Engineering Proceedings

Other

8 pages, 431 KB

Open AccessProceeding Paper

Compressive Strength, Density, and Setting Time of Concrete Blended with Rice Husk Ash

by Edidiong Eseme Ambrose, Okiemute Roland Ogirigbo, Tirimisiu Bayonle Bello and Saviour Umoh Akpando

Eng. Proc. 2026, 124(1), 1; https://doi.org/10.3390/engproc2026124001 - 14 Jan 2026

Cited by 3 | Viewed by 1889

This study investigated the effects of incorporating rice husk ash (RHA) as a partial replacement for cement on the properties of concrete. To determine the optimal replacement level, RHA was used to replace cement in varying proportions, ranging from 0% to 25% in [...] Read more.

This study investigated the effects of incorporating rice husk ash (RHA) as a partial replacement for cement on the properties of concrete. To determine the optimal replacement level, RHA was used to replace cement in varying proportions, ranging from 0% to 25% in 5% increments. The mix with 0% RHA served as the control. The properties evaluated included setting time, density, and compressive strength. The results revealed that blending RHA with cement increased the initial setting time. This was attributed to the lower calcium oxide (CaO) content of RHA, which slows early-age hydration reactions. Conversely, the final setting time was reduced due to the pozzolanic activity of RHA, which enhances later-stage reactions. Additionally, the inclusion of RHA resulted in a decrease in concrete density, owing to its lower specific gravity and bulk density compared to Portland cement. Despite this, RHA-modified specimens exhibited higher compressive strengths than the control specimens. This strength enhancement was linked to the formation of additional calcium–silicate–hydrate (C-S-H) gel due to the pozzolanic reaction between amorphous silica in RHA and calcium hydroxide (Ca(OH)₂) from hydration reaction. The gel fills concrete voids at the microstructural level, producing a denser and more compact concrete matrix. Based on the balance between strength and durability, the optimal RHA replacement level was identified as 10%. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 347 KB

Open AccessProceeding Paper

Determination of Conditions of Divergence for Antenna Array Measurements Due to Changes in Satellite Attitude

by Marcello Asciolla, Angela Cratere and Francesco Dell’Olio

Eng. Proc. 2026, 124(1), 2; https://doi.org/10.3390/engproc2026124002 - 19 Jan 2026

Cited by 1 | Viewed by 295

Abstract

This study focused on determining the conditions leading to variance in the measurements of an antenna array capable of measuring the direction of electromagnetic waves. The payload of the study is a cross-array of antennas that is able to measure direction through array [...] Read more.

This study focused on determining the conditions leading to variance in the measurements of an antenna array capable of measuring the direction of electromagnetic waves. The payload of the study is a cross-array of antennas that is able to measure direction through array beamforming and angle of arrival (AOA) technology. Starting from the modeling of satellite kinematics (in terms of the satellite’s position and attitude combined with its relative position with respect to an electromagnetic wave emitter located on Earth’s surface), this study provides the mathematical fundamentals to identify potential cases that lead to divergence in the estimation variance for the position of a signal emitter. The numerical and analytical predictions, conducted through an evaluation of the Cramér–Rao lower bound (CRLB) metrics, were on the azimuth, elevation, and broadside angles through the generation of errors in the attitude with Monte Carlo simulations. Recent advancements in the miniaturization of electronics make these studies of particular interest for a new set of technological demonstrators equipped with payloads composed of antenna arrays. Applications of interest include Earth-scanning missions, with exemplary cases of search-and-rescue operations or the spectrum monitoring of jamming in the E1/L1 band for the GNSS. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1024 KB

Open AccessProceeding Paper

Simulation of a POCKETQUBE Nanosatellite Swarm Control System via a Linear Quadratic Regulator

by Jacques B. Ngoua Ndong Avele, Dalia A. Karaf and Vladimir K. Orlov

Eng. Proc. 2026, 124(1), 3; https://doi.org/10.3390/engproc2026124003 - 20 Jan 2026

Viewed by 617

Abstract

Developing an advanced simulation to control a swarm of 20 PocketQube nanosatellites using a linear quadratic regulator (LQR) involves several crucial steps that go beyond the initial scheme. A comprehensive approach requires a deep understanding of orbital mechanics and, in particular, the challenges [...] Read more.

Developing an advanced simulation to control a swarm of 20 PocketQube nanosatellites using a linear quadratic regulator (LQR) involves several crucial steps that go beyond the initial scheme. A comprehensive approach requires a deep understanding of orbital mechanics and, in particular, the challenges presented by the nanosatellite platform. The inherent limitations in terms of nanosatellite power, propulsion, and communications systems necessitate careful orbital selection and maneuver planning to achieve mission objectives efficiently and reliably. This includes optimizing launch windows, understanding atmospheric drag effects in low Earth orbits (LEOs), and designing robust attitude control systems to maintain the desired pointing for scientific instruments or communications links. Our work focused on simulating the attitude control of PocketQube nanosatellites in a swarm using the R2022a release of the Matlab/Simulink environment. First, we provided a mathematical model for the relative coordinates of a nanosatellite swarm. Second, we developed a mathematical model of the linear quadratic regulator implementation in the relative navigation. Third, we simulated the attitude control of 20 PocketQube nanosatellites using the Matlab/Simulink environment. Finally, we provided the swarm scenario and attitude control system data. The simulation of an attitude control system for 20 PocketQube nanosatellites using an LQR controller in a swarm successfully demonstrated the stabilization capabilities essential for swarm operations in the space environment. A link to a video of the simulation is provided in the Results section. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 2536 KB

Open AccessProceeding Paper

AutoML with Explainable AI Analysis: Optimization and Interpretation of Machine Learning Models for the Prediction of Hysteresis Behavior in Shape Memory Alloys

by Dmytro Tymoshchuk and Oleh Yasniy

Eng. Proc. 2026, 124(1), 4; https://doi.org/10.3390/engproc2026124004 - 20 Jan 2026

Viewed by 565

Abstract

This study presents an approach for predicting the hysteresis behavior of shape memory alloys (SMAs) based on automated machine learning (AutoML) integrated with explainable artificial intelligence (XAI). Experimental data from cyclic tests of NiTi wire under loading frequencies of 0.3, 0.5, 1, and [...] Read more.

This study presents an approach for predicting the hysteresis behavior of shape memory alloys (SMAs) based on automated machine learning (AutoML) integrated with explainable artificial intelligence (XAI). Experimental data from cyclic tests of NiTi wire under loading frequencies of 0.3, 0.5, 1, and 5 Hz were used for model development. The AutoML framework PyCaret enabled automated model selection, hyperparameter optimization, and performance comparison of regression algorithms. The highest prediction accuracy was achieved by the LightGBM model (for 0.3 Hz and 1 Hz) and the CatBoost model (for 0.5 Hz and 5 Hz), both demonstrating a coefficient of determination R² > 0.997 and low MSE, MAE, and MAPE values. Integration of XAI through the SHAP method provided both global and local interpretability of the model’s behavior. The analysis revealed the dominant influence of the Stress parameter, the physically meaningful role of the loading or unloading stage (UpDown), and a gradual increase in the contribution of the Cycle parameter in later cycles, reflecting fatigue accumulation effects. The obtained results confirm the high accuracy, interpretability, and physical consistency of the developed models. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

5 pages, 1208 KB

Open AccessProceeding Paper

High-Energy Ball Milling Strategies for the Synthesis of Cu/TiO₂ Catalysts

by Matías G. Rinaudo, Luis E. Cadús and Maria R. Morales

Eng. Proc. 2026, 124(1), 5; https://doi.org/10.3390/engproc2026124005 - 21 Jan 2026

Viewed by 696

Abstract

In this work, Cu/TiO₂ catalysts were prepared by several high-energy ball milling strategies (dry and semi-wet milling) using different copper reagents and compared with a sample synthesized by a conventional impregnation method. Crystal structures were identified by means of X-ray Diffraction (XRD), [...] Read more.

In this work, Cu/TiO₂ catalysts were prepared by several high-energy ball milling strategies (dry and semi-wet milling) using different copper reagents and compared with a sample synthesized by a conventional impregnation method. Crystal structures were identified by means of X-ray Diffraction (XRD), including anatase, rutile, high-pressure TiO₂ (II) and W species due to mill vial erosion at some conditions, highlighting the effect of a copper precursor on the rate of titania polymorphic transformation. Specific Surface Area (S_BET) values were calculated from N₂ physisorption, showing a correlation between the energy supplied to the powder and the milling conditions. Moreover, Scanning Electron Microscopy (SEM) was able to display the morphologies while a semi-quantification of present elements could be performed by Electron Dispersive X-ray Spectroscopy (EDS). Catalysts obtained through this green and one-pot process could be suitable for a variety of reactions, including CO₂ hydrogenation and glycerol valorization. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 3364 KB

Open AccessProceeding Paper

Effect of Stirring Efficiency on Fatigue Behavior of Graphene Nanoplatelets-Reinforced Friction Stir Spot Welded Aluminum Sheets

by Amir Alkhafaji and Daniel Camas

Eng. Proc. 2026, 124(1), 6; https://doi.org/10.3390/engproc2026124006 - 23 Jan 2026

Cited by 1 | Viewed by 376

Abstract

Friction stir spot welding (FSSW) is a novel variant of Friction Stir welding (FSW), developed by Mazda Motors and Kawasaki Heavy Industries to join similar and dissimilar materials in a solid state. It is an economic and environmentally friendly alternative to resistance spot [...] Read more.

Friction stir spot welding (FSSW) is a novel variant of Friction Stir welding (FSW), developed by Mazda Motors and Kawasaki Heavy Industries to join similar and dissimilar materials in a solid state. It is an economic and environmentally friendly alternative to resistance spot welding (RSW). The FSSW technique, however, includes some structural defects imbedded within the weld joint, such as keyhole formation, hook crack, and bond line oxidation challenging the joint strength. The unique properties of nanomaterials in the reinforcement of metal matrices motivated researchers to enhance the FSSW joints’ strength. Previous studies successfully fabricated nano-reinforced FSSW joints. At different volumetric ratios of nano-reinforcement, nanoparticles may agglomerate due to inefficient stirring of the welding tool pin, forming stress concentration sites and brittle phases, affecting tensile and fatigue strength under static and cyclic loading conditions, respectively. This work investigated how the welding tool pin affects stirring efficiency by controlling the distribution of a nano-reinforcing material within the joint stir zone (SZ), and thus the tensile and fatigue strength of the FSSW joints. Sheets of AA6061-T6 of 1.8 mm thickness were used as a base material. In addition, graphene nanoplatelets (GNPs) with lateral sizes of 1–10 µm and thicknesses of 3–9 nm were used as nano-reinforcements. GNP-reinforced FSSW specimens were prepared and successfully fabricated. Optical microscope (OM) and field emission scanning electron microscope (FE-SEM) methods were employed to visualize the GNPs’ incorporation into the SZs of the FSSW joints. Micrographs of as-welded specimens showed lower formations of scattered, clustered GNPs achieved by the threaded pin tool compared to continuous agglomerations observed when the cylindrical pin tool was used. Tensile test results revealed a significant improvement of about 30% exhibited by the threaded pin tool compared to the cylindrical pin tool, while fatigue test showed an improvement of 46–24% for the low- and high-cycle fatigue, respectively. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 2135 KB

Open AccessProceeding Paper

Improving Earthquake Resilience—The Role of RC Frame Asymmetry Under Successive Events: Nonlinear Dynamic Insights for Safer Building Codes

by Paraskevi K. Askouni

Eng. Proc. 2026, 124(1), 7; https://doi.org/10.3390/engproc2026124007 - 26 Jan 2026

Viewed by 349

Abstract

This study addresses a critical gap in seismic design by quantifying how plan asymmetry and multiple earthquake sequences interact to affect the nonlinear reaction of reinforced concrete (RC)-framed models. While earthquake-resistant design provisions have evolved, most current codes are based on single-event assumptions [...] Read more.

This study addresses a critical gap in seismic design by quantifying how plan asymmetry and multiple earthquake sequences interact to affect the nonlinear reaction of reinforced concrete (RC)-framed models. While earthquake-resistant design provisions have evolved, most current codes are based on single-event assumptions and simplified symmetry considerations, overlooking the cumulative effects of repeated ground motions observed in recent international studies. In this research, symmetrical and asymmetrical low-rise RC buildings are analyzed through nonlinear dynamic simulations, with both single- and multiple-event ground excitations considered for comparison. The analyses incorporate three-dimensional ground motions in horizontal and vertical orientations, while explicitly modeling the nonlinear inelastic response of RC sections under severe seismic demands. The evaluation of elastoplastic findings relies on normalized indices, by considering a simple dimensionless parameter to quantify the physical symmetry or asymmetry of the RC models. Results show that increasing plan asymmetry amplifies inter-story drift, torsional rotations, and plastic hinge concentrations, particularly under successive earthquake sequences. These findings indicate that existing design provisions may underestimate the vulnerability of irregular RC buildings. This work is among the first to integrate plan asymmetry and multi-event seismic loading into a unified evaluation framework, offering a novel tool for refining earthquake-resistant design standards. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 526 KB

Open AccessProceeding Paper

Characterization of Three Ion Chambers for High-Energy Photons Reference Dosimetry

by Sara Mohamed, Sahar Awad, Yasser Hassan, Aly Wagdy and Ahmed M. Maghraby

Eng. Proc. 2026, 124(1), 8; https://doi.org/10.3390/engproc2026124008 - 19 Jan 2026

Viewed by 886

Abstract

Introduction: Many standards, codes of practice, and protocols were issued internationally in order to standardize the methodologies and formalism of the use of ionization chambers for the purposes of evaluating absorbed radiation doses in high-energy photon and electron beams from medical linear accelerators. [...] Read more.

Introduction: Many standards, codes of practice, and protocols were issued internationally in order to standardize the methodologies and formalism of the use of ionization chambers for the purposes of evaluating absorbed radiation doses in high-energy photon and electron beams from medical linear accelerators. Methods: Three ion chambers were selected for this study: PTW Semiflex 3D (PTW 31021), PTW Farmer type (PTW 30013), and PTW PinPoint 3D (PTW 31022) ion chambers. Many correction factors and parameters controlling the behavior of ionization chambers were included in the study, such as polarity, ion recombination, and response to high-energy photons for each ion chamber. Results and discussion: The collection efficiencies of each ion chamber were calculated and evaluated numerically. Additionally, the tissue-phantom ratio (TPR_20,10) was used as a beam quality index, and the beam quality correction factors were determined for each chamber for two high-energy photon beams, 6 MV and 10 MV, where the reference beam quality is assumed to be that of Cobalt-60 photon energy. The volume averaging correction factor for each ion chamber was evaluated in order to account for the non-uniformity of the beam and for the two beam qualities. Conclusion: All the studied parameters are of great importance and should be considered for the purposes of radiation metrology. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 902 KB

Open AccessProceeding Paper

A Critical Review on the Influence of Additive Manufacturing on Climate Change and Environmental Sustainability

by Anthony C. Ogazi

Eng. Proc. 2026, 124(1), 9; https://doi.org/10.3390/engproc2026124009 - 27 Jan 2026

Viewed by 1466

Abstract

Additive manufacturing (AM), or 3D printing, has a significant, largely beneficial influence on climate change by decreasing material waste and requiring less energy use. The application of AM in the construction and industrial sectors has the potential to reduce carbon emissions. This goal [...] Read more.

Additive manufacturing (AM), or 3D printing, has a significant, largely beneficial influence on climate change by decreasing material waste and requiring less energy use. The application of AM in the construction and industrial sectors has the potential to reduce carbon emissions. This goal may be accomplished by using material and energy-saving measures, improving manufacturing processes, designing lightweight structures, and reducing transportation operations. While 3DP has the potential to help reduce environmental degradation, it is crucial to recognize the inherent setbacks associated with the technology. Certain AM processes have the potential to emit volatile organic compounds, which contribute to air pollution and hence need improved control. Industrial 3D printers can be excessively expensive, greatly increasing the initial expenditure required to begin a project. Despite these limitations, AM can reduce greenhouse gas emissions, generate better-built environments, and provide a means to reduce energy usage while supporting global carbon neutrality objectives. Governments should extend financial assistance in the form of subsidies to help reduce equipment purchase costs. Furthermore, AM’s capacity to foster a circular economy and minimize overall environmental effects is dependent on the improvement of material recycling and scalability. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 877 KB

Open AccessProceeding Paper

Impact of Operating Conditions on the Reliability of SRAM-Based Physical Unclonable Functions (PUFs)

by Marco Grossi, Martin Omaña, Simone Bisi, Cecilia Metra and Andrea Acquaviva

Eng. Proc. 2026, 124(1), 10; https://doi.org/10.3390/engproc2026124010 - 27 Jan 2026

Viewed by 809

Abstract

Wireless sensor systems can collect and share a large amount of data for different kinds of applications, but are also vulnerable to cyberattacks. The impact of cyberattacks on systems’ confidentiality, integrity, and availability can be mitigated by using authentication procedures and cryptographic algorithms. [...] Read more.

Wireless sensor systems can collect and share a large amount of data for different kinds of applications, but are also vulnerable to cyberattacks. The impact of cyberattacks on systems’ confidentiality, integrity, and availability can be mitigated by using authentication procedures and cryptographic algorithms. Authentication passwords and cryptographic keys may be stored in a non-volatile memory, which may be easily tampered with. Alternately, Physical Unclonable Functions (PUFs) can be adopted. They generate a chip’s unique fingerprint, by exploiting the randomness of process parameters’ variations occurring during chip fabrication, thus constituting a more secure alternative to the adoption of non-volatile memories for password storage. PUF reliability is of primary concern to guarantee a system’s availability. In this paper, the reliability of a Static Random Access Memory (SRAM)-based PUF implemented by a standard 32 nm CMOS technology is investigated, as a function of different operating conditions, such as noise, power supply voltage, and temperature, and considering different values of transistor conduction threshold voltages. The achieved results will show that transistor threshold voltage and noise are the operating conditions mostly affecting PUF reliability, while the impact of temperature variations is lower, and that of power supply variations is negligible. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 3729 KB

Open AccessProceeding Paper

Generalized Extreme Value-Based Fragility Curves

by Zahra Haqi, Matteo Dalmasso and Marco Civera

Eng. Proc. 2026, 124(1), 11; https://doi.org/10.3390/engproc2026124011 - 30 Jan 2026

Viewed by 447

Abstract

Fragility curves are essential for assessing the vulnerability of buildings to earthquake-induced damage, representing the probability of exceeding various damage states as a function of seismic intensity. They enable rapid pre-screening of large building stocks, guiding focused analyses, monitoring, and mitigation strategies. This [...] Read more.

Fragility curves are essential for assessing the vulnerability of buildings to earthquake-induced damage, representing the probability of exceeding various damage states as a function of seismic intensity. They enable rapid pre-screening of large building stocks, guiding focused analyses, monitoring, and mitigation strategies. This study introduces an empirical approach using the Generalized Extreme Value (GEV) distribution to model fragility curves, offering greater flexibility than the conventional lognormal method. In this work, GEV-based curves were derived from empirical data retrieved from the Italian Da.D.O. platform using Python 3.10.1 tool. The approach provides a practical framework for accurate, large-scale seismic risk assessment. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 2674 KB

Open AccessProceeding Paper

Retention of Tuning for Vibro-Impact and Linear Dampers Under Periodic Excitation

by Petro Lizunov, Olga Pogorelova and Tetyana Postnikova

Eng. Proc. 2026, 124(1), 12; https://doi.org/10.3390/engproc2026124012 - 3 Feb 2026

Viewed by 370

Abstract

This work studies the ability of a single-sided vibro-impact nonlinear energy sink (SSVI NES) and a tuned mass damper (TMD) to maintain their vibration reduction performance when the natural frequency of the primary structure (PS), which is determined by its stiffness, changes. Both [...] Read more.

This work studies the ability of a single-sided vibro-impact nonlinear energy sink (SSVI NES) and a tuned mass damper (TMD) to maintain their vibration reduction performance when the natural frequency of the primary structure (PS), which is determined by its stiffness, changes. Both types of dampers demonstrate high efficiency in mitigating the PS vibrations under periodic excitation if their parameters are optimized at a specific PS natural frequency. Their ability to reduce PS vibrations changes similarly for both types of dampers when this structural parameter is changed. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

19 pages, 1502 KB

Open AccessProceeding Paper

Machine Learning-Based Prognostic Modeling of Thyroid Cancer Recurrence

by Duppala Rohan, Kasaraneni Purna Prakash, Yellapragada Venkata Pavan Kumar, Gogulamudi Pradeep Reddy, Maddikera Kalyan Chakravarthi and Pradeep Reddy Challa

Eng. Proc. 2026, 124(1), 13; https://doi.org/10.3390/engproc2026124013 - 3 Feb 2026

Cited by 1 | Viewed by 1026

Abstract

Thyroid cancer is the most common type of endocrine cancer. Most cases are called differentiated thyroid cancer (DTC), which includes papillary, follicular, and hurthle cell types. DTC usually grows slowly and has a good prognosis, especially when found early and treated with surgery, [...] Read more.

Thyroid cancer is the most common type of endocrine cancer. Most cases are called differentiated thyroid cancer (DTC), which includes papillary, follicular, and hurthle cell types. DTC usually grows slowly and has a good prognosis, especially when found early and treated with surgery, radioactive iodine, and thyroid hormone therapy. However, cancer can come back sometimes even years after treatment. This recurrence can appear as abnormal blood tests or as lumps in the neck or other parts of the body. Being able to predict and detect these recurrences early is important for improving patient care and planning follow-up treatment. In this view, this research explores different machine learning algorithms and neural networks to effectively predict DTC recurrence. A total of 17 classifiers were utilized for the experiment, namely, logistic regression, random forest, k-nearest neighbours, Gaussian naïve Bayes, multi-layered perceptron, extreme gradient boosting, adaptive boosting, gradient boosting classifier, extra tree classifier (ETC), light gradient boosting machine, categorical boosting, Bernoulli naïve Bayes, complement naïve Bayes, multinomial naïve Bayes, histogram-based gradient boosting, and nearest centroid, followed by building an artificial neural network. Among the classifiers, ETC performed best with 95.3% accuracy, 95.1% precision, 87.92% recall, 98.18% specificity, 91.21% F1-score, 98.84% AUROC and 97.66% AUPRC on the first dataset, and 99.47% accuracy, 94.83% precision, 98.62% sensitivity, 99.54% specificity, 96.65% F1-score, 99.95% AUROC, and 99.37% AUPRC on the second dataset. To improve model interpretability, Shapley Additive Explanations (SHAP) was also used to explain the contribution of each clinical feature to the model’s predictions, allowing for transparent, patient-specific insights into which factors were most important for predicting recurrence, thereby supporting the proposed model’s clinical relevance. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 1551 KB

Open AccessProceeding Paper

Deep Learning and Transfer Learning Models for Indian Food Classification

by Jigarkumar Ambalal Patel, Dileep Laxmansinh Labana, Gaurang Vinodray Lakhani and Rashmika Ketan Vaghela

Eng. Proc. 2026, 124(1), 14; https://doi.org/10.3390/engproc2026124014 - 3 Feb 2026

Viewed by 786

Abstract

This study examines the utilization of deep learning and transfer learning models for classifying photos of Indian cuisine. Indian cuisine, characterized by its extensive diversity and intricate presentation, poses considerable hurdles in food recognition owing to changes in ingredients, texture, and visual aesthetics. [...] Read more.

This study examines the utilization of deep learning and transfer learning models for classifying photos of Indian cuisine. Indian cuisine, characterized by its extensive diversity and intricate presentation, poses considerable hurdles in food recognition owing to changes in ingredients, texture, and visual aesthetics. To tackle these challenges, we utilized a bespoke Convolutional Neural Network (CNN) and harnessed cutting-edge transfer learning models such as DenseNet121, InceptionV3, MobileNet, VGG16, and Xception. The research employed a varied dataset comprising 13 food categories and executed preprocessing techniques like HSV conversion, noise reduction, and edge identification to improve image quality. Metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were employed to assess model efficacy. The CNN model demonstrated a mediocre performance, revealing overfitting concerns due to a substantial disparity between training and validation accuracy. In contrast, transfer learning models, particularly DenseNet121, InceptionV3, and Xception, exhibited an enhanced generalization ability, each attaining above 92% accuracy across all criteria. MobileNet and VGG16 produced reliable outcomes with marginally reduced performances. The results highlight the efficacy of transfer learning in food image classification and indicate that fine-tuned, pre-trained models markedly improve classification accuracy. This research advances the creation of intelligent food recognition systems applicable in dietary monitoring, nutrition tracking, and health management. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 690 KB

Open AccessProceeding Paper

Optimization of Parameters for Supercritical Carbon Dioxide Extraction of Mongolian Sea Buckthorn Oil

by Gangerel Khorloo, Ulziisaikhan Purevsuren and Chimid-Ochir Gonchig

Eng. Proc. 2026, 124(1), 15; https://doi.org/10.3390/engproc2026124015 - 4 Feb 2026

Viewed by 398

Abstract

This study aims to model and optimize the process parameters influencing the efficiency and yield of oil extraction from Mongolian sea buckthorn seeds using supercritical carbon dioxide (CO₂). The experiments were planned using response surface methodology (RSM) based on a central [...] Read more.

This study aims to model and optimize the process parameters influencing the efficiency and yield of oil extraction from Mongolian sea buckthorn seeds using supercritical carbon dioxide (CO₂). The experiments were planned using response surface methodology (RSM) based on a central composite rotatable design (CCRD) to evaluate the effects of extraction pressure, temperature, and time, while maintaining a constant solvent flow rate of 2.0 L/min to balance extraction efficiency and selectivity. Following data refinement and outlier exclusion, the developed second-order polynomial model exhibited excellent accuracy with a coefficient of determination R² of 0.9375. Among the parameters studied, pressure was identified as the most critical factor affecting oil yield. Furthermore, significant interaction effects were observed, particularly between extraction time and the other variables, pressure–time (A * C) and temperature–time (B * C), indicating the time-dependent nature of mass transfer. The predicted optimal conditions for maximum yield were determined to be 5075 psi, 70 °C, and an extraction time of 10 h. Validation experiments under these conditions resulted in an oil yield of 800 g, confirming the reliability of the model. These findings demonstrate the feasibility of optimizing supercritical CO₂ extraction for the industrial-scale production of high-quality functional oils and nutraceuticals. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 625 KB

Open AccessProceeding Paper

Surface Hydrophilicity of Dental Copolymer Modified with Dimethacrylates Possessing Quaternary Ammonium Groups

by Patryk Drejka and Izabela Barszczewska-Rybarek

Eng. Proc. 2026, 124(1), 16; https://doi.org/10.3390/engproc2026124016 - 4 Feb 2026

Viewed by 300

Abstract

Dental composite reconstructive materials (DCRMs) used in caries treatment possess satisfactory functional properties but lack antimicrobial activity, which may lead to secondary caries. This research aimed to modify the DCRM matrix with urethane-dimethacrylate monomers derived from cycloaliphatic and aromatic diisocyanates bearing quaternary ammonium [...] Read more.

Dental composite reconstructive materials (DCRMs) used in caries treatment possess satisfactory functional properties but lack antimicrobial activity, which may lead to secondary caries. This research aimed to modify the DCRM matrix with urethane-dimethacrylate monomers derived from cycloaliphatic and aromatic diisocyanates bearing quaternary ammonium groups. The diisocyanates used were 1,3-bis(1-isocyanato-1-methylethyl)benzene (TMXDI), isophorone diisocyanate (IPDI), dicyclohexylmethane-4,4′-diisocyanate (CHMDI), and 1,1′-methylenebis(4-isocyanatobenzene) (MDI). As a result, eight modified copolymers were obtained and tested for the surface water contact angle (WCA), water sorption (WS), and water solubility (SL). The WCA results indicated predominantly hydrophilic surfaces, while the WS and SL values were generally satisfactory. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 510 KB

Open AccessProceeding Paper

AI-Driven Spatiotemporal Mapping and Grid Optimization for Solar and Wind Energy

by Rahul Jain, Sushil Kumar Singh, Habib Khan, Om Prakash Pal, Sejal Mishra and Bhavisha Suthar

Eng. Proc. 2026, 124(1), 17; https://doi.org/10.3390/engproc2026124017 - 5 Feb 2026

Cited by 1 | Viewed by 2753

Abstract

Renewable energy sources play a critical role in modern energy production and transmission systems. This paper presents a GIS-enhanced deep learning framework for spatially informed renewable energy potential assessment, integrating environmental variables with Geographic Information Systems (GIS) to support sustainable energy planning aligned [...] Read more.

Renewable energy sources play a critical role in modern energy production and transmission systems. This paper presents a GIS-enhanced deep learning framework for spatially informed renewable energy potential assessment, integrating environmental variables with Geographic Information Systems (GIS) to support sustainable energy planning aligned with the United Nations Sustainable Development Goals (SDGs). A synthetic dataset comprising 100 distinct geographical regions was constructed using key environmental parameters, including solar irradiance, wind speed, temperature, relative humidity, and altitude. The dataset was further enriched with GIS-based spatial attributes (latitude and longitude) and aggregated historical energy production records used as reference values for supervised learning, without explicit temporal modeling. The standardized dataset was divided into training and testing subsets using an 80:20 split and employed to train a neural network implemented using TensorFlow’s Sequential API. The architecture incorporated dense layers and dropout regularization to prevent overfitting, and was trained for 50 epochs with a batch size of 16 using the Adam optimizer and mean squared error (MSE) loss. The model achieved stable convergence, with training loss reducing from 98,273.70 to 16,651.12 and consistent validation performance, indicating strong generalization. Model outputs were integrated with GIS tools to generate spatial visualizations of energy potential, revealing distinct geographical patterns and clusters relevant for grid planning and resource allocation. By explicitly embedding spatial features into the learning process, the proposed approach provides accurate and interpretable energy potential estimates, supporting informed decision-making for renewable energy deployment and contributing to SDG 7 (clean energy), SDG 9 (resilient infrastructure), and SDG 13 (climate action). Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 293 KB

Open AccessProceeding Paper

Design of a Fault-Tolerant BCD to Excess-3 Code Converter Using Clifford+T Quantum Gates

by Sandip Das, Shankar Prasad Mitra, Sushmita Chaudhari and Riya Sen

Eng. Proc. 2026, 124(1), 18; https://doi.org/10.3390/engproc2026124018 - 4 Feb 2026

Cited by 1 | Viewed by 627

Abstract

Quantum computing has the potential to transform modern computation by offering exponential advantages in areas such as cryptography, optimization, and intelligent data processing. To effectively realize these advantages, particularly in fault-tolerant and Noisy Intermediate-Scale Quantum (NISQ) environments, quantum circuits must be both resource-efficient [...] Read more.

Quantum computing has the potential to transform modern computation by offering exponential advantages in areas such as cryptography, optimization, and intelligent data processing. To effectively realize these advantages, particularly in fault-tolerant and Noisy Intermediate-Scale Quantum (NISQ) environments, quantum circuits must be both resource-efficient and error-resilient. This paper presents a novel Binary-Coded Decimal (BCD) to Excess-3 code converter designed exclusively using the Clifford+T gate set, which is widely supported by fault-tolerant quantum hardware. The proposed design eliminates conventional 4-bit reversible adder-based implementations and instead employs an optimized logic structure based on Clifford+T-decomposed Peres gates. By leveraging Temporary Logical-AND gates and CNOT operations, the circuit achieves reduced T-count, circuit depth, and quantum cost as key metrics in fault-tolerant quantum computation. Functional correctness is verified through IBM Qiskit, Version 2.1 simulations for all valid BCD inputs. The proposed converter serves as a scalable and hardware-compatible arithmetic building block for resource-aware and AI-oriented quantum architectures. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 12951 KB

Open AccessProceeding Paper

A Forest Mapping Model for Algeria Using Noisy Labels and Few Clean Data

by Lilia Ammar Khodja, Meziane Iftene and Mohammed El Amin Larabi

Eng. Proc. 2026, 124(1), 19; https://doi.org/10.3390/engproc2026124019 - 6 Feb 2026

Viewed by 618

Abstract

This study proposes a forest mapping framework for Algeria that addresses the challenge of limited clean data and noisy global land cover labels. The approach combines a small set of manually curated annotations with noisy ESA WorldCover data, leveraging Sentinel-2 multispectral imagery and [...] Read more.

This study proposes a forest mapping framework for Algeria that addresses the challenge of limited clean data and noisy global land cover labels. The approach combines a small set of manually curated annotations with noisy ESA WorldCover data, leveraging Sentinel-2 multispectral imagery and Digital Elevation Model (DEM) features such as slope, aspect, and the Normalized Difference Vegetation Index (NDVI). A modified ResNet-18 architecture was fine-tuned using both clean and pseudo-labeled noisy data, enabling the model to effectively mitigate label noise. The framework achieved an overall accuracy of 98.5%, demonstrating strong generalization across Algeria’s diverse forest ecosystems. These results highlight the potential of semi-supervised deep learning to improve large-scale forest monitoring, with applications in conservation, sustainable resource management, and climate change mitigation. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 2141 KB

Open AccessProceeding Paper

Blue and Green Phosphate Coatings Formed on Steel Without Heating

by Viktoriya S. Konovalova

Eng. Proc. 2026, 124(1), 20; https://doi.org/10.3390/engproc2026124020 - 6 Feb 2026

Viewed by 973

Abstract

Phosphate coatings were obtained by cold method from solutions based on Mazev Salt (containing Mn(H₂PO₄)₂∙2H₂O and iron phosphates). Metal nitrates and nitrites were introduced into solutions as accelerators of the phosphating process. To obtain green [...] Read more.

Phosphate coatings were obtained by cold method from solutions based on Mazev Salt (containing Mn(H₂PO₄)₂∙2H₂O and iron phosphates). Metal nitrates and nitrites were introduced into solutions as accelerators of the phosphating process. To obtain green and blue phosphate coatings, procyon olive green and methylene blue dyes (8 g/L) were added into the solutions. Colored phosphate coatings are deposited unevenly on the steel surface. The thickness of the modified phosphate films was estimated from SEM images of the cross-section samples and determined to be 3–4 microns. Colored phosphate coatings are fine-grained with a grain size of 170–190 nm, which was determined using an atomic force microscope. Phosphate films continue to exhibit protective properties when heated to 100 °C. With a further increase in temperature, the protective ability of the film is significantly reduced. Colored phosphate films have a low coefficient of friction (0.1–0.15). The breakdown voltage of colored phosphate coatings is 180–200 V, which characterizes low electrical insulation ability. Based on the established properties, colored phosphate coatings can be used as protective and decorative. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 1064 KB

Open AccessProceeding Paper

Ensemble-Based Imputation for Handling Missing Values in Healthcare Datasets: A Comparative Study of Machine Learning Models

by Bilal Ibrahim Maijamaa, Salim Ahmad, Aminu Musa, Abdullahi Ishaq and Abida Ayuba

Eng. Proc. 2026, 124(1), 21; https://doi.org/10.3390/engproc2026124021 - 9 Feb 2026

Viewed by 847

Abstract

This study addresses the challenge of missing numerical values in healthcare datasets by proposing a Particle Swarm Optimization (PSO)-optimized stacking ensemble model for data imputation. The framework combines Random Forest, XGBoost, and Linear Regression within a stacking architecture, with PSO used to optimize [...] Read more.

This study addresses the challenge of missing numerical values in healthcare datasets by proposing a Particle Swarm Optimization (PSO)-optimized stacking ensemble model for data imputation. The framework combines Random Forest, XGBoost, and Linear Regression within a stacking architecture, with PSO used to optimize model selection and hyperparameters for improved accuracy. The approach was evaluated on the Breast Cancer Wisconsin and Heart Disease datasets under Missing Completely at Random (MCAR) conditions at 30%, 20%, and 10% missingness levels, using RMSE, MAE, R², and processing time as performance metrics. Experimental results show that the proposed model consistently outperforms individual learners across all missingness scenarios, achieving an RMSE of 0.0446, MAE of 0.0303, and R² of 86.56% on the Breast Cancer dataset at 10% MCAR, and an RMSE of 0.1388 with an R² of 75.19% on the Heart Disease dataset. Compared with a MissForest-based existing approach, the proposed framework demonstrates substantial reductions in imputation error, confirming the effectiveness of combining ensemble learning with evolutionary optimization. Although the PSO-based stacking model incurs higher computational cost, the findings indicate that it provides a robust, accurate, and generalizable solution for numerical data imputation in healthcare applications under varying levels of missingness. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 781 KB

Open AccessProceeding Paper

Bayesian Optimization-Driven U-Net Architecture Tuning for Brain Tumor Segmentation

by Shoffan Saifullah and Rafał Dreżewski

Eng. Proc. 2026, 124(1), 22; https://doi.org/10.3390/engproc2026124022 - 9 Feb 2026

Cited by 1 | Viewed by 989

Abstract

Precise brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for clinical diagnosis and treatment planning. However, determining an optimal deep learning architecture for such tasks remains a challenge due to the vast hyperparameter space and structural variations. This paper presents [...] Read more.

Precise brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for clinical diagnosis and treatment planning. However, determining an optimal deep learning architecture for such tasks remains a challenge due to the vast hyperparameter space and structural variations. This paper presents a novel approach that integrates Bayesian Optimization (BO) to automatically tune the U-Net architecture for effective brain tumor segmentation. The proposed BO-UNet framework searches over encoder, bottleneck, and decoder configurations using a Gaussian Process-based surrogate model, guided by a fitness function derived from Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Experiments were conducted on two benchmark datasets: the Figshare Brain Tumor Segmentation (FBTS) dataset and the BraTS 2021 dataset (focused on Whole Tumor segmentation). The best-discovered architecture [64, 64, 64, 256, 64, 128, 256] achieved notable performance: on the FBTS dataset, it reached 0.9503 DSC and 0.9054 JI; on BraTS 2021, it obtained 0.9261 DSC and 0.8631 JI, outperforming several state-of-the-art methods. Convergence and segmentation-map evolution confirm that BO effectively guided the architectural search process. These findings demonstrate the potential of BO-driven deep learning in medical imaging, opening new avenues for architecture-level optimization with minimal manual intervention. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 826 KB

Open AccessProceeding Paper

Photocatalytic Degradation of Brilliant Blue FCF Dye Using Biosynthesized ZnO Nanoparticles

by Ekhlakh Veg, Nashra Fatima and Tahmeena Khan

Eng. Proc. 2026, 124(1), 23; https://doi.org/10.3390/engproc2026124023 - 9 Feb 2026

Viewed by 782

Abstract

In recent years, photocatalysis based on metal oxides has gained significant attention as an effective and environmentally sustainable strategy for the degradation of dye pollutants under mild operating conditions. Among the various metal oxide photocatalysts, zinc oxide (ZnO) is particularly attractive due to [...] Read more.

In recent years, photocatalysis based on metal oxides has gained significant attention as an effective and environmentally sustainable strategy for the degradation of dye pollutants under mild operating conditions. Among the various metal oxide photocatalysts, zinc oxide (ZnO) is particularly attractive due to its appropriate band gap, excellent chemical stability, low cost, and capability to operate under visible light. In this work, ZnO nanoparticles (NPs) were green-synthesized using Livistona chinensis leaf extract and subsequently assessed for their photocatalytic performance in the degradation of the synthetic dye Brilliant Blue FCF. The synthesized ZnO NPs achieved approximately 76% dye removal within 90 min of visible light irradiation. These findings demonstrate the potential of ZnO NPs as an efficient, economical, and eco-friendly visible-light-driven photocatalyst, supporting their application in sustainable wastewater remediation. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 792 KB

Open AccessProceeding Paper

Prediction of Metastatic Risk in Breast Cancer by the Expression of Mechanobiological Markers

by Ksenia Maksimova, Margarita Pustovalova, Sergey Leonov and Yulia Merkher

Eng. Proc. 2026, 124(1), 24; https://doi.org/10.3390/engproc2026124024 - 10 Feb 2026

Viewed by 738

Abstract

Distant metastasis is the leading cause of breast cancer-related mortality, yet its prediction from primary tumor profiles remains challenging. Cytoskeletal remodeling and cell motility are central to metastatic dissemination, suggesting mechanobiological genes as biologically relevant biomarkers. Here, we evaluated the ability of supervised [...] Read more.

Distant metastasis is the leading cause of breast cancer-related mortality, yet its prediction from primary tumor profiles remains challenging. Cytoskeletal remodeling and cell motility are central to metastatic dissemination, suggesting mechanobiological genes as biologically relevant biomarkers. Here, we evaluated the ability of supervised machine learning models to distinguish metastatic from non-metastatic breast cancer samples using expression profiles of 11 actin cytoskeleton-related genes from the TCGA cohort. Ensemble models, particularly Random Forest and XGBoost, demonstrated strong discriminative ability and consistently outperformed other approaches after employing SMOTE for class balancing in exploratory analyses. CFL1, ANXA2, and MYH9 consistently emerged as the most informative predictors, highlighting mechanobiological processes as key drivers of metastatic risk. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 683 KB

Open AccessProceeding Paper

Adaptive Marine Predators Algorithm for Optimizing CNNs in Malaria Detection

by Abubakar Salisu Bashir, Usman Mahmud, Abdulkadir Abubakar Bichi, Abubakar Ado, Abdulrauf Garba Sharifai and Mansir Abubakar

Eng. Proc. 2026, 124(1), 25; https://doi.org/10.3390/engproc2026124025 - 11 Feb 2026

Viewed by 453

Abstract

Malaria remains a major global health burden, requiring rapid and reliable diagnostic tools to complement or replace labor-intensive manual microscopy. Although deep learning methods have demonstrated strong potential for automated malaria diagnosis, many existing approaches depend on computationally expensive transfer learning architectures or [...] Read more.

Malaria remains a major global health burden, requiring rapid and reliable diagnostic tools to complement or replace labor-intensive manual microscopy. Although deep learning methods have demonstrated strong potential for automated malaria diagnosis, many existing approaches depend on computationally expensive transfer learning architectures or exhibit sensitivity to suboptimal hyperparameter configurations. This study proposes a lightweight automated framework for binary classification of malaria cell images using a custom Convolutional Neural Network (CNN) optimized by a novel Adaptive Marine Predators Algorithm (AMPA). The proposed AMPA integrates a state-aware adaptive control factor that dynamically adjusts step size based on population loss, thereby improving search efficiency and reducing susceptibility to local optima. The framework was evaluated on the NIH Malaria Cell Image Dataset containing 27,558 single-cell images. Experimental results show that the AMPA-optimized CNN achieves a testing accuracy of 95.00% and an Area Under the Curve of 0.986. Comparative experiments indicate that the proposed model outperforms several reported lightweight architectures, including MobileNetV2 (92.00%) and YOLO-based detectors (94.07%), while achieving performance comparable to deeper networks such as VGG-16 (94.88%), with substantially lower computational complexity. The model further attains high sensitivity (0.94) and precision (0.96), supporting its suitability as a robust and resource-efficient approach for automated malaria screening research. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 535 KB

Open AccessProceeding Paper

A Comprehensive Review and Experimental Study on Biodiesel Upgrade Through Selective Partial Catalytic Hydrogenation

by Alexandros Psalidas, Elissavet Emmanouilidou and Nikolaos C. Kokkinos

Eng. Proc. 2026, 124(1), 26; https://doi.org/10.3390/engproc2026124026 - 11 Feb 2026

Viewed by 911

Abstract

Biodiesel is a promising alternative to conventional diesel, but its widespread use is inhibited by oxidative stability issues. To address this problem, various strategies have been tested, and among them, the partial hydrogenation of biodiesel FAMEs has shown promising results. Within the framework [...] Read more.

Biodiesel is a promising alternative to conventional diesel, but its widespread use is inhibited by oxidative stability issues. To address this problem, various strategies have been tested, and among them, the partial hydrogenation of biodiesel FAMEs has shown promising results. Within the framework of the present study, a comprehensive review and an experimental study on biodiesel upgrading through selective partial catalytic hydrogenation have been conducted. The literature indicates that biphasic aqueous/organic catalytic systems have great potential for biodiesel catalytic upgrade, offering high reaction rates, good selectivity and convenient catalyst separation. In this context, an aqueous/organic biphasic system with a Ru/TPPTS catalyst was tested for the partial hydrogenation of biodiesel derived from WCOs. The results were comparable to those reported in the literature, indicating the potential of this process and contributing to the scarce body of research on these systems. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

13 pages, 3258 KB

Open AccessProceeding Paper

Integration of Solar Thermal Energy Conversion with a Novel Multilevel Inverter Circuit for Low-Power Applications

by Vijayaraja Loganathan, Dhanasekar Ravikumar, Mohamed Raffi Sheik Alaudeen, Abinandhan Jeevagan and Rupa Kesavan

Eng. Proc. 2026, 124(1), 27; https://doi.org/10.3390/engproc2026124027 - 11 Feb 2026

Viewed by 611

Abstract

The rise of carbon emissions from fossil fuel-based power generation has intensified the need for efficient and low-carbon energy systems. The global CO₂ concentration has risen from 285 ppm in the pre-industrial era to nearly 420 ppm today, and this contributes to [...] Read more.

The rise of carbon emissions from fossil fuel-based power generation has intensified the need for efficient and low-carbon energy systems. The global CO₂ concentration has risen from 285 ppm in the pre-industrial era to nearly 420 ppm today, and this contributes to a 1°C increase in average temperature. Therefore, in this article, a hybrid photovoltaic–thermoelectric generator (PV–TEG) system integrated with a reduced-switch multilevel inverter (MLI) is proposed. This enhances renewable energy utilization and power quality. The proposed PV–TEG model recovers waste heat from PV modules, which yields an overall efficiency improvement of approximately 2–8% compared to standalone PV systems. Further, the proposed MLI operates in symmetric (seven-level) and asymmetric (11-level) modes using eight switches. The system develops high-quality stepped output voltages with a minimum component count. Simulation work is performed, and the results show a peak output voltage of ±220 V with Total Harmonic Distortion (THD) of 7.2% under R-load and reduced THD below 5% under RL and variable load conditions. The integrated system demonstrates improved efficiency, reliability, and suitability for sustainable power generation and rural electrification. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1026 KB

Open AccessProceeding Paper

IoT-Based Sensor Technologies for Object Detection in Low-Visibility Environments: Development and Validation of a Functional Prototype

by Pedro Escudero-Villa and Cristian Escudero

Eng. Proc. 2026, 124(1), 28; https://doi.org/10.3390/engproc2026124028 - 12 Feb 2026

Viewed by 944

Abstract

In emergency scenarios where visibility is compromised, rapid and accurate object detection becomes critical. This study addresses this challenge by proposing an IoT-enabled robotic solution capable of operating in low-visibility environments, with a focus on supporting search and rescue missions through autonomous sensing [...] Read more.

In emergency scenarios where visibility is compromised, rapid and accurate object detection becomes critical. This study addresses this challenge by proposing an IoT-enabled robotic solution capable of operating in low-visibility environments, with a focus on supporting search and rescue missions through autonomous sensing and real-time data communication. This research presents the development and implementation of an IoT-based sensorized system designed to detect objects in low-visibility environments. The system aims to enhance search and rescue operations by identifying potential human presence in areas with limited access due to smoke, darkness, or hazardous conditions. The platform integrates distance sensors, a thermal camera (AMG8833), a PIR motion sensor, and wireless communication through the Arduino MKR1000 and ESP32-CAM boards. The mobile robot is equipped with obstacle avoidance, person detection, and IoT communication modules, allowing data to be sent to the cloud via ThingSpeak and enabling remote commands through TalkBack. A structured methodology was followed, including technology selection, hardware/software design, and testing under various lighting and opacity conditions. Experimental results showed the effectiveness of the system in identifying obstacles and detecting heat signatures representing human body, with optimal performance observed at a 15 cm detection threshold. The system demonstrated robust operation in simulated rescue environments, providing real-time data transmission and remote-control capabilities. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 218 KB

Open AccessProceeding Paper

Phytochemical Screening and In Vitro Antioxidant Analysis of Colebrookea oppositifolia Sm. Extract

by Rohit Malik, Santosh Kumar Singh and Prashant Kumar

Eng. Proc. 2026, 124(1), 30; https://doi.org/10.3390/engproc2026124030 - 13 Feb 2026

Viewed by 658

Abstract

Dementia, a major cause of dependency, disability, and mortality, is characterized by a progressive cognitive decline. Alzheimer’s disease, a major neurodegenerative dementia, primarily affects the elderly. This study aimed to investigate the antioxidant and neuroprotective potential of plant phytoconstituents for the treatment of [...] Read more.

Dementia, a major cause of dependency, disability, and mortality, is characterized by a progressive cognitive decline. Alzheimer’s disease, a major neurodegenerative dementia, primarily affects the elderly. This study aimed to investigate the antioxidant and neuroprotective potential of plant phytoconstituents for the treatment of Alzheimer’s disease. Phytoconstituents of Colebrookea oppositifolia Sm. were investigated using aerial and root extracts. The antioxidant potential of the plant phytoconstituents was assessed using in vitro antioxidant assays such as 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and Ferric-Reducing Antioxidant Power (FRAP) assay. The plant extract (root) showed significant antioxidant potential. Additional studies are underway to comprehensively evaluate its potential applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

11 pages, 716 KB

Open AccessProceeding Paper

Advanced Control of MEA-Based CO₂ Capture Systems

by Adham Norkobilov, Abror Turakulov, Qilichbek Safarov, Sanjar Ergashev, Zafar Turakulov, Azizbek Kamolov, Aziza Maksudova and Jaloliddin Eshbobaev

Eng. Proc. 2026, 124(1), 31; https://doi.org/10.3390/engproc2026124031 - 13 Feb 2026

Viewed by 696

Abstract

Post-combustion CO₂ capture using monoethanolamine (MEA) is a mature mitigation technology, yet its high energy demand and complex dynamics remain major challenges. This study presents a unified dynamic modeling and control framework for an MEA-based absorption–regeneration system, focusing on a comparative evaluation [...] Read more.

Post-combustion CO₂ capture using monoethanolamine (MEA) is a mature mitigation technology, yet its high energy demand and complex dynamics remain major challenges. This study presents a unified dynamic modeling and control framework for an MEA-based absorption–regeneration system, focusing on a comparative evaluation of PID, fuzzy logic control (FLC), and model predictive control (MPC) under realistic operating disturbances. A control-oriented surrogate model was developed in MATLAB R2024b/Simulink and validated against published benchmark trends. The control objective was to maintain CO₂ capture efficiency above 90% while minimizing reboiler energy consumption under ±10% inlet CO₂ concentration and flue gas flow disturbances. Simulation results showed that PID control ensures basic stability but exhibits slow recovery and high energy usage, while FLC improves robustness with limited dynamic improvement. MPC consistently maintained capture efficiency above the target value, reduced the settling time by approximately 37%, and achieved a 12.4% reduction in average reboiler duty compared to PID control. The results demonstrate that a unified, implementation-oriented modeling framework enables the effective assessment of advanced control strategies and supports the energy-efficient operation of industrial MEA-based CO₂ capture systems. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 1306 KB

Open AccessProceeding Paper

Trunk and Lower-Extremity Kinematics During Gait After Posterior Fixation for Thoracolumbar Fracture

by Battugs Borkhuu, Batbayar Khuyagbaatar, Ganbat Danaa and Sonomjamts Munkhbayarlakh

Eng. Proc. 2026, 124(1), 32; https://doi.org/10.3390/engproc2026124032 - 14 Feb 2026

Viewed by 399

Abstract

Posterior fixation is usually performed to restore spinal stability and decompress the spinal canal for unstable thoracolumbar burst fractures. The purpose of this study was to compare trunk and lower-extremity kinematics during gait between healthy adults and patients who had undergone posterior fixation [...] Read more.

Posterior fixation is usually performed to restore spinal stability and decompress the spinal canal for unstable thoracolumbar burst fractures. The purpose of this study was to compare trunk and lower-extremity kinematics during gait between healthy adults and patients who had undergone posterior fixation surgery after thoracolumbar fractures. Optical motion capture was used to record joint kinematics during walking. The trunk, hip, knee, and ankle joint angles and excursions in sagittal, frontal, and transverse planes were calculated, averaged, and compared between patients and control groups. The patient group had significantly increased total hip excursion in the frontal plane and reduced ankle dorsiflexion in the sagittal plane, with mean differences of 4.2° and 6.4°, respectively. However, there were no differences in knee joint kinematics. The patient group exhibited a more upright trunk position during walking than the control group, with both peak trunk flexion and extension significantly different, possibly indicating stiffer trunk movement. This study provides the fundamentals of the joint kinematics of the trunk and lower extremities after posterior surgical treatment for thoracolumbar fractures, which may help in evaluating surgical outcomes. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 667 KB

Open AccessProceeding Paper

Secure and Efficient Biometric Data Streaming with IoT for Wearable Healthcare

by Nikolaos Tournatzis, Stylianos Katsoulis, Ioannis Chrysovalantis Panagou, Evangelos Nannos, Ioannis Christakis and Grigorios Koulouras

Eng. Proc. 2026, 124(1), 33; https://doi.org/10.3390/engproc2026124033 - 15 Feb 2026

Viewed by 940

Abstract

The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integrated dashboard visualization. This research addresses these [...] Read more.

The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integrated dashboard visualization. This research addresses these gaps by investigating and evaluating an IoT framework leveraging lightweight communication and real-time visualization for improved healthcare monitoring. Drawing primarily on recent peer-reviewed journals and reputable conference proceedings, we evaluate an IoT architecture that securely integrates wearable biometric data into a cloud-based dashboard. The system utilizes encrypted advertising packets (e.g., AES-128-CCM) to broadcast biometric signals, eliminating the need for permanent device pairing and minimizing energy consumption. These packets are captured by our prototype ESP32-based (Espressif Systems, Shanghai, China) gateway node, decrypted and forwarded to a secure cloud environment that ensures persistent storage and accessibility. The cloud-based dashboard provides medical staff and end-users with real-time insights and long-term data tracking. Emphasis was placed on evaluating the system’s low latency performance, energy efficiency and data confidentiality. System evaluation demonstrates that encrypted advertising packets can securely transmit biometric signals, while drastically reducing energy consumption and latency. System evaluation demonstrates that encrypted BLE advertising serves as a superior alternative to traditional pairing-based methods for long-term medical monitoring. By implementing a dual-optimization strategy that balances data confidentiality with power efficiency, the proposed system achieved a 33-fold increase in operational autonomy compared with standard permanent BLE connections. These results represent a significant advancement in battery longevity for the IoMT ecosystem, providing a scalable solution for continuous, secure biometric signal transmission with minimal energy overhead. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 828 KB

Open AccessProceeding Paper

Simulation of Hydrogen Production from Crude Glycerol Using Steam Reforming

by Dorcas Museme Mabulay, Shonisani Salvation Muthubi and Pascal Kilunji Mwenge

Eng. Proc. 2026, 124(1), 34; https://doi.org/10.3390/engproc2026124034 - 15 Feb 2026

Viewed by 750

Abstract

The rising global production of biodiesel has led to a surplus of crude glycerol, a byproduct accounting for about 10% of biodiesel’s weight. Crude glycerol contains various impurities, including unreacted alcohol, soap, free fatty acids, water, and leftover reagents, which are often considered [...] Read more.

The rising global production of biodiesel has led to a surplus of crude glycerol, a byproduct accounting for about 10% of biodiesel’s weight. Crude glycerol contains various impurities, including unreacted alcohol, soap, free fatty acids, water, and leftover reagents, which are often considered waste. Several methods have been explored to utilise this surplus, such as combustion for energy recovery, composting, animal feed, and purification. However, purification can be expensive and is often not economically viable. While there is growing interest in hydrogen production via the steam reforming of glycerol, there is a significant lack of detailed information and research on simulating this process using ChemCAD 8.1.0 software. This study aimed to simulate glycerol steam reforming (GSR) using ChemCAD, a process that converts crude glycerol from biodiesel into hydrogen. The process operates on a Gibbs free energy reactor, simulating GSR using the UNIFAC thermodynamic model under various conditions: temperatures ranging from 200 °C to 1000 °C, steam-to-glycerol mass ratios from 2:1 to 12:1, and a nickel catalyst maintained at 1 wt.%. The results demonstrate maximum glycerol consumption at temperatures above 600 °C and at a steam-to-glycerol mass ratio of 6:1. The optimum conditions for achieving a hydrogen yield of 65.23% occur at 800 °C and a ratio of 8:1 while minimising the formation of byproducts such as CO₂, CO, and CH₄. These findings provide valuable insights for optimising GSR processes and promoting the sustainable utilisation of renewable energy sources, thereby contributing to the circular economy and supporting the United Nations Sustainable Development Goal 7 (Affordable and Clean Energy). Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

17 pages, 6352 KB

Open AccessProceeding Paper

Comparative Evaluation of Machine Learning Classifiers for Breast Cancer Diagnosis: A Comprehensive Statistical Analysis

by Sambit Subhankar Das, Atal Mahaprasad, Neelamadhab Padhy, Srikant Misra, Rasmita Panigrahi, Pradeep Kumar Mahapatro and Dasaradha Arangi

Eng. Proc. 2026, 124(1), 35; https://doi.org/10.3390/engproc2026124035 - 15 Feb 2026

Cited by 1 | Viewed by 959

Abstract

Content/Background: Breast cancer is one of the most fatal cancers among women around the globe. The chances of surviving this cancer increase with early tumor detection, which is necessary for effective treatment. Traditional diagnostic techniques are ineffective and time-consuming, and they yield results [...] Read more.

Content/Background: Breast cancer is one of the most fatal cancers among women around the globe. The chances of surviving this cancer increase with early tumor detection, which is necessary for effective treatment. Traditional diagnostic techniques are ineffective and time-consuming, and they yield results that may be accurate or inaccurate. Therefore, our primary objective is to determine how a machine learning model can reduce diagnostic errors and provide accurate results. Objective: The main objective of this project is to build an ML-based classification model that can help doctors detect breast cancer early and more accurately. This project also aims to provide an interactive interface for easy access in healthcare settings. Materials/Methods: For this study, twelve machine learning classification algorithms are implemented and tested: Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, XGBOOST, Naive Bayes, AdaBoosting, Light GBM, CatBoost, and the Artificial Neural Network (ANN). This study used the Wisconsin Breast Cancer Dataset (WBCD) from the UCI ML Repository. It contains 569 patient samples and 30 features. This dataset has the following features: Radius, Texture, Area, Perimeter, Smoothness, Compactness, Concavity, and Fractional Dimension. The target variable is diagnosis, which is categorized as malignant vs. benign. Results: The fifteen models were analyzed, evaluated, and compared using five performance metrics: Accuracy, Precision, Recall, F1-Score, and AUC-ROC. Among the evaluated models, CatBoost, LoGR, and AdaBoost outperformed the others, with an Accuracy of 97.%, Precision of 97%, Recall of 97%, and AUC-ROC score of 99%. The AUC-ROC is nearly 99%, and the model has a high ability to differentiate between malignant and benign tumors. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 784 KB

Open AccessProceeding Paper

Forecasting PM2.5 Concentrations with Machine Learning: Accuracy, Efficiency, and Public Health Implications

by Kyriakos Ovaliadis, Spyridon Mitropoulos, Vassilios Tsiantos and Ioannis Christakis

Eng. Proc. 2026, 124(1), 36; https://doi.org/10.3390/engproc2026124036 - 16 Feb 2026

Viewed by 710

Abstract

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. [...] Read more.

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 996 KB

Open AccessProceeding Paper

TransQSAR-pf: A Bio-Informed QSAR Framework Using Plasmodium falciparum Stress Signatures for Enhanced Antiplasmodial Activity Prediction

by Favour O. Igwezeke and Charles O. Nnadi

Eng. Proc. 2026, 124(1), 37; https://doi.org/10.3390/engproc2026124037 - 13 Feb 2026

Viewed by 485

Abstract

Traditional QSAR modeling relies solely on molecular descriptors, neglecting the biological state of target organisms. While prior approaches have integrated biological data with molecular features for activity prediction, we developed TransQSAR-pf, a methodological framework that integrates Plasmodium falciparum transcriptomic stress signatures with molecular [...] Read more.

Traditional QSAR modeling relies solely on molecular descriptors, neglecting the biological state of target organisms. While prior approaches have integrated biological data with molecular features for activity prediction, we developed TransQSAR-pf, a methodological framework that integrates Plasmodium falciparum transcriptomic stress signatures with molecular descriptors to construct biologically informed activity prediction models. Applied to 125 triazolopyrimidine derivatives, the framework distilled 764 transcriptomic features into 13 key predictors through Boruta selection, constructing an interpretable model (R² = 0.762, RMSE = 0.470) that demonstrated improved performance over the baseline QSAR-only model (R² = 0.719, RMSE = 0.529). Biological mapping revealed that 71.2% of feature importance derived from conserved unknown-function genes, representing largely uncharacterized stress response pathways that correlate with compound efficacy and warrant experimental characterization, demonstrating the framework’s utility for generating mechanistic hypotheses. This work presents a novel computational pipeline for building biology-aware QSAR models that prioritize experimental targets for antimalarial discovery. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 1772 KB

Open AccessProceeding Paper

Design and Performance Analysis of Double-Gate TFETs Using High-k Dielectrics and Silicon Thickness Scaling for Low-Power Applications

by Pallabi Pahari, Sushanta Kumar Mohapatra, Jitendra Kumar Das and Om Prakash Acharya

Eng. Proc. 2026, 124(1), 38; https://doi.org/10.3390/engproc2026124038 - 19 Feb 2026

Cited by 1 | Viewed by 817

Abstract

Tunnel Field-Effect Transistors (TFETs) are being explored for ultra-low-power very-large-scale integrated circuits (VLSI) because their band-to-band tunnelling (BTBT) transport permits subthreshold swings (SS) below the 60 mV/dec thermionic limit at room temperature, along with significantly lower leakage than MOSFETs. This paper presents a [...] Read more.

Tunnel Field-Effect Transistors (TFETs) are being explored for ultra-low-power very-large-scale integrated circuits (VLSI) because their band-to-band tunnelling (BTBT) transport permits subthreshold swings (SS) below the 60 mV/dec thermionic limit at room temperature, along with significantly lower leakage than MOSFETs. This paper presents a systematic TCAD study of DG-TFETs that maps how four primary knobs–gate dielectric materials, silicon channel thickness, temperature variation, and different channel material shape key figures of merit: the ON current (I_ON), OFF current (I_OFF), threshold voltage (V_TH), SS, and the I_ON/I_OFF switching ratio. High-k gate enhances gate-to-channel coupling and boost tunnelling efficiency; rigorous body scaling enhances electrostatic control; and targeted source-proximal doping profiles elevate I_ON while minimizing leakage. We also measure the trade-offs between I_ON, SS, and I_OFF that occur when scaling is performed at the same time. This shows that careful coordination is needed instead of just tuning one parameter. This is a simulated work, and the physical models are calibrated to experimental TFET data and all parameters are checked against previously reported results. The device reaches SS = 31.4 mV/dec, V_TH = 0.46 V, I_ON = 5.91 × 10⁻⁵ A and an I_ON/I_OFF of about 4.5 × 10¹¹. This shows that it can switch quickly with little leakage. The design insights that come from this work provide useful advice regarding how to choose gate dielectric material, structures, and doping strategies to add DG-TFETs to the next generation of low-power semiconductor technologies. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 538 KB

Open AccessProceeding Paper

Effect of Cultivation Region on the Physicochemical and Quality Characteristics of Arabica Coffee (Red Bourbon Variety) from Bean to Brew

by Ivan Hrab, Anastasiia Sachko, Oksana Sema, Kristina Gavrysh and Yuriy Khalavka

Eng. Proc. 2026, 124(1), 39; https://doi.org/10.3390/engproc2026124039 - 20 Feb 2026

Viewed by 942

Abstract

Caffeine is one of the most well-known biologically active compounds in coffee beans, and its content largely determines the taste and stimulating properties of the drink. However, the amount of caffeine in beans can vary significantly depending on growing conditions, even within the [...] Read more.

Caffeine is one of the most well-known biologically active compounds in coffee beans, and its content largely determines the taste and stimulating properties of the drink. However, the amount of caffeine in beans can vary significantly depending on growing conditions, even within the same coffee variety. The growing global demand for coffee and the current market dynamics emphasize the necessity to investigate how the origin of coffee beans influences beverage quality. Arabica beans, particularly the Red Bourbon variety, are known to exhibit variations in chemical composition, sensory characteristics, and technological behavior depending on their cultivation environment. The study aimed to evaluate the physicochemical and sensory properties of Arabica Red Bourbon beans sourced from distinct geographic regions, considering factors such as altitude and local environmental conditions. The sensory characteristics of the resulting beverages were evaluated using the capping method, and water activity, density, moisture content, color, pH, extractivity and caffeine content were determined. Roasted bean color ranged from 61.4 to 62.5, while ground coffee color was 72.5–75.4. Moisture content was highest in Col and R (3.4%) and lowest in Con (3.1%). The greatest moisture loss during roasting occurred in S and R (13.4%). Water activity decreased from 0.50–0.56 in green beans to 0.18–0.30 post-roasting. Extraction yield ranged from 20.03 to 21.21%, and total dissolved solids (TDS) varied at 1.23–1.30%. The least acidic sample was S (pH 5.04). Colombian beans contained unusually high caffeine. The conducted research confirmed that the geographical origin of Arabica Red Bourbon beans significantly impacts their physicochemical and sensory attributes. Variations in moisture, acidity, and caffeine content were observed among the samples, despite a consistent roasting profile. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 1832 KB

Open AccessProceeding Paper

IOTAfy: An ESP32-Based OTA Firmware Management Platform for Scalable IoT Deployments

by Ioannis Chrysovalantis Panagou, Stylianos Katsoulis, Evangelos Nannos, Fotios Zantalis and Grigorios Koulouras

Eng. Proc. 2026, 124(1), 40; https://doi.org/10.3390/engproc2026124040 - 19 Feb 2026

Viewed by 1117

Abstract

Managing firmware updates in large-scale IoT deployments presents significant challenges regarding security, reliability, and operational cost. This paper presents the design and implementation of “IOTAfy”, a new, open-source, comprehensive, Over-The-Air (OTA) firmware management platform tailored for ESP32-based IoT devices. “IOTAfy” distinguishes itself through [...] Read more.

Managing firmware updates in large-scale IoT deployments presents significant challenges regarding security, reliability, and operational cost. This paper presents the design and implementation of “IOTAfy”, a new, open-source, comprehensive, Over-The-Air (OTA) firmware management platform tailored for ESP32-based IoT devices. “IOTAfy” distinguishes itself through its efficient database design and asynchronous update mechanism, enabling scalable deployments. The proposed system comprises a device-side solution for ESP32 microcontrollers, incorporating a custom bootloader, an application firmware boilerplate code, and an OTA library, alongside a web-based management interface developed using PHP, SQLite3, and Bootstrap. This integrated approach facilitates secure and reliable OTA application firmware updates. The platform enables centralized monitoring of device status, scheduled firmware updates, and robust version control for multiple IoT devices. OTA updates are implemented directly on the ESP32, eliminating the need for physical access or intervention. The web interface provides administrators with features for group upgrades, rollback capabilities, real-time update status monitoring and alerts. The system was tested in a controlled environment with 50 ESP32 devices, achieving a 98% success rate for OTA updates. The results demonstrate the potential for significant cost savings and reduced maintenance time compared to manual update processes. The architecture of the “IOTAfy” platform, leveraging an efficient database design and asynchronous update mechanism, facilitates scalability to hundreds or thousands of devices. This work offers a practical and scalable solution for managing firmware in large-scale IoT deployments, contributing to enhanced device security, improved reliability, and reduced operational expenditures. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 6390 KB

Open AccessProceeding Paper

Satellite Vegetation Monitoring Challenges for Oil Pollution in the Niger Delta Community

by Jennifer Akuchinyere Anucha, Bhaskar Das, Sandhya Patidar, Ambrose Onne Okpu, Ikenna Light Nkwocha and Bhaskar Sen Gupta

Eng. Proc. 2026, 124(1), 41; https://doi.org/10.3390/engproc2026124041 - 22 Feb 2026

Viewed by 890

Abstract

Monitoring vegetation and land cover changes over time in oil-impacted regions is crucial for assessing ecological degradation and informing remediation options. This study aimed to identify the challenges encountered when using Landsat imagery to detect changes in vegetation health and land cover in [...] Read more.

Monitoring vegetation and land cover changes over time in oil-impacted regions is crucial for assessing ecological degradation and informing remediation options. This study aimed to identify the challenges encountered when using Landsat imagery to detect changes in vegetation health and land cover in Bodo, a hydrocarbon-impacted community in the Niger Delta region of Nigeria, over a 20-year period. Landsat 7 ETM+ and Landsat 8 OLI imagery were used to derive the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) from 2003 to 2023. Data continuity was affected by the Landsat 7 Scan Line Corrector malfunction in the 2008 images and by high cloud coverage in the Landsat 8 OLI 2013 images. Hence, 2008 and 2013 were excluded from the analysis, limiting multi-year comparisons. Results from the available years indicated that NDBI values increased gradually, suggesting minor urban expansion. Stable but low NDWI levels suggest water stress, while changing NDVI values indicate alterations in vegetative health. However, this study highlights observable environmental changes and the challenges involved in using satellite imagery for environmental monitoring in oil-impacted regions, underscoring the need for improved cloud-masking methodologies and radar datasets to enhance long-term environmental assessment. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 2494 KB

Open AccessProceeding Paper

Study on the Surface Quality of Quartz Glass Ground Using Trochoidal Trajectory with Cup Wheel Grinding

by Pengcheng Zhao, Bin Lin, Jingguo Zhou and Tianyi Sui

Eng. Proc. 2026, 124(1), 42; https://doi.org/10.3390/engproc2026124042 - 24 Feb 2026

Viewed by 556

Abstract

With regard to space telescopes, the processing of large optical mirrors has always been a highlight in the field of optical processing. These mirrors are typically made of hard and brittle materials such as quartz glass, microcrystalline glass, and silicon carbide. These materials [...] Read more.

With regard to space telescopes, the processing of large optical mirrors has always been a highlight in the field of optical processing. These mirrors are typically made of hard and brittle materials such as quartz glass, microcrystalline glass, and silicon carbide. These materials have long been considered challenging to work with due to their processing efficiency and propensity for damage. This study proposes a trochoid model considering the actual motion trajectory of the cup wheel with discrete consolidated abrasive grains. Through the establishment of a process parameter–mathematical model to establish the multi-grain coupled motion trajectory, the uniformity of the trajectory is optimized to increase the material removal rate and reduce the surface damage caused by abrasive interference. The results show that the process parameter optimization using this model can effectively reduce the surface roughness of quartz glass grinding. The surface and sub-surface damage caused by grinding stress are significantly reduced, and the edge fracture area of quartz glass is decreased. The large contact area at the end face of the cup-grinding wheel enables a larger grinding depth while ensuring that cracks do not extend to the sub-surface, improving the overall surface integrity of the mirror. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 2548 KB

Open AccessProceeding Paper

Optimization of Ultrasound-Assisted Solvothermal Synthesis of N-Doped Carbon Dots Derived from Water Hyacinth (Pontederia crassipes) for Carbon Monoxide Sensing

by Maria Angeline Magalong, Shayne Ruzzel Galvez, Kristine Oira Flordeliza, Jenuelle Lui Caballero, Peniel Jean Gildo and Rugi Vicente Rubi

Eng. Proc. 2026, 124(1), 43; https://doi.org/10.3390/engproc2026124043 - 24 Feb 2026

Viewed by 747

Abstract

Carbon monoxide (CO) is an odorless, colorless, and toxic gas that requires effective detection due to health risks upon exposure. This study investigates the synthesis of nitrogen-doped carbon dots (N-CDs) from water hyacinth using an ultrasound-assisted solvothermal method for CO sensing. A Box–Behnken [...] Read more.

Carbon monoxide (CO) is an odorless, colorless, and toxic gas that requires effective detection due to health risks upon exposure. This study investigates the synthesis of nitrogen-doped carbon dots (N-CDs) from water hyacinth using an ultrasound-assisted solvothermal method for CO sensing. A Box–Behnken design under response surface methodology (RSM) optimized the synthesis parameters at 177 C, 6.25 h, and 2.62 g dopant, achieving a maximum quantum yield of 20.15%. UV-vis and PL analysis confirmed successful nitrogen doping and stable excitation-independent photoluminescence. FESEM-EDX revealed spherical to quasi-spherical particles ranging from 8 to 55 nm with carbon, nitrogen, and oxygen composition. Gas sensing results revealed enhanced CO response for N-doped CDs compared to undoped CDs due to improved charge transfer and increased adsorption sites, demonstrating their potential for CO detection at low concentrations. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 865 KB

Open AccessProceeding Paper

Upcycling Spent Palm Oil into High-Performance Polyurethane Adhesives for Dimensionally Stable Bagasse Particleboards

by June Marxis Binasoy, Sherwin Kent Compuesto, Jhanine Dungca, Charlene Elaisa Gravador, Rose Mae Mirabueno, Janelou Marielle Rosaldo, Andrea Salvador, Jerry Olay, Rugi Vicente Rubi and Rich Jhon Paul Latiza

Eng. Proc. 2026, 124(1), 44; https://doi.org/10.3390/engproc2026124044 - 24 Feb 2026

Viewed by 470

Abstract

The construction industry faces intensifying pressure to mitigate its environmental impact, particularly concerning the reliance on non-biodegradable materials and hazardous formaldehyde-based adhesives. Although bio-based alternatives are emerging, many still depend on virgin feedstocks, and the valorization rates for abundant waste streams like used [...] Read more.

The construction industry faces intensifying pressure to mitigate its environmental impact, particularly concerning the reliance on non-biodegradable materials and hazardous formaldehyde-based adhesives. Although bio-based alternatives are emerging, many still depend on virgin feedstocks, and the valorization rates for abundant waste streams like used cooking oil remain critically low. To bridge this gap, this study developed a sustainable, formaldehyde-free Modified Reused Palm Oil-Polyurethane (MRPO-PU) adhesive specifically for binding sugarcane bagasse particleboards. The synthesis process involved filtering used palm oil and subjecting it to epoxidation and hydroxylation reactions to yield a functional bio-polyol, the chemical structure of which was validated via Fourier Transform Infrared Spectroscopy (FTIR). This bio-polyol was subsequently mixed with polymeric diphenylmethane diisocyanate (pMDI) and combined with alkali-treated bagasse at varying adhesive ratios ranging from 15 to 85 wt%. Physical and mechanical evaluations demonstrated a robust positive correlation between adhesive content and composite integrity. Specifically, increasing the adhesive loading enhanced density up to 444 kg/m³ and minimized thickness swelling to 5.1%, while flexural and compressive strengths significantly improved. The data suggests an optimal efficiency range between 45 and 55 wt%. Ultimately, this research validates a dual-waste valorization strategy, offering a scalable circular economy model that transforms agricultural residues and spent oils into high-performance, eco-friendly construction materials. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 733 KB

Open AccessProceeding Paper

Synthesis, Spectral Characteristics, and Molecular Structure of N-(2,2,2-Trichloro-1-((4-phenylthiazol-2-yl)amino)ethyl)carboxamides

by Yelyzaveta R. Lomynoha, Pavlo V. Zadorozhnii, Pavlo V. Romanenko, Vadym V. Kiselev, Oxana V. Okhtina and Aleksandr V. Kharchenko

Eng. Proc. 2026, 124(1), 45; https://doi.org/10.3390/engproc2026124045 - 24 Feb 2026

Viewed by 721

Abstract

1,3-Thiazole derivatives are of interest in pharmacy, medicine, and agriculture as potential biologically active substances. We have proposed for the first time a convenient and effective method for the synthesis of amidoalkylated derivatives of 2-amino-1,3-thiazole. This approach is based on the reaction of [...] Read more.

1,3-Thiazole derivatives are of interest in pharmacy, medicine, and agriculture as potential biologically active substances. We have proposed for the first time a convenient and effective method for the synthesis of amidoalkylated derivatives of 2-amino-1,3-thiazole. This approach is based on the reaction of amidoalkylated thioureas with α-halocarbonyl compounds. The reaction was carried out under stirring at 20 °C in ethanol with the addition of an equimolar amount of triethylamine to bind the released hydrogen halide. The yield of the obtained 1,3-thiazole derivatives was 68–75%. An attempt to carry out a counter-synthesis by amidoalkylation of the corresponding 2-amino-1,3-thiazole derivative was unsuccessful due to strong resinification of the reaction mass. The structure of the compounds obtained was confirmed by ¹H and ¹³C NMR spectroscopy. The structure was finally confirmed by X-ray structural analysis performed for N-(2,2,2-trichloro-1-((4-phenylthiazol-2-yl)amino)ethyl)acetamide. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 1244 KB

Open AccessProceeding Paper

Formulation Strategies for Mayonnaise-Type Sauces: The Role of Hydrocolloid Combinations

by Anastasiia Sachko and Oksana Sema

Eng. Proc. 2026, 124(1), 46; https://doi.org/10.3390/engproc2026124046 - 18 Feb 2026

Viewed by 709

Abstract

The aim of this study was to investigate the substitution of egg yolk in mayonnaise-type sauces with alternative protein components and to optimize the hydrocolloid composition for improved stability and rheological properties. Mustard powder (1%), soybean flour (1%), casein (2%), and cream powder [...] Read more.

The aim of this study was to investigate the substitution of egg yolk in mayonnaise-type sauces with alternative protein components and to optimize the hydrocolloid composition for improved stability and rheological properties. Mustard powder (1%), soybean flour (1%), casein (2%), and cream powder (1%) blends were employed as emulsifiers. The influence of the ratio of potato starch, carboxymethylcellulose (CMC), pectin, and xanthan gum (0–1% each) on the properties of low-fat mayonnaise formulations with 30% oil content was examined. Sedimentation and thermal stability tests revealed high resistance of all samples (98–99%) after 24 h of storage. Optical microscopy confirmed a homogeneous structure with individual dispersed particles of 100–150 μm corresponding to plant protein inclusions. The particle size distribution D [3,4] exhibited a bimodal profile with peaks at 0.1–1 μm and 2–8 μm, indicating efficient homogenization. Storage experiments demonstrated an increase in particle size by 1.4–1.6 times and a decrease in viscosity, likely due to flocculation and aggregation of polysaccharide clusters into larger agglomerates. Among the tested formulations, the sample containing 0.3% CMC, 0.3% xanthan gum, and 0.4% pectin showed the most favorable physicochemical and sensory properties, highlighting the synergistic effect of hydrocolloid blends in stabilizing reduced-fat mayonnaise-type emulsions. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

6 pages, 908 KB

Open AccessProceeding Paper

Fabrication of Supercapacitor Based on Conducting Polyaniline and Graphene Oxide Nanocomposites

by Achanai Buasri, Montree Sangthongdee, Rattaruj Chodsatidpokin, Sunisa Chamnanwichit and Vorrada Loryuenyong

Eng. Proc. 2026, 124(1), 47; https://doi.org/10.3390/engproc2026124047 - 25 Feb 2026

Viewed by 593

Abstract

This study aims to enhance and develop the properties of materials used as supercapacitors. The synthesis of graphene oxide (GO) was achieved via a modified Hummer’s method, whereas the fabrication of polyaniline (PANI)/GO nanocomposites was conducted utilizing an in situ chemical polymerization technique. [...] Read more.

This study aims to enhance and develop the properties of materials used as supercapacitors. The synthesis of graphene oxide (GO) was achieved via a modified Hummer’s method, whereas the fabrication of polyaniline (PANI)/GO nanocomposites was conducted utilizing an in situ chemical polymerization technique. Subsequently, the PANI/GO layered films were deposited on fluorine-doped tin oxide (FTO) glass for supercapacitor applications. The materials were analyzed using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and cyclic voltammetry (CV). The experimental results demonstrated that a reaction time of 30 min, along with a weight ratio of aniline (ANI) monomer to GO of 1:1.5, yielded an optimal specific capacitance of 13.30 F/g. The robust electrochemical performance of the PANI/GO electrode may stem from the increased active sites for PANI deposition, attributable to the large surface areas of GO. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Graphical abstract

10 pages, 3091 KB

Open AccessProceeding Paper

Energy Absorption Characteristics of Biodegradable and Recyclable Composite with Interlocking Periodic Honeycomb Sandwich Structure

by Quanjin Ma, Mohd Ruzaimi Mat Rejab, Nasrul Hadi, Yiheng Song, Sivasubramanian Palanisamy and Zahidah Ansari

Eng. Proc. 2026, 124(1), 48; https://doi.org/10.3390/engproc2026124048 - 25 Feb 2026

Viewed by 500

Abstract

The demand for biodegradable, recyclable, natural composites with lightweight structures is driven by the fact that advanced structures can withstand quasi-static and dynamic loadings. This study examined the energy-absorbing characteristics of interlocking periodic honeycomb sandwich structures made from short sugar palm, kenaf, and [...] Read more.

The demand for biodegradable, recyclable, natural composites with lightweight structures is driven by the fact that advanced structures can withstand quasi-static and dynamic loadings. This study examined the energy-absorbing characteristics of interlocking periodic honeycomb sandwich structures made from short sugar palm, kenaf, and pineapple leaf fibres (PALFs) reinforced with a polylactic acid (PLA) composite. The biodegradable sugar palm, kenaf, and PALF/PLA composite sheets were subjected to hot compression and cut into single- and double-slot square plates. The interlocking technique was used to assemble periodic two-dimensional square-honeycomb sandwich structures. Moreover, new and recyclable PLA-based composites with three fibres were tested for tensile properties. The biodegradable PLA-based composite honeycomb sandwich structure underwent a quasi-static compression test. Finite element modelling was used to simulate the load–displacement curve, energy-absorption characteristics, and failure behaviour, incorporating tensile properties and geometric imperfections. The results revealed that the double-slot design of the pineapple/PLA sandwich structure significantly increased by 1.33 times compared to the sugar palm/PLA sandwich structure. Notably, it reduced the compressive strength of recyclable pineapple/PLA (66.4%) and recyclable sugar palm/PLA (31.5%) composite sandwich structures compared to the new pineapple/sugar palm PLA-based composite. In addition, finite element analysis (FEA) showed reasonable agreement with experimental data, with a 7.11% error in energy absorption (EA). It was highlighted that biodegradable, recyclable, interlocking sandwich-structured composites have potential for advanced, sustainable energy-absorbing structures. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 4498 KB

Open AccessProceeding Paper

An Automated Medical Diagnosis System for Neoplasm Medical Image Classification Using Supervised and Unsupervised Techniques

by Sreedhar Kumar Seetharaman, Basant Kumar, Manjunath Chikkanjinappa Rajanna and Syed Thouheed Ahmed

Eng. Proc. 2026, 124(1), 49; https://doi.org/10.3390/engproc2026124049 - 11 Feb 2026

Cited by 9 | Viewed by 373

Abstract

In this research, an improved automated medical prediction system, namely, the Neoplasm Medical Image Classification System (NMICS), is proposed. The proposed NMICS aims to robotically identify whether the given test magnetic resonance image (MRI) belongs to the tumor group or the non-tumor group [...] Read more.

In this research, an improved automated medical prediction system, namely, the Neoplasm Medical Image Classification System (NMICS), is proposed. The proposed NMICS aims to robotically identify whether the given test magnetic resonance image (MRI) belongs to the tumor group or the non-tumor group using machine learning techniques. The proposed NMICS is divided into two stages, namely, the Train Medical Image Model (TMIM) and the Medical Image Prediction Stage (MIPS), respectively. In the TMIM stage, the NMICS performs various distinct operations including improving input medical image data set quality and consistency through standard arithmetic operations; extracting specific features (edge) from every individual medical image in the input medical image set using the CNN method; and separating the feature vector set of the input medical image set into two distinct clusters, namely, tumor and non-tumor, respectively, using the unsupervised k-means clustering technique. In the MIPS stage, the proposed (NMIC) system performs the same types of operations, including preprocessing and feature extraction, on the test medical image samples. Next, the NMICS maps and classifies the feature vector of the test medical image sample against trained medical image data set clusters using a KNN classifier. The investigation results show that the NMICS is well-suited to diagnosing whether the given medical image is grouped into the neoplasm category or the non-neoplasm group. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

14 pages, 1328 KB

Open AccessProceeding Paper

An Intelligent Prediction–Optimization Framework for Free Chlorine Removal from Industrial Wastewater Using Activated Carbon Filtration

by Alisher Rakhimov, Rustam Bozorov, Shuhrat Mutalov, Jaloliddin Eshbobaev, Mirjalol Yusupov, Farida Islomova and Bokhodir Yunusov

Eng. Proc. 2026, 124(1), 50; https://doi.org/10.3390/engproc2026124050 - 26 Feb 2026

Viewed by 586

Abstract

Free chlorine removal from industrial wastewater using activated carbon filtration requires accurate modeling and optimal control to balance treatment efficiency and adsorbent consumption. In this study, a combined experimental–machine learning–optimization framework was developed to predict and optimize residual chlorine concentration in a pilot-scale [...] Read more.

Free chlorine removal from industrial wastewater using activated carbon filtration requires accurate modeling and optimal control to balance treatment efficiency and adsorbent consumption. In this study, a combined experimental–machine learning–optimization framework was developed to predict and optimize residual chlorine concentration in a pilot-scale activated carbon filtration unit. A total of 200 experimental runs were collected using a pilot activated carbon filtration system by varying flow rate, initial chlorine concentration, pressure, pH, temperature, and carbon dose. Two ensemble learning models, Random Forest (RF) and Gradient Boosting (GB), were trained and validated using five-fold cross-validation. Both models exhibited high predictive accuracy, with GB outperforming RF on the full dataset (R² = 0.9995, Root Mean Square Error (RMSE) = 0.0355 mg·L⁻¹, Mean Absolute Error (MAE) = 0.0276 mg·L⁻¹) and on the independent test set (R² = 0.9417). Feature importance and partial dependence analyses revealed that the initial chlorine concentration and activated carbon dose were the dominant controlling variables, while increasing flow rate led to higher residual chlorine levels. A multi-objective optimization strategy based on Pareto dominance was implemented using the trained GB model as a surrogate to simultaneously minimize residual chlorine and carbon consumption. The optimal compromise solution corresponded to an activated carbon dose of approximately 51.5 kg and a residual chlorine concentration of 0.156 mg·L⁻¹ at a flow rate of 43.1 m³·h⁻¹. The proposed framework demonstrates a reliable and cost-effective approach for predictive control and sustainable optimization of dechlorination processes in industrial wastewater treatment. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 557 KB

Open AccessProceeding Paper

Moderation Effects: How Body Length Modifies the Effect of Temperature on Parasite Abundance

by Svitlana Shvydka, Martin Kalina, Ievgen Tkach and Volodimir Sarabeev

Eng. Proc. 2026, 124(1), 51; https://doi.org/10.3390/engproc2026124051 - 2 Mar 2026

Viewed by 516

Abstract

Accurate testing and interpretation of interaction effects are essential for a robust understanding of the dynamics of biological systems. However, exclusive reliance on the statistical significance of the interaction term in regression models can lead to either an underestimation or an overestimation of [...] Read more.

Accurate testing and interpretation of interaction effects are essential for a robust understanding of the dynamics of biological systems. However, exclusive reliance on the statistical significance of the interaction term in regression models can lead to either an underestimation or an overestimation of interaction effects. Using empirical data of monogenean ectoparasite abundance in the Pacific so-iuy mullet host, Planiliza haematocheila (Temminck & Schlegel, 1845), we detected context-dependent patterns that standard coefficients failed to capture fully. Specifically, our findings suggest that the temperature–abundance relationship is not uniform across all body length–age classes but is modulated by host ontogeny/size, a pattern only resolved by explicitly evaluating conditional effects. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

19 pages, 4439 KB

Open AccessProceeding Paper

Comparative Analysis of Machine-Learning and Deep-Learning Approaches for Accurate Animal Disease Prediction and Health Risk Assessment

by Bhagyashree Panigrahy, Akhil Subudhi, Tanushree Harichandan, Neelamadhab Padhy and Rasmita Panigrahi

Eng. Proc. 2026, 124(1), 52; https://doi.org/10.3390/engproc2026124052 - 2 Mar 2026

Viewed by 1207

Abstract

Effective, efficient, and early animal disease prediction is a challenging task. Identifying and reducing animal health risks is important for preventing disease outbreaks and improving cattle management. This study presents the machine-learning and hybrid deep-learning models for animal risk prediction. We employed eight [...] Read more.

Effective, efficient, and early animal disease prediction is a challenging task. Identifying and reducing animal health risks is important for preventing disease outbreaks and improving cattle management. This study presents the machine-learning and hybrid deep-learning models for animal risk prediction. We employed eight classifiers (Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbors, Gaussian Naive Bayes, and Random Forest) along with feature-enhanced hybrid variants (RF–CNN and RF–ANN) to early detect risk to animals’ health. Our main objective is to develop and evaluate robust ML models for predicting animal health risks. Apart from these, we also present a comparative study of the conventional and hybrid models to construct a decision support system for early disease prediction. The experimental work reveals that RF obtained the highest accuracy of 95.77%, a macro F1-score of 0.9343, and a weighted F1-score of 0.9515. We also conduct the statistical test to confirm the robustness of the model for animal disease prediction. The proposed framework provides a scalable, interpretable decision-support system for real-world animal health monitoring and early disease intervention. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 207 KB

Open AccessProceeding Paper

Techno-Environmental Perspectives on Chemical Looping for Green Hydrogen: Oxygen Carrier Performance and Life Cycle Implications

by Alejandra Balaguera Quintero, María Camila Zapata Chaverra and Gloria Isabel Carvajal

Eng. Proc. 2026, 124(1), 53; https://doi.org/10.3390/engproc2026124053 - 28 Feb 2026

Viewed by 445

Abstract

Green hydrogen has emerged as a key energy vector for decarbonizing industrial sectors that are difficult to electrify. Among the available production pathways, Chemical Looping technologies have attracted increasing attention due to their intrinsic CO₂ capture capability, high energy efficiency, and flexibility [...] Read more.

Green hydrogen has emerged as a key energy vector for decarbonizing industrial sectors that are difficult to electrify. Among the available production pathways, Chemical Looping technologies have attracted increasing attention due to their intrinsic CO₂ capture capability, high energy efficiency, and flexibility in feedstock utilization. This review provides a comprehensive analysis of recent advances in Chemical Looping processes for green hydrogen production, with particular emphasis on the role of oxygen carriers and the integration of environmental and life cycle assessment perspectives. The main Chemical Looping configurations are discussed, along with the performance and sustainability challenges associated with different oxygen carrier materials. In addition, the relevance of Life Cycle Assessment as a decision-support tool for identifying environmental hotspots and improvement pathways is examined. By integrating technological and environmental insights, this review identifies current research gaps and outlines future directions for the sustainable deployment of Chemical Looping systems within the hydrogen economy. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

12 pages, 1940 KB

Open AccessProceeding Paper

Visible-Light-Induced Photocatalytic Degradation of Fast Green FCF and Orange II Dye by Yb₂O₃ Nanoparticles

by Nashra Fatima, Ekhlakh Veg and Tahmeena Khan

Eng. Proc. 2026, 124(1), 54; https://doi.org/10.3390/engproc2026124054 - 5 Mar 2026

Viewed by 577

Abstract

The quest for efficient photocatalytic materials for eliminating synthetic dyes like Orange II Sodium Salt and Fast Green FCF (For Coloring Food) has been spurred by the mounting environmental problems associated with these dyes. Due to their excellent electrical characteristics, thermal stability, and [...] Read more.

The quest for efficient photocatalytic materials for eliminating synthetic dyes like Orange II Sodium Salt and Fast Green FCF (For Coloring Food) has been spurred by the mounting environmental problems associated with these dyes. Due to their excellent electrical characteristics, thermal stability, and potential to reduce electron–hole recombination, ytterbium oxide (Yb₂O₃) and other rare-earth metal oxides are gaining popularity. This study synthesized Yb₂O₃ nanoparticles and assessed their photocatalytic activity when exposed to visible light. Significant degradation efficiencies were revealed by the spectrophotometric analysis, suggesting that Yb₂O₃ is an effective nanocatalyst for dye remediation applications. The results demonstrate how rare-earth-based nanomaterials can improve environmentally friendly and sustainable wastewater treatment methods, supporting ongoing environmental cleanup initiatives. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 252 KB

Open AccessProceeding Paper

Enhancing Candidate Generation in Recommendation Systems Through LLM-Powered Semantic Enrichment in a Distributed Environment

by Balagangadhar Reddy Kandula and Lija Jacob

Eng. Proc. 2026, 124(1), 55; https://doi.org/10.3390/engproc2026124055 - 6 Mar 2026

Viewed by 1248

Abstract

Effective candidate generation is a critical component of two-stage recommender systems; however, traditional methods such as Term Frequency–Inverse Document Frequency (TF-IDF) often fail to capture deep semantic context. This limitation leads to suboptimal recall rates, particularly for new or niche items—a challenge commonly [...] Read more.

Effective candidate generation is a critical component of two-stage recommender systems; however, traditional methods such as Term Frequency–Inverse Document Frequency (TF-IDF) often fail to capture deep semantic context. This limitation leads to suboptimal recall rates, particularly for new or niche items—a challenge commonly referred to as the cold start problem—thereby degrading overall recommendation quality and user experience. This study proposes a semantically aware approach to improve the initial recall phase of recommendation pipelines. The methodology integrates Large Language Models (LLMs) into a distributed Apache Spark pipeline for large-scale content enrichment, generating 768-dimensional vector embeddings and concise, context-aware summaries for each content item. These enriched representations are indexed in Elasticsearch to enable efficient vector-based retrieval during candidate generation. Quantitative evaluation on a corpus of 143,000 Wikipedia articles demonstrates that the LLM-enriched method achieves a Recall@10 of 62%, representing a 37% relative improvement over the TF-IDF baseline (45%). When relevance is measured using only embedding-independent signals (category overlap and keyword similarity), the method still achieves a Recall@10 of 58%, confirming that gains are not an artifact of the evaluation metric. The resulting candidate pools exhibit improved semantic diversity and broader category coverage, delivering richer input for downstream ranking models. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 4864 KB

Open AccessProceeding Paper

Investigation of Mediterranean Cyclones and Their Contribution to Heavy Precipitation over North Africa Using ERA5 Reanalysis Data

by Amal Saber El-Sehwagy, Zeinab Salah, Magdy M. Abdel Wahab, Moetasm H. ElTaweel and Albenis Pérez-Alarcón

Eng. Proc. 2026, 124(1), 56; https://doi.org/10.3390/engproc2026124056 - 6 Mar 2026

Viewed by 657

Abstract

Mediterranean cyclones over North Africa were analyzed using CyTRACK and ERA5 reanalysis data for the period 2015–2025. Cyclones were classified by minimum sea level pressure into Very Deep, Deep, Moderate, and Weak categories, and their structural characteristics—including spatial extent, lifetime, and associated synoptic-scale [...] Read more.

Mediterranean cyclones over North Africa were analyzed using CyTRACK and ERA5 reanalysis data for the period 2015–2025. Cyclones were classified by minimum sea level pressure into Very Deep, Deep, Moderate, and Weak categories, and their structural characteristics—including spatial extent, lifetime, and associated synoptic-scale systems—were examined. The relationship between cyclone activity and monthly precipitation was assessed for Morocco, Algeria, Tunisia, Libya, and Egypt, revealing substantial spatial variability in rainfall response. Egypt exhibited the strongest correspondence between cyclone frequency and precipitation, while other countries showed weaker or inconsistent associations, highlighting the role of cyclone intensity and moisture availability in driving regional hydroclimatic impacts. This intensity-resolved, region-specific analysis provides a comprehensive view of Mediterranean cyclone behavior and its influence on rainfall extremes, offering a valuable framework for improved forecasting, risk assessment, and climate resilience planning in the southern Mediterranean. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 255 KB

Open AccessProceeding Paper

Adaptive Multimodal LSTM with Online Learning for Evolving IoT Data Streams

by Osaretin Edith Okoro, Nurudeen Mahmud Ibrahim, Prema Kirubakan and Suleiman Aliyu Muhammad

Eng. Proc. 2026, 124(1), 57; https://doi.org/10.3390/engproc2026124057 - 7 Mar 2026

Viewed by 603

Abstract

The Internet of Things (IoT) uses networked devices, dispersed sensors, and cameras to create huge, diverse data streams. Concept drift, in which the underlying data distribution shifts over time, is frequently caused by the non-stationary and multimodal character of these streams. Static machine [...] Read more.

The Internet of Things (IoT) uses networked devices, dispersed sensors, and cameras to create huge, diverse data streams. Concept drift, in which the underlying data distribution shifts over time, is frequently caused by the non-stationary and multimodal character of these streams. Static machine learning models, based on fixed data distributions, reduce forecast accuracy and system reliability since they are unable to adapt to such changes. This paper proposes an Adaptive Multimodal Long Short-Term Memory (AM-LSTM) architecture to address these challenges by combining modality-specific temporal modelling, attention-based dynamic fusion, and drift-aware online learning. An attention mechanism adaptively weights informative streams to mitigate the impact of noisy or missing input, while specialist LSTM encoders capture the temporal correlations of each modality. Concept drift is detected using a sliding-window error monitoring technique, and adaptive learning rate adjustment and selective retraining are started when significant distributional changes occur. The proposed system is tested under synthetic drift conditions using the Edge-IoT and UNSW-NB15 benchmark datasets. Experimental results demonstrate that AM-LSTM achieves 88.7% accuracy and an F1-score of 0.85, adapting to drift within 620 samples while maintaining an average update latency of 47 ms per batch. Compared with static and existing adaptive baselines, the proposed approach provides improved robustness, faster drift adaptation, and computational efficiency suitable for real-time IoT environments. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

6 pages, 388 KB

Open AccessProceeding Paper

Purification of Waste Cooking Oil Using Coconut Shell Derived Activated Carbon: Reduction in Free Fatty Acids and Quality Enhancement

by Lasitha Madhusanka, Lakshan Priyankara, Isuranga Abejeewa, Rumesha Thathsarani, Helitha Nilmalgoda, Ashan Induranga, Chanaka Galpaya and Kaveenga Koswattage

Eng. Proc. 2026, 124(1), 58; https://doi.org/10.3390/engproc2026124058 - 4 Mar 2026

Viewed by 924

Abstract

Waste cooking oil (WCO) from households and the food industry causes environmental pollution when improperly disposed of. Repeated oil use leads to oxidation, hydrolysis, and polymerization, increasing free fatty acids (FFAs) and degrading quality. This study evaluated coconut shell-derived activated carbon for WCO [...] Read more.

Waste cooking oil (WCO) from households and the food industry causes environmental pollution when improperly disposed of. Repeated oil use leads to oxidation, hydrolysis, and polymerization, increasing free fatty acids (FFAs) and degrading quality. This study evaluated coconut shell-derived activated carbon for WCO purification. Activated carbon was produced by carbonization at 450 °C for 3 h and KOH activation. Adsorption experiments used 2.5% (w/w) adsorbent in 450 mL WCO for 30–90 min. FFAs decreased from 0.286% to 0.049% (82.9%), pH increased from 4.62 to 5.91, and calorific value rose from 8981 to 9019 Cal/g, demonstrating suitability for biodiesel and circular economy applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 1065 KB

Open AccessProceeding Paper

Reconfigurable Metasurface-Enabled AIoT Framework for Intelligent and Sustainable Smart Cities

by Shubham Gupta and Suhaib Ahmed

Eng. Proc. 2026, 124(1), 59; https://doi.org/10.3390/engproc2026124059 - 9 Mar 2026

Viewed by 538

Abstract

The fast growth of smart city systems requires sensing and intelligence systems that are dynamic, power-efficient, and have capabilities of real-time decision-making. The traditional IoT-based smart city systems are subject to constraints like nonflexible sensing architectures, high energy use, and high-latency because of [...] Read more.

The fast growth of smart city systems requires sensing and intelligence systems that are dynamic, power-efficient, and have capabilities of real-time decision-making. The traditional IoT-based smart city systems are subject to constraints like nonflexible sensing architectures, high energy use, and high-latency because of processing on clouds. To solve these problems, in this paper, a reconfigurable metasurface-based Artificial Intelligence of Things (AIoT) architecture of smart cities is proposed. The proposed system incorporates programmable electromagnetic metasurface-based sensing, edge-level Artificial Intelligence, and AIoT gateways to implement ultra-sensitive sensing, low-latency analytics, and effective resource utilization. A computer algorithm with a hybrid realization between metasurface physics and neural network-based learning can be used to improve the accuracy and flexibility of sensing. The experimental analysis with publicly available data of a smart city proves that the proposed framework can attain an accuracy of sensing in the range of 92% and 97%, by far surpassing traditional IoT sensors, with 78% and 83% as the accuracy limits. Moreover, the suggested system shortens end-to-end latency to as low as 3645 ms, as compared to 8490 ms, and also reduces the power usage. The improved sensing efficiency, which is defined as the ratio of power consumption to accuracy, is obtained in all test conditions. These findings validate that the suggested AIoT framework, powered by the metasurface, can be used to offer a scalable and low-latency solution that uses less energy when it is deployed in applications linked to smart cities of the next generation. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 3347 KB

Open AccessProceeding Paper

Comparison of Magnetic Data from Swarm and CSES Satellites Flying in Opposite Hemispheres on the Occasion of Pi2 Pulsations

by Dedalo Marchetti, Essam Ghamry and Daniele Bailo

Eng. Proc. 2026, 124(1), 60; https://doi.org/10.3390/engproc2026124060 - 9 Mar 2026

Viewed by 892

Abstract

Swarm is a three-satellite mission operated by the European Space Agency to monitor the Earth’s magnetic field. The China Seismo-Electromagnetic Satellite (CSES) is a satellite dedicated to studying the possible seismo-induced effects of earthquake activity on the ionosphere, operated by the China National [...] Read more.

Swarm is a three-satellite mission operated by the European Space Agency to monitor the Earth’s magnetic field. The China Seismo-Electromagnetic Satellite (CSES) is a satellite dedicated to studying the possible seismo-induced effects of earthquake activity on the ionosphere, operated by the China National Space Administration in cooperation with the Italian Space Agency. Such satellites are placed in Low Earth Orbit at an altitude ranging from 460 km to 510 km. We selected orbital combinations with the Swarm satellite in one hemisphere and CSES-01 in the opposite one to study the impact of magnetic pulsations on the ionospheric environment. The data have been filtered in the frequency range of Pi2 pulsations (period between 40 s and 150 s). Similar oscillations of a few nanoTeslas of the magnetic field intensity were detected by both satellites, sometimes in phase and at other times in counterphase. Detected oscillations could be explained by interactions between the Sun’s and Earth’s magnetic fields or the effect of a satellite crossing the auroral ring currents at the Northern and Southern Poles. This work supports the cross-validation of magnetic data from multiple satellite missions in Low Earth Orbit, such as Swarm and CSES. Our results confirm the scientific reliability of magnetic data acquired from the above-cited satellite missions. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 1279 KB

Open AccessProceeding Paper

High-Performance Harmonic Filter Design for Electric Vehicle Charging Stations to Enhance Power Quality

by Sugunakar Mamidala and Yellapragada Venkata Pavan Kumar

Eng. Proc. 2026, 124(1), 61; https://doi.org/10.3390/engproc2026124061 - 9 Mar 2026

Viewed by 637

Abstract

The recent advent of charging infrastructure on an Electric Vehicles (EVs) poses a severe problem with effect on the power grid in terms of harmonic distortion, mostly caused by the nonlinear loads on the electric power produced by charging stations, diode bridge rectifiers, [...] Read more.

The recent advent of charging infrastructure on an Electric Vehicles (EVs) poses a severe problem with effect on the power grid in terms of harmonic distortion, mostly caused by the nonlinear loads on the electric power produced by charging stations, diode bridge rectifiers, and switching converters. These harmonics continuously negatively influence power quality by increasing system and grid current, voltage total harmonic distortion (THD), power factor, and voltage regulation, and lowering the overall efficiency of the system at high rates that exceed IEEE 519 harmonic standards. This paper develops a thorough design and critical analysis of four topologies of harmonic passive filter, including single-tuned filter (STF), double-tuned filter (DTF), high-pass filter (HPF), and C-type high-pass filter (CHPF), to alleviate harmonics and enhance power quality on grid-tied charging stations of electric vehicles. A generalized structure is modeled and simulated in MATLAB/Simulink R2021a at a charging load of an EV charging load for all the filters under the same conditions and evaluated based on the current THD (ITHD), voltage THD (VTHD), input power factor (PF), voltage regulation (VR), and efficiency (η). The findings show that STF has an ITHD of 8.3%, VTHD of 4.6%, PF of 0.92, VR of 6.2%, and efficiency of 91.3%; DTF has an ITHD of 6.1%, VTHD of 3.9%, PF of 0.95, VR of 5.4%, and 93.5%; HPF has an ITHD of 5.6%, VTHD of 3.5%, 0.96 PF, 5.0% of VR, and 94.2% efficiency. The effectiveness of the proposed CHPH is superior to all other traditional approaches and has the lowest ITHD and VTHD, 3.7% and 2.1%, respectively, the highest PF of 0.987, a better VR of 3.8%, and a higher efficiency of 96.2%. The proposed CHPF shows the high-performance characteristics as reflected in the harmonic reduction, improved voltage stability, power factor, and efficiency. The suggested CHPF complies with IEEE 519 standards and provides better grid compatibility with modern EV charging applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

14 pages, 1245 KB

Open AccessProceeding Paper

Multi-Dimensional Taylor Network-Based Predefined-Time Output-Feedback Adaptive Control with Full-State Error Constraints for PMSM Drives in Electric Vehicles

by Mohammed Haddad and Badis Lekouaghet

Eng. Proc. 2026, 124(1), 62; https://doi.org/10.3390/engproc2026124062 - 5 Mar 2026

Viewed by 211

Abstract

The accelerating adoption of electric vehicles (EVs) has positioned them among the fastest-growing sectors in the electricity market. Since reliability, energy efficiency, and robustness are the fundamental criteria in motor drive selection, the permanent magnet synchronous motor (PMSM) has emerged as a preferred [...] Read more.

The accelerating adoption of electric vehicles (EVs) has positioned them among the fastest-growing sectors in the electricity market. Since reliability, energy efficiency, and robustness are the fundamental criteria in motor drive selection, the permanent magnet synchronous motor (PMSM) has emerged as a preferred choice for EV applications. Nevertheless, achieving high-performance control of PMSM systems remains challenging due to nonlinear dynamics, parameter uncertainties, and external disturbances. To address these issues, this paper proposes a predefined-time output-feedback tracking control strategy for PMSMs subject to full-state error constraints, unknown nonlinear dynamics, external disturbances, and unmeasured states. Multi-dimensional Taylor Networks (MTNs) are employed to approximate unknown nonlinearities, while MTN-based observers are designed to estimate unmeasured states. The proposed controller integrates predefined-time stability theory, a general potential Lyapunov function, dynamic surface control (DSC), and backstepping to guarantee constraint satisfaction and rapid convergence. A hyperbolic tangent function is incorporated to eliminate singularities, and a predefined-time filter is introduced to mitigate the computational complexity of recursive backstepping. Theoretical analysis based on Lyapunov methods proves that all closed-loop signals remain bounded and that the tracking error converges to zero within a prespecified time. Simulation results confirm the effectiveness, robustness, and practical feasibility of the proposed approach for PMSM-driven EV applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 755 KB

Open AccessProceeding Paper

Determination of the Diffusion Coefficient of Butylparaben and Bisphenol-A via UV-Vis Spectrometry

by Emmanuel Mismanos, Leana Rose Evano, Allan Soriano, Rugi Vicente Rubi and Carlou Siga-an Eguico

Eng. Proc. 2026, 124(1), 63; https://doi.org/10.3390/engproc2026124063 - 9 Mar 2026

Viewed by 457

Abstract

Bisphenol-A (BPA) and butylparaben (BP) are recognized as emerging contaminants due to their extensive use in plastics and personal care products, posing significant risks to ecosystems and human health. Understanding their transport behavior is vital for predicting environmental fate and designing mitigation measures. [...] Read more.

Bisphenol-A (BPA) and butylparaben (BP) are recognized as emerging contaminants due to their extensive use in plastics and personal care products, posing significant risks to ecosystems and human health. Understanding their transport behavior is vital for predicting environmental fate and designing mitigation measures. This study quantifies the diffusion coefficients of BPA and BP under infinite dilution conditions to simulate realistic environmental scenarios. Laboratory experiments employed a UV-Visible spectrophotometer to monitor concentration changes over time at four initial BP concentrations (0.0005–0.0025 M) and at temperatures between 294.85 K and 304.15 K. Experimental data show that BP concentrations at lower initial values (0.0005 M and 0.00075 M) remained constant, indicating minimal diffusion. Theoretical estimations using the Stokes–Einstein equation yielded diffusion coefficients at 299.38 K of 1.51 × 10⁻¹³ m²/s for BP and 8.47 × 10⁻¹⁴ m²/s for BPA. The Wilke–Chang equation estimated higher values: 1.21 × 10⁻¹⁰ m²/s for BP and 1.18 × 10⁻¹⁰ m²/s for BPA at the same temperature. Results confirm that temperature increases enhance diffusion, while molecular size differences cause BP to diffuse faster than BPA. The robust experimental dataset produced here supports the refinement of predictive models for contaminant mobility. These insights are critical for risk assessment and for developing targeted strategies to minimize the persistence and spread of endocrine-disrupting chemicals in aquatic and terrestrial systems. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 217 KB

Open AccessProceeding Paper

Linking Wastewater Treatment Performance to River Ecosystem Health: Insights from Atlantis WWTW (Wesfleur) in Western Cape

by Sinalo Mapatwana, John Baptist Nzukizi Mudumbi, Patrick Bukenya, Seteno Karabo Obed Ntwampe and Kevin Musungu

Eng. Proc. 2026, 124(1), 64; https://doi.org/10.3390/engproc2026124064 - 3 Mar 2026

Viewed by 502

Abstract

The semi-arid nation of South Africa deals with critical water shortages and deteriorating water quality because of rising population numbers as well as expanding urban areas and industrial operations. The protection of water resources depends heavily on wastewater treatment, yet treated wastewater continues [...] Read more.

The semi-arid nation of South Africa deals with critical water shortages and deteriorating water quality because of rising population numbers as well as expanding urban areas and industrial operations. The protection of water resources depends heavily on wastewater treatment, yet treated wastewater continues to serve as a significant pollution source. The study assesses environmental effects from the Atlantis (Wesfleur) Wastewater Treatment Works (WWTW) located in the Western Cape while studying its pollution impact on the Donkergat River. The research team obtained water samples from the treatment plant effluent and river water downstream between October and November 2023 to measure the COD, TSS, nitrates, orthophosphates, chloride, conductivity, sulphates, and pH levels. The Atlantis WWTW effluent met all the requirements set by the Department of Water and Sanitation (DWS) and the National Water Act (NWA, 36 of 1998). The COD, TSS, and nutrient measurements in the treated water stayed within the established limits. The river water quality showed high chloride (264 mg/L) and sulphate (1543 mg/L) levels during multiple sampling events because of wastewater discharge accumulation and suspected human-made pollution sources. The WWTW achieves satisfactory treatment performance, but the research demonstrates that ongoing surveillance, facility enhancements, and environmental protection strategies are essential to protect water bodies. The results support the ongoing discussion about achieving proper wastewater treatment and environmental protection in areas with limited water resources. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

13 pages, 306 KB

Open AccessProceeding Paper

GravRank: A Gravitational Extractive Preprocessing Framework for Abstractive Summarization of Long Documents

by Abubakar Salisu Bashir, Abdulkadir Abubakar Bichi and Abubakar Ado

Eng. Proc. 2026, 124(1), 65; https://doi.org/10.3390/engproc2026124065 - 10 Mar 2026

Viewed by 313

Abstract

Transformer-based models face persistent challenges in long-document summarization due to fixed input-length constraints. Hybrid approaches address this limitation by applying extractive preprocessing to select salient sentences for downstream abstractive summarization. However, many unsupervised extractive methods, including TextRank and LexRank, rely on heuristic graph [...] Read more.

Transformer-based models face persistent challenges in long-document summarization due to fixed input-length constraints. Hybrid approaches address this limitation by applying extractive preprocessing to select salient sentences for downstream abstractive summarization. However, many unsupervised extractive methods, including TextRank and LexRank, rely on heuristic graph centrality and often struggle to preserve semantic coherence or control redundancy. This paper proposes GravRank, an unsupervised and deterministic extractive summarization framework that models sentence importance as an emergent property of pairwise semantic interactions governed by a softened Plummer potential. Sentences are embedded in a shared semantic space, and a global energy function is defined over all sentence pairs using a softened interaction kernel. This formulation jointly encodes relevance and redundancy within a single scoring function, avoiding iterative graph propagation, supervised training, and post hoc diversity filtering. The deterministic extractive output is used as input to a BART-based abstractive summarization model, forming a hybrid pipeline for long and semantically dense documents. Experiments on the BillSum, PubMed, and GovReport datasets show that GravRank improves over classical unsupervised baselines, remains competitive with recent extractive methods, and yields a competitive result in downstream abstractive summarization when combined with BART. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

13 pages, 3766 KB

Open AccessProceeding Paper

Synoptic Analysis of a Rare Convective Storm over Alexandria, Egypt, in May 2025

by Mona M. Labib, Zeinab Salah, Fatma R. A. Ismail, M. M. Abdel Wahab and Mostafa E. Hamouda

Eng. Proc. 2026, 124(1), 66; https://doi.org/10.3390/engproc2026124066 - 10 Mar 2026

Viewed by 753

Abstract

Egypt generally experiences a hot and arid climate, with rainfall primarily confined to the northern coast during winter season. However, on 31 May 2025, Alexandria experienced an unusual late-spring convective storm that was associated with heavy rainfall, strong winds, intense lightning, and localized [...] Read more.

Egypt generally experiences a hot and arid climate, with rainfall primarily confined to the northern coast during winter season. However, on 31 May 2025, Alexandria experienced an unusual late-spring convective storm that was associated with heavy rainfall, strong winds, intense lightning, and localized hail. This rare event caused temporary disruptions to urban life and underscored the growing vulnerability of coastal cities to short-duration, high-intensity precipitation events occurring outside the climatological rainy season. This study investigates the atmospheric mechanisms underlying this event through a comprehensive synoptic and dynamic analysis of pressure systems, wind fields, and temperature structures extending from the surface to the 200 hPa level. Particular emphasis is placed on the role of moisture convergence and upper-level dynamical forcing in triggering the rapid development of deep convection. Furthermore, the influence of anomalous large-scale circulation patterns on storm initiation and intensification is systematically examined. Improved understanding of these processes provides valuable insight into off-season convective activity over the southeastern Mediterranean and enhances forecasting capability, risk assessment, and early warning strategies for similar extreme events in the region. Furthermore, the influence of anomalous large-scale circulation patterns on storm initiation and intensification is quantitatively assessed to clarify their contribution to the event’s development. A deeper understanding of these processes offers critical insight into the mechanisms governing off-season convective activity over the southeastern Mediterranean and strengthens forecasting skill, risk assessment frameworks, and early warning systems for comparable extreme events in the region. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

14 pages, 268 KB

Open AccessProceeding Paper

IoT and AI-Driven Approaches for Energy Optimization in Off-Grid Solar Systems

by Panagiotis Priamos Koumoulos, Leonidas Mazarakis, Stylianos Katsoulis, Fotios Zantalis and Grigorios Koulouras

Eng. Proc. 2026, 124(1), 67; https://doi.org/10.3390/engproc2026124067 - 10 Mar 2026

Viewed by 1918

Abstract

The growing reliance on renewable energy sources, particularly solar photovoltaics (PVs), requires intelligent management strategies to address challenges of intermittency, storage, and efficiency in autonomous microgrids. This review investigates IoT-based solutions for energy optimization, focusing on hardware platforms, communication protocols, and intelligent control [...] Read more.

The growing reliance on renewable energy sources, particularly solar photovoltaics (PVs), requires intelligent management strategies to address challenges of intermittency, storage, and efficiency in autonomous microgrids. This review investigates IoT-based solutions for energy optimization, focusing on hardware platforms, communication protocols, and intelligent control strategies that enhance the reliability and autonomy of PV-powered systems. This review follows a structured methodological protocol including predefined research questions, database selection, screening criteria, and systematic categorization of studies of IoT-enabled solar microgrid applications, relying on peer-reviewed journal articles, reputable conference proceedings, and scholarly works published between 2020 and 2025. The focus centers on microcontroller-based platforms (e.g., Arduino, ESP32, NodeMCU, TTGO LoRa32) and Single-Board Computers (SBCs) (e.g., Raspberry Pi), alongside the integration of optimization algorithms with Machine Learning (ML) and Neural Network (NN) approaches. Results highlight that lightweight microcontrollers offer cost-effective monitoring, ESP32 and NodeMCU balance real-time analytics with energy efficiency, Raspberry Pi supports edge-level AI processing, and LoRa enables scalable long-range communication for remote PV systems. Furthermore, optimization algorithms (PSO, WOA-SA) and neural models (ANN, LSTM, CNN–LSTM) are explored as methods to improve forecasting accuracy, fault detection, and demand-side management. Conclusions indicate that IoT-based architectures significantly improve energy efficiency, support predictive maintenance, and enable scalable deployment of autonomous solar microgrids. The study emphasizes the necessity of hybrid IoT architectures, combining edge and cloud intelligence, to balance computational complexity, power constraints, and cybersecurity requirements. These findings provide practical insights into designing robust, cost-effective, and scalable IoT-enabled PV microgrids that contribute to decentralized and sustainable energy transitions. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

12 pages, 1348 KB

Open AccessProceeding Paper

LDDm-YOLO: A Distilled YOLOv8 Model for Efficient Real-Time UAV Detection on Edge Devices

by Maryam Lawan Salisu and Aminu Musa

Eng. Proc. 2026, 124(1), 68; https://doi.org/10.3390/engproc2026124068 - 4 Mar 2026

Viewed by 901

Abstract

Lightweight deep-learning models, including MobileNet and LDDm-CNN, have demonstrated significant potential for distinguishing drones from other aerial objects, making them well suited for deployment in resource-constrained environments. However, classification-based approaches face inherent limitations for real-time surveillance, as they rely on prior object cropping [...] Read more.

Lightweight deep-learning models, including MobileNet and LDDm-CNN, have demonstrated significant potential for distinguishing drones from other aerial objects, making them well suited for deployment in resource-constrained environments. However, classification-based approaches face inherent limitations for real-time surveillance, as they rely on prior object cropping or manual region-of-interest extraction and lack the capability to localize drones directly within a complex scene. This limitation significantly restricts their applicability and effectiveness in dynamic and safety-critical environments such as airspace monitoring and critical infrastructure protection, where both recognition and spatial localization are crucial. To address this gap, we proposed LDDm-YOLO, which uses the YOLO-v8n as a compact feature extractor and integrates a lightweight, anchor-free detection head with a shallow feature pyramid for multi-scale object localization. We employed knowledge distillation to transfer rich spatial and semantic features from a larger teacher detector (YOLO-V8x), while incorporating Bayesian optimization for hyperparameter tuning. All experiments were conducted on the Google Colab platform with NVIDIA T4 GPU. The proposed LDDm-YOLO achieves competitive mean Average Precision (mAP = 0.96), Precision 0.92, Recall 0.94, and 127.06 FPS, retaining a smaller model size of only 6.25 MB and low computational complexity (8.9 GFLOPs). These results indicate the potential of the proposed model for edge device deployment. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 261 KB

Open AccessProceeding Paper

In Vitro Inhibition of Cardiometabolic Health-Related Enzymes by Monofloral Stingless Bee Honey

by Fuen Ann Tan, Ai Ling Ho and Fook Yee Chye

Eng. Proc. 2026, 124(1), 69; https://doi.org/10.3390/engproc2026124069 - 10 Mar 2026

Viewed by 615

Abstract

Stingless bee honey (SBH) contains bioactive compounds which are influenced by its botanical origin, and these constituents are closely associated with its health-promoting properties. Interest in monofloral honey has been increasing owing to its distinctive sensory characteristics, relatively consistent nutritional composition, and higher [...] Read more.

Stingless bee honey (SBH) contains bioactive compounds which are influenced by its botanical origin, and these constituents are closely associated with its health-promoting properties. Interest in monofloral honey has been increasing owing to its distinctive sensory characteristics, relatively consistent nutritional composition, and higher market value. The growing burden of cardiometabolic disease underscores the need for additional studies examining the inhibition of enzymes relevant to these pathways. This study analyzed SBH from five botanical origins (acacia, coconut, elderberry, Singapore rhododendron, sunflecks). In vitro inhibition was evaluated against enzymes associated with cholesterol biosynthesis (HMG-CoA reductase), blood pressure regulation (ACE), lipid digestion (pancreatic lipase, cholesterol esterase), and postprandial glycaemia (α-amylase, α-glucosidase). Acacia SBH showed the most potent ACE inhibition (16.80 ± 0.30 mg/mL), while Singapore rhododendron SBH exhibited the strongest HMG-CoA reductase inhibition (18.05 ± 0.50 mg/mL). Sunflecks SBH showed the most potent inhibition of cholesterol esterase, with the lowest IC₅₀ (57.00 ± 0.01 mg/mL). The findings suggest the potential of SBH as a cardioprotective functional food with significant health benefits. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

10 pages, 804 KB

Open AccessProceeding Paper

Application of a New Heliomycin Derivative Against Breast Cancer Under Normoxia and Hypoxia

by Diana I. Salnikova, Alexander S. Tikhomirov, Alexandra L. Mikhaylova, Alvina I. Khamidullina, Andrey E. Shchekotikhin and Alexander M. Scherbakov

Eng. Proc. 2026, 124(1), 70; https://doi.org/10.3390/engproc2026124070 - 11 Mar 2026

Viewed by 492

Abstract

This study investigates the effects of heliomycin and its derivative LCTA-2614 on both hormone-dependent and hormone-independent breast cancer cell lines. Biological activity was assessed using the MTT assay, flow cytometry, and immunoblotting. Heliomycin exhibited potent antiproliferative activity across breast cancer cell lines of [...] Read more.

This study investigates the effects of heliomycin and its derivative LCTA-2614 on both hormone-dependent and hormone-independent breast cancer cell lines. Biological activity was assessed using the MTT assay, flow cytometry, and immunoblotting. Heliomycin exhibited potent antiproliferative activity across breast cancer cell lines of various molecular subtypes, with half-maximal inhibitory concentrations (IC₅₀) of 0.65 μM in MCF7, 0.95 μM in MDA-MB-231, and 0.79 μM in HCC1954 cells. The water-soluble derivative LCTA-2614 showed comparable activity, with IC₅₀ values of 0.86 μM in MCF7, 0.68 μM in MDA-MB-231, and 0.60 μM in HCC1954 cells. The compound LCTA-2614 demonstrated a more selective effect on tumor cells compared to heliomycin. Importantly, both compounds maintained their antiproliferative potency under hypoxic conditions, a known driver of chemoresistance. Additionally, compound LCTA-2614 induced apoptosis in hormone-dependent MCF7 cells through a p53-associated pathways. These findings highlight heliomycin as promising molecular scaffolds for the development of new chemotherapeutic agents. Their retained activity under hypoxia suggests particular potential for the treatment of solid tumors with extensive hypoxic regions. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

16 pages, 6718 KB

Open AccessProceeding Paper

Spatiotemporal Variability of Heat Waves in Egypt: Duration, Intensity, and Frequency (1990–2023)

by Fatma R. A. Ismail, Zeinab Salah, Moetasm H. ElTaweel and M. M. Abdel Wahab

Eng. Proc. 2026, 124(1), 71; https://doi.org/10.3390/engproc2026124071 - 10 Mar 2026

Viewed by 857

Abstract

Heatwaves are among the most significant climate extremes affecting Egypt, with direct impacts on human health, energy demand, water resources, and overall thermal comfort. Although several previous studies have examined heatwave characteristics in Egypt, most have relied on station-based or localized analyses, limiting [...] Read more.

Heatwaves are among the most significant climate extremes affecting Egypt, with direct impacts on human health, energy demand, water resources, and overall thermal comfort. Although several previous studies have examined heatwave characteristics in Egypt, most have relied on station-based or localized analyses, limiting the understanding of national-scale patterns and recurrence behavior. To address this gap, this study provides a comprehensive national-scale assessment of the spatiotemporal characteristics of heatwave occurrences across Egypt from 1990 to 2023 using daily maximum and minimum temperatures derived from the ERA5 reanalysis dataset. Daytime and nighttime heatwaves were defined using the 90th percentile temperature thresholds and a minimum duration of three consecutive days. This made it possible to study their frequency, duration, severity, seasonal distribution, and how often they happened again. The results demonstrate that heatwaves happen more often and with more severity in late July and August. This is especially true for nighttime heatwaves. These findings indicate that daily baseline temperatures in Egypt have been rising steadily since 2010. Nighttime heatwaves show a notable increase in frequency and persistence, indicating a sustained rise in baseline temperatures and reduced nocturnal cooling. By providing the first long-term, spatially consistent national-scale heatwave assessment over Egypt, this study contributes to a more comprehensive understanding of extreme temperature behavior under ongoing climate change. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

13 pages, 5042 KB

Open AccessProceeding Paper

Deep Learning-Based Time-Frequency Attention Network Model for Water-Body Segmentation

by Sivaramakrishna Yechuri, Sandireddy Ramadevi, M. Anand, Vijaya Kumar Velpula, Ganesh Miriyala and V. Siddhartha

Eng. Proc. 2026, 124(1), 72; https://doi.org/10.3390/engproc2026124072 - 11 Mar 2026

Viewed by 496

Abstract

Satellite imagery is increasingly being scrutinized through deep learning methodologies for remote sensing applications, particularly focusing on the detection of water bodies. Identification and analysis of rivers, lakes, and reservoirs through segmentation have become feasible, enabling the exploration of their statistical information. During [...] Read more.

Satellite imagery is increasingly being scrutinized through deep learning methodologies for remote sensing applications, particularly focusing on the detection of water bodies. Identification and analysis of rivers, lakes, and reservoirs through segmentation have become feasible, enabling the exploration of their statistical information. During crises such as floods and changes in river pathways, real-time detection of water bodies via remote sensing proves to be highly advantageous. Nevertheless, achieving precise segmentation of water bodies presents a notable challenge, mainly due to the necessity of high-resolution multi-channel satellite images. Existing literature predominantly relies on satellite data from multi-band satellites for water-body extraction. Conversely, the current research emphasizes the segmentation of water-body regions using relatively lower-resolution RGB images without the incorporation of extra multi-spectral channels. To tackle this challenge, a unique methodology is suggested, involving a customized U-Net model integrated with a time-frequency attention network for segmentation. To assess the comprehensive performance of the proposed model, it is evaluated against a publicly available Sentinel-2 satellite dataset, and the outcomes are compared against standard benchmark metrics. The proposed TFA-U-Net model demonstrates superior performance compared to several recent state-of-the-art water-body segmentation models. Experimental results show that the proposed model achieves a precision of 0.94, sensitivity of 0.96, Dice score of 0.93, accuracy of 0.97, and mean IoU of 0.85, indicating its effectiveness for accurate water-body segmentation using low-resolution satellite images. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 1450 KB

Open AccessProceeding Paper

Hypoxia Triggers Subtype-Specific Membrane Damage and Morphological Changes in Breast Cancer Cell Lines

by Elizaveta Pershikova, Elizaveta Kontareva, Irina Ilyina, Anastasia Alexandrova, Margarita Pustovalova, Sergey Leonov and Yulia Merkher

Eng. Proc. 2026, 124(1), 73; https://doi.org/10.3390/engproc2026124073 - 5 Mar 2026

Viewed by 558

Abstract

Hypoxia is a key microenvironmental factor shaping tumor cell survival and plasticity, but its cellular consequences depend on the cancer subtype. Here, we compare breast cancer cell lines with varying metastatic potential (MP) in response to CoCl₂-induced hypoxia (100 μM, 20 [...] Read more.

Hypoxia is a key microenvironmental factor shaping tumor cell survival and plasticity, but its cellular consequences depend on the cancer subtype. Here, we compare breast cancer cell lines with varying metastatic potential (MP) in response to CoCl₂-induced hypoxia (100 μM, 20 h), analyzing the results immediately (T0) and after 24 h (T1). Plasma membrane damage was measured by the number of propidium iodide-positive cells. Hypoxia pathway activation was evaluated using HIF-1α immunofluorescence, while phenotypic changes were assessed through quantitative morphometric analysis. Chemically induced hypoxia promoted a delayed increase in membrane failure in low-MP cells, consistent with reduced tolerance to hypoxia-mimetic stress. In contrast, high-MP cultures showed a lower PI-positive fraction at T1, consistent with enhanced survival under these conditions. This divergence was accompanied by higher basal and hypoxia-induced HIF-1α levels in high-MP cells. High-MP cells uniquely demonstrated delayed shape changes after exposure to hypoxia-mimetic conditions, evolving into a more compact and rounded form, suggesting phenotypic plasticity instead of structural deterioration. Together, our findings provide insight into how oxygen deprivation impacts cell destiny and phenotypic plasticity in breast cancer models with varying metastatic risks. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1862 KB

Open AccessProceeding Paper

Charging Speed vs. Daily Performance: A Comparative Analysis of Battery Duration in Smartphones Under Different Charging Regimens

by Dimitrios Rimpas, Nikolaos Rimpas, Vasilios A. Orfanos, Sofia Fragouli and Ioannis Christakis

Eng. Proc. 2026, 124(1), 74; https://doi.org/10.3390/engproc2026124074 - 11 Mar 2026

Viewed by 1366

Abstract

This study focuses on the instantaneous effects of fast charging technologies, in terms of the daily operation of mobile devices, and specifically on the trade-off between fast charge and discharge efficiency. A controlled experimental layout is used, containing three smart devices, iPhone 17 [...] Read more.

This study focuses on the instantaneous effects of fast charging technologies, in terms of the daily operation of mobile devices, and specifically on the trade-off between fast charge and discharge efficiency. A controlled experimental layout is used, containing three smart devices, iPhone 17 Pro, iPad 11 Air and MacBook Pro, and four variations in chargers. The research monitored important values like the voltage, current, power and thermal behavior of the selected devices. These comparative results showed that high-speed charging at 67 Watts causes peak temperatures in the battery to be 41.5 °C, which is significantly higher compared to charging under standard protocols of 20 W, with values of 33.1 °C. This thermal stress forces the battery outside of its optimum operating window and consequently increases the internal resistance of the battery which results in a reduction of about 5% of the subsequent discharge runtime. Although fast charging offers a rapid energy replenishment, the thermal penalty incurred by the fast charging process reduces the battery’s short-term utility, suggesting that standard charging is the best option to maximize the single-cycle duration. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

17 pages, 369 KB

Open AccessProceeding Paper

Net-Zero Now: Pathways to Accelerate Building Decarbonisation

by Olusegun Oguntona, Thabang Modula and Clinton Aigbavboa

Eng. Proc. 2026, 124(1), 75; https://doi.org/10.3390/engproc2026124075 - 12 Mar 2026

Viewed by 659

Abstract

The global built environment accounts for a substantial share of greenhouse gas emissions, driven by energy-intensive operations, carbon-heavy construction materials, and ageing building stock. Achieving the climate commitments under the Paris Agreement and South Africa’s Nationally Determined Contributions (NDCs) demands an urgent transition [...] Read more.

The global built environment accounts for a substantial share of greenhouse gas emissions, driven by energy-intensive operations, carbon-heavy construction materials, and ageing building stock. Achieving the climate commitments under the Paris Agreement and South Africa’s Nationally Determined Contributions (NDCs) demands an urgent transition toward net-zero carbon buildings. This paper explores strategic interventions that can fast-track decarbonisation across residential, commercial, and public infrastructure, combining technological innovation with enabling policies and market mechanisms. A structured, closed-ended questionnaire survey was administered to registered and practising construction professionals in the South African construction industry. The retrieved data were subjected to exploratory factor analysis (EFA). Findings from the EFA revealed five clusters: sustainable building advancement, policy and investment, building energy optimisation, comprehensive support, and sustainable design and technology integration strategies. The study concludes that achieving net-zero buildings at scale requires a coordinated “whole-system” approach, such as stringent regulatory frameworks, innovative financing, skilled human capital, and a cultural shift among stakeholders. South Africa’s experience can provide a template for other emerging economies, showing that rapid decarbonisation of buildings is technically feasible and economically advantageous when immediate and collaborative action is taken. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

13 pages, 1381 KB

Open AccessProceeding Paper

Comparative Analysis of Drying Techniques on Mineral Retention and Quality of Apricots (Prunus armeniaca L.)

by Sarvar Rejabov, Botir Usmonov, Komil Usmanov, Jaloliddin Eshbobaev, Bekzod Madaminov, Abbos Elmanov and Zafar Turakulov

Eng. Proc. 2026, 124(1), 76; https://doi.org/10.3390/engproc2026124076 - 12 Mar 2026

Cited by 2 | Viewed by 759

Abstract

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Institute of Chemical-Technology [...] Read more.

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Institute of Chemical-Technology using equal quantities of fresh apricots. Drying was continued until the moisture content, measured gravimetrically, dropped below 20% (wet basis), followed by spectroscopic analysis to determine macro- and microelement concentrations. Solar-dried apricots showed higher retention of essential nutrients in this experimental trial: potassium (2.37%), silicon (0.538%), magnesium (0.145%), calcium (0.176%), and sulfur (0.152%). In contrast, open sun drying led to significant nutrient degradation and poor visual quality. Microwave drying preserved some micronutrients but resulted in surface scorching due to uneven heating. Infrared drying yielded acceptable results but required substantial energy input. Among all methods, solar drying provided the optimal balance of high product quality and energy efficiency. The drying process required negligible electrical energy owing to exclusive reliance on solar radiation. This method supports sustainable food processing by reducing energy demand and greenhouse gas emissions while preserving nutritional quality. The results highlight solar drying as a promising, eco-friendly technique for preserving the nutritional integrity of agricultural products. These findings offer valuable scientific guidance for selecting appropriate drying technologies in the food processing industry, especially in regions with high solar potential. However, the study is limited to a single fruit variety and seasonal conditions. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 2482 KB

Open AccessProceeding Paper

AClustering-Enhanced Explainable Approach Involving Convolutional Neural Networks for Predicting the Compressive Strength of Lightweight Aggregate Concrete

by Violeta Migallón, Héctor Penadés and José Penadés

Eng. Proc. 2026, 124(1), 77; https://doi.org/10.3390/engproc2026124077 - 11 Mar 2026

Viewed by 220

Abstract

Lightweight aggregate concrete (LWAC) is a practical alternative to conventional concrete in civil engineering, offering advantages such as reduced density, enhanced insulation properties, and improved seismic performance. However, segregation during compaction remains a limitation, as it can lead to non-uniform material distribution and [...] Read more.

Lightweight aggregate concrete (LWAC) is a practical alternative to conventional concrete in civil engineering, offering advantages such as reduced density, enhanced insulation properties, and improved seismic performance. However, segregation during compaction remains a limitation, as it can lead to non-uniform material distribution and reduced compressive strength. This study addresses this issue by combining non-destructive techniques with deep learning methods to predict the compressive strength of LWAC. We propose an explainable approach based on a convolutional recurrent neural network architecture, enhanced by unsupervised clustering and SHapley Additive exPlanations (SHAP), to improve interpretability. To optimize predictive performance, several aggregation strategies are evaluated at the recurrent layer before the dense layers, including full-sequence flattening, max pooling, average pooling, and an attention mechanism over the full sequence. Experimental results show that the proposed model outperforms conventional machine learning methods such as multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR), as well as ensemble methods such as gradient boosting (GBR), XGBoost, and weighted average ensemble (WAE). Furthermore, when combined with unsupervised clustering, the model identifies latent behavioral patterns that are not observable through traditional evaluation techniques. This demonstrates the potential of integrating non-destructive testing with interpretable deep learning as a reliable approach for the structural assessment of LWAC. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 866 KB

Open AccessProceeding Paper

Development of LiI-Doped PEO/PMMA-Based Solid Polymer Electrolytes Reinforced with SnO₂ Nanofillers

by Amudha Subramanian, Mohammed Tasleem Tahira and Rajalakshmi Kumaraiah

Eng. Proc. 2026, 124(1), 78; https://doi.org/10.3390/engproc2026124078 - 13 Mar 2026

Viewed by 390

Abstract

The current research investigates the electrochemical performance of plasticized nanocomposite solid polymer electrolytes derived from a polyethylene oxide (PEO)–polymethyl methacrylate (PMMA) blended system with lithium iodide (LiI) as the dopant salt and tin dioxide (SnO₂) nanoparticles as the inorganic nanofillers. Thin [...] Read more.

The current research investigates the electrochemical performance of plasticized nanocomposite solid polymer electrolytes derived from a polyethylene oxide (PEO)–polymethyl methacrylate (PMMA) blended system with lithium iodide (LiI) as the dopant salt and tin dioxide (SnO₂) nanoparticles as the inorganic nanofillers. Thin nanofilms of the synthesized electrolytes were prepared and progressively examined by using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), Ultraviolet visible (UV–Vis) spectroscopy, and Scanning electron microscopy (SEM). XRD characterization confirmed the successful establishment of the polymer electrolyte matrix and reflected a significant decrease in crystallinity upon the incorporation of nanofillers, whereas crystallite size was estimated using the Debye–Scherrer equation. FT-IR spectra showed prominent molecular interactions and complexation of polymer, salt, and nanofiller components. UV–Vis spectroscopy provides information on the optical absorption behavior, whereas the SEM micrograph shows the morphological features and homogeneity of plasticized nanocomposite solid polymer electrolyte films. The addition of SnO₂ nanofillers was shown to improve both the structural and electrochemical properties of the electrolyte system, highlighting its potential usage in solid-state batteries and other high-end electrochemical devices. These enhancements make the developed nanocomposite solid polymer electrolytes viable candidates for high-performance, flexible lithium-ion battery applications, offering a promising route toward safer and more efficient energy storage systems. Comprehensive electrochemical performance evaluation will be addressed in future studies. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 3645 KB

Open AccessProceeding Paper

Towards Predictive Models of Mechanical Properties in 3D-Printed Polymers: An Exploratory Study

by Bruno A. G. Sousa, César M. A. Vasques and Adélio M. S. Cavadas

Eng. Proc. 2026, 124(1), 79; https://doi.org/10.3390/engproc2026124079 - 16 Mar 2026

Viewed by 559

Abstract

Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed [...] Read more.

Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed to provide the experimental basis for the development and calibration of predictive models of mechanical properties in 3D-printed components. Standard ISO 527-2 Type 1A specimens were fabricated using thermoplastic PLA (polylactic acid) with systematic variations in layer orientation, infill overlap, and printing velocity. Mechanical characterization was carried out through uniaxial tensile testing to determine tensile strength and stiffness of the material specimens, while scanning electron microscopy (SEM) provided complementary insights into interlayer bonding, filament alignment, porosity, and fracture morphology. Results showed that material type and processing strategies strongly influenced mechanical response, with SEM highlighting microstructural features that govern interlayer adhesion and failure mechanisms. These findings contribute to a deeper understanding of process–structure–property relationships in additive manufacturing and establish the groundwork for predictive model development. Ongoing efforts will integrate these experimental insights into numerical simulations employing homogenized material models, thereby enhancing design optimization and reliability of 3D-printed structural components. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 642 KB

Open AccessProceeding Paper

Microstructural and Spectral Characterization of ZrO₂-Doped PEO/PMMA Nanocomposite Polymer Electrolytes

by Amudha Subramanian, Rajalakshmi Kumaraiah and Mohammed Tasleem Tahira

Eng. Proc. 2026, 124(1), 80; https://doi.org/10.3390/engproc2026124080 - 17 Mar 2026

Viewed by 482

Abstract

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer [...] Read more.

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO₂) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO₂ nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behavior of the PEO/PMMA blend system. The inclusion of ZrO₂ nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making them promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO₂ nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behavior of the solid polymer electrolytes. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 2448 KB

Open AccessProceeding Paper

Solvent-Based Simulation and Techno-Economic Evaluation of CO₂/H₂S Separation at Shurtan Gas Chemical Complex

by Adham Norkobilov, Rakhmatullo Muradov, Sanjar Ergashev, Zafar Turakulov, Yulduz Safarova and Noilakhon Yakubova

Eng. Proc. 2026, 124(1), 81; https://doi.org/10.3390/engproc2026124081 - 17 Mar 2026

Cited by 1 | Viewed by 728

Abstract

The separation of carbon dioxide (CO₂) and hydrogen sulfide (H₂S) from sour natural gas is an important step in gas processing and emission control. This study applies a rate-based Aspen Plus simulation to examine solvent-based CO₂/H₂ [...] Read more.

The separation of carbon dioxide (CO₂) and hydrogen sulfide (H₂S) from sour natural gas is an important step in gas processing and emission control. This study applies a rate-based Aspen Plus simulation to examine solvent-based CO₂/H₂S removal under conditions representative of the Shurtan Gas Chemical Complex in Uzbekistan. Monoethanolamine (MEA) and methyldiethanolamine (MDEA) are evaluated as reference solvents with respect to separation performance and energy demand. The rate-based modeling framework accounts for reaction kinetics and mass transfer effects in the absorber–regenerator system. Simulation results indicate that both solvents achieve high acid gas removal efficiencies. From an engineering perspective, the results provide practical guidance for solvent selection and energy optimization in existing acid gas removal units, supporting pilot-scale deployment under industrial operating conditions. Sensitivity analysis suggests that increasing gas throughput beyond 30 t/h leads to a gradual reduction in CO₂ capture efficiency, primarily due to mass transfer limitations. From a techno-economic perspective, the lower energy demand of the MDEA-based system may imply reduced operating costs. The captured CO₂ stream reaches a purity of around 99.5%, which is compatible with downstream soda ash production. Overall, the results provide a screening-level assessment supporting further detailed evaluation. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

12 pages, 1019 KB

Open AccessProceeding Paper

Intelligent Drone Patrolling with Real-Time Object Detection and GPS-Based Path Adaptation

by Gurugubelli V. S. Narayana, Shiba Prasad Swain, Debabrata Pattnayak, Manas Ranjan Pradhan and P. Ankit Krishna

Eng. Proc. 2026, 124(1), 82; https://doi.org/10.3390/engproc2026124082 - 18 Mar 2026

Cited by 1 | Viewed by 979

Abstract

Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we [...] Read more.

Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we aim at designing and validating experimentally a low-cost drone-based unmanned autonomous mission patrolling system with waypoint navigation, real-time video backhauling, AI-based human/object detection and GPS path re-planning when an event occurs to ensure the safety of patrol missions under battery constraints. Methods: The proposed architecture combines autonomous navigation and embedded flight-control with online analog video streaming and ground-station-based computer vision processing. Object detection based on deep learning for live aerial video is used, and the proposed system’s performance is tested at different altitudes, lighting states and GPS patrol plans. Results: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system is able to adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Conclusions: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system can adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 720 KB

Open AccessProceeding Paper

Emerging Environmental Pollutants and Diabetes: A Pilot Study on PFASs in a South African Mixed-Ancestry Population

by John Baptist Nzukizi Mudumbi, Seteno Karabo Obed Ntwampe, Adegbenro Peter Daso, Okechukwu Jonathan Okonkwo, Thomas Joel Farrar, Didier Mugisho Nyambwe and Tandi Edith Matsha

Eng. Proc. 2026, 124(1), 83; https://doi.org/10.3390/engproc2026124083 - 17 Mar 2026

Viewed by 456

Abstract

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and consumer products due to their thermal stability and resistance to degradation. These properties also contribute to their environmental persistence and potential adverse health effects. Despite increasing global concern regarding [...] Read more.

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and consumer products due to their thermal stability and resistance to degradation. These properties also contribute to their environmental persistence and potential adverse health effects. Despite increasing global concern regarding PFAS exposure, biomonitoring data from African populations remain limited. This study investigated the serum concentrations of perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), and perfluorobutane sulfonate (PFBS), and their association with diabetes mellitus (DM) in a mixed-ancestry population from Bellville South, Cape Town, South Africa. Serum samples (n = 179) were analysed using liquid chromatography–tandem mass spectrometry (LC–MS-8030), and statistical analyses were performed using STATISTICA 13.5 software. All three PFASs were detected in 100% of samples, with PFOA exhibiting the highest mean concentration (9.43 ± 13.16 ng/mL), followed by PFBS and PFOS. PFAS concentrations were generally higher in females than in males, with significantly elevated PFOA levels observed among women (p = 0.0116). No statistically significant associations were identified between PFAS concentrations and glycemic status, obesity, or related metabolic indicators (p > 0.05). However, PFOS showed a modest positive correlation with HbA1c in females, suggesting potential gender-specific interactions. These findings confirm measurable PFAS exposure in the South African population and highlight the need for larger longitudinal studies implications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 640 KB

Open AccessProceeding Paper

Physicochemical Characterization of Emerging Contaminants: A Conductance-Based Determination of Diffusion Coefficients for Butylparaben and Triclosan in Aqueous Solution

by Jesse Louise Javier, Karl Steven Narte, Mohammad Naif Sali, Rolex Villaflor, Janine Renz Villegas, Rugi Vicente Rubi, Allan Soriano and Rich Jhon Paul Latiza

Eng. Proc. 2026, 124(1), 84; https://doi.org/10.3390/engproc2026124084 - 19 Mar 2026

Viewed by 357

Abstract

The escalating accumulation of pharmaceutical micropollutants in global water systems represents a significant challenge to current circular economy frameworks, highlighting a critical gap in the management of environmental persistence. Although advanced remediation technologies are often proposed to mitigate this crisis, their engineering optimization [...] Read more.

The escalating accumulation of pharmaceutical micropollutants in global water systems represents a significant challenge to current circular economy frameworks, highlighting a critical gap in the management of environmental persistence. Although advanced remediation technologies are often proposed to mitigate this crisis, their engineering optimization is frequently compromised by a reliance on empirical approximations rather than precise physicochemical constants. Addressing this fundamental deficit, this study executes a rigorous determination of mass transfer properties for two ubiquitous contaminants: Butylparaben and Triclosan. Utilizing a high-precision electrolytic conductance method under infinite dilution, we investigated transport dynamics across varying temperature gradients (305.15–319.15 K). Experimental data were subjected to advanced mathematical modeling, where the Modified Robinson–Stokes (MRS) quadratic model significantly outperformed classical linear approaches (

R^{2} > 0.98

), accurately capturing non-ideal solute–solvent interactions. The derived limiting molar conductivities facilitated the calculation of infinite dilution diffusion coefficients via the Nernst–Haskell equation, yielding values of

0.99 \times 10^{- 8}

m²/s for Butylparaben and

0.98 \times 10^{- 8}

m²/s for Triclosan. Furthermore, Stokes–Einstein analysis quantified the hydrodynamic radii, elucidating the steric mechanisms governing the sluggish migration of bulky chlorinated ethers compared to single-ring esters. These precise transport parameters are not merely theoretical values; they are essential inputs for developing accurate computational fate models and designing regenerable separation processes, thereby providing the hard physics required to engineer solutions for the perpetual pollution era. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1389 KB

Open AccessProceeding Paper

Impact of Hyperthermia on Gut Microbial Adaptation: Role of Thermophilic Bacteria in Host Physiology

by Sugandha Jaiswal, Vinod Kumar Nigam and Rakesh Kumar Sinha

Eng. Proc. 2026, 124(1), 85; https://doi.org/10.3390/engproc2026124085 - 20 Mar 2026

Viewed by 481

Abstract

Heat stress (HS) is one of the most challenging environmental conditions, responsible for impaired growth and reproduction in living systems. It also leads to altering the release of different biochemicals responsible for controlling the metabolic pathway. Five White Wistar rats were exposed at [...] Read more.

Heat stress (HS) is one of the most challenging environmental conditions, responsible for impaired growth and reproduction in living systems. It also leads to altering the release of different biochemicals responsible for controlling the metabolic pathway. Five White Wistar rats were exposed at 42 ± 1 °C inside a closed chamber for the induction of hyperthermia. Their rectal temperature was recorded before and after heat exposure. The semi-digested food from the gut (colon) of sacrificed rats was collected under sterilized conditions for the isolation of gut bacteria on a nutrient agar plate at 50 °C, 60 °C, and 70 °C. The sample was incubated for 24 h and isolates were further purified. The proteolytic, amylolytic, cellulolytic, and xylanolytic activities were measured via plate assay and the enzymatic index was calculated. Total protein and estimation of heat shock protein 70 (HSP70) were also quantified. Initially, the rectal temperature of the animal was 37.1 ± 0.2 °C, but after exposure to heat, the temperature was 40.8 ± 0.2 °C. The number of purified isolates was recorded, i.e., at 50 °C (04), at 60 °C (01), and at 70 °C (03). Among eight isolates, Bacillus licheniformis (50 °C) showed all four enzymatic activities with a higher enzymatic index. Further, this novel isolate also exhibited a maximum concentration of HSP70. This preliminary study reveals the survival of a bacterium (B. licheniformis) capable of producing key metabolites, highlighting its significance in supporting host physiology and other pathophysiological conditions. As a probiotic, it may serve as a potential therapeutic bridge connecting HSP70, host physiological function, and gut health. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Graphical abstract

8 pages, 997 KB

Open AccessProceeding Paper

Proton Beam Irradiation Affects the Way Breast Cancer Cells Take Up Nanoparticles in Relation to the Stiffness of Their Microenvironment

by Elizaveta Kontareva, Philipp Malakhov, Yulia Merkher, Sergey Leonov and Margarita Pustovalova

Eng. Proc. 2026, 124(1), 86; https://doi.org/10.3390/engproc2026124086 - 31 Mar 2026

Viewed by 537

Abstract

High-frequency proton therapy shows promise for breast cancer (BC) treatment. We previously showed that BC cells’ metastatic potential (MP) correlates with their nanoparticle (NP) uptake efficiency. MP is known to be associated with microenvironment stiffness and radiosensitivity. Here, proton beam-irradiated MCF-7 and MDA-MB-231 [...] Read more.

High-frequency proton therapy shows promise for breast cancer (BC) treatment. We previously showed that BC cells’ metastatic potential (MP) correlates with their nanoparticle (NP) uptake efficiency. MP is known to be associated with microenvironment stiffness and radiosensitivity. Here, proton beam-irradiated MCF-7 and MDA-MB-231 cells were assessed for NP uptake efficiency under stiff (plastic) or soft (fibrin gel) conditions. In a stiff microenvironment, control MDA-MB-231 cells internalized 1.35-fold more NPs than MCF-7 (p < 0.0017), with comparably low uptake in soft conditions. After proton beam irradiation at a dose of 6 Gy, in stiff conditions, MDA-MB-231 cells showed a 1.6-fold increase in NP internalization compared to non-treated MDA-MB-231 (p < 0.0001), while MCF-7 cells showed no change, leading to an overall 1.86-fold difference between proton-treated MDA-MB-231 and MCF-7 cells (p < 0.0001). In soft conditions, irradiated MDA-MB-231 retained a 1.47-fold higher uptake of NPs than MCF-7 cells (p < 0.0172), but this value was 1.7-fold lower (p < 0.0001) compared to non-irradiated MDA-MB-231 cells on stiff plastic. Hence, therapeutic strategies combining proton irradiation with targeting tumor microenvironment softening may reduce post-irradiation metastasis risk. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 2384 KB

Open AccessProceeding Paper

AI-Powered Algerian Forest Mapping

by Maha Bazouzi, Mouncef Mohammed Kadri, Mohammed Anis Zemali, Meziane Iftene and Mohammed El Amin Larabi

Eng. Proc. 2026, 124(1), 87; https://doi.org/10.3390/engproc2026124087 - 23 Mar 2026

Viewed by 462

Abstract

This paper presents an AI-powered approach for mapping Algerian forests using Sentinel-2 satellite imagery and advanced deep learning techniques. We leverage ESA WorldCover for large-scale weak supervision and mitigate label noise through a robust training strategy centered on Deep Abstaining Classifier (DAC) loss. [...] Read more.

This paper presents an AI-powered approach for mapping Algerian forests using Sentinel-2 satellite imagery and advanced deep learning techniques. We leverage ESA WorldCover for large-scale weak supervision and mitigate label noise through a robust training strategy centered on Deep Abstaining Classifier (DAC) loss. Our core model is DeepLabV3+ with six Sentinel-2 spectral bands and vegetation indices (NDVI, EVI, and SAVI). Validation uses a high-quality manually annotated dataset derived from Google Earth Pro imagery. The DeepLabV3+ model with DAC training achieves strong segmentation performance (accuracy:

96.26 %

; Dice:

92.04 %

; IoU:

85.26 %

; recall:

94.91 %

), outperforming the baseline U-Net. The DAC remains stronger than both CE and SCE under matched settings, clean/noisy ratio sensitivity identifies a stable optimum around a clean weight of 0.85–0.90, and spatial five-fold cross-validation provides explicit cross-region variance estimates. Overall, the framework produces spatially coherent forest predictions with robust behavior under noisy supervision. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 694 KB

Open AccessProceeding Paper

Urea-Modified Activated Carbons and Their Application in Methylene Blue Removal from Wastewater

by Pedro Francisco Geraldo, Isabel Pestana da Paixão Cansado, Paulo Alexandre Mira Mourão and José Eduardo dos Santos Felix Castanheiro

Eng. Proc. 2026, 124(1), 88; https://doi.org/10.3390/engproc2026124088 - 24 Mar 2026

Cited by 1 | Viewed by 538

Abstract

This study aims to evaluate the use of Tectona grandis sawdust (Teak) and activated carbons (ACs) prepared from Teak and modified with urea on the removal of methylene blue (MB) from the aqueous phase. Activation is performed with potassium hydroxide (KOH), and urea [...] Read more.

This study aims to evaluate the use of Tectona grandis sawdust (Teak) and activated carbons (ACs) prepared from Teak and modified with urea on the removal of methylene blue (MB) from the aqueous phase. Activation is performed with potassium hydroxide (KOH), and urea is added during chemical activation to increase nitrogen content in the AC matrix and improve textural properties. ACs are physiochemically characterized by elemental and Fourier Transform Infrared (FTIR) spectroscopy analysis, with the determination of the point of zero charge (pHpzc) and nitrogen adsorption at 77 K. The addition of urea allows for obtaining ACs with a higher pHpzc and carbon and nitrogen content, with improved textural properties when compared with the original AC. The addition of urea also promotes an increase in surface area and porous volume (1246 m² g⁻¹ and 0.64 cm³ g⁻¹). The modifications slightly affect the performance of the ACs in removing MB from water. While the original AC (AC_Teak_KOH_1_2) has a maximum MB adsorption capacity of 270.2 mg g⁻¹, the modified one (AC_Teak_KOH_urea (1_2_1)) has a maximum adsorption capacity of 281.7 mg g⁻¹. MB adsorption isotherms fit well with the Freundlich equation. Kinetic data fit well with the pseudo-second-order model, and the Weber–Morris representation shows that MB adsorption is described as a succession of two diffusion steps. The results make clear that it is possible to recover Teak waste through its transformation into ACs, presenting high application in the removal of dyes from water. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 215 KB

Open AccessProceeding Paper

Towards a News Authenticity Predictor (NAP AI)

by Arif Wali, Stelios Kapetanakis and Giacomo Nalli

Eng. Proc. 2026, 124(1), 89; https://doi.org/10.3390/engproc2026124089 - 24 Mar 2026

Viewed by 481

Abstract

The rapid spread of misinformation on social media has emerged as a major societal issue. Over 40% of British social media news-sharers admitted they had shared inaccurate or fake news. The extensive distribution of false information causes public trust deterioration while modifying public opinions and potentially destabilizing social [...] Read more.

The rapid spread of misinformation on social media has emerged as a major societal issue. Over 40% of British social media news-sharers admitted they had shared inaccurate or fake news. The extensive distribution of false information causes public trust deterioration while modifying public opinions and potentially destabilizing social and political systems. There are profound challenges due to this hard-to-detect, hard-to-stop reality and the financials and societal implications are remarkable. As an attempt to limit the challenges created from misinformation this paper introduces some preliminary work on detection of fake news and verification of their reliability based on online content. Large language models (LLMs) are being used along with natural language processing (NLP) techniques to evaluate news articles through their linguistic and contextual characteristics. Several models are compared on how they can typically identify typical indicators of misinformation through the analysis of extensive verified datasets to develop an ability to classify content as authentic or fabricated. This work has been through thorough testing to determine its operational effectiveness and dependability after completion. We present a relatively easy-to-use tool which enables a wide range of people also for those without a background in computer science to easily verify news accuracy before sharing or trusting it. This work could help to stop false information from spreading while promoting fact-based discussions and improving digital literacy skills. The research demonstrates how technology fights the fake news crisis to create an informed digital environment which supports public conversation protection and information integrity in the modern digital age. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

12 pages, 918 KB

Open AccessProceeding Paper

Computational Evaluation of Philippine Vitex negundo Phytochemicals as Potential Inhibitors of Rhinovirus 3C Protease: Molecular Docking, Pharmacokinetic Analysis, and ADMET Studies

by Francis Ceniza, Harll Fawwenn Hayes Paderanga, Sheena Alexa Yacapin and Nesteve John Agosto

Eng. Proc. 2026, 124(1), 90; https://doi.org/10.3390/engproc2026124090 - 25 Mar 2026

Viewed by 1171

Abstract

Human rhinoviruses (HRVs) are the primary cause of the common cold, a highly contagious upper respiratory tract infection characterized by nasal congestion, sneezing, and sore throat. HRV replication depends on its 3C protease (HRV-3Cpro), a key enzyme that cleaves the viral polyprotein into [...] Read more.

Human rhinoviruses (HRVs) are the primary cause of the common cold, a highly contagious upper respiratory tract infection characterized by nasal congestion, sneezing, and sore throat. HRV replication depends on its 3C protease (HRV-3Cpro), a key enzyme that cleaves the viral polyprotein into functional proteins essential for viral maturation. Currently, no FDA-approved inhibitors specifically target HRV-3Cpro. While rupintrivir, a synthetic inhibitor, advanced to clinical trials, it ultimately failed due to limited efficacy. This study investigated the potential of Vitex negundo (or lagundi)—a medicinal plant traditionally used in the Philippines for treating colds and respiratory ailments—as a source of natural HRV-3Cpro inhibitors through in silico molecular docking and pharmacokinetic (ADMET) evaluation. Fifteen phytochemicals were screened, with five compounds exhibiting strong binding affinities exceeding that of the reference inhibitor rupintrivir (−6.1 kcal/mol): agnuside (−6.9 kcal/mol), luteolin 7-O-glucoside (−6.7 kcal/mol), 2′-p-hydroxybenzoyl mussaenosidic acid (−6.5 kcal/mol), 6′-(p-hydroxybenzoyl) mussaenosidic acid (−6.5 kcal/mol), and luteolin (−6.2 kcal/mol). Among these, luteolin emerged as a particularly promising lead compound, forming stable hydrogen bonding and hydrophobic interactions with HRV-3Cpro. Luteolin also demonstrates a favorable ADMET and safety profile, predicted to be non-mutagenic and non-hepatotoxic. These findings position luteolin as a potential plant-based HRV-3Cpro inhibitor, warranting further in vitro and in vivo studies to validate its antiviral efficacy and pharmacokinetic properties. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 1393 KB

Open AccessProceeding Paper

Phytofabrication of Silver Nanoparticles from Water Hyacinth (Eichhornia crassipes) as a Potential Pest Control Tool for Spodoptera frugiperda

by Joserie Joice Reyes, Jeremy Kyle Edson Austria, Ma. Angelica Chua, Anna Maria Parzuelo, Sean Carlo Castro, Jerry Go Olay, Rugi Vicente Rubi and Carlou Siga-an Eguico

Eng. Proc. 2026, 124(1), 91; https://doi.org/10.3390/engproc2026124091 - 26 Mar 2026

Viewed by 561

Abstract

The invasive fall armyworm (Spodoptera frugiperda) threatens Philippine crops, highlighting the need for sustainable pest management. This study therefore optimizes the green synthesis of silver nanoparticles (AgNPs) from water hyacinth (Eichhornia crassipes), an abundant and problematic aquatic weed, as [...] Read more.

The invasive fall armyworm (Spodoptera frugiperda) threatens Philippine crops, highlighting the need for sustainable pest management. This study therefore optimizes the green synthesis of silver nanoparticles (AgNPs) from water hyacinth (Eichhornia crassipes), an abundant and problematic aquatic weed, as a potential pest control tool. Methanolic leaf extracts were prepared under varying methanol concentrations, temperatures, and extraction times, and total phenolic content was quantified using the Folin–Ciocalteu method. SEM–EDX confirmed the formation of silver nanoparticles synthesized from Eichhornia crassipes (Ec-AgNPs), with particles observed at ≤100 nm. Optimal extraction occurred at 47 °C, 90% methanol, and 76 min, maximizing phenolic yield. Overall, results suggest phenolic content and extract volume influence nanoparticle size and stability, with larger extract volumes increasing agglomeration risk. Pesticidal efficacy was not evaluated; further work is needed to assess pest control performance. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1829 KB

Open AccessProceeding Paper

Parameter Extraction and State-of-Charge Estimation of Li-Ion Batteries for BMS Applications

by Badis Lekouaghet, Hani Terfa and Mohammed Haddad

Eng. Proc. 2026, 124(1), 92; https://doi.org/10.3390/engproc2026124092 - 26 Mar 2026

Viewed by 589

Abstract

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the [...] Read more.

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

9 pages, 2818 KB

Open AccessProceeding Paper

Alternating Sequential Model Predictive Control in Multimodular Direct Matrix Converters

by Rodrigo Romero, Edgar Maqueda, Sergio Toledo, Carlos Romero, Sergio Núñez, Raúl Gregor and Marco Rivera

Eng. Proc. 2026, 124(1), 93; https://doi.org/10.3390/engproc2026124093 - 30 Mar 2026

Viewed by 525

Abstract

This work presents an alternating sequential model predictive control (ASMPC) scheme applied to multimodular matrix converters. The proposed strategy alternately evaluates two control objectives: load current tracking and input reactive power minimization. The algorithm was implemented in MATLAB/Simulink on an architecture composed of [...] Read more.

This work presents an alternating sequential model predictive control (ASMPC) scheme applied to multimodular matrix converters. The proposed strategy alternately evaluates two control objectives: load current tracking and input reactive power minimization. The algorithm was implemented in MATLAB/Simulink on an architecture composed of two direct matrix converters in a multimodular configuration. The influence of parameter

N_{2}

on system performance was analyzed under step changes in reference current of 30 A and 60 A. To this end, performance metrics such as THD and MSE were used, along with a descriptive statistical analysis including the mean, standard deviation, mean absolute deviation (MAD), and coefficient of variation (CV). Simulation results show stable performance for variations in

N_{2}

, with an input current THD of

8.10 %

and load THD reduced to

1.00 %

, demonstrating improved harmonic performance compared with classical weighted MPC approaches. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 2190 KB

Open AccessProceeding Paper

Site-Specific Challenges for VAWT Installation in Remote Island Environments: A Case Study in Pulau Tioman

by Ali Jamaludin, Mohd Azimin Elias and Mohd Azwan Rashid

Eng. Proc. 2026, 124(1), 94; https://doi.org/10.3390/engproc2026124094 - 30 Mar 2026

Viewed by 550

Abstract

The implementation of small-scale renewable energy systems in remote island environments poses unique engineering and logistical challenges. This case study examines the installation of a 1 kW vertical-axis wind turbine (VAWT) in Pulau Tioman, Malaysia, to enhance localized energy generation in coastal regions. [...] Read more.

The implementation of small-scale renewable energy systems in remote island environments poses unique engineering and logistical challenges. This case study examines the installation of a 1 kW vertical-axis wind turbine (VAWT) in Pulau Tioman, Malaysia, to enhance localized energy generation in coastal regions. Constraints such as limited heavy lifting equipment, reliance on basic machinery, and restricted transport required modular handling, manual assembly, and terrain-adapted foundations. Environmental conditions, including humidity, rainfall, and irregular wind patterns, influenced planning and execution. While the installation was successfully completed, prolonged low-wind conditions prevented post-installation performance monitoring. This study documents the installation challenges, engineering adaptations, and lessons learned, offering guidance for future renewable energy deployments in Southeast Asia island environments. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1159 KB

Open AccessProceeding Paper

Integration of Deep Learning Methods into the Design of Microwave Transceiver Components for a 5G Mid-Band System

by Pedro Escudero-Villa, Santiago Huebla-Huilca and Jenny Paredes-Fierro

Eng. Proc. 2026, 124(1), 95; https://doi.org/10.3390/engproc2026124095 - 30 Mar 2026

Viewed by 527

Abstract

This study evaluates the application of deep learning techniques to the design of a microwave transmitter–receiver system operating in the 5G mid-band. The proposed architecture consists of four stages—signal generation, amplification, mixing, and filtering—each initially designed using conventional microwave methods and subsequently integrated [...] Read more.

This study evaluates the application of deep learning techniques to the design of a microwave transmitter–receiver system operating in the 5G mid-band. The proposed architecture consists of four stages—signal generation, amplification, mixing, and filtering—each initially designed using conventional microwave methods and subsequently integrated into a complete transceiver. Simulation data were generated and component-specific convolutional neural networks (CNNs) were implemented in Python using TensorFlow/Keras. Across all models, an average error reduction exceeding 90% was achieved, with most networks converging after the third training cycle. System-level integration shows that the baseline design achieved a transmitted power of −32.637 dBm and a gain of 1.116 dB, while the deep learning-based design yielded −33.912 dBm and 0.738 dB. Additional analysis of S-parameters confirms acceptable impedance matching and a frequency response of around 3.5 GHz. These results illustrate that deep learning provides an effective complementary methodology for multi-component microwave system modeling and optimization in 5G applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 1159 KB

Open AccessProceeding Paper

Microwave-Assisted Extraction (MAE) of Propolis from Tetragonula biroi (Philippine Kiwot Bee) Hives

by Nicole D. Llarena, John Andrei G. Allam, Ella B. Angelan, Christine Mae C. Cahayon, Mikhaela L. Lumunsad, Peniel Jean A. Gildo, Joseph Rey H. Sta. Agueda and John Ray C. Estrellado

Eng. Proc. 2026, 124(1), 96; https://doi.org/10.3390/engproc2026124096 - 27 Mar 2026

Viewed by 528

Abstract

Propolis is a resinous substance known for its bioactive compounds, such as phenolics and flavonoids, which exhibit antioxidant, antibacterial, and anti-inflammatory benefits. While it can be harvested from sources globally, it can also be derived from the native stingless Philippine kiwot bees. This [...] Read more.

Propolis is a resinous substance known for its bioactive compounds, such as phenolics and flavonoids, which exhibit antioxidant, antibacterial, and anti-inflammatory benefits. While it can be harvested from sources globally, it can also be derived from the native stingless Philippine kiwot bees. This study used microwave-assisted extraction (MAE) using ethanol, changing the solvent-to-sample ratio (5 mL/g, 10 mL/g, and 15 mL/g), solvent concentration (35% v/v, 65% v/v, and 95% v/v), and extraction time (60 s, 180 s, and 300 s), to determine their effects on the yield and phenolic and flavonoid contents. The extracted propolis was concentrated using rotary evaporator and freeze-dried. Phenolic content was determined through the Folin–Ciocalteu method, while flavonoid content was identified through aluminum chloride complex formation in methanol solution. Interactions between solvent-to-sample ratio and solvent concentration significantly affected yield, while extraction time did not. Results show that using MAE with ethanol as a solvent successfully extracted propolis. Lower solvent-to-sample ratios with increasing solvent concentration resulted in higher yields. Conversely, high solvent concentrations with increasing solvent-to-sample ratio decreases yield. The highest yield (0.144 g/g) was obtained with 5 mL/g solvent-to-sample ratio, 95% v/v solvent concentration, and 300 s extraction time. Phenolic and flavonoid contents of the propolis produced using the parameters resulting in the highest yield were determined to be 142.660 mg GAE/g propolis and 65.900 mg QE/g propolis, comparable to propolis extracted using traditional methods. Effects of extraction parameters were determined, which can further optimize the quantity and quality of propolis. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 2510 KB

Open AccessProceeding Paper

Development and Synthesis of a Novel Carbon Dioxide-Capturing Polyacrylic Sorbent

by Shahnozakhon Shavkatjon kizi Khakimova and Oytura Sitdikovna Maksumova

Eng. Proc. 2026, 124(1), 97; https://doi.org/10.3390/engproc2026124097 - 30 Mar 2026

Viewed by 514

Abstract

The release of CO₂ gas into the atmosphere is one of the most prolific causes of global climate change. To solve this problem, cost-effective technologies are being sought. Polymer membranes are innovative materials that can be widely used in the process of [...] Read more.

The release of CO₂ gas into the atmosphere is one of the most prolific causes of global climate change. To solve this problem, cost-effective technologies are being sought. Polymer membranes are innovative materials that can be widely used in the process of capturing and separating CO₂ gas. In this work, an amine impregnated and amidated solid sorbent (AISS) containing a copolymer (PMMA-co-AA), which consists of acrylic acid (AA) and methyl methacrylate (MMA), and PEPA (polyethylene polyamine), was synthesized. For the first time, sorbents based on homopolymers and copolymers of acrylic acid and methyl methacrylate were compared for their ability to capture CO₂ gas. Other than the synthesis of low swelling AISS, a calculation of its energy consumption, and a comparison of its cyclic capacity with 30% water solutions of monoethanolamine and methyldiethanolamine (MEA and MDEA) were performed. The solid sorbent PMMA-co-AAS showed a higher cyclic capacity than others, corresponding to the order PMMA-co-AAS (23 mg/g) > PAAS (16 mg/g) > MDEA (10 mg/g) > MEA (6 mg/g). The average absorption rate for these sorbents was in the sequence of MEA > PMMA-co-AAS > PAAS > MDEA at 40 °C, and the desorption rates were PMMA-co-AAS > PAAS > MDEA > MEA for these sorbents at 70 °C, correspondingly. When the amount of acrylic acid in the copolymer was varied from 0 to 100%, the copolymer’s water absorption capacity ranged from 0.2 to 1359.63%. Among them, the swelling ability of the chosen sorbent prepared from the 10% AA-containing copolymer and PEPA was 0.64%. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 904 KB

Open AccessProceeding Paper

Predictive Modeling of Malaria Risk Using the Nigerian Demographic and Health Survey Data

by JohnPaul C. Ugwu, Thecla O. Ayoka, Charles O. Nnadi and Wilfred O. Obonga

Eng. Proc. 2026, 124(1), 98; https://doi.org/10.3390/engproc2026124098 - 31 Mar 2026

Viewed by 629

Abstract

Malaria continues to pose a significant public health challenge in Nigeria, yet there has not been much research utilizing machine-learning techniques to forecast malaria risk. This study developed a machine-learning model that predicts malaria risk by leveraging demographic, environmental, and GPS data from [...] Read more.

Malaria continues to pose a significant public health challenge in Nigeria, yet there has not been much research utilizing machine-learning techniques to forecast malaria risk. This study developed a machine-learning model that predicts malaria risk by leveraging demographic, environmental, and GPS data from the Nigerian Demographic and Health Survey (DHS) covering the years 2000 to 2020. The dataset was pre-processed and split into a training set (with 406 respondents) and a test set (with 102 respondents). Random Forest (RF), Gradient Boosting (GB) and Linear Regression (LR) algorithms were employed to assess their predictive performance. The RF stood out with the best accuracy, achieving the lowest mean squared error (MSE = 0.0053) and the highest coefficient of determination (R² = 0.6364). Thus, RF was recognized as the most effective model for predicting malaria risk. The regression equation with positive coefficients (like population density = 0.0141, travel time = 0.0019, minimum temperature = 0.0082, temperature in January = 0.0265, and dry land surface temp = 0.0368) indicate that higher feature values are associated with increased malaria prevalence, while negative coefficients (such as rainfall = −0.0122, nightlights composite = −0.03, potential evapotranspiration = −0.09 and insecticide treated nets = −0.02) suggest that as the feature increases, the prevalence decreases. This study underscores the potential of the RF approach in improving early predictions of malaria risk and can guide targeted interventions to control malaria in areas at high risk. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

15 pages, 287 KB

Open AccessProceeding Paper

Computer Vision for Collaborative Robots in Industry 5.0: A Survey of Techniques, Gaps, and Future Directions

by Himani Varolia, César M. A. Vasques and Adélio M. S. Cavadas

Eng. Proc. 2026, 124(1), 99; https://doi.org/10.3390/engproc2026124099 - 24 Mar 2026

Viewed by 1186

Abstract

Collaborative robots are increasingly deployed in human-shared industrial workspaces, where perception is a key enabler for safe interaction, flexible manipulation, and human-aware task execution. In the context of Industry 5.0, computer vision for cobots must meet not only accuracy requirements but also human-centered [...] Read more.

Collaborative robots are increasingly deployed in human-shared industrial workspaces, where perception is a key enabler for safe interaction, flexible manipulation, and human-aware task execution. In the context of Industry 5.0, computer vision for cobots must meet not only accuracy requirements but also human-centered constraints such as safety, transparency, robustness, and practical deployability. This paper surveys computer-vision approaches used in collaborative robotics and organizes them through a task-driven taxonomy covering detection, segmentation, tracking, pose estimation, action/gesture recognition, and safety monitoring. Beyond a descriptive literature review, the paper provides a task-driven qualitative analytical perspective that relates families of computer vision methods to key industrial constraints, including occlusion, lighting variability, clutter, domain shift, real-time latency, and annotation cost, and summarizes comparative strengths and failure modes using unified criteria. We further discuss challenges related to data availability and evaluation practices, highlighting gaps in reproducibility, standardized metrics, and real-world validation in shared human–robot environments. Finally, we outline implementation and deployment considerations across common software stacks (e.g., Python-based pipelines and MATLAB-based prototyping), emphasizing ROS2 integration, edge inference, and lifecycle maintenance. The survey concludes with research directions toward robust multimodal perception, explainable human-aware vision, and benchmarkable safety-critical perception for next-generation collaborative robotic systems. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

8 pages, 540 KB

Open AccessProceeding Paper

A Federated Learning Approach for Privacy-Preserving Automated Signature Verification

by Haris Veraros, Fotios Zantalis, Stylianos Katsoulis, Elias N. Zois and Grigorios Koulouras

Eng. Proc. 2026, 124(1), 100; https://doi.org/10.3390/engproc2026124100 - 1 Apr 2026

Viewed by 800

Abstract

The growing interconnectivity of digital systems has led to the massive collection and centralization of sensitive data, raising serious concerns about confidentiality and compliance with privacy regulations. Biometric authentication systems, such as offline signature verification, are particularly vulnerable. Federated learning (FL) provides a [...] Read more.

The growing interconnectivity of digital systems has led to the massive collection and centralization of sensitive data, raising serious concerns about confidentiality and compliance with privacy regulations. Biometric authentication systems, such as offline signature verification, are particularly vulnerable. Federated learning (FL) provides a promising framework by enabling model training without exposing raw client data. However, keeping data strictly localized inherently creates severe data scarcity, which is a significant barrier to building robust deep learning (DL) models. This work investigates the feasibility of a privacy-preserving writer-dependent (WD) offline signature verification (OSV) system within an FL framework. To make local training viable under these constraints, we integrate complementary techniques into the federated pipeline: data augmentation is utilized to increase local sample diversity, while transfer learning provides robust pre-trained feature representations, drastically reducing the volume of data required for effective local fine-tuning. The proposed WD-OSV system was trained and evaluated on the popular CEDAR signature dataset, for which an average area under the curve of 0.8893, along with an average binary accuracy (ACC) of 80.12%, are reported as preliminary results. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 590 KB

Open AccessProceeding Paper

High-Gain Artificial Magnetic Conductor-Integrated Antenna for 5G Communication Systems

by Ganesh Miriyala, Vijaya Kumar Velpula, Sivaramakrishna Yechuri and Sista Venkata Surya Prasad

Eng. Proc. 2026, 124(1), 101; https://doi.org/10.3390/engproc2026124101 - 17 Mar 2026

Viewed by 415

Abstract

This article presents a meta-surface-based antenna configuration aimed at enhancing the gain performance for millimeter-wave wireless communication systems. The proposed structure consists of a rectangular meta-surface with circular cut-outs placed beneath a rectangular ring to improve the electromagnetic characteristics of the antenna. A [...] Read more.

This article presents a meta-surface-based antenna configuration aimed at enhancing the gain performance for millimeter-wave wireless communication systems. The proposed structure consists of a rectangular meta-surface with circular cut-outs placed beneath a rectangular ring to improve the electromagnetic characteristics of the antenna. A rectangular monopole antenna is designed to operate at dual frequency bands around 38 GHz and 43 GHz. To further enhance radiation performance, Artificial Magnetic Conductor (AMC) structures are incorporated beneath the antenna element. The AMC surface improves the radiation efficiency and stabilizes the antenna characteristics by providing in-phase reflection near the operating frequencies. Simulation results demonstrate that the integration of the AMC structure significantly enhances the antenna gain and impedance matching performance. In particular, the incorporation of a

4 \times 4

AMC array increases the antenna gain from approximately 3.4 dB to 6.4 dB while maintaining stable reflection coefficient characteristics. The proposed design demonstrates improved gain performance and compact structure, making it a promising candidate for millimeter-wave wireless communication applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

19 pages, 1119 KB

Open AccessProceeding Paper

Quantum-Fuzzy Adaptive Control Architecture for Nonlinear Dynamic Systems in Industrial Automation

by Noilakhon Yakubova, Isomiddin Siddiqov, Komil Usmanov, Zafar Turakulov and Yoldoshkhon Akramkhodjayev

Eng. Proc. 2026, 124(1), 102; https://doi.org/10.3390/engproc2026124102 - 1 Apr 2026

Viewed by 641

Abstract

Maintaining optimal control of heating boiler systems using intelligent control strategies remains a significant challenge due to strong nonlinearities, time delays, and unpredictable variations in fuel quality and thermal load. Conventional fuzzy logic controllers, while effective under nominal conditions, often exhibit limited robustness [...] Read more.

Maintaining optimal control of heating boiler systems using intelligent control strategies remains a significant challenge due to strong nonlinearities, time delays, and unpredictable variations in fuel quality and thermal load. Conventional fuzzy logic controllers, while effective under nominal conditions, often exhibit limited robustness when exposed to abrupt parameter changes. To address this limitation, this study proposes a novel Quantum-Fuzzy Adaptive Intelligent Proportional-Integral-Derivative (QFAI-PID) control architecture, in which probabilistic inference mechanisms inspired by quantum principles are implemented algorithmically within a classical computing framework and validated through MATLAB/Simulink simulations. The proposed approach enhances the adaptability of fuzzy rule-based control by enabling probabilistic superposition and dynamic activation of control rules, allowing the knowledge base to self-organize in real time. The control system is evaluated using a nonlinear heating boiler model developed in MATLAB/Simulink under realistic industrial disturbances, including ±25% fuel flow variations, up to 30% changes in thermal demand, and measurement delays of 5–8 s. Simulation results demonstrate that the proposed controller achieves up to 36% improvement in control stability, 30% faster response time, and 22% reduction in energy-related control effort compared with conventional fuzzy control systems. These results confirm that the proposed quantum-inspired fuzzy approach provides a robust, energy-efficient, and practically implementable solution for intelligent control of nonlinear thermal energy systems. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 1440 KB

Open AccessProceeding Paper

Influence of Geometric Scaling on the Stiffness and Stress Behavior of a Robotic Gripper

by Hugo Miguel Silva, Jhonny Rodrigues, Justino Cruz, Filipe Silva and Augusto Rego

Eng. Proc. 2026, 124(1), 103; https://doi.org/10.3390/engproc2026124103 - 8 Apr 2026

Viewed by 1025

Abstract

Robotic grippers play a key role in industrial automation and precision manipulation, where structural stiffness critically influences performance, load capacity, and accuracy. This study investigates how variations in geometric dimensions affect the stiffness and stresses of the gripper, thereby supporting more informed design [...] Read more.

Robotic grippers play a key role in industrial automation and precision manipulation, where structural stiffness critically influences performance, load capacity, and accuracy. This study investigates how variations in geometric dimensions affect the stiffness and stresses of the gripper, thereby supporting more informed design decisions. A three-dimensional baseline model of a parallel-jaw robotic gripper was developed and systematically scaled along the three principal axes to evaluate the independent effects of geometric variation. Numerical simulations were conducted using ANSYS Workbench 2025 R1 to evaluate the stiffness and stress responses resulting from geometric scaling. The results provide insight into how scaling strategies influence mechanical behavior, offering a foundation for the optimization of gripper geometry in future designs. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

7 pages, 392 KB

Open AccessProceeding Paper

Evaluation of the Quality Parameters of a Sunflower–Rapeseed Oil Blend

by Natalia Murlykina and Olena Upatova

Eng. Proc. 2026, 124(1), 104; https://doi.org/10.3390/engproc2026124104 - 9 Apr 2026

Viewed by 459

Abstract

Blending traditional vegetable oils is a cost-effective and practical approach to designing products with targeted levels and ratios of polyunsaturated fatty acids. A blend of 52% sunflower oil and 48% rapeseed oil exhibited a favourable fatty acid profile for balanced nutrition, with high [...] Read more.

Blending traditional vegetable oils is a cost-effective and practical approach to designing products with targeted levels and ratios of polyunsaturated fatty acids. A blend of 52% sunflower oil and 48% rapeseed oil exhibited a favourable fatty acid profile for balanced nutrition, with high content of monounsaturated oleic acid 42.61 ± 0.25% and sufficient ω-3 linolenic acid 4.29 ± 0.20%. It demonstrated improved hydrolytic and oxidative stability, confirmed by significantly lower acid and peroxide values after 30 days of storage at 20 ± 1 °C compared to pure sunflower oil—by 5.3% and 19.7%, respectively. The accumulation rate of primary oxidation products was 1.5 times lower in the blend (p < 0.05). The developed blend is a promising option for functional fat-containing products aimed at dietary improvement and disease prevention. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 6900 KB

Open AccessProceeding Paper

A Data-Centric Approach to Urban Building Footprint Extraction Using Graph Neural Networks and Assessed OpenStreetMap Data

by Anouar Adel, Meziane Iftene and Mohammed El Amin Larabi

Eng. Proc. 2026, 124(1), 105; https://doi.org/10.3390/engproc2026124105 - 10 Apr 2026

Viewed by 584

Abstract

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by [...] Read more.

The accurate and timely identification of urban building footprints is critical for sustainable urban planning and disaster management. Traditional remote sensing methods for this task often face limitations in scalability, accuracy, and adaptability to complex urban morphologies. This paper addresses these challenges by developing and evaluating a novel data-centric framework that synergistically integrates Graph Neural Networks (GNNs) with zero-shot superpixel segmentation derived from the Segment Anything Model (SAM) applied to Sentinel-2 imagery. A cornerstone of our methodology is a rigorous assessment of OpenStreetMap (OSM) data, refined through temporal NDVI stability analysis to generate high-quality ground truth. We propose an optimized UrbanGraphSAGE model, enhanced with spectral data augmentation and trained using a robust loss function with label smoothing to mitigate label noise. In the complex urban landscape of Algiers, Algeria, our approach achieves a Test F1-Score of 0.7131, demonstrating highly competitive performance with standard pixel-based baselines like U-Net while offering significant topological and computational advantages. Specifically, our model operates with merely 19,585 parameters—orders of magnitude fewer than pixel-based CNNs. A rigorous Gold Standard evaluation against manually labeled imagery confirms the model’s high recall (0.8484) and reliability for automated urban monitoring. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

16 pages, 2011 KB

Open AccessProceeding Paper

Prescribed Performance-Adaptive Sliding-Mode Control for a Morphing Quadcopter UAV

by Ibrahim Abdullahi Shehu, Zaharuddeen Haruna, Muhammed Bashir Mu’azu, Norhaliza Bint Abdulwahab, Sani Salisu and Umar Musa

Eng. Proc. 2026, 124(1), 106; https://doi.org/10.3390/engproc2026124106 - 8 Apr 2026

Viewed by 215

Abstract

Foldable quadcopters represent a new frontier in aerial robotics technology. The ability of a foldable quadcopter to reconfigure its geometry in flight and adapt to various flight scenarios enhances agility, maneuverability, aerodynamic efficiency, and mission versatility compared to a traditional quadcopter. However, the [...] Read more.

Foldable quadcopters represent a new frontier in aerial robotics technology. The ability of a foldable quadcopter to reconfigure its geometry in flight and adapt to various flight scenarios enhances agility, maneuverability, aerodynamic efficiency, and mission versatility compared to a traditional quadcopter. However, the morphing function introduces significant variations in parameters such as center of gravity, inertia, and nonlinear dynamics, in addition to inherent underactuation, coupling dynamics, and external disturbances. Thus, the folding mechanism presents significant challenges to conventional control approaches. To solve the drawbacks of the conventional control approach, nonlinear control methods have been investigated. This article proposed the development of a prescribed performance-adaptive sliding-mode control for a foldable quadcopter UAV. It models the morphing quadcopter as a rigid body system with five morphing formations (X, Y, H, O, and T). The prescribed performance sliding mode control approach systematically addresses the time-varying parameter and aerodynamic properties impact resulting from the morphing formation. Using Lyapunov theory, a sliding mode controller is designed that ensures the error evolution remains within prescribed performance bounds, maintains closed-loop stability, and tracks the trajectory under uncertainties. The effectiveness of the proposed control algorithm is evaluated and benchmarked in structured and unstructured trajectories against conventional nonlinear sliding mode control (SMC), PID, and LQR control methods. The simulation results indicate that the prescribed performance adaptive SMC achieves better performance and improved robustness compared to benchmarked control methods. The simulation results demonstrated that the adaptive control approach is a viable and effective solution for managing the complex dynamics of foldable quadcopters UAV. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 1487 KB

Open AccessProceeding Paper

Structural and Optical Characterization of Co₃O₄ Nanostructures Synthesized via Sol–Gel Method and Calcined at Different Temperatures

by Baskar Sumathi Samyuktha, Arumugasamy Sathiya Priya and Ragupathi Indhumathi

Eng. Proc. 2026, 124(1), 107; https://doi.org/10.3390/engproc2026124107 - 15 Apr 2026

Viewed by 631

Abstract

In this study, cobalt oxide (Co₃O₄) ceramics were synthesized using the sol–gel method and calcined at 300 °C and 600 °C to investigate the influence of thermal treatment on their structural, thermal and optical properties. X-ray diffraction (XRD) analysis [...] Read more.

In this study, cobalt oxide (Co₃O₄) ceramics were synthesized using the sol–gel method and calcined at 300 °C and 600 °C to investigate the influence of thermal treatment on their structural, thermal and optical properties. X-ray diffraction (XRD) analysis confirmed the successful formation of a pure cubic spinel Co₃O₄ phase with nanocrystalline features, belonging to the Fd3m space group. As the calcined temperature increased, the samples exhibited enhanced crystallinity, with the average crystallite size ranging from 15 to 26 nm, sharper and more intense diffraction peaks, indicating grain growth and improved structural ordering. Thermogravimetric analysis (TGA) indicated the elimination of surfaceadsorbed species and residual organics during the initial stages, succeeded by the stabilization of a pure cubic spinel Co₃O₄ phase, which exhibits remarkable thermal stability without any additional phase transitions. UV–Vis diffuse reflectance spectroscopy (DRS) analysis showed that the Co₃O₄ nanostructures displayed significant absorption in the visible region, consistent with their intrinsic narrow band gap characteristics. Unlike earlier sol–gel synthesized Co₃O₄ ceramics, the present work highlights enhanced crystallinity and structural development with increasing calcination temperature. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

13 pages, 3022 KB

Open AccessProceeding Paper

An Enhanced Lightweight IoT-Based Pipeline Leak Detection Model

by Abida Ayuba, Farouk Lawan Gambo, Aminu Musa, Hauwa Aliyu Yakubu, Bilal Ibrahim Maijamaa and Abdullahi Ishaq

Eng. Proc. 2026, 124(1), 108; https://doi.org/10.3390/engproc2026124108 - 16 Apr 2026

Viewed by 743

Abstract

Monitoring oil pipelines is crucial for effective infrastructure management and maintenance, as it helps prevent threats such as vandalism and leaks that can lead to catastrophic events. Pipeline leaks pose significant environmental and economic risks; however, existing detection methods are often expensive, slow, [...] Read more.

Monitoring oil pipelines is crucial for effective infrastructure management and maintenance, as it helps prevent threats such as vandalism and leaks that can lead to catastrophic events. Pipeline leaks pose significant environmental and economic risks; however, existing detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This study proposes a lightweight thermal-imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNN), Autoencoder (AE), and Knowledge Distillation (KD), suitable for deployment on edge devices. The proposed system addresses challenges associated with existing pipeline detection techniques, including large model sizes, high transmission latency, and excessive energy consumption. Thermal images of pipelines are captured and compressed using an autoencoder before being processed by a CNN model optimized through knowledge distillation. The model was trained and tested on a locally collected thermal image dataset and designed for deployment on edge devices such as Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate that the proposed CNN + KD + AE model achieved 98% accuracy, 98% precision, 98% recall, and an F1-score of 98%, outperforming baseline models such as MobileNetV2 (91%), InceptionV3 (84%), EfficientNet-Lite (81%), and ResNet (74%). Furthermore, the number of trainable parameters was significantly reduced to 1.18 million, with a compact model size of 4.51 MB. These findings confirm the system’s suitability for real-time leak detection in remote and resource-constrained environments, contributing to the development of cost-effective, scalable, and energy-efficient solutions for intelligent pipeline monitoring. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

14 pages, 1428 KB

Open AccessProceeding Paper

Comparative Evaluation of Flavonoids and Water-Soluble Vitamins in Solar- and Open-Air-Dried Plantago major L. Leaves for Functional Food Applications

by Komil Usmanov, Shakhnoza Sultanova, Noilakhon Yakubova, Jaloliddin Eshbobaev, Sarvar Rejabov and Jasur Safarov

Eng. Proc. 2026, 124(1), 109; https://doi.org/10.3390/engproc2026124109 - 20 Apr 2026

Viewed by 334

Abstract

This study presents a comparative evaluation of solar cabinet drying and traditional open-air sun drying with respect to their influence on the retention of water-soluble vitamins and flavonoids in Plantago major L. leaves, aiming to identify an effective and sustainable drying strategy for [...] Read more.

This study presents a comparative evaluation of solar cabinet drying and traditional open-air sun drying with respect to their influence on the retention of water-soluble vitamins and flavonoids in Plantago major L. leaves, aiming to identify an effective and sustainable drying strategy for functional food applications. Freshly harvested leaves were subjected to both drying methods under comparable environmental conditions. To account for possible structural heterogeneity, external and internal leaf tissues were analyzed separately. Qualitative and quantitative determination of bioactive compounds was performed using high-performance liquid chromatography with diode-array detection (HPLC-DAD). Flavonoids were analyzed at detection wavelengths of 254 and 276 nm, while water-soluble vitamins (C, B₂, B₃, B₆, and B₉) were determined at 250 nm. Quantification was carried out using external calibration, and results were expressed as concentrations (mg/g dry matter). The results demonstrate that solar cabinet drying provides superior preservation of oxidation- and light-sensitive bioactive compounds compared to open-air sun drying. In particular, vitamin C content in solar cabinet-dried samples reached 91.62 mg/g, which was more than three times higher than that observed after open-air drying (26.90 mg/g). Solar cabinet drying also enhanced the retention of key antioxidant flavonoids, notably dihydroquercetin (14.23 mg/g vs. 11.21 mg/g) and luteolin (0.38 mg/g vs. 0.26 mg/g). Although slightly higher concentrations of certain compounds, such as rutin and vitamins B₆ and B₉, were detected in open-air-dried samples, the overall nutraceutical profile favored solar cabinet drying. In conclusion, the controlled microclimate of the solar cabinet dryer significantly improves the stability and retention of critical water-soluble vitamins and antioxidant flavonoids in Plantago major L. leaves. These findings confirm that solar cabinet drying is a nutritionally advantageous, energy-efficient, and sustainable approach for producing high-quality plant-based ingredients suitable for functional food and nutraceutical applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

14 pages, 1848 KB

Open AccessProceeding Paper

Efficient and Explainable Glaucoma Detection Using an Attention-Enhanced Custom CNN on Retinal Fundus Images

by Vijaya Kumar Velpula, Jonnala Naga Surekha, Nekkanti Gowthami, S. Anuradha, Muvva Venkateswara Rao, J. Rajendra Prasad and Jyothisri Vadlamudi

Eng. Proc. 2026, 124(1), 110; https://doi.org/10.3390/engproc2026124110 - 21 Apr 2026

Viewed by 512

Abstract

Glaucoma is an increasing ocular disease and one of the major causes of irreversible blindness globally. Thus, it is of utmost importance to diagnose glaucoma accurately and in a timely manner for appropriate clinical intervention. Retinal fundus image analysis is a non-invasive approach [...] Read more.

Glaucoma is an increasing ocular disease and one of the major causes of irreversible blindness globally. Thus, it is of utmost importance to diagnose glaucoma accurately and in a timely manner for appropriate clinical intervention. Retinal fundus image analysis is a non-invasive approach for glaucoma screening. However, it is a tedious and subjective process for accurate glaucoma detection. In this work, a customized convolutional neural network (CNN) model is proposed for glaucoma classification using fundus images, and an attention mechanism is employed for improved discriminative feature learning. Experiments were carried out on two publicly available datasets, namely Drishti-GS1 and ACRIMA, for unbalanced and balanced datasets, respectively. Data augmentation and hyperparameter tuning were employed for improved model generalization. To increase model explainability and trust for accurate glaucoma screening, gradient-weighted class activation mapping (Grad-CAM) is employed for accurate glaucoma screening. For accurate glaucoma screening, the proposed model attained 90.32% and 96.45% accuracy on the Drishti-GS1 and ACRIMA datasets, respectively, with higher sensitivity and AUC scores. Thus, it is evident that the proposed model employing attention mechanisms and explainable AI attained higher accuracy for accurate glaucoma screening. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

10 pages, 60623 KB

Open AccessProceeding Paper

Hyaluronic Acid for Wound Healing: Experience in Deep-Burn Rat Model

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

Eng. Proc. 2026, 124(1), 111; https://doi.org/10.3390/engproc2026124111 - 23 Apr 2026

Viewed by 414

Abstract

Hyaluronic acid (HA), a major extracellular matrix component, is used therapeutically to aid healing and deliver drugs to injury sites. Burns create serious clinical and aesthetic problems needing fast skin repair to prevent complications. This study compared 1.8% pharmaceutical-grade HA with panthenol-containing gel [...] Read more.

Hyaluronic acid (HA), a major extracellular matrix component, is used therapeutically to aid healing and deliver drugs to injury sites. Burns create serious clinical and aesthetic problems needing fast skin repair to prevent complications. This study compared 1.8% pharmaceutical-grade HA with panthenol-containing gel (PCG) in deep-burn healing in rats against spontaneous healing. HA slightly accelerated wound closure from day 3 compared to PCG; both induced granulation by day 7 and epithelialization by day 28. HA caused early collagen drop (day 3), later matched PCG levels with abnormal distribution, and both exceeded control by day 28. HA normalized systemic leukocyte counts by day 14 while strongly increasing local leukocyte infiltration in the wound area. HA dual immune effect depends on source and properties; further research is required for clinical use in wound healing. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

13 pages, 1028 KB

Open AccessProceeding Paper

Comparative Study of Rigid and Flexible Multibody Dynamics in a 3D-Printed Two-Link Robotic Mechanism

by Hassan Ali, César M. A. Vasques and Adélio M. S. Cavadas

Eng. Proc. 2026, 124(1), 112; https://doi.org/10.3390/engproc2026124112 - 5 May 2026

Cited by 1 | Viewed by 441

Abstract

The use of 3D printing in robotics enables lightweight, customized, and geometrically complex structures, but the resulting structural compliance challenges accurate dynamic prediction. Traditional rigid multibody models often neglect structural deformations and vibrations that can critically affect performance and control. This work presents [...] Read more.

The use of 3D printing in robotics enables lightweight, customized, and geometrically complex structures, but the resulting structural compliance challenges accurate dynamic prediction. Traditional rigid multibody models often neglect structural deformations and vibrations that can critically affect performance and control. This work presents initial advances toward a computational framework for flexible multibody dynamics of 3D-printed robotic structures. A two-link mechanism is modeled in MATLAB Simscape Multibody under both rigid and flexible assumptions, and parametric analyses are conducted to assess the influence of geometry, mass distribution, and stiffness on system dynamics. The proposed framework is formulated to accommodate reduced-order and data-driven modeling approaches for efficient simulation and analysis of flexible robotic mechanisms. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

15 pages, 747 KB

Open AccessProceeding Paper

Explainable Artificial Intelligence for Social Sciences and Humanities: A Systematic Bibliometric Analysis

by Nikos Koutsoupias and Marios Nosios

Eng. Proc. 2026, 124(1), 113; https://doi.org/10.3390/engproc2026124113 - 7 May 2026

Viewed by 674

Abstract

The increasing adoption of artificial intelligence in the social sciences and humanities has intensified concerns regarding transparency, interpretability, and epistemic accountability, thereby contributing to the growing prominence of explainable artificial intelligence. This study examines how explainable artificial intelligence has been structured and integrated [...] Read more.

The increasing adoption of artificial intelligence in the social sciences and humanities has intensified concerns regarding transparency, interpretability, and epistemic accountability, thereby contributing to the growing prominence of explainable artificial intelligence. This study examines how explainable artificial intelligence has been structured and integrated within social sciences and humanities research through a systematic bibliometric analysis of peer-reviewed journal articles indexed in Scopus between 1975 and 2026. To ensure disciplinary delimitation, a corpus restricted to the Social Sciences and Arts and Humanities subject areas was constructed and analyzed alongside a matched corpus focused on computer science. Using the bibliometrix package in R, the analysis assessed publication trajectories, keyword configurations, thematic structures, and citation patterns. The findings indicate that explainable artificial intelligence research in social sciences and humanities is firmly embedded within broader machine learning infrastructures while exhibiting distinctive patterns of methodological adoption and thematic emphasis. Feature attribution techniques, particularly SHAP and LIME, have emerged as the dominant explanatory tools, whereas deep learning-centered and model-theoretic debates on interpretability remain comparatively less prominent. Thematic mapping reveals a consolidated core linking explainable artificial intelligence to established computational paradigms, alongside more specialized methodological niches. Citation patterns further underscore the prominence of human-centered and application-oriented research domains. Overall, the study demonstrates that explainable artificial intelligence within the social sciences and humanities constitutes a selectively adapted and contextually embedded research formation, shaped by disciplinary priorities and applied research environments. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 1329 KB

Open AccessProceeding Paper

Neuromorphic AI-Based e-Skin for Emotion-Sensitive Humanoid Robots

by Shubham Gupta and Suhaib Ahmed

Eng. Proc. 2026, 124(1), 114; https://doi.org/10.3390/engproc2026124114 - 7 May 2026

Viewed by 838

Abstract

Humanoid robots operating in proximity to humans require the ability to perceive and interpret emotional cues conveyed through touch to achieve safe, natural, and socially intelligent interaction. Conventional tactile sensing systems primarily focus on force or pressure detection and cannot infer affective intent, [...] Read more.

Humanoid robots operating in proximity to humans require the ability to perceive and interpret emotional cues conveyed through touch to achieve safe, natural, and socially intelligent interaction. Conventional tactile sensing systems primarily focus on force or pressure detection and cannot infer affective intent, while frame-based deep learning models often suffer from high latency and energy consumption when deployed on embedded platforms. To address these limitations, this paper presents a neuromorphic AI-based multimodal electronic skin (e-skin) framework for emotion-sensitive touch perception in humanoid robots. The proposed system integrates pressure, temperature, and electrostatic sensing with a bio-inspired signal conditioning pipeline and a Spiking Neural Network (SNN) for event-driven, low-power processing. A custom multimodal tactile dataset was collected using the proposed e-skin prototype to model four emotional touch interactions: stress, neutral, comfort, and affection. Experimental results demonstrate that the proposed approach achieves a high emotion classification accuracy of up to 92%, with an average accuracy of 88.75% across all classes. The neuromorphic SNN significantly reduces inference latency to approximately 8 ms, compared to 38 ms for a conventional CNN-based model, while maintaining energy-efficient operation suitable for edge deployment. The results validate the effectiveness of combining multimodal tactile sensing with neuromorphic processing to enable real-time, emotion-aware human–robot interaction. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 232 KB

Open AccessProceeding Paper

Evaluating Thread, Zigbee and Z-Wave Against Common Criteria Cryptographic Requirements

by Evangelos Nannos, Stylianos Katsoulis, Fotios Zantalis, Ioannis Chrysovalantis Panagou, Konstantinos Boukouras and Grigorios Koulouras

Eng. Proc. 2026, 124(1), 115; https://doi.org/10.3390/engproc2026124115 - 22 May 2026

Viewed by 435

Abstract

The explosive growth of the Internet of Things (IoT) has brought an array of resource-constrained devices to domains such as smart homes, industrial automation, and healthcare, raising substantial cybersecurity challenges. Lightweight wireless protocols, such as Thread, Zigbee, and Z-Wave, are integral to IoT [...] Read more.

The explosive growth of the Internet of Things (IoT) has brought an array of resource-constrained devices to domains such as smart homes, industrial automation, and healthcare, raising substantial cybersecurity challenges. Lightweight wireless protocols, such as Thread, Zigbee, and Z-Wave, are integral to IoT connectivity, but the degree to which their embedded cryptographic mechanisms satisfy formal cybersecurity certification schemes remains underexplored. This work draws primarily on recent peer-reviewed publications and major conference proceedings to rigorously evaluate Thread, Zigbee, and Z-Wave against the Common Criteria (CC) Functional Requirements for Cryptography (FCS) as specified in CC:2022 and the EU cybersecurity certification scheme on Common Criteria (EUCC). The assessment focuses on essential CC cryptographic components, including key generation (FCS_CKM.1), secure key distribution (FCS_CKM.2), agreement protocols (FCS_CKM_EXT.7), cryptographic operations (FCS_COP.1), and random bit generators (FCS_RBG.1). The analysis reveals that Thread demonstrates the strongest alignment with CC requirements by leveraging Advanced Encryption Standard—Counter with CBC-MAC mode (AES-CCM) authenticated encryption and Elliptic Curve Diffie-Hellman (ECDH)-based key exchange within a decentralized trust framework. Zigbee matches this cryptographic strength at the primitive level, but its dependency on a centralized Trust Center for key management complicates full compliance with key lifecycle and distribution controls. Z-Wave, especially through its S2 Security framework, improves by incorporating authenticated ECDH exchanges, though proprietary constraints and limited protocol transparency remain obstacles to independent assurance. This comparative study concludes that while all three protocols provide a baseline of robust cryptographic security, only Thread currently aligns with CC and EUCC certification schemes. Zigbee and Z-Wave will require additional protocol hardening and enhancement of cryptographic key lifecycle management to achieve comparable assurance levels. Ensuring conformance with formal cybersecurity standards is imperative for building trust and resilience across critical IoT infrastructures. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

16 pages, 2476 KB

Open AccessProceeding Paper

An In-Depth Comparative Analysis of Machine Learning Models for Soil Fertility Prediction

by Harmesh Behera, Bibhukalyan Nayak, Ritesh Kumar Gouda, Neelamadhab Padhy, Rasmita Panigrahi and Pradeep Kumar Mahapatro

Eng. Proc. 2026, 124(1), 116; https://doi.org/10.3390/engproc2026124116 - 19 May 2026

Viewed by 331

Abstract

One of the major determinants of crop productivity and sustainable agricultural practices is soil fertility. Proper soil assessment helps farmers make informed decisions about nutrients and fertilizers. This study utilizes 16 machine learning classifiers for soil fertility prediction, including learner-based, ensemble-based, instance-based, and [...] Read more.

One of the major determinants of crop productivity and sustainable agricultural practices is soil fertility. Proper soil assessment helps farmers make informed decisions about nutrients and fertilizers. This study utilizes 16 machine learning classifiers for soil fertility prediction, including learner-based, ensemble-based, instance-based, and probabilistic-based models. The model’s performance is assessed using accuracy, precision, recall, and F1-score. This paper presents a machine learning model for predicting soil fertility based on soil physicochemical characteristics. The data used in the research comprise vital soil parameters: nitrogen, phosphorus, potassium, pH, organic carbon, electrical conductivity, and micronutrients. Missing-value imputation, label encoding, and feature standardization are among the data preprocessing methods used to enhance data quality. Correlation analysis, ANOVA F-score, and mutual information were used to assess feature importance and determine the most significant soil characteristics. The experimental observation reveals that the RF model achieves an accuracy of 90.91% compared to the other models. Additional assessment using multi-class Receiver Operating Characteristic (ROC) and Precision–Recall (PR) curves showed excellent discriminative ability across the dominant soil fertility, which was of high quality. The findings show that machine learning models, especially ensemble-based models, are effective at estimating soil fertility levels. The proposed framework provides a data-driven, reliable decision-support system to assess soil fertility, enabling farmers and agricultural experts to enhance nutrient management and crop production. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

16 pages, 4094 KB

Open AccessProceeding Paper

Integrated Linear Transformer-Based Diode Bridge Rectifier for Improved Power Quality in Electric Vehicle Charging Stations

by Sugunakar Mamidala and Yellapragada Venkata Pavan Kumar

Eng. Proc. 2026, 124(1), 117; https://doi.org/10.3390/engproc2026124117 - 20 May 2026

Viewed by 378

Abstract

As electric vehicle (EV) charging stations are increasingly common, the front-end rectifier stage of the charging infrastructure tends to degrade grid power quality by introducing high input current harmonics, poor power factor, and voltage distortion. Despite their simplicity and low cost, the conventional [...] Read more.

As electric vehicle (EV) charging stations are increasingly common, the front-end rectifier stage of the charging infrastructure tends to degrade grid power quality by introducing high input current harmonics, poor power factor, and voltage distortion. Despite their simplicity and low cost, the conventional diode bridge rectifiers (DBR) usually have a total harmonic distortion (THD) of over 25% and have power factors of below 0.80. These issues have been handled with the active power factor correction (PFC) techniques, which increase system complexity, the cost of the system, and the increased sophistication of the control algorithm. This article proposes an integrated linear transformer (LT) based diode-bridge rectifier (DBR) that is intended to enhance the quality of power of the EV charging stations without invoking active control mechanisms. The suggested arrangement combines a linear transformer, a passive filter network, and a diode bridge to obtain multipurpose voltage step-down (galvanic isolation) and harmonic mitigation in a single structure. The system provides improved voltage regulation, flux balancing, and filter resonance, and reduced current distortion. The proposed system is validated with MATLAB/Simulink R2021a, and the results show that the proposed system has a THD of 4.32% that complies with the IEEE 519 harmonic standards, and also the input power factor is increased to 0.98. It also decreases DC output voltage ripple by 4.8% to 0.7% and improves its voltage regulation by 9.1%, as well as increases its system efficiency to 96.3%. The findings make integrated LT + DBR an affordable, robust, and less massive implementation of the next-generation EV charging infrastructure, specially designed to meet the needs of smart grid deployment and integration in Tier-2 and Tier-3 cities, where simplicity and power quality compliance remain a priority. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

19 pages, 6080 KB

Open AccessProceeding Paper

Advancing Colorectal Polyp Detection in Colonoscopy Through Region-Guided Deep Learning

by Fairooz Nahiyan, Simoon Nahar, Taslim Alam, Md. Khaliluzzaman and Mohammad Mahadi Hassan

Eng. Proc. 2026, 124(1), 118; https://doi.org/10.3390/engproc2026124118 - 22 May 2026

Viewed by 589

Abstract

In terms of the detection of colorectal polyps during a colonoscopy, the accuracy of the diagnosis is key to effective prevention and treatment, and can be hindered by manual identification. Colorectal polyps are abnormal tissue growths in the colon or rectum, and their [...] Read more.

In terms of the detection of colorectal polyps during a colonoscopy, the accuracy of the diagnosis is key to effective prevention and treatment, and can be hindered by manual identification. Colorectal polyps are abnormal tissue growths in the colon or rectum, and their sizes, shapes and textures can make them difficult to find. Researchers have now turned to deep learning techniques and the YOLOv11 detection framework in particular to provide a method to automate the recognition and accurate identification of these abnormal growths. Specifically, the proposed method modifies the conventional YOLOv11 detection workflow by generating bounding box annotations from polyp segmentation masks, applying region-aware data preprocessing and augmentation, and training the detector under region-guided supervision to enhance localization precision and detection robustness. polyp segmentation masks are utilized to generate bounding box annotations which not only contribute exact spatial supervision but also avoid manual box labeling inconstancy. Region-aware data preprocessing and augmentation pay more attention to polyp-relevant regions and suppress background noise, which leads to clearer feature discrimination for small or irregular polyps. Additionally, region-guided supervision serves as explicit guidance for localizing objects with the anatomical polyp regions, which largely helps achieve accurate boundaries and prevent false detections. The proposed YOLOv11-based polyp detection system was tested and evaluated on the publicly available Kvasir-SEG dataset, which is comprised of annotated colonoscopy images. Enhanced data pre-processing and exhaustive training with appropriate choice of hyper-parameters fortified the reliability and useability of the model. The results confirmed high-grade results, and gave an Intersection over Union score of 0.9764, and an overall correctness rate of 99.00%, with well-balanced precision, recollection and F1-scores. Coming in with a mean Average Precision (mAP) of 0.9937 at a Intersection over Union threshold of 0.5 and 0.9935 over the full spectrum of thresholds from 0.5 to 0.95, this shows that the model is able to consistently and reliably detect polyps. The proposed system was also compared with Segment Anything Model, YOLO-Seg, and SAM2 and confirmed the efficacy of its method. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

8 pages, 2092 KB

Open AccessProceeding Paper

Prediction of Unsaturated Hydraulic Conductivity in Bio-Treated Stabilized Lateritic Soil

by Roland K. Etim, Paul Yohana, Adrian O. Eberemu, Thomas S. Ijimdiya and Kolawole J. Osinubi

Eng. Proc. 2026, 124(1), 119; https://doi.org/10.3390/engproc2026124119 - 29 May 2026

Viewed by 76

Abstract

The measurement and/or evaluation of unsaturated hydraulic conductivity (USHC) is time-consuming and, at the same time, requires the deployment of specialized equipment. Due to this problem, several studies have used analytical methods to evaluate and predict the USHC of soil and modified soil [...] Read more.

The measurement and/or evaluation of unsaturated hydraulic conductivity (USHC) is time-consuming and, at the same time, requires the deployment of specialized equipment. Due to this problem, several studies have used analytical methods to evaluate and predict the USHC of soil and modified soil matrix. Since there is a lack of adequate data on studies or cases of USHC in bio-treated soil specimens, this research examines the subject, though not without limitation. This research examines the USHC behaviour of bio-modified lateritic soil using fitting parameters of the soil-water retention curve. These parameters were fitted into the relative permeability function, k_r, for van Genuchten (VG), Brooks–Corey (BC), and Fredlund–Xing (FX). The numerical measure of the USHC is the product of k_r and the measured saturated permeability value. The saturated hydraulic conductivity and soil–water retention curve of specimens were prepared at −2, 0, and +2% moulding water content relative to optimum (MWCRO), 0 to 2.4 × 10⁹ cells/mL bacteria suspension densities, and RBSL to BSH compactive efforts. At higher suction stress, USHC in most instances decreased as MWCRO increased, culminating in its lowest value of 1.4 × 10⁻¹⁹ m/s for BC at +2% wet of optimum, while increased microbial suspension resulted in a slight decrease and/or variations that translated to the lowest value of 3.32 × 10⁻³⁰ m/s for BC at 1.5 × 10⁸ cells/mL. The USHC decreased with suction in the order BC ˂ FX ˂ VG, presenting how moisture condition, bio-treatment, and compaction interact to govern USHC and confirm the relevance of SWCC-based models in bio-stabilized soil assessment. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

4337 KB

Open AccessProceeding Paper

Next-Day Forest Fire Risk Prediction Using Machine Learning and Multimodal Satellite Data

by Prajwal Mohapatra, Swayam Subhankar Sahoo, Adyasha Das and Rururaj Pradhan

Eng. Proc. 2026, 124(1), 120; https://doi.org/10.3390/engproc2026124120 (registering DOI) - 17 Jun 2026

Abstract

Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal [...] Read more.

Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal anomaly detection (e.g., MODIS or VIIRS-SNPP), our approach is fully predictive, generating pixel-wise fire risk maps a day in advance. Our study focuses on Uttarakhand, India, which is an ecologically sensitive region that experiences frequent and severe forest fires. We curated a domain-specific geospatial dataset spanning 1 April to 29 May 2016. It includes daily 30 m GeoTIFF images with 10 bands comprising weather (e.g., temperature, wind, precipitation), topography (slope, aspect), fuel map, and fire mask. We constructed this dataset from diverse sources and aligned all bands spatially and temporally. To demonstrate the usefulness of this dataset, we implement a deep convolutional neural network (CNN) using the ResUNet-A architecture, chosen for its robust performance in the semantic segmentation of high-resolution remote sensing data. Our model is trained from scratch to produce high-resolution fire probability maps and classify fire/no-fire pixels. Our solution helps with planning and decision-making for early intervention, especially in areas with high risk. It supports the UN’s SDG 13 (Climate Action) and SDG 15 (Life on Land) by enhancing resilience and conserving ecosystems. The presented dataset and methodology can serve as a benchmark for future research on wildfire risk prediction using Earth observation data. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

14 pages, 1879 KB

Open AccessProceeding Paper

Altitude Control in an Unmanned Aerial Vehicle Through Deflection of Elevator

by Muhammad Hashier Muneeb Farrukh, Syed Irtiza Ali Shah, Ibtesam Hayat, Hafiz Usama Tanveer, Rai Faisal Aslam and Hasham Tanveer

Eng. Proc. 2026, 124(1), 121; https://doi.org/10.3390/engproc2026124121 (registering DOI) - 10 Jun 2026

Viewed by 7

Abstract

This paper investigates altitude control of the Unmanned Aerial Vehicle (UAV) through the elevator. Elevators are flight control surfaces, which control lateral altitude by changing the pitch balance. The angle deflection along with the thrust from propulsion system is matched and guided by [...] Read more.

This paper investigates altitude control of the Unmanned Aerial Vehicle (UAV) through the elevator. Elevators are flight control surfaces, which control lateral altitude by changing the pitch balance. The angle deflection along with the thrust from propulsion system is matched and guided by the system for the gain or loss of altitude over desired range of distance. A linear time-invariant elevator–altitude channel model is obtained by linearizing the six-degree-of-freedom equations of motion about a steady, level-flight trim condition. The resulting transfer function is analyzed using state-space representation and root-locus techniques, revealing that the uncompensated unity-feedback system is unstable. A proportional-integral (PI) controller is then designed and implemented in a unity-feedback configuration. The closed-loop dynamics are evaluated through time-domain simulations under step, ramp, and parabolic altitude commands, and key performance indices such as rise time, settling time, overshoot, and steady-state error are extracted. The Routh–Hurwitz criterion is used to derive an admissible gain range and to select a gain that balances response speed and robustness. The steady-state error is quantified analytically for step, ramp, and parabolic inputs, confirming a finite error for step inputs and infinite error for ramp and parabolic inputs, consistent with a type-0 system. The results demonstrate that a simple PI-based elevator controller can stabilize the linearized altitude channel and significantly improve transient performance, providing a useful baseline for more advanced nonlinear or adaptive designs in UAV flight-control applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

11 pages, 1246 KB

Open AccessProceeding Paper

Comparison of Intelligent and Traditional Control Systems in Wastewater Treatment Process Control

by Jaloliddin Eshbobaev, Alisher Rakhimov, Adham Norkobilov, Komil Usmanov, Zafar Turakulov, Azizbek Kamolov, Sarvar Rejabov and Bakhodir Khamidov

Eng. Proc. 2026, 124(1), 4029; https://doi.org/10.3390/engproc2026124029 - 12 Feb 2026

Cited by 1 | Viewed by 649

Abstract

Ion-exchange-based wastewater treatment processes exhibit nonlinear and time-varying dynamics, making the control of total dissolved solids (TDS) and water hardness a complex task. Conventional Proportional–Integral–Derivative (PID) controllers often show limited performance under such conditions due to fixed tuning parameters and linear assumptions. To [...] Read more.

Ion-exchange-based wastewater treatment processes exhibit nonlinear and time-varying dynamics, making the control of total dissolved solids (TDS) and water hardness a complex task. Conventional Proportional–Integral–Derivative (PID) controllers often show limited performance under such conditions due to fixed tuning parameters and linear assumptions. To address these limitations, this study presents a comparative evaluation of traditional and intelligent control strategies for regulating TDS and water hardness through influent flow control. A classical PID controller is compared with fuzzy logic and Adaptive neuro-fuzzy inference system (ANFIS) controllers using a unified MATLAB/Simulink simulation framework. The control performance is evaluated based on dynamic response characteristics, including rise time, settling time, and overshoot. For TDS control, the PID controller exhibits a rise time of 15.9 s and a settling time of 50.9 s, while the fuzzy logic controller improves the response with a rise time of 13.6 s and settling time of 44.1 s. The ANFIS controller achieves the fastest response, with a rise time of 8.31 s and a settling time of 27.1 s. Similar trends are observed for water hardness control, where the PID controller shows a rise time of 17.0 s and settling time of 55.8 s, the fuzzy logic controller reduces these values to 12.3 s and 40.4 s, respectively, and the ANFIS controller further improves performance with a rise time of 9.23 s and settling time of 30.3 s. The overshoot values for all controllers remain comparable, within the range of approximately 4.4–5.0%. The results clearly demonstrate that intelligent control strategies, particularly ANFIS, provide significantly faster convergence and improved dynamic performance compared to conventional PID control. The reduced settling time implies lower control effort and decreased energy consumption, highlighting the potential of intelligent controllers for efficient and reliable industrial wastewater treatment applications. Full article

(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Eng. Proc., 2026, ASEC 2025

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI