditional use of L. javanica in treating infectious and inflammatory conditions. The results also highlight its potential as a source of natural antimicrobial and anti-inflammatory agents.

1.22. Evaluation of the Chemical Composition and the Histochemical Localization of Salvia nemorosa L. Essential Oils

• Stanislava Ivanova ^1,2, Ralitsa Parova ¹, Zoya Dzhakova ¹, Diana Karcheva-Bahchevanska ^1,2 and Kalin Ivanov ^1,2

¹ 

Department of Pharmacognosy and Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria

² 

Research Institute, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria

Introduction: Salvia is the largest genus within the Lamiaceae family, comprising over 1000 species of considerable importance to the pharmaceutical, food, and cosmetic industries. Salvia nemorosa L. (S. nemorosa) is a less explored representative of this genus, associated with antioxidant, analgesic, antimicrobial, antifungal, antidiabetic, and acetylcholinesterase inhibitory activities. This study aimed to evaluate the phytochemical profiles of essential oils (EOs) obtained from four wild populations of S. nemorosa L. collected across different regions of Bulgaria. Methods: The phytochemical profiles of the four samples were analyzed using gas chromatography-mass spectrometry (GC–MS). In addition, histochemical analysis of lipid accumulation was carried out with Sudan III staining, and sections were examined under a light microscope equipped with a digital camera and image processing software. Results: GC–MS analyses revealed the presence of sesquiterpene hydrocarbons, monoterpene hydrocarbons, oxygenated sesquiterpenes, and oxygenated monoterpenes. The predominant compounds were germacrene D (17.64–41.34%), β-caryophyllene (9.50–22.38%), and caryophyllene oxide (4.66–7.35%), with other notable constituents including sabinene (6.88–21.89%), bicyclogermacrene (7.67%), and phytol (5.21%). Based on the high content of these compounds, the EOs may exhibit considerable antioxidant, antimicrobial, and anti-inflammatory activities. Histochemical analysis confirmed the presence of lipid structures, as indicated by orange-stained droplets. These findings provide valuable insights into the phytochemistry of S. nemorosa L., highlighting its promise for future pharmacological and industrial applications.

1.23. Exploring Heat Resilience in Cotton Through Integrated Morpho-Physiological and Biochemical Analysis

• Muhammad Affan Akram ¹, Muhammad Abubakkar Azmat ¹, Saman Arshad ¹ and Sajjid Mehmood Nadeem ²

¹ 

Department of Plant Breeding and Genetics, University of Agriculture Faisalabad Sub Campus Burewala, Burewala 61010, Pakistan

² 

Department of Soil Science, University of Agriculture Faisalabad Sub Campus Burewala, Burewala 61010, Pakistan

The major fiber crop cotton (Gossypium hirsutum L.) holds worldwide economic value yet its production faces increasing threats from the temperature rises caused by climate change. Heat stress, particularly during the reproductive phase, impairs floral development, boll retention, and fiber elongation, resulting in yield and quality losses. This study aimed to identify heat-tolerant upland cotton genotypes through the combined evaluation of morphological and physiological traits under field conditions reflecting regional thermal extremes. The experiment was conducted at the Cotton Research Station, Faisalabad, under both optimal and elevated temperature regimes. Significant genotypic variation was observed in yield-related parameters, including boll retention, number of bolls per plant, seed cotton yield, and plant height. High temperature reduced photosystem II efficiency and relative water content, while tolerant genotypes maintained higher chlorophyll stability, photosynthetic rate, and overall plant vigor. The results indicate that sustained photosynthetic efficiency and water balance under heat stress are key factors contributing to thermotolerance. Future studies involving biochemical assays, such as antioxidant enzyme activity and osmolyte accumulation, will be undertaken to validate the physiological basis of heat tolerance further and improve screening accuracy for resilient cotton cultivars. Hence, integrated approach is expected to provide reliable basis for selection of heat resilient cotton genotypes.

1.24. Extraction, Purification, and Partial Characterization of Novel Serine Protease Inhibitors from Aegle marmelos Against Drug-Resistant Staphylococcus aureus

• Rudra Awdhesh Kumar Mishra and Gothandam Kodiveri Muthukaliannan

• School of Biosciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India

Serine protease inhibitor (AMPI) was extracted from Aegle marmelos fruit pulp to explore its antimicrobial potential. Extraction was performed using a phosphate buffer with protease inhibitors, followed by ammonium sulfate precipitation and chromatographic purification using DEAE-cellulose ion-exchange and Sephadex G-75 and G-50 gel filtration columns. Biochemical characterization of AMPI showed that it retained inhibitory activity over a wide temperature range, with optimal performance at 30 °C. Activity decreased sharply above 50 °C, indicating thermal sensitivity beyond physiological limits. pH profiling revealed maximal stability between pH 6 and 8, with a notable decline under strongly acidic or alkaline conditions. AMPI maintained its function in the presence of non-ionic surfactants, but its activity was diminished by certain metal ions, particularly Fe³⁺ and Cu²⁺, suggesting potential oxidative vulnerability. AMPI was tested against S. aureus strains for its ability to inhibit and eradicate biofilms. At higher concentrations, the inhibitor demonstrated significant anti-biofilm effects, with MSSA showing the highest response. Biofilm eradication at 8× MIC levels exceeded 80% in certain strains, supporting AMPI’s potential as a biofilm-targeting agent. Membrane permeabilization studies using standard fluorescence-based methods showed that AMPI induced gradual damage to both the outer and inner membranes in a time-dependent manner. To evaluate the regulatory impact of AMPI, sRNA expression profiling was conducted using quantitative real-time PCR. At 12 h post treatment, early upregulation of RNAIII, Teg41, and Teg49 was observed in MSSA, possibly reflecting a transient stress response. By 24 h, however, the expression of all tested sRNAs was markedly downregulated across MSSA, MRSA, and MDR-SA.

1.25. Forensic Entomology Insights: Effects of Cocaine and Carbamate on Cadaveric Entomofauna and Postmortem Interval Estimation

• Maria Luiza Cavallari ¹ and José Eduardo Tolezano ²

¹ 

School of Nursing, FASIG—IGESP Faculty of Health Sciences, São Paulo 01301-000, Brazil

² 

Parasitology and Mycology Center, Adolfo Lutz Institute, São Paulo 01246-000, Brazil

Forensic entomology utilizes insect succession patterns to estimate the postmortem interval (PMI), a vital tool in criminal investigations, especially for corpses in advanced decomposition. This study explored how toxic substances—cocaine and carbamate (“chumbinho”)—influence decomposition and entomofauna succession in pig carcasses (Sus scrofa domesticus) in an urban setting in São Paulo, Brazil. Three carcasses (two experimental, one control) were euthanized, intoxicated accordingly, and exposed in cages with interception traps from July to September 2016. Over this period, 15,870 adult insects (Diptera, Coleoptera, Hymenoptera, Lepidoptera) and 1952 immature insects (Diptera, Hymenoptera) were collected and analyzed. Cocaine accelerated larval colonization within 24 h, compared to Day 2 in the control, while carbamate delayed it until Day 15, suggesting differential toxicological impacts on insect behavior. The cocaine-exposed carcass showed the highest insect attraction, though species visitation did not differ significantly across models. Decomposition phases progressed synchronously in all carcasses, unaffected by the toxins. These substances altered PMI estimation: cocaine shortened it, while carbamate extended it, potentially skewing forensic timelines. The findings underscore the need to account for toxicological factors in PMI calculations, enhancing the reliability of entomological evidence in death investigations involving drug overdoses or poisoning. This research bridges applied biosciences and forensic science, offering practical insights for criminalistics.

1.26. Green and Biocompatible Chitosan Nanoparticles for Enhanced Delivery of Bioactive Molecules

• Annalisa Bianco

• Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Via Arnesano, 73100 Lecce, LE, Italy

A green and biocompatible nanoparticle system based on chitosan was developed via a one-step Ionic Gelation approach, eliminating the use of organic solvents and toxic crosslinkers. The resulting nanoparticles (NPs) were subsequently functionalized with epigallocatechin gallate (EGCG), a polyphenolic compound with potent antioxidant and anticancer activity. Morphological analysis using Atomic Force Microscopy (AFM) and Transmission Electron Microscopy (TEM) confirmed the formation of uniformly sized, spherical nanoparticles. Biocompatibility was assessed through in vitro cytotoxicity assays and quantification of intracellular Reactive Oxygen Species (ROS), demonstrating minimal cytotoxicity and significant antioxidant capacity. Cellular uptake studies performed on tumor cell lines at various time points using flow cytometry and confocal microscopy revealed efficient internalization of the EGCG-functionalized NPs. Subsequent evaluations of cell viability and morphostructural changes showed a pronounced antiproliferative effect and cellular remodeling. This nanostructured delivery system, developed through a sustainable and biocompatible synthesis route, demonstrates a low biological impact while offering high potential for improving the delivery and therapeutic efficacy of bioactive compounds. Its ability to enhance cellular uptake and reduce oxidative stress highlights its value as an innovative platform for targeted drug delivery and advanced pharmacological strategies. The preliminary results demostrated the potential use of NPs as effective and safe drug delivery system, paving the way to their use in future nanomedicine.

1.27. Green Tea Polyphenols Inhibit Helicobacter pylori Virulence Factors CagA and VacA: A Computational Study Using Molecular Docking

• Suraj N. Mali ¹, Parasuram Ayyappan ², Janavi Rao ², Susmita Yadav ³, Pratik Ganpule ² and Rakesh Somani ²

¹ 

School of Pharmacy, D.Y. Patil University (Deemed to be University), Sector 7, Nerul, Navi Mumbai 400706, India

² 

Department of Pharmaceutical Chemistry, School of Pharmacy, D.Y. Patil University (Deemed to be University), Nerul, Navi Mumbai 400706, India

³ 

Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Mesra, Jharkhand, India

Helicobacter pylori (H. pylori) is a Gram-negative bacterium that colonizes the gastric mucosa and plays a central role in the development of peptic ulcers, chronic gastritis, and gastric cancer. A significant portion of the global population is infected, emphasizing the need for early detection and effective treatment strategies. Natural compounds, especially green tea flavonoids (GTFs), have recently gained attention as promising anti-H. pylori agents. Among them, Epigallocatechin Gallate (EGCG), the major catechin in green tea, shows notable inhibitory effects on H. pylori growth and colonization through mechanisms such as membrane disruption, enzyme inhibition, and oxidative stress modulation. Additionally, the antioxidant and anti-inflammatory properties of GTFs may help mitigate H. pylori-induced gastric inflammation. In this computational study, we employed molecular docking simulations using AutoDock Vina to evaluate the binding potential of key GTFs against H. pylori virulence factors CagA and VacA. EGCG exhibited a docking score of −198.72 kcal/mol against CagA, while Theaflavin-3-gallate scored −177.19 kcal/mol against VacA, indicating strong binding affinities. These in silico results suggest that GTFs, particularly EGCG, may effectively target H. pylori pathogenic proteins and impair their function. Overall, the findings support the therapeutic potential of GTFs and highlight the value of computational approaches in screening natural antibacterial compounds.

1.28. Hyaluronic Acid as Burn Healing Modulator: Experience in Rat Model

• Daria Cherkashina, Olena Revenko, Serhii Balak and Oleksandr Petrenko

• Institute for Problems of Cryobiology and Cryomedicine, National Academy of Sciences of Ukraine, Kharkiv, Ukraine

Hyaluronic acid (HA) is a well-known key extracellular matrix component, which as therapeutic agent is believed to participate in healing and may be used for treatment delivery to the injury site. Burns pose significant clinical and aesthetic challenges requiring rapid skin restoration to avoid complications. This study compares HA efficacy in burn healing in rats against commonly used panthenol-containing gel (PCG).

Deep burns were induced in rats with 200 °C copper plate. Pharmaceutical-grade HA (1.8%) or PCG were applied 24 h post-injury, with spontaneous healing as control. Wound recovery was assessed over 28 days. Collagens of I and III types were quantified using PicroSirius Red staining and ImageJ software.

Control and PCG-treated burns closed significantly just by day 21, remaining open till the end. HA decreased wound area a little faster from day 3, slightly outperforming PCG. Both treatments showed granulation from day 7 and epithelialization by day 28. Initial collagen content in HA-group dropped paradoxically by day 3, matched PCG by day 14 in total number, but with abnormal distribution in derma. By day 28, both groups exceeded control collagen levels. HA suppressed systemic leukocyte number to normal levels by day 14, while drastically enhancing local inflammation till this observation point.

Observed strong stimulation of local inflammatory reaction with HA can be explained by some data suggesting that HA can either stimulate or suppress immune response in skin, depending on its source and physicochemical characteristics; therefore, further research is needed for wide clinical applications of HA as a wound cover.

1.29. Investigation of Adherent 3T3 Cell Line Growth on Electrospun Polyacrylonitrile–Polyethylene Oxide (PAN-PEO) Nanofiber Nonwovens with Varying Material Ratios

• Timo Grothe, Minh Anh Nguyen, Ewin Tanzli, Bennet Brockhagen, Yusuf Topuz, Hannah Blattner and Anke Rattenholl

• Faculty of Engineering and Mathematics, Bielefeld University of Applied Sciences and Arts, 33619 Bielefeld, Germany

The fourth industrial revolution encompasses not only advanced production technologies, but also fields such as nanotechnology, biotechnology, and new materials. Tissue engineering, which involves using various types of fiber scaffolds to grow tissue, overlaps with three of the four fields when nanofibers are used as tissue. Due to their high surface-to-volume ratio, nanofibers are a promising area of research in this field. Biocompatibility plays a decisive role here, which is why the weakly biocompatible polyacrylonitrile (PAN) nanofibers, commonly found in current research, must be combined with biocompatible polymers such as polyethylene oxide (PEO).

Here, we investigated the influence of different molecular weights of PEO in combination with different PAN-PEO ratios on an adherent 3T3 cell line. In order not to compromise the stability of the spun nanofibers, the ratios 9:1 and 8:2 were chosen, which represent a trade-off between PEO content and fiber stability. The molecular weights investigated were 40 kDa, 300 kDa, and 1000 kDa to cover a broad range of available molecular weights. The percentage of the stained cells that grew over the surface was used as a key parameter for successful cell growth and was examined by using optical analysis. The cell growth was investigated after one, two, and three days.

The combination of a 9:1 ratio and a molecular weight of 300 kDa showed the highest percentage of growth. Further investigations with the atomic force microscope showed that the pores created by the water-soluble PEO were particularly uniform in this sample, which provided the cells with the possibility of stronger adhesion.

1.30. Iron Oxidation by Ferroxidans Cultures: The Effect of Operational Conditions

• Maria Teresa Pines-Pozo, Ester Lopez Fernandez, Javier Llanos and Francisco Jesus Fernandez-Morales

• Chemical Engineering Department, Faculty of Chemical Sciences and Technologies, University Castilla-La Mancha, Ciudad Real, Avda. Camilo Jose Cela S/N, 13071, Spain

In this work, the effect of the most relevant operational conditions on ferrous iron oxidation by a mixed culture of ferroxidans microorganisms was thoroughly investigated at the laboratory scale. The study focused on identifying the optimal values for key parameters such as pH and ferrous iron concentration, which are crucial for maximizing the efficiency of the biological oxidation processes taking place. Through a series of controlled experiments, it was determined that pH and ferrous iron concentration significantly affect the activity of the mixed culture of ferroxidans microorganisms, leading to more efficient iron oxidation.

To further understand and predict the behavior of the mixed culture of ferroxidans microorganisms under various operational conditions, a Monod-based mathematical model was developed. This matematical model incorporates the main biological process parameters and describes the kinetic behavior of the mixed culture of ferroxidans microorganisms in bioreactors. The predictions made by the model for the rate of ferrous iron oxidation were found to be in close agreement with the experimental data obtained in the laboratory experiments, demonstrating a very good correlation coefficient. This indicates that the model is robust and reliable for accurately predicting the performance of the mixed culture of ferroxidans microorganisms in different operational scenarios. The findings of this study provide valuable insights into optimizing bioreactor conditions for industrial applications, such as bioleaching and bioremediation, where efficient iron oxidation is essential.

1.31. Low-Cost Optical Chemical Sensors via MIPs and Optical Fibers

• Rosalba Pitruzzella ¹, Antimo Amato De Serpis ¹, Natalia Abategiovanni ¹, Raffaele Fusco ¹, Chiara Marzano ¹, Francesco Arcadio ¹, Maria Pesavento ², Giancarla Alberti ², Luigi Zeni ¹ and Nunzio Cennamo ¹

¹ 

Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy

² 

Department of Chemistry, University of Pavia, Via Taramelli 12, 27100 Pavia, Italy

Molecularly imprinted polymers (MIPs) have been combined with plasmonic probes to realize optical chemical sensors. In particular, plasmonic optical fiber probes are used as transducers to monitor several kinds of MIPs, such as nanoMIPs, MIP layers, and microbeads of MIPs. Plastic Optical Fibers (POFs) can be used to develop several bio/chemical sensor configurations, exploiting their excellent flexibility, easy manipulation, great numerical aperture, large diameter, and large number of modes. Therefore, extrinsic and intrinsic optical fiber sensing schemes can be achieved using POFs’ characteristics combined with cheap equipment, such as white light sources and spectrometers. In this work, we propose a low-cost sensing strategy to monitor MIPs without relying on plasmonic phenomena. Intensity-based sensor configurations can be implemented by exploiting MIPs as the core of sensitive optical waveguides. In this case, when the binding between the substance of interest and specific sites of MIPs occurs, the refractive index of the core in the sensitive waveguide changes, and the intensity of the transmitted light also changes. This sensing strategy can be implemented using an LED as the source, photodetectors as the receiver, and an Arduino system to record and process experimental data. Several configurations will be presented in this work to demonstrate the capability of this sensing approach. In particular, as a proof-of-concept, furfural (2-FAL) detection in water solutions in food applications is presented, demonstrating high performance by achieving an ultra-low detection limit at the pico- to nanomolar level and a wide detection range spanning approximately four orders of magnitude.

1.32. Modeling of Total Reducible Sugars Using Artificial Neural Networks in Agro-Waste Pretreatment Using Nepenthes mirabilis Pitcher Fluids as Enzymatic Agents

• Seteno Karabo Obed Ntwampe ¹ and Abiola Ezekiel Taiwo ²

¹ 

Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Corner of Hanover and Tennant Street, Zonnebloem, Cape Town 8000, South Africa

² 

Faculty of Engineering, Mangosuthu University of Technology, 511 Griffiths Mxenge Hwy, Umlazi, Durban 4031, South Africa

This study explores the application of artificial neural networks (ANNs) to predict the concentration of total reducible sugars (TRS) in hydrolysates derived from pretreated mixed agro-waste, with implications for environmental engineering, biotechnology, and sustainable biorefinery processes. Experimental data were generated using bench-scale hydrolysis experiments conducted at the Bioresource Engineering Research Group la-boratory and the Centre for Proteomic and Genomic Research (Cape Town, South Af-rica). Nepenthes mirabilis (N. mirabilis) pitcher fluids, sampled from Pan’s Carnivores Plant Nursery (Cape Town, South Africa), served as enzymatic agents for agro-waste pretreatment. The ANN analysis was conducted using MATLAB’s Neural Network Toolbox, employing a feed-forward topology with a 1-5-2-2 network structure. Input parameters included particle size and enzyme fraction, while outputs corresponded to TRS concentrations at 24 and 72 h. Validation identified three epochs as optimal for model training, albeit with a mean squared error of 0.54. Experimental runs coded 2, 3, and 4 demonstrated minimal prediction errors (5%), though runs 3 and 4 exhibited high phenolic concentrations, which are undesired in TRS hydrolysates destined for fer-mentation. In contrast, Run 12 (>106 µm particle size/>3 kDa enzyme fraction) showed promising predictability (R² = 0.93) with low phenolic content, highlighting its suitabil-ity for future biorefinery applications. Statistical validation of the ANN predictions against experimental TRS data confirmed the model’s robustness. This research pro-vides a foundation for optimizing agro-waste pretreatment processes using N. mirabilis pitcher fluids and advances studies through ANN-based modeling. The dataset offers a platform for researchers to explore alternative enzymatic agents or integrate advanced optimization software for scalable bioresource valorization.

1.33. Multichannel Plasmonic Point-of-Care Device for Salivary Detection of Periodontal MIP-1α: Analytical Comparison with ELISA

• Marco Annunziata ¹, Francesco Arcadio ², Emanuela Stampone ³, Debora Bencivenga ³, Gennaro Cecoro ¹, Chiara Marzano ², Angelantonio Piccirillo ¹, Fulvio Della Ragione ³, Nunzio Cennamo ², Luigi Zeni ², Adriana Borriello ³ and Luigi Guida ¹

¹ 

Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania “Luigi Vanvitelli”, Via L. De Crecchio, 6—80138 Naples, Italy

² 

Department of Engineering, University of Campania “Luigi Vanvitelli”, Via Roma, 9—81031 Aversa, CE, Italy

³ 

Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, via L. De Crecchio, 7—80138 Naples, Italy

Introduction: Salivary biomarkers are increasingly gaining trust as promising candidates for the non-invasive diagnosis of periodontal diseases and the monitoring of periodontal tissue health. This study investigated the analytical capabilities of a multichannel, plasmonic point-of-care (POC) test based on optical fiber technology for detecting and quantifying salivary macrophage inflammatory protein-1 alpha (MIP-1α), using enzyme-linked immunosorbent assay (ELISA) as a comparison.

Methods: Three plastic optical fibers (POFs) were functionalized with a self-assembled monolayer (SAM) containing MIP-1α antibodies. The POFs were placed between a spectrometer and a light source to monitor refractive index shifts at the POF-SAM interface, corresponding to the occurrence of surface plasmon resonance (SPR) during antigen–antibody interaction. A dose–response curve was generated using a range of MIP-1α concentrations. Salivary samples were collected from a cohort of fifty participants and analyzed using both the SPR-based biosensor and ELISA. Spearman’s Rank test assessed the correlation between the two techniques. Differences in MIP-1α expression were further analyzed in relation to clinical variables, including periodontal status, age, and gender (Mann–Whitney U-test).

Results: A significant correlation was observed between the measurements obtained from the biosensor and those from ELISA. The sensitivity of the SPR-POF device allowed for the detection of MIP-1α at lower concentrations than ELISA. Patients with periodontal disease exhibited significantly higher levels of MIP-1α compared to those without the disease, supporting its potential as a diagnostic biomarker.

Conclusions: The developed three-channel plasmonic POCT exhibited comparable accuracy and superior sensitivity to ELISA for detecting salivary MIP-1α. Moreover, the multichannel plasmonic configuration enhanced both measurement efficiency and the reliability of the results.

1.34. New Developed Spectrophotometric Visible Analysis for Metformin Hydrochloride in Tablets of a Pharmaceutical

• CRISTIAN-CATALIN GAVAT ¹ and Afrodita Doina Marculescu ²

¹ 

Biomedical Sciences Department, Faculty of Medical Bioengineering, GRIGORE T. POPA University of Medicine and Pharmacy, 16 Universitatii Street, 700115 Iasi, Romania

² 

Morpho-Functional Sciences II Department, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 16 Universitatii Street, 700115 Iasi, Romania

The main aim of this research was to find and develop a new spectrophotometric method for Visible analysis of Metformin hydrochloride in a pharmaceutical. The method was based on the quantitative reaction of metformin hydrochloride with 0.1% beta-naphthol alkaline solution in the presence of 5% sodium nitrite and 15% hydrochloric acid by resting in cold conditions for 25 min. The following was observed: the quantitative formation of an intense yellow azo dye with an absorption maximum at λ = 408 nm, which was subsequently dosed in relation to double distilled water as a blank. Method was linear over the studied concentration range between 0.80 μg/mL–8.00 μg/mL. Linear regression coefficient was R² = 0.9994, R² ≥ 0.9990 and correlation coefficient R = 0.9997, R > 0.9990; both fit perfectly within the normal limits. Limit of Detection (LOD) = 0.17496 μg/ mL, and limit of quantitation (LOQ) = 0.5832 μg/ mL, LD 1 and LQ 1. Standard Error of the regression line was SE = 0.0065902, SE 1, which had a very small value that was statistically accepted. Pure Metformin hydrochloride content found was 998.9858 mg in tablet, very close to the official stated amount of 1000 mg pure Metformin hydrochloride on extended-release tablet. The relative percentage deviation of the calculated amount 998.9858 mg was 0.1014% below the official reference value of 1000 mg and fell below the maximum average percentage deviation allowed (±5%) imposed by the Romanian European Pharmacopoeias Rules.

1.35. Numerical Modeling of Polydopamine Nanoparticle-Enhanced Photothermal Therapy for the Treatment of Skin Cancer

• Abby Chapman ¹, William Whelan ² and Sundeep Singh ¹

¹ 

Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

² 

Department of Physics, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada

The incidence of skin cancer is rising worldwide, with non-melanoma skin cancer ranked as the fifth most prevalent cancer in 2022, presenting a significant burden on public health. Photothermal therapy has emerged as a promising treatment that employs near-infrared light to selectively destroy cancerous tissue. While the integration of metallic nanoparticles has demonstrated enhanced thermal performance, concerns over their low tissue clearance rate and long-term toxicity have hindered their clinical translation. Polydopamine (PDA) nanoparticles have recently garnered attention as a promising alternative due to their biodegradability and biocompatibility. This study presents a finite element-based multiscale modeling framework to investigate PDA nanoparticle-enhanced photothermal therapy for skin cancer treatment. Numerically characterized optical properties of PDA nanoparticles were incorporated into a three-dimensional, multi-layered skin tissue model that includes a region of squamous cell carcinoma. Heat transfer was simulated by coupling the Pennes’ bioheat transfer equation with the Beer–Lambert law to compute the spatiotemporal temperature distribution during laser irradiation. Model validation against experimental temperature data from PDA suspensions at various concentrations showed strong agreement. Parametric studies explored the effect of PDA nanoparticle size, concentration, laser intensity, and beam profile on temperature profiles. The results demonstrated that PDA nanoparticles increased tumor temperature compared to treatments without nanoparticles. The temperature increased by 6 °C when 1000 μg/mL was irradiated for 10 min with a 1.4 W/cm² laser intensity. These findings help to deepen our understanding of the thermal behavior of PDA nanoparticles in biological tissues and support their potential as a biocompatible alternative for enhanced photothermal therapy.

1.36. Photophysical Properties and Singlet Oxygen Generation by Zn-Protoporphyrin IX Embedded in Hemoglobin

• Marina V. Parkhats ¹, Dmitry V. Berdnikovich ² and Sergei V. Lepeshkevich ¹

¹ 

B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, 68 Nezavisimosti Ave, 220072 Minsk, Belarus

² 

Belarussian State University, 4 Nezavisimosti Avenue, 220030 Minsk, Belarus

Photodynamic therapy (PDT) is a modern minimally invasive method of treating oncological diseases. PDT is based on the use of a photosensitizer (PS), a light-sensitive drug that triggers a chain of photochemical reactions leading to the formation of cytotoxic singlet oxygen and/or reactive oxygen species that destroy tumor cells. An important challenge for further progress in PDT is to overcome the limitations associated with PS delivery and oxygen availability in tumors. Therefore, the development of drug delivery systems based on hemoglobin (Hb) has attracted increasing attention. Hb serves as a delivery system for both PS and molecular oxygen in hypoxic tumor cells.

Here, we synthesized Zn-substituted hemoglobin (ZnHb), in which heme was replaced by Zn-protoporphyrin IX (ZnPP), an effective PS. The photophysical properties and singlet oxygen generation by ZnPP in a complex with Hb were studied. It was shown that interaction of ZnPP with Hb leads to the increase in the PS’s triplet state lifetime by more than 10 times, which is associated with a significant decrease in the access of molecular oxygen to ZnPP embedded in the heme pocket. ZnPP in the complex with Hb does not lose the ability to generate singlet oxygen. It was found that laser irradiation causes photodestruction of ZnHb, with ZnPP not leaving the heme pocket at a pH of 7.2. The release of ZnPP from the protein occurs with an increase in the acidity of the medium, which leads to the aggregation of ZnPP and a significant decrease in singlet oxygen generation.

1.37. Plant-Derived Antimicrobial Agents: Bioactivity Screening of Nelsonia canescens Against Clinically Relevant Pathogens

• Abubakar Abdulhameed Abdullahi ¹, DAUDA GARBA ², Yahaya Muhammed Sani ¹ and Mohammed Ibrahim Sule ¹

¹ 

Department of Pharmaceutical and Medicinal Chemistry, Ahmadu Bello University, Zaria 810107, Nigeria

² 

Department of Pharmaceutical and Medicinal Chemistry, University of Abuja, Abuja 900105, Nigeria

Nelsonia canescens is traditionally used to treat infections. This study evaluated its phytochemical composition and antimicrobial activity to validate ethnomedicinal claims. The methanol extract and fractions (hexane, chloroform, ethyl acetate, and butanol) were screened for phytochemicals and tested against Staphylococcus aureus, Escherichia coli, Salmonella typhi, Candida albicans, and Aspergillus niger using agar diffusion and broth dilution methods, with ciprofloxacin and fluconazole as controls. Phytochemical analysis revealed flavonoids, saponins, tannins, alkaloids, and cardiac glycosides. Antimicrobial assays showed that the butanol fraction (BF) and an isolated compound (IS) exhibited strong activity, with inhibition zones of 30–36 mm (S. aureus) and 28–30 mm (E. coli). The ethyl acetate fraction (EAF) showed moderate antifungal effects (17 mm against C. albicans). IS had the lowest MIC values (2.5–10 mg/mL), comparable to ciprofloxacin (1.25–2.5 µg/mL), and demonstrated bactericidal effects (MBC: 5–50 mg/mL). Non-polar fractions (CME, HF, and CF) were inactive, while fluconazole was effective only against fungi (MIC/MFC: 1.25–2.5 µg/mL). Polar fractions (BF, EAF) and IS displayed promising antimicrobial activity, particularly against Gram-positive bacteria and C. albicans, though standard drugs were more potent. The presence of bioactive compounds supports N. canescens traditional use, highlighting its potential as a source of natural antimicrobial agents. Further optimization could enhance efficacy.

1.38. Pongamia pinnata-Derived Phytochemicals as Potent Antibacterial and Anti-Biofilm Leads Against Multidrug-Resistant Pathogens

• Muhammad Waqas ¹ and Tehmina Saddique ²

¹ 

Department of Zoology, Division of Science and Technology, University of Education, Lahore, Pakistan

² 

Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan

Background: The rapid rise of multidrug-resistant bacteria is undermining conventional antibiotics, driving the search for novel scaffolds. Pongamia pinnata, a leguminous medicinal plant rich in flavonoids, karanjin, and pongamol, represents an untapped source of antibacterial leads with strong translational potential.

Methods: Bioactive fractions from P. pinnata seeds and leaves were extracted using methanol and ethyl acetate, followed by LC-MS and NMR characterization. Antibacterial activity was assessed against MDR Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli via CLSI-standard broth microdilution. Biofilm formation and eradication assays were quantified by crystal violet staining and confocal microscopy. Synergy with ceftriaxone and azithromycin was tested using checkerboard and time–kill kinetics. BALB/c mice infected with MDR S. aureus were used for in vivo efficacy and toxicity evaluation.

Results: P. pinnata fractions showed strong antibacterial effects, with MICs of 16 µg/mL (S. aureus), 32 µg/mL (P. aeruginosa), and 24 µg/mL (E. coli). Biofilm inhibition exceeded 72% at sub-MIC levels, while eradication of mature biofilms reached 61% at 32 µg/mL. Synergistic assays revealed marked potentiation with ceftriaxone (FICI 0.34) and azithromycin (FICI 0.39), reducing effective antibiotic concentrations by fourfold. In vivo, alkaloid-rich fractions (50 mg/kg, i.p.) improved survival to 78% versus 18% in controls and reduced bacterial loads in spleen and liver by >2.5 log CFU. Histological analysis confirmed no acute toxicity in major organs.

Conclusion: Pongamia pinnata phytochemicals exhibit robust antibacterial, anti-biofilm, and synergistic activity with conventional antibiotics, validated by in vivo efficacy. These results position P. pinnata as a powerful natural reservoir for developing next-generation antibacterial leads against MDR pathogens.

1.39. Synergistic Anti-Inflammatory Potential of a Pulegone-Rich Fraction from Satureja calamintha: Efficacy and Safety Evaluation

• Manel Fellahi, Mohammed El Amine DIB, Okkacha BENSAID and Yazid DATOUSSAID

• Department of Chemistry, Faculty of Science, University of Abou Bekr Belkaid Tlemcen, Tlemcen 13000, Algeria

This study explores the chemical composition and anti-inflammatory potential of the essential oil extracted from the aerial parts of Satureja calamintha, a medicinal and aromatic plant belonging to the Lamiaceae family. The essential oil was analyzed by gas chromatography (GC) and gas chromatography–mass spectrometry (GC-MS), which led to the identification of twenty compounds, representing 96.7% of the total oil. The composition was dominated by oxygenated monoterpenes (74%) and, to a lesser extent, by hydrocarbon monoterpenes (7.4%) and sesquiterpenes (9.52%). Pulegone was identified as the major constituent (50%), followed by iso-menthone, β-caryophyllene, menthol, and cubebol.

To better understand the contribution of pulegone to the biological activity of the oil, a pulegone-rich fraction (F2) was obtained through column chromatography and subsequently characterized by GC-MS. This fraction contained pulegone as the predominant compound (80%), along with E-β-caryophyllene and pipertenone oxide. Anti-inflammatory activity was assessed using the heat-induced albumin denaturation method. The enriched fraction exhibited a significantly higher inhibitory effect (IC₅₀ = 0.128 mg/mL) compared to the crude oil (IC₅₀ = 0.86 mg/mL), suggesting antagonistic interactions in the unfractionated oil. Furthermore, combination studies with diclofenac sodium revealed a pronounced synergistic effect. Notably, the pulegone-rich fraction combined with diclofenac displayed enhanced activity (IC₅₀ = 0.085 mg/mL), more than twice as effective as diclofenac alone (IC₅₀ = 0.194 mg/mL). Additionally, hemolysis assays conducted on human red blood cells indicated a low cytotoxic potential for both the crude essential oil and the F2 fraction, supporting their potential as safe and effective natural anti-inflammatory agents.

1.40. Therapeutic Opportunities in Disorder: Integrated Analysis of Cancer-Linked IDPs Reveals Druggable Motifs and Interaction Hubs

• DEEPAK CHAURASIYA ¹, Puja Kumari ² and Lalit Pratap Singh ³

¹ 

Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Prayagraj 211015, India

² 

Department of Infectious Diseases Biology, ICMR-National Institute for Research In Reproductive and Child Health, Parel, Mumbai 400012, Maharashtra, India

³ 

Department of Animal Biotechnology, NATIONAL Dairy Research Institute, Karnal 132001, Haryana, India

Intrinsically disordered proteins (IDPs), defined by their lack of stable tertiary structures, play pivotal roles in cancer development by modulating signaling cascades, transcriptional activity, and essential cellular processes. In the present analysis, experimentally validated cancer-associated IDPs from the DisProt database were systematically categorized into Fully Intrinsically Disordered Proteins (FIDPs, ≥90% disorder), Moderately Intrinsically Disordered Proteins (MIDPs, 30–90% disorder), and Ordered Proteins (ODPs, 30% disorder). A multi-layered computational framework, incorporating machine learning-based tools including PONDR, PONDR-DEPP, Depictor2, FuzDrop, and STRING, enabled functional profiling of these classes. FIDPs demonstrated a higher density of molecular recognition features (MoRFs), increased phosphorylation site prediction, and elevated liquid–liquid phase separation (LLPS) propensity compared to MIDPs and ODPs. Protein–protein interaction network analysis via STRING revealed that FIDPs are frequently positioned as central hubs in networks governing apoptosis, DNA repair, and cell cycle regulation. Furthermore, detailed mapping identified functionally relevant disordered regions enriched with predicted phosphorylation hotspots, MoRF segments, and LLPS-favorable domains. These intrinsically disordered segments reflect a high potential for dynamic regulation and molecular adaptability. The annotated IDR sites possess notable translational relevance, offering promising avenues for the design of natural disordered-based biosensors, the development of disorder-targeted therapeutics, and the exploitation of post-translational modification (PTM) patterns in cancer diagnostics and treatment strategies.

1.41. Toward Climate-Resilient Cotton: Molecular Drivers, Abscission Zone Dynamics, and Translational Breeding Strategies

• Muhammad Abubakar ¹, Malaika Zaheer ², Muhammad Affan Akram ¹ and Muhammad Shaban ¹

¹ 

Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan

² 

Department of Agricultural Biotechnology, Faculty of Agriculture, Ondokuz Mayis University, Samsun 55270, Turkey

Climate-induced heat stress poses a critical threat to cotton (Gossypium spp.) productivity, especially during the flowering and boll development stages. Elevated temperatures exceeding 35 °C disrupt key physiological processes, impair photosynthesis, and alter hormonal homeostasis, ultimately triggering premature boll abscission. Central to this process is the formation of the abscission zone (AZ), driven by the upregulation of ethylene and abscisic acid (ABA) biosynthetic genes, which antagonize auxin transport and compromise cell wall integrity. This disruption results in reduced boll retention and significant yield losses under heat-stressed conditions. This review synthesizes current advances in understanding the molecular and physiological mechanisms underlying heat-induced boll shedding. We highlight the roles of heat shock proteins (HSPs), stress-responsive transcription factors, reactive oxygen species (ROS) signaling, and hormone crosstalk in AZ regulation. Furthermore, we explore integrative breeding approaches combining quantitative trait loci (QTL) mapping, transcriptomics, and CRISPR/Cas9-based gene editing to enhance thermotolerance in cotton. Agronomic interventions—including exogenous application of plant growth regulators and precision irrigation techniques—are also examined as complementary strategies for mitigating heat stress effects. Emerging technologies, such as nanotechnology-enabled delivery systems for stress modulators, offer promising avenues for targeted intervention. Finally, we propose a research framework centered on AZ-specific gene expression profiling, gene–hormone interaction networks, and translational breeding pipelines. These multidisciplinary insights form a robust foundation for the development of climate-resilient cotton cultivars suited to increasingly extreme agro-climatic conditions.

1.42. Water Quality Characterization Procedures for Poultry Slaughterhouse Treatment Systems

• Nazaire Nsazimana ¹, Mncedisi Trinity Dewa ², Seteno Karabo Obed Ntwampe ¹ and Moses Batisere ³

¹ 

Department of Environmental and Occupational Studies, Faculty of Applied Sciences, Cape Peninsula University of Technology, Corner of Hanover and Tennant Street, Zonnebloem, Cape Town 8000, South Africa

² 

School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Johannesburg 2194, South Africa

³ 

Academic Support for Engineering in Cape Town (ASPECT), Centre for Higher Education Development, Upper Campus, University of Cape Town, Rondebosch 7701, South Africa

Slaughterhouses release a significant amount of wastewater containing varying concentration of organic matter, nutrients, and other pollutants that necessitate robust treatment solutions. In this paper, a multi-stage lab-scale plant that consists of a pre-treatment stage, two trains with either a Static Granular Bed Reactor (SGBR) and Expanded Granular Sludge-bed Bioreactor (EGSB) coupled individually to membrane bioreactors (MBRs), were used. This configuration was designed to systematically evaluate and compare the efficacy of each anaerobic bioreactor type within an integrated treatment train. The lab-scale plant was operated for 77 days, a duration selected to ensure process stability and collect sufficient data for statistical analysis, and quality performance parameters were investigated using capability indices (Cp, Cpk, Pp, and Ppk). The secondary experimental data was analysed through QI Macros (SPC software for Microsoft Excel) to provide a rigorous, statistical evaluation of process control and performance. The lab-scale plant designed with the three stages, i.e., bio-physical pre-treatment stage—SGBR or EGSB units—MBRs, indicated a significant potential and capability of the overall process to treat PSW, whereby treatment stages had Pp = Ppk = 1.00 with 99.73% of each treatment stage outputs being within specifications. This high degree of statistical capability demonstrates exceptional process control and a low probability of producing non-conforming effluent. Furthermore, all stages performed with Cpk equal to 0.99, 1.06, 0.83, 0.86, and 0.79, respectively. The key findings of the paper reveal the treatment efficiency, Operational Stability, and Statistical Performance; which is a focused interpretation of the Capability Indices (Cp, Cpk) for each reactor, explicitly stating which system showed better performance.

2. Nanosciences, Chemistry and Materials Science

2.1. Machine Learning and Molecular Dynamics for the Thermal Conductivity of Doped Semiconductors

• Emiliano Cerón-Vasilescu, Gerardo Rodriguez-Hernandez and Alejandro Guajardo-Cuéllar

• Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Zapopan, 45201, Mexico

Semiconductors are fundamental components of modern industry, as they serve as the backbone of electronics and energy technologies. Among their many characteristics, thermal properties play a crucial role in ensuring both performance and long-term reliability. Doped semiconductors, in particular, display unique and useful electronic properties; however, the introduction of impurities generally alters their thermal behavior, often leading to a reduction in thermal conductivity. To study this effect, equilibrium molecular dynamics (EMD) combined with the Green–Kubo formalism can be employed to calculate the thermal conductivity of doped semiconductor systems. In addition, the use of interatomic potentials, such as the Tersoff model, provides a framework to capture the underlying atomic interactions. By integrating machine learning techniques with molecular dynamics, it becomes possible to predict thermal properties across different doping levels and defect concentrations. Machine learning models, trained on simulation data, can reduce the computational cost of traditional simulations, which are typically both time- and resource-intensive. The results highlight how doping and defects modify thermal conductivity and help establish practical limits for impurity levels that still allow semiconductors to remain attractive for technological applications. The results are also of interest to determine the figure of merit of doped semiconductors. Understanding this relationship is essential for designing advanced materials that balance performance, efficiency, and reliability.

2.2. Antifungal Properties of 3-(Morpholin-4-yl)propane-2,3-dione 4-Phenylthiosemicarbazone and Its Mixed Ligand–Copper(II) Complexes Toward Cryptococcus neoformans

• Ianina Graur ¹, Vasilii Graur ¹, Irina Usataia ¹, Victor Tsapkov ¹, Carolina Lozan-Tirsu ², Greta Balan ² and Aurelian Gulea ¹

¹ 

Institute of Chemistry, Moldova State University, Chișinău, Republic of Moldova

² 

“Nicolae Testemitanu” State University of Medicine and Pharmacy, Chisinau, Republic of Moldova

The rising incidence of fungal infections and the limited effectiveness of current antifungal drugs emphasize the need for new active substances. Cryptococcus neoformans is a major opportunistic pathogen, responsible for cryptococcal meningitis in immunocompromised patients and associated with high mortality worldwide. Thiosemicarbazones and their metal complexes are reported to exhibit diverse biological activities, including antifungal effects.

For our study, we have synthesized 3-(morpholin-4-yl)propane-2,3-dione 4-phenylthiosemicarbazone (HL) and its mixed ligand–copper(II) complexes [CuL(Im)NO₃], [CuL(Py)NO₃], [CuL(β-Pic)NO₃], and [CuL(γ-Pic)NO₃]. The composition and structure of all compounds were confirmed using ¹H and ¹³C NMR, FTIR, elemental analysis, molar conductivity measurements, and X-ray crystallographic analysis.

The antifungal activity of all synthesized compounds was evaluated towards Cryptococcus neoformans (CECT 1043) using the broth microdilution method. The mixed ligand–copper(II) complexes exhibited higher activity compared to both the HL and the copper(II) nitrate complex from which they were obtained. This finding confirms that the introduction of an additional ligand into the inner coordination sphere can enhance biological activity. The most active compound was [CuL(γ-Pic)NO₃], which showed MIC and MBC values of 7.81 µg/mL and 15.63 µg/mL, respectively.

These results highlight the potential of mixed ligand–copper(II) complexes as promising leads for the development of new antifungal agents, particularly against pathogenic fungi such as Cryptococcus neoformans.

The work was performed with financial support from subprogram 010602 of the institutional project.

2.3. Application of Cryogels with Noble Metal Nanoparticles as Flow Through Catalyst for “Green” Decomposition of Phenol Derivatives

• Dmitriy Berillo ^1,2

¹ 

Department chemistry, Kozybayev University, Petropavl 150000, Kazakhstan

² 

School of Applied Sciences, University of Brighton, Huxley Building, Lewes Road, Brighton BN2 4GJ, UK

Chlorinated derivatives, including pesticide fungicides and insecticides, are extensively applied in agriculture, disinfection, and industry, yet their persistence leads to severe contamination of aquatic environments. Conventional bioremediation methods are insufficient for removing these pollutants, which has stimulated the development of catalytic approaches to degrade stable chloro-organic compounds effectively. In the current study a range of catalytic strategies has been designed to address the degradation of persistent organic pollutants. In this investigation, the formation of metal-coordinated chitosan gels were analyzed via rheological measurements (G′ and G″). using Medusa modeling mechanism of gel formation was proposed. Ionic gels were further transformed into covalently cross-linked macroporous cryogels containing in situ immobilized Pd or Pt nanoparticles through redox-driven reactions. The catalytic efficiency of these cryogels for degrading chloro-organic contaminants in continuous water treatment was evaluated, with PdNPs and PtNPs uniformly dispersed as nanosized particles within the porous polymer structure. The degradation kinetics of o-chlorophenol, p-chlorophenol, and 2,4-dichloro-phenol were investigated using catalysts with varying Pd and Pt loadings. Conversion increased with higher formic acid excess and elevated temperatures, reaching 80–90% at 80 °C. The CHI–GA–PdNPs cryogel demonstrated superior hydrogenation activity at pH 6 compared to CHI–GA–PtNPs, though no marked difference was observed at pH 3. Batch-mode termination studies and control experiments were conducted to explore catalyst deactivation, with silver nitrate addition showing limited benefit. Overall, this catalytic platform holds promise for application in flow-through systems and for broader use in the synthesis of valuable chemicals.

2.4. Development and Validation of a Novel Detergent Formulation for Dermal Decontamination: From Phorate Screening to VX Efficacy Testing

• Antonio Peña-Fernández ¹, Hazem Matar ², Robert P. Chilcott ³, Borja Martínez-Alonso ⁴, Victor Guarnizo ⁴, Antonio Juberías ⁵, María de los Ángeles Peña ⁴, Norma S. Torres ⁴ and Guillermo Torrado ⁴

¹ 

Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Ctra. Madrid-Barcelona, Km. 33.600, 28871 Alcalá de Henares, Madrid, Spain

² 

Ereuna Ltd., Porton Science Park, Bybrook road, Porton Down, Wiltshire SP4 0BF, UK

³ 

Research Centre for Topical Drug Delivery and Toxicology, University of Hertfordshire, Hatfield AL10 9AB, UK

⁴ 

Departamento de Ciencias Biomédicas, Universidad de Alcalá, Crta. Madrid-Barcelona Km, 33.6, 28871 Alcalá de Henares, Madrid, Spain

⁵ 

Centro Militar de Farmacia de la Defensa. Carretera M-609 de Miraflores, Km 34, Colmenar Viejo, 28770 Madrid, Spain

Organophosphorus nerve agents, such as VX, pose an extreme risk due to their high dermal toxicity, environmental persistence, and rapid systemic absorption. Timely and effective decontamination is crucial for reducing exposure and minimizing health effects. This study outlines a two-phase approach to develop and validate a new surfactant-based decontamination solution, beginning with simulant testing using phorate and progressing to efficacy trials with real VX. In phase one, phorate was used as a simulant for VX in dermal exposure studies. Two prototype detergents (F1 and F2) were tested on full-thickness porcine skin using static Franz-type diffusion cells. A ¹⁴C-labelled phorate droplet was applied, followed by no treatment, water-only rinse, or detergent application using the ORCHIDS microfibre protocol. Absorption was measured via liquid scintillation counting. Phase two involved testing an optimized formulation (Formula 3), informed by the results of Phase one, against ¹⁴C-labelled VX on full-thickness human abdominal skin. Decontamination occurred 60 min post-application, comparing water-only and detergent treatment. Autoradiography and scintillation counting were used to quantify residual agent. In the simulant phase, both detergents significantly reduced phorate absorption compared to water or no treatment, with Formula 2 performing best by limiting wash-in effects. Subsequent VX testing showed Formula 3 significantly reduced residual agent on the skin surface compared to water alone, indicating effective decontamination under realistic exposure conditions. This two-stage research strategy demonstrates that simulant screening is a valuable tool for optimizing decontamination solutions. The final detergent formulation provides an effective countermeasure for dermal exposure to VX, with potential applications in defense and civilian emergency response.

2.5. Effects of Carrier Gas and Substrate Temperature on Fluorine-Doped ZnO Thin Films Deposited on Polymer Substrates by Ultrasonic Spray Pyrolysis

• Héctor Eduardo Petlacalco Ramírez, Salvador Alcantara Iniesta and Blanca Susana Soto Cruz

• Centro de Investigaciones en Dispositivos Semiconductores (CIDS-ICUAP), Benemérita Universidad Autónoma de Puebla (BUAP), Puebla 72570, Mexico

ZnO is a relevant semiconductor that continues to expand its applications. The development of new devices requires overcoming the limitations of rigid substrates. Various techniques have been used to deposit materials on flexible substrates; however, their costs are high. Therefore, the spray pyrolysis technique is promising due to its homogeneity over large surfaces, low cost, and simplicity.

For the first time, fluorine-doped ZnO films at an atomic ratio [F/Zn] of 15 at.% were deposited on polyimide substrates in a temperature range of 320–400 °C under air or N2 carrier gas using the spray pyrolysis technique. Structural, morphological, and electrical properties were investigated using X-ray diffraction, scanning electron microscopy, four-point technique, and profilometry. All films were polycrystalline with a hexagonal wurtzite structure. Films deposited at 320 and 350 °C with air gas exhibited a preferential (100) orientation, while those deposited at 350 and 400 °C with N₂ gas showed a preferential (002) orientation. The size of the crystallites increased with the temperature and N₂ gas. The morphology changed between columnar and wedge-shaped, depending on the temperature and the type of carrier gas. The resistivity decreased from 1.117 × 10² to 9.45 × 10⁻² Ω-cm with increasing temperature. At a temperature of 350 °C, the resistivity decreased by two orders of magnitude when using N₂ gas compared to the deposition with air.

The improved incorporation of fluorine and hydrogen into ZnO, using N2 gas, probably contributed to the decrease in resistivity. Spray pyrolysis was presented as an alternative method for depositing ZnO:F films on polyimide for applications in flexible electronics.

2.6. Improving the Thermomechanical Properties of Aerated Concrete Using Ecological Additives

• Atigui Malika, Youssef Maaloufa, Asma Souidi, Mina Amazal, Slimane Oubeddou, Hassan Demrati, Soumia Mounir and Ahmed Aharoune

• Laboratory of Thermodynamics and Energetics, Faculty of Science (University of Ibn Zohr), City Dakhla, Agadir 80000, Morocco

In this study, we developed a new type of composite with improved thermophysical and mechanical properties. This composite is made of aerated concrete based on two types of additives, and we aimed to choose the best one. The main raw materials for manufacturing cellular concrete are sand, lime, cement, water, and aluminium powder. We replaced natural sand with olive pomace sand and shell sand, using replacement percentages of 10%, 20%, 30%, and 40%. We conducted a comparative study of the results to ultimately choose the one that yielded the best results. The thermomechanical properties, durability, and physical properties of the composites were studied. We finally concluded that both types of replacement reduce the mechanical properties of the material, but shell sand gives better results than olive pomace sand. We also found that composites based on olive pomace sand have a higher water absorption coefficient than those with shell sand, which weakens the material, probably due to the porous structure of olive waste and the lack of bonding between the matrix and grain. In terms of thermal properties, both sands exhibited increased thermal conductivity by 28.5% and 32.7% for a 10% replacement rate for olive pomace waste and shell waste, respectively, but this remains an acceptable value, as aerated concrete is already known for its thermal insulation properties.

2.7. Machine Learning-Guided Optimization of Catalyst and Reaction Parameters for CO₂-to-Gasoline-Range Hydrocarbon Production via Fischer–Tropsch Synthesis

• Adham Norkobilov ^1,2, Mansurbek Urol ugli Abdullaev ^3,4, Zafar Safarovich Turakulov ² and Azizbek Kamolov ²

¹ 

Department of Food Engineering, Karshi State Technical University, Shahrisabz, Uzbekistan

² 

Department of Automation and Digital Control, Tashkent Institute of Chemical Technology, Tashkent, Uzbekistan

³ 

Hydrogen & C1 gas research center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea

⁴ 

Department of Advanced Materials and Chemical Engineering, University of Science and Technology (UST), Gajeong-dong, Yuseong, Daejeon 34113, Republic of Korea

The conversion of CO₂ into gasoline-range hydrocarbons (C₅–C₁₂) using Fischer–Tropsch synthesis (FTS) represents a compelling pathway toward sustainable fuel production. In this study, we compiled and statistically analyzed a dataset of over 100 experimental records from the published literature focused on CO₂-FTS performance, predominantly featuring Co- and Fe-based catalysts, which are the most frequently reported in gasoline-range studies. We evaluated four machine learning models—XGBoost, CatBoost, Random Forest, and Neural Networks—to predict CO₂ conversion and gasoline-range selectivity. CatBoost achieved the highest predictive accuracy with a test R² score of approximately 0.8, and was selected for further interpretation using SHAP-based post hoc analysis. The model revealed that the optimal operational conditions for maximizing gasoline-range hydrocarbon yield are aligned with ranges commonly reported: a temperature of 280–320 °C, pressure of around 2 MPa, and space velocity (GHSV) between 900 and 120,000 mL h⁻¹ g⁻¹ (most studies cluster in the 1000–5000 range). Conditions were associated with enhanced chain growth probability and suppressed methane formation, especially in Co-based systems. The SHAP analysis also highlighted the principal role of catalysts containing cobalt (often supported on γ-Al₂O₃ with Re promoter) in increasing C₅⁺ chain growth and gasoline-range selectivity. Additionally, Co-based catalysts demonstrated clear benefits: increased chain-growth probability, reduced methane selectivity, and higher selectivity toward gasoline fractions under the identified optimal conditions. Our ML-driven framework not only predicts performance but also provides mechanistic insights into the influence of catalyst composition and reaction parameters. This integrated approach accelerates rational catalyst and process design for CO₂-to-fuel technology.

2.8. Silver Decorated Titanium Dioxide for Enhanced Photocatalytic Hydrogen Production

• Fupeng Xu, Zixiang Yu, Xinying Shi

• School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221116, China

Titanium dioxide (TiO₂) has been widely recognized as a promising material for addressing fossil fuel dependence and environmental degradation due to its robust photocatalytic activity, stability, and low toxicity. However, pristine TiO₂ suffers from the rapid recombination of photogenerated charge carriers and restricted visible-light absorption. Given this, suppressing charge recombination while extending its photoresponse to visible light would establish pristine TiO₂ as a viable candidate for scalable photocatalysis. Efforts have emphasized depositing noble-metal cocatalysts or creating heterojunctions via advanced synthesis; however, most strategies involve complex procedures, high costs, or poor reproducibility that hinder real-world implementation.

In our recent work, we developed a controllable synthesis strategy [1] to directly integrate silver (Ag) nanoparticles with TiO₂. Typically, Ag nanoparticles ranging from 5 to 20 nm in size were uniformly anchored onto both the {101} and {001} facets of TiO₂. This composite exhibited improved performance in photocatalytic hydrogen generation and organic pollutant degradation. The enhanced photocatalytic ability is attributed to the formation of stable Ti–O–Ag interfacial bonds. These bonds create an efficient electron-shuttling pathway, accelerating the transfer of photogenerated electrons from the TiO₂ conduction band to the catalytic Ag sites, thereby facilitating charge carrier separation and enhancing light harvesting.

• Reference

• Shi, X.; Zhang, M.; Wang, X.; Kistanov, A.A.; Li, T.; Cao, W.; Huttula, M. Nickel nanoparticle-activated MoS₂ for efficient visible light photocatalytic hydrogen evolution. Nanoscale 2022, 14, 8601–8610.

2.9. A Comparative Assessment of Dye Degradation by Green-Synthesised ZnO Nanostructures Under Solar Light Irradiation

• SUNEEL− ¹, Abdul Rahman Khan ¹ and Devendra Pratap Mishra ²

¹ 

Research Lab-B043, Department of Chemistry, Integral University, Lucknow 226026, Uttar Pradesh, India.

² 

Rajkiya Engineering College Ambedkar Nagar Affiliated to Dr A. P. J. Abdul Kalam Technical University, Lucknow 226031, India

Recent studies have demonstrated a dramatic escalation in global water consumption, with an approximate 600% increase over the past century. Due to the expansion of the textile industry, there is an increase in the release of synthetic organic dye effluents, which are contributing to the deterioration of water quality, posing significant threats to both human health and aquatic ecosystems. Notably, the textile sector alone utilises approximately 100,000 metric tons of dyes annually, with approximately 15% of these chromophonic compounds being inappropriately discharged into aquatic environments.

Methodology, result, and conclusion: Here, the facile route is applied to synthesise zinc oxide nanoparticles (ZnO NPs) using 80 and 100 mL of aqueous leaf extract of Psidium guajava and Syzygium cumini. The crystalline nature of the synthesised ZnO NPs—80P, 80S, 100P, and 100S—was analysed by XRD; however, Fourier transform spectroscopy and analysis were used to confirm the functional group purity. To examine the photocatalytic activity, 0.1 g/L of synthesised ZnO NPs—80P, 80 S, 100P, and 100B—was immersed in 100 mL of 10 ppm brilliant cresyl green, Malachite green, Eyosin Y, and congored dye solution, stirred for 20 min, and kept in the dark to establish an adsorption–desorption equilibrium, followed by exposure to natural sunlight. Moreover, the ZnO NPs 80S and 100S displayed good photocatalytic activity in 140 min of sunlight exposure compared to ZnO NPs 80P and 100P. This study aims to green-synthesise ZnO nanoclusters using two distinct phytochemical reducers and compare their efficiency in solar-assisted photocatalytic degradation of organic dyes in textile wastewater.

2.10. A Predictive Framework for Investigating Nanoscale Elastic Modulus in PVDF/Fe₃O₄ Nanocomposite Fibers

• Ashfaqul Hoque Khadem and Lihua Lou

• NanoBio Mechanics & Manufacturing Laboratory, Department of Mechanical Engineering, College of Engineering, Computing, and Applied Science, Clemson University, Clemson, SC 29634, USA

Accurately predicting the elastic modulus of polymer-nanoparticle composites presents a critical challenge, as conventional micromechanical models rely on idealized assumptions that are fundamentally invalid at the nanoscale. In this study, we demonstrate this significant discrepancy in a system of electrospun polyvinylidene fluoride (PVDF) nanofibers reinforced with iron oxide (Fe₃O₄) nanoparticles. Our experimental measurements reveal a substantial 23% increase in the composite’s elastic modulus, confirming significant nanoparticle reinforcement and the material’s enhanced performance. However, we show that established predictive frameworks—including the rule of mixtures, Kerner’s model, and the Guth model—fail to predict this experimental outcome. The failure of these models is largely attributed to their flawed foundational assumptions, such as ideal interfacial bonding between the polymer and nanoparticle, uniform particle dispersion, and the inapplicability of bulk-scale mechanics to nanoscale phenomena. To address this predictive gap, we propose a new, more sophisticated predictive model that moves beyond these idealizations. Our framework successfully incorporates critical nanoscale parameters that govern composite behavior, including quantified nanoparticle dispersion characteristics and the properties of the crucial polymer-nanoparticle interfacial zone. The resulting model provides a far more accurate description of the elastic modulus in PVDF/Fe₃O₄ systems, establishing a robust foundation for the future rational design of advanced materials with precisely tunable mechanical properties.

2.11. A Systematic Investigation of Microstructure, Thermal Stability, Conductivity, and Solubility in Polyaniline Doped with Oxalic Acid

• Souad Djellali, Chaima BENNAI, Aya DJATI and Rahma BENDRIS

• Department of Chemistry, Faculty of Sciences, University Ferhat Abbas, Setif 1, Algeria

Introduction: Conductive polymers, particularly polyaniline (PANI), are crucial in materials science, but their application is often limited by the use of corrosive mineral acids like HCl for doping. This study investigates the use of oxalic acid, a safer organic acid, as a functional dopant to create an environmentally benign and more processable form of PANI.

Methods: Polyaniline was synthesized and doped separately with hydrochloric acid (PANI-HCl) and oxalic acid (PANI-OA). The resulting materials were comprehensively characterized using FTIR and UV-Vis spectroscopy, Scanning Electron Microscopy (SEM), Thermogravimetric Analysis (TGA), four-point probe conductivity measurements, and qualitative solubility tests.

Results: FTIR analysis confirmed the successful doping of PANI with oxalic acid, evidenced by significant shifts in the benzenoid and quinoid ring stretching vibrations and an enhanced band at ~1158 cm⁻¹, indicating charge delocalization. Furthermore, SEM imaging revealed a significant morphological transformation from a classic “cauliflower-like” structure in PANI-HCl to an ordered “rod-like” microstructure in PANI-OA. This structural change was accompanied by a trade-off in performance where PANI-OA exhibited lower electrical conductivity (1.23 S/cm) and reduced thermal stability compared to PANI-HCl (329 S/cm). However, these drawbacks were offset by a critical gain in processability, as PANI-OA demonstrated excellent solubility in polar aprotic solvents, whereas PANI-HCl remained intractable.

Conclusion: Doping polyaniline with oxalic acid sacrifices conductivity and thermal stability for major gains in processability, safety, and morphological control. This work validates the use of functional organic dopants to engineer task-specific conductive polymers with tailored properties for solution-based fabrication.

2.12. Application of Polymer Nanocomposites in the Design of Prosthetic Sockets That Feature Auxetic Meta-Structures

• Sumit Kolte, Vinayak Vijayan and Lihua Lou

• Department of Mechanical Engineering, College of Engineering, Computing and Applied Sciences, Clemson University, 219 Fluor Daniel Building, Clemson, SC 29634-0921, USA

Lower limb amputees, comprising the majority, rely heavily on prosthetic devices for mobility. However, conventional prosthetic sockets often create uneven pressure distributions on the residual limb, leading to localized pressure points that cause skin irritation, discomfort, and long-term tissue damage, ultimately contributing to prosthesis abandonment. This study investigates the application of auxetic meta-structures and polymer nanocomposites to improve prosthetic socket design by enhancing interface pressure distribution, reducing deflection, and improving energy dissipation. Three prosthetic socket designs featuring internal meta-structures, chiral (auxetic), reentrant hexagon (auxetic), and regular honeycomb (non-auxetic), were modeled in SolidWorks and analyzed using finite element analysis (FEA) in Ansys under single-leg stance loading conditions. Each socket was simulated using five materials: polypropylene, ultra-high molecular weight polyethylene (UHMWPE), polypropylene with 5% zinc oxide (ZnO), polypropylene with 5% titanium dioxide (TiO₂), and UHMWPE with 0.5% graphene nanoplatelets. The results demonstrated that auxetic chiral prosthetic sockets consistently exhibited a 44.6% reduction in average interfacial pressure compared to non-auxetic hexagon sockets. The chiral design also showed a more uniform pressure distribution and enhanced energy absorption. While nanoparticle inclusion generally leads to less favorable contact pressure profiles, it offers a promising strategy for tailoring socket stiffness. These findings highlight the benefits of integrating auxetic geometries and nanocomposite materials in socket design to enhance user comfort and minimize prosthesis rejection. Additive manufacturing enables the scalable production of such customized prosthetic sockets, offering a promising pathway to improve access and quality of life for individuals with limb loss.

2.13. Application of Response Surface Methodology for Oil Adsorption by Using Fish Scales as Low-Cost Adsorbent

• Achanai Buasri, Romchat Buaban, Kaewpilin Pattanpornpong, Theapparat Doksoy and Vorrada Loryuenyong

• Department of Materials Science and Engineering, Faculty of Engineering and Industrial Technology, Silpakorn University, Nakhon Pathom 73000, Thailand

Oil-contaminated water is now a serious environmental issue caused by a variety of industries and manufacturing processes. Because of its toxicity, it endangers both the environment and other living things. Most methods for treating oily water and removing oil from water are time-consuming, expensive, need a large number of staff and equipment, and, in most cases, cause environmental damage. Adsorbents have gained popularity among available approaches in recent years due to their ease of use and low cost. The present research focuses on the possibilities of using fish scales as low-cost adsorbents for oil adsorption in water via batch processing. Fish scales were cleaned with water, dried in sunlight, and then heated in an oven at 70 °C for 1 h before being carefully ground. The samples were passed through a 60–200-mesh sieve (74–250 mm). The materials were characterized using X-ray diffraction (XRD), X-ray fluorescence (XRF), and scanning electron microscopy (SEM). Response surface methodology (RSM) was employed to investigate the effect of different experimental conditions on oil adsorption. To achieve the highest engine oil adsorption capacity of 90.04 mL, the following parameters were optimized: adsorption time of 33.17 min, agitation speed of 203.19 rpm, and adsorbent weight of 19.74 g. As a result, fish scales are a promising and environmentally benign biosorption material for the removal of contaminants from natural and wastewater sources.

2.14. Atto-Plasmonic Sensors for Point-of-Care Tests

• Nunzio Cennamo ¹, Francesco Arcadio ¹, Natalia Abategiovanni ¹, Antimo Amato De Serpis ¹, Dalila Cicatiello ¹, Chiara Marzano ¹, Rosalba Pitruzzella ¹, Laura Pasquardini ², Olivier Soppera ³ and Luigi Zeni ¹

¹ 

Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy

² 

Indivenire srl, Via Sommarive 18, 38123 Trento, Italy

³ 

Centre National de Recherche Scientifique, Institut de Science des Matériaux de Mulhouse (IS2M CNRS UMR 7361), Université de Haute-Alsace, 68100 Mulhouse, France

The ultra-low detection of substances of interest in atto-molar concentration ranges can be exploited to detect the analyte in real-world scenarios via simple dilution steps. The high sensitivity of several plasmonic-based biosensors is utilized to detect biomarkers or pollutants at an atto-molar level. This goal can be achieved via low-cost and simple equipment in small-sized setups combined with sensor chips based on ultra-high-sensitive plasmonic probes or ultra-efficient receptor layers. More specifically, extrinsic and intrinsic optical fiber sensing schemes can be achieved using plastic optical fiber (POF) characteristics combined with cheap equipment. The reported sensing strategy is simply implemented using a white light source and spectrometers. For instance, this work recalls atto-plasmonic sensors achieved via hybrid plasmonic probes combined with several receptors or ultra-efficient receptors and integrated into conventional plasmonic probes. These atto-plasmonic sensors, which utilize simple setups, can be useful for detecting substances of interest via Point-of-Care Tests (PoCTs) in various application fields. The sensor systems enable on-site measurement of the substance of interest in just a few minutes, with the results transmitted via an Internet connection. Moreover, via the Internet connection, remote control of sensor systems for on-site measurement can be carried out, combining the sensors with mechatronics and robotics.

2.15. Carbon-Paper Transducer for Detection of Venlafaxine

• Petra Albuquerque, Miguel Tavares, Vitória Dibo, Cristina Delerue-Matos, Simone Morais and Álvaro Torrinha

• REQUIMTE/LAQV, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal

Pollution is a concern in modern society, with pharmaceutical compounds being increasingly recognized as a major cause. Their improper disposal, along with their increased use, makes them reach the aquatic environment, causing potential harm to the aquatic ecosystem and consequently to human health. As a result, it is of extreme importance to develop sensors capable of monitoring pharmaceutical compounds in a sustainable and affordable way, with a rapid response [1]. The aim of this work is the development of an electrochemical sensor for the determination of venlafaxine in environmental waters, a widely prescribed antidepressant drug. The sensor is based on a carbon paper transducer modified with an iron-based metal-organic framework (MOF), MIL-100, by electrodeposition. Cyclic voltammetry analysis showed an irreversible oxidation peak at around 0.7 V (vs. Ag/AgCl), with higher intensity compared with the unmodified carbon paper. Square-wave voltammetry was then applied to perform the optimization studies regarding electrolyte pH, technique parameters (frequency, step potential, amplitude), and analyte deposition, as well the study of its analytical performance. This electrochemical sensor shows promising analytical features in the determination of venlafaxine, taking advantage of the higher porosity and surface area of the MOF material, resulting in higher adsorption of the drug and thus higher electrochemical efficiency.

• Reference

• Tavares, M.; Morais, S.; Torrinha, Á. Metal-organic frameworks based electrochemical sensors for emerging pharmaceutical contaminants in aquatic environment. Trends Environ. Anal. Chem. 2025, 47, e00271.

2.16. Cerium Oxide Enhanced Electrospun PVDF Nanofibers: Nanoscale Surface Mapping Towards Biomedical Scaffold Development

• Aditya Chauhan, Ashfaqul Hoque Khadem and Lihua Lou

• Department of Mechanical Engineering, Clemson University, Clemson, SC 29631, USA

Electrospun nanocomposite fibers have emerged as promising scaffold materials in tissue engineering due to their high surface area, tunable porosity, and structural resemblance to the native extracellular matrix. However, their limited surface functionality and poor bioactivity often restrict effective cell–material interactions, posing challenges for successful tissue integration and regeneration. In this study, cerium oxide (CeO₂) nanoparticles were incorporated into polyvinylidene fluoride (PVDF) nanocomposite fibers to enhance their mechanical and interfacial properties for scaffold-based biomedical applications. CeO₂, known for its antioxidant, anti-inflammatory, and regenerative characteristics, was embedded into the PVDF matrix via electrospinning. The inclusion of CeO₂ led to a significant increase in surface roughness, Young’s modulus, and surface energy key features that positively influence cell adhesion, spreading, and proliferation. Additionally, uniform nanoparticle dispersion within the PVDF matrix ensured consistent fiber morphology and improved mechanical stability. The engineered nanofibers demonstrate a synergistic combination of enhanced mechanical integrity and surface bioactivity, directly addressing limitations associated with conventional polymer scaffolds. These improvements suggest that CeO₂-functionalized PVDF nanocomposite fibers represent a viable approach for developing advanced tissue engineering scaffolds. This work contributes to the advancement of functional biomaterials designed to support favorable cellular responses and improve therapeutic outcomes in regenerative medicine and tissue repair.

2.17. Characteristics and Analytical Study of Ni-Zr-Melamine-Based Coordination Polymer/Charcoal Composite

• Wahidat Ibrahim ¹, Mary Gojeh ², Hauwa Mohammed Mustafa ¹, Bako Myek ¹, Lucky Sunday ¹ and Musa Julius Gyoghaing ¹

¹ 

Department of Pure & Applied Chemistry, Kaduna State University, P. M. B. 2339 Kaduna, Kaduna State, Nigeria

² 

Department of Pure & Applied Chemistry, College of Computing, Science and Engineering, Faculty of Physical Sciences, Kaduna State University, P. M. B. 2339, Kaduna, Kaduna State, 234, Nigeria

This study investigates the synthesis and characterization of Ni-Zr melamine based coordination polymer (CP) modified using charcoal by mechanochemical synthesis. The materials were analyzed using XRD, SEM-EDS, BET, TGA, and FTIR techniques. The results show that Ni-Zr melamine CP exhibit significantly enhanced surface area at (1178.176 m²/g) and porosity (2.800 nm) compared to the pure melamine ligand (83.803 m²/g). The incorporation of charcoal further increased the surface area to 1873.196 m²/g and pore diameter to 3.120 nm. TGA analysis indicated improved thermal stability of the CP, with total weight loss of 7% compared to 17% of the melamine ligand. X-ray diffraction analysis showed distinct peaks of Ni-Zr melamine at 27.7° and 29.58°, contrasting with pure melamine’s broader peak range (13–55°), which suggests new crystalline phases due to metal ligand interaction. The charcoal-modified composite exhibited a lower angle XRD peak (3–36°), indicating increase disorder and charcoal-induced structural changes. Fourier transform infrared spectroscopy identified characteristic N-H, C≡N, and C=O vibrations, with new peaks in the composite (2849 cm⁻¹) indicating the influence of charcoal. Scanning electron microscopy revealed a complex, agglomerated morphology for the composite compared to the uniform crystalline structure of pure melamine, highlighted enhanced porosity. Energy-dispersive X-ray spectroscopy revealed dominant nitrogen (77.91%) and carbon (17.64%) content, consistent with melamine chemical composition (C₃H₆N₆), with trace impurities (Al, Na, Mg, Ti, Si) suggesting residual contaminants. The study highlighted the synergistic effects of combining CP with charcoal, leading to enhanced material properties and performance. Ni-Zr melamine CPs, especially those modified with charcoal, demonstrated promising characteristics for various environmental applications.

2.18. Chemical Synthesis of High-Purity Silica from Algerian Diatomite for Photovoltaic Applications

• Asmaa Zeboudj and Saad Hamzaoui

• Department of Physics, University of Science and Technology Mohamed Boudiaf (USTO-MB) Oran, Algeria

This study focuses on the chemical synthesis of high-purity silica derived from Algerian diatomite, targeting its use in advanced technological applications, particularly in the photovoltaic sector. Diatomite, a naturally abundant siliceous mineral, was subjected to a controlled chemical purification process using a 4 mol/L sodium hydroxide (NaOH) solution. This alkaline leaching aims to dissolve impurities, enhance silica content, and improve the material’s structural order. The treated samples were extensively characterized by X-ray diffraction (XRD), to assess crystallinity and phase evolution, and X-ray photoelectron spectroscopy (XPS), to examine surface chemistry and impurity levels. The untreated diatomite contained approximately 80% SiO₂. Post-treatment, XRD patterns indicated a clear improvement in crystallinity and the removal of amorphous or non-siliceous phases. XPS analysis revealed a marked reduction in surface impurities, particularly metallic elements and carbon-containing species, with a significant drop in carbon peak intensity, indicating a cleaner silica surface. This enhanced surface purity is crucial for the downstream production of solar-grade silicon. Overall, the results confirm that sodium hydroxide NaOH-based chemical treatment is a simple, effective, and scalable approach for producing high-purity silica from natural diatomite. The purified material demonstrates excellent potential for use in the photovoltaic industry and other advanced applications requiring ultra-clean silica sources.

2.19. Combining Zeolite 5A and Biosynthesized Nanomaghemite for Synergetic Cd²⁺ Removal from Water

• Juan Adrian Ramos Guivar ¹, Mercedes del Pilar Marcos Carrillo ¹, Renzo Rueda Vellasmin ¹, Edson C. Passamani ², Noemi-Raquel Checca-Huaman ³ and César Oswaldo Arévalo-Hernández ⁴

¹ 

Grupo de Investigación de Nanotecnología Aplicada para Biorremediación Ambiental, Energía, Biomedicina y Agricultura (NANOTECH), Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru

² 

Departamento de Física, Universidade Federal do Espírito Santo -UFES, Vitória 29075-910, ES, Brazil

³ 

Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro 22290-180, RJ, Brazil

⁴ 

Departamento de Agronomía, Facultad de Ingeniería, Universidad Nacional Autónoma de Alto Amazonas, Prolongación Libertad 1228, Yurimaguas, Loreto 16501, Peru

This study reports the structural, morphological, and magnetic characterization of two magnetic samples, 8 nm maghemite nanoparticles (MCRES) and MCRES plus zeolite type 5A (MCZ0), the last one developed for Cd²⁺ remediation. Both materials were synthesized via eco-friendly methods using ferruginous precursors and Citrus reticulata peel extract. X-ray diffraction confirmed the presence of maghemite as the dominant magnetic crystalline phase in both samples. The MCRES sample did not exhibit additional diffraction peaks associated with residual precursor phases, whereas MCZ0 displayed improved crystallinity due to the presence of zeolite 5A. Transmission electron microscopy revealed distinct surface morphologies: MCRES featured 2D quasi-spherical nanoparticles, while MCZ0 showed better particle dispersion with more defined contours. Magnetic characterization through vibrating sample magnetometry demonstrated soft ferromagnetic behavior, with MCZ0 exhibiting a lesser saturation magnetization (M_s) of 23 emu g⁻¹ at 300 K than MCRES (M_s ~ 62 emu g⁻¹ at 300 K), suggesting efficient magnetic recovery potential. Fourier transform infrared spectroscopy (FTIR) confirmed the presence of functional groups associated with natural organic residues in MCRES, while MCZ0 showed reduced organic content due to thermal treatment. These findings highlight that the MCZ0 composite, owing to its enhanced crystallinity, reduced organic interference, and superior magnetic response, is a promising candidate for sustainable pollutant removal of Cd²⁺ from aqueous systems. The study provides evidence that the magnetic hybrid significantly uptakes up to 99% for an initial concentration of 50 mg L⁻¹, pH = 6, and adsorbent dose of 1.7 g L⁻¹.

2.20. Complexes of N,N-Donor Dihydrazone Derivative: Perspective on Synthesis, Characterization and In-Vitro Antimicrobial Antioxidant and Anti-Inflammatory Evaluation

• Sadi Abdullahi Hassan ¹, Habu Nuhu Aliyu ² and Mustapha Danlami Garba ²

¹ 

Department of Pure and Industrial Chemistry, College of Natural and Pharmaceutical Sciences, Bayero University, Kano, Nigeria

² 

Department of Pure and Industrial Chemistry, Bayero University, Kano, Nigeria

Hydrazone complexes are gaining more attention in the design of drugs. This had led to lots of studies on metal-base drugs. Mn(II), Fe(II), Co(II), Ni(II), Cu(II) and Zn(II) complexes of dihydrazone derivative were synthesized by conventional reflux method in ethanolic medium and characterized by physicochemical and spectral techniques. The molar conductance values of the complexes in DMF were in the range of 14.5–25.2 Ohm⁻¹cm²mol⁻¹, signifying their non-electrolytic nature. Magnetic susceptibility measurement confirmed the paramagnetic behaviour of the complexes, except Zn(II) complex that was proved to be diamagnetic. Electronic spectra showed pie bonding to pie-antibonding and non-bonding to pie-antibonding electronic transitions at 280–287 nm (absorbance range of 4.54–7.41) and 400–414 nm (absorbance of 3.06–6.32) respectively attributed to C=N moiety. Infrared spectrum of the ligand showed C=N absorption band at 1603 cm⁻¹ which was shifted to 1592–1618 cm⁻¹ in the spectra of the complexes due to coordination through nitrogen atom of azomethine groups. Microanalysis agreed with the proposed formulae of the compounds (1:2 metal to ligand ratio). The mass spectrum of the ligand displayed the molecular ion peak at m/z 381 in a positive mode due to [M + H]+ ion. The TGA/DTGA curve of the ligand revealed two sequential stages of weight loss. In-vitro biological evaluation indicated that, all the complexes exhibited enhanced antibacterial, antifungal, antioxidant and anti-inflammatory activities compared to the free ligand. These findings highlighted the potentials of the compounds as promising antimicrobial, antioxidant and anti-inflammatory candidates.

2.21. Comprehensive Characterization of Moringa oleifera from Ghardaïa: Phytochemical Profiling, Antioxidant Capacities, and Antimicrobial Efficacy Against Pathogens Bacterial

• Farid Bennabi ¹, Mohamedi Mohamed walid ¹, Yasmina Khane ^2,3, Khaled Rahmani ¹, Ali Khalfa ¹, Djamila Boukraa ⁴, Abdelkader Nebatti Ech-chergui ¹ and Abderrahmane Bellaouar ^2,3

¹ 

Department of Biology, University of Belhadj Bouchaib, Ain Temouchent, Algeria

² 

Faculty of Sciences and Technology, University of Ghardaïa, BP 455, Ghardaïa 47000, Algeria

³ 

Materials, Energy Systems Technology and Environment Laboratory, Faculty of Sciences and Technology, University of Ghardaïa, BP 455, Ghardaïa 47000, Algeria

⁴ 

Department of Biology University of Mascara Mustapha Stambouli, Mascara, Algeria

In the field of medicine and the development of new therapeutic techniques, the demand for medicinal plants is very high. This work presents the results of a comparison between some biological activities of aqueous extract and nanoparticles based on aqueous extract of the medicinal plant Pistacia lentiscus fron Ain t’émouchent, Algeria. Phytochemical screening revealed the presence of secondary metabolites such as: tannins, saponins, flavonoids, alkaloids, free quinones, sterols and terpenoids. Two aqueous extracts prepared from Moringa oleifera were obtained from maceration extraction. The estimation of the reducing power of its residues of M. oleifera and the nanoparticles based on aqueous extract was calculated using the method of trapping the free radical DPPH. We noted that this plant has good antioxidant activity and the results reveal that the aqueous extract of the fruits and leaves has better activity with an IC50 of 0.9093 mg/mL and 1.2145 mg/mL, respectively. The nanoparticles based on aqueous extract of the leaves were the least active with an IC50 of 1.988 mg/mL.

The antibacterial effect of the extracts and nanoparticles was evaluated by the agar diffusion method (Muelleur-Hinton). The results indicate that this species also has a high antibacterial activity against the four strains tested (P. aeruginosa ATCC 27853, E. coli ATCC 25922, S. aureus ATCC 25923, S. aureus ATCC 43300), with inhibition zones of variable diameters. We found that the antibacterial effect of the nanoparticles was the best compared to the aqueous extract and compared to silver nitrate regardless of the bacterial strain.

2.22. Conductive Polymer–Natural Clay Nanocomposites for Efficient Heavy Metal Removal from Aqueous Solutions

• Abdelqader El Guerraf ¹, Abdelaziz El Mouden ², Kamal Essifi ³ and Ridouan El Yousfi ⁴

¹ 

Laboratory of Applied Chemistry and Environment, Faculty of Sciences and Technologies, Hassan First University, Settat 26002, Morocco

² 

Laboratory of Applied Chemistry and Environment, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco

³ 

Coordination and Analytical Chemistry Laboratory, Faculty of Sciences, University of Chouaïb Doukkali, El Jadida, Morocco

⁴ 

Laboratory of Applied Chemistry and Environment, Faculty of Sciences, Mohamed First University, Oujda 60000, Morocco

Water pollution caused by heavy metals such as zinc (Zn(II)), copper (Cu(II)), and cadmium (Cd(II)) poses significant environmental and health risks due to their persistence and toxicity. This study investigates the synthesis and characterization of novel conductive polymer (polypyrrole and polyaniline) montmorillonite nanocomposites (PPy@Mont and PAni@Mont) as efficient adsorbents for heavy metal removal from aqueous solutions. The composites were prepared via intercalation of the polymers into sodium-exchanged montmorillonite using in situ polymerization, yielding materials with enhanced adsorption properties. Comprehensive characterization was performed using XRD, FTIR, TGA, SEM, and EDX spectroscopic and microscopic techniques to confirm successful polymer incorporation and evaluate structural and thermal stability. Batch adsorption experiments assessed the effects of pH, contact time, adsorbent dosage, initial metal concentration, and temperature on the adsorption performance. Both composites exhibited optimal adsorption at pH 7.5 and equilibrium at 110 min. PPy@Mont demonstrated higher adsorption capacities for Zn(II) and Cu(II), while PAni@Mont showed superior performance for Cd(II). Adsorption isotherms and kinetic studies revealed that the process aligns with Langmuir and pseudo-second-order models, indicating chemisorption as the dominant mechanism. These findings highlight the potential of PPy@Mont and PAni@Mont as cost-effective, efficient, and reusable adsorbents for heavy metal remediation.

2.23. Development and Characterization of Cellulose Triacetate-Based Membranes for Water Filtration Applications

• Fares Fenniche ¹, Khane Yasmina ¹, Zoulikha Hafsi ¹, Djaber Aouf ², Ilyes Bouchlaghem ², Sofiane Khane ³ and Abdelhalim Zoukel ⁴

¹ 

Materials, Energy Systems Technology and Environment Laboratory, Faculty of Sciences and Technology, University of Ghardaia, Ghardaia 47000, Algeria

² 

Department of process engineering, Faculty of Sciences and Technology, University of Ghardaïa, BP 455, Ghardaïa 47000, Algeria

³ 

University of Ghardaia, Ghardaia, Algeria

⁴ 

Center for Scientific and Technical Research in Physicochemical Analysis (PTAPC-Laghouat-CRAPC), Laghouat 03000, Algeria

The principal objective of this study is to develop and evaluate membranes based on cellulose derivatives for water treatment applications. In the first phase, cellulose triacetate (CTA) was synthesized through a chemical modification process involving the esterification of natural cotton, collected from the Ghardaia region, using acetic anhydride as an acetylating agent. The resulting CTA polymer was then used to fabricate membranes either in their pure form or blended with another biodegradable polymer—chitosan—through a phase inversion technique. The synthesized CTA was structurally characterized using Fourier Transform Infrared Spectroscopy (FTIR), Proton Nuclear Magnetic Resonance (1H-NMR), and Carbon-13 NMR (13C-NMR). The resulting membranes were analyzed by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) to investigate their surface morphology and elemental composition. The structural analyses confirmed the successful acetylation of cellulose and the formation of CTA. The pure CTA membrane exhibited a dense structure with small pore sizes, making it suitable for microfiltration applications. In contrast, the membranes made from chitosan and chitosan/CTA blends showed smoother surfaces and smaller, more uniform pores, making them more appropriate for nanofiltration. The addition of chitosan significantly altered the membrane morphology, enhancing its potential for selective separation. These findings suggest that the developed membranes hold strong promise for future testing in wastewater treatment and environmental remediation applications.

2.24. Development of Bio-Based Carboxymethyl Cellulose/Chitosan and Its Utilization in Controlled Release Fertilizer

• Achanai Buasri, Natacha Janpom, Putita Phetcharat, Witchayaporn Matcha and Vorrada Loryuenyong

• Department of Materials Science and Engineering, Faculty of Engineering and Industrial Technology, Silpakorn University, Nakhon Pathom 73000, Thailand

Fertilizers with slow release and water retention have received a lot of attention lately because of their importance in agriculture and horticultural applications. In this study, a novel controlled release fertilizer system based on carboxymethyl cellulose (CMC) and chitosan (CH) was developed to boost biomass usage efficiency while reducing pollution. The bio-based CMC and CH were made from sugarcane bagasse and golden apple snail shell, respectively. The produced materials were analyzed using a Fourier transform infrared spectrometer (FTIR), scanning electron microscopy (SEM), and moisture absorption techniques. The behaviors of nutrient release in slow release fertilizer (SRF) were examined thoroughly. SEM images reveal that a fertilizer coated with 5 wt% CMC exhibits an effective distribution of CMC across its surface, providing a comprehensive coverage of the fertilizer. However, if the quantity is elevated, the CMC becomes aggregate, leading to a comparatively inadequate coverage of the fertilizer surface. The experimental data revealed that the fertilizer utilizing CMC and CH as coating substances exhibits advantageous slow-release characteristics. The uncoated fertilizer exhibits a water absorption value of 53.3%, while the one-coating layer fertilizer shows a value of 154.0%, and the two-coating layers fertilizer reaches 223.2%. Therefore, incorporating natural polymers can enhance the efficiency of biomass utilization, minimize nutrient loss, and optimize water use efficiency.

2.25. Development of High-Swelling Double-Network Sliver Nanocomposite Reusable Beads for Environmental Remediation

• Fatima Yahya Zabara, Farah Suhail Alhasan, Amal AbdulHakeem Abdulla, Sirine Mounir Zamouri, Noor Ali Mahdi and Roshan Deen

• Materials for Medicine Research Group, School of Medicine, Royal College of Surgeons in Ireland, Medical University of Bahrain, Busaiteen, Bahrain

Introduction: Sodium alginate has been manufactured using biomaterial nanotechnology in the nanometer size range. Nanocomposites exhibit features of high solubility and effective degradation of toxic substances, including heavy metals, pharmaceuticals, and healthcare wastes. This project observes the swelling capacity of sustainably developed sodium alginate-poly sodium acrylate silver nanocomposites and their potential enhanced degradation of toxic organic materials, especially in wastewater.

Methodology: Sodium alginate-poly sodium acrylate polymer beads embedded with silver nanoparticles were prepared through ionotropic crosslinking in calcium chloride solution, followed by free radical polymerization initiated by ammonium persulfate. The synthesized beads were characterized using UV-Vis spectroscopy, FTIR spectroscopy, and electron microscopy: Swelling behavior was assessed gravimetrically, while antibacterial activity was evaluated against clinically relevant pathogens, including Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa, via the incubation method. Additionally, the catalytic efficiency of the beads in degrading Congo red and 2-nitrophenol was investigated in the presence of sodium borohydride.

Preliminary results: The spherical and porous nanocomposite beads demonstrated a significant swelling ability, attributed to their composition of poly sodium acrylate. A distinct surface plasmon resonance (SPR) peak near 400 nm confirmed the presence of silver nanoparticles. These beads further effectively suppressed the growth of E. coli and P. aeruginosa and achieved almost a complete breakdown of Congo red and 2-nitrophenol within 30 min.

Conclusion and work-in-progress: The nanocomposite beads exhibited antimicrobial and catalytic capabilities, suggesting their suitability for future hospital wastewater treatment. Current efforts are directed toward assessing their reusability and performance with actual wastewater samples.

2.26. DNA Binding and In Silico Pharmacokinetics Studies of Salophene Schiff Base and Its Mn(II), Fe(II) and Zn(II) Complexes

• Ibrahim Abubakar Sadiq and Ibrahim Tajo Siraj

• Department of Pure and Industrial Chemistry, Bayero University, Kano, Nigeria

A salophene ligand was successfully synthesized from 4-bromobenzene-1,2-diamine and 5-bromo-2-hydroxybenzaldehyde, followed by the preparation of its manganese(II), iron(II), and zinc(II) metal complexes. These compounds were thoroughly characterized using a range of physical, spectroscopic, and analytical techniques, including Infrared (IR) and UV–Visible spectroscopy, molar conductance, and magnetic susceptibility measurements. These methods confirmed the successful formation and coordination behavior of the metal complexes. To evaluate their biological relevance, the interaction of the ligand and its metal complexes with calf thymus DNA (ct-DNA) was investigated through UV–Visible titration. The binding studies revealed that the free ligand exhibited a binding constant (Kb) of 1.65 × 10⁴ M⁻¹, while the metal complexes demonstrated significantly stronger binding, with Kb values ranging from 3.32 × 10⁵ to 9.29 × 10⁵ M⁻¹, indicating strong DNA affinity and suggesting potential anticancer activity. Furthermore, in silico pharmacokinetic assessments were performed using the SwissADME and pkCSM software tools. These studies revealed that the ligand and complexes exhibited favorable lipophilicity, low fraction unbound values (0.17 F and 0.55 F, respectively), good distribution profiles, and acceptable levels of oral bioavailability. The toxicity profiles were within safe limits. Importantly, the compounds adhered to drug-likeness criteria based on the Lipinski, Veber, and Egan rules, indicating their potential as therapeutic agents against diseases such as cancer, bacterial, and fungal infections.

2.27. Effects of Copper or Germanium Additions on the Stabilized Formation of α-Phase Formamidinium Lead Triiodide Perovskites

• Takeo Oku, Riku Okumura, Ayu Enomoto and Atsushi Suzuki

• Department of Materials Chemistry, The University of Shiga Prefecture, Hikone 522-8533, Shiga, Japan

Formamidinium lead triiodide (FAPbI₃) is one of the candidate materials for stable perovskite solar cells. There exists an optically active cubic α-FAPbI₃ phase, an optically inactive hexagonal δ-FAPbI₃ phase, and a one-dimensional phase. Since the δ-phase is thermodynamically stable, the structural phase transition from the α-phase to the δ-phase causes a serious problem on the photovoltaic efficiencies. The aim of this study is to investigate the effects of copper (Cu) or germanium (Ge) additions on the formation of FAPbI₃. Cu or Ge were added in the present study, as a method for stabilizing the α-phase, to suppress formation of the δ-phase and one-dimensional phase. When Cu was added at the lead site, diffraction peaks of the α-phase increased. Ge addition also increased the diffraction intensity of the α-phase and decreased the diffraction intensity of PbI₂. The possibility of stabilization of FAPbI₃ by Cu introduction at the FA site was also demonstrated. The first-principles band calculation on the Cu-doped FAPbI₃ at the FA site indicated that the total energy value of the crystal decreased. From the calculated partial density of states, the valence band and conduction band are dominated by I-p orbitals and Pb-p orbitals, respectively, and the energy level of the Cu-d orbital is formed at a position slightly lower than the valence band maximum. The effectiveness of Cu introduction in stabilizing the formation of α-FAPbI₃ was also demonstrated in the synthesized FAPbI₃ crystal.

2.28. Electronic and Optical Characterization of DPP for Photodetection and Sensing Applications

• Oumaima El ouardi, Hassane Chadli and Brahim Fakrach

• Laboratory of Advanced Materials Study and Applications (LEM2A), Moulay Ismail University, Faculty of Sciences, BP 11201, Zitoune, Meknes 50000, Morocco

This theoretical study, conducted using Density Functional Theory (DFT) with the Vienna Ab Initio Simulation Package (VASP), investigates the electronic and optical properties of a diketopyrrolopyrrole (DPP)-based material, focusing on its potential applications in semiconductor sensors and photodetectors. DPP is an organic semiconductor material that shows great promise for use in various optoelectronic devices.

The results reveal that DPP exhibits a direct band gap of 2.30 eV, which is crucial for its ability to absorb and emit light effectively. The Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energy levels are clearly defined at −4.47 eV and −2.17 eV, respectively, confirming the material’s semiconducting nature and making it suitable for electronic applications requiring controlled charge transport.

The calculated optical absorption spectrum shows a significant peak centered at 538 nm in the visible range, which corresponds to the HOMO–LUMO transition, making it effective for light detection in the visible spectrum. Additionally, significant absorption is observed in the ultraviolet (UV) region (below 400 nm), which further extends the material’s potential applications in UV-sensitive devices.

These optical and electronic properties make DPP a promising material for photodetectors, where efficient light absorption and signal generation are needed. Its tunable electronic structure also makes it suitable for semiconductor sensors, which can be used to detect a wide range of chemical or environmental factors. This study emphasizes the great potential of DPP for advancing high-performance optoelectronic devices and sensing technologies, with applications ranging from solar energy conversion to environmental monitoring, offering new possibilities for future technologies.

2.29. Electronic Structures, Optical and Acoustic Phonons, and Electronic and Thermal Conductivities of Cesium Ytterbium Chloride Perovskite Crystal

• Atsushi Suzuki and Takeo Oku

• Department of Materials Chemistry, The University of Shiga Prefecture, 2500 Hassaka, Hikone 522-8533, Shiga, Japan

The optoelectronic and transport properties of lead-free ytterbium (CsYbCl₃) perovskites have been characterized. The purpose of this study is to predict the electronic structure of the CsYbCl₃ perovskite crystal and propose guidelines for material design to improve its photovoltaic performance. The CsYbCl₃ crystal was prepared based on the cesium lead chloride crystal structure, structurally optimized, and the band structures and absorption characteristics predicted using first-principles calculations. The electronic structure consists of an occupied 4f orbital of the Yb²⁺ ion in the valence band state and a 5d orbital of the Yb²⁺ ion in the conduction band state, and it exhibits a direct transition band gap of 0.55 eV. Real (Re) and imaginary (Im) dielectric functions were found to be Re = 26.5 at 1.7 eV and Im = 32.2 at 2.9 eV. The absorption coefficient was widely distributed in the range of 163–1768 nm. From the intercept of photon energy with the slope of the Tauc plot, the band gap was found to be 0.55 eV. The acoustic phonon as lattice vibrations exhibited dynamic instability derived from the tilt of the octahedral structure. The temperature behavior of electrical conductivity decreased with increasing thermal conductivity. The hole conductivity was based on the combination of carrier diffusion with lattice vibration as acoustic phonons in the high-temperature region. As a novelty, CsYbCl₃ crystals, due to theoretical predictions, are expected to be applicable as photoactive materials operating at high temperatures for optoelectronic applications such as solar cells and fluorescent devices.

2.30. Electrospun Indium-Doped Nanofibers Based on Gallium Oxide: Fabrication and Characterization

• Aleksandr Vladimirovich Rybalka, Petr Petrovich Snetkov, Maksim Vladimirovich Dorogov, Svetlana Nikolaevna Morozkina and Alexey Evgenievich Romanov

• Institute of Advanced Data Transfer Systems, ITMO University, Kronverkskiy Prospekt, 49, Bldg. A, 197101 Saint Petersburg, Russia

Gallium oxide-based nanofibers (NFs) have gained increasing attention due to their unique combination of physical, optical, and chemical properties. These features make Ga₂O₃ NFs promising materials for UV photodetectors, gas sensors, and optoelectronic devices. However, the effects of dopants on the properties of Ga₂O₃ NFs remain insufficiently studied. This work focuses on the synthesis of undoped and indium-doped Ga₂O₃ nanofibers by electrospinning and the investigation of their structural, morphological, and optical properties.

Polyvinylpyrrolidone was used as a polymer matrix. Gallium nitrate Ga(NO₃)₃·8H₂O (99.9%) and indium nitrate In(NO₃)₃·4½H₂O (99.99%) served as metal precursors. The Ga precursor concentration was 2 wt.%, while indium was introduced at 5 and 10 wt.% relative to gallium. A 1:1 mixture of distilled water and ethanol was used as a solvent.

Electrospinning was carried out under 25 kV, with a feed rate of 1.8 mL/h, at 25 ± 1 °C and 30 ± 1% RH for 40 min. The as-spun fibers were dried for 15 min in a chamber and then for 48 h at room temperature. Thermal annealing was performed at 900 °C for 4 h (heating rate: 5 °C/min) with natural cooling.

Morphological analysis was conducted using SEM and EDS. The optical bandgap was determined by the Tauc-plot method. Indium doping at 10 wt.% led to a 0.35 eV reduction in bandgap, in good agreement with theoretical predictions.

This research was funded by the Ministry of Science and Higher Education of the Russian Federation (project No. FSER-2025-0005).

2.31. Enhanced Energy Storage: A Carbon Paste Electrode Mechanochemically Fabricated with ZnO

• Mary Gojeh ¹, Bemgba B Nyakuma ², Ishaya Sugbeh Alhassan ³, Sadi Shuaibu ⁴ and Hafsat A Tukur ¹

¹ 

Department of Pure & Applied Chemistry, College of Computing, Science and Engineering, Faculty of Physical Sciences, Kaduna State University, P. M. B. 2339, Kaduna, Kaduna State, 234, Nigeria

² 

Department of Chemical Sciences, Faculty of Science and Computing, North-Eastern University, P. M. B. 0198 Gombe, Gombe State, 234, Nigeria

³ 

Department of Engineering, Naval Institute of Technology, Sapele Urban VIII 331107, Delta, 234, Nigeria

⁴ 

Department of Pure & Applied Chemistry, College of Computing, Science and Engineering, Faculty of Physical Sciences, Kaduna State University, P. M. B. 2339 Kaduna, Kaduna State, 234, Nigeria

This study investigates the mechanochemical modification of graphite electrodes with zinc oxide (ZnO) to enhance their electrochemical performance, particularly in the anodic response to ferricyanide. The structural and morphological characteristics of the modified graphite were analyzed using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). SEM images revealed the smoothened surface morphology of the graphite post-ZnO incorporation, indicating an increase in surface area critical for electrochemical reactions. X-ray Diffraction (XRD) analysis confirmed the formation of new compounds upon the mechanochemical synthesis of ZnO-modified graphite, suggesting the successful integration of ZnO into the graphite matrix. Cyclic voltammetry experiments demonstrated a significant enhancement in anodic response when ferricyanide was used as an electrolyte, with increased peak currents observed at elevated scan rates. Varying the scan rate allowed for the differentiation between diffusion-controlled and surface-controlled processes, providing insights into charge transfer mechanisms and the stability of the electrode material. Higher scan rates revealed surface-adsorbed species or fast electron transfer, while lower scan rates are more indicative of diffusion-limited processes. This helped in optimizing the electrode’s performance for energy storage applications. These findings indicate that the mechanochemically modified ZnO-graphite electrode exhibits superior electrochemical properties compared to unmodified graphite, positioning it as a promising candidate for future electrical and battery applications. The results underscore the potential of mechanochemical methods in developing advanced materials for energy storage technologies.

2.32. Enhanced Oral Delivery of Abacavir via Eudragit-Based Nanosuspension System

• Rohit Katkar, Shyam Suryakant Awate and Ashwini Subhash Labade

• IVM’s Krishnarao Bhegade Institute of Pharmaceutical Education and Research, Talegaon Dabhade—Pune, India

The objective of this study was to develop and evaluate a nanosuspension of Abacavir, a poorly water-soluble antiretroviral drug classified under BCS Class II, to enhance its solubility and oral bioavailability. Nanosuspensions were prepared using the quasi-emulsification solvent diffusion method, employing Eudragit RS100 and RL100 polymers in combination with Poloxamer 407 (Pluronic F127) as a stabilizer. A total of eight formulations were developed by varying polymer and stabilizer ratios. The prepared nanosuspensions were characterized for particle size, zeta potential, drug entrapment efficiency, saturation solubility, and in vitro drug release.

Among the tested formulations, ABC-F4 (Drug:Polymer:Stabilizer ratio of 1:2:1 using Eudragit RS100) exhibited optimal characteristics, including a particle size of 92.20 nm, zeta potential of −14.55 mV, and drug entrapment efficiency of 91.21%. In vitro dissolution studies revealed a sustained drug release of 99.87% over 10 h, indicating effective control of drug release. Saturation solubility of the nanosized Abacavir increased nearly fivefold compared to the pure drug, confirming significant solubility enhancement. Compatibility studies using FTIR and DSC showed no interaction between the drug and excipients.

The study concludes that nanosuspension technology using Eudragit RS100 and Poloxamer 407 is a promising strategy to improve the solubility, stability, and oral bioavailability of Abacavir. Such a formulation could contribute to improved therapeutic outcomes in HIV treatment by enabling better absorption and sustained drug release.

2.33. Ensuring the Joint Work of Steel and Concrete Using Multicomponent Composite Materials

• Oleksandra Shevchenko

• Department of Construction Computer Technologies, Kyiv Aviation Institute, 03058 Kyiv, Ukraine

Introduction and Aim: Steel-reinforced concrete is an integral part of construction, but its durability and reliability can be compromised by problems arising from the interaction between steel and concrete. Differences in the properties of these materials often lead to microcracking, reduced load-bearing capacity, increased corrosion, adhesion problems, and a shorter service life of structures.

The study’s main aim is to investigate the effectiveness of multicomponent composite materials (MCM) that improve the interaction between steel and concrete.

Methods: The study was conducted in two stages. First, we selected materials, among which were the following: acrylic polymer compositions; epoxy resins with modified hardeners; liquid glass-based materials with mineral fillers; hybrid organo-mineral systems.

In the second stage, experimental tests were conducted, which included assessment of adhesive strength, corrosion resistance, crack resistance, and durability under cyclic freezing/thawing conditions.

Results and Discussion: The results demonstrate significant improvement in the compatibility of steel and concrete through the use of MCM. In particular, the adhesive strength increased by 20–40% compared to unmodified samples, indicating a denser and more homogeneous structure at the interface. MCM also provided effective protection of permanent formwork, reducing the corrosion rate in aggressive environments by 50% or more. The MCM matrix’s polymer components partially compensated for stresses from thermal expansion differences, reducing the risk of microcracking.

Conclusion: The results of the study confirm that MCM are an effective solution for ensuring the compatibility of steel and concrete. The prospects of using such materials in construction will significantly increase the reliability of structures and extend their service life.

2.34. Evaluation of New Bio-Based Hybrid Composite Materials Reinforced with Basalt Fiber and Recycled Carbon Fiber

• Zahra Esmaeili Dehaghi ¹, Alejandro Cortés Fernández ¹, Andrea Pantín Cordero ¹, Isaac Isarn Garcia ¹, Silvia González Prolongo ¹ and Alberto Jiménez Suárez ^1,2

¹ 

Materials Science and Engineering Area, Escuela Superior de Ciencias Experimentales y Tecnología, Rey Juan Carlos University, Calle Tulipán s/n, 28933 Móstoles (Madrid), Spain

² 

Instituto de Tecnologías para la Sostenibilidad, Rey Juan Carlos University, C/Tulipán s/n, 28933 Móstoles (Madrid), Spain

Developing green materials is essential for the transition toward more sustainable engineering solutions. Among these, biopolymers and their composites represent promising alternatives to petroleum-based polymers due to their biodegradability, lower toxicity, and potential cost-efficiency—especially when derived from waste. In this work, we explore the feasibility of using a bio-based resin, epoxidized resveratrol (RESEP), reinforced with continuous basalt fibers as a sustainable composite for non-structural components in railway vehicles.

Three composite laminates were fabricated via the hand lay-up method, a reference laminate (RESEP + basalt), a laminate with 5%wt DOPO (a flame-retardant agent), and a laminate with 7.5%wt mechanically recycled carbon fiber (RCF), to obtain smart materials by developing electrically conductive composites. The composites were characterized by density measurements, mechanical and thermomechanical testing, and fire resistance evaluations. Additionally, the RCF-reinforced laminate was assessed for structural health monitoring (SHM) capabilities and de-icing performance via Joule heating.

Our results show that the RCF-reinforced laminate combines good mechanical behavior with added functionalities such as SHM and active de-icing, despite slight mechanical trade-offs. In contrast, DOPO inclusion negatively impacted mechanical and fire performance. Here, a significant decrease in interlaminar shear strength was observed, likely due to the increased resin viscosity, hindering fiber impregnation. These defects compromise both mechanical integrity and fire resistance. Consequently, the RCF laminate is proposed for external applications requiring multifunctionality, while the reference laminate is more suitable for fire-sensitive interior uses.

This study supports the advancement of sustainable, high-performance composites for the transportation sector in alignment with circular economy and emission reduction goals.

2.35. Evaluation of Silicon–Graphene Oxide and Silicon Dioxide–Graphene Oxide Composite Anodes for High-Capacity Lithium-Ion Batteries in Terms of Electrochemical Performance

• Amani Azaizia ¹, Maksim Dorogov ¹ and Nada Redjimi ²

¹ 

Institute of Advanced Data Transfer Systems, ITMO University, 197101 St. Petersburg, Russia

² 

Nice Physics Institute, Université Côte d’Azur, 06000 Nice, France

Elevated density in energy-advanced anodes is required for LIBs; however, problems like volume growth or reduced capacity make silicon (Si) and silicon dioxide (SiO₂) unsuitable. Using graphene to overcome these real-world constraints, this study compares Si–graphene oxide (Si-GO) and SiO₂–graphene oxide (SiO₂-GO) composite anodes.

The purpose of this study is to elucidate the different performance characteristics of Si-GO and SiO₂-GO anodes. By comprehending these distinctions, silicon-based materials may be rationally designed to meet specific energy density and cycle life needs for new batteries. This will allow for a customized selection or ideal blend of Si and SiO₂ in graphene composites.

A modified Hummer’s method is proposed to manufacture graphene oxide (GO). GO is combined with either silicon from magnesiothermic reduction (for Si-GO) or a silica precursor (for SiO₂-GO) to generate composites. The morphology and structure of the materials are examined using elemental analysis, TGA, SEM, XRD, Raman spectroscopy, and XPS. Impedance spectroscopy, rate capability testing, galvanostatic cycling, and cyclic voltammetry are used to assess the electrochemical performance of LiFePO₄-based cells.

Si-GO is anticipated to provide a greater initial specific capacity, and because of its conversion reaction mechanism, SiO₂-GO should exhibit improved long-term cycle stability. Both composites should benefit from graphene’s ability to improve electrical conductivity and buffer volume expansion. The trade-off between large capacity and long cycle life will be described in the analysis.

Acknowledgements. This research was supported by the Ministry of Science and Higher Education of the Russian Federation (project No. FSER-2025-0005).

2.36. Exploiting an Invasive Plant (Tithonia diversifolia) for Green Synthesis of CuO Nanoparticles: Petals vs. Leaves

• S.S. Millavithanachchi ^1,2,3, M.D.K.M Gunasena ^1,3, G.D.C.P. Galpaya ³, K.R. Koswattage ^3,4 and D.V.S. Kaluthanthri ⁵

¹ 

Department of Biosystems Technology, Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka

² 

Faculty of Graduate Studies, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka

³ 

Center for Nanodevice Fabrication and Characterization, Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka

⁴ 

Department of Engineering Technology, Faculty of Technology, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka

⁵ 

Department of Plant and Molecular Biology, Faculty of Science, University of Kelaniya, Kelaniya 11600, Sri Lanka

Green synthesis of nanoparticles provides an alternative to conventional methods, which often involve harsh conditions and hazardous chemicals. This study utilized Tithonia diversifolia, an invasive plant species in Sri Lanka. Rich in phytochemicals, it is a promising candidate for green synthesis while converting it into a useful application. CuSO₄·5H₂O (Copper (II) sulfate) was used as the metal precursor. This research compared the petals and leaves of T. diversifolia to determine the optimal source for green synthesis of copper oxide (CuO) nanoparticles. Plant extracts (dried, ground, and sieved petals and leaves) were prepared by dissolving the powders in distilled water [1:10 (w/v)], heating at 70 °C for 15 min, and then centrifuging at 10,000 rpm. The supernatant was mixed with CuSO₄·5H₂O (0.5 M) and distilled water in a 1:1:1 ratio, heated to 70 °C for 15 min, and stirred at room temperature. The colour change indicated nanoparticle production. The solution was centrifuged at 10,000 rpm, washed three times with distilled water, dried at 60 °C, and characterized. Results for petals and leaves were as follows: UV–Vis absorbances at 200–250 nm and 265–285 nm; SEM revealed spherical shapes (20–50 nm) and flake-like structures (~100 nm); FTIR confirmed Cu–O bonding at 500–650 cm⁻¹ with phytochemical residues; zeta potential values were −0.2 mV (petals) and −1.8 mV (leaves), with hydrodynamic diameters of 262 nm and 385 nm, respectively. The study confirms that both are effective, with petals showing improved characteristics. Further research on purification and applications is recommended.

2.37. Exploring the Antifungal Potential of Co(Ii) and Mn(Ii) Schiff Base Derived from O-Aminophenol and Benzaldehyde Complexes, Synthesis, Spectroscopic Studies

• Umar Mudi Ahmad ¹, Harun Ibrahim Khalil ², Junaidu Naaliya ², Zakariyya Uba Zango ³ and Saidu Iliyasu Musa ¹

¹ 

Department of Chemistry, School of Sciences, Kano State College of Education and Preliminary Studies, Kano 234, Nigeria

² 

Department of Pure and Industrial Chemistry, Faculty of Physical Sciences, Bayero University, Kano 234, Nigeria

³ 

Department of Chemistry, College of Natural and Applied Sciences, Al-Qalam University, Katsina State 234, Nigeria

A Schiff base derived from benzaldehyde and o-aminophenol was synthesized, along with its Mn(II) and Co(II) complexes. The synthesized compounds were characterized using standard analytical and spectroscopic methods, including melting/decomposition temperature determination, solubility testing, magnetic susceptibility, infrared (IR) spectroscopy, atomic absorption spectroscopy (AAS), and elemental analysis. Formation of the azomethine bond was confirmed by an IR absorption band at 1614 cm⁻¹ for the free ligand, which shifted to 1603 cm⁻¹ and 1602 cm⁻¹ in the metal complexes, indicating coordination. The melting point of the Schiff base was 189 °C, while the metal complexes decomposed at 273 °C and 220 °C, respectively. Magnetic moment values of 5.78 and 4.81 B.M confirmed paramagnetic behavior. Low molar conductance values (18.5 and 14.1 Ω⁻¹ cm² mol⁻¹) indicated non-electrolytic nature of the complexes. AAS and elemental data suggested a 1:2 metal-to-ligand ratio, with the Schiff base functioning as a bidentate ligand. Biological evaluations revealed that the Schiff base and its metal complexes exhibited significant antibacterial activity against Staphylococcus aureus, Escherichia coli, and Salmonella typhi, and potent antifungal activity against Aspergillus niger, Aspergillus flavus, and Candida albicans. Notably, the antifungal effects were more pronounced at higher concentrations. Cytotoxicity studies showed moderate toxicity, with LC₅₀ values of 132.705 and 147.932 µg/mL for the free ligand and complexes, respectively. These findings suggest that Schiff base metal complexes of Mn(II) and Co(II) hold promise as effective bioactive agents, particularly in antifungal applications.

2.38. Fabrication of Physically Crosslinked Polyvinyl Alcohol and Sodium Alginate Hydrogels for the Delivery of Curcumin and Tranexamic Acid

• Roshan Deen, Dhoha Alshameri, Khadija Mohsen Altaweel, Zainab Alaali and Maha Alhoda

• Materials for Medicine Research Group, School of Medicine, Royal College of Surgeons in Ireland-Medical University of Bahrain, Building No. 2441, Road 2835, Busaiteen Block 228, Bahrain

Introduction: Hydrogels are three-dimensional (3D) crosslinked polymeric materials that has been explored for a wide range of applications. In this work, we have fabricated hydrogels with different morphologies for the delivery of a therapeutic, curcumin and an anti-fibrinolytic agent, tranexamic acid.

Method: Polyvinyl alcohol-sodium alginate hydrogel films containing various ratios of monomers were prepared by performing three freeze-thaw cycles and ionotropic crosslinking with calcium ions (3%). The swelling capacity, equilibrium water content, water diffusion kinetics, network parameters, and morphology were studied using standard methods. Drug loading and release under various external conditions were studied using UV-Vis absorption spectroscopy.

Results: The hydrogels prepared by solvent casting method exhibited a smooth morphology while hydrogels prepared by freeze drawing exhibited a porous and less dense structure. The hydrogels showed good swelling capacity with no disintegration of the material.

Conclusions and on-going work: The hydrogels exhibited properties required for wound dressings. With loaded curcumin and tranexamic acid these materials may have potential application as wound dressing bandages. Other studies such as antibacterial features, cell cytotoxicity, mechanical properties are currently in progress.

2.39. Facile Spin-Coating of 2D Ti₃C₂-MXene/AuNPs Nanocomposites in a PMMA Matrix: Toward Stable Coatings for Memristive Applications

• Ayesha Zaheer ¹, Aldobenedetto Zotti ², Vincenzo Iannotti ^1,3, Ulf Wiedwald ⁴, Anna Borriello ² and Antonio Cassinese ¹

¹ 

Department of Physics “E. Pancini”, University of Naples Federico II, Via Cintia 26, 80126 Naples, Italy

² 

Institute for Polymers, Composites and Biomaterials, National Research Council (CNR-IPCB), P. le Fermi, 1, 80055 Portici, Naples, Italy

³ 

CNR-SPIN c/o Department of Physics “E. Pancini”, Piazzale V. Tecchio 80, 80125 Naples, Italy

⁴ 

Faculty of Physics and Center for Nanointegration Duisburg-Essen, University of Duisburg-Essen, 47057 Duisburg, Germany

Developing reliable processing protocols for two-dimensional materials is essential for their integration into future energy and electronic systems. In this study, we optimized a spin-coating strategy for depositing 2D Ti₃C₂ MXene decorated with gold nanoparticles (MX@AuNPs), dispersed in a polymethyl methacrylate (PMMA) matrix, yielding homogeneous coatings. The MAX-phase precursor was etched using a mixture of HF and HCl¹ and delaminated with LiCl to obtain stable single- and few-layer MXene flakes (single layers being ~200 nm in lateral size), which were subsequently decorated with AuNPs. Characterization using SEM, TEM, and UV–Vis confirmed successful composite formation, with TEM images showing uniform AuNP decoration on MXene flakes2 and UV–Vis spectra revealing the characteristic plasmonic peak of AuNPs alongside MXene features. For spin coating, colloidal solutions were prepared in different solvents, including chloroform, acetone, and N,N-dimethylformamide (DMF), and systematically evaluated. DMF yielded the most stable dispersions, exhibiting excellent solubility for both pristine MXene and MX@AuNP nanocomposites. In addition, the effect of polymer and composite concentration was investigated by varying PMMA content in DMF (1.6 wt% and 8.3 wt%) and MX@AuNPs content in PMMA (1.5 wt% and 3.0 wt%) under different spin-coating speeds and times. Overall, this work establishes a robust spin-coating protocol for MXene-based composites providing a practical pathway toward semiconductor technologies, including memristive devices and future electronic networks.

2.40. Facile Synthesis, Characterization and Antibacterial Study of Carboxymethyl Cellulose Derived from Wheat Husk

• KHALID S K ¹, AISHA Y S ² and UMAR MUDI AHMAD ³

¹ 

Department of Chemistry, Nigerian Police Academy, Kano, Nigeria

² 

Department of Pure and Industrial Chemistry, Faculty of Physical Sciences, Bayero University, Kano, Nigeria

³ 

Department of Chemistry, School of Sciences, Kano State College of Education and Preliminary Studies, Kano, Nigeria

This study investigated the synthesis and characterization of carboxymethyl cellulose (CMC) derived from wheat husk cellulose and evaluated its antimicrobial efficacy. Cellulose was extracted using acidified sodium chlorite and subsequently converted to CMC through etherification involving sodium hydroxide and monochloroacetic acid. The process resulted in a cellulose yield of 15.6% and a CMC yield of 60.8%, with a degree of substitution (DS) of 0.2. Fourier-transform infrared (FT-IR) spectroscopy confirmed successful chemical modification, indicated by the appearance of a carbonyl absorption band at 1640 cm⁻¹. X-ray diffraction (XRD) analysis showed a decrease in crystallinity in the CMC compared with the native cellulose, while scanning electron microscopy (SEM) revealed a shift from a slightly cracked to a smoother surface morphology in the CMC. The antimicrobial properties of the synthesized CMC and zinc oxide-incorporated CMC (ZnO-CMC) biofilms were tested against Escherichia coli, Staphylococcus aureus, Salmonella typhi, and the fungus Cladosporium spherospermum. ZnO-CMC biofilms exhibited enhanced antimicrobial activity and greater resistance to degradation than CMC biofilms alone. These findings suggest that CMC synthesized from agricultural biomass such as wheat husk is a promising biodegradable material for food and pharmaceutical packaging. Additionally, the incorporation of ZnO nanoparticles further enhances its potential as an antimicrobial material for use in pharmaceutical applications.

2.41. Finite Element Modeling of Bilayer P3HT/C60 Organic Solar Cells: Influence of Active-Layer Thickness on Optical Performance

• Fathi BRIOUA ¹ and Chouaib Daoudi ²

¹ 

Electrical Engineering Department, Ahmed Draia University, Route Nationale N°6, Adrar, Algeria

² 

Département Génie Électrique, Université 20 Août 1955, Skikda, Algeria

Introduction: Polymer–fullerene organic solar cells (OSCs) are attractive for their low cost, mechanical flexibility, and compatibility with large-area fabrication. Bilayer architectures using poly(3-hexylthiophene) (P3HT) as the donor and fullerene (C60) as the acceptor provide a simple geometry with well-defined donor–acceptor interfaces. However, their performance is highly dependent on the optimization of active-layer thicknesses to balance light absorption and charge generation.

Methods: Two-dimensional optical simulations were conducted using the finite element method (FEM) to analyze a bilayer OSC stack composed of a glass substrate, a SiO₂ buffer layer, an indium tin oxide (ITO) anode, a PEDOT:PSS hole transport layer, a P3HT/C60 active region, a lithium fluoride (LiF) electron transport layer, and an aluminum (Al) cathode. The optical field distribution and exciton generation rate (G) were evaluated under monochromatic illumination at incident wavelengths of 350, 530, 740, and 860 nm, as well as under the AM1.5G solar spectrum at 100 mW/cm².

Results: The simulations revealed that both the spectral response and the active-layer thicknesses strongly influence device performance. At the selected wavelengths, distinct resonance patterns were observed, showing enhanced exciton generation within the absorber. The optimization study further demonstrated that maximum absorption and short-circuit current density (JSC) were achieved for a 100 nm P3HT layer combined with a 55 nm C60 layer, yielding balanced light confinement and charge generation efficiency across the investigated spectrum.

Conclusion: This numerical investigation highlights the combined impact of wavelength-dependent optical behavior and active-layer thickness on bilayer P3HT/C60 OSCs. The results confirm that precise control of geometry enables substantial improvements in light harvesting and photocurrent generation. These findings are consistent with experimental reports, validating FEM-based modeling as a reliable approach for guiding the optimization of organic photovoltaic devices.

2.42. Fluid Loss Control and Strength Development in Latex-Modified Geopolymer Well Cement for Elevated Temperature and Pressure Subsurface Applications

• NURUL NAZMIN ZULKARNAIN ¹ and Afif Izwan A Hamid ²

¹ 

PETRONAS Research Sdn Bhd, Kajang 43000, Selangor, Malaysia

² 

Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia

Geopolymer cement has garnered increasing attention as a low-carbon alternative to ordinary Portland cement (OPC) for wellbore cementing applications, particularly in CO₂-rich subsurface environments where OPC is prone to chemical degradation. The study investigates the mechanical and structural effects of incorporating styrene-butadiene latex—a colloidal polymer dispersion commonly employed for fluid loss mitigation—into geopolymer formulations subjected to elevated temperature and pressure conditions.

Geopolymer cement slurries were prepared using Class F fly ash, activated by sodium silicate and sodium hydroxide solutions, and modified with varying latex dosages ranging from 0 to 10 wt.% relative to fly ash mass. The specimens were cured at 100 °C and 3000 psi for 48 h, followed by evaluations of compressive strength, microstructure, and elemental distribution. The optimal latex concentration of 4 wt.% yielded a maximum compressive strength of 2937 psi, marginally outperforming the control sample. Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDX) revealed that latex incorporation facilitated silica-rich film formation, needle-like structure, and enhanced microstructural connectivity. In contrast, excessive latex concentrations produced flocculated domains and phase irregularities, resulting in mechanical impairment.

The preliminary findings establish styrene-butadiene latex as a promising fluid loss control agent capable of contributing to early strength gain and structural refinement in geopolymer cement systems under elevated conditions. Future studies should explore co-additive synergies, long-term durability and phase stability to optimize cement integrity for demanding wellbore environments.

2.43. Formation, Characterization and Photocatalytic Activity of Orange Peel-Mediated-Synthesis TiO₂ Nanoparticles

• Yasmina Khane ^1,2, Zoulikha Hafsi ^1,2, Farid Bennabi ³, Fares Fenniche ^1,2, Djaber Aouf ¹, Sofiane khane ¹ and Abderrahmane Bellaouar ^1,2

¹ 

Faculty of Sciences and Technology, University of Ghardaïa, BP 455, Ghardaïa 47000, Algeria

² 

Materials, Energy Systems Technology and Environment Laboratory, Faculty of Sciences and Technology, University of Ghardaïa, BP 455, Ghardaïa 47000, Algeria

³ 

Department of Biology, University of Belhadj Bouchaib, Ain Temouchent, Algeria

Titanium dioxide nanoparticles (TiO₂ NPs) were produced as a consequence of the synthesis procedure by making use of an aqueous extract of navel orange (Citrus sinensis L.) and the co-precipitation approach. The structural and morphological characteristics of the nanocomposites were validated by the use of X-ray diffraction (XRD) as well as scanning electron microscopy (SEM). The findings from both methods revealed that the nanoparticles were effectively generated. These nanoparticles displayed the structure of anatase and ranged in size from fifty to one hundred fifty nanometers for each particle. In order to test the photocatalytic activity of the TiO₂NPs under natural sun irradiation utilizing a batch approach, the ability of the bio-synthesized TiO₂NPs to destroy Malachite green, which is a deadly organic dye, was used. The results of this study showed that the TiO₂ nanoparticles exhibited outstanding photocatalytic activity of green malachite after being exposed to natural solar irradiation for a period of forty-five minutes. Additionally, they were successful in achieving a deterioration rate of 78% or higher. When the findings of this study are taken into consideration, it seems that titanium dioxide nanoparticles are a material that has the potential to be effective for the photodegradation of textiles. This material might be used in applications that are connected to environmental remediation.

2.44. Image-Based Quantification of Aggregate Segregation and Orientation in Hardened Concrete

• Arsalaan Khan Yousafzai ^1,2, Rawid Khan ², Nasir Khan ¹, Ahmed Wajeh Hamza Mushtaha ¹ and Abdullah O. Baarimah ³

¹ 

Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

² 

Department of Civil Engineering, Faculty of Civil, Agricultural & Mining Engineering, University of Engineering & Technology Peshawar, Peshawar 25120, Pakistan

³ 

Department of Civil and Construction Engineering, College of Engineering, A’Sharqiyah University, Ibra 400, Oman

The segregation and orientation of coarse aggregates significantly influence the mechanical behaviour and durability of cement concrete. This study investigates these characteristics using digital image analysis techniques through ImageJ and MATLAB software. Concrete samples were prepared following ASTM C33-01 standards, using two aggregate sizes (1½” and 1”), and produced under both controlled (laboratory) and uncontrolled (field-simulated) conditions to examine the effects of boundary confinement on aggregate distribution. Additionally, the influence of vertical reinforcement (simulated using three #4 steel bars) on aggregate behaviour was also assessed. Concrete cylinders and slabs were cast, and core samples were extracted and sectioned at bottom, middle, and top levels. Surface images were acquired using a flatbed scanner, yielding 48 high-resolution digital cross-sections. ImageJ macros and MATLAB algorithms were employed to process and quantify aggregate segregation and orientation. Results indicate that image-based analysis provides a reliable and effective method for assessing aggregate distribution in hardened concrete. Findings reveal that segregation varies significantly with casting conditions and height within the specimens. Uncontrolled samples exhibited greater peripheral segregation compared to centrally confined ones. Reinforced samples also showed altered distribution patterns due to the presence of steel bars. These insights contribute to a better understanding of mix performance and can guide improvements in construction quality control and concrete durability.

2.45. Improved Optical and Electrical Characteristics of PTB7:PCBM Organic Solar Cells via ZnO Spacer Integration

• Fathi Brioua ¹ and Chouaib Daoudi ²

¹ 

Electrical Engineering Department, Ahmed Draia University, Route Nationale N°6, Adrar, Algeria

² 

Département Génie Électrique, Université 20 Août 1955, Skikda, Algeria

Introduction: Organic solar cells (OSCs) are attractive for low-cost, lightweight, and mechanically flexible photovoltaic technologies. However, their efficiency is limited by suboptimal light absorption and charge extraction. Incorporating an optical spacer, such as zinc oxide (ZnO), has been proposed to improve device performance by enhancing both optical and electrical responses.

Methods: Finite element method (FEM)-based simulations were carried out to examine the effect of a ZnO optical spacer in OSCs. The active layer consisted of a PTB7 donor blended with a PCBM acceptor. Two device structures were analyzed: a reference design (Glass/SiO₂/ITO/PEDOT:PSS/PTB7:PCBM/Al) and a modified configuration with a ZnO spacer inserted between the PEDOT:PSS layer and the active blend (Glass/SiO₂/ITO/PEDOT:PSS/ZnO/PTB7:PCBM/Al). Optical field distribution, exciton generation rate (G), and short-circuit current density (JSC) were evaluated under monochromatic illumination (450–850 nm) and standard AM1.5 solar conditions at 100 mW/cm².

Results: The inclusion of ZnO significantly enhanced the internal electric field distribution and increased exciton generation across the active layer. This improvement is attributed to the dual role of ZnO: (i) its optical spacer effect, which reduces reflection losses at the PEDOT:PSS/active interface and redistributes light more effectively within the absorber, and (ii) its favorable electronic properties, which facilitate electron transport and mitigate interfacial recombination. Under AM1.5 illumination, the ZnO-modified structure exhibited nearly 30% higher light absorption and a notable increase in JSC compared to the reference cell.

Conclusion: This numerical study demonstrates that integrating a ZnO spacer layer into OSCs simultaneously improves light harvesting and charge extraction. The findings, in agreement with experimental reports, confirm the multifunctional role of ZnO as both an optical spacer and an electron-transporting interfacial layer, providing a practical strategy for enhancing the efficiency of organic photovoltaics.

2.46. Influence of Zr Doping on the Structural and Dielectric Characteristics of ZnSnO₃ Ceramics

• Indhumathi R, Sathiya Priya A and Samyuktha B. S.

• Department of Physics, Sri Sai Ram Engineering College, Chennai 600 044, India

Developing lead-free, eco-friendly dielectric materials is essential for the advancement of next-generation electronic and energy storage devices. Among these, perovskite-type oxides such as zinc stannate (ZnSnO₃) are particularly promising due to their high dielectric constant, wide bandgap, and flexible crystal structure. In this study, zirconium (Zr⁴⁺)-doped ZnSnO₃ ceramics with the general formula ZnSn_1−xZr_xO₃ (x = 0.1–0.5) were synthesized using the chemical precipitation method to investigate the influence of Zr⁴⁺ substitution at the Sn⁴⁺ (B-site) position on the structural, optical, and dielectric properties. X-ray diffraction (XRD) confirmed the formation of a single-phase orthorhombic perovskite structure at all doping levels, with peak shifts indicating lattice distortion and unit cell modification due to Zr⁴⁺ incorporation. Fourier-transform infrared (FTIR) spectroscopy exhibited well-defined metal–oxygen bonds, supporting the structural stability and bonding environment of the perovskite framework. Ultraviolet–visible (UV–Vis) spectroscopy revealed shifts in absorption behavior, implying changes in the electronic structure and possible bandgap modulation. Dielectric analysis demonstrated a consistent and progressive increase in the dielectric constant with increasing Zr content, attributed to enhanced ionic polarization, lattice distortion, and subtle structural modifications. These findings underscore the suitability of Zr-doped ZnSnO₃ ceramics as versatile, lead-free dielectric materials with strong potential for use in advanced electronic systems and sustainable technologies.

2.47. Investigation of Anisotropic Thermal Conductivity in Sb-Doped ZnO Thin Films Using Infrared Radiometry and Thermoreflectance Techniques Along with First-Principles Calculation

• Yassmine Sallaki ¹, Misha khalid ², Ankur Chatterjee ^2,3, Hadiqa Naaz ⁴ and Michal Pawlak ²

¹ 

Laboratory of Nanostructures and Advanced Materials, Mechanics and Thermofluids, Physics Department, Faculty of Sciences and Technologies, Hassan II University of Casablanca, Mohammedia, Morocco

² 

Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziądzka 5, 87-100 Torun, Poland

³ 

Applied Solid-State Physics, Experimental Physics VI, Ruhr-University Bochum, Universitaetsstrasse 150, D-44780 Bochum, Germany

⁴ 

School of Interdisciplinary Engineering & Science (SINES), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan

Antimony (Sb) doped ZnO thin films are investigated to elucidate their anisotropic heat-transport properties for advanced thermal management and thermoelectric applications. Films with Sb concentrations from 0 to 0.4 at.% were deposited by pulsed-laser deposition onto sapphire substrates to ensure high crystalline quality, and characterized by X-ray diffraction and atomic force microscopy to confirm phase purity, uniform dopant distribution, and surface morphology. We employ complementary infrared radiometry and frequency-domain thermoreflectance to measure cross-plane and in-plane thermal conductivities at room temperature, with repeat measurements across a 300–500 K range to evaluate temperature dependence. Both conductivity components decrease monotonically with increasing Sb content reaching up to 40% reduction at 0.4 at.% Sb consistent with enhanced phonon scattering induced by mass-defect and strain-field perturbations. All doped samples exhibit pronounced thermal anisotropy: in-plane conductivities exceed cross-plane values by 30–60%, with measurement uncertainties below ±5%. Complementary density functional theory calculations using Quantum ESPRESSO reveal that Sb-induced lattice distortions and local strain fields modify phonon dispersion relations and increase scattering rates, corroborating experimental observations. Figure 1 illustrates the evolution of both conductivity components with doping concentration and underscores the persistent anisotropic behavior. These high-precision, multi-technique measurements deliver unprecedented insight into phonon-mediated heat conduction in doped semiconducting oxides, establish critical benchmarks for the rational design of high-performance thermal barrier coatings, and inform optimization strategies for next-generation thermoelectric modules and integrated thermal management systems.

2.48. Investigation on Ammonium Hydroxide Based Process for the Production of High-Grade Titanium Dioxide from Ilmenite Mud

• Sundararajan Mayappan ¹, Prasanna Muddarangappa ^1,2, Aparna A M ¹, Soumya Sudhakaran ¹ and Peer Mohamed ^1,2

¹ 

Materials Science and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram 695 019, India

² 

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

The decomposition of ilmenite slag by ammonium hydroxide was investigated as a potential enhancement to produce high-purity titanium oxide. It was discovered that the powdered ilmenite slag broke down when it was digested in 4 M NH₄OH at 150 °C. Ammonium titanate ((NH₄)₂TiO₃) was created, and this material was easily hydrolysed in hot water to produce high-purity rutile (TiO₂). It can be determined that it is in the rutile phase using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM-EDX), and other analytical techniques. A flow sheet was created and tested in accordance with the experimental findings. Present study we focus on conversion of waste ilmenite mud produced from VV titanium pigment private limited Thuthukudi. Ilmenite mud generated during production of TiO₂ through H₂SO₄ digestion. Unreacted mass of ilmenite basically comprised of acidic resistive TiO₂ phases such as rutile and anatase. Other mineral mainly silicates (SiO₂, ZrSiO₄). This study is primarily focused on the reutilization of secondary waste generated from TiO₂ production, aiming to recover valuable elements while ensuring the production of zero secondary waste. Ammonia based technology gaining importance due to the factor like high selectivity of transitional ammonia complex, recyclability of ammonia and low temperature reactions. In this we aimed to lower the production cost of TiO₂ from secondary resources.

2.49. Kinetic Study of Diclofenac Removal on Biocomposite Microcapsules in Aqueous Systems

• IKRAM DRAI ¹, MOHAMMED BELDJILALI ¹, Elomari Kawthar ² and Adel Keddou ³

¹ 

Institute of Science and Technology, University of Ain Temouchent, Route Sidi Bel Abbes BP 284, Aïn Témouchent 46000, Algeria

² 

Laboratoire de Matériaux LABMAT, Ecole National polytechnique d’Oran Maurice-Audin ENPO-MA, Oran, Algeria

³ 

Laboratoire de Matériaux et Environnement LME, Université de Médea, Médea, Algeria

The study of sodium diclofenac (SDF) adsorption on alginate/inorganic filler (Alg/PZ) microcapsules addresses the environmental issues related to the presence of pharmaceutical contaminants in water. The Alg/PZ biomaterial microcapsules were synthesized by crosslinking under various conditions (polymer: 0.75–1.3 g; inorganic filler: 0.4–1.2 g). The product and raw materials were characterized by thermogravimetric analysis (TGA); Fourier transform infrared spectroscopy (FTIR); and scanning electron microscopy (SEM).

TGA revealed optimal encapsulation or synthesis for the 0.75/1.2 mass ratio (Alg/PZ), with 63.87% PZ encapsulation, indicating enhanced thermal stability. Effective entrapment of PZ within the polymeric matrix was proven by FTIR and TGA analyses, highlighting hydrogen and electrostatic bonds, while SEM images confirmed a spherical, uniform, and porous bead morphology with a diameter of 1.73 mm. Sallow-bed adsorption revealed instantaneous pseudo-second-order kinetics, reaching equilibrium in 45 min, with a maximum adsorption capacity (q_max) of 21 mg·g⁻¹ for an adsorbent mass of 0.2 g. Furthermore, kinetic studies, supported by the Weber–Morris and Crank (squared driving force model) models, highlighted pore accessibility as well as a concentration-dependent effective diffusion coefficient (D_eff). The squared driving force mass transfer model validated diffusion-limited kinetics (D_eff = 2.96 × 10⁻⁷ cm²·s⁻¹, R² = 0.9832). This study validates Alg/PZ composites as sustainable and scalable solutions compared to conventional adsorbents, enabling 98% SDF removal under optimal conditions.

2.50. Magnetic Nanovehicles Functionalized with Chlorins for Antimicrobial Photodynamic Therapy

• Maribel Lopez, Daniel A. Heredia and Edgardo N. Durantini

• IDAS-CONICET, Departamento de Química, FCEFQyN, Universidad Nacional de Río Cuarto, Río Cuarto X5804BYA, Argentina

Photodynamic inactivation (PDI) has emerged as an effective and selective strategy for microbial inactivation, where close contact between microorganisms and the photosensitizer (PS) is essential to ensure its efficacy. Achieving proper dispersion of the PS in aqueous media is crucial for its performance. In this work, magnetic nanoparticles (MNPs) were designed as platforms to transport PSs in aqueous environments.

Iron oxide (Fe₃O₄) MNPs were synthesized using the conventional co-precipitation method. FeCl₂·4H₂O and FeCl₃·6H₂O were dissolved in water and reacted under mild conditions. The resulting MNPs were then coated with metasilicate, incorporating Na₂SiO₃·5H₂O, to provide colloidal stability. The obtained MNPs-SiO₂ was washed by magnetic decantation and functionalized with aminopropyltriethoxysilane (APTES) in a toluene/THF (1:1) mixture. This spacer introduced aliphatic amine groups capable of reacting through nucleophilic aromatic substitution (S_NAr) with the PSs. Finally, the MNPs-SiO₂-NH₂ was suspended in DMF to react with 5,10,15,20-tetrakis (pentafluorophenyl)chlorin (TPCF₂₀) and its Zn(II) complex (ZnTPCF₂₀). This covalent coupling allowed for the preparation of MNPs-TPCF₂₀ and MNPs-ZnTPCF₂₀, which remained stable in aqueous solutions. Moreover, the magnetic nature of the MNPs-PS facilitated their removal from the medium using external magnetic fields. Spectroscopic characterizations confirmed the retention of the photophysical properties of the attached chlorins. The materials showed the ability to generate reactive oxygen species (ROS) and photodynamic efficacy against both Gram-positive and Gram-negative microorganisms. These results highlight the potential of chlorin-functionalized magnetic nanoparticles as effective antimicrobial agents for applications in aqueous environments.

2.51. Mechanical Modeling of Biomimetic Scaffolds to Recreate Enthesis Function

• Siddhi Sunil, Ashfaqul Hoque Khadem, Vinayak Vijayan and Lihua Lou

• NanoBio Mechanics & Manufacturing Laboratory, Department of Mechanical Engineering, College of Engineering, Computing, and Applied Science, Clemson University, Clemson, SC 29634, USA

The enthesis is the anatomical interface between the soft and hard tissues where tendons or ligaments connect into bone, the stress distribution under shear and tension loads in these areas is defined by complex transitions in collagen fiber architecture, mineral content, and stiffness. There are numerous experimental studies that detail mineral phase gradients and mesoscale mineralized spherules, however, there is limited study into their integration into scaffold design. Aiming to address this gap, this study focuses on the development of accurate models of scaffolds that replicate key functional characteristics of native enthesis function by incorporating multiscale structural features. This study makes use of the CAD software to generate scaffold geometries in order to incorporate site-specific features such as collagen fiber bundle orientation and spatial mineralization patterns. Using Finite Element Analysis (FEA) these models are then subjected to simulate physiological loading scenarios and assess mechanical behavior across the scaffold, including stiffness gradients, stress distribution, and potential failure zones. This research aims to establish a robust design framework and foundation for developing enthesis-mimicking scaffolds by integrating structural hierarch into the scaffold model and validating its mechanical performance. The goal is the development of biomaterials that support seamless tissue integration, aiding improved mechanical resilience and clinical outcomes in tissue engineering and regenerative medicine.

2.52. Micro-Coagulated Ion-Doping Protein Corona “Repulsive Barrier” for Liposomal Metered Dose Inhalers: Construction and Aerosolization Properties

• Junli Zhu, Qian Chen, Han Xie, Chuanbin Wu and Zhengwei Huang

• Department of Pharmacy, College of Pharmacy, Jinan University, Guangzhou 511436, China

Introduction: Liposomal metered dose inhalers possess ample superiorities in pulmonary disease therapy. Nevertheless, liposomes are vulnerable to aggregate in the propellant medium, giving the bottleneck low stability for the clinical translation. Current stabilization strategies based on the physical barrier become invalid in the propellant medium, and there is anurgent need to seek a valid approach to overcome the bottleneck issue. To achieve this goal, we have proposed a new strategy: a micro-coagulated ion-doping protein corona “repulsive barrier”.

Methods: In this research, a liposomal inhalation aerosol was prepared based on the encapsulation system of liposome-Ca²⁺-Cl^--bovine serum albumin. Its size, charge, and morphology were characterized, followed by in vitro release and encapsulation capacity analysis by dialysis. Finally, fluorescent labelling was assessed for aerosol particle distribution and deposition to evaluate its aerodynamic properties.

Results: The findings indicated that the coronated nanoparticles were more homogeneous than other control formulations. Concurrently, the zeta potential was approximately −19 mV, suggesting the surface charge properties were unaffected. The formulation achieved 90% cumulative in vitro release without burst release. The three prepared systems exhibited comparable drug encapsulation efficiencies, reaching 80%. In addition, strong fluorescence signals were detected by fluorescence imaging, which confirmed the excellent encapsulation effect.

Conclusions: The consistent and reproducible results of particle size, zeta potential, morphology, drug release profile, and encapsulation efficiency collectively demonstrated the successful fabrication of the micro-coagulated ion-doping protein corona “repulsive barrier” system. Furthermore, the aerodynamic property assessment showed that the particle size distribution was reasonable and the aerodynamic properties were satisfactory after nebulization.

2.53. Modification of Acrylamide-N,N’-methylenebisacrylamide Hydrogel with Quaternary Ammonium Monomethacrylate

• Patrycja Kula and Izabela Barszczewska-Rybarek

• Department of Physical Chemistry and Technology of Polymers, Faculty of Chemistry, Silesian University of Technology, Gliwice, Poland

Introduction: Wound hydrogel dressings are valued for maintaining a moist environment that promotes tissue regeneration. Incorporating antibacterial components may reduce infections and accelerate healing. This study evaluated the visual integrity of hydrogels by varying quaternary ammonium monomer and crosslinker content.

Methods: As monomers containing quaternary ammonium groups, 2-(methacryloyloxy)ethyl-2-hydroxyethylmethyloctylammonium bromide (QAHAMA-8) and 2-(methacryloyloxy)ethyl-2-decylhydroxyethylmethylammonium bromide (QAHAMA-10) were used in amounts ranging from 2.5 to 100 mol.%. As the crosslinker N,N′-methylenebisacrylamide (bis-AA) was used, and its content ranged from 0.5 to 5 mol.%. As the basic monomer acrylamide (AA) was used, and its content ranged from 0 to 97.5 mol.%, in respect to QAHAMA-10.

Results: Significant differences in sample integrity were observed. The mechanical integrity of the obtained hydrogels depended on both cross-link density and the content of quaternary ammonium monomer. At lower QAHAMA-8 and QAHAMA-10 contents, hydrogels with lower cross-linker amount, i.e., 0.5 and 1 mol.%, were integral. At higher QAHAMA-8 and QAHAMA-10 contents, hydrogels cross-linked with higher bis-AA contents were more integral. For example, for 0.5 mol.% bis-AA integral hydrogels were obtained for 2.5 mol.% to 10 mol.%, whereas for 50 and 100 mol.% of the quaternary ammonium monomer, integral hydrogels were obtained for 5 mol.% bis-AA. Overall, hydrogels of the higher visual quality were obtained for 1 mol.% of bis-AA and quaternary ammonium monomer for 5 mol.% to 30 mol.% (QAHAMA-8) and for 2.5 mol.% to 30 mol.% (QAHAMA-10).

Conclusions: The higher the bis-AA content, the greater the hydrogel brittleness. Only hydrogels cross-linked with 0.5 and 1 mol.% bis AA maintained their integrity over the entire concentration range of QAHAMA-8 and QAHAMA-10.

2.54. Modular Composite Containers for Electric Marine Operations: Lightweight Design Using Vacuum-Infused Basalt Fibre Sandwich Panels

• Benjamin Andoh-Appiah ^1,2, Susana P.B. Sousa ^2,3, Helena M.C Teixeira ² and Andreia Araújo ^2,3

¹ 

Faculty of Engineering, University of Porto (FEUP), Porto, Portugal

² 

INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal

³ 

LAETA-Associated Laboratory of Energy, Transports and Aeronautics, Porto, Portugal

The integration of modular container for Onshore Power Supply (OPS) systems for electric boats presents a strategic advancement in sustainable maritime infrastructures which are under-developed. This study explores the design and production of lightweight, modular containers made using composite sandwich panels with basalt fibre skins, extruded polystyrene (XPS) cores, and epoxy resin matrices. The sandwich panel production process uses one-shot vacuum infusion, ensuring uniform resin distribution and minimized void content. Basalt fibre, known for its high tensile strength, thermal stability, and resistance to corrosion, offers a sustainable alternative to conventional synthetic fibres. Combined with XPS, which contributes thermal insulation and low density, the resulting sandwich structure achieves an optimal balance between weight reduction and mechanical performance. These modular units are tailored to meet the general operational demands, providing adaptable, durable storage and systems housing. Experimental tests confirm the composites’ favourable strength-to-weight ratio and environmental resilience, highlighting their suitability for marine applications. Furthermore, the modularity supports scalability and ease of maintenance, aligning with the dynamic logistical needs of modern electric boat operations. This research underscores the potential of advanced composite manufacturing techniques in maritime applications, contributing to the broader objectives of energy efficiency, operational flexibility, and sustainable design in the naval sector.

2.55. New Stable Hole Transport Material for Perovskite Solar Cells: Decaphenylcyclopentasilane Polysilane Material

• Taiga Nasu ¹, Takeo Oku ¹, Atsushi Suzuki ¹, Tomoharu Tachikawa ² and Sakiko Fukunishi ²

¹ 

Department of Materials Chemistry, The University of Shiga Prefecture, Hikone 522-8533, Shiga, Japan

² 

Frontier Materials Laboratories, Osaka Gas Chemicals Co., Ltd., Osaka 554-0051, Japan

Spiro-OMeTAD, currently the most widely used hole transport material, has high hole mobility. However, its poor thermal durability and low long-term stability when applied to photovoltaic devices are due to the moisture absorption and ion diffusion of the dopant. The objectives of this research are to fabricate and evaluate perovskite solar cells consisting of various perovskite compositions and stacked layered structures using decaphenylcyclopentasilane (DPPS), and to clarify the effects of DPPS on the perovskite layers. The novelty of this study is that bilayer stacked structures were fabricated using different types of perovskites using DPPS as a protective and hole transport layer, and to propose the design principles for highly reliable photovoltaic devices. Due to the high-temperature durability and chemical inertness of DPPS, high-temperature annealing of devices is possible, which is expected to improve the thermal stability and long-term stability of perovskite solar cells. Current–voltage measurements showed that devices using DPPS and heat-treated at higher temperatures than the ordinary annealing temperatures maintained their photoconversion efficiency even after one year. Thermal stability tests also showed that the devices maintainedefficiencies higher than 85% of their initial efficiencies after 3600 s at elevated temperatures. In addition, microstructure analysis through XRD measurements revealed that DPPS crystallized after the heat treatment. The results of optical absorption measurements showed that the crystallization of DPPS suppressed the desorption of MA⁺ from the perovskite, thereby increasing the optical absorption intensity, which indicated contribution to the improvement of the photoelectric conversion efficiencies of the devices.

2.56. Numerical Investigation of Gold Nanoshell Heating Dynamics for Optimized Nanoshell-Assisted Cancer Photothermal Therapy

• Chouaib Daoudi ¹ and Fathi Brioua ²

¹ 

Department of Electrical Engineering, University of Skikda, Skikda, Algeria

² 

Department Electrical Engineering, University of Ahmed Draia Adrar, Adrar, Algeria

Introduction: Gold nanoshells (AuNSs) with dielectric cores have emerged as promising agents for biomedical photothermal therapy due to their tunable plasmonic properties in the near-infrared region. Understanding their thermal responses under various laser excitations is crucial for optimizing their therapeutic efficiency and safety.

Methods: We employed finite element modeling (FEM) in COMSOL Multiphysics to investigate the spatiotemporal temperature evolution of SiO₂@Au and BaTiO₃@Au nanoshells, as well as nanobars, under continuous-wave (CW), nanosecond (ns), and femtosecond (fs) laser irradiation. The models coupled electromagnetic absorption with heat transfer, accounting for electron–phonon and phonon–environment interactions through one-, two-, and three-temperature models depending on the pulse duration.

Results: Our simulations show that BaTiO₃@Au nanoshells exhibit significantly higher absorption and heating than SiO₂@Au, particularly at 800 nm, a wavelength relevant for biomedical applications. Under CW excitation, the temperature rise is moderate and spatially uniform, reaching equilibrium within hundreds of nanoseconds. In contrast, ns pulses produced localized heating with delayed peak temperatures (~203 K for BaTiO₃@Au vs. ~34 K for SiO₂@Au at 5 mJ/cm²), while fs pulses induced ultrafast electron heating (>3000 K) followed by energy transfer to the lattice and environment within a few nanoseconds. Parametric studies revealed a strong dependence of the thermal response on shell thickness, pulse duration, and fluence.

Conclusion: This work highlights the distinct thermal dynamics of gold nanoshells under different irradiation regimes and identifies BaTiO₃@Au as a highly efficient photothermal agent. These insights provide valuable guidance for designing nanoshell-assisted cancer therapies with controlled heating and minimal collateral damage.

2.57. One-Pot Hydrothermal Synthesis of Fe-Doped ZnO Nanoparticles

• C.M. Lopez-Badillo and Diego Antonio Corona-Martinez

• Facultad de Ciencias Químicas, Universidad Autónoma de Coahuila, Blvd. V. Carranza y José Cárdenas Valdés, C.P. 25280, Saltillo, Coah., Mexico

Zinc oxide is a highly studied material due to its versatile properties and widespread use in multiple fields of application. Therefore, it is important to develop methods to synthesize zinc oxide particles, capable of achieving homogeneous morphology and size, to maximize their applicability as well as improve and control their properties.

This study focuses on the production of Fe-doped zinc nanoparticles via one-pot hydrothermal synthesis, since although there is currently a wide variety of well-established procedures for their production, these methods tend to require many steps and highly specialized equipment to achieve adequate control of the nanoparticles produced.

Therefore, hydrothermal synthesis is proposed as a simpler method to achieve better control of crystallized nanoparticles via a single step without requiring any further purification or refinement of the crystalline structures.

To enhance the properties of the ZnO nanoparticles, doping of the wurtzite structure with Fe ³⁺ ions in quantities from 1 to 10% was proposed, and experiments were carried out on Teflon-lined stainless-steel autoclaves at 160 °C during various reaction intervals (1–12 h) using Zn(NO₃)₂ 6H₂O, FeCl₃·6H₂O and NaOH solutions as hydrothermal media.

All the obtained powders analyzed by XRD displayed defined peaks coinciding with the ZnO in wurtzite phase, indicating that no secondary phases crystallized during the treatment. In addition, displacement was concurrent with the decreasing size of the crystallite as the Fe ³⁺ content increased (32 to 28 nm). Finally, FT-IR and UV-Vis spectroscopy indicated the formation of ZnO nanoparticles doped with high Fe content.

2.58. Optimizing Piezoelectric Performance of ZnO Nanowire Arrays Through Integration with Metallic Substrates

• Mariana Chelu ¹, Hermine Stroescu ¹, Jose María Calderon Moreno ¹, Peter Petrik ², Zoltan Labadi ² and Mariuca gartner ¹

¹ 

“Ilie Murgulescu” Institute of Physical Chemistry, Spl. Independentei 202, 060021 Bucharest, Romania

² 

Centre for Energy Research, Hungarian Academy of Sciences, Konkoly-Thege Str. 29-33, H-1121 Budapest, Hungary

The development of advanced energy harvesting technologies requires piezoelectric architectures that combine high efficiency with mechanical flexibility. In this study, we conduct a comparative investigation of zinc oxide (ZnO) nanowires (NW) arrays grown on rigid Au/Pt-coated substrates and on flexible titanium foils, with the aim of evaluating their potential for integration into next-generation piezoelectric devices.

Vertically aligned ZnO NWs were grown via a low-temperature hydrothermal process on a crystalline seed layer prepared by sol–gel spin deposition. To enhance structural integrity and device compatibility, the NW arrays were encapsulated within a polymethylmethacrylate polymer matrix. The nanostructures were characterized throughout fabrication using transmission electron microscopy, scanning electron microscopy, and spectroscopic ellipsometry, confirming uniform morphology, high crystallinity, and consistent alignment. Piezoelectric properties were directly evaluated by measurements of the effective longitudinal piezoelectric coefficient (d₃₃), enabling a quantitative comparison between rigid and flexible device platforms. The results revealed a significant enhancement in piezoelectric performance for ZnO NWs integrated on titanium foil compared to those grown on Pt/Au substrates. This improvement is attributed to superior vertical integration and enhanced mechanical adaptability of the NWs when supported by a flexible substrate. These findings highlight the critical role of substrate choice in optimizing nanoscale piezoelectric performance. Moreover, the demonstrated low-cost, solution-processed growth of ZnO NWs on flexible metal foils underscores their potential for scalable fabrication of energy harvesting systems.

These results indicate that solution-processed ZnO nanowires networks on metal foils provide a cost-effective, scalable pathway to developing efficient, environmentally sustainable energy harvesting devices.

2.59. Preparation of Composite Nanofibers with M-Type Nickel Ferrite and Graphene Oxide and Investigation of Their Application in Dye Pollutant Adsorption

• Seyyed Ghasem Nasiri Salvash, Mahdi Heidari-Golafzani and Mahboubeh Rabbani

• Department of Chemistry, Iran University of Science and Technology, Narmak, Tehran 16846-13114, Iran

Introduction: Water contamination by synthetic dyes from industrial effluents presents a severe threat to ecosystems and public health. There is a critical need for the development of efficient, sustainable, and easily separable adsorbents to remove these pollutants. This study focuses on synthesizing and evaluating a novel magnetic nanocomposite adsorbent that combines the high adsorption capacity of graphene oxide (GO) with the magnetic properties of nickel ferrite for the effective removal of dye pollutants from wastewater.

Methods: Magnetic M-type nickel ferrite (NiFe₁₉O₂₀) nanoparticles were first synthesized via a combustion method. Subsequently, composite nanofibers were fabricated by incorporating these nanoparticles and graphene oxide (GO) nanosheets into a polymer matrix using the electrospinning technique. The resulting NiFe₁₉O₂₀/GO composite nanofibers were thoroughly characterized using SEM, TEM, XRD, FT-IR, and VSM. Their adsorption performance was evaluated using Methylene Blue (MB) as a model cationic dye.

Results: The characterization results confirmed the successful integration of NiFe₁₉O₂₀ nanoparticles and GO nanosheets within the nanofiber matrix. The composite exhibited excellent superparamagnetic properties, allowing for rapid separation from water using an external magnet.

Conclusions: The NiFe₁₉O₂₀/GO composite nanofibers successfully combine the exceptional adsorption capacity of GO, provided by its high surface area and functional groups, with the superb magnetic separability of nickel ferrite. This study conclusively demonstrates that this material is a highly effective, reusable, and easily retrievable adsorbent. It presents a promising and sustainable solution for the advanced treatment of dye-laden wastewater, offering significant potential for environmental remediation applications.

2.60. Pulse-Atomic Force Lithography Nanopatterning of Chitosan Film: A Novel Approach for the Eco-Sustainable Manufacturing of Nanowires

• Lorenzo Vincenti ^1,2

¹ 

Department of Mathematics and Physics “E. De Giorgi”, University of Salento, Lecce, 73100 Via Monteroni, Italy

² 

Institute for Microelectronics and Microsystems (IMM), CNR, Lecce, 73100 Via Monteroni, Italy

This experimental work explores the application of an innovative, eco-friendly, and highly-reproducible nanofabrication technique, namely Pulse-Atomic Force Lithography (P-AFL), as the starting point of a process that employs eco-sustainable materials to manufacture nanowires. A thin film made of chitosan, a biopolymer, was obtained by spin-coating a solution of medium-molecular-weight chitosan at a concentration of 0.8% w/v in acetic acid 1% v/v on a silicon oxide substrate. The surface morphology and thickness of the resulting chitosan film was characterized by Atomic Force Microscopy (AFM): the film was homogeneous, with a roughness of about 1 nm and a thickness of about 52 nm. Then, the P-AFL technique was optimized to pattern a set of nanogrooves on chitosan. A metal layer of chrome and titanium, with a 1:10 ratio, was deposited on the chitosan film by Electron Bem Evaporation (EBE). Successively, the samples were submitted to a lift-off process by an acetic acid solution (1% v/v) with the aim of dissolving the chitosan layer. The resulting nanowires were then characterized by AFM and Scanning Electron Microscopy (SEM): the nanostructures appeared well-fabricated, with a length of 10 μm and a height of (71 ± 20) nm. The entire process is based on the use of the P-AFL technique, which does not require the use of toxic chemicals, i.e., the developer resists for conventional optical lithography. Moreover, chitosan is eco-sustainable, as it is derived from natural sources, bio-compatible, and bio-degradable, and the lift-off step is performed by using acetic acid, a non-harmful chemical.

2.61. Quantum Mechanical Insights into the Extraction of Quercetin and Caffeic Acid Using Choline Chloride–Glycerol Deep Eutectic Solvent

• Lucia Kuyet CHRISTOPHER, Toyese OYEGOKE and Sharon OLORUNFEMI

• CAD Engineering of Processes and Reactive Interfaces Group, Chemical Engineering Department, Ahmadu Bello University, Samaru, Sabon-gari LGA, Zaria 810106, Kaduna State, Nigeria

Quercetin (QUE) and caffeic acid (CAF) are bioactive compounds with proven antioxidant, anti-inflammatory, and anticancer properties. The development of efficient and eco-friendly extraction techniques for these compounds is essential for advancing sustainable pharmaceutical and nutraceutical applications. In this study, a choline chloride–glycerol (CLC–GLY) deep eutectic solvent (DES) was explored as a green alternative to conventional solvents such as ethanol (ETOH) for extracting QUE and CAF. A quantum mechanical approach was employed to investigate the molecular-level interactions between the DES and the target compounds and to compare their extraction potential with that of ethanol. All calculations were carried out using the wB97X-D functional, which accounts for dispersion interactions, starting from PM3-optimized geometries. A dual basis set (6-31G(d)/3-21G*) was applied in the gas phase. The results revealed that CLC–GLY exhibits a lower HOMO–LUMO energy gap (11.19 eV) compared to ethanol (14.06 eV), indicating that CLC–GLY is less electronically stable but more chemically reactive. More importantly, the DES demonstrated stronger binding interactions with caffeic acid (–1.12 eV) and quercetin (–0.96 eV) than ethanol, which showed significantly weaker binding energies. These enhanced interactions suggest that the CLC–GLY solvent has greater extraction potential for both QUE and CAF due to its higher chemical reactivity and stronger affinity toward the target molecules. Overall, this study supports the application of DESs, particularly choline chloride–glycerol, as promising green solvents for the extraction of bioactive phytochemicals, offering a more sustainable and effective alternative to conventional solvents in pharmaceutical development.

2.62. Rapid Green Synthesis of Reusable Alginate-Silver Nanocomposite Beads Using Yerba Mate (Ilex paraguariensis) for Catalytic and Antibacterial Applications

• Adnan Abdulkarim Alsaei, Abdulla Fawzi Albalooshi, Ahmed Jawad Alaraibi, Fatima AlHannan, Fryad Henari and G. Roshan Deen

• School of Medicine, Royal college of surgeons in Ireland—Bahrain (RCSI), Busaiteen, Bahrain

Introduction: Green synthesis of NPs has gained momentum as a promising and alternative approach. The use of aqueous extracts of plants is an attractive approach as the protocol is simple, cost-effective, and does not produce any toxic wastes. Our aim was to leverage the high content of phytochemicals in fresh leaf extract of Yerba mate (Ym) to sustainably produce AgNPs and AgNc beads, and to evaluate their catalytic degradation of hazardous compounds and antibacterial efficacy.

Method: Sodium alginate-silver nanocomposite (SA-AgNc) beads were prepared by reduction and ionotropic crosslinking (gelation). The beads were characterized by UV-Vis absorption spectroscopy, electron microscopy, and energy dispersive X-ray spectroscopy (EDX). The catalytic potential was studied by following the degradation of organic compounds such as 2-nitrophenol, Congo red, and methylene blue. The antibacterial efficiency was tested against Escherichia coli.

Results: The SA-AgNc beads were close-to spherical shape with cauliflower-like morphology. The presence of pores and AgNPs on the beads were confirmed by electron microscopy and EDX mapping. Sigmoidal degradation profile reflects initial adsorption on the surface, then rapid breakdown. The beads were effective in disinfection of the bacterial solution.

Conclusions: SA-AgNc beads containing Ym reduced AgNPs was successfully prepared by reduction and ionotropic crosslinking with calcium ions. The catalytic and antibacterial activities of SA-AgNc were promising. In future, these types of materials hold promise in the treatment of hospital wastewater.

2.63. Removal of Thermal Stable Salts from MDEA and DEA Solvents via Vacuum Distillation

• Jonibek Norqulov ¹, Muhriddin Ibodullayev ², Orifjon Kodirov ³, Olim Abdurahmanov ⁴ and Adham Norkobilov ¹

¹ 

Department of Food Engineering, Karshi State Technical University, 20, Shahrisabz Street, Shahrisabz, Uzbekistan

² 

Department of Engineering Technologies, Shahrisabz branch of the Tashkent Institute of Chemical Technology, Shahrisabz, Uzbekistan

³ 

Centre of Information Technologies, Tashkent State Technical University, Tashkent 100095, Uzbekistan

⁴ 

Department of Automation of Technological Procesesses and Production, Bukhara State Technical University, 15, Murtazayev Street, Bukhara 200100, Uzbekistan

The formation of thermal stable salts (TSS) in amine-based natural gas sweetening processes is a major operational challenge that compromises the efficiency and longevity of solvent systems. This study explores the degradation mechanisms of methyldiethanolamine (MDEA) and diethanolamine (DEA) solvents used in the gas treatment units at the Shurtan Oil and Gas Production Department. It was identified that the presence of impurities such as COS and chloride ions—introduced from upstream zeolite purification units—significantly accelerates solvent degradation, leading to the accumulation of TSS, surfactants, and organometallic compounds. These degradation products not only reduce absorption efficiency but also promote corrosion, foaming, and viscosity increases, resulting in higher energy consumption and more frequent solvent replacement. Elemental analysis of degraded solvent samples revealed high concentrations of sulfur, calcium, potassium, and corrosion-related metals such as iron, chromium, and manganese. To address these issues, a two-stage vacuum distillation system was designed and evaluated. The first stage involves atmospheric distillation to concentrate the solution, followed by vacuum steam stripping in the second stage to remove degradation products and recover purified amine. Experimental results demonstrate a solvent recovery rate exceeding 90%, with significant reductions in TSS and degradation byproduct concentrations. This method offers a practical and efficient approach to solvent reclamation, extending the service life of amine solutions and improving the stability and sustainability of gas sweetening operations. The proposed technology aligns with international practices and holds potential for broader adoption in natural gas processing facilities where solvent degradation is a persistent issue.

2.64. Reutilization of Raffinate for the Benefication of Siliceous Manganese Ore

• Sundararajan Mayappan ^1,2, Prasanna Muddarangappa ^1,2, Safa Fathima ¹ and Peer Mohamed ¹

¹ 

Materials Science and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram 695019, India

² 

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Siliceous manganese ores are the manganese ore which contain low percent of Mn, SiO₂ and Fe. Due to widespread application of manganese in the world, it is necessary to increase the production of manganese. In this work, waste acid and Raffinate are mainly used for the beneficiation process of siliceous manganese ore hydrometallurgical. The acid leaching ores performed using different solvents such as 2 M HCl, waste acid and Raffinate with different ratios at 95 °C for two hours. The leached residues and filtrate are investigated with characterisation techniques. All the studies were compared with analytical grade HCl. It revealed the secondary leaching of manganese in pure acids over waste acid. Additive or catalytic activities may increase the rate of liberation of manganese. The beneficiation of siliceous manganese ore presents significant challenges due to the high silica content, which reduces the efficiency of conventional processing methods. At the same time, hydrometallurgical processes used in metal extraction generate large volumes of raffinate, a waste solution that is often acidic or alkaline and contains residual metal ions. Disposing of this raffinate poses serious environmental concerns and contributes to increased treatment costs. Hence, this study is necessary to evaluate the feasibility, effectiveness, and environmental impact of raffinate reutilization in manganese ore processing, offering a more sustainable and economical approach for the industry.

2.65. Selective Chloroacetylation of Methoxyphenol Isomers in the Presence of Various Catalysts: Product Distribution and Mechanistic Insights

• Ruzimurod Jurayev ¹, Azimjon Choriyev ² and Anvar Abdushukurov ³

¹ 

Department of “Chemical Engineering”, Karshi State Technical University Shahrisabz Faculty of Food Engineering, 20, Shakhrisabz Str., Shakhrisabz 181306, Uzbekistan

² 

Department of “Organic Chemistry”, Karshi State University, 17, Kuchabog Str., Karshi 180103, Uzbekistan

³ 

Department of “Organic Chemistry”, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent 100174, Uzbekistan

The selective chloroacetylation of methoxyphenol isomers (ortho-, meta-, and para-) was studied under mild conditions using catalytic amounts of various Lewis acids, including FeCl₃, FeCl₃·6H₂O, MoCl₅, WCl₆, ZnCl₂, SnCl₄, VCl₃, TAA, and TSA. The reactivity and selectivity of each isomer toward chloroacetyl chloride were evaluated in nonpolar solvents such as benzene. The influence of catalyst type, molar ratio, temperature, and reaction time on product yield and distribution was systematically investigated. Spectroscopic characterization of the reaction products was performed using IR, UV, and NMR techniques, confirming the formation of O-acylation products (methoxyphenyl chloroacetates) as well as regioisomeric C-acylation products (hydroxy-methoxyphenacyl chlorides). Notably, o-methoxyphenol gave a mixture of three main products, while m- and p-isomers primarily yielded two. Among the catalysts tested, FeCl₃ provided the highest overall yield and favored O-acylation, while stronger Lewis acids like SnCl₄ and MoCl₅ showed greater influence on C-acylation pathways. Mechanistic studies indicate that the reaction proceeds via nucleophilic substitution at the carbonyl carbon of chloroacetyl chloride rather than through a classical acylium ion intermediate, due to the aprotic solvent system and low catalyst concentration. The product distribution correlated strongly with electronic and steric effects of substituents on the aromatic ring. The findings offer valuable insights into structure–reactivity relationships in electrophilic aromatic substitution reactions of substituted phenols and demonstrate the potential of such transformations in the synthesis of bioactive intermediates.

2.66. Silver Nanoparticle-Based Delivery of Mebeverine: A Targeted Approach for Irritable Bowel Syndrome

• Vera Nikolaeva Gledacheva ¹, Miglena Milusheva ², Mihaela Stoyanova ², Iliyana Stefanova ¹, Mina Todorova ² and Stoyanka Nikolova ²

¹ 

Department of Medical Physics and Biophysics, Faculty of Pharmacy, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria

² 

Department of Organic Chemistry, Faculty of Chemistry, University of Plovdiv, 4000 Plovdiv, Bulgaria

Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder characterized by abdominal cramps, pain, bloating, and altered bowel habits, severely affecting the quality of life of the patients. Mebeverine is a common antispasmodic agent that relaxes smooth intestinal muscle, yet its clinical application is limited by systemic side effects. To improve therapeutic targeting and efficacy, we developed a silver nanoparticle (AgNP)-based drug delivery system loaded with mebeverine.

Methods: Drug-loaded AgNPs were synthesized and their effects on smooth muscle contractility were assessed in vitro in the presence of cholinergic inhibitors, selective receptor antagonists, calcium blockers, and neurotransmitters. Anti-inflammatory activity was evaluated via albumin denaturation inhibition.

Results: Mebeverine-loaded AgNPs exhibited distinct effects on contractile response parameters, although they did not directly block cholinergic receptors. In the anti-inflammatory assay, mebeverine demonstrated higher inhibition of albumin denaturation compared to diclofenac, while the nanoparticle formulation retained superior activity relative to diclofenac but was slightly less potent than free mebeverine.

Conclusions: The mebeverine-loaded AgNP system shows promising spasmolytic and anti-inflammatory potential in vitro, supporting its further evaluation as a targeted therapeutic strategy for IBS management.

Acknowledgments:

This study is supported by the Bulgarian Ministry of Education under the National Program “Young Scientists and Postdoctoral Students–2”, Project № MUPD-HF-016.

2.67. Strength and Consolidation Characteristics of Expansive Clay Improved with Lime–Nano-Metakaolin and Sisal Fibre Reinforcement

• Roland K. Etim ¹, Cyril Etim ², Kenneth E. Andem ³ and Ini-Obong N. Stephen ⁴

¹ 

Department of Civil Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria

² 

Department of Civil Engineering, Nigeria Defense Academy, Kaduna, Nigeria

³ 

Department of Marine Engineering, Maritime Academy of Nigeria, Oron, Nigeria

⁴ 

Department of Civil Engineering, University of Uyo, Uyo, Nigeria

Expansive clay soils are known for their low strength and high compressibility, which often lead to severe geotechnical challenges in construction. This study evaluates the combined effects of lime, nano-metakaolin (NMK), and sisal fibre on the strength and consolidation behavior of expansive clay. Lime content was maintained at 4%, while NMK was varied at 4, 8, and 12%, and sisal fibre was incorporated at 2, 3, 4, and 5% by dry weight of soil. A series of laboratory tests, including unconfined compressive strength (UCS) and one-dimensional consolidation tests, were conducted to determine the mechanical and compressibility characteristics of the treated soils. The results indicate that the inclusion of NMK significantly enhanced pozzolanic reactivity with lime, leading to improved soil bonding and higher UCS values. The addition of sisal fibre provided tensile bridging within the soil matrix, which increased ductility and contributed to sustained strength at higher fibre dosages. In terms of consolidation, the treated specimens showed reduced compressibility and lower coefficient of consolidation compared with untreated expansive clay, reflecting the densification and pore-filling effects of lime–NMK reactions and fibre interlocking. The optimum performance was observed at intermediate NMK contents (8%) and fibre reinforcement of 3–4%, where a balance between strength gain and reduced compressibility was achieved. Overall, the study demonstrates that a combination of lime, NMK, and sisal fibre provides a sustainable and effective technique for improving the strength and consolidation properties of expansive soils, with promising applications in geotechnical engineering practice.

2.68. Study of the Inclusion Complexes of γ-Cyclodextrin with Non-Steroidal Anti-Inflammatory Drugs Using Differential Scanning Calorimetry

• Victoria Andreevna Pavlova and Elena Grekhneva

• Department of Chemistry, Kursk State University, Kursk, Russia

The formation of cyclodextrin clathrates with NSAIDs leads to an improvement in their physicochemical properties. To confirm the structure of the inclusion complex, a combination of physicochemical analysis methods was used, including differential scanning calorimetry (DSC).

To obtain the clathrate, γ-cyclodextrin (γ-CD) and the NSAID nimesulide, which is used in the treatment of acute and chronic pain, were employed. The possibility of complex formation was previously evaluated using the ChemOffice 16.0 and Gaussian 09 W software packages. Complexation was achieved through co-precipitation and co-evaporation methods. Changes in the thermal properties of the obtained complexes were recorded by the DSC method.

During the study, thermograms and thermodynamic characteristics of γ-CD, nimesulide, and the proposed complexes were obtained. The shift of the endothermic peak of nimesulide (from 148 °C to 191 °C), corresponding to its melting point, indicates its possible incorporation into the γ-CD cavity and transition to an amorphous state. The endothermic peak of γ-CD at around 75 °C decreases in intensity due to the expected change in hydration during complexation. The appearance of an endothermic peak near 283 °C may correspond to the decomposition of the complex or a change in its structure.

Thus, the DSC study confirms the formation of an inclusion complex between γ-CD and nimesulide, and the data are consistent with the results of computer modeling, indicating the possibility of successful complexation.

2.69. Studying Geopolymer as an Eco-Friendly Material in the Restoration of Mural Paintings

• Gehan Mostafa Mohammed Hassan El Kempshawy

• Faculty of Archaeology, Restoration Department, Cairo University, Badrasheen, Giza 12918, Egypt

The study is handling the application of geopolymer, as one of the eco-friendly green binder materials. The experimental study provided composites of geopolymer, as an alternative restoration mortar to traditional mortars and employed them according to the needs of restoration operations (blocks, mortars, adhesives, etc.). The study presents a composite of an inorganic geopolymer. Examinations and tests were also carried out on stone, mortar, the components of the proposed geopolymer mixtures and the prepared samples.

The components of the geopolymer mixtures were selected from two geopolymer sources, metakaolin or kaolin as a natural source, fly ash as a secondary industrial product, limestone powder, natural hydraulic lime (NHL-5), sand, distilled water and sodium hydroxide (NaOH) was used as an activating agent.

Mixtures were prepared in varying proportions with three molarities (6, 4, 2 mol) and composites were also applied to experimental models of stone samples and clay mortars to choose the best geopolymer composites for their application in the restoration of mural paintings at Saqqara necropolis in Egypt.

The results for the selected geopolymer mixtures showed densities ranging from 2.0 to 2.17 g/cm³, porosity ranging from 6.98% to 18.99%, and compressive strength ranging from 31.91 to 53.81 MPa, which is approximately 2.3 times more than the compressive strength of wall painting support.

2.70. SWCNT for Gas Detection Using Raman Spectroscopy

• Ahmed Kreta

• Faculty of Engineering, May University in Cairo, Cairo, Egypt

The development of high-performance gas sensors is crucial for environmental monitoring, industrial safety, and medical diagnostics. Single-walled carbon nanotubes (SWCNTs) are a promising material for gas sensing due to their high surface-area-to-volume ratio, exceptional electrical properties, and sensitivity to charge transfer. While conventional SWCNT gas sensors rely on measuring changes in electrical resistance, Raman spectroscopy offers a powerful and non-destructive optical method for detecting and characterizing gas molecules. Raman spectroscopy provides unique vibrational fingerprints of materials. The characteristic Raman bands of SWCNTs, such as the radial breathing mode (RBM), D-band, and G-band, are highly sensitive to their local environment. The adsorption of gas molecules onto the SWCNT surface leads to a charge transfer interaction, which perturbs the electronic and vibrational properties of the nanotubes. This interaction results in observable changes in the Raman spectrum, including shifts in the peak positions, alterations in intensity, and the appearance of new peaks. This work investigates the changes in the Raman spectrum of SWCNT films upon exposure to various gases, such as Carbon Dioxide (CO2). Our findings demonstrate that Raman spectroscopy, particularly when utilizing resonant excitation, offers a highly sensitive and selective method for gas detection. This approach could lead to the development of robust, real-time optical gas sensors that complement or surpass traditional electrical-based sensors, providing a new pathway for advanced gas sensing technologies.

2.71. Synthesis of High Purity Sodium Silicate Material from Clay Industry Waste Silica

• Veena Suresh ¹, Sundararajan Mayappan ^1,2, Prasanna Muddarangappa ^1,2 and Peer Mohamed A ¹

¹ 

Materials Science and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram 695 019, India

² 

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Silica is one of the major wastes produced as a result of mining in the clay industries. With the goal of turning industrial waste into valuable chemical materials, this project investigates the sustainable synthesis of sodium silicate from silica-rich waste produced by the clay industry. Waste silica sand is roasted at high temperatures with potassium hydroxide (KOH) and sodium hydroxide (NaOH) to transform inert silica into soluble silicate compounds. To recover solid sodium silicate, the roasted mass is subsequently leached with hot water, filtered, and the filtrate is then concentrated by evaporation. The synthesized material was thoroughly characterized using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), and Raman spectroscopy to verify its composition, structure, and purity. This technique shows a practical, environmentally responsible way to value the waste from the clay industry while supporting resource recovery and circular economy principles. The elemental composition, surface morphology, molecular structure, and successful preparation of sodium silicate were all confirmed by these analyses. The study shows how to turn industrial waste into a highly sought-after chemical product in an efficient and environmentally responsible manner.

The need of the present study is to investigate a novel approach to synthesize sodium silicate from an unusual, underutilized source clay industry waste. It draws attention to the potential of industrial waste products as substitute raw materials, advancing green chemistry and material science.

2.72. Synthesis of Nanosilica and TiO₂ Products from Ilmenite Mud Through Alkali Roasting Route

• Safa Fathima T ¹, Sundararajan Mayappan ^1,2, Prasanna Muddarangappa ¹, Anagha M ¹ and Peer Mohamed A ¹

¹ 

Materials Science and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram 695 019, India

² 

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India

Ilmenite and rutile are major contributors to titanium dioxide production, depleting high-grade deposits in Ti production industries. In the present study, we investigate the possibility of using unreacted ilmenite mud as feed material for production of commercial TiO₂ production routes. Indian ilmenite mud generally consists of a rutile phase and is generally resistant to leaching. The alkali-assisted leaching process converts unreacted TiO₂ phases into easily leachable NaTiO₃ phases. Removal of silica, vanadium, and aluminium is performed as ternndite, vanadite, and aluminite, which yield valuable components for the whole process. In general, a huge quantity of ilmenite mud is dumped or stored near production sites, posing athreat to the environment. Ilmenite mud comprises valuable elements such as titanium, zirconium, and rare earth elements. Recovery and seperation are difficult due to their non-reactive nature with acids. In the present study, we designed an alkali-based process for the conversion of non-leachable TiO2 phases into easily leachable phases such as sodium titanate and sodium silicates. Byproducts such as silica and vanadium were important components for balancing the cost of production. The process involved the optimised removal of free acid through saline treatment, followed by roasting with sodium hydroxide to convert into easily leachable phases such as sodium silicates. The results of XRD, XRF, and SEM characterisation indicate that water-leached products contain 85% TiO2 content with low silica levels that can be used as the feed material in existing sulphate industries.

2.73. Synthesis of Naphthalen-2-yl 2-thiocyanatoacetate and Its Application as a Selective Photometric Reagent for Ni(II) Detection

• Ruzimurod Jurayev ¹, Azimjon Choriyev ², Anvar Abdushukurov ³ and Innat Nakhatov ²

¹ 

Department of “Chemical Engineering”, Karshi State Technical University Shahrisabz Faculty of Food Engineering, 20, Shakhrisabz Str., Shakhrisabz 181306, Uzbekistan

² 

Department of “Organic Chemistry”, Karshi State University, 17, Kuchabog Str., Karshi 180103, Uzbekistan

³ 

Department of “Organic Chemistry”, National University of Uzbekistan named after Mirzo Ulugbek, Tashkent 100174, Uzbekistan

In this study, a novel organic compound—naphthalen-2-yl 2-thiocyanatoacetate—was synthesized via a nucleophilic substitution reaction (SN2 mechanism) in dimethylformamide (DMF) as the solvent. The molecular structure of the synthesized compound was confirmed by infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy. The compound was evaluated for its analytical properties, particularly its ability to form a colored complex with Ni(II) ions, which was investigated using a photometric detection method. A comprehensive optimization of the detection conditions was performed. Factors such as the acidity of the reaction medium (pH), the order of reagent addition, the amount of reagent, the selection of optical filter, maximum absorption wavelength (λmax), Beer’s law linearity range, molar absorptivity, Sandell sensitivity, limit of detection (LOD), equilibrium constant, and the molar ratio of the complex components were studied in detail. The results demonstrated that the synthesized compound exhibits high sensitivity, selectivity, and stability in complex formation with Ni(II) ions. The developed photometric method is simple, cost-effective, and rapid, and can be implemented using basic instrumentation. Therefore, the method shows great potential for application in environmental monitoring, industrial wastewater analysis, and trace metal detection. This work contributes to the development of novel thiocyanate-based analytical reagents and expands the toolbox for photometric determination of transition metal ions, particularly Ni(II).

2.74. Thermal and Electrical Conductivity of Modified Asphalt Mixes Reinforced with Waste Tyre Metal Fiber and Graphite

• Aiman Hakim bin Helmi, Mohamed Mubarak Abdul Wahab and Arsalaan Khan Yousafzai

• Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

Asphalt is a vital material in road construction due to its durability and flexibility. However, conventional asphalt mixtures often face performance challenges, such as cracking, rutting, and thermal deformation under heavy traffic loads and temperature fluctuations. This study aims to enhance the thermal and electrical conductivity of asphalt mixtures by incorporating Waste Tire Metal Fiber (WTMF) and Graphite, thereby improving overall performance and pavement longevity. The study begins with the evaluation of volumetric properties and Optimum Bitumen Content (OBC) of the control mix, which was determined to be 4.9%, in compliance with Jabatan Kerja Raya (JKR) specifications. Modified asphalt samples with 0.2%, 0.5%, and 1.0% WTMF and 1.0%, 3.0%, and 5.0% graphite were prepared and tested for Marshall stability, flow, volumetric properties, electrical resistivity, and thermal conductivity. Results showed that air voids (AV) increased with higher WTMF content, while voids filled with bitumen (VFB) decreased. Stability values decreased beyond 0.5% WTMF, while graphite-modified samples improved stability up to 3.0% graphite. For conductivity properties, electrical resistivity decreased by 92% (1.0% WTMF) and 72% (5.0% graphite), while thermal conductivity improved by 56% and 48%, respectively. The optimal composition of 0.5% WTMF and 3.0% graphite achieved the best balance between conductivity enhancement and mechanical performance, paving the way for resilient, sustainable asphalt pavement solutions. Future work will focus on enhancing fiber distribution and further optimizing bitumen content to maximize long-term performance.

2.75. Thermomechanical Study of Ecological Hollow Bricks Based on Cork and Light Concrete

• Youssef Maaloufa ^1,2, Soumia Mounir ^1,2 and Hassan DEMRATI ²

¹ 

MECAD, National School of Architecture, New Complex Ibn Zohr, Agadir 80 000, Morocco

² 

Thermodynamics and Energetics Laboratory, Faculty of Science, Ibn Zohr University, BP8106, Agadir 80006, Morocco

The need for ecological materials became an important strategy to reduce the carbon footprint and the heating loss of the building envelope, and this has become a necessity according to the huge problem that agonizes the world actually. The need to use insulating materials in envelopes prompts the authors to consider developing a new composite material. Cork is considering a friendly Mediterranean insulation for more applications in building thanks to its excellent thermo physical properties. The aim of this study is to develop new ecological composite bricks from light concrete resembling the same form used in construction. Several steps have been conducted for this work; firstly, the characterization of thermal conductivity and thermal resistance, and secondly, the characterization of the compressive strength of bricks has been done according to the variation of the additive compared to the ones of the light concrete bricks. The results show an improvement of the thermal properties of the bricks and a stabilization of the compressive strength of the bricks for the maximum volume fraction of the insulating materials, ensuring good thermomechanical properties of the new bricks produced based on cork according to the standards NF EN 1996-1-1 for using brick in walls. A simulation study at the scale of walls was conducted by the software COMSOL Multiphysics in order to show the impact of the new bricks on the energy efficiency of the building envelope.

2.76. Topical Transferosomal Nanogel of Tridax procumbens: Formulation, Characterization, and Antifungal Evaluation

• Ashwini Subhash Labade, Dr. Amol S Rakte, Rohit Eknath Katkar and Dr. Sanjay Arote

• IVM’s Krishnarao Bhegade Institute of Pharmaceutical Education and Research, Talegaon Dabhade—Pune, India

The present study focuses on the formulation and evaluation of a novel transferosomal nanogel containing Tridax procumbens extract, aimed at enhancing topical antifungal therapy. Transferosomes, known for their ultra-deformable lipid bilayer and high skin penetration potential, were prepared using the ethanol injection method with varying concentrations of phospholipids (soya lecithin) and edge activator (Tween 80). The optimized formulation (F7) exhibited the highest entrapment efficiency (80.23%) and nanoscale particle size (~183 nm), with a stable zeta potential of −27 mV, indicating good colloidal stability.

The optimized transferosomal formulation was incorporated into a gel base using Carbopol 934 to form the transferosomal nanogel. Among the various formulations, TF7G2 showed optimal physicochemical properties including suitable pH (6.72), excellent spreadability (75.23 g.cm/s), viscosity (41,256 cP), and high drug content (81.56%). In vitro diffusion studies using a Franz diffusion cell demonstrated a sustained drug release of 82.54% over 8 h, following Peppas release kinetics.

The results suggest that the transferosomal nanogel significantly improves the skin permeability and retention of Tridax procumbens extract, making it a promising candidate for the effective topical treatment of fungal infections such as Tinea corporis. The integration of herbal medicine with nanocarrier systems like transferosomes offers a synergistic approach to enhance therapeutic efficacy, patient compliance, and stability in dermatological formulations.

2.77. Treatment Performance of Nanoclay-Assisted Biochar (NAB) Adsorption Process for Fluoride-Polluted Groundwater Remediation

• PELİN SOYERTAŞ YAPICIOĞLU and Fatma Elisa

• DEPARTMENT OF ENVIRONMENTAL ENGINEERING, ENGINEERING FACULTY, HARRAN UNIVERSITY, New District Lake, Fish Lake St. No:40, 63210 Eyyubiye, 63200 Sanliurfa Center, Turkey

In recent years, biochar has been regarded as an efficient adsorbent in potable water treatment owing to its low-cost, larger surface area, functional groups, and carbon-negative texture. However, biochar might not have higher pollutant adsorption capacity owing to its heterogeneous texture and some surface characteristics. Furthermore, biochar can include both positive and negative charges depending on the pyrolysis temperature, which might decrease the adsorption capacity of biochar in order to remove anionic pollutant substances such as fluoride. In this study, the modification of biochar (B) by bentonite, which is a type of nanoclay (NC) for the treatment of fluoride polluted groundwater, was achieved. The nanoclay-assisted biochar (NAB) process for groundwater treatment was also investigated. This research developed a novel modified malt-dust-derived biochar by bentonite for efficient potable water treatment, obtaining new insights into NAB adsorption performance. The water samples were provided from Sarım and Karataş villages located in an arid/semi-arid region in Türkiye. The mixing ratios (NC:B) were 1:1, 1:2 and 1:4, respectively. The results demonstrated that the highest fluoride removal efficiency was achieved by using 15 g/L-groundwater of bentonite and biochar (1:1). Bentonite addition promoted the fluoride removal from groundwater by 18.1% on average. A groundwater remediation index (GRI) was developed and validated by a sensitivity analysis as a result of the Monte Carlo simulation following the NAB process based on treatment efficiencies and water quality parameters. The highest GRI was reported in the range of 0.983–0.99 as a result of the NAB process.

2.78. Valorization of Agro-Waste: A Green Synthesis of Magnesium Oxide Nanoparticles Using Strychnos pungens Fruit Shell Extract

• Alinanuswe Joel Mwakalesi ¹ and John Chagu ²

¹ 

Department of Chemistry and Physics, College of Natural and Applied Sciences, Sokoine University of Agriculture, P.O. Box 3038, Tanzania

² 

Department of Natural Sciences, Mbeya University of Science and Technology, P.O. Box 131, Mbeya, Tanzania

The conventional synthesis of nanoparticles often relies on toxic synthetic chemicals, raising concerns regarding environmental sustainability and ecological impact. In response, green synthesis utilizing phytochemicals has emerged as a promising alternative, offering a renewable, biodegradable, and environmentally benign platform for nanomaterial fabrication.

This study reports a green synthesis of magnesium oxide nanoparticles (MgO NPs) using an aqueous extract from the fruit shells of Strychnos pungens as an underutilized agro-waste product. The water-soluble phytochemicals present in the shell extract served as effective chelating and stabilizing agents during the chemical precipitation of magnesium ions, facilitating the formation of MgO NPs.

The structural and morphological properties of the biosynthesized nanoparticles were thoroughly characterized. X-ray diffraction (XRD) analysis confirmed the crystalline nature of the NPs, revealing a face-centered cubic structure with characteristic peaks at 2θ = 37.94°, 42.99°, 62.33°, 74.83°, and 78.76°. The average crystallite size was determined to be 9.8 nm and 18.59 nm via the Debye–Scherer and Williamson–Hall methods, respectively. Scanning electron microscopy (SEM) revealed an irregular morphology with microparticle aggregates ranging from 0.5 to 1.0 µm. Elemental composition analysis by energy dispersive X-ray spectroscopy (EDX) confirmed the predominant presence of magnesium and oxygen, unequivocally verifying the successful formation of MgO.

This work not only demonstrates the successful green synthesis and characterization of MgO NPs but also highlights the potential of Strychnos pungens fruit shells as a valuable, sustainable resource for advanced nanomaterial production, aligning with the principles of circular economy and green chemistry.

2.79. Voltammetric Sensor Based on Carbon Nanotubes and Cerium Dioxide Nanoparticles for Ponceau 4R

• Guzel Ziyatdinova and Marina Vasilevskaya

• Analytical Chemistry Department, Kazan Federal University, Kazan 420008, Russia

Synthetic azo dyes including red Ponceau 4R (E124) are widely applied in the food industry to provide a bright, attractive, and stable color to foodstuff. Nevertheless, negative health effects can appear with high dye consumption. Therefore, the Ponceau 4R content in foods is strictly controlled. Voltammetric sensors are a promising tool for solving this problem. A glassy carbon electrode with layer-by-layer coverage of multi-walled carbon nanotubes and cerium dioxide nanoparticle dispersion in cetylpyridinium bromide has been designed as a novel sensitive voltammetric sensor for Ponceau 4R. The modifier combination provides an 80 mV cathodic shift of the Ponceau 4R oxidation peak and 1.7-fold higher oxidation currents compared to the bare GCE due to the synergistic effect of nanomaterials. Moreover, the appearance of cathodic step on the Ponceau 4R voltammograms indicates an increase in the electron transfer rate that is clearly confirmed by the electrochemical impedance spectroscopy data (k_et = 1.15 × 10⁻³ and 5.19 × 10⁻⁵ cm s⁻¹ for the modified and bare GCE, respectively). Ponceau 4R electrooxidation is a quasi-reversible, pH-independent, and diffusion-driven process. Differential pulse mode (pulse amplitude of 75 mV and time of 25 ms) in Britton–Robinson buffer pH 2.0 has been applied for Ponceau 4R quantification. The sensor response at 0.76 V is linear in the range of 0.10–1.0 and 1.0–7.5 μM of Ponceau 4R with the detection limit of 23 nM, which is sufficient for practical application. Recovery of dye in model solutions (99–101%) confirms high accuracy of the sensor developed.

3. Computing and Artificial Intelligence

3.1. Integrating Drone-Based Visual Inspection and AI-Powered Object Detection for Remote Powerline Monitoring

• Ezgi Tukel ¹ and Berk Sönmez ²

¹ 

Department of Remote Sensing and Geographical Information Science, Eskişehir Technical University, Eskişehir, Türkiye

² 

Başarsoft Information Technologies, Ankara, Türkiye

Introduction: Electrical distribution networks often traverse remote or hazardous terrains, making conventional ground-based inspections both risky and inefficient. Recent advances in UAV technology and AI-based computer vision have opened new avenues for remote asset monitoring. In this study, we introduce Powerline AI, an integrated system leveraging drones and object detection to automate powerline inspection tasks.

Methods: Using drone-mounted high-resolution cameras, field images are captured from previously inaccessible areas. A deep learning-based object detection module, trained on annotated electrical infrastructure datasets, is employed to extract inventory features (e.g., pole types, insulators) and detect anomalies such as broken elements, corrosion, or vegetation encroachment. The system is integrated into a GIS-backed web and mobile application, enabling real-time reporting and visualisation.

Results: Field deployment across rural regions revealed that Powerline AI achieved over 92% mean Average Precision (mAP) in anomaly detection. Time spent on routine inspections decreased by 60% compared to manual methods, while early anomaly alerts enabled preemptive maintenance actions. In mountainous terrain, drone accessibility has significantly improved inspection coverage.

Conclusion: This work demonstrates that AI-powered UAV inspection systems can enhance the accuracy, safety, and operational efficiency of powerline monitoring. Their integration with enterprise systems ensures daily usability, contributing to predictive maintenance frameworks and reducing long-term asset failure risks.

3.2. An AI-Based Approach for Forecasting Drought Exposure in Semi-Arid Tropical Regions: Case of Haute Matsiatra (Madagascar)

• Ramiandrisoa Marie Larissa ^1,2, Razakamanantsoa Andry ², Saint-Fleur Bob E. ², Fanjaniaina Marie Lucia ¹ and Hajalalaina Aimé Richard ¹

¹ 

LIMAD, University of Fianarantsoa, Fianarantsoa, Madagascar

² 

GERS-EE, University of Gustave Eiffel, 44344 Bouguenais, France

Drought constitutes a major threat to food security and the sustainability of agricultural systems in tropical developing countries. These systems are highly vulnerable due to their strong dependence on rainfall, their low resilience to climatic hazards, and the scarcity of reliable data. This study proposes a methodological framework for assessing and forecasting agricultural drought exposure in semi-arid tropical regions, using Haute Matsiatra in Madagascar as a case study. The framework integrates multi-source geospatial data with artificial intelligence to construct and predict a drought exposure index. Climate data are retrieved from the NASA POWER LARC platform and local institutions, while Landsat satellite imagery (30 m resolution) is sourced from the USGS platform to derive radiometric indices. The approach consists of three main steps: (i) selection and preprocessing of climate variables, satellite imagery, and radiometric indices such as NDWI and SPEI; (ii) development of a composite exposure index through weighted aggregation, incorporating local expertise using Saaty’s Analytic Hierarchy Process (AHP); and (iii) forecasting the index with a Long Short-Term Memory (LSTM) recurrent neural network, using horizons of 6 to 36 months aligned with the local agricultural calendar. To adapt to these horizons, daily data are aggregated to reduce noise due to data granularity. The outputs will be presented as thematic maps illustrating exposure levels under different horizons and climate scenarios. This framework offers a decision-support tool for climate risk management and can be replicated or adapted to other regions facing similar vulnerabilities.

3.3. A Task-Oriented Review of Medical Image Segmentation: Methods, Applications, and Trends

• Sijia Zhu ¹, Juan Liao ² and Zhe Liu ^2,3

¹ 

Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA

² 

School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia

³ 

College of Mathematics and Computer, Xinyu University, Xinyu 338004, China

Medical image segmentation has emerged as a cornerstone in modern healthcare, enabling precise delineation of anatomical structures and pathological regions for diagnosis, treatment planning, and image-guided interventions. Over the past decade, the field has witnessed a paradigm shift driven by methodological advances and the increasing availability of large-scale datasets. From a methodological perspective, segmentation research has evolved from traditional techniques such as thresholding, edge detection, and graph-based models to machine learning approaches leveraging hand-crafted features. More recently, deep learning has dominated the landscape with architectures such as U-Net, V-Net, and SegNet, followed by transformer-based models and generative frameworks. These innovations have greatly improved segmentation accuracy, robustness, and generalization across diverse imaging modalities. In terms of task-oriented progress, research has addressed a wide spectrum of clinical applications, including organ segmentation (e.g., brain, heart, and liver), lesion and tumor delineation (e.g., glioma, liver tumor, and lung nodules), vascular segmentation (e.g., retinal and coronary vessels), and histopathology-based cellular segmentation. Benchmark challenges and open datasets such as BRATS, LiTS, KiTS, and DRIVE have played a central role in accelerating algorithm development and standardizing evaluation protocols. Looking forward, the field faces several challenges and opportunities. Future research will likely emphasize foundation and multi-modal models, federated and privacy-preserving learning, and the development of explainable and clinically reliable systems. Addressing these challenges will be critical to translating state-of-the-art segmentation techniques into routine clinical practice.

3.4. Advanced Estimation of Higher-Order Cumulants in Bi-Additive Statistical Models for Applied Sciences

• Patrícia Antunes ^1,2, Sandra Ferreira ³ and Dário Ferreira ³

¹ 

Polytechnic University of Castelo Branco, Sport Physical activity and health Research & INnovation CenTer (SPRINT-IPCB), Castelo Branco, Portugal

² 

Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal

³ 

Department of Mathematics, Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal

This work introduces improved estimators for fourth-order cumulants in bi-additive models involving both fixed and random effects. Cumulants are powerful statistical measures that extend beyond traditional moments, offering more nuanced descriptions of probability distributions, including characteristics such as skewness and kurtosis. By utilizing the cumulant-generating function and least-squares methods, we develop advanced estimators that enable accurate inference from independent and identically distributed (i.i.d.) data. The proposed bi-additive models, which incorporate deterministic fixed components and independent random terms, facilitate precise estimation of fourth-order cumulants while accounting for the variability commonly observed in real-world data. These models also enable the systematic analysis of distributions with parameters related to location, dispersion, and shape, thus shedding light on their underlying structure. We demonstrate that the proposed methods offer both theoretical robustness and practical utility across diverse applied contexts. We illustrate the applications of this methodology in two domains: anomaly detection in sensor networks, where higher-order cumulants help identify deviations from expected patterns, and variability analysis in materials science, where they capture subtle differences in material properties. Our results emphasize the significance of higher-order cumulants in revealing complex features in data that are often obscured in mean-variance-based analyses. This contribution emphasizes both the theoretical significance and practical benefits of adopting advanced cumulant estimators in bi-additive models, equipping researchers in the applied sciences with novel tools for exploring and understanding the complexity of empirical distributions.

3.5. Adversarial U-Net Adaptation with Targeted Augmentation Boosts Crop Classification in Data-Scarce Regions

• Nada Naili ¹, Meziane Iftene ² and Mohammed El Amin Larabi ²

¹ 

Higher School of Computer Science (ESI-SBA), Sidi Bel Abbès 22000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

Deep learning models for crop classification are crucial for food security (SDG 2) but often fail when deployed in new geographic regions due to domain shift. This is a major barrier in nations like Algeria, which lack large-scale labeled datasets. To address this, we propose an adapted Domain-Adversarial Neural Network (DANN) that effectively transfers knowledge from data-rich European regions to data-scarce Algerian environments for Sentinel-2 imagery. Our methodology is centered on a U-Net segmentation architecture trained with a DANN framework. Our primary contribution is the introduction of a feature-matching loss at the U-Net bottleneck, which forces the model to learn more robust, domain-invariant representations. To address the limited availability of local labeled data, we apply a targeted data augmentation pipeline (including random rotations and scaling) to the small set of labeled Algerian wheat and potato parcels. The proposed model demonstrates significant performance gains. A baseline U-Net trained only on European data achieved 62% accuracy on the Algerian test set. In contrast, our adapted DANN model, trained with only 50% of the available Algerian labels, increased the overall accuracy to 89%. This data-efficient approach yielded high class-specific F1-Scores of 0.93 for wheat and 0.89 for potatoes. This work provides a validated and scalable pathway for developing accurate crop classification systems in regions with limited data.

3.6. Benchmarking Large Language Models (Llms) for Data-Driven End-Use Energy Analysis in U.S. Residential Buildings

• Sorena Vosoughkhosravi ¹ and Sahand Vosoughkhosravi ²

¹ 

Department of Construction Management, Thomas Jefferson University, East Falls, Philadelphia, PA, USA

² 

University of Tehran, Tehran, Iran

Buildings consume approximately 40% of national energy in the U.S., significantly contributing to greenhouse gas emissions. Data-driven analysis and accurate prediction of energy consumption is vital for advancing sustainability and climate objectives. In this regard, Large Language Models (LLMs), advanced AI systems trained on vast datasets to process and generate human-like text, are pivotal in enhancing data-driven analysis and prediction of building energy use. With a variety of LLMs available, selecting the most effective model is critical for optimizing energy consumption forecasts. This study investigates, analyzes, and benchmarks multiple LLMs to identify the optimal model for data-driven analysis of energy consumption in U.S. residential buildings, leveraging data from the Residential Energy Consumption Survey (RECS). Through rigorous evaluation of model performance across different end-use energy levels—including space heating, air conditioning, water heating, lighting, and appliances—this research identifies the most accurate and efficient LLM. The identification of the best-performing LLM informs retrofit planning, energy policy development, and demand-side management, enabling more effective energy-saving strategies.

3.7. Classification of Chemical Coating Quality in Soybeans Using Convolutional Neural Networks

• Caio Ulisses Silva Carmassi, Artur Sousa Silva, Francisco Guilhien Gomes Junior and Hae Yong Kim

¹ 

Dept. of Electronic Systems Engineering, Polytechnic School, University of São Paulo, São Paulo 05508-080, Brazil

² 

Dept. of Crop Science, Escola Superior de Agricultura “Luiz de Queiroz” (ESALQ), University of São Paulo, Piracicaba 13418-900, Brazil

The chemical treatment of seeds is a fundamental practice that ensures protection against pests and diseases, thus promoting robust plant establishment. However, this process is susceptible to failures, particularly in the form of inadequate coating. As such, the precise assessment of treatment quality emerges as a critical factor in securing high-performance crop yields. In this work, we present an approach based on image processing and convolutional neural networks (CNNs) to segment and predict the quality of chemical coverage on seeds from RGB images. The seeds were arranged on a homogeneous surface and labeled in six categories (C1 to C6), according to the level of chemical coating, with C1 corresponding to no treatment and C6 to adequate treatment, totaling 1165 seed images, with half of the images captured under natural light and the other half under artificial lighting. For segmentation, granulometric analysis and morphological segmentation techniques were applied, allowing the individual isolation of each seed. For classification, a CNN based on the MobileNetV2 architecture was used, with fine-tuning and data augmentation techniques. The model achieved an average F1 score of 0.96, performing well in all classes. The results demonstrate that the proposed approach is capable of identifying subtle variations in color and uniformity of coverage with excellence, indicating its potential for embedded automated screening applications. The proposal contributes to the standardization and automation of seed evaluation, with direct applicability in the agribusiness sector.

3.8. Edge-Optimized Lightweight Convolutional Framework for Real-Time Human Activity Recognition

• Kriti Sankhla, Harish Kumar Pamnani and Shikha Khullar

• Department of Computer Science and Engineering, Poornima University, Jaipur, India

Human Activity Recognition (HAR) is the keystone for healthcare, smart homes, and mobile computing; nevertheless, the process of putting deep learning models right on edge devices has always been a hard nut to crack because of the high computational requirements. This research article is a perfect example of a work that solves what we might call the paradox of real-time and efficient HAR with accuracy kept intact. In this paper, this is achieved by creating a lightweight convolutional neural network (CNN) and using TensorFlow Lite to perform the quantization and related optimizations on the UCI HAR dataset.

The design reaches 93.5% of the accuracy before quantization. After the quantization, there is a loss of less than 1% in accuracy. The dimension of the model decreases from ~4.2 MB to ~0.9 MB, and the inference latency is almost half, thus making it IoT as well as mobile devices user-friendly. The findings validate the possibility of employing compact CNNs as they can strike a balance between the accuracy of the solution and computational efficiency, thus making it possible to perform HAR on platforms with limited resources.

This is a project that merges the possibility of achieving the highest accuracy for HAR and the feasibility of deploying it in edge devices. The research works that lie ahead include multimodal HAR, lightweight transformer architectures, and real-world streaming applications.

3.9. Enhancing Emergency Medical Communication: A Multi-Model Information Extraction Pipeline for Ambulance Communication Using LLMs

• Nagasandeepa Basvoju ¹, Sheikh Faisal Rashid ², Yahya Almarashli ³, Nusyba Al Semadi ³, Michael Hahn ⁴ and Iram Ashraf ⁵

¹ 

Computer Science, Saarland University, 66123 Saarbrücken, Germany

² 

Educational Technology Lab, German Research Center for Artificial Intelligence (DFKI), 10587 Berlin, Germany

³ 

Hochschule Bremerhaven, 27568 Bremerhaven, Germany

⁴ 

Saarland Informatics Campus, Saarland University, 66123 Saarbrücken, Germany

⁵ 

University of South Wales, Cardiff CF24 2FN, UK

Effective communication in emergency medical services is critical in high-stakes scenarios, where information must be conveyed with speed, precision, and clarity. However, background noise, stress-induced speech patterns, and the use of specialized medical terminology frequently hinder comprehension. Improving the reliability of emergency communication is therefore a pressing challenge for both clinical outcomes and operational efficiency. This paper introduces a robust multi-model information extraction pipeline designed to enhance the accuracy and efficiency of emergency medical communication. The pipeline integrates advanced Speech-to-Text (STT) systems with Large Language Models (LLMs) to both improve transcription fidelity and extract mission-critical medical data. It comprises four modules: (1) audio capture of simulated German emergency communications under varied acoustic conditions, (2) STT transcription using Whisper, Azure, and IBM Watson, (3) LLM-driven refinement of transcriptions with GPT-4 to correct grammatical and terminological errors, and (4) structured information extraction with GPT-4, LLaMA 3.2, and Mixtral-8, guided by Chain-of-Thought and role-based prompting. The whole pipeline is evaluated using Word Error Rate (WER), BLEU, ROUGE-L, and semantic similarity, alongside accuracy, completeness, and relevance of extracted data. Azure STT with GPT-4 proved optimal, achieving the lowest post-refinement WER (0.1812, a 32.7% improvement), and high semantic similarity (0.9736), ROUGE-L (0.8802), and BLEU (0.7457). GPT-4 reached near-perfect extraction accuracy (0.995), surpassing LLaMA 3.2 and Mixtral-8, though Mixtral-8 remained highly competitive (0.980 accuracy with Whisper). Overall, the proposed pipeline demonstrates how combining STT and LLMs can transform noisy emergency dialogues into precise, structured clinical data, advancing responsive and reliable emergency management systems.

3.10. Ethical Implications of Using Artificial Intelligence and Gamification to Improve Teaching in English Courses: A Case Study in Mexican Education

• Juan Badillo-de Loera ¹, Susana Durán-Hernández ¹, Ma. del Refugio Piña-Arellano ¹, Antonio Guzmán-Fernández ¹, Martin de Jesús Cardoso-Pérez ¹, Hector Duran-Muñoz ¹ and Oscar Cruz-Domínguez ²

¹ 

Department of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico

² 

Department of Industrial Engineering, Polytechnic University of Zacatecas, Fresnillo 99059, Mexico

Artificial intelligence (AI) has revolutionized the world, generating new perspectives in industry and educational institutions. In English language learning, ChatGPt (a generative pre-trained transformer) has become an extremely useful and easy-to-implement tool in the classroom, revolutionizing conventional teaching methods. Using ChatGPt and gamification for teaching English offers tremendous versatility for interacting with students, generating curiosity, motivating exploration, and encouraging the development of new ideas. However, ChatGPt has been reported to have several problems, including fake references, a high risk of plagiarism, limited analytical capabilities, and putting user privacy at risk. Therefore, the aim of this work is to develop an innovative teaching sequence using ChatGPT and gamification, which promotes students’ critical and analytical thinking to improve the English language learning process. This work focuses on the impact of the recent national English language teaching program in Mexican public primary schools, called the “Programa Nacional de Inglés en Educación Básica” (PNIEB). This program, promoted by the Ministry of Public Education, is part of the national curriculum. Among the main results of this work, it is worth highlighting that ChatGPt is recommended only as an assistant for certain course activities, so in the early stages of its use, it is recommended to combine it with the gamification stream. The gamification stream combined with ChatGPt resulted in increased motivation and active participation, the development of language skills, and an active and participatory approach to the learning process.

3.11. FedHeart-MM: A Privacy-Preserving Federated Multimodal Framework for Accurate Heart Disease Prediction

• T. Monika, Neelamadhab Padhy and Rasmita Panigrahi

• Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur 765022, Odisha, India

Background: Currently, in medical analysis, the mortality rate is enhanced due to cardiovascular diseases. The WHO reports that approximately 17.9 million people are afflicted with heart diseases, which leads to death across the globe. Cardiovascular diseases are illnesses that affect the heart and blood vessels, including cerebrovascular disease, coronary heart disease, rheumatic heart disease, etc. Objective: In this article, our objective is to predict early heart disease using deep learning. We designed a framework that addresses the critical gaps in heart disease prediction, as well as ensuring privacy, clinical utility, and equity. Materials/methods: We used several classification models, such as LogR, RF, XGBoost, 1D-CNN (Centralized), LSTM (Centralized), FedAvg (EHR Only), FedHeart-MM and FedHeart-MM + DP (ε = 2) to predict early heart disease. We proposed a novel framework called FedHeart-MM to achieve this task. The FedHeart-MM architecture was designed to facilitate distributed training among several client nodes while maintaining the confidentiality of raw patient data. The federated configuration enables each client (e.g., hospitals or data centres) to conduct local training on their data while transmitting just model parameters to a central server. Result: This study’s proposed model, FedHeart-MM achieved 95% AUC-ROC, sensitivity of 92%, specificity of 93%, and an F1-score of 82%. In comparison to another model, our proposed model performs well and identifies early heart disease. We also observed that there is a 14% higher AUC vs. our traditional approaches. Federated models exhibited enhanced privacy-preserving capabilities.

3.12. FusionX-Net: Cross-Attention Enhanced Masked Autoencoders for Multi-Modal Remote Sensing Data Fusion

• Nowshad Hasan

• Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh

Introduction: Recent developments in self-supervised learning techniques for representation learning have gained considerable attention in the remote sensing sector, particularly for reducing the substantial costs related to annotating large satellite image datasets. In the context of multimodal data fusion, contrastive learning has become widely adopted to address domain discrepancies between various sensor types. However, contrastive methods heavily rely on data augmentation techniques, which require significant expertise, especially when dealing with multispectral remote sensing data. A promising yet often overlooked alternative is to employ masked image modeling-based pretraining techniques to bypass these challenges.

Methods: In this research, we introduce FusionX-Net, a self-supervised learning framework that utilizes masked autoencoders and incorporates cross-attention mechanisms for the early and feature-level integration of synthetic aperture radar (SAR) and multispectral optical data. These two data modalities generally exhibit a significant domain gap, which complicates the fusion process. FusionX-Net effectively addresses this challenge by using its cross-attention design, improving the representation learning process.

Results: FusionX-Net achieves state-of-the-art performance well above 95% on a number of benchmarks. On BigEarthNet-MM, FusionX-Net achieves 95.2% mean average precision (mAP), outperforming Dino-MM (88.1%) and SatViT (90.4%). Even in the low-label regime (1% labels), it achieves an impressive 92.0% mAP, leaving Dino-MM (81.5%) and SatViT (79.8%) by a wide margin. On SEN12MS, FusionX-Net achieves a Top-1 accuracy of 96.1%, significantly better than competitive baselines.

Conclusions: The proposed approach offers an effective alternative to contrastive learning techniques, which typically require extensive data augmentation. It demonstrates the potential of self-supervised learning, particularly masked autoencoders, in solving challenges associated with multispectral data fusion.

3.13. Hybrid Federated Learning with Client-Side Personalization for Privacy-Preserving and Scalable Medical Imaging Analytics

• Kriti Sankhla and Sumit Kumar Kapoor

• Department of Computer Science and Engineering, Poornima University, Rajasthan, India

The rapid growth of healthcare data across hospitals, imaging centers, and wearable devices creates opportunities for data-driven clinical decision support, yet strict privacy regulations prevent centralized aggregation of sensitive records. Federated Learning (FL) enables decentralized model training without sharing raw data; however, traditional FL is still shown to degrade performance under heterogeneous client distributions and shows limited adaptability to local environments. This feasibility study presents a Hybrid Federated Learning Framework that combines global model aggregation with local, client-based personalization to promote scalability and accuracy at the local level in multi-institutional contexts. A ResNet-18 backbone was trained on the NIH ChestX-ray14 dataset with patient-level data partitioning. The hybrid FL model achieved mean AUROC scores between 0.74 and 0.87, which closely approximated a centrally developed model. Localized, personalized improvements were also found to augment holdout AUROC by 2–6% as compared to a global-only baseline. To bolster privacy, we employed differential privacy on the client-level and showed that moderate differential privacy budgets (ε approximated 2–5) provided a similar level of accuracy as the global model, with less than minimal utility loss. The results suggest that personalized hybrid FL is a secure, privacy-preserving, and scalable framework for healthcare analytics and achieves near-centralized computational performance while maintaining the privacy of patient information. Future work will expand this framework for real-time Internet of Things (IoT) medical devices and utilize communication-efficient aggregation methods and compression techniques, including blockchain-assisted secure protocols.

3.14. Impact of AI Technologies on the Challenges of Rural Education in Mexico

• Juan Badillo-de Loera ¹, Jose Ruiz Gomez ¹, Antonio Guzmán Fernández ¹, Martin de Jesús Cardoso-Pérez ¹, Beatriz Adriana Rodríguez González ², Oscar Cruz-Domínguez ² and Hector Duran-Muñoz ³

¹ 

Academic Unit of History, Master’s Degree in Teacher Training. Autonomous University of Zacatecas. Jdn. Juárez #147, Historic Center, Zacatecas 98000, Zacatecas, Mexico

² 

Department of Industrial Engineering, Polytechnic University of Zacatecas, Fresnillo 99059, Mexico

³ 

Department of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico

Artificial intelligence (AI) has generated a new technological revolution in education. AI can be an excellent tool for solving a wide range of problems. For example, the appropriate use of AI can have a beneficial impact on public education in Mexico and can help thousands of students avoid dropping out of school. Even the most acute socio-educational problem is school dropout, and in rural regions of Mexico, this problem is even more serious. Because once students leave school, they are exposed to violence and crime. Therefore, the aim of this work is to use statistical regression analysis through Generative AI, specifically using DeepSeek, to identify those students with a higher probability of school dropout in rural regions of Mexico. This work can be a first pilot test and subsequently be implemented in all rural regions, helping hundreds of students. Fundamentally, a survey was implemented to identify the socioeconomic and academic conditions of the students. The data obtained was analyzed using DeepSeek, and the students most likely to drop out of school were identified. Aditionally, DeepSeek is an attractive option for teachers and the general public without a math degree, as developing a sophisticated mathematical model to predict the probability of dropping out of school is not an easy task. Among the key results of this work is a significant reduction in the dropout rate. DeepSeek’s simple features allow for the rapid monitoring of the socioeconomic and academic status of a large number of students.

3.15. Phase-Aware and Sensor-Level Interpretability in Human Activity Recognition via Consistency-Regularized CNN-LSTM-SE Networks

• Abdulaziz Aminu, Sani Danjuma, Sani Bature Sufyan, Aminu Usman Jibril, Abdulaziz Ahmad, Umar Shafiu Haruna and Muktar Danlami

• Faculty of Computing, Northwest University, Kano, Nigeria

Human Activity Recognition (HAR) has applications in healthcare, assistive technology, and security, where both interpretability and accuracy are necessary for real-world implementation. Existing deep learning methods such as CNN-LSTM hybrids have a tendency to behave as uninterpretable “black boxes,” lowering the confidence of users in real deployments. In this work, we present a novel HAR framework with explainability built directly into the architecture and training process. Our approach integrates Squeeze-and-Excitation (SE) attention into a CNN-LSTM backbone for recalibrating feature importance, and introduces a hierarchical interpretability strategy that uncovers both sensor-level and temporal phase-level relevance for activity recognition. To render explanations reliable, we design a consistency-based regularization objective that fosters stable and sparse attention patterns across samples, making interpretability intrinsic to the learning process rather than an afterthought. Furthermore, we present a phase-aware visualization method that maps attention weights to sensor modalities and activity phases, offering intuitive and actionable insights to domain experts. Experimental evaluation on a real-life HAR dataset demonstrates that the proposed framework achieves above 96% classification accuracy, outperforming conventional multiheaded CNN-LSTM, while offering robust and interpretable explanations of activity patterns. This work takes HAR to the next level by integrating high predictive power with intrinsic trust and interpretability, paving the way for deployment in safety-critical domains.

3.16. Secure and Adaptive Federated Learning with Knowledge Distillation and Hierarchical Homomorphic Encryption for Non-IID Data

• Bruno da Luz and Hae Yong Kim

• Polytechnic School, Universidade de São Paulo (USP), São Paulo 05508-010, Brazil

Federated Learning (FL) offers a promising paradigm for privacy-preserving, collaborative machine learning; however, the presence of non-independent and identically distributed (non-IID) data among clients significantly affects global model performance. This research proposes a novel architecture that combines Knowledge Distillation (KD) with Vision Transformer (ViT) models and hierarchical fully homomorphic encryption (FHE) to address both the non-IID data challenge and privacy preservation in FL. The proposed framework employs an aggregator server to homomorphically aggregate encrypted local model parameters, which are then decrypted and averaged by a separate federated server, ensuring that only clients retain access to their unencrypted parameters. Traditional approaches that modify aggregation algorithms are computationally prohibitive or incompatible with FHE; in contrast, KD facilitates robust model adaptation to local client distributions, supports heterogeneous client architectures, and integrates seamlessly with encrypted workflows. Experimental results with two clients, one utilizing the CIFAR-10 dataset and another utilizing the Pascal VOC 2007 dataset (sharing common classes), demonstrate the efficacy of the approach. EfficientNet was used for local training with Pyfhel-based FHE applied to model parameter exchange. Without knowledge distillation, the system obtained an AUC of 0.78, which improved to 0.84 when applying ViT-based knowledge distillation. The findings highlight the proposed method’s potential to enhance FL robustness, adaptability, and privacy, representing a viable and scalable solution for privacy-preserving collaborative learning in heterogeneous environments.

3.17. SightSeeingGemma: Enhancing Assistive AI for the Visually Impaired via Object Detection and Monocular Depth Estimation with Language-Based Scene Understanding

• Anh Trac Duc Dinh, Minh Tue Hua, Tai Ta Tien, Tri Huu Trinh and Tho Thanh Quan

• Unlimited Research Group of AI (URA), Ho Chi Minh City University of Technology (HCMUT), Vietnam National University—Ho Chi Minh City (VNU-HCM), Vietnam

This paper presents an integrated assistive approach that combines multimodal vision-language models with advanced computer vision techniques to support visually impaired individuals. The proposed system utilizes object detection via a custom-trained YOLOv8 model on an expanded dataset, along with MiDaS for monocular depth estimation. This enables the extraction of visual cues about surrounding objects and their relative distances, allowing near real-time hazard recognition and contextual descriptions via voice feedback. The system is deployed on a cloud/server infrastructure using a lightweight prototype (Google Colab + Ngrok), which introduces an average latency of 15–17 s per response. A dedicated Vietnamese dataset of annotated images, warnings, and context-specific descriptions was developed. To evaluate semantic alignment between model-generated and human-written descriptions, cosine similarity (using SBERT embeddings) achieved approximately 95%, far above the 0.5 threshold. Natural Language Inference (NLI) techniques were used to assess logical consistency. For overall descriptions, 72.5% were labeled neutral, 14.5% entailment, and 13.0% contradiction. For hazard warnings, 73.5% were entailment, 25.5% contradiction, and only 1.0% neutral. These results demonstrate that the model produces reliable hazard descriptions while general summaries may omit minor contextual details. In conclusion, the integration of vision-language models with object detection and depth estimation offers a scalable and effective assistive solution for the visually impaired. The system achieves high semantic fidelity in descriptive tasks and robust hazard communication, proving its potential for real-world deployment in accessibility technologies.

3.18. A Bio-Inspired Hybrid Optimization Framework for Precision Agriculture Using PSO–ACO and Neural Networks

• Mansir Abubakar ¹, Abubakar Bashir Salisu ², Usman Mahmud ³, Abdulkadir Abubakar Bichi ⁴, Abubakar Ado ³ and Abdulrauf Garba Sharifai ⁵

¹ 

Faculty of Computer Science and Mathematics, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia

² 

Department of Computer Science, Faculty of Computing and Mathematical Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria

³ 

Department of Software Engineering, Faculty of Computing, Northwest University, Kano, Nigeria

⁴ 

Software Department, Faculty of Computing, Northwest University, Kano, Nigeria

⁵ 

Science Department, Faculty of Computing Northwest University, Kano, Nigeria

Agricultural productivity is influenced by a complex interplay of environmental conditions, soil characteristics, and farm management practices. Traditional farming methods often lack the precision and adaptability required to optimize these dynamic variables, limiting crop yield potential and sustainability. In response to this challenge, this study presents an intelligent crop optimization framework that leverages the capabilities of bio-inspired metaheuristic algorithms, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Inspired by natural phenomena such as bird flocking and ant foraging, PSO and ACO are employed to explore optimal combinations of key agricultural parameters, including soil nutrient composition, pH levels, rainfall, and temperature. A neural network is trained to evaluate these parameter configurations, providing a performance-guided feedback mechanism that directs the search toward high-yield solutions. By integrating PSO and ACO with a neural predictive model, the proposed hybrid system combines the global search power of evolutionary algorithms with the pattern recognition strength of deep learning. This synergy enhances both the accuracy and robustness of decision-making in agricultural settings. The model not only adapts to changing environmental inputs but also supports real-time optimization, making it highly suitable for precision agriculture applications. Experimental results demonstrate that the system can effectively recommend parameter configurations that maximize yield while maintaining resource efficiency. The proposed approach offers a scalable, data-driven solution that empowers farmers with intelligent tools for informed and sustainable agricultural planning. This study contributes a novel and adaptive computational framework for optimizing crop yields, bridging the gap between artificial intelligence and modern farming practices.

3.19. A Hybrid Machine Learning and Multi-Criteria Decision-Making Framework for Selecting High-Temperature Thermochemical Energy Storage Materials

• Sara Samy Alkafas ^1,2 and Evgeniy A. Kalashnikov ¹

¹ 

Department of Automation and Control of Technological Processes and Production, Institute of Information Technology and Computer Science, National University of Science and Technology MISIS, Moscow 119049, Russia

² 

Production Engineering and Mechanical Design Department, Faculty of Engineering, Menofia University, Menofia 32511, Egypt

The decarbonization of energy systems requires efficient and reliable high-temperature thermochemical energy storage (HT-TCES) materials to stabilize renewable electricity supply and meet industrial heat needs. Finding the best HT-TCES materials is a complex multi-criteria decision-making (MCDM) problem, as it involves balancing thermodynamic performance, thermal stability, cost, and environmental sustainability among many potential options. This study presents a machine learning-integrated multi-criteria decision-making framework for the methodical selection of HT-TCES materials. Eighteen material alternatives are rigorously evaluated based on eight crucial criteria, including reaction enthalpy, energy density, operating temperature range, cycle stability, heat transfer characteristics, raw material cost, environmental impact, and scalability. The proposed method uses Multiple-Criteria Ranking by Alternative Trace (MCRAT) as the main ranking tool, supported by three weighting strategies—Analytical Hierarchy Process (AHP) for expert-driven prioritization, CRITIC (Criteria Importance Through Intercriteria Correlation) for objective variability assessment, and MEREC (Method based on the Removal Effects of Criteria) to ensure robustness. A random forest machine learning algorithm verifies the MCDM rankings, identifies non-linear relationships among criteria, and conducts sensitivity analysis to identify the most impactful parameters. The integrated ML-MCDM approach provides consistent, data-based material rankings and highlights key properties that determine HT-TCES suitability. This hybrid framework offers a reproducible and scalable decision-support tool to accelerate the deployment of advanced thermochemical storage systems, fostering improved grid flexibility, industrial decarbonization, and wider adoption of renewable energy.

3.20. A Machine Learning Algorithm for Urban Vegetation Classification Based on Radar and Multispectral Imagery from Sentinel Satellites Data

• Medfranck Obiang Mba Dit and Mikhailov Vyacheslav Nikolaevich

• Department of Radio Engineering System, Faculty Radio Engineering and Telecommunications, Saint Petersburg State Electrotechnical University, Saint Petersburg 197227, Russia

The study of the Vegetation is a crucial factor in the ecosystem. This study investigates the improved classification for urban vegetation on Giglio Island (in Italia) by integrating data from Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (Multispectral) from the European Space Agency’s Sentinel constellation. The analysis leverages the power of Random Forest Algorithm and Sklearn python’s library to create a classification map. Usually, urban vegetation mapping relies on a single data source resulting in limitations in accuracy and the abilities to differentiate vegetation types. The vegetation treated were: Mediterranean macchia, grasslands, coastal vegetation, pine forest. First, we performed independent classification vegetation using Normalized Difference Vegetation Index and Radar Vegetation Index. Secondly, to enhance classification accuracy, by incorporating a combination of each index with the Modified Normalized Difference Water Index and Soil Adjusted Vegetation Index we identified the vegetation classes exhibiting the highest prevalence. Thirdly using the proposed fusion method we combined the Modified Normalized Difference Water Index and Soil Adjusted Vegetation Index with the fusion between Normalized Difference Vegetation Index and Radar Vegetation Index and we compared the density variations among vegetation types. The results showed the effectiveness of the fusion approach significantly improved the classification accuracy, a perfect 100% of overall accuracy and Kappa index in all prediction elements. It showed a classification result by class for Random Forest Algorithm where each vegetation types were “present”, and showed the agreement level by class for Random Forest Algorithm some vegetation types were perfect, moderate, fair, slight, poor, very poor, inexistent.

3.21. A Machine Learning-Integrated Decision Tree and AHP Multi-Criteria Decision-Making Approach for High-Temperature Thermochemical Energy Storage Materials

• Sara Samy Alkafas ^1,2 and Evgeniy A. Kalashnikov ¹

¹ 

Department of Automation and Control of Technological Processes and Production, Institute of Information Technology and Computer Science, National University of Science and Technology MISIS, Leninsky Prospekt 4, Moscow 119049, Russia

² 

Production Engineering and Mechanical Design Department, Faculty of Engineering, Menofia University, Menofia 32511, Egypt

High-temperature thermochemical energy storage (HT-TCES) materials are essential to enable efficient industrial waste heat recovery and widespread deployment of renewable energy. However, determining the most appropriate material requires addressing several interrelated criteria, including thermal stability, reaction enthalpy, cycling behavior, cost, and environmental impact. To manage this complexity, this study proposes a hybrid framework between Decision Tree (DT) and Analytical Hierarchy Process (AHP) as a multi-criteria decision-making (MCDM) methodology integrated with machine learning for systematic HT-TCES material selection. The framework begins with a DT-based feature selection stage, which automatically determines the relative importance of evaluation criteria and filters out less significant attributes from a large initial set. DTs are chosen for their high interpretability and ability to provide explicit, rule-based explanations, allowing decision makers to understand why certain criteria are prioritized. The refined set of critical criteria is then analyzed through AHP, which structures pairwise comparisons and calculates consistent priority weights to rank candidate materials. Applied to high-temperature industrial waste-heat recovery (above 500 °C), the integrated DT–AHP model identifies the most suitable materials by simplifying the decision process, clarifying the choices, and ensuring a robust selection method. Sensitivity analysis shows that the material rankings remain consistent even when input conditions vary. This interpretable ML+MCDM approach offers a scalable decision-support tool for energy planners and policymakers, facilitating the sustainable deployment of thermochemical storage technologies and supporting global decarbonization objectives.

3.22. A Multi-Modal Approach for Early Detection and Classification of Alzheimer’s Disease

• Rozy Sultana, Maimuna Akter Shawon and Md. Khaliluzzaman

• Dept. of Computer Science and Engineering, International Islamic University Chittagong (IIUC), Chittagong-4318, Bangladesh

Alzheimer’s disease is one of the most frequent neurodegenerative diseases, leading to a disruption in the cognitive process of the human brain. Using this type of dataset to implement machine learning and deep learning techniques is a common approach for detecting and classifying Alzheimer’s disease. In this study, we addressed primary research problems, including early diagnosis and accurate classification of Alzheimer’s disease, effective preprocessing of imaging and non-imaging data, and identifying the most accurate modelling strategy between machine learning and deep learning techniques. We made an effort to present an advanced neuroimaging-based analysis of Alzheimer’s disease early detection, implementing various machine learning and deep learning techniques. We collected our dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For our work, we extracted attributes such as structural MRI, clinical assessments, and cognitive data from our collected dataset. In this study, we employed machine learning and deep learning techniques separately to evaluate their precision and accuracy in detecting and classifying Alzheimer’s detection, which led to identifying the most optimized results. We utilized a custom CNN and Self-Attention (SA) model, along with DenseNet, ResNet-50, and VGG-16, to implement deep learning techniques. For machine learning techniques, we employed Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), CatBoost, and Gradient Boosting models. To compare our method with state-of-the-art strategies, we used metrics such as accuracy, precision, and F1 scores. Our approach outperformed existing machine learning and deep learning models. In our approach, CNN and Self-Attention model achieved an accuracy of 98.20%.

3.23. Accurate Health Risk Detection and Disease Prediction in Animals Using Machine and Deep Learning Approaches

• Bhagyashree Panigrahy, Akhil Subhidhi, Tanushree Harichandan, Neelamadhab Padhy and Rasmita Panigrahi

• Department of Computer science and engineering, school of engineering and technology, GIET University, Gunupur 765022, Odisha, India

In order to maintain public health, food security, and livestock productivity, animal health monitoring is essential. Traditional illness detection techniques frequently depend on laboratory testing and manual observation, which are expensive, time-consuming, and prone to causing delays in early intervention. Humans can easily communicate and share a problem; however, when it comes to animals, they cannot communicate or share their discomfort. Our objective in this paper is to detect and predict various health risks in different animals with high accuracy, thereby promoting a healthy lifestyle and minimizing the risk of death. Additionally, our model facilitates early intervention, contributing to reduced mortality rates. We used machine learning algorithms like random forest, SVM, LoGR, DT, Naive Bayes, and KNN, which were trained and evaluated for baseline performance. Apart from this, we also used the DL models ANN and CNN to capture complex nonlinear patterns and high-dimensional feature interactions. The performance metrics were evaluated to determine which model performed well. The proposed ML and DL models achieved high classification performance, with the random forest and CNN models outperforming the others. We measured the accuracy of the CNN model to be 94.8%, with a precision of 93.2%, recall of 95.1%, and F1 score of 94.1%. This means that the CNN has one of the strongest predictive capabilities for identifying early health risk across multiple animal species. We also found that the AUC-ROC score was 0.96, which indicates that our model perfectly classifies healthy and diseased cases for early diagnosis and intervention in animal healthcare.

3.24. Advancements in Water Resource Engineering: A Review of Artificial Intelligence Applications

• Nadir Murtaza ¹, Zeeshan Akbar ², Ghufran Ahmed Pasha ² and Sohail Iqbal ³

¹ 

Department of Civil Engineering, CECOS University of IT and Emerging Sciences, Peshawar 25000, Pakistan

² 

Department of Civil Engineering, University of Engineering and Technology Taxila, 47050, Pakistan

³ 

Department of Civil Engineering, Faculty of Science and Technology, Tokyo University of Science, Chiba 278-8510, Japan

Artificial Intelligence (AI) has brought significant changes to the traditional field of water resource engineering in the hydrological and environmental management domain. Conventional techniques in water management, flood forecasting, sediment transport analysis, and groundwater, even though useful, are not necessarily well-suited for large-scale, time-varying data. Various advances in AI range from machine learning and natural language processing, which have undoubtedly helped in sharpening predictive ability and decision-making in the areas in question. Out of these, ChatGPT, a current-generation AI model, is proving to be the new wave intelligent system for data analysis, information retrieval, and solving problems. In this brief review, the authors have covered their exploration of AI integration in water resource engineering, taking into account advancements in water management, flood forecasting, sediment dynamics, erosion modeling, groundwater assessment, and rainfall prediction. The paper also demonstrates how native approaches differ from the AI methods, with an emphasis on both their advantages and disadvantages. Lastly, it reflects some of the limitations, including the quality of input data, computation overhead, and ethical issues, before going through opportunities and directions for further studies. Therefore, it is important that we achieve the review’s binary objectives of mapping AI and ChatGPT progress to highlight the potential directions for future research and application in sustainable water resource management.

3.25. Advancing Colorectal Cancer Prevention: Region-Guided Polyp Detection in Colonoscopy

• Fairooz Nahiyan, Taslim Alam, Simoon Nahar, Md. Khaliluzzaman and Mohammad Mahadi Hassan

• Dept. of Computer Science and Engineering, International Islamic University Chittagong (IIUC), Chittagong-4318, Bangladesh

Colorectal polyps are unusual tissue growths in the colon or rectum that can progress into colorectal cancer if left undetected at an early stage. Early and accurate polyp detection during colonoscopy is crucial for effective prevention and treatment. However, manual detection is challenging due to variability in polyp size, shape and texture which can lead to missed or false diagnoses. To address these challenges, we propose a deep learning-based approach to automate polyp detection, capable of identifying even the smallest polyps and aiding early cancer prevention. This research utilizes the Kvasir-SEG dataset, a publicly available collection of annotated polyp images to train and evaluate an advanced detection model. We employed YOLO (You Only Look Once), a state-of-the-art object detection framework and trained its latest version, YOLOv11 on the Kvasir-SEG dataset. The model was enhanced through advanced data preprocessing and hyperparameter tuning, making it suitable for clinical deployment. Our approach achieved excellent results, with an Intersection over Union (IoU) score of 0.9764 and an overall accuracy of 99.00%. Also achieved a balanced precision, recall and F1-score. The detection metrics showed a mean Average Precision (mAP) of 0.9937 at 0.5 IoU threshold and 0.9935 across thresholds from 0.5 to 0.95, indicating robust and reliable performance. The model was comprehensively analyzed with SAM (Segment Anything Model), YOLO-Seg and SAM2. These results demonstrate the effectiveness of our model in accurate and consistent polyp detection. The proposed method can assist clinicians by reducing missed detections and enabling early colorectal cancer diagnosis.

3.26. Advancing Precision Agriculture via Few-Shot Learning: A Mixture of Experts Approach with Vision Foundation Models for Cereal Mapping

• Amir Moncef Tighlit ¹, Mohammed El Amin Larabi ² and Meziane Iftene ²

¹ 

Departement of Compuer Science, École Supérieure en Informatique, Sidi Bel Abbès 22000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

Vision Foundation Models (VFMs) offer transformative potential for geospatial AI, but their application in data-constrained regions like Algeria is hindered by massive data requirements and high computational costs. This work introduces a novel framework that integrates VFMs with Few-Shot Learning (FSL) and an advanced ensemble technique, delivering a high-performance, data-efficient solution for mapping cereal crops, which are vital for Algerian food security.

Our objective was to develop a semantic segmentation pipeline for cereal mapping using a very limited, custom-collected dataset. We fine-tuned two architecturally distinct VFMs: the ViT-based Prithvi and the Swin Transformer-based Satlas. We then developed a Mixture of Experts (MoE) system, which combines these two fine-tuned “expert” models. A lightweight, trainable “gating network” learns to dynamically weigh the output of each expert on a per-image basis, synergistically leveraging their unique strengths.

The results highlight the exceptional performance of VFMs in a low-data regime. The fine-tuned Satlas model achieved a remarkable Overall Accuracy of 96.93% and a Cereal Class Intersection over Union (IoU) of 94.12%. The MoE system advanced this performance further, setting a new benchmark with an Overall Accuracy of 97.82% and a Cereal Class IoU of 95.58%. The MoE model demonstrated rapid convergence, showcasing its efficiency.

This study validates a highly effective framework for precision agriculture, proving that VFM ensembles can overcome data scarcity and deliver state-of-the-art performance, providing a tangible pathway for nations like Algeria to leverage cutting-edge AI for food security and sustainable resource management.

3.27. AI-Enabled Personalized Cybersecurity Education for Adolescents: Deep Learning Methods and Impact Assessment

• Zhifang Sun ¹, Sijia Zhu ² and Zhe Liu ^3,4

¹ 

College of Artificial Intelligence, Shandong University of Engineering and Vocational Technology, Jinan 250200, China

² 

Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA

³ 

School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia

⁴ 

College of Mathematics and Computer, Xinyu University, Xinyu 338004, China

In response to the escalating cybersecurity threats targeting adolescents, this study proposes an innovative artificial intelligence (AI)-driven framework designed to transform conventional cybersecurity education through personalized and adaptive learning. The system integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and natural language processing (NLP) to establish a multimodal behavioral perception mechanism, generating a cybersecurity behavior vector that quantifies vulnerabilities in areas such as phishing susceptibility and privacy protection. A Transformer-based cognitive mapping engine aligns these behavioral features with a structured knowledge graph built from extensive cybersecurity databases, enabling dynamic generation of personalized learning units—including interactive comics, gamified phishing challenges, and AR-based scenarios—optimized via a contextual multi-armed bandit algorithm to enhance long-term retention while minimizing cognitive load. A 12-week randomized controlled trial with 412 middle school students demonstrated that the AI-enabled approach significantly outperformed traditional methods, yielding a 32% increase in knowledge retention, a 2.1-fold improvement in threat detection accuracy, a 28% rise in self-reported security behaviors, and a 41% boost in engagement metrics. The results validate the efficacy of adaptive, data-rich interventions in fostering sustainable cybersecurity habits. The study concludes with policy recommendations for integrating AI-driven personalized education into national cybersecurity strategies, promoting cross-departmental collaboration for resource sharing, and establishing ethical guidelines for equitable and transparent educational AI systems.

3.28. AI-Powered Smart Urban Navigation and Safety Alert System for Visually Impaired Pedestrians

• Ajay B. Gadicha ¹, Vijay Gadicha ², Mayur Burange ², Mohammad Zuhair ³ and Zeeshan I. Khan ²

¹ 

Department of Artificial Intelligence and Data Science, P R Pote Patil College of Engineering and Management, Amravati 444604, Maharashtra, India

² 

Department of Computer Science and Engineering, P R Pote Patil College of Engineering and Management, Amravati 444604, Maharashtra, India

³ 

Department of Civil Engineering, P R Pote Patil College of Engineering and Management, Amravati 444604, Maharashtra, India

AI-Powered Smart Urban Navigation and Safety Alert System for Visually Impaired Pedestrians.

Navigating urban environments remains a significant challenge for visually impaired individuals due to dynamic obstacles, unclear signage, and inconsistent auditory cues. This research presents an AI-powered smart urban assistive system designed to provide real-time navigation guidance and safety alerts to visually impaired pedestrians. The system integrates computer vision, sensor fusion, GPS-based routing, and audio feedback mechanisms to create a wearable and responsive mobility aid.

A prototype was deployed in both controlled campus and real-world urban scenarios involving 20 visually impaired users across various navigation tasks (e.g., crossing roads, avoiding obstacles, locating entrances). The proposed system achieved an accuracy of 93.2% in obstacle detection, 87.6% route adherence, and 95% user satisfaction in safety perception, significantly outperforming traditional cane-based mobility methods.

In conclusion, the AI-driven smart assistive system demonstrates high potential in enhancing urban mobility for the visually impaired. It empowers users with greater independence, real-time situational awareness, and reduced anxiety in navigating complex environments. The modular and scalable design ensures adaptability to various urban infrastructures and user preferences. Future work will explore integration with edge AI for offline inference, voice-command interfaces, and context-aware navigation recommendations based on pedestrian density and time of day, contributing toward inclusive smart cities in alignment with Sustainable Development Goals (SDG 11: Sustainable Cities and Communities).

3.29. ALSAT Satellite Imagery Enhancement Using Generative Models for Forest Mapping

• Asma Haichour ¹, Maroua Rezig ¹, Mohammed El Amin Larabi ² and Meziane Iftene ²

¹ 

Departement of Artificial Intelligence and Data Sciences, École supérieure en Sciences et Technologies de l’Informatique et du Numérique, Béjaia 06000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

National satellite programs offer strategic autonomy but are often constrained by sensor hardware limitations. Algeria’s ALSAT-2B satellite lacks the critical Short-Wave Infrared (SWIR) and Red-Edge spectral bands essential for quantitative forest monitoring, limiting the utility of this sovereign data asset. This research introduces a novel AI-driven framework to digitally enhance ALSAT-2B imagery, unlocking its full potential for national forest management (SDG 13 & 15).

Our methodology employs a synergistic, two-stage process. Stage 1 (Spectral Enrichment): A Generative Adversarial Network (GAN) with a SwinUNet architecture was trained to synthetically reconstruct the five missing spectral bands by learning the relationship between ALSAT-2B and Sentinel-2 data. Stage 2 (Spatial Enhancement): The now nine-band data was fused with ALSAT-2B’s 2.5 m panchromatic band using the Gram–Schmidt pansharpening algorithm, enhancing its spatial resolution.

The framework’s efficacy was validated through a forest cover classification task. Results showed a dramatic improvement at each stage. The classification F1-Score on the baseline four-band, 10 m ALSAT-2B data was 60.28%. After spectral enrichment, the score increased to 70.08%. The final, fully enhanced nine-band, 2.5 m product achieved an F1-Score of 74.03%—a total improvement of nearly 14 percentage points.

This research presents a powerful framework for valorizing sovereign satellite data through AI. By successfully generating critical spectral bands and enhancing spatial detail, we can transform existing imagery into a high-value, analysis-ready product, providing Algerian institutions with a tangible, deployable methodology to significantly improve national forest monitoring and management.

3.30. An AI-Based Risk Prediction System for Maternal Health in Pregnant Women

• Biswamohan Padhi, Dipak Kumar Mohanty, Neelamadhab Padhy, Soumya Ranjan Dandapat and Rasmita Panigrahi

• Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur, Odisha, India

Objective: The purpose of this paper is to develop a machine learning-based classification model that can predict the risk level of maternal disease during pregnancy. The risk levels are classified as low or high based on clinical and physiological features. This model can provide early warnings, helping to reduce complications that typically occur during pregnancy.

Material/method: In this study fifteen classification algorithms were implemented and evaluated: Logistic Regression, Linear SVM (L1), RBF SVM, Decision Tree, Random Forest, XGBoost, AdaBoost, Bagging, KNN, Gaussian Naïve Bayes, Bernoulli Naïve Bayes, Ridge Classifier, Linear Discriminant Analysis, LightGBM, Extra Trees, and Deep Learning algorithms such as Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), which are planned for future enhancements to capture nonlinear patterns. The dataset used in this model is Mendeley’s Maternal Health Risk Assessment Dataset. The target variable in this dataset is the Risk Level, which is categorized as Low or High. The datasets include nearly 1187 patient records. The key features in this dataset are Age, Systolic and Diastolic Blood Pressure, Blood Sugar (BS), Body Temperature, BMI, Heart Rate, Previous Complications, Preexisting Diabetes, Gestational Diabetes, and Mental Disease Indicators.

Results: Among the fifteen classification algorithms tested, XGBoost achieved the highest accuracy of 99% along with strong precision and recall for high-risk cases. Feature importance analysis showed that preexisting Diabetes, Blood Sugar, BMI, Heart Rate, and Mental Disease Indicators were the most influential predictors.

3.31. An Enhanced Hybrid CNN-LSTM with Attention Mechanism for SMS Phishing Detection

• Abdullahi Ishaq, Zaharaddeen Salele Iro, Aminu Musa, Abida Ayuba, Bilal Ibrahim Maijamaa and Abubakar M. Miyim

¹ 

Department of Computer Science, Faculty of Computing, Federal University Dutse, Jigawa State, Nigeria

² 

Department of Cybersecurity, Faculty of Computing, Federal University Dutse, Jigawa State, Nigeria

³ 

Department of Computer Science, Faculty of Computing, Bayero University Kano, Kano State, Nigeria

Short Message Service (SMS) is still a vital communication tool in our daily life activities. Despite the growing popularity of internet-based messaging platforms, Short Message Service (SMS) remains a widely used means of communication. However, this continued reliance has given rise to SMS phishing commonly known as smishing, which poses a significant cybersecurity threats. Traditional detection methods, including heuristic analysis, rule-based systems, and blacklists, often struggle to identify evolving smishing tactics. Similarly, conventional machine learning models such as Random Forest, SVM, RNN, CNN, and LSTM face limitations when handling long text sequences due to the vanishing gradient problem. To address these challenges, this study proposes SmishNet, an enhanced hybrid model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism for improved smishing detection. The model is trained on a combined dataset comprising the Kaggle SMS Smishing Collection and locally sourced phishing messages from Nigeria, ensuring contextual and linguistic relevance. The model’s performance was evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Experimental results show that SmishNet achieves a high accuracy of 99.3%, outperforming CNN with 98.6%, and LSTM with 71.9%. These findings demonstrate the effectiveness of attention mechanism in handling vanishing gradient problem, and the efficacy of hybrid approach in smishing detection.

3.32. An Enhanced Lightweight IoT-Based Pipeline Leak Detection Model Using CNN and Autoencoder

• Abida Ayuba ¹, Dr Farouk Lawan Gambo ², Aminu Musa ¹, Hauwa Aliyu Yakubu ³, Bilal Ibrahim Maijamaa ¹ and Abdullahi Ishaq ¹

¹ 

Computer Science Department, Federal University Dutse, Dutse 720211, Nigeria

² 

Department of Cyber Security, Federal University Dutse, Dutse, Nigeria

³ 

Computer Science, Bayero University Kano, 700006, Nigeria

Abstract: Monitoring oil pipelines is crucial for effective infrastructure management and maintenance. It helps prevent threats such as vandalism and leaks, which can result in catastrophic events. Pipeline leaks pose significant environmental and economic risks, yet current detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This research introduces a lightweight thermal imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNNs), autoencoders, and knowledge distillation for deployment on edge devices. The proposed system addresses the challenges associated with existing pipeline detection techniques, such as large model sizes, high transmission latency, and excessive energy consumption. It utilizes thermal cameras to capture images of the pipeline, which are then compressed using an autoencoder. This compressed data is used to train a CNN model, which is further optimized through knowledge distillation. The model is trained and tested on real and synthetic data and deployed on a Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate improvement in detection accuracy, low inference latency, and an efficient transmission rate, confirming the system’s suitability for real-time leak detection in remote and resource-constrained environments. This work contributes to the development of cost-effective, scalable, and energy-efficient solutions for pipeline monitoring.

3.33. An Improved Breast Cancer Classification Using Ensemble Learning and Data Resampling Techniques: A Machine Learning Approach

• Mohammed Kabir Bashir ¹, Sanusi Abu Darma ², Usman Mahmud ³ and Mansir Abubakar ⁴

¹ 

Department of Computer Science and Infor. Tech, Al-Qalam University Katsina, Katsina, Nigeria

² 

Department of Computer and Information Technology, Al-Qalam University, Katsina, Nigeria

³ 

Department of Software Engineering, Northwest University, Kano, Nigeria

⁴ 

Faculty of Computer Science and Mathematics, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia

Breast cancer remains a major global health challenge and is one of the leading causes of mortality among women worldwide. Despite significant advancements in diagnostic imaging and clinical assessment methods, traditional detection approaches often suffer from high false-positive rates and considerable diagnostic subjectivity. These limitations can delay early treatment and increase patient anxiety. To address these challenges, this study presents a machine learning-based framework aimed at improving breast cancer classification using structured clinical data. The proposed system utilizes an ensemble learning model, specifically the Extreme Gradient Boosting (XGBoost) classifier, known for its accuracy and speed in predictive modeling tasks. To address the issue of class imbalance and improve sensitivity to malignant cases, the Synthetic Minority Over-sampling Technique combined with Tomek Links (SMOTE Tomek) was employed. Furthermore, feature importance analysis using the Random Forest algorithm was conducted to identify the most relevant clinical variables, thereby enhancing the model’s interpretability and computational efficiency. Evaluation of the model yielded promising results, with an accuracy of 94.03%, precision of 91.89%, and an AUC score of 97.0 These metrics indicate that the model is robust and highly effective in classifying breast cancer cases. The findings underscore the potential of integrating advanced machine learning techniques into healthcare workflows, offering a more accurate, consistent, and early diagnostic aid for breast cancer. The study supports the growing role of data-driven solutions in enhancing clinical decision-making and improving patient outcomes.

3.34. An Improved Graph-Based Method for Hausa Text Single-Document Summary Extraction Using a Hybrid Similarity Function

• Abdulkadir Abubakar Bichi ¹, Abubakar Ado ², Abdulrauf Garba Sharifai ³, Mansir Abubakar ⁴, Usman Mahmud ⁵ and Abubakar Salisu Bashir ⁶

¹ 

Software Enigeering Department, Faculty of Computing Northwest University, Kano, Nigeria

² 

Faculty of Computing, Northwest University, Kano, Nigeria

³ 

Science Department, Faculty of Computing Northwest University, Kano, Nigeria

⁴ 

Faculty of Computer Science and Mathematics, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia

⁵ 

Department of Software Engineering, Faculty of Computing Northwest University, Kano, Nigeria

⁶ 

Department of Computer Science, Faculty of Computing and Mathematical Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria

Extractive text summarization is a technique that automatically generates a concise version of a document by selecting and rearranging its most important sentences verbatim. This paper proposes an improved graph-based method for Hausa single-document extractive summarization. The improvement is achieved through the use of a hybrid similarity function, created by first evaluating the performance of four distinct similarity measures individually within a ranking algorithm. These measures are cosine similarity, Jaccard similarity, the overlap coefficient, and n-gram/Dice’s coefficient similarity. Three other similarity measures were then each combined with the n-gram/Dice’s coefficient similarity using the simple harmonic mean to form hybrid similarity functions. To evaluate the effectiveness of the proposed method, the Hausa extractive text summarization corpus was used. Performance was assessed using standard evaluation metrics, including precision, recall, and F-score. Among the tested combinations, cosine similarity combined with n-gram/Dice’s coefficient similarity yielded the best performance. It achieved F-score values of 0.8085 for ROUGE-1, 0.3705 for ROUGE-2, and 0.6946 for ROUGE-L, outperforming the other similarity pairings. These results demonstrate that integrating cosine similarity with n-gram/Dice’s coefficient similarity significantly enhances the performance of graph-based extractive summarization for Hausa text. This study contributes to the advancement of natural language processing tools for under-resourced languages like Hausa and provides a foundation for further development in multi-lingual text summarization systems.

3.35. Analyzing Seasonal Vegetation Variations in Southwestern Madagascar with Unsupervised Classification of Long-Term MODIS Data

• Rafanoharana Elisa Thérèse Baovola ¹, Delaitre Eric ², Razanaka Samuel Jean ³, Rakotonirainy Hasina Lalaina ⁴ and Hajalalaina Aimé Richard ⁴

¹ 

Doctoral School Modeling—Computer Science, University of Fianarantsoa, Andrainjato 1264, Madagascar

² 

UMR Espace Dev, Research Institute for Development, Montpellier, 500 Rue Jean François Breton 34090, France

³ 

National Center for Environmental Research, Antananarivo, Fiadanana 1739, Madagascar

⁴ 

School of Management and Technological Innovation, University of Fianarantsoa, Andrainjato 1264, Madagascar

Satellite data have become an essential tool in environmental monitoring and ecosystem assessment. This study investigates the application of unsupervised classification to characterize the spatio-temporal dynamics of vegetation in southwestern Madagascar, a region highly vulnerable to climatic variability. MODIS Collection MOD13Q1 products were selected despite their relatively coarse spatial resolution, due to their dense temporal coverage, enabling the analysis of a long time series from 2001 to 2024. The methodological framework is based on clustering pixels according to their monthly growth profiles derived from the Normalized Difference Vegetation Index (NDVI). Seasonal variations, including the wet and dry seasons, were explicitly considered. To ensure robustness, results from K-means clustering were cross-validated with Hierarchical Ascendant Classification (HAC), allowing us to compare and consolidate class stability. The classification identified seven distinct profile classes, reflecting both seasonal phenological patterns and dominant vegetation cover types. These results provide crucial insights for spatio-temporal monitoring and mapping of ecosystems, contributing to improved environmental surveillance in the region. Overall, the study demonstrates the effectiveness of unsupervised classification in extracting meaningful information from satellite time series. By offering a detailed understanding of vegetation dynamics over two decades, this approach highlights valuable opportunities for sustainable management and conservation of natural resources in southwestern Madagascar.

3.36. Application of AI-Powered Chatbots in Teaching Applied Sciences

• Marta Bochniak and Ewelina Książek

• Department of Agroengineering and Quality Analysis, Faculty of Production Engineering, Wroclaw University of Economics and Business, Wroclaw, Poland

Applied sciences and engineering education increasingly rely on advanced digital tools that foster practical skills and analytical thinking. Artificial intelligence (AI) solutions are gaining importance as they support the development of data-driven decision-making and problem-solving competencies.

The aim of this study was to develop and evaluate an AI-based educational chatbot to enhance student learning in data analysis and statistical process control, ensuring content quality and safe use through exclusive reliance on instructor-provided materials.

The chatbot was implemented on the ChatGPT-4o (Custom GPT) platform and generates tasks, data files, step-by-step instructions in Statistica, quizzes, and control questions, enabling self-paced and interactive learning. It was integrated into the Standardization of Production Processes course and evaluated using a student survey and a Computer-Assisted Video Interview (CAVI).

Evaluation results demonstrated high acceptance: 86.4% of students found the chatbot easy to use, 95.5% rated its responses as clear and understandable, 100% found Statistica’s instructions helpful, and 95.5% reported that the content was tailored to their individual preferences. Quizzes and tasks supported learning for 90.9% of students and improved preparation for assignments and tests, while 77.2% reported increased motivation for regular revision, and 77.3% noted improvement in prompt formulation skills.

The AI chatbot thus provides a controlled environment for personalized learning and practical training in statistical process control, linking theoretical concepts with hands-on applications. Its design can be adapted to other technical and engineering courses, offering a scalable model for integrating AI-driven tools into higher education curricula focused on applied competencies.

3.37. Application of Artificial Intelligence and Machine Learning in a Nuclear Power Industry to Address Environmental Problems

• Pavlo Kuznietsov, Olha Biedunkova, Alla Pryshchepa, Vasyl Korbutiak and Ihor Statnyk

• Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, 33028 Rivne, Ukraine

The objective of this study is to integrate artificial intelligence (AI) and machine learning (ML) in the nuclear power industry to address environmental issues. The problem addressed stems from the nuclear sector’s growing need to modernize aging infrastructure and meet stricter environmental and regulatory standards, all while facing data scarcity, cybersecurity concerns, and a shortage of skilled personnel.

Methods: This study employs a systematic literature review and analysis of case studies to assess the current applications of AI and ML in nuclear power. It evaluates various AI techniques, including neural networks, fuzzy logic, and deep learning, in tasks such as fault diagnostics, reactor control, and performance optimization. Additionally, this study examines challenges related to the deployment of these technologies, focusing on data requirements, model complexity, and compliance with nuclear safety regulations.

Results: AI and ML have demonstrated considerable success across multiple domains within nuclear operations. Neural networks and fuzzy logic systems have enhanced the accuracy of reactor monitoring and the stability of control processes. Deep learning models have enabled the real-time optimization of operational parameters and predictive maintenance, resulting in significant reductions in downtime and maintenance costs. However, the implementation of these systems is hindered by the lack of explainable AI frameworks and robust datasets necessary for training high-performance models.

Conclusion: Our findings underscore the transformative potential of AI and ML in the nuclear sector. To fully harness their capabilities, the industry must overcome existing barriers through targeted research focusing on explainable AI, improved data governance, and adaptive regulatory frameworks.

3.38. Artificial Intelligence as a Decision-Support Tool in Crisis Management

• Daniel Chovanec, Jozef Ristvej, Boris Kollár and Jozef Kubás

• Department of Crisis Management, University of Žilina, Žilina 01026, Slovakia

Artificial Intelligence (AI) is becoming a game-changing and increasingly important tool in various fields, as well in disaster management. However, its true value lies in how effectively decision-makers and public authorities are able to implement it in real-world practise operations. This paper addresses a gap between technological potential and practical application by combining insights from academic research, expert analyses, and an overview of the current state of AI integration in Slovakia. Despite the growing relevance of digital tools in crisis response, the integration of modern technologies into the national crisis management structure remains limited. Even in the recently announced reform of crisis management in Slovakia, there is a noticeable absence of concrete steps aimed at leveraging AI or other advanced technologies. Yet AI holds significant potential to enhance the efficiency and quality of decision-making, support faster and more effective responses, improve resource planning and risk assessment, and ultimately contribute to better crisis-related processes. The paper highlights key areas where AI can be implemented for decision support, including predictive data analytics, resource optimization, and strategic governance. At the same time, it discusses the risks associated with the use of AI in high-stakes environments, such as concerns over data quality and accountability. The findings underscore the need for targeted education and training of crisis responders and public officials to ensure AI tools are used responsibly and effectively. Strengthening human competencies is essential to ensure that AI serves as a reliable decision support, enhancing rather than replacing human judgment in crisis management.

3.39. Assessing Pre-Exam Nervousness Levels in Students Using Neural Networks for Emotion Recognition

• Edward Pinto Pimenta Junior, Daniel Guzmán Del Río, Miguel Angel Orellana Postigo and Israel Gondres Torné

• PPGEEL—Postgraduate Program in Electrical Engineering, State, University of Amazonas, Manaus 69050-020, Brazil

Nervousness is a key emotional factor affecting student performance in high-stakes evaluations. Excessive anxiety before or during exams can impair concentration, reduce problem-solving efficiency, and compromise outcomes. Measuring students’ nervousness is therefore essential for fair and effective assessment. This work proposes a neural network-based framework to estimate and monitor nervousness in pre-exam scenarios. A convolutional neural network (CNN), trained for facial emotion recognition, analyzed real-time video streams, classifying seven emotions: anger, disgust, fear, happiness, neutrality, sadness, and surprise. Each emotion was translated into quantitative nervousness scores using a weighted scoring model, allowing continuous tracking of emotional tension during question answering.

The CNN achieved around 70% accuracy in training, ensuring reliable emotion detection and nervousness estimation. Nervousness scores ranged from 0 to 100, derived from weighted associations of emotions such as fear, anger, and sadness, based on psychology and education studies highlighting their impact on learning and test performance. The system produces per-question and overall assessments, culminating in a readiness report indicating if a student is in an adequate emotional state to proceed. By combining emotion recognition with an interpretable scoring model, the framework provides educators with a practical tool to monitor emotional readiness and identify students at risk of underperforming due to anxiety. Preliminary findings show the approach effectively captures variations in nervousness and offers insights into learners’ emotional states. This research contributes to affective computing in education, demonstrating the potential of neural networks to enhance fairness, well-being, and adaptability in assessment environments.

3.40. Assistive Communication for Visual and Speech Impairments

• Mohammed Kadri ¹, Souad Alaoui ¹, Abdelhalim Hnini ² and Imane Chlioui ³

¹ 

Engineering Sciences Laboratory (LSI), FP Taza, USMBA, Fez, Morocco

² 

LAVETTE FST, National School of Applied Sciences, Hassan First University of Settat, Berrechid, Morocco

³ 

Software Project Management Research Team, ENSIAS, Mohammed V University Rabat, Morocco

In the field of assistive technology (AT), individuals with visual impairments (VI) and speech impairments (SI) often encounter significant barriers to effective daily communication and social participation. This work presents the design and evaluation of an AI-powered integrated communication system aimed at bridging these gaps. The proposed solution combines multimodal interfaces that include voice commands, with real-time text and speech conversion to enhance user interaction. Adopting a user-centered design methodology, the system was iteratively refined through usability testing conducted in both controlled environments and real-world contexts. The results demonstrated notable improvements, with average task completion times decreasing from 45 to 28 s and communication success rates increasing from 76% to 91%. Additionally, user feedback emphasized clearer interactions, improved adaptability across contexts, and reduced frustration during use. Despite remaining challenges related to device compatibility, latency, and cost, the system demonstrated practical feasibility and significant value in supporting daily communication for users with VI and SI. This research provides a strong foundation for future developments, including multilingual capabilities, broader device support, and advanced AI features (e.g., predictive text and emotion recognition) to increase user autonomy and inclusion. Furthermore, the integration of customizable settings allows users to tailor the system according to their specific needs and preferences, enhancing accessibility and personal comfort. Continuous updates and machine learning algorithms ensure the system adapts dynamically to user behavior, improving efficiency over time. The scalability of the system suggests potential application beyond individual use, including educational and workplace environments, thereby promoting inclusivity on a larger scale.

3.41. Automatic Classification of Legal Cases: A Comparative Study of AutoGluon and the Gemini Family

• José Maria Dias Filho and Carlos Mauricio Figueiredo

• Embedded Systems Laboratory, State University of Amazonas, Manaus 69050-020, Brazil

The growing volume of litigation in the Brazilian judiciary imposes significant challenges to procedural speed and efficiency. One critical bottleneck lies in the initial classification of petitions, where the correct assignment of the procedural “Class”, as defined by the Unified Procedural Tables (TPU) of the National Council of Justice (CNJ), is essential for the subsequent procedural flow. Errors at this stage lead to rework and delays. This article investigates the potential of Artificial Intelligence to mitigate this issue by presenting a comparative analysis of the performance of two distinct technological approaches: the Automated Machine Learning (AutoML) framework AutoGluon and a suite of Large Language Models (LLMs) from Google’s Gemini and Gemma families. Using a private and robust dataset of 27,000 initial petitions from the Court of Justice of the State of Amazonas (TJ-AM), distributed across nine procedural classes, the models were evaluated in a zero-shot scenario simulating implementation with minimal configuration effort. The results for both technologies demonstrate remarkable feasibility. AutoGluon, leveraging the full dataset, achieved a performance ceiling of 95% accuracy. Impressively, the LLMs, evaluated on a smaller sample without specific training, delivered highly competitive results, with Gemini-2.0-flash reaching 94% and Gemma-3-27b-it achieving 93% accuracy. The study concludes that “out-of-the-box” AI solutions are promising tools for assisting lawyers and judicial staff, with the potential to improve classification accuracy, streamline workflows, and contribute to greater efficiency in the delivery of justice.

3.42. Automating the Detection of Unplanned Urban Constructions Through AI-Powered Super-Resolution and Multi-Modal Data Fusion

• Lina Abbedou ¹, Meziane Iftene ² and Mohammed El Amin Larabi ²

¹ 

Departement of Computer Science, University of Algiers Benyoucef Benkhedda, Algiers 16000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

Unauthorized urban development significantly threatens sustainable development objectives (SDG 11), requiring robust and scalable surveillance systems. While Algeria’s ALSAT-2 satellite offers consistent territorial coverage, its inherent 10 m spatial resolution (2.5 m post-pansharpening) proves inadequate for detecting small-scale illegal structures. This study presents a novel dual-stage artificial intelligence pipeline designed to augment existing national satellite data for urban sprawl monitoring. Initially, a Generative Adversarial Network was developed using Sentinel-2 training datasets to execute spectral enhancement, generating higher-resolution imagery with supplementary spectral channels from original ALSAT-2 data. Subsequently, a U-Net architecture processed this refined imagery to automatically identify and delineate unauthorized construction areas. To address local material discrimination challenges, Land Surface Temperature information was incorporated to distinguish concrete surfaces from prevalent alternative building materials in the study region. Performance analysis revealed that spectral enhancement significantly boosted the classification accuracy relative to standard pansharpened baselines, with thermal data integration providing further improvements in the precision and sensitivity of detection. The methodology successfully mapped illegal construction zones, achieving an IoU of 0.7712, Dice coefficient of 0.8350, Precision of 0.8210, Recall of 0.9285, and Overall Accuracy of 0.9516, which was verified through field validation in Arzew, Oran, Algeria, during the years 2022 and 2025. This research demonstrates artificial intelligence’s potential to transcend the hardware limitations of satellite sensors, providing an economical and adaptable solution for urban governance, planning compliance, and sustainable territorial management.

3.43. Balancing Time, Memory, and Accuracy: Algorithmic Insights into the N-Queens Challenge

• Kimia Sadat Karbasi ¹, Umme Rabab Syed ² and Aiman Darakhshan ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

School of Management Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

The N-Queens problem remains a benchmark for evaluating optimization strategies in artificial intelligence due to its combinatorial complexity. This study investigates the performance of four algorithmic approaches—Exhaustive Search (Depth-First Search with Backtracking), Greedy Search (Hill Climbing), Simulated Annealing, and Genetic Algorithm—on varying board sizes (N = 10, 30, 50, 100, 200). The aim is to assess each method’s ability to find conflict-free queen placements, and to evaluate their computational efficiency, scalability, and memory usage.

Each algorithm was implemented in Python and tested under identical hardware settings. We designed specific parameter tuning strategies for metaheuristic methods, including cooling schedules for Simulated Annealing and crossover/mutation rates for Genetic Algorithms. Experiments revealed that Exhaustive Search is suitable only for small boards (N ≤ 10), while Greedy Search performs effectively up to N = 50 but struggles with local optima on larger boards. Simulated Annealing offered the best trade-off between time and memory, successfully solving instances up to N = 100. Genetic Algorithm also scaled to N = 100 but with significantly higher memory demands and runtime.

These findings demonstrate that no single algorithm excels universally, highlighting the need for hybrid strategies and parameter optimization to extend scalability. This comparative analysis contributes to understanding algorithm behavior under combinatorial constraints and supports the development of more adaptive optimization techniques for complex real-world problems.

3.44. Clustering Student Profiles with Parental Responsibilities Using Unsupervised Learning Algorithms

• Michael Cutipa-Santi, Daisy Crishyely Quelca Velásquez and Yeni Cutipa Santi

• Professional School of Statistical Engineering and Computer Science, National University of the Altiplano, Puno 21001, Peru

This study explores the identification of student profiles with parental responsibilities at the National University of Altiplano, Puno, using clustering algorithms. A total of 206 records of students with parental responsibilities were analyzed, employing a variety of sociodemographic, academic, and family-related variables. Dimensionality reduction was performed using Principal Component Analysis (PCA), retaining 10 key components to enhance computational efficiency and interpretability. Following this, three clustering algorithms—K-Means, DBSCAN, and Agglomerative Clustering—were implemented to segment students and evaluate the effectiveness of these methods in identifying distinct behavioral profiles based on their responsibilities and academic challenges.

The K-Means algorithm proved to be the most effective, generating two distinct clusters with a Silhouette Score of 0.1611, a Davies-Bouldin Index of 2.1475, and a Calinski-Harabasz Index of 33.7629. Cluster 0 included students with greater academic stability and fewer interruptions, while Cluster 1 represented students facing greater challenges, such as frequent study pauses and lower academic performance. DBSCAN identified a noise cluster, while Agglomerative Clustering produced intermediate results with less defined clusters.

These findings underscore the usefulness of clustering techniques in understanding the academic dynamics of students with parental responsibilities, offering valuable insights for developing personalized interventions. This approach helps fill a gap in the existing literature and provides opportunities for future research with larger datasets and additional variables, ultimately improving support strategies for this unique student population.

3.45. Comparative Performance Analysis of Quantum and Classical Models in Brain Tumor Classification

• Emine Akpinar and Murat Oduncuoglu

• Department of Physics, Yildiz Technical University, Istanbul, Turkey

Brain tumors are abnormal cell masses in the brain. For early-stage tumor classification based on neuroimaging techniques, particularly MRI, DL approaches such as DNNs and CNNs are frequently employed and have achieved moderate success. Recently, to address limitations of classical AI—such as data-driven challenges (e.g., high complexity, correlations) and limited computational resources (CPU, GPU)—quantum computing-based AI approaches have been developed, leveraging quantum mechanics principles and properties of quantum particles. This study proposes a hybrid quantum–classical integrated neural network (HQCINN) model for multi-class brain tumor classification, comparing its performance with two DNN and CNN models, with trainable parameters controlled at a similar level. The HQCINN model consists of quantum and classical components: the quantum part incorporates amplitude encoding, a multi-layer parameterized quantum circuit, and measurement operations, while the classical part includes the softmax function, loss computation, and optimization steps. Furthermore, the proposed quantum model was executed on the default.qubit state-vector simulator provided by PennyLane 0.35.1, whereas Keras was used for designing the DL models. The HQCINN model demonstrated the highest performance in distinguishing four different brain tumor types, with training/validation losses of 0.24/0.23 and accuracies of 0.91/0.92. For the DNN model, the losses were 1.31/1.03 and accuracies 0.47/0.51, while for the CNN model, losses were 0.42/0.80 and accuracies 0.78/0.65. The total execution time for HQCINN was 16 h longer than that of the CNN. In conclusion, model selection should be scenario-dependent: HQCINN offers superior performance on complex datasets, whereas classical CNNs remain more efficient when speed is prioritized.

3.46. Date Palm Tree Counting from Satellite and UAV Imagery: Challenges, Methods, and Future Directions

• Bouthina Rechachi ¹, Khaled Rezeg ¹, Okba Kazar ² and Imam Barket Ghiloubi ³

¹ 

Laboratoire de l’INFormatique Intelligente (LINFI), Department of Computer Science, University of Mohamed Khider Biskra, Algeria

² 

College of Computing and Intelligent Systems Department of Computer Science, University of Kalba, Sharjah, United Arab Emirates

³ 

Identification, Command, Control and Communication Laboratory (LI3CUB), Mohamed Khider University, Biskra, Algeria

Date palms are vital for food security, cultural heritage, and the economy in arid and semi-arid regions. Accurate estimation of their number and spatial distribution is crucial for sustainable agricultural management and yield prediction. Recent advances in remote sensing and deep learning have enabled automated palm tree counting using both satellite and unmanned aerial vehicle (UAV) imagery. This review synthesizes the state of the art in palm tree detection and enumeration, emphasizing two main data sources: high-resolution satellite images, which provide large-scale coverage, and UAV-based imagery, which offers fine-grained details. While early studies relied on traditional image processing techniques such as crown segmentation and vegetation indices, recent research increasingly adopts deep learning approaches, including object detection (e.g., YOLO, Faster R-CNN) and semantic or instance segmentation (e.g., Mask R-CNN, U-Net). We analyze the strengths and limitations of these methods, with particular attention to challenges such as shadow interference, crown overlap, and scarcity of annotated datasets. Performance evaluation metrics (precision, recall, mAP, MAE) are discussed, along with the role of transfer learning, multispectral data integration, and emerging transformer-based architectures. Finally, we outline future research directions, including the development of benchmark datasets, hybrid workflows that integrate multi-source imagery, and real-time monitoring systems to support farmers, researchers, and decision-makers in managing palm plantations more effectively.

3.47. Deep Learning-Based Vision System for Real-Time Gesture Recognition and Speech Synthesis to Assist Non-Verbal Users

• Ashutosh Das, B. Sahithi, Pallavi Choudhury, P. Ankit Krishna and Gurugubelli V.S. Narayana

• School of Engineering and Technology, Department of Computer Science and Engineering, GIET University, Gunupur 765022, Odisha, India

Background: Individuals with speech impairments often face significant challenges in daily communication, limiting their ability to interact effectively. Traditional communication aids, though helpful, can be costly or inflexible. Recent advancements in computer vision and deep learning offer new opportunities to develop logical, real-time, and affordable assistive technologies. Objective: This study aims to design and implement a low-cost, vision-based gesture-to-speech system that enables nonverbal individuals to communicate through hand gestures. The goal is to translate recognized gestures into audible speech, bridging the communication gap and enhancing quality of life. Methods: The system uses a standard webcam to capture hand gestures, processed in real-time using OpenCV. A Convolutional Neural Network (CNN) developed with TensorFlow is trained on a custom dataset to classify hand signs accurately. The workflow includes image preprocessing, data augmentation, model training, and deployment. Each recognized gesture is mapped to a corresponding text, which is then converted into speech using a text-to-speech (TTS) engine. Results: The captured hand image is first passed through a filter, and the filtered image is then input to a CNN-based classifier that predicts the gesture class. Once classified, the corresponding word is displayed as output and then converted into audible speech. The system achieved 98% accuracy across 26 alphabetic gestures and performed reliably in real-time with minimal latency under varying lighting and background conditions. Conclusion: The proposed system is an effective and affordable communication aid for individuals with speech impairments. Its modular, real-time design makes it suitable for deployment in resource-constrained settings.

3.48. Design and Analysis of a 9 GHZ Inset Fed Microstrip Patch Antenna for X-Band Applications

• Yechuri Sivaramakrishna ¹, M. Anand ², Ramadevi Sandireddy ³, Vijaya Kumar Velpula ⁴ and Ganesh Miryala ⁴

¹ 

Department of ECE, MLR Institute of Technology, Dundigal, Hyderabad, India

² 

Department of Electronics Communication Enginnering, Geethanjali College of Enginnering and technology, Hyderbad, India

³ 

Department of Electronics Communication Enginnering, NRI Institute of Technology, Agiripalli, Vijayawada, India

⁴ 

Department of Electronics Communication Engineering, MLR Institute of Technology, Hyderabad, India

This paper presents the design and comprehensive analysis of a 9 GHz inset-fed rectangular microstrip patch antenna intended for X-band applications. Microstrip patch antennas are widely recognized for their low-profile structure, ease of fabrication, and seamless integration with modern microwave circuits, making them suitable for radar, satellite communication, and defense systems. The proposed antenna is designed and simulated using Ansys HFSS software on four distinct substrate materials, FR4, air, Bakelite, and Rogers RT Duroid 5880, with dielectric constants of 4.4, 1.0, 4.8, and 2.2, respectively. The study emphasizes the impact of substrate material on antenna performance by evaluating critical parameters such as return loss, gain, directivity, bandwidth, and VSWR. Simulation results demonstrate that FR4 provides the best impedance matching with a return loss of −37.13 dB, while the air substrate offers maximum gain of 9.58 dBi. Rogers 5880 achieves balanced performance in terms of gain and return loss, making it suitable for high-performance applications despite higher fabrication costs. Bakelite shows moderate performance but remains viable for low-cost solutions. This investigation highlights the trade-offs between performance and cost in substrate selection, offering antenna designers practical guidelines for optimizing X-band antenna designs. The findings contribute to improving antenna design strategies for reliable communication systems operating in the X-band spectrum.

3.49. Developing a Unified Framework for Contextual Multi-Modal Reasoning in Document Understanding

• Goni Mahmud Mustapha ¹ and Austin Olom Ogar ²

¹ 

Computer Science Department, Nile University of Nigeria, Abuja 900108, Nigeria

² 

Software Engineering Department, Nile University of Nigeria, Abuja 900108, Nigeria

Understanding real-world documents is no longer just about reading text. Modern documents combine written content with layouts, tables, forms, and even images, creating a level of complexity that traditional natural language processing tools struggle to handle. Many existing approaches treat each part of the documents as text, structure, or visuals separately or fuse them too late, which means they often miss the context that comes from how these elements interact. This research presents a unified framework that integrates text, layout, and visuals into a single, explainable system. Built on a transformer backbone, the framework uses cross-modal attention and graph-based reasoning to capture relationships across different modalities more naturally. The framework will be tested on benchmark datasets, including FUNSD, DocVQA, SROIE, RVL-CDIP, and PubLayNet, which cover tasks such as key-value extraction, document classification, and visual question answering. We will evaluate results using standard metrics (F1, exact match, IoU) and add interpretability checks to ensure transparency. We expect the system to deliver stronger accuracy, adaptability, and interpretability than current methods. Beyond technical gains, this research aims to support practical automation in many precision domains like healthcare, legal services, and government, while advancing the foundations of multimodal reasoning in the field of Artificial Intelligence.

3.50. Efficient Deep Learning Framework for Automated Pneumonia Classification

• Dominic Kipkemboi Koimet ¹, Yasir Jamal ², Raja Hashim Ali ¹ and Qamar Abbas ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

Department of Artificial Intelligence, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly among children and the elderly. Early and accurate diagnosis is vital to improving patient outcomes; however, manual chest X-ray interpretation requires specialized expertise and is prone to subjectivity. Deep learning offers a promising solution by automating diagnosis while reducing diagnostic delays and improving accessibility. This study develops and evaluates a lightweight Convolutional Neural Network (CNN) model for binary classification of chest X-ray images into “Normal” and “Pneumonia” categories.

The publicly available Kaggle Chest X-Ray Pneumonia dataset, comprising 5863 pediatric radiographs, was used for training, validation, and testing. Images were preprocessed through resizing, normalization, and data augmentation techniques including flipping and rotation to enhance model generalization. The CNN architecture included three convolutional blocks followed by dense layers, dropout regularization, and a final sigmoid classifier. Training was conducted for 15 epochs with the Adam optimizer, and performance was assessed using accuracy, precision, recall, and F1-score.

Results demonstrate a test accuracy of 76.6%, with precision of 90% for pneumonia cases and recall of 94% for normal cases. The model showed strong diagnostic capability for normal scans but occasionally misclassified subtle pneumonia features, as confirmed by means of qualitative error analysis. Despite these limitations, the CNN achieved balanced performance with reduced computational complexity, making it suitable for deployment in resource-limited settings.

In conclusion, this study highlights the potential of efficient CNN architectures for supporting pneumonia diagnosis, offering a scalable and interpretable tool for clinical decision support and preliminary screening.

3.51. Enhancing Explainability in Diabetic Retinopathy Detection Using Lesion-Based Image Analysis

• Lija Jacob, Thomas K. T. and Sharon Susan Thomas

• School of Sciences, Christ University, Bengaluru 560029, India

Diabetic Retinopathy (DR) remains one of the leading causes of vision damage worldwide, emphasizing the urgent need for reliable and interpretable diagnostic tools. While deep learning models have demonstrated remarkable performance in DR detection, their “black-box” nature limits clinical adoption. This study explores an explainable AI (XAI) framework that integrates lesion-level analysis with retinal fundus images to improve both accuracy and interpretability. Lesions such as microaneurysms, hemorrhages, and exudates are detected and highlighted as clinically relevant biomarkers, which are then mapped to disease severity grading. The model not only classifies DR stages but also generates visual explanations that correspond to ophthalmologists’ diagnostic reasoning. Preliminary results suggest that lesion-based explainability enhances clinician trust, facilitates validation of automated outputs, and supports better decision-making in screening programs. This approach underscores the potential of combining AI precision with medical interpretability, paving the way for practical integration of DR screening tools in real-world healthcare settings.

In the primary stage of experiments, the images were categorized into multiple classes representing the severity of diabetic retinopathy (e.g., No DR, Mild, Moderate, Severe, and Proliferative DR). The MobileNet model achieved over 82% classification accuracy, indicating its ability to distinguish between different stages of the disease with reasonable reliability. This performance demonstrates the feasibility of using lesion-focused features for automated DR grading. At the same time, the lesion-based explainability maps generated during these trials showed alignment with clinically relevant structures such as microaneurysms, hemorrhages, and exudates, supporting both the accuracy and interpretability of the predictions.

3.52. Evaluating Deep Architectures for Pneumonia Detection in Resource-Constrained Healthcare

• Tarik Kizildere ¹, Umme Rabab Syed ² and Haider Ali Khan ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

Pneumonia remains a leading cause of mortality globally, necessitating early and accurate diagnosis to improve patient outcomes. This study presents a comparative evaluation of three deep learning models—Custom Convolutional Neural Network (CNN), ResNet50, and EfficientNet-B0—for automated pneumonia detection using chest X-ray images. The analysis is conducted on the publicly available Kaggle Chest X-ray Pneumonia dataset, comprising 5863 pediatric images, preprocessed and augmented to enhance model generalization.

Each model was assessed based on classification accuracy, AUC-ROC scores, training time, and diagnostic sensitivity. The custom CNN was designed and trained from scratch, while ResNet50 and EfficientNet-B0 utilized transfer learning with pre-trained ImageNet weights and customized classification heads. Experiments were executed in a PyTorch environment with GPU acceleration and early stopping to prevent overfitting.

Among the three, ResNet50 demonstrated superior performance with 85.42% accuracy and an AUC of 0.946, achieving the best trade-off between diagnostic precision and computational efficiency (10.2 min training time). EfficientNet-B0 achieved moderate accuracy (78.21%) and AUC (0.891) but required longer training time. The custom CNN, while competitive in training speed (12.4 min), achieved lower accuracy (71.15%) and was more prone to overfitting.

These results confirm the advantage of transfer learning in medical imaging, particularly for limited datasets. ResNet50 emerges as a robust candidate for clinical screening applications in resource-constrained settings. Future work should focus on domain-specific fine-tuning, multiclass classification, and external validation across diverse populations and imaging protocols.

3.53. Evaluating Shallow vs. Deep CNNs for Particle Jet Classification in High-Energy Physics

• Daria Carlberg ¹, Umme Rabab Syed ² and Ali Gohar ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

School of Management Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

Jet classification plays a vital role in high-energy physics by helping distinguish between fundamental particles such as quarks and gluons in particle collision events. This study explores the potential of deep learning models—specifically, a custom Convolutional Neural Network (CNN) and a pre-trained ResNet50—in classifying quark and gluon jets using 2D histogram representations derived from Pythia8 simulations. The goal is to evaluate the performance of these models in a binary classification task using only visual features, without incorporating high-level physical attributes like jet substructures.

The dataset consists of 40,000 samples (20,000 quark and 20,000 gluon jets), transformed into normalized 64 × 64 grayscale histograms. The CNN was carefully tuned for optimal architecture and regularization, while the ResNet50 was applied with matching parameters to allow fair comparison. Performance was evaluated using precision, recall, F1-score, and ROC-AUC metrics.

Results showed that both models achieved moderate classification performance, with the CNN slightly outperforming ResNet50. The best CNN configuration reached an accuracy of 68.2% and an AUC of 0.74, surpassing random guessing but falling short of deployment-ready reliability.

This work highlights both the potential and limitations of image-based jet classification using deep learning. It also emphasizes the importance of model architecture, preprocessing, and domain-specific features. The findings serve as a stepping stone for future research on integrating physics-aware features and advanced architectures for more robust particle identification.

3.54. Evaluating Unsupervised Learning Frameworks for Marine Wildlife Re-Identification

• Armina Jawali and Jazzie Jao

• College of Computer Studies, Department of Software Technology, De La Salle University, Manila 1004, Philippines

Scalable animal re-identification without labels is essential for wildlife monitoring, especially in resource-limited settings; however, most unsupervised re-ID frameworks remain limited to human datasets. This study systematically evaluates three state-of-the-art frameworks—Self-paced Contrastive Learning (SpCL), Cluster Contrast (CC), and Transformer-Based Multi-Granular Features (TMGF)—on the NDD20 dolphin dataset, a curated underwater image collection featuring white-beaked dolphins (Lagenorhynchus albirostris).

Dolphin viewpoints were manually annotated to address the lack of camera ID labels and camera-aware proxies were substituted with pose-aware proxies to isolate pose variation. All frameworks were trained fully unsupervised using clustering-derived pseudo-labels and contrastive objectives, with ground-truth identities reserved solely for evaluation. Retrieval performance was assessed using mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC) scores. TMGF consistently outperformed SpCL and CC, boosting mAP by 3% and demonstrating greater robustness to pose variation and intra-class variability. View-specific evaluation outperformed aggregated retrieval, suggesting that flank-dependent identity cues are significant for dolphin re-ID. In contrast, SpCL and CC, despite competitive Top-10 accuracy, exhibited lower mAP, indicating reduced consistency.

This study offers the first comprehensive assessment of unsupervised re-ID models on a marine wildlife dataset. It reveals that pose-aware proxies are effective for species with view-invariant or bilaterally consistent identifiers (e.g., humans, dorsal fins, tail flukes), but less so for species with asymmetric or view-dependent cues (e.g., flank markings). These findings underscore the importance of species-aware design when adapting unsupervised learning to ecological domains, advancing the development of AI-driven tools for biodiversity monitoring and marine conservation.

3.55. Explainable AI for Remote Sensing Image Processing: Advanced Interpretation Techniques for Agricultural Monitoring

• Radjaa Bekkouche ¹, Fatma Zohra Albatoul Djoghlaf ¹, Mohammed El Amin Larabi ² and Meziane Iftene ²

¹ 

Departement of Computer Science, University of Algiers Benyoucef Benkhedda, Algiers 16000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

The “black-box” nature of deep learning models remains a critical barrier to their adoption in high-stakes fields like precision agriculture, where trust and accountability are paramount. This research addresses this challenge by developing and validating two novel Explainable AI (XAI) frameworks designed to make crop segmentation models both transparent and highly accurate.

The first framework, SpectroXAI-LLaMA, is a post hoc tool that synergizes multiple attribution methods (e.g., SHAP, LIME) and uses a Chain-of-Thought (CoT) reasoning engine to generate logical, human-readable explanations. The second, IMPACTX-GC-RS, is a self-explaining U-Net architecture trained to simultaneously predict segmentation masks and generate its own Grad-CAM explanation heatmap, thereby making interpretability an intrinsic part of the model.

The results were transformative. The SpectroXAI-LLaMA framework successfully produced detailed explanations that were faithful to model behavior and consistent with agronomic principles. Most remarkably, the IMPACTX-GC-RS model, by learning to explain its own reasoning process, became more accurate than its non-explainable baseline. The mean Intersection over Union (IoU) increased from 0.9625 to 0.975, and the model completely eliminated a key misclassification error between cereal and potato classes.

This work makes a significant contribution by demonstrating that, contrary to the assumed trade-off, integrating explainability directly into AI models can enhance their predictive performance. Our frameworks provide a vital pathway to developing accountable, verifiable, and trustworthy AI systems, accelerating their adoption for sustainable agriculture and other critical applications.

3.56. Explainable Artificial Intelligence for Social Sciences and Humanities: A Systematic Review

• Nikos Koutsoupias and Marios Nosios

• International and European Studies, University of Macedonia, Thessaloniki, Greece

This systematic review examines the integration of Explainable Artificial Intelligence (XAI) methodologies within social sciences and humanities research, focusing on three principal approaches, feature attribution, counterfactual analysis, and model-agnostic visualization, and their application across diverse empirical domains. Feature attribution techniques, such as SHAP and LIME, have been adopted in archival text studies to quantify the contribution of individual lexical elements to topic model outputs, thereby elucidating latent thematic structures. Counterfactual analysis has proven instrumental in social media sentiment research, wherein minimally perturbed inputs expose classifier decision boundaries and reveal embedded biases. Model-agnostic visualization tools further enable scholars to interactively explore decision surfaces in network models of historical social structures, facilitating critical interrogation of community detection and relational dynamics. By synthesizing documented methodological workflows and available open-source toolkits, we identify best practices for harmonizing disciplinary expertise with computational frameworks, including guidelines for model selection, XAI implementation, and domain-expert validation. Evaluation metrics, explanation fidelity, coherence, and end-user interpretability are extracted from empirical studies to benchmark transparency and reproducibility. The proposed workflow begins with the articulation of a precise research question, proceeds through iterative model development and explanation generation, and culminates in collaborative validation with subject-matter experts. This integrated approach advances robust, accountable, and contextually informed computational inquiry, thereby fostering the maturation of XAI as an indispensable instrument in social sciences and humanities scholarship.

3.57. From Global Noise to Local Accuracy: An Abstaining Classifier Approach for Robust Forest Mapping with Noisy Global Data

• Mohammed Mouncef Kadri ¹, Maha Bazouzi ¹, Mohammed Anis Zemali ¹, Meziane Iftene ² and Mohammed El Amin Larabi ²

¹ 

Departement of Computer Science, The National Higher School of Artificial Intelligence (ENSIA), Algiers 16000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

Accurate forest mapping is frequently hindered by the label noise inherent in large-scale global land cover products and the scarcity of high-quality local ground-truth data. This paper presents a novel AI-driven framework that effectively addresses this challenge by synergistically leveraging the broad coverage of noisy global datasets with a small, trusted set of clean annotations. Our approach utilizes a DeepLabV3+ architecture with Sentinel-2 multispectral imagery and derived vegetation indices as input.

The core of our methodology lies in a hybrid data strategy and a specialized composite loss function. We employ strategic batch sampling to prioritize learning from the clean dataset (85% of each batch) while still benefiting from the contextual coverage of the noisy data. Our composite loss function, integrating Dice Loss, Categorical Focal Loss, and a Deep Abstaining Classifier (DAC) Loss, is explicitly designed to manage label noise and boundary uncertainty by empowering the model to abstain from predictions on highly uncertain pixels.

The framework’s efficacy was validated on the complex forested landscapes of North Africa, a region under-represented in many global training datasets. Our model achieved a validation F1-score of 91% and an Intersection over Union (IoU) of 83% on the held-out clean data. Critically, it achieved a recall of 95.4%, substantially minimizing the omission of forest areas compared to a baseline U-Net. This work provides a reliable, scalable methodology for refining large-scale, imperfect datasets, offering a robust solution for forest monitoring in data-challenged regions worldwide.

3.58. From Waves to Wisdom: Leveraging Transformers and CNNs for ECG Signal Classification

• Fatima Guendouzi ¹ and Awatif Guendouzi ²

¹ 

Electrical and Computer Engineering Department, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada

² 

Research Center in Industrial Technologies (CRTI), PB 64, Cheraga, Algiers 16014, Algeria

Diagnosing heart disease is a complex and critical task that requires extracting meaningful patterns from large electrocardiogram (ECG) datasets. As the demand for faster and more reliable diagnostic tools increases, deep learning has emerged as a transformative solution, enabling automated ECG interpretation and reducing the risk of human error. However, traditional models often face challenges such as high computational complexity and limited adaptability to the dynamic nature of ECG signals.

In this study, we investigate the potential of transformer-based architectures to overcome these limitations and enhance classification performance. We explore two distinct strategies: the first employs a standalone transformer encoder to classify ECG signals into five categories—Normal beats (N), Unknown beats (Q), Ventricular ectopic beats (V), Supraventricular ectopic beats (S), and Fusion beats (F)—achieving an accuracy of 91%. The second approach integrates a Convolutional Neural Network (CNN) with the transformer encoder, where the CNN extracts relevant features that are subsequently refined and classified by the transformer, resulting in a significantly higher accuracy of 98%.

These findings demonstrate the effectiveness of transformer models, particularly when combined with CNNs, in improving the precision and robustness of ECG signal classification. This research contributes to the growing field of AI-assisted healthcare and highlights the promise of hybrid deep learning frameworks in supporting more efficient and accurate cardiac diagnostics.

3.59. GAN-Based Image Segmentation for Extraction of Horticulture Plantation Type Using VHRS Data in Parts of Arid Regions of Rajasthan

• Gunavathi R. and Angelo P. Chery

• School of Sciences, Christ University, Bangalore 412112, India

Deep Learning methodologies have been shown to assist vegetation plantations and their stakeholders. Extraction of horticultural plantations from satellite imagery is an important example for agricultural monitoring, food security, and sustainable land management. Traditional techniques often fall short in generalizing across seasons or heterogeneous landscapes, necessitating more adaptive approaches. To address this, we utilized Conditional Generative Adversarial Networks to segment sample plantations of pomegranate and date palm in the arid regions of Rajasthan, India (Jaisalmer and Barmer districts), using Very High-Resolution Satellite (VHRS) imagery from CARTOSAT-2E. The framework, inspired by the existing Pix2Pix image translation model by Philip Isola, incorporated a U-Net generator and Patch-GAN discriminator for segmentation, trained on over 13,000 annotated image–mask pairs. Using loss functions, generator skip connections, and various activation functions, the Pix2Pix U-Net cGAN model demonstrated strong segmentation capabilities. Dice coefficient of 99.49%, IoU of 98.99%, pixel accuracy of 99.28%, precision of 99.66%, and recall of 99.32%. Focal and edge-aware loss functions further enhanced class differentiation, yielding a Dice coefficient of 94.35%, an IoU of 91.52%, a pixel accuracy of 97.95%, a precision of 97.06%, and a recall of 94.09%. While these metrics may have shown a strong performance, the true effectiveness of this approach was demonstrated through accurate vegetation classification and recreating the vegetation masks for extraction and segmentation. The workflow automates plantation detection from VHRS data. It establishes a foundation for future opportunities in horticulture segmentation and analysis, as well as its potential integration into decision-support systems for sustainable resource management.

3.60. Generative AI as a Pedagogical Scaffold: A Scoping Review in the Context of Post-Method English Language Learning

• Md. Ripon Ahmed ¹ and Mohammad Mohi Uddin ²

¹ 

Department of English, Starlight College, Sylhet, Bangladesh

² 

Department of Educational Leadership, Policy, and Technology Studies (College of Education), The University of Alabama, Tuscaloosa, AL 35487, USA

Due to the diverse cultural backgrounds and varying cognitive levels of foreign English language learners, personalized scaffolding is essential for developing language competency across all four language skills: reading, writing, listening, and speaking. However, this need is not adequately addressed in the existing literature. In the post-method era, glocal (global and local) teaching and learning are encouraged, where learners’ cultural foundations and cognitive stages help define the learning approach. In this context, while human scaffolding is limited, the advancement of Generative AI, in English as a Foreign Language (EFL) education, promotes personalized learning and fosters learner autonomy in both formal and informal settings. Grounded in Constructivist theory, which emphasizes contextualized learning, GenAI is integrated as a scaffold to enhance the Zone of Proximal Development (ZPD). Guided by the PICO model and PRISMA screening process, this study selected 45 secondary documents for coding from an initial pool of 383, sourced from SCOPUS through a Boolean search. A thematic qualitative approach was employed for analysis. Our findings reveal that a single method is not sufficient and that GenAI tools play a crucial role in navigating contextual learning pathways, enhancing students’ engagement and learning outcomes in English language learning. These tools improve proficiency across all four language skills and reflect a shift from method-bound instruction to personalized, technology-enhanced learning. The findings will assist students, teachers, and curriculum designers in redefining their understanding of English language acquisition by examining how AI can transform English education from a traditional, teacher-controlled model to a learner-driven system.

3.61. GravSpike: A Neuro-Inspired Gravitational Preprocessing Framework for Abstractive Summarization of Long Documents

• Abubakar Salisu Bashir ¹, Abdulkadir Abubakar Bichi ² and Abubakar Rogo Ado ¹

¹ 

Department of Computer Science, Faculty of Computing, Northwest University, Kano PMB 3099, Kano, Nigeria

² 

Department of Software Engineering, Northwest University, Kano PMB 3099, Kano, Nigeria

Transformer-based models struggle with long-document summarization due to fixed input length constraints. To mitigate this issue, hybrid approaches typically perform an extractive preprocessing step, selecting salient sentences as input to an abstractive summarization model. However, most unsupervised extractive methods, such as TextRank and LexRank, rely on shallow heuristics and fail to preserve semantic coherence or minimize redundancy. We propose GravSpike, a neuro-inspired preprocessing framework for extractive–abstractive summarization. GravSpike integrates SBERT-based sentence embeddings with a gravitational ranking model that scores sentences based on lexical salience, positional weight, and semantic proximity, modeled using the gravitational force equation. To further enhance content diversity and reduce redundancy, we introduce a spiking neuron-inspired filtering mechanism that iteratively activates informative sentences based on adaptive firing thresholds. A multi-objective Ant Colony Optimization (ACO) algorithm then selects an optimal subset, balancing ROUGE-based relevance and SBERT-based semantic cohesion. We evaluate GravSpike on three long-document datasets, BillSum, PubMed, and arXiv, by comparing abstractive summaries generated by BART and T5 with and without GravSpike preprocessing. Experimental results show that GravSpike-enhanced inputs consistently yield higher ROUGE-1, ROUGE-2, and ROUGE-L scores than the same models applied directly to truncated or full-length documents. On the BillSum dataset, GravSpike achieves ROUGE-1, ROUGE-2, and ROUGE-L scores of 58.83, 37.63, and 44.47, respectively (p < 0.01). These findings demonstrate GravSpike’s effectiveness as a modular, unsupervised filtering pipeline that significantly improves the performance of large language models on long-form summarization tasks.

3.62. Heuristic and Evolutionary Strategies for the N-Queens Problem: Strengths and Trade-Offs

• Daria Carlberg ¹, Umme Rabab Syed ² and Ali Raza ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

The N-Queens problem is a classic combinatorial challenge in artificial intelligence, where the objective is to place n queens on an n × n chessboard such that no two queens threaten each other. As n increases, the search space expands factorially, demanding robust and scalable optimisation strategies. This study presents a comparative analysis of four algorithmic approaches: Depth-First Search (DFS), Hill Climbing, Simulated Annealing, and Genetic Algorithms. Each method was implemented in Python and tested across varying board sizes (n = 10, 30, 50, 100, 200) to evaluate solution accuracy, execution time, memory usage, and scalability.

DFS proved reliable for smaller instances but became computationally infeasible as n grew. Hill Climbing showed efficiency for mid-sized boards but often stagnated in local optima beyond n = 100. Simulated Annealing consistently delivered high-quality solutions with excellent time and memory efficiency across all test cases. The Genetic Algorithm, while slower and more memory-intensive, achieved solutions for all tested n values after extensive tuning of hyperparameters.

The results highlight trade-offs between deterministic and stochastic approaches, emphasizing that no single algorithm outperforms across all dimensions. Simulated Annealing emerged as the most balanced method in terms of scalability and efficiency. This work contributes a practical benchmark for researchers evaluating optimisation techniques on NP-complete problems and provides a foundation for developing hybrid or parallelised methods for large-scale constraint satisfaction challenges.

3.63. Hybrid VGG19-TCN with Multi-Channel Temporal Attention for Phishing Attack Detection

• Abdulrauf Garba Sharifai ¹, Abubakar Salisu Bashir ², Usman Abubakar Mahmud ³, Abdulkadir Abubakar Bichi ³, Abubakar Ado ⁴ and Mansir Abubakar ⁵

¹ 

Computer Science Department, Faculty of Computing Northwest University, Kano, Nigeria

² 

Department of Computer Science, Faculty of Computing and Mathematical Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria

³ 

Software Engineering Department, Faculty of Computing, Northwest University, Kano, Nigeria

⁴ 

Faculty of Computing, Northwest University, Kano, Nigeria

⁵ 

Faculty of Computer Science and Mathematics, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia

Phishing attacks continue to be a constant and evolving menace in cyberspace, taking advantage of users’ confidence to gain access to private data. To improve the detection of phishing attacks, this research study proposes a novel hybrid architecture that combines the robust spatial feature extraction capabilities of VGG19 with the temporal sequence modelling advantage of a Temporal Convolutional Network (TCN), enhanced with a Multi-Channel Temporal Attention Mechanism. The TCN temporarily captures the deep spatial information that the VGG19 network extracts from embedded URLs and email content to detect time-based attack patterns and sequential dependencies. The model can focus on the most discriminative temporal features, especially in imbalanced datasets, based on the Multi-Channel Temporal Attention Module, which dynamically weights temporal features across different data streams. The proposed Hybrid VGG19–TCN model with Multi-Channel Temporal Attention outperforms conventional CNN-RNN, CNN-LSTM, CNN-GRU, CNN, LSTM, GRU, TCN, and BiLSTM models and other baseline machine learning classifiers regarding accuracy, recall, and AUC, according to an experimental evaluation on three benchmark phishing datasets. The experiment results demonstrate that the proposed model is a more robust and precise solution for detecting advanced phishing attacks than state-of-the-art models, and it can be deployed in real-time phishing detection systems.

3.64. Improved Taxonomy Re-Structuring Using Modified K-Means Clustering for Efficient Large-Scale Text Classification

• Abubakar Ado ¹, Abdurrauf Sharifai Garba ¹, Mansir Abubakar ², Bashir Salisu ³, Usman Mahmud ⁴ and Abdulkadir Bichi Abubakar ¹

¹ 

Faculty of Computing, Northwest University, Kano, Nigeria

² 

Faculty of Computer Science and Mathematics, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia

³ 

Department of Computer Science, Faculty of Computing and Mathematical Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria

⁴ 

Department of Software Engineering, Northwest University, Kano, Nigeria

Textual classification for a hierarchical taxonomy of classes is a common and well-known problem associated with Large-Scale Text classifications (LSTCs). Existing approaches simply re-structure the hierarchy of classes prior to classification and have achieved better results. However, when there are many classes with an increased number of features, traditional hierarchy re-structuring tends to produce many nodes with similar granularities. This results in misclassification, and it is computationally expensive or not scalable for many classification models, especially when the hierarchy is longer. In this paper, we propose an improved hierarchy re-structuring algorithm that uses modified k-means clustering. The method uses a k-weight and backtracking, where necessary, to cluster nodes with similar granularities into a few generalized classes, reducing the number of nodes and hierarchy length as well. In addition, the proposed approach can handle overfitting, which usually occurs as a result of the unbalanced nature of LSHT datasets, where the features in each class vary extensively. Experimental results on 20NG, IPC, and DMOZ-small datasets using TD-LR and TD-SVM show that our approach can effectively improve large-scale hierarchical text classification performance over traditional and existing re-structuring approaches. In terms of scalability, our approach increases the number of scalable instances by about 10%; hence, it records the best and fastest running time.

3.65. Interpretable AI in Healthcare: Parkinson’s Disease Detection from Spirals

• Kaab Mohammed Arzoo ¹, Yasir Jamal ², Raja Hashim Ali ¹ and Adnan Shah ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

Department of Artificial Intelligence, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder where early detection is essential to slow disease progression and improve patient outcomes. Handwriting analysis, particularly spiral drawings, provides a low-cost, non-invasive biomarker for identifying motor impairments linked to PD. However, existing approaches often depend on multimodal inputs or specialized hardware, limiting scalability and interpretability in clinical use. This study proposes an explainable deep learning framework that detects PD solely from spiral handwriting images.

Using the NewHandPD dataset, a lightweight Convolutional Neural Network (CNN) was trained on grayscale spiral drawings after standard preprocessing steps such as resizing and normalization. The model was evaluated using accuracy, precision, recall, and AUC metrics, and interpretability was ensured through Gradient-Weighted Class Activation Mapping (Grad-CAM), which highlights input regions influencing predictions.

The CNN achieved an overall accuracy of 87%, with a precision of 86.5%, a recall of 87.3%, and an AUC of 0.91 on the test set. Grad-CAM heatmaps confirmed that the network consistently focused on tremor-induced distortions and irregular stroke patterns, aligning with clinically relevant features of PD. This interpretability bridges the gap between deep learning performance and clinical trust, addressing the common “black box” limitation of AI systems in healthcare.

The results demonstrate that handwriting-based biometrics can serve as an effective, explainable, and deployable tool for early PD screening. This work provides a foundation for integrating transparent AI-driven diagnostics into clinical workflows and expanding research toward multimodal approaches in neurodegenerative disease detection.

3.66. Kidney Cancer Diagnosis Using Bagging Ensemble Method

• Mohamed Lakhdar Tiar, Nadjiba Terki and Fadi Elislam Rouag

• VSC Laboratory, University of Mohamed Khider Biskra, Biskra 07000, Algeria

Early and accurate diagnosis of kidney cancer is important for effective treatment planning and improved patient prognosis. This work proposed a strong ensemble of deep learning for binary classification of kidney histopathological images into tumor and normal classes. The dataset employed was obtained from the publicly accessible Multi Cancer Dataset on Kaggle, with all images resized to 128 × 128 pixels to ensure consistency. We implemented a bagging ensemble strategy by training three distinct convolutional neural network models, each based on a pre-trained ResNet50 architecture with frozen base layers. Each model was trained on a different subset of the training data to promote diversity within the ensemble. The predictions of each model were aggregated with soft voting for the final prediction. Based on the evaluation of the test set, our ensemble achieved an accuracy of 94.46% with high precision, recall, and F1-scores. Our results demonstrate that the bagging ensemble effectively has robustness in automated kidney cancer detection and has potential as a decision-support tool in clinical practice. The proposed method not only reduces variance and improves classification stability but also highlights the effectiveness of using ensemble learning with transfer learning for histopathological image analysis, as we note that it was a successful collaboration.

3.67. Legal Document Classification into High-Frequency Procedural Categories Using Machine Learning

• Rhedson Esashika, Israel Gondres Torné and Marcelo Chamy Machado

• PPGEEL—Postgraduate Program in Electrical Engineering, State University of Amazonas, Manaus, Amazonas, Brazil

The continuous increase in the number of legal cases submitted to the judiciary has imposed a significant burden on the court system, making the analysis, identification, and classification of similar actions within large and complex datasets increasingly challenging. When performed manually, this task becomes not only time-consuming but also highly prone to human error, potentially compromising the efficiency and reliability of judicial procedures. This study investigates the application of Natural Language Processing (NLP) techniques to automate and enhance the classification of procedural acts into high-frequency categories. Specifically, the Continuous Bag of Words (CBOW) and Skip-gram models—both based on word embedding strategies—were implemented in conjunction with the Logistic Regression algorithm for supervised classification. The dataset, comprising approximately 311,000 legal documents from the Court of Justice of the State of Amazonas (TJAM), was processed through a robust pipeline, including automated web scraping, advanced text preprocessing, vocabulary construction, and model training. The experimental results were highly promising: the models achieved an accuracy rate of 95% and an F1-score of 95%, demonstrating the strong potential of integrating NLP with machine learning to optimize procedural management. By automating repetitive and labor-intensive classification tasks, the proposed approach not only reduces processing time and human workload but also enables judicial institutions to allocate more resources to complex cases requiring expert human judgment, thereby improving efficiency, reducing backlog, and enhancing access to justice.

3.68. MarineSumm: A Multi-Objective, Semantic-Aware Optimization Framework for Extractive Summarization of Legal Texts

• Abubakar Salisu Bashir, Abdulrahman Muhammed Bello, Ahmad Jibril Zainab and Mahmud Usman

• Department of Computer Science, Faculty of Computing, Northwest University, Kano PMB 3099, Kano, Nigeria

Automatic text summarization is essential for managing information overload, particularly in domains such as law, where documents are lengthy and complex. Existing extractive summarization methods based on metaheuristic algorithms often suffer from poor initialization and single-objective optimization, resulting in redundant and semantically weak summaries. This work presents MarineSumm, a novel extractive summarization framework designed to improve both lexical quality and semantic relevance in long legal texts. MarineSumm enhances the Marine Predator Optimization (MPA) algorithm through three key contributions. First, it replaces random initialization with a PageRank-based strategy that uses sentence centrality to guide the starting population. Second, it incorporates SBERT embeddings to model semantic similarity between sentences more effectively. Third, it introduces a dynamic multi-objective fitness function that adaptively balances ROUGE scores and SBERT-based cosine similarity across iterations, optimizing for both surface-level relevance and deeper semantic alignment. To further refine candidate solutions, MarineSumm applies a controlled Lévy-flight mutation, sigmoid-based binarized encoding, and sentence-length constraints, which improve search diversity and ensure concise, coherent outputs. Evaluated on the BillSum dataset, MarineSumm achieves ROUGE-1 of 0.5348, ROUGE-2 of 0.2547, and ROUGE-L of 0.3344, outperforming standard metaheuristic baselines including Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. These results demonstrate the effectiveness of integrating graph-based initialization, semantic-aware scoring, and adaptive optimization into the MPA framework. MarineSumm offers a robust, unsupervised solution for summarizing legal and technical documents and can serve as a reliable extractive component within hybrid summarization pipelines.

3.69. Mitigating Label Noise in Remote Sensing: A Pseudo-Labeling Method for Forest Classification with Sentinel-2

• Lilia Ammar Khodja ¹, Mohammed El Amin Larabi ² and Meziane Iftene ²

¹ 

Departement of Computer Science, The National Higher School of Artificial Intelligence (ENSIA), Algiers 16000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

The accuracy of large-area forest mapping is often compromised by the label noise present in global land cover products like ESA WorldCover. This study introduces a robust semi-supervised framework designed to mitigate this issue by leveraging a small, trusted set of manually curated clean data to refine a large, noisy dataset.

Our approach employs a modified ResNet-18 architecture in a two-stage training process. First, the model is trained exclusively on the high-quality, manually labeled clean dataset. This initial “teacher” model is then used to generate high-confidence pseudo-labels for the extensive but noisy WorldCover data, effectively filtering and re-labeling uncertain or incorrect regions. In the second stage, the model is fine-tuned on a composite dataset containing both the original clean labels and the newly generated, reliable pseudo-labels. This strategy leverages the accuracy of the clean data to improve the utility of the noisy data, significantly enhancing model robustness and generalization. The methodology was tested using Sentinel-2 and Digital Elevation Model (DEM) data in a case study covering the diverse forest ecosystems of North Africa.

Our semi-supervised methodology demonstrated exceptional performance, achieving a final classification accuracy of 98.50% on a combined validation set. The initial training on clean data showed rapid convergence, underscoring the power of a high-quality seed dataset. This research offers a practical and highly effective strategy for improving land cover classification in any region where large, noisy datasets are available alongside limited high-quality ground truth, providing a scalable solution to support global conservation efforts.

3.70. Multimodal Sentiment Analysis with Transformer Networks: Bridging Speech, Text, and Facial Expressions

• Shikha Khullar, Harish Kumar Pamnani and Kriti Sankhla

• Department of Computer Science and Engineering, Poornima University, Jaipur, India

The conventional approaches to sentiment analysis often use only one type of modality, e.g., text or speech; hence, they cannot be used to identify the richness of human expression of emotions. As the concept of deep learning rapidly expands, Multimodal Sentiment Analysis (MSA) has become a potent technology that allows the merging of various sources of data to enhance emotional comprehension. This paper introduces a transformer-based architecture, integrating speech, text and faces to improve the accuracy of sentiment classification. Transformer networks are able to use the self-attention mechanism to capture long-range interactions as well as cross-modal interactions, which are difficult to capture using more traditional recurrent or convolutional models. The suggested system derives textual embeddings based on a pre-trained language model, acoustic features based on spectrogram-based encoders, and visual interpreters based on facial landmark and expression recognition systems. A cross-modal attention fusion approach synchronizes and dynamically balances features between modalities to produce higher-level and more context-initiative sentiment cues. Results of experiments on benchmark datasets, including CMU-MOSEI and IEMOCAP, show that the proposed model provides an accuracy of 87.6 and an F1-score of 86.9, surpassing unimodal and early-fusion baselines by 6.4 and 5.8, respectively. The architecture has been found to be accurate at recognizing subtle or ambiguous emotions. These results show the possibilities of using transformer-based MSA systems in real-life scenarios, such as human–computer interaction, healthcare, social robotics, and in digital learning environments, creating a path to emotionally intelligent and responsive AI systems.

3.71. Neural Network-Based Emotion Recognition for Student Assessment and Test Readiness

• Edward Pinto Pimenta Junior, Daniel Guzmán Del Río, Miguel Angel Orellana Postigo and Israel Gondres Torné

• PPGEEL—Postgraduate Program in Electrical Engineering, State University of Amazonas, Manaus 69050-020, Brazil

This work presents the development of an educational support system based on Convolutional Neural Networks (CNNs) applied to facial emotion recognition. The model was trained using public datasets of emotional expressions, enabling the real-time identification of affective states such as happiness, sadness, anger, surprise, and neutrality. From these detections, two dynamic indicators were defined: concentration and nervousness. Both were computed through a weighted mapping of emotions, where each recognized emotion contributed with specific coefficients to quantify levels of focus and stress. This methodology was inspired by studies in affective computing and educational psychology, which emphasize the influence of emotional states on attention and test anxiety.

The CNN model achieved an accuracy of approximately 70% during training and validation, ensuring reliable emotion detection for subsequent analysis. To determine readiness, a rule-based mechanism was applied: students were considered prepared when concentration reached at least 60 out of 100 while nervousness remained below 50. By combining these two indicators, the system provided an objective and interpretable evaluation of the student’s emotional readiness to answer questions or undertake an assessment.

The system was designed to support teachers in better understanding students’ emotional states during evaluative activities. By integrating emotional and cognitive factors, educators gain a more holistic view of the learning process, promoting fairer and more inclusive evaluation practices.

Experimental results confirmed consistent estimations of concentration and nervousness, with reliable classification of test readiness. These findings highlight the potential of artificial intelligence as an innovative tool in contemporary education.

3.72. Optimizing Distillation Column Internals Design Using Reinforcement Learning Algorithms for Hybrid Discrete–Continuous Action Spaces

• Holden B. Broussard ¹, Dhan Lord B. Fortela ¹, Ashley P. Mikolajczyk ¹ and Magdy Bayoumi ²

¹ 

Department of Chemical Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

² 

Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

Introduction: Column internals design is an integral part of chemical engineering applications. Traditionally reliant on computational/time-intensive optimization, this process is often limited in terms of efficiency and adaptability. With the recent emergence of A.I. and reinforcement learning, integrating these tools into the design process offers opportunities to further efficiency and optimization. The main challenge faced in this integration process is the navigation of complex, hybrid action spaces that contain, or combine, both continuous and discrete variables. Methods: By using custom reinforcement learning environments integrated with Aspen Plus, a digital twin framework was developed, allowing a machine learning agent to interact with process simulations. Two reinforcement learning algorithms were implemented, a hybrid Soft Actor–Critic and Deep Q-Network approach, which allocates continuous actions to the SAC algorithm and the discrete actions to the DQN algorithm, and a more unified Parametrized Deep Q-Network approach, which integrates discrete–continuous actions into one architecture. In both cases, the reward function is based on the percent approach to flooding at each section of the column, providing insight into the hydraulic stability of the column. While this is the metric chosen for current studies, the framework can be extended to include others. Results: Our results indicate that both reinforcement learning strategies navigated the hybrid action space, generated hydraulically feasible designs, and adapted to different column configurations. Conclusions: This research indicates that reinforcement learning is a plausible option for optimizing distillation internals design. The reinforcement learning strategies develop a pathway for scalable, multi-objective optimization in process design.

3.73. Optimizing Security with Enhanced CNN in 5G/6G Networks and Using Deep Learning for Disease Prediction

• Shahnazeer C. K. and Sureshkumar G.

• Department of Computer Science, School of Engineering & Technology, Pondicherry University Karaikal Campus, Karaikal 609605, Puducherry (UT), India

Introduction: Integrating machine learning algorithms into the medical field has become essential for improving disease diagnosis and predicting conditions at an early stage. However, conventional machine learning techniques often struggle with large, complex datasets, limiting their effectiveness. This work proposes a novel deep-learning approach to enhance disease prediction accuracy using medical databases to address this. Methods: This study introduces a two-stage deep learning model utilizing a Convolutional Neural Network (CNN) for disease prediction. CNNs, known for their strengths in pattern recognition and regression, are applied to classify medical data. In the first stage, initial classification is performed, while the second stage focuses on analysing the experimental dataset to assess accuracy. To evaluate the performance of the proposed CNN model, a comparative study is conducted against two established models: VGG16 and Recurrent Neural Network (RNN). Results: The proposed CNN model achieved an accuracy of 98.5%, significantly surpassing the performance of both VGG16 (85%) and RNN (90%). The CNN’s ability to handle complex datasets with diverse medical parameters effectively highlights its superiority in disease prediction tasks. Conclusion: The study demonstrates that the CNN-based deep learning model offers a highly accurate and efficient solution for early disease prediction, outperforming traditional models. With its improved accuracy and robustness, the proposed approach has the potential to enhance diagnostic capabilities, enabling timely medical interventions and better patient care outcomes.

3.74. Performance Evaluation of Stop Word Influence in Hausa Extractive Summarization Using Enhanced TextRank

• Usman Mahmud ¹, Abdulkadir Abubakar Bichi ², Abubakar Ado ³, Abdulrauf Garba Sharifai ⁴, Mansir Abubakar ⁵, Abubakar Salisu Bashir ⁶ and Abubakar Bashir Salisu ⁶

¹ 

Department of Software Engineering, Faculty of Computing, Northwest University, Kano, Nigeria

² 

Software Department, Faculty of Computing Northwest University, Kano, Nigeria

³ 

Faculty of Computing, Northwest University, Kano, Nigeria

⁴ 

Computer Science Department, Faculty of Computing Northwest University, Kano, Nigeria

⁵ 

Faculty of Computer Science and Mathematics, Universiti Teknologi Mara, Shah Alam, Selangor, Malaysia

⁶ 

Department of Computer Science, Faculty of Computing and Mathematical Science, Aliko Dangote University of Science and Technology, Wudil, Nigeria

Stop word removal is a fundamental preprocessing step in Natural Language Processing (NLP), aiming to eliminate non-informative words that may degrade the performance of downstream tasks. While its impact has been widely explored in high-resource languages like English, its effectiveness in low-resource languages such as Hausa remains under-investigated, particularly in the context of extractive text summarization. This study addresses that gap by examining the role of stop word removal in enhancing summarization performance for Hausa academic texts. We introduce a new benchmark dataset specifically curated for Hausa extractive summarization, composed of academic abstracts. Furthermore, we propose an enhanced variant of the TextRank algorithm that leverages a combination of sentence-level features including positional weight, lexical similarity, and semantic similarity to compute edge weights in the sentence similarity graph. This feature-rich graph structure allows for a more context-aware sentence ranking process. The proposed model is evaluated against standard baselines, namely, TextRank and LexRank, using the ROUGE evaluation metric. Experimental results demonstrate that our method significantly outperforms the baselines across ROUGE-1, ROUGE-2, and ROUGE-L scores. Additionally, ablation studies with and without a tailored Hausa stop word list reveal a notable performance gain when stop words are removed. These findings highlight the importance of language-specific preprocessing strategies in improving NLP outcomes for low-resource languages.

3.75. PetSense: An Integrated System for Real-Time Cat Activity and Affective Monitoring

• Marvin Cheng, Hugo Camargo, Athena Cheng and Nicole Camargo

• The Davis Crew Kittens, Inc., Morgantown, WV 26505, USA

This work proposes a home-centric monitoring system that integrates machine vision, YOLO-based object detection, and household sensing platforms, fixed indoor cameras and mobile robots such as robot vacuums, to track the activities and partial affective indicators of indoor cats. The system targets scenarios in which owners are away for work or travel and is especially relevant for multi-cat households, where social dynamics, resource access, and routine changes can influence wellbeing. By operating primarily on-device, the approach aims to deliver real-time insights while preserving privacy and minimizing bandwidth.

The architecture combines YOLO for fast detection of cats and relevant objects, multi-object tracking and re-identification for per-cat continuity, and posture and action recognition for behavior classification. Temporal analytics convert frame-level outputs into interpretable daily and weekly metrics, including locomotion patterns, play bouts, resource visits, zone occupancy, and trend deviations from individualized baselines. The mobile robot augments fixed viewpoints by patrolling occluded areas and following up on uncertain detections, improving coverage and robustness in cluttered indoor environments.

To estimate partial emotion-related indicators, the system aggregates proxies such as posture cues (ear position, tail carriage, body curvature), activity balance, hiding or exploration patterns, and resource interaction changes, optionally fused with audio or smart-home signals. It highlights potential stress or discomfort when multi-cue deviations persist, while emphasizing that such inferences are approximations and not medical diagnoses. Deployment guidance addresses model selection for edge devices, dataset curation tailored to the home, per-cat baselining, and owner-in-the-loop corrections.

3.76. Pixel Reflectance Estimation with Deep Learning Pansharpening Methods

• Hind Hallabia

¹ 

UMR CNRS 7347—Materiaux, microéléctronique, acoustique, nanotechnologies (GREMAN), Université de Tours, Tours, France

² 

Institut National des Sciences Appliquées (INSA) Centre-Val de Loire, Campus Blois, Blois, France

Pansharpening consists of fusing a multispectral (MS) image and a panchromatic (PAN) image to generate a high quality MS image. Several pansharpening techniques have been developed to enhance the spatial quality of MS data. The estimation of objects in the scene, such as cars, trees or buildings, require accurate pixels synthesis during fusion process. In this paper, we proposed to estimate the pixel reflectance of fused multispectral images using a generalized UIQI band-wise metric. This criteria is validated on pansharpened results at reduced-scale using Wald protocol. In this context, we presented two comparative studies. In the first case, we compared statistically the proposed criteria to the pixel correlation and the Euclidian distance. The proposed criteria presented promising results quantitatively. Concerning the second case study, we considered the assessment of deep learning-based fusion methods versus the state-of-the-art. Indeed, the pansharpening based on Neural Networks (PNN), Convolutional Neural Networks (CNN) and the adaptive PNN with fine tuning have been trained and tested. The state-of-the art pansharpening methods include the Generalized Laplacian Pyramids (GLP), Additive Wavelet Luminance Proportion (AWLP), Gram-Schmidt adaptive (GSA), Total variation (TV), Model-based fusion using principle component and wavelets (PWMBF) and Filter estimation based on a semi-blind deconvolution framework (FE). The experimental results have been performed on two remote sensing data sets captured by GeoEye-1 and Worldview-3 satellites. The comparative study allows a better understanding of the displacement of objects or the misregistration of PAN and MS images.

3.77. Real-Time Multi-Class Face Recognition Using Deep Embedding and a Novel Lightweight Deep Learning Model

• MD Jiabul Hoque, MD Iftakhar Kabir Sakur, Ahmed Musa Chowdhury and Dr. Mohammed Saifuddin

• Department of Computer and Communication Engineering, International Islamic University Chittagong, Kumira, Bangladesh

Accurate and real-time facial recognition remains a cornerstone of modern computer vision applications. However, existing systems often suffer from high computational costs, limited scalability, and poor adaptability to large, heterogeneous datasets. This paper presents a lightweight yet robust deep learning-based face recognition framework that leverages embedding-based classification using a custom-designed Deep Neural Network (DNN) architecture. The proposed system integrates Dlib’s ResNet-based face embedding extractor with a bespoke DNN classifier, trained and evaluated on both a custom 68-label dataset and the publicly available Labeled Faces dataset comprising 5817 identities. The framework encompasses a comprehensive pipeline including face detection using HOG and Haarcascade algorithms, landmark-based alignment using 68 facial points, grayscale preprocessing, and extensive data augmentation to enhance generalization. A 128-dimensional facial encoding is used as input to the DNN, which employs dropout regularization and ReLU activation across fully connected layers to optimize classification performance. Extensive experimentation demonstrates the superiority of the proposed model over traditional and pretrained models. On the 68-label dataset, the DNN achieved 99.37% accuracy, 98% precision, and 89% F1-score, outperforming both Dlib+SVC and conventional methods such as LBPH, Eigenface, and Fisherface. Furthermore, for the large-scale 5817-label dataset, it attained a 94% accuracy, significantly higher than the 54% achieved by the pretrained SVC model. Real-time testing using a live camera further confirms the framework’s practicality, delivering high-confidence recognition with low latency. This research not only bridges the gap between lightweight deployment and high accuracy but also paves the way for scalable and efficient face recognition in resource-constrained environments.

3.78. Robotic System to Identify Finite Number of Significant Image Frames on Large Video Data Using Unsupervised Machine Learning Technique

• Sreedhar Kumar Seetharaman ^1,2 and Basant Kumar ³

¹ 

Department of Information Science and Engineering, Sir M Visvesvaraya Institute of Technology, Bengaluru, Karnataka, India

² 

Lincoln University College, 47301 Petaling Jaya, Selangor Darul Ehsan, Malaysia

³ 

Department of Mathematics and Computer Science, Modern College of Business and Science, Bowshar, Oman

In this research work, a video-related automated system, namely Robotic Key Image Frame Identification System (RKIFIS), is proposed, and it aims to instinctively identify the finite number of representative image frames over the video through the process of splitting the video contents into the optimal number of distinct clusters with different sizes using the Optimal-N-Means ONM clustering technique. The proposed RKIFI system contains five stages; in the beginning stage, the RKIFI converts the input video into a sequence of image frames using the standard open CV tool. Subsequently, the proposed system improves the image frame quality through pre-processing every individual image frame from the result of the previous stage. Afterward, the RKIFI system extracts highly relevant features from each image frame in the image frame set of the input video using standard arithmetic operations. Consecutively, the proposed system is iteratively split into the image frame vector set into a finite number of clusters through the process of iteratively identifying the optimal number of representative image frames over the input image frame set of the input video using the Optimal-N-Means clustering technique, where N denotes the optimal number of representative image frames in the image feature vector set of the input video. In the final stage, the RKIFI system validates the dissimilarity level among the key image frames which are identified in the clustering stage. The experimental result shows how the RKIFI system is well suited to automatically identifying the essential key image frames in the video data.

3.79. Safe Robot Navigation Through Low- and High-Risk Zones: Evaluation of A*, D*, and RRT Algorithms

• Sourav Mondal ¹, Saumya Das ¹, Suman Das ² and Dipanjan Bhattacharjee ³

¹ 

Department of Computer Science and Engineering—Artificial Intelligence, Brainware University, Kolkata 700125, India

² 

Department of Computer Science and Engineering, Brainware University, Kolkata 700125, India

³ 

Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737136, India

Autonomous navigation in hazardous environments demands path planning strategies that balance computational efficiency with safety considerations. This study compares the performance of three widely used algorithms—A*, D*, and Rapidly Exploring Random Trees (RRTs)—across varying risk conditions. A grid-based framework was employed to simulate three types of environments: mixed-risk scenarios with randomly distributed obstacles, a fully high-risk environment, and a fully low-risk environment. Performance was assessed using execution time, path length, and collision behavior as evaluation metrics. Results demonstrate that A* consistently achieves the fastest execution across all scenarios, confirming its computational efficiency. However, in fully high-risk and low-risk environments, A* tends to generate longer paths compared to RRT. While RRT frequently identifies shorter and more economical paths, its sampling-based approach results in longer computation times than A* and D*, and in some cases, introduces instability in path safety. D* shows performance similar to A* in terms of path length but with slightly higher computation time. Overall, A* emerges as the most reliable option for time-critical applications, whereas RRT offers path-length advantages at the expense of speed and stability. The findings highlight the trade-offs between graph-based and sampling-based methods and suggest that hybrid or risk-aware planners may provide more robust solutions for real-world rescue, surveillance, and hazardous material handling scenarios.

3.80. Satellite Image Classification for Early Wildfire Detection Using Deep Learning

• Bhagavan Naik Megavath ¹, Yasir Jamal ², Raja Hashim Ali ¹ and Qamar Abbas ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

Department of Artificial Intelligence, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan

Wildfires have emerged as one of the most pressing environmental hazards, fueled by climate change, deforestation, and human activity. Early detection is critical to reducing damage to ecosystems and human settlements, yet traditional monitoring methods such as ground sensors or manual observation are slow and limited in scope. Recent advances in deep learning have shown promise, but many approaches rely on heavy segmentation or object detection models that are unsuitable for real-time or resource-constrained environments.

This study proposes a lightweight wildfire detection framework using EfficientNetV2-S with transfer learning for binary image classification. A publicly available satellite image dataset was preprocessed through resizing, normalization, and train–test splitting. Transfer learning was applied by freezing pretrained ImageNet weights and fine-tuning the classifier head for two categories: fire and no-fire. The model was trained using the AdamW optimizer with cross-entropy loss and evaluated through accuracy, precision, recall, F1-score, and confusion matrices.

The system achieved 97.54% validation accuracy and 92.65% test accuracy, with balanced precision and recall across both classes. Robustness testing on real-world satellite images confirmed strong generalization, while inference times of 15–20 ms per image demonstrated real-time viability. Unlike heavier segmentation-based pipelines, this lightweight model can be deployed on drones, edge devices, and early-warning platforms.

In conclusion, EfficientNetV2-S provides an efficient, accurate, and scalable solution for wildfire detection, offering a deployable alternative to computationally intensive models and supporting rapid-response systems for disaster prevention.

3.81. Segmentation of an Atypical Teratoid Rhabdoid Tumor Using UNet+ Fork with ResNext and ResNet for Improved MRI Analysis

• Nadenlla Rajamohan Reddy and G. Muneeswari

• School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India

Atypical Teratoid Rhabdoid Tumor (ATRT) is a highly aggressive pediatric brain tumor, which poses substantial clinical challenges due to its rapid proliferation and complex morphological diversity. Precise segmentation of ATRT in Magnetic Resonance Imaging (MRI) is essential for accurate diagnosis, effective treatment planning, and thorough outcome evaluation. However, manual segmentation is time-consuming and susceptible to errors, particularly given the tumor’s intricate structure. To overcome these limitations, deep learning-based automated segmentation techniques have attracted significant attention, with UNet architectures emerging as a leading solution in medical image analysis. This study introduces an advanced segmentation approach, utilizing a Fork of the Tumor-Segmentation-UNet+ model integrated with residual networks, namely ResNext and ResNet. These architectures enhance the model’s ability to delineate complex tumor boundaries and account for the high degree of heterogeneity seen in ATRT. The inclusion of residual blocks from ResNext and ResNet facilitates more efficient feature extraction while also mitigating common issues in deep neural networks, such as vanishing gradients. The proposed model was trained and validated on a dataset comprising ATRT-specific MRI scans and compared against conventional segmentation approaches. Performance metrics, including the Dice coefficient, Intersection over Union (IoU), and sensitivity, were used to measure segmentation accuracy. The results indicate that the UNet+ model enhanced with ResNext and ResNet significantly outperforms standard UNet configurations, delivering more accurate and reliable segmentation of ATRT tumors.

3.82. Soil-Aware Deep Learning for Pile Integrity Testing: A CNN-Based Approach Using Real-World PIT Data

• Bora Canbula, Tugba Ozacar and Ovunc Ozturk

• Department of Computer Engineering, Manisa Celal Bayar University, Yunusemre, 45140 Manisa, Türkiye

In this study, several well-established convolutional neural network (CNN) architectures were employed to enable the automatic and reliable interpretation of Pile Integrity Test (PIT) data, with a particular emphasis on integrating soil characteristics as auxiliary input features. While previous studies have primarily focused on analyzing signal data alone, the significant impact of subsurface soil conditions on wave propagation and signal interpretation has been widely recognized by geotechnical experts. However, this critical aspect has often been overlooked in data-driven approaches. To address this limitation, we collected PIT data from 278 foundation piles constructed across sites with diverse geotechnical profiles. Each data sample was augmented with relevant soil-related features, including soil classification, stiffness parameters, and localized stratigraphic information. Among the tested CNN models, the highest classification accuracy achieved was 95%. Importantly, all data used in the study were obtained from real-world PIT measurements, as opposed to synthetic reflectograms commonly used in earlier research, thereby enhancing the practical relevance and generalizability of the results. The inclusion of soil characteristics was found to substantially improve model performance, increasing accuracy from 90% (signal-only input) to 95% when soil features were incorporated. This study represents the first comprehensive effort to explicitly include soil influence in PIT data analysis using deep learning and offers a novel contribution to AI-powered decision-support systems in structural and geotechnical engineering. The proposed soil-aware approach opens up new opportunities for more accurate and context-sensitive defect detection in pile foundations.

3.83. Spatial and Temporal Feature Fusion for Enhanced Phishing Attack Detection in Web Environments

• Abdullahi Egigogo Raji ^1,2, Idris Ismaila ¹, Morufu Olalere ³, Barira Hamisu ², Abisoye Opeyemi Aderiike ⁴ and Ojeniyi Adebayo Joseph ¹

¹ 

Department of Cyber Security Science, School of Information Communication Technology, Federal University of Technology, Gidan Kwanu, P.M.B 65, Minna, Niger State, Nigeria

² 

Department of Software Engineering and Cyber Security, College of Computing and Information Science Al-Qalam University, Tafawa Balewa Way, Dutsin-ma, Road PMB 213, Kastina, Nigeria

³ 

National Open University of Nigeria (NOUN) Plot 91, Cadastral Zone Nnamdi Azikiwe Expressway Jabi, Abuja, Nigeria

⁴ 

Department of Computer Science, School of Information Communication Technology, Federal university of technology, Gidan Kwanu, P.M.B 65, Minna, Niger State, Nigeria

Phishing attacks remain a dominant and evolving cybersecurity threat, exploiting deceptive techniques to compromise user credentials and sensitive data. Traditional detection systems, often rule-based or reliant on manually engineered features, struggle to cope with the dynamic nature of phishing patterns. This study proposes a hybridized deep learning model that integrates Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) to effectively detect phishing websites. CNN is employed for spatial feature extraction from URL and HTML structures, while BiGRU captures temporal dependencies, enabling a comprehensive understanding of phishing behaviors. The model processes a rich dataset of 80,000 website instances—sourced from PhishTank, OpenPhish, and other repositories, and applies Min-Max scaling during preprocessing to normalize feature values. A dual-pathway architecture fuses spatial and sequential features into a unified representation, enhancing detection performance. Experimental evaluations using train–test split and 5-fold stratified cross-validation demonstrate outstanding results, achieving 99.97% accuracy, 99.98% recall, and 99.96% specificity. The model further exhibits strong generalizability when tested on an external dataset, reinforcing its robustness across diverse phishing patterns. Comparative analysis with existing deep learning methods, including CNN-LSTM and CNN-BiLSTM, confirms that the CNN-BiGRU architecture delivers superior performance with reduced false positives and false negatives. This work highlights the potential of hybrid deep learning frameworks in building resilient, scalable, and real-time phishing detection systems suited for deployment in modern web security infrastructures.

3.84. Toward Intelligent Agent-Based Scientific Storytelling

• Angelica Lo Duca

• Institute of Informatics and Telematics, National Research Council, via G. Moruzzi, 1, 56124 Pisa, Italy

Introduction: Over the past few decades, a significant quantity of scientific papers have been published. However, many of them remain unread, despite their potential level of novelty. Traditional dissemination formats, based on static papers or technical reports, are often inaccessible to policymakers, executives, or the general public. This paper proposes a vision for an intelligent framework that utilizes Generative AI agents to transform scientific papers into structured, audience-aware narratives, leveraging storytelling principles to enhance comprehension and engagement.

Methods: The proposed methodology introduces a multi-agent pipeline with three components. A Narrative Planner Agent first analyzes the paper and constructs a storyline using a three-act structure (Context, Problem, and Solution. A Generative Presenter Agent then produces a presentation tailored to the target audience. Finally, a Rubric-Based Evaluator Agent assesses the generated presentation on criteria such as clarity, scientific rigor, and audience alignment. The entire process is envisioned within an open-source workflow automation environment (e.g., n8n) to ensure scalability and modularity.

Results: At this stage, the methodology is in the design phase. However, preliminary texts in other domains have demonstrated that Generative AI can produce audience-based stories originating from domain-specific material.

Conclusions: This work outlines a path toward AI-driven scientific storytelling, where research is transformed into meaningful, contextualized narratives. Such systems could pave the way towards interdisciplinary communication, science education, and public engagement in applied sciences.

3.85. Toward Reliable Deepfake Detection: A CNN-Based Study on Synthetic Faces

• Eslam Mahmoud Mohamed Mahmoud Aly ¹, Umme Rabab Syed ² and Muhammad Maaz Hamid ²

¹ 

Department of Business, University of Europe for Applied Sciences, 14469 Potsdam, Germany

² 

School of Management Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi 23460, Pakistan

The proliferation of AI-generated synthetic faces has raised significant concerns about media authenticity, identity theft, and misinformation. Detecting such fake faces reliably is critical for securing biometric systems and restoring public trust in digital content. This study investigates the effectiveness of transfer learning using the Xception model—a deep convolutional neural network originally trained on ImageNet—for binary classification of real versus AI-generated face images.

We combined and preprocessed two publicly available datasets, resulting in a balanced corpus of authentic and synthetic face images. The data was resized to 299 × 299 pixels, normalized, and split into training (70%), validation (20%), and test (10%) sets. A fully unfrozen Xception model was fine-tuned using an optimized architecture and trained over 30 epochs with the Adam optimizer and binary cross-entropy loss. Performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix, along with qualitative analysis through prediction visualizations.

The fine-tuned model achieved nearly 100% training accuracy and 90% validation accuracy. On the unseen test set of 1205 images, it attained 89.88% accuracy and an F1-score of 0.90, indicating high reliability across both real and fake face classes. The model performed slightly better at identifying synthetic faces, highlighting detectable artifacts introduced during generation.

Our findings confirm that transfer learning with the Xception model is a practical, reproducible solution for fake face detection, even in resource-limited academic settings. This study contributes a streamlined pipeline and benchmarks for future work in visual deepfake detection and media forensics.

3.86. Two Stage Extractive Text Summarization

• Abdulrahman Mohammed Bello ¹, Bashir Salisu Abubakar ¹, Abdulkadir Abubakar Bichi ² and Usman Mahmud ²

¹ 

Department of Computer Science, Aliko Dangote University of Science and Technology, Kano, Nigeria

² 

Department of Software Engineering, Northwest University, Kano, Nigeria

The rapid growth of digital text highlights the need for effective summarization. Traditional graph based methods, like TextRank, often fall short by relying primarily on lexical similarity, which can miss crucial semantic connections and deeper contextual meaning. This study proposes a two stage summarization framework that integrates an enhanced graph-based ranking mechanism with a metaheuristic optimization strategy. In the initial phase, we modified the conventional TextRank algorithm by redefining edge weights through a combination of lexical, structural, and semantic attributes, specifically sentence position, bigram overlap, and SBERT based semantic similarity. This multi feature integration enhances the estimation of sentence significance by effectively capturing both surface level and contextual relationships. In the subsequent phase, we present a refined Snake Optimization Algorithm that identifies optimal subset of sentences through the application of a fitness function. This function integrates ROUGE-1, ROUGE-2, ROUGE-L metrics, SBERT-based semantic similarity aligned with the reference summary, as well as a sentence threshold penalty to control redundancy and length. Findings on the Medium Article datasets demonstrate improved summarization quality in terms of both lexical and semantic metrics, validating the effectiveness of the proposed two stage strategy. This research significantly contributes to the advancement of extractive summarization models by combining graph based ranking with semantically informed optimization.

3.87. Urban Building Footprint Extraction Using Graph Neural Networks and Assessed OpenStreetMap Data with Sentinel-2 Imagery

• Anouar Adel ¹, Meziane Iftene ² and Mohammed El Amin Larabi ²

¹ 

Department of Artificial intelligence, University of Khemis Miliana, Aïn Defla 44000, Algeria

² 

Departement of Scientific and Technological Watch, Algerian Space Agency, Algiers 16000, Algeria

Accurate urban building footprint data are essential for sustainable urban planning (SDG 11). This study introduces a novel framework that overcomes the dual challenges of extracting detailed features from medium-resolution Sentinel-2 imagery and the inherent quality issues of crowdsourced OpenStreetMap (OSM) data. We achieve this by integrating Graph Neural Networks (GNNs) with a rigorous, multi-source data assessment pipeline.

Our objective was to evaluate an UrbanGraphSAGE GNN architecture for segmenting building footprints in Algiers. A foundational component was the creation of a high-confidence ground truth dataset by cross-validating OSM data against Google Open Buildings and Overture Maps, followed by a temporal stability analysis. Our model departs from standard CNNs by first segmenting the imagery into superpixels, which then serve as nodes in a graph. The GNN classifies these nodes by learning from both their spectral features and their spatial context, allowing it to model complex urban morphologies effectively.

The results confirmed the framework’s robustness. The final augmented model achieved a strong Test F1-Score of 0.7579 and an excellent recall of 0.9192. This high recall is critical for creating comprehensive urban inventories, as it minimizes the number of missed buildings.

This study validates a powerful framework for leveraging GNNs and rigorously assessed open data for urban monitoring. The methodology offers a scalable and low-cost solution for creating reliable building footprint datasets, providing a valuable tool for planners in rapidly urbanizing cities.