International Journal of Molecular Sciences

Editorial

Jump to: Other

3 pages, 134 KB

Open AccessEditorial

Preface of the International Symposium on Nanotechnology for Medicine, Environment and Energy

by Luis Zamora-Peredo and Marcos Luna-Cervantes

Mater. Proc. 2025, 28(1), 11; https://doi.org/10.3390/materproc2025028011 - 6 Jan 2026

Viewed by 300

Abstract

n/a Full article

(This article belongs to the Proceedings of The International Symposium on Nanotechnology for Medicine, Environment and Energy)

Other

Jump to: Editorial

7 pages, 754 KB

Open AccessProceeding Paper

Ultrafast Sonochemical Synthesis of SBA-15 Mesoporous Silica at 25 °C

by Jorge Gajardo, Julio Colmenares-Zerpa, Giancarlo González, Francesc Gispert-Guirado, Adolfo Henríquez and Ricardo J. Chimentão

Mater. Proc. 2026, 30(1), 2; https://doi.org/10.3390/materproc2026030002 - 11 Mar 2026

Viewed by 229

Abstract

Ultrafast sonochemical synthesis of SBA-15 performed via the pH-adjustment method at 25 °C was reported. Ultrasound treatment was applied to the entire synthesis process for a period of 90 min. The sonication synthesis was compared with the aging-mediated sonication method. Ultrasound assistance under [...] Read more.

Ultrafast sonochemical synthesis of SBA-15 performed via the pH-adjustment method at 25 °C was reported. Ultrasound treatment was applied to the entire synthesis process for a period of 90 min. The sonication synthesis was compared with the aging-mediated sonication method. Ultrasound assistance under the studied conditions allows the aging step to be replaced and minimizes the structural deterioration of SBA-15 due to the pH-adjustment effect. In addition, the hydrophilic character and CO₂ adsorption capacity of these materials were studied using contact-angle techniques and CO₂ adsorption, respectively. Ultrasonic synthesis at 25 °C results in the best uniformity of a mesopore structure relative to its peers. Full article

(This article belongs to the Proceedings of The International Conference on Advanced Nano Materials)

► Show Figures

Figure 1

13 pages, 2495 KB

Open AccessProceeding Paper

Synthesis, Integration with Textiles, and Application in Sensors of SrMoO₄:Ag

by Vinícius Prado Corrallo, Vitória Silva Novoa, Noemy Rodrigues Santos, Daniel Tetsuo Gonçalves Mori, Julia Carina Orfão Costa, Rogério de Almeida Vieira, Paulo Henrique Silva Marques de Azevedo, Graça Soares, Roseli Künzel and Ana Paula de Azevedo Marques

Mater. Proc. 2026, 30(1), 3; https://doi.org/10.3390/materproc2026030003 - 9 Mar 2026

Viewed by 479

Abstract

This study investigates pure and Ag-doped SrMoO₄ powders (Sr_1−xAg_xMoO₄, x = 0, 0.01, 0.07), focusing on structural, optical, and functional properties. We evaluate its photocatalytic performance, capacitance response in lactate solution and water, and antimicrobial activity [...] Read more.

This study investigates pure and Ag-doped SrMoO₄ powders (Sr_1−xAg_xMoO₄, x = 0, 0.01, 0.07), focusing on structural, optical, and functional properties. We evaluate its photocatalytic performance, capacitance response in lactate solution and water, and antimicrobial activity in textiles. The diffraction patterns could be indexed to the pure tetragonal phase SrMoO₄. The doping of SrMoO₄ with Ag⁺ ions affects the morphology and particle size of the samples designed by co-precipitation. SrMoO₄ pure and Ag⁺-doped samples exhibited promising results in detecting water and lactate solutions, as well as photocatalysis. Pure SrMoO₄ was more efficient in the photodegradation of methylene blue (MB) than the sample doped with Ag⁺. Among the bactericidal test results, sample SMO:0.01-P4, without light, in S. aureus, and SMO:0.07-P3, with light in E. coli, showed a slight distance from the inhibition halo. These results suggest that the treated textile may possess a characteristic bactericidal capacity that deserves further exploration. This comprehensive analysis offers insights into the structure–function relationship of SrMoO₄:Ag and advances the development of multifunctional materials. Full article

(This article belongs to the Proceedings of The International Conference on Advanced Nano Materials)

► Show Figures

Figure 1

13 pages, 4447 KB

Open AccessProceeding Paper

Environmental Applications of Quantum Dots in Photocatalytic Treatment of Urban Wastewater

by Sabbir Hossain, Sk. Tanjim Jaman Supto, Tahzib Ibrahim Protik and Md. Nurjaman Ridoy

Mater. Proc. 2025, 26(1), 15; https://doi.org/10.3390/materproc2025026015 - 9 Mar 2026

Viewed by 315

Abstract

Quantum dots (QDs) have drawn a lot of attention as photocatalytic materials due to the growing need for environmentally friendly wastewater treatment technologies. Among these, carbon-based QDs, including graphene oxide quantum dots (GOQDs), graphitic carbon nitride (g-C₃N₄), and carbon [...] Read more.

Quantum dots (QDs) have drawn a lot of attention as photocatalytic materials due to the growing need for environmentally friendly wastewater treatment technologies. Among these, carbon-based QDs, including graphene oxide quantum dots (GOQDs), graphitic carbon nitride (g-C₃N₄), and carbon quantum dots (CQDs), have exceptional optical, electronic, and surface characteristics that increase their suitability for degrading pollutants when exposed to sunlight or visible light. These composites are better at transferring charges, staying stable in light, and breaking down pollutants. Metal-based QDs like ZnO and CdS also have strong photocatalytic activity, but their sustainability remains a concern due to the potential release of toxic ions when they corrode in light. The green synthesis approach addresses these challenges. Using natural extracts, like polyphenols from tea leaves, to biofunctionalize surfaces has been shown to reduce toxicity and improve photocatalytic performance. Green synthesis using renewable precursors solves problems with toxicity, resource depletion, and environmental pollution, which supports a low-impact and circular technological approach. This study examines recent developments in the making, modifying, and use of QD-based photocatalysts in the environment, with a focus on CQD/g-C₃N₄ hybrid systems. Future research should focus on making green, non-toxic, regenerable, and highly active carbon-based QDs for safe large-scale water treatment. Full article

(This article belongs to the Proceedings of The 4th International Online Conference on Materials)

► Show Figures

Figure 1

6 pages, 3322 KB

Open AccessProceeding Paper

Structural Properties of Supramolecular Metallogel Derived from Vanadium and Hydrazone Ligand: Metallogelation Triggered by Hydrogen Bonding, pi–pi Interactions, and Other Non-Covalent Interactions

by Sunshine Dominic Kurbah

Mater. Proc. 2026, 29(1), 3; https://doi.org/10.3390/materproc2026029003 - 12 Feb 2026

Viewed by 145

Abstract

The metallogelation process has been successfully achieved by utilizing a crystal engineering approach to generate a new metallogel. While the coordination of metal ions to ligands plays a very important role for building the primary structure, the stabilization and morphology of metallogels are [...] Read more.

The metallogelation process has been successfully achieved by utilizing a crystal engineering approach to generate a new metallogel. While the coordination of metal ions to ligands plays a very important role for building the primary structure, the stabilization and morphology of metallogels are heavily dependent on various intra-molecular interactions and non-covalent interactions, with hydrogen bonding (HB) often playing a dominant and structurally organizing role. In the present study, gelation experiments were achieved successfully by reacting vanadium acetylacetonate with a hydrazone ligand using different solvents. The metallogel shows excellent gelation ability with 1.7 wt% minimum gelator concentrations and the gel–sol dissociation temperature, T_gel is 55 °C (water/methanol). The structural properties of the metallogel were studied using single-crystal X-ray crystallography. The crystal structure analysis of the metallogel shows the presence of various interactions such as hydrogen bonding, pi–pi interactions, pnictogen bonding, and other weak non-covalent interactions. These molecular interactions play a very important role in the gelation process and also affect the gel’s properties like swelling behavior, viscosity, and elasticity. Full article

(This article belongs to the Proceedings of The 1st International Online Conference on Gels)

► Show Figures

Figure 1

8 pages, 1802 KB

Open AccessProceeding Paper

Graphene–MXene Heterostructure for Biomedical and Environmental Antimicrobial Applications

by Avdhesh Kumar, Ankit Singh and Manish Pratap Singh

Mater. Proc. 2025, 26(1), 10; https://doi.org/10.3390/materproc2025026010 - 10 Feb 2026

Viewed by 501

Abstract

The increasing threat of bacterial infections and the limitations of conventional antibiotics have intensified the search for innovative antimicrobial substances. This study investigates a heterostructure nanomaterial of graphene and MXene designed to efficiently inhibit bacterial growth. The graphene–MXene heterostructure was prepared via eco-friendly [...] Read more.

The increasing threat of bacterial infections and the limitations of conventional antibiotics have intensified the search for innovative antimicrobial substances. This study investigates a heterostructure nanomaterial of graphene and MXene designed to efficiently inhibit bacterial growth. The graphene–MXene heterostructure was prepared via eco-friendly and non-hazardous ultrasonication to ensure uniform dispersion and interfacial interaction between the 2D components. Powder X-ray diffraction (PXRD), Fourier-Transform Infrared Spectroscopy (FTIR), and High-Resolution Transmission Electron Microscopy (HR-TEM) confirmed the successful integration of the graphene-and-MXene-based heterostructure. Antibacterial activity has assessed using colony-forming unit (CFU) quantification against Escherichia coli (E. coli). Substantially reduced CFU counts and significant inhibition of bacterial growth are observed in the presence of graphene–MXene heterostructure compared to pristine materials. This study opens new avenues for the future development of 2D heterostructures engineered for microbial resistance under diverse conditions. Thus, the design of graphene–MXene heterostructure is a promising strategy for next-generation antimicrobial applications. Full article

(This article belongs to the Proceedings of The 4th International Online Conference on Materials)

► Show Figures

Figure 1

13 pages, 73269 KB

Open AccessProceeding Paper

Advanced Machine Learning Approaches for Predicting ADHD in Females: A Data-Driven Study Employing the WIDS Dataset

by Parth Patil, Karthik Kamaldinni, Sanjana Patil and Sakshi Gaitonde

Comput. Sci. Math. Forum 2025, 12(1), 17; https://doi.org/10.3390/cmsf2025012017 - 3 Feb 2026

Viewed by 477

Abstract

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is found in both children and adults. While this disorder often continues in adulthood, diagnosis can be challenging, particularly in females. Unlike males, who are often diagnosed with ADHD due to their externalizing behaviors [...] Read more.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is found in both children and adults. While this disorder often continues in adulthood, diagnosis can be challenging, particularly in females. Unlike males, who are often diagnosed with ADHD due to their externalizing behaviors (i.e., impulsive nature), most females show inattentive symptoms (i.e., in focusing, disorganization), which makes this disorder hard to detect. This paper proposes a machine learning approach to detect ADHD among females. The Wids Datathon 2025 provides three datasets: categorical data, quantitative data, and function connectomes. It contains information on 1213 participants who are seeking to take a test to detect ADHD. Categorical data includes 10 attributes, quantitative data has 19 attributes, and functional connectomes contain 19,901 attributes which are relevant to studying the participants’ overall condition. By combining both XGBoost and Random Forest, an accuracy of 79.42% was achieved. The results show that machine learning algorithms can help in improving ADHD detection in females, leading to better diagnoses in future. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

9 pages, 647 KB

Open AccessProceeding Paper

Vesicle-Associated Membrane Proteins (VAMPs) 3 and 7, Crucial Membrane Proteins Instrumental in Constitutive and Regulated Secretion in Cells, Are Most Likely Not Involved in Exocytosis of PLGA Nanoparticles

by Suman Saha, Subrata Sinha and Parthaprasad Chattopadhyay

Mater. Proc. 2025, 25(1), 24; https://doi.org/10.3390/materproc2025025024 - 29 Jan 2026

Viewed by 454

Abstract

Background: Poly(lactic-co-glycolic) acid (PLGA) nanoparticles were found to be actively exocytosed from cells in a previous study in our lab. The exocytosis process can be modulated to increase the retention of nanoparticles within the cells so that the therapeutic efficacy of any drug [...] Read more.

Background: Poly(lactic-co-glycolic) acid (PLGA) nanoparticles were found to be actively exocytosed from cells in a previous study in our lab. The exocytosis process can be modulated to increase the retention of nanoparticles within the cells so that the therapeutic efficacy of any drug encapsulated within the nanoparticles is increased. So, we wanted to know which membrane proteins were involved in the exocytosis process of the nanoparticles. The roles of VAMP3 and VAMP7, two crucial membrane proteins associated mainly with constitutive and regulated secretion, respectively, in cells, were studied in the context of exocytosis of PLGA nanoparticles. Materials and Methods: The siRNA-mediated knockdown of VAMP3 and VAMP7 genes was performed in the LN229 cancer cell line, and the intracellular accumulation of PLGA nanoparticles was studied by fluorescence microscopy. Results: There was no significant difference in the intracellular accumulation of the PLGA nanoparticles after siRNA-mediated knockdown of VAMP3 or VAMP7. Conclusion: This study shows that VAMP3 and VAMP7, which serve as important membrane proteins associated with the conventional constitutive and regulated secretion of different molecules in cells, are most likely not involved in the exocytosis/secretion of PLGA nanoparticles. So, the pathway of intracellular trafficking of PLGA nanoparticles needs to be deciphered, as it appears to be a non-conventional one. Full article

(This article belongs to the Proceedings of The 5th International Online Conference on Nanomaterials)

► Show Figures

Figure 1

8 pages, 2677 KB

Open AccessProceeding Paper

Synthesis and Characterization of Spermidine-Modified Alginic Acid Hydrogels with Possible Tissue Regeneration Applications

by Harim Galilea Díaz-Corte, Itzia Irene Padilla-Martínez, Gabriela Martínez-Mejía and Mónica Corea

Mater. Proc. 2025, 25(1), 23; https://doi.org/10.3390/materproc2025025023 - 26 Jan 2026

Viewed by 393

Abstract

Hydrogels are 3D networks of hydrophilic crosslinked polymers, which are synthesized from synthetic or natural sources such as chitosan and alginic acid derived from shrimp shell and brown seaweed, respectively. These materials exhibit biodegradability, biocompatibility, and non-cytotoxic properties to be used as scaffolds [...] Read more.

Hydrogels are 3D networks of hydrophilic crosslinked polymers, which are synthesized from synthetic or natural sources such as chitosan and alginic acid derived from shrimp shell and brown seaweed, respectively. These materials exhibit biodegradability, biocompatibility, and non-cytotoxic properties to be used as scaffolds for tissue engineering applications. In this study, four types of alginic acid hydrogels were chemically synthesized using spermidine as a crosslinking agent with concentrations ranging from 5% (w/w) to 100% (w/w). The results of scanning electron microscopy (SEM) revealed a small average pore size (≤5 μm), while electrospray ionization mass spectrometry (ESI-MASS) and Fourier transform infrared spectroscopy (FT-IR) showed the characteristic vibrations and formed bonds between alginic acid and spermidine, respectively. Finally, the alginic acid hydrogels demonstrated potential ability for tissue regeneration treatments. Full article

(This article belongs to the Proceedings of The 5th International Online Conference on Nanomaterials)

► Show Figures

Figure 1

6 pages, 1519 KB

Open AccessProceeding Paper

A Comparative Assessment of XFEM and FEM for Stress Concentration at Circular Holes near Bi-Material Interfaces

by Huu-Dien Nguyen

Mater. Proc. 2025, 26(1), 3; https://doi.org/10.3390/materproc2025026003 - 5 Jan 2026

Viewed by 420

Abstract

Accurately predicting stress concentration factors (SCFs) is essential for assessing the structural integrity of components containing holes or discontinuities, especially in multi-material systems. Traditional Finite Element Method (FEM) models often require substantial mesh refinement near geometric discontinuities, whereas the Extended Finite Element Method [...] Read more.

Accurately predicting stress concentration factors (SCFs) is essential for assessing the structural integrity of components containing holes or discontinuities, especially in multi-material systems. Traditional Finite Element Method (FEM) models often require substantial mesh refinement near geometric discontinuities, whereas the Extended Finite Element Method (XFEM) allows discontinuities to be represented independently of the mesh through enrichment functions. This study provides a comparative assessment of FEM and XFEM for evaluating SCFs around a circular hole located near a bi-material interface. Both methods are implemented in MATLAB R2019a using the level-set approach to describe the hole. The displacement and stress fields obtained from FEM and XFEM are compared, followed by an evaluation against an established analytical reference solution. The findings show that while both methods reproduce global fields with good agreement, differences arise in the accuracy of SCF prediction. These results highlight the conditions under which XFEM may offer advantages over conventional FEM when modeling discontinuities in heterogeneous materials. Full article

(This article belongs to the Proceedings of The 4th International Online Conference on Materials)

► Show Figures

Figure 1

9 pages, 1165 KB

Open AccessProceeding Paper

LSTM-Based News Article Category Classification

by Yusra Rafat, Potu Narayana, R. Madana Mohana and Kolukuluri Srilatha

Comput. Sci. Math. Forum 2025, 12(1), 8; https://doi.org/10.3390/cmsf2025012008 - 18 Dec 2025

Viewed by 823

Abstract

A substantial amount of data is generated day-to-day, to which news articles are a major contributor. Most of this data is not well-structured, highlighting the need for efficient ways to manage, process, and analyze said data. One useful approach involves the categorization of [...] Read more.

A substantial amount of data is generated day-to-day, to which news articles are a major contributor. Most of this data is not well-structured, highlighting the need for efficient ways to manage, process, and analyze said data. One useful approach involves the categorization of the data. The work “News Article Category Classification” develops a Long Short-Term Memory (LSTM) model for classifying news articles into 14 categories. LSTM networks are suitable for text classification tasks, as they efficiently capture contextual and sequential dependencies. They have a special ability to retain long-term information which makes them perfect for understanding the meaning of news articles. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

8 pages, 910 KB

Open AccessProceeding Paper

Deep Learning Approaches to Chronic Venous Disease Classification

by Ankur Goyal, Vikas Honmane, Kumarsagar Dange and Shiv Kant

Comput. Sci. Math. Forum 2025, 12(1), 7; https://doi.org/10.3390/cmsf2025012007 - 18 Dec 2025

Viewed by 438

Abstract

Millions of people suffer from chronic venous disease (CVD), a common vascular condition that frequently causes pain, edema, and skin ulcers. For treatment to be effective, its stages must be accurately and promptly classified. This study offers a deep learning-based framework for classifying [...] Read more.

Millions of people suffer from chronic venous disease (CVD), a common vascular condition that frequently causes pain, edema, and skin ulcers. For treatment to be effective, its stages must be accurately and promptly classified. This study offers a deep learning-based framework for classifying CVD stages using medical images, such as limb photos or ultrasound scans. For training and assessment, convolutional neural networks (CNNs) are used in conjunction with pre-trained models like ResNet, VGG, and Efficient Net. Metrics like accuracy, precision, recall, and F1-score are used to evaluate the model’s performance. The encouraging findings suggest that deep learning tools can greatly facilitate the diagnosis of CVD and may be integrated into clinical decision support systems for quicker, more precise evaluations. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

13 pages, 2431 KB

Open AccessProceeding Paper

Gender-Aware ADHD Detection Framework Combining XGBoost and FLAML Models: Exploring Predictive Features in Women Advancing Personalized ADHD Diagnosis

by Srushti Honnangi, Anushri Kajagar, Shashank Shetgeri, Tanvi Korgaonkar, Salma Shahapur and Rajashri Khanai

Comput. Sci. Math. Forum 2025, 12(1), 6; https://doi.org/10.3390/cmsf2025012006 - 18 Dec 2025

Viewed by 602

Abstract

A machine learning architecture is introduced to predict attention deficit hyperactivity disorder (ADHD) and biological sex from multimodal inputs. The problem sidesteps the clinical task of early ADHD detection and adds prediction of sex as a meta-feature to enhance robustness. The architecture is [...] Read more.

A machine learning architecture is introduced to predict attention deficit hyperactivity disorder (ADHD) and biological sex from multimodal inputs. The problem sidesteps the clinical task of early ADHD detection and adds prediction of sex as a meta-feature to enhance robustness. The architecture is applied to demographic profiles, quantitative tests, and functional brain connectomes as 200 × 200 matrices. Preprocessing includes data harmonization, matrix symmetrization, graph-based descriptor extraction, including total strength, mean, and standard deviation, categorical encoding, variance thresholding, and imputation of missing values using k-nearest neighbors. Sex classification is performed using XGBoost with stratified cross-validation to generate probability outputs that enhance the ADHD model. ADHD classification is tuned using FLAML’s automatic hyperparameter search for XGBoost and class-weighting to address imbalance. Findings show that combining imaging-derived features and automated model selection yields a robust method of ADHD detection, underscoring the utility of multimodal data fusion in neuropsychiatric studies. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

1316 KB

Open AccessProceeding Paper

Silicon Fiber Optic Coating with Zinc Oxide Nanoparticles Characterized by AFM

by Saira Ximena Mendoza-Lopez, Jaime Gutiérrez-Gutiérrez, Marciano Vargas-Treviño, Antonio Canseco-Urbieta, Rosa María Velázquez-Cueto, Ivonne Arisbeth Díaz-Santiago and José Luis Cano-Pérez

Mater. Proc. 2025, 28(1), 8; https://doi.org/10.3390/materproc2025028008 - 17 Dec 2025

Viewed by 885

Abstract

This paper presents the preparation and characterization of single-mode optical fibers coated with zinc oxide (ZnO) nanoparticles using the immersion technique. The study was carried out in three stages: the first consisted of pretreating the fiber by means of controlled immersion in HCl [...] Read more.

This paper presents the preparation and characterization of single-mode optical fibers coated with zinc oxide (ZnO) nanoparticles using the immersion technique. The study was carried out in three stages: the first consisted of pretreating the fiber by means of controlled immersion in HCl and H₂SO₄ solutions and exposure in a muffle furnace; the second involved the growth and deposition of ZnO nanoparticles synthesized in a laboratory; and the third was characterization by means of atomic force microscopy (AFM). In this last stage, we obtained through AFM that Sample 1, considered optimized, presented high particle density (9.203 particles/µm²), an RMS roughness (Rq) of 2.98 nm, and average roughness (Ra) of 1.82 nm, as well as an average height of 1.117 nm. These parameters reflect a uniform and stable surface, desirable conditions for applications in the development of high-sensitivity optical sensors and biosensors. Full article

(This article belongs to the Proceedings of The International Symposium on Nanotechnology for Medicine, Environment and Energy)

► Show Figures

Figure 1

8 pages, 446 KB

Open AccessProceeding Paper

Enhanced Early Detection of Epileptic Seizures Through Advanced Line Spectral Estimation and XGBoost Machine Learning

by K. Rama Krishna and B. B. Shabarinath

Comput. Sci. Math. Forum 2025, 12(1), 4; https://doi.org/10.3390/cmsf2025012004 - 17 Dec 2025

Viewed by 736

Abstract

This paper proposes a fast epileptic seizure detection method to allow for early clinical intervention. The primary goal is to enhance computational and predictive performance to make the method viable for online implementation. An advanced Line Spectral Estimation (LSE)-based method for EEG analysis [...] Read more.

This paper proposes a fast epileptic seizure detection method to allow for early clinical intervention. The primary goal is to enhance computational and predictive performance to make the method viable for online implementation. An advanced Line Spectral Estimation (LSE)-based method for EEG analysis was developed with Bayesian inference and Toeplitz structure-based fast inversion with Capon and non-uniform Fourier transforms to reduce computational requirements. XGBoost classifier with parallel boosting was employed to increase prediction performance. The method was tested with patients’ EEG data using multiple embedded Graphic Processing Unit (GPU) platforms and achieved 95.5% accuracy, and 23.48 and 33.46 min average and maximum lead times before a seizure, respectively. The sensitivity and specificity values (92.23% and 93.38%) show the method to be reliable. The integration of LSE and XGBoost can be extended to create an efficient and practical online seizure detection and management tool. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

10 pages, 791 KB

Open AccessProceeding Paper

Data-Driven Approach for Asthma Classification: Ensemble Learning with Random Forest and XGBoost

by Bhavana Santosh Pansare, Anagha Deepak Kulkarni and Priyanka Prabhakar Pawar

Comput. Sci. Math. Forum 2025, 12(1), 3; https://doi.org/10.3390/cmsf2025012003 - 17 Dec 2025

Viewed by 533

Abstract

Across the world, asthma is a prominent and widespread respiratory disorder that has a substantial clinical and socioeconomic influence. The classification of asthma subtypes should be performed precisely and effectively, with objectives such as personalized treatments, improved rehabilitation outcomes, and preventing tragic exacerbations. [...] Read more.

Across the world, asthma is a prominent and widespread respiratory disorder that has a substantial clinical and socioeconomic influence. The classification of asthma subtypes should be performed precisely and effectively, with objectives such as personalized treatments, improved rehabilitation outcomes, and preventing tragic exacerbations. Typical screening approaches are primarily based on spirometry measures, immunologic assessments, and individual clinical diagnoses, and they are commonly affected by limitations such as uncertainty, crossover disparities, and restricted generalizability among various groups of patients. This study utilizes machine learning (ML) methodologies as a Data-Driven Approach (DDA)-based framework for asthma classification to overcome the mentioned challenges. Methodically constructed and evaluated classifiers, such as Random Forest and XGBoost, use the Asthma Disease Dataset from Kaggle, which consists of demographic data, lung function metrics (FEV1, FVC, FEV1/FVC ratio, and PEFR), and immunoglobulin E (IgE) biomarkers. A wide range of metrics such as accuracy, precision, recall, F1-score, receiver operating characteristic area under the curve (ROC-AUC), and average precision (AP) are used exhaustively to assess the performance of the model. The results indicate that though each model exhibits outstanding forecasting abilities, XGBoost has an enhanced classification capability, especially in recall and AP, which minimizes the proportion of false negatives, resulting in a clinically noteworthy result. The significance of the FEV1/FVC ratio, IgE levels, and PEFR as key indicators is recognized by feature interpretability analysis. These results emphasize the ability of ML-powered evaluation in advancing personalized healthcare and revolutionizing the clinical management of asthma. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

15 pages, 2426 KB

Open AccessProceeding Paper

Scalable Machine Learning Solutions for High-Volume Financial Transaction Fraud Detection

by Sourav Yallur, Jiya Patil, Tanvi Shikhari, Prajwal Dabbanavar, Rajashri Khanai and Salma Shahpur

Comput. Sci. Math. Forum 2025, 12(1), 1; https://doi.org/10.3390/cmsf2025012001 - 17 Dec 2025

Viewed by 773

Abstract

More reliable and intelligent detection systems are required because of the rise in fraudulent activities brought on by the volume of digital financial transactions. In this work, the data used is from a publicly accessible dataset with more than a million transaction records [...] Read more.

More reliable and intelligent detection systems are required because of the rise in fraudulent activities brought on by the volume of digital financial transactions. In this work, the data used is from a publicly accessible dataset with more than a million transaction records to investigate a machine learning strategy to identify hidden patterns in the fraud transaction. Data preprocessing included applying Z-score normalization, eliminating outliers using the IQR method, and handling missing values according to the skewness of each attribute. The selection of important features was guided by correlation analysis using Chi-square tests and Pearson coefficients. This study implemented multiple supervised learning techniques, comprising Random Forest, Logistic Regression, K-Nearest Neighbors, and Gradient Boost to evaluate and compare their effectiveness in accurately detecting fraudulent transactions. Full article

(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))

► Show Figures

Figure 1

8 pages, 518 KB

Open AccessProceeding Paper

Utilization of TiO₂ Nanoparticles for Methylene Blue Degradation

by Md. Golam Sazid, Harunur Rashid, Md. Redwanur Rashid Nafi and Asraf Ibna Helal

Mater. Proc. 2025, 25(1), 13; https://doi.org/10.3390/materproc2025025013 - 8 Dec 2025

Viewed by 1140

Abstract

Titanium dioxide (TiO₂) nanoparticles (NPs) are useful as a potential photocatalyst for the degradation of dyes such as methyl orange, rhodamine B, and methylene blue (MB). Understanding the mechanism of photocatalysis and the factors influencing photocatalysis is important for engineering TiO [...] Read more.

Titanium dioxide (TiO₂) nanoparticles (NPs) are useful as a potential photocatalyst for the degradation of dyes such as methyl orange, rhodamine B, and methylene blue (MB). Understanding the mechanism of photocatalysis and the factors influencing photocatalysis is important for engineering TiO₂ NPs to achieve an unprecedented photocatalysis rate. For TiO₂ NPs, their unique physicochemical qualities, such as small size, large surface area, optimum semiconductor bandgap, substantial oxidative potential, and outstanding chemical stability are factors which influence the MB degradation rate. The electron–hole pair separation in TiO₂ NPs allows for photocatalysis, which is not possible in their bulk form. The formation of reactive oxygen species (ROS) via photoinduced generation of electron–hole pairs under light irradiation is the starting point of the mechanism of photocatalysis for TiO₂ NPs. By generating ROS, TiO₂ NPs catalyze the degradation of MB. The photocatalytic performance of TiO₂ NPs is also different for different crystal phases, such as anatase, rutile, and brookite. The addition of metal or non-metal dopants into TiO₂ NPs enhances photocatalysis by enhancing light absorption, which enhances the generation of electron–hole pairs and of ROS. This review article will explain the mechanism of photocatalysis, the parameters influencing photocatalytic activity, active sites and recombination rates, disadvantages, and strategies to overcome these challenges that can improve TiO₂ NPs for a future wastewater treatment that is both efficient and sustainable. Full article

(This article belongs to the Proceedings of The 5th International Online Conference on Nanomaterials)

► Show Figures

Figure 1

9 pages, 757 KB

Open AccessProceeding Paper

Nanotechnology for Sustainable Cities: Benefits and Risks of Nano-Enabled Building Materials

by Djamil BenGhida, Riad BenGhida, Sabrina BenGhida and Sonia BenGhida

Mater. Proc. 2025, 25(1), 11; https://doi.org/10.3390/materproc2025025011 - 4 Dec 2025

Viewed by 1138

Abstract

Nanotechnology is reshaping the built environment by enabling the development of materials that improve structural performance, energy efficiency, durability, and environmental quality. This paper reviews nano-enabled construction materials through a micro–meso–macro lens, linking material mechanisms to building behavior and urban impacts. It highlights [...] Read more.

Nanotechnology is reshaping the built environment by enabling the development of materials that improve structural performance, energy efficiency, durability, and environmental quality. This paper reviews nano-enabled construction materials through a micro–meso–macro lens, linking material mechanisms to building behavior and urban impacts. It highlights both their potential contributions to decarbonization, public health, and urban resilience, and the parallel challenges of energy-intensive production, uncertain toxicological profiles, and regulatory gaps. Finally, it argues for responsible integration based on life-cycle thinking, precautionary risk governance, and updated architectural and engineering education so that nano-enabled innovation supports truly sustainable, equitable cities rather than new forms of hidden risk. Full article

(This article belongs to the Proceedings of The 5th International Online Conference on Nanomaterials)

► Show Figures

Figure 1

8 pages, 2083 KB

Open AccessProceeding Paper

Coffee Waste-Based Nanostructures: A Cost-Effective Fluorescent Material for Ni²⁺ Detection in Water

by Sepideh Dadashi, Gabriele Giancane and Giuseppe Mele

Mater. Proc. 2025, 25(1), 9; https://doi.org/10.3390/materproc2025025009 - 1 Dec 2025

Viewed by 499

Abstract

Nickel ions (Ni²⁺) are persistent heavy metal pollutants that pose significant risks to human health due to their toxicity. Conventional treatment technologies, while effective, are often costly, energy-intensive, and limited in removing emerging pollutants. In this study, we report an eco-friendly, [...] Read more.

Nickel ions (Ni²⁺) are persistent heavy metal pollutants that pose significant risks to human health due to their toxicity. Conventional treatment technologies, while effective, are often costly, energy-intensive, and limited in removing emerging pollutants. In this study, we report an eco-friendly, fluorescence-based sensing platform using carbon nanostructures (CNs) synthesized from coffee waste via pyrolysis at 600 °C. The CNs were characterized by Fourier transform infrared (FTIR) spectroscopy and evaluated for their fluorescence response toward Ni²⁺, Co²⁺, Cu²⁺, and Cd²⁺ ions. Distinct ion-specific behaviors were observed, with Ni²⁺ inducing the strongest fluorescence quenching. Sensitivity studies revealed reliable detection across 10⁻⁸–10⁻³ M, with a detection limit of 10⁻⁴ M (≈5.9 mg/L). Fluorescence stability was maintained for up to six hours, with one hour identified as the optimal detection window. Performance in real water samples highlighted consistent responses in mineral water, reflecting reliable sensing capability in a realistic aqueous matrix. While the current detection limit is above the World Health Organization guideline for drinking water, the CNs show promise for monitoring Ni²⁺ in contaminated or industrial effluents. Overall, this work demonstrates that coffee waste-derived CNs provide a cost-effective, sustainable approach to heavy metal sensing, linking waste valorization with environmental monitoring. Full article

(This article belongs to the Proceedings of The 5th International Online Conference on Nanomaterials)

► Show Figures

Figure 1

8 pages, 5882 KB

Open AccessProceeding Paper

Approaching the Quantum Limit in Axion Detection at IBS-CAPP and IBS-DMAG

by Sergey V. Uchaikin, Boris I. Ivanov, Arjan F. van Loo, Yasunobu Nakamura, MinSu Ko, Jinmyeong Kim, Saebyeok Ahn, Seonjeong Oh, Yannis K. Semertzidis and SungWoo Youn

Phys. Sci. Forum 2025, 11(1), 5; https://doi.org/10.3390/psf2025011005 - 26 Nov 2025

Viewed by 661

Abstract

We present the development of two complementary amplifier architectures for axion haloscope experiments, based on two types of Josephson Parametric Amplifiers (JPAs). The first employs a multi-chip module of flux-driven JPAs in a parallel–series configuration, enabling near quantum-limited amplification over an extended tunable [...] Read more.

We present the development of two complementary amplifier architectures for axion haloscope experiments, based on two types of Josephson Parametric Amplifiers (JPAs). The first employs a multi-chip module of flux-driven JPAs in a parallel–series configuration, enabling near quantum-limited amplification over an extended tunable range of between 1.2 and 1.5 GHz. The second design features a lumped-element JPA, offering continuous tunability across a wide frequency range from 2.4 to 4 GHz. Both approaches demonstrate near-quantum-limited noise performance and are compatible with operation in cryogenic environments. These amplifiers significantly enhance the sensitivity and frequency coverage of axion search experiments, and also provide new opportunities for broadband quantum sensing applications. Full article

(This article belongs to the Proceedings of The 19th Patras Workshop on Axions, WIMPs and WISPs)

► Show Figures

Figure 1

7 pages, 1595 KB

Open AccessProceeding Paper

Progress in GrAHal-CAPP/DMAG for Axion Dark Matter Search in the 1–3 μeV Range

by Pierre Pugnat, Rafik Ballou, Philippe Camus, Guillaume Donnier-Valentin, Thierry Grenet, Ohjoon Kwon, Jérôme Lacipière, Mickaël Pelloux, Rolf Pfister, Yannis K. Semertzidis, Arthur Talarmin, Jérémy Vessaire and SungWoo Youn

Phys. Sci. Forum 2025, 11(1), 3; https://doi.org/10.3390/psf2025011003 - 24 Oct 2025

Viewed by 755

Abstract

Two outstanding problems of particle physics and cosmology, namely the strong-CP problem and the nature of dark matter, can be solved with the discovery of a single new particle, the axion. The modular high magnetic field and flux hybrid magnet platform of LNCMI-Grenoble, [...] Read more.

Two outstanding problems of particle physics and cosmology, namely the strong-CP problem and the nature of dark matter, can be solved with the discovery of a single new particle, the axion. The modular high magnetic field and flux hybrid magnet platform of LNCMI-Grenoble, which was recently put in operation up to 42 T, offers unique opportunities for axion/axion-like particle search using Sikivie-type haloscopes. In this paper, the focus will be on the 350–600 MHz frequency range corresponding to the 1–3 μeV axion mass range requiring a large-bore RF-cavity. It will be built by DMAG and integrated within the large-bore superconducting hybrid magnet outsert, providing a central magnetic field up to 9 T in 812 mm warm bore diameter. The progress achieved by Néel Institute in the design of the complex cryostat with its double dilution refrigerators to cooldown below 50 mK the ultra-light Cu RF-cavity of 650 mm inner diameter and the first stage of the RF measurement chain are presented. Perspectives for the targeted sensitivity, assuming less than 2-year integration time, are recalled. Full article

(This article belongs to the Proceedings of The 19th Patras Workshop on Axions, WIMPs and WISPs)

► Show Figures

Figure 1

17 pages, 3452 KB

Open AccessProceeding Paper

Design and Performance Optimization of Battery Pack with AI-Driven Thermal Runaway Prediction

by Jalal Khan, Sher Jan, Sami Ifitkhar, Ajmal Yaqoob, Ubaid Ur Rehman, Taqi Ahmad Cheema, Shahid Alam and Usman Habib

Mater. Proc. 2025, 23(1), 17; https://doi.org/10.3390/materproc2025023017 - 8 Aug 2025

Cited by 2 | Viewed by 2775

Abstract

Battery thermal management is a critical factor in ensuring the performance, safety, and longevity of electric vehicle (EV) battery packs. This study investigates the effectiveness of a forced air convection cooling system, optimized cell spacing and suitable configuration in maintaining optimal battery cell [...] Read more.

Battery thermal management is a critical factor in ensuring the performance, safety, and longevity of electric vehicle (EV) battery packs. This study investigates the effectiveness of a forced air convection cooling system, optimized cell spacing and suitable configuration in maintaining optimal battery cell temperatures. A 3D computational model was developed to analyze the temperature distribution of a battery pack under varying airflow velocities, cell spacings and configurations. The numerical simulations were validated through experimental testing, demonstrating a strong correlation between simulated and measured results. The findings reveal that with a 2 m/s velocity of the fan, the battery’s maximum temperature is reduced by 7% compared to the case of natural convection, while the fan consumed only 4% of the battery pack available capacity. An AI algorithm was trained on the experimental data obtained to perform data-driven predictions of failures. The results provide valuable insights for optimizing air cooling systems in EV applications. Future work will explore the effect of non-uniform air flow distribution in reducing the risk of thermal runaway and avoiding hot spots in the battery pack for optimal performance. Full article

(This article belongs to the Proceedings of The 3rd International Conference on Modern Technologies in Mechanical & Materials Engineering)

► Show Figures

Figure 1

13 pages, 4100 KB

Open AccessProceeding Paper

Simulation and Experimental Validation of a Microfluidic Device Used for Cell Focusing and Sorting Based on an Inertial Microfluidics Technique

by Muhammad Zulfiqar, Fizzah Asif, Emad Uddin, Muhammad Irfan, Ch Abdullah, Sibghat Ullah and Danish Manshad

Mater. Proc. 2025, 23(1), 13; https://doi.org/10.3390/materproc2025023013 - 6 Aug 2025

Viewed by 1296

Abstract

Cell separation is a major process in biomedicine and diagnostics and in the food and pharmaceutical industries. In this paper, a channel design is proposed for cell separation based on a passive cell sorting technique and sheath less flow. Initially, erythrocytes and monocytes [...] Read more.

Cell separation is a major process in biomedicine and diagnostics and in the food and pharmaceutical industries. In this paper, a channel design is proposed for cell separation based on a passive cell sorting technique and sheath less flow. Initially, erythrocytes and monocytes are injected into the designed channel, and the behavior of the particles is observed. The erythrocyte and monocyte are 8 μm and 20 μm in size, respectively. The final design is tested for different cross-sectional areas and particle sizes; 20 μm is the largest particle size that can be sorted with this design. Particles are separated due to inertial migration because the forces that focus the particles in the channels, in the form of different streams, deepen the lift force on the inertia of the moving particles. The lift force pushes the particles toward the wall, while the Dean force causes them to rotate near to the wall, stabilizing their positions. The lift and Dean forces depend on the inertia of the particles and topology of the channel, respectively. In this research, cell sorting is quantified by the distance between the two separated particles, and the trend of Δ x x versus Q is discussed. The channel throughput is also quantified in terms of the minimum and maximum allowable flow rates. Particles are best sorted by critical flow rate and Dean number. This hook-shaped design is created using polymethyl siloxane (PDMS), which is ideally suited for use in lab-on-chip (LOC) devices for continuous filtration and particle separation. The design is also experimentally tested and validated with the simulation results. Full article

(This article belongs to the Proceedings of The 3rd International Conference on Modern Technologies in Mechanical & Materials Engineering)

► Show Figures

Figure 1

10 pages, 228 KB

Open AccessProceeding Paper

A Numerical Assessment of Some Recurrent Crime Series in the State of Pittsburg

by Yuvraj Sunecher, Naushad Mamode Khan and Paulo Canas Rodrigues

Comput. Sci. Math. Forum 2025, 11(1), 35; https://doi.org/10.3390/cmsf2025011035 - 31 Jul 2025

Viewed by 239

Abstract

The city of Pittsburg, Pennsylvania, remains -the epicenter of aggravated assaults this year. Compared to its pre-pandemic figures, violent crimes saw an upsurge with theft topping the city crime list. This study assessed the trend of crime series, particularly thefts, robberies, and burglaries, [...] Read more.

The city of Pittsburg, Pennsylvania, remains -the epicenter of aggravated assaults this year. Compared to its pre-pandemic figures, violent crimes saw an upsurge with theft topping the city crime list. This study assessed the trend of crime series, particularly thefts, robberies, and burglaries, in two specific periods, namely from January 1990 to December 2001 and from 1 July 2023 to 30 September 2023, in Pittsburg using the discrete valued time series processes, with some popular innovation distributions that have recently emerged. The upward trend in thefts, robberies, and burglaries was affiliated with a shortage of police, existing police officers’ low morale, the latter’s anti-police demeanours, weak crime laws, gun proliferation, falling inflation rates, a rise in the consumers’ price index, uncomfortable homes, life insecurity, poverty, alcohol, drugs, and a devalued society. Thus, the implications include a need to strengthen existing crime laws, to create a diversion judiciary system offering alternatives to high-cost incarcerations provided that culprits adhere to the programs, and to establish evidence-based policies rooted in effective approaches. Full article

(This article belongs to the Proceedings of The 11th International Conference on Time Series and Forecasting)

Journal Menu

Journal Browser

Lysomal Hydroles

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (25 papers)

Editorial

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI