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6 pages, 1556 KiB

Open AccessProceeding Paper

Mitigating Environmental Risks: Efficient Removal of Metronidazole from Pharmaceutical Wastewater Using Functionalized Graphene Membrane

by Toyese Oyegoke

Eng. Proc. 2025, 87(1), 1; https://doi.org/10.3390/engproc2025087001 - 16 Jan 2025

Viewed by 634

Abstract

Metronidazole, an antibiotic widely used in human and veterinary medicine, poses significant environmental risks when discharged into aquatic environments. This study explores the potential of functionalized graphene membranes for the removal of metronidazole from industrial and pharmaceutical wastewater. Employing molecular simulations and the [...] Read more.

Metronidazole, an antibiotic widely used in human and veterinary medicine, poses significant environmental risks when discharged into aquatic environments. This study explores the potential of functionalized graphene membranes for the removal of metronidazole from industrial and pharmaceutical wastewater. Employing molecular simulations and the AM1 semi-empirical-calculation method in solvent (water), we designed and simulated functionalized membranes to enhance metronidazole removal efficiency. Pharmaceutical effluent that contains metronidazole can have detrimental effects on aquatic ecosystems, including toxicity to aquatic organisms and the potential development of antibiotic-resistant bacteria. Our findings show that specific functionalized membranes exhibit selective adsorption for metronidazole, indicating promising results for efficient wastewater treatment. In the study, it was confirmed that a significant drop occurs in the adsorptive property of all functions for metronidazole removal, except for membranes decorated with aldehyde (-CHO) and secondary amine (-CHNH) function. Further analysis of the functionalized graphene membranes confirms one decorated with aldehyde function to have demonstrated superior selective adsorption of metronidazole over water, compared to the other membrane decorated with other functions in the presence of water. The use of functionalized graphene membranes for metronidazole removal shows great potential in mitigating the environmental risks associated with pharmaceutical effluent, which is in line with the study findings and related literature. By improving our understanding of adsorption processes and membrane interactions, we can develop more effective wastewater treatment technologies to safeguard our environment. Full article

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8 pages, 1441 KiB

Open AccessProceeding Paper

Peripheral Venous Simulator Development for Medical Training

by Pedro Escudero-Villa, Jéssica Núñez-Sánchez and Jenny Paredes-Fierro

Eng. Proc. 2025, 87(1), 2; https://doi.org/10.3390/engproc2025087002 - 6 Feb 2025

Viewed by 768

Abstract

The necessity to develop skills in medical training, from simple procedures such as sutures, venipunctures, and peripheral venous cannulations to complex surgeries, has driven innovation in the fabrication of medical simulators throughout history. These simulators are crafted using materials that mimic the physical [...] Read more.

The necessity to develop skills in medical training, from simple procedures such as sutures, venipunctures, and peripheral venous cannulations to complex surgeries, has driven innovation in the fabrication of medical simulators throughout history. These simulators are crafted using materials that mimic the physical and mechanical characteristics of human body parts, providing realistic training experiences. However, the costs associated with developing these simulators pose a significant challenge, especially for low-income areas. This work explores practical options for creating cost-effective and useful simulators by fabricating pieces that represent the forearm, a common site for venipunctures and peripheral venous cannulations. The fabrication process involved combining three types of materials: polydimethylsiloxane (PDMS), food-grade silicone, and Artesil Shore 20 silicone, along with a Foley catheter to simulate the arm veins. The compatibility of these materials was thoroughly evaluated to produce valid prototypes, ensuring that the stress ratios closely matched the properties of human tissue. Preliminary evaluations showed a good acceptability rating from users. Medical students who tested the simulators found them effective for explaining the behavior of fluids in the body during venoclysis simulations and recommended elaborating on the replication of more complex structures. Full article

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8 pages, 1753 KiB

Open AccessProceeding Paper

DenseMobile Net: Deep Ensemble Model for Precision and Innovation in Indian Food Recognition

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

Eng. Proc. 2025, 87(1), 3; https://doi.org/10.3390/engproc2025087003 - 7 Feb 2025

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Abstract

Precision and efficacy are vital in the constantly advancing field of food image identification, particularly in the domains of medicine and healthcare. Transfer learning and deep ensemble learning techniques are employed to enhance the accuracy and efficiency of the Indian Food Classification System. [...] Read more.

Precision and efficacy are vital in the constantly advancing field of food image identification, particularly in the domains of medicine and healthcare. Transfer learning and deep ensemble learning techniques are employed to enhance the accuracy and efficiency of the Indian Food Classification System. The ensemble model effectively captures various patterns and correlations within the information by employing many machine learning techniques. The ensemble method we employ utilizes the MobileNetV3 and DenseNet-121 transfer learning models to construct a robust model. The ensemble model benefits from the integration of model predictions, resulting in enhanced recognition of Indian food. The study utilized a dataset consisting of 6000 photographs of Indian cuisine, categorized into 26 distinct groups. The picture dataset is divided into two subsets: 80% is allocated for training and 20% is reserved for testing. The experimental results demonstrate that DenseNet-121 surpasses MobileNetv3 in terms of testing accuracy, achieving a rate of 90%. The MobileNetV3 model achieves an accuracy of 87.64% on the Indian food image dataset. The integration of both models in ensemble learning yields a model accuracy of 92.38%, surpassing the performance of each individual model. This research revolutionizes our food relationship with the use of state-of-the-art technologies. By utilizing the most advanced transfer learning algorithm specifically designed for the precise classification of Indian cuisine, our aim is to establish a new standard in both technology and gastronomy. This will facilitate innovation in food perception, comprehension, and engagement. Full article

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10 pages, 1883 KiB

Open AccessProceeding Paper

Analyzing the Thermal Behavior and Phase Transitions of ZnSnO₃ Prepared via Chemical Precipitation

by Ragupathi Indhumathi, Arumugasamy Sathiya Priya and Baskar Sumathi Samyuktha

Eng. Proc. 2025, 87(1), 4; https://doi.org/10.3390/engproc2025087004 - 14 Feb 2025

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Abstract

ZnSnO₃ ceramics were prepared via chemical precipitation at various calcination temperatures of 200, 300, 400, 500, and 600 °C. The prepared ceramics were analyzed using thermogravimetric analysis–differential scanning calorimetry (TGA–DSC), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV-visible spectroscopy (UV-Vis). [...] Read more.

ZnSnO₃ ceramics were prepared via chemical precipitation at various calcination temperatures of 200, 300, 400, 500, and 600 °C. The prepared ceramics were analyzed using thermogravimetric analysis–differential scanning calorimetry (TGA–DSC), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV-visible spectroscopy (UV-Vis). Thermal analysis identified critical phase transitions, including the decomposition of ZnSn(OH)₆ into ZnSnO₃ and its subsequent transformation into Zn₂SnO₄ at elevated temperatures. XRD confirmed the orthorhombic crystal structure of the prepared ceramics. Further, increasing calcination temperatures led to enhanced crystallinity and reduced crystallite sizes, with the average crystallite size ranging from 22 to 45 nm. FTIR analysis revealed the chemical bonding and functional groups present in ZnSnO₃. The energy band gap values observed from UV-Vis spectroscopy ranged from 3.64 eV to 3.53 eV. These findings show the role of calcination temperature in tailoring the structural and optical properties of ZnSnO₃ ceramics, with potential applications in energy conversion technologies, including solar cells and optoelectronic devices. The optimization and development of ZnSnO₃-based materials hold promise for efficient energy harvesting and storage applications. Full article

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8 pages, 7121 KiB

Open AccessProceeding Paper

Influence of Optical Feedback Strength on Intensity Noise and Photon Number Probability Distribution of InGaAsP/InP Laser

by Salah Abdulrhmann, Abu Mohamed Alhasan and Jabir Hakami

Eng. Proc. 2025, 87(1), 5; https://doi.org/10.3390/engproc2025087005 - 18 Feb 2025

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Abstract

We have systematically investigated how the strength of optical feedback (OFB) affects the dynamics, noise levels, and photon number probability density distribution (PNPDD) in time-delayed semiconductor lasers (SLs). We find that intensity noise decreases in both weak and strong OFB regimes. The shape [...] Read more.

We have systematically investigated how the strength of optical feedback (OFB) affects the dynamics, noise levels, and photon number probability density distribution (PNPDD) in time-delayed semiconductor lasers (SLs). We find that intensity noise decreases in both weak and strong OFB regimes. The shape of the PNPDDs changes based on OFB strength: it shifts from symmetric to asymmetric based on the OFB strength. In the chaotic region, the PNPDDs display a peak at low intensity and taper off at multiples of the average photon number. The results of this work suggest that operating SLs under weak or strong OFB conditions may help to minimize instability. Full article

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10 pages, 1579 KiB

Open AccessProceeding Paper

Fabrication and Characterization of Perovskite Solar Cells Using Metal Phthalocyanines and Naphthalocyanines

by Atsushi Suzuki, Naoki Ohashi, Takeo Oku, Tomoharu Tachikawa, Tomoya Hasegawa and Sakiko Fukunishi

Eng. Proc. 2025, 87(1), 6; https://doi.org/10.3390/engproc2025087006 - 18 Feb 2025

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Abstract

Fabrication and characterization based on experimental results for methylammonium lead iodide (MAPbI₃) perovskite solar cells using chemical-substituted metal phthalocyanines (MPc) and naphthalocyanines (MNc) as hole-transport materials have been performed to improve conversion efficiency (η) and stability. The purpose of [...] Read more.

Fabrication and characterization based on experimental results for methylammonium lead iodide (MAPbI₃) perovskite solar cells using chemical-substituted metal phthalocyanines (MPc) and naphthalocyanines (MNc) as hole-transport materials have been performed to improve conversion efficiency (η) and stability. The purpose of this study was to fabricate and characterize a MAPbI₃ perovskite solar cell using t-butyl MPc and MNc as a hole-transporting layer to improve the photovoltaic performance and stability of η. Photovoltaic characteristics, morphology, crystallinity, and electronic structures were characterized in perovskite solar cells using MPc and MNc. The photovoltaic performance of the perovskite solar cell using t-butyl nickel phthalocyanine (NiPc) reached the maximum value of η at 13.4%. Incorporation of NiPc passivated the surface morphology by increasing the crystal grain size and supporting the carrier diffusion while suppressing carrier recombination near the grain boundary in the perovskite layer. Simulation using a SCAPS-1D program predicted the photovoltaic characteristics of the perovskite solar cell using NiPc. The photovoltaic mechanism was discussed on the basis of an energy diagram of the perovskite solar cell. The insertion of NiPc optimized energy levels near the highest occupied molecular orbital of NiPc and the valence band state of MAPbI₃, supporting a charge transfer related to short-circuit current density and η. Full article

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9 pages, 721 KiB

Open AccessProceeding Paper

Comparative Analysis of Long Short-Term Memory and Gated Recurrent Unit Models for Chicken Egg Fertility Classification Using Deep Learning

by Shoffan Saifullah

Eng. Proc. 2025, 87(1), 7; https://doi.org/10.3390/engproc2025087007 - 20 Feb 2025

Cited by 1 | Viewed by 445

Abstract

This study explores the application of advanced Recurrent Neural Network (RNN) architectures—specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—for classifying chicken egg fertility based on embryonic development detected in egg images. Traditional methods, such as candling, are labor-intensive and often inaccurate, [...] Read more.

This study explores the application of advanced Recurrent Neural Network (RNN) architectures—specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—for classifying chicken egg fertility based on embryonic development detected in egg images. Traditional methods, such as candling, are labor-intensive and often inaccurate, making them unsuitable for large-scale poultry operations. By leveraging the capabilities of LSTM and GRU models, this research aims to automate and enhance the accuracy of egg fertility classification, thereby contributing to agricultural automation. A dataset comprising 240 high-resolution egg images was employed, resized to 256 × 256 pixels for optimal processing efficiency. LSTM and GRU models were trained to discern fertile from infertile eggs by analyzing the sequential data represented by the pixel rows in these images. The LSTM model demonstrated superior performance, achieving a validation accuracy of 89.58%, significantly surpassing the GRU model (66.67%). Compared to classical methods such as Decision Tree (85%), Logistic Regression (88.3%), the LSTM model demonstrated superior performance, achieving a validation accuracy of 89.58%, significantly surpassing the GRU model (66.67%). Compared to Decision Tree (85%), Logistic Regression (88.3%), SVM (84.57%), K-means (82.9%), and R-CNN (70%), the LSTM model achieved the highest classification accuracy. Unlike classical machine learning approaches that rely on handcrafted features and predefined decision rules, LSTM effectively learns complex sequential dependencies within images, improving fertility classification accuracy in real-world poultry farming applications. In contrast, GRU models, while more computationally efficient, may struggle with generalization under constrained data conditions. This study underscores the potential of advanced RNNs in enhancing the efficiency and accuracy of automated farming systems, paving the way for future research to further optimize these models for real-world agricultural applications. Full article

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15 pages, 290 KiB

Open AccessProceeding Paper

Effect of Partial Replacement of Wheat with Fava Bean and Black Cumin Flours on Nutritional Properties and Sensory Attributes of Bread

by Melaku Tafese Awulachew

Eng. Proc. 2025, 87(1), 8; https://doi.org/10.3390/engproc2025087008 - 20 Feb 2025

Viewed by 694

Abstract

Blending wheat with fava bean and black cumin flours can improve the nutritional content of wheat-based bread. The current study investigated the effects of flour blending ratios of wheat, germinated fava bean, and black cumin on the physicochemical and sensory attributes of bread. [...] Read more.

Blending wheat with fava bean and black cumin flours can improve the nutritional content of wheat-based bread. The current study investigated the effects of flour blending ratios of wheat, germinated fava bean, and black cumin on the physicochemical and sensory attributes of bread. A total of sixteen bread formulations were produced using the Design Expert software version 13.0.5.0: mixtures of wheat (64–100%), fava bean (0–30%), and black cumin (0–6%). The findings showed that the mixed fraction of composite flours affected the sensory attributes and nutritional value of bread. The mineral contents [Fe, Zn, and Ca] and proximate compositions [ash, fiber, fat, and crude protein] increased with an increase in fava bean and black cumin flour content and decreased with an increase in wheat flour content. The carbohydrate content and crumb lightness (L* value) increased with a decrease in black cumin and germinated fava bean flour proportion. The sensory attributes were significantly affected by the blend proportion (p < 0.05). Sensory scores increased with an increase in the level of germinated fava bean flour and decreased with an increase in the level of black cumin. Generally, the best bread blending ratio was found to be 72.5% wheat, 25.6% germinated fava bean, and 1.9% black cumin, in terms of overall qualitative attributes. This could lead to healthier and more appealing bread options. Full article

10 pages, 1395 KiB

Open AccessProceeding Paper

Patent Analysis and Trends Related to 2D Nanomaterials for Active Food Packaging

by Massimo Barbieri

Eng. Proc. 2025, 87(1), 9; https://doi.org/10.3390/engproc2025087009 - 24 Feb 2025

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Abstract

Active food packaging technology encompasses systems that incorporate active substances into the polymeric matrix. The embedded components exhibit antimicrobial, antifungal, and antioxidant properties and are able to absorb or reduce oxygen, carbon dioxide or ethylene, thereby enhancing the quality and safety of food [...] Read more.

Active food packaging technology encompasses systems that incorporate active substances into the polymeric matrix. The embedded components exhibit antimicrobial, antifungal, and antioxidant properties and are able to absorb or reduce oxygen, carbon dioxide or ethylene, thereby enhancing the quality and safety of food products. The utilization of 2D nanomaterials, such as graphene, has facilitated the advent of novel avenues for the advancement of active packaging (AP). The integration of these materials with polymers has the potential to enhance the barrier, thermal, and mechanical properties of packaging materials. The objective of this paper is to provide a comprehensive overview of patented two-dimensional materials in the field of active packaging. Full article

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8 pages, 2371 KiB

Open AccessProceeding Paper

Development of Jaw Controlled Wireless Navigation Governing System for Wheelchair to Empower Person with Impaired Upper Limb

by Dhanasekar Ravikumar, Vijayaraja Loganathan, Narenthira Sai Raam Pasumponthangaperumal, Mirthulaa Suresh Kumar, Pranav Ponnovian and Benita Evangeline Balan

Eng. Proc. 2025, 87(1), 10; https://doi.org/10.3390/engproc2025087010 - 28 Feb 2025

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Abstract

The central focus of this work is to implement an effective and cost-friendly wheelchair motion control system for individuals with impaired upper body movements by utilizing the mandibular movement of an individual. The initial part of the system is the signal-gathering system that [...] Read more.

The central focus of this work is to implement an effective and cost-friendly wheelchair motion control system for individuals with impaired upper body movements by utilizing the mandibular movement of an individual. The initial part of the system is the signal-gathering system that is built of two functional blocks, the magnet and sensing block. A magnet is affixed to the inferior region of the user’s mandible, and the sensing block, which incorporates two static HMCL 5883L sensors, quantifies the magnetic field intensity modulated by the magnet’s displacement. The processing unit deciphers these sensor signals to ascertain the wheelchair’s trajectory, while the mechanical unit affects the movement directives. The methodology is embedding the HMCL 5883L sensor into the microcontroller to detect the required motion for the wheelchair. The HMCL 5883L sensors are incorporated to identify each change in the orientation of the magnet. HMCL 5883L is a sophisticated and budget technology. The sensor partitions the magnet’s strength path into three hypothetical axes to trace the magnet in the user’s jaw region. The magnet’s configuration in the mandibular region will not create unease, and a user jaw action that requires a certain level is not new. This development empowers the mobility of patients with Quadriplegia, and because of the device’s smaller footprint and feasible modules, it infuses sustainable development and availability. Full article

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8 pages, 2089 KiB

Open AccessProceeding Paper

Optimal Sizing of a Photovoltaic System: A Case Study of a Poultry Plant in Ecuador

by Pedro Escudero-Villa, Jhonny Chicaiza-Zapata, Jéssica Núñez-Sánchez and Jenny Paredes-Fierro

Eng. Proc. 2025, 87(1), 11; https://doi.org/10.3390/engproc2025087011 - 28 Feb 2025

Viewed by 321

Abstract

The poultry sector in Ecuador relies heavily on non-renewable energy, particularly electricity from the public grid. A typical poultry plant consumes an average of 57,313 MWh per year, with sheds accounting for 36% of the total energy consumption. This study aims to reduce [...] Read more.

The poultry sector in Ecuador relies heavily on non-renewable energy, particularly electricity from the public grid. A typical poultry plant consumes an average of 57,313 MWh per year, with sheds accounting for 36% of the total energy consumption. This study aims to reduce operating costs and transition the energy matrix by modeling an optimal photovoltaic system tailored to Ecuador’s geographical conditions. To achieve this, historical data on solar radiation, geographical resources, and energy consumption patterns were collected. Based on this analysis, an isolated photovoltaic system was designed to meet the energy needs of the Type A shed (5.89 kWh) and Type B shed (6.59 kWh). The system was sized to account for the lower annual solar radiation values of 4.58 kWh/m², ensuring effectiveness even under reduced solar input. The approach aims to standardize the energy supply in rural poultry plants by using a modular solar energy system. It also enables the scalable addition of photovoltaic modules based on each plant’s consumption requirements. Full article

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10 pages, 2079 KiB

Open AccessProceeding Paper

Optimisation of Biodiesel Production from Waste Margarine Oil Using Response Surface Methodology

by Pascal Mwenge, Salvation Muthubi and Hilary Rutto

Eng. Proc. 2025, 87(1), 12; https://doi.org/10.3390/engproc2025087012 - 6 Mar 2025

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Abstract

This work presents biodiesel production using waste margarine oil and response surface methodology (RSM) for optimisation. The transesterification of waste margarine oil was carried out using sodium hydroxide (NaOH) as a catalyst under atmospheric pressure in a lab-scale batch reactor. Central composite design [...] Read more.

This work presents biodiesel production using waste margarine oil and response surface methodology (RSM) for optimisation. The transesterification of waste margarine oil was carried out using sodium hydroxide (NaOH) as a catalyst under atmospheric pressure in a lab-scale batch reactor. Central composite design (CCD) was used to optimise four parameters: methanol-to-oil ratio (3–15 mol/mol), catalyst ratio (0.3–1.5 wt.%), reaction time (30–90 min), and reaction temperature (30–70 °C). Numeral optimisation was performed, and an optimum yield of 99.1% was obtained at an 11.906 methanol-to-oil mol ratio, 1.113 wt.% catalyst ratio, 59.646 min reaction time, 52.459 °C temperature, and a low percentage error yield of 0.942%. Analysis of variance (ANOVA) showed that the methanol-to-oil ratio had the highest influence on the biodiesel yield, followed by the catalyst ratio, and reaction time had the least impact after temperature. The kinetics study revealed that the reaction is controlled by a pseudo-first order, and the activation energy was found to be 62.41 kJ/mol. It was concluded that biodiesel could be produced using waste margarine oil as a cost-effective feedstock optimised by RSM. Full article

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16 pages, 2000 KiB

Open AccessProceeding Paper

The Utilization of Printed Circuit Boards (PCBs) in Axial Flux Machines: A Systematic Review

by Isiaka Shuaibu, Eric Ho Tatt Wei, Ramani Kannan and Yau Alhaji Samaila

Eng. Proc. 2025, 87(1), 13; https://doi.org/10.3390/engproc2025087013 - 6 Mar 2025

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Abstract

The rapid advancement of technology has increased our reliance on axial flux permanent magnet machines (AFPMMs), making Printed Circuit Boards (PCBs) essential for modern, lightweight designs. This study reviews PCB roles in AFPMMs for low- and high-power applications by examining research from 2019 [...] Read more.

The rapid advancement of technology has increased our reliance on axial flux permanent magnet machines (AFPMMs), making Printed Circuit Boards (PCBs) essential for modern, lightweight designs. This study reviews PCB roles in AFPMMs for low- and high-power applications by examining research from 2019 to 2024. Using the PRISMA methodology, 38 articles from IEEE Xplore and Web of Science were analyzed. This review focuses on advancements in PCB manufacturing, defect mitigation, winding topologies, software tools, and optimization methods. A structured Boolean search strategy (“Printed Circuit Board” OR “PCB” AND “axial flux permanent magnet machine” OR “AFPM”) guided the literature retrieval process. Articles were meticulously screened using the Rayyan software for titles, abstracts, and content, with duplicate removal performed via the Mendeley software V2.120.0. Findings show significant progress in lightweight AFPMMs with PCBs, improving power quality and performance. Research activity over the 6 years showed inconsistent growth, with concentrated trapezoidal winding emerging as the dominant configuration, followed by distributed winding designs. These configurations were particularly applied in single stator double rotor (SSDR) coreless AFPM machines, characterized by minimal defects, minimal losses, and optimized single-layer winding designs utilizing tools such as ANSYS and COMSOL. Growing interest in double stator single rotor (DSSR) and multi-disk configurations highlights opportunities for innovative designs and advanced optimization techniques. Full article

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8 pages, 1256 KiB

Open AccessProceeding Paper

Green Upgrading of Biodiesel Derived from Biomass Wastes

by Elissavet Emmanouilidou, Alexandros Psalidas, Anastasia Lazaridou, Sophia Mitkidou and Nikolaos C. Kokkinos

Eng. Proc. 2025, 87(1), 14; https://doi.org/10.3390/engproc2025087014 - 10 Mar 2025

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Abstract

The rising demand for edible oils underscores the potential of non-edible oils for biodiesel production. However, biodiesel’s low oxidative stability (OS) and poor cold flow properties due to high unsaturation levels limit its use. This study aims to improve OS through the partial [...] Read more.

The rising demand for edible oils underscores the potential of non-edible oils for biodiesel production. However, biodiesel’s low oxidative stability (OS) and poor cold flow properties due to high unsaturation levels limit its use. This study aims to improve OS through the partial hydrogenation of polyunsaturated FAMEs using a Ru-TPPTS biphasic catalytic system. GC-MS analysis showed that the pre-hydrogenated biodiesel contained over 85% of unsaturated FAMEs, mainly linoleic (C18:2) and oleic acid (C18:1). Hydrogenation reduced C18:2 FAME content by over 70% while increasing stearic acid level (C18:0 FAME), significantly enhancing OS by more than 135%. Further optimization is needed to meet the required quality and performance standards. Full article

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7 pages, 1049 KiB

Open AccessProceeding Paper

An Analytical Model for the Prediction of the Stiffness Behavior of Thin-Walled Beams

by Hugo Miguel Silva and Jerzy Wojewoda

Eng. Proc. 2025, 87(1), 15; https://doi.org/10.3390/engproc2025087015 - 11 Mar 2025

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Abstract

The aim of this work is to develop and test an analytical model that is deemed to be more accurate than the traditional method in the prediction of the mechanical behavior of hollow box beams. The methodology was tested with a hollow box [...] Read more.

The aim of this work is to develop and test an analytical model that is deemed to be more accurate than the traditional method in the prediction of the mechanical behavior of hollow box beams. The methodology was tested with a hollow box beam of a rectangular section. To achieve the aim, a new analytical model was derived. An FEM model of a simple box beam was built, and the results of the comparison between the classical theory, the novel equation, and the l numerical method are presented. It was possible to validate the new equation with the numerical model and the classical equation. It was observed that the novel equation can predict the mechanical behavior of the studied geometries with better accuracy than the classical equation. Full article

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7 pages, 1587 KiB

Open AccessProceeding Paper

Sensitivity Analysis of Conformal Cooling Channels for Injection Molds: Two-Dimensional Transient Heat Transfer Analysis

by Hugo Miguel Silva, João Tiago Noversa, Leandro Fernandes, Hugo Luís Rodrigues and António José Pontes

Eng. Proc. 2025, 87(1), 16; https://doi.org/10.3390/engproc2025087016 - 12 Mar 2025

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Abstract

In recent years, conformal cooling channels (CCCs) have become simpler and less costly to produce. This was largely the product of recent developments in additive manufacturing. In injection molding engineering applications, CCCs provide superior cooling performance compared to the usual usage of straight-drilled [...] Read more.

In recent years, conformal cooling channels (CCCs) have become simpler and less costly to produce. This was largely the product of recent developments in additive manufacturing. In injection molding engineering applications, CCCs provide superior cooling performance compared to the usual usage of straight-drilled channels. This is because CCCs can be conformed for more uniform cooling of the molded part. Using CCCs decreases cooling time, total injection time, thermal stresses, and warpage by a significant amount. Despite this, CCC design is more difficult than conventional channel design. The production of a cost-effective and efficient design is dependent upon CAE simulations. This inquiry focuses on the sensitivity analysis of design features in preparation for the adoption of a design optimization approach in the future. The goal is to optimize the position of cooling channels (CCs) so as to decrease ejection time and promote temperature distribution uniformity. The ANSYS Parametric Design Language (APDL) parametrization and the given design variables are useable and may be used in future optimization attempts. Full article

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6 pages, 651 KiB

Open AccessProceeding Paper

The Development of an Affordable Graphite-Based Conductive Ink for Printed Electronics

by Anandita Dey, Ankur Jyoti Kalita, Hiramoni Khatun and Utpal Sarma

Eng. Proc. 2025, 87(1), 17; https://doi.org/10.3390/engproc2025087017 - 13 Mar 2025

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Abstract

Printed electronics (PEs) are rapidly attracting interest, especially in wearable sensors, smart textiles, and IoT devices. Conductive inks, essential for the fabrication of PE, must be highly conductive, cost-effective, biocompatible, easy to prepare, and less viscous. Conductive inks comprise a conducting material (metals [...] Read more.

Printed electronics (PEs) are rapidly attracting interest, especially in wearable sensors, smart textiles, and IoT devices. Conductive inks, essential for the fabrication of PE, must be highly conductive, cost-effective, biocompatible, easy to prepare, and less viscous. Conductive inks comprise a conducting material (metals like silver, gold, copper, or carbon-based alternatives like graphite, graphene, and carbon nanotubes), a binder, and a solvent. In this work, a water-based graphite conductive ink is developed using graphite as a conductive material, corn starch powder (non-toxic and biodegradable) as a binder, and distilled water as a solvent. Firstly, corn starch powder is added to distilled water, which is heated up to 100 °C and stirred continuously until a homogeneous gel-like mixture is formed. After cooling the mixture, graphite powder is added to it, and it is stirred for an hour at 450 rpm to obtain the ink. To check the conductivity, the ink is brush-painted on a paper substrate with a dimension of 20 mm × 10 mm and the result shows a low ohmic resistance of ~560 Ω, confirming the highly conductive nature of the ink. Additionally, thermogravimetric analysis (TGA) is performed on the prepared ink to evaluate its thermal stability, and a very strong X-ray diffraction (XRD) peak obtained at 2θ° = 26.5426° and a small peak at 2θ° = 54.6145°, along with a few other small peaks, confirms the presence of graphite with corn starch. Thus, this conductive ink can be used for PEs owing to its affordability, biocompatibility, and ease of preparation. Full article

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9 pages, 1388 KiB

Open AccessProceeding Paper

Projected Changes in Wind Power Potential over a Vulnerable Eastern Mediterranean Area Using EURO-CORDEX RCMs According to rcp4.5 and rcp8.5 Scenarios

by Ioannis Logothetis, Kleareti Tourpali and Dimitrios Melas

Eng. Proc. 2025, 87(1), 18; https://doi.org/10.3390/engproc2025087018 - 12 Mar 2025

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Abstract

Under the threat of the climate crisis, renewables are an alternative that are aligned to European principles for clean energy and green transition strategies. Past studies have shown that the Eastern Mediterranean will present notable short- and long-term wind speed variability due to [...] Read more.

Under the threat of the climate crisis, renewables are an alternative that are aligned to European principles for clean energy and green transition strategies. Past studies have shown that the Eastern Mediterranean will present notable short- and long-term wind speed variability due to climate change. In this context, this study investigates the mean changes in wind energy potential (WEP) of a typical height of offshore turbines (80 m) over the climate sensitive area of the Aegean Sea during early, middle and late periods of the 21st century with reference to a base period (the historical period from 1970 to 2005). Data, available from EURO-CORDEX project under the moderate and extreme future scenarios (rcp4.5 and rcp8.5) as well as the recent past (historical) period (from 1970 to 2005), are analyzed here. In both future scenarios, the majority of model simulations indicates an increase in the WEP over the Aegean area as compared to the base period. In particular, the maximum increase in WEP is higher in the rcp8.5 scenario as compared to the rcp4.5 scenario. The most significant changes are shown over the southeastern (the straights between Crete and Rhodes Island) and the central-eastern Aegean area. Full article

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7 pages, 1013 KiB

Open AccessProceeding Paper

Modeling of Stress Concentration Factors in CFRP-Reinforced Circular Hollow Section KT-Joints Under Axial Compression

by Mohsin Iqbal, Saravanan Karuppanan, Veeradasan Perumal, Mark Ovinis, Muhammad Iqbal and Adnan Rasul

Eng. Proc. 2025, 87(1), 19; https://doi.org/10.3390/engproc2025087019 - 17 Mar 2025

Cited by 1 | Viewed by 293

Abstract

Tubular structures are critical in renewable energy and offshore industries but face significant loads over time, leading to joint degradation. Carbon fiber-reinforced polymers (CFRPs) offer promising rehabilitation solutions, yet existing studies often overlook stress concentration factors (SCFs) along the weld toe. This study [...] Read more.

Tubular structures are critical in renewable energy and offshore industries but face significant loads over time, leading to joint degradation. Carbon fiber-reinforced polymers (CFRPs) offer promising rehabilitation solutions, yet existing studies often overlook stress concentration factors (SCFs) along the weld toe. This study examines SCFs at 24 weld toe positions in CFRP-reinforced KT-joints under axial compression. Using 5429 simulations and artificial neural networks, precise estimations of CFRPs’ impact on SCFs were achieved, with <10% error. These findings demonstrate CFRPs’ potential to reduce SCFs and improve fatigue life prediction for tubular joints under axial compression. Full article

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8 pages, 13911 KiB

Open AccessProceeding Paper

Synthesis and Structural Characterization of Novel Urethane-Dimethacrylate Monomers with Two Quaternary Ammonium Groups Based on Cycloaliphatic Diisocyanates

by Patryk Drejka, Patrycja Kula and Izabela Barszczewska-Rybarek

Eng. Proc. 2025, 87(1), 20; https://doi.org/10.3390/engproc2025087020 - 17 Mar 2025

Viewed by 150

Abstract

Diseases such are caries affect approximately 25% of the worldwide population. Such a state requires novel, antibacterial materials. This research aimed to synthesize and characterize the structures of two urethane-dimethacrylate monomers showing possible antibacterial activity for dental composite restorative materials (DCRMs). The monomers [...] Read more.

Diseases such are caries affect approximately 25% of the worldwide population. Such a state requires novel, antibacterial materials. This research aimed to synthesize and characterize the structures of two urethane-dimethacrylate monomers showing possible antibacterial activity for dental composite restorative materials (DCRMs). The monomers were based on isophorone diisocyanate (IPDI) and dicyclohexylmethane 4,4′-diisocyanate (CHMDI). The structures of the monomers and their key elements were confirmed with the application of spectroscopy methods. Nuclear Magnetic Resonance Spectroscopy (¹H and ¹³C NMR) and Fourier Transform Infrared Spectroscopy (FTIR) were applied. The monomers were synthesized and their structures were confirmed with the abovementioned techniques. Full article

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8 pages, 1427 KiB

Open AccessProceeding Paper

Utilizing Spent Yeast for Tannin Adsorption in Chestnut Shell Treatment Solutions

by Elsa F. Vieira, Tomás Amaral, Ricardo Ferraz and Cristina Delerue-Matos

Eng. Proc. 2025, 87(1), 21; https://doi.org/10.3390/engproc2025087021 - 19 Mar 2025

Viewed by 180

Abstract

This study evaluated the use of brewer’s spent yeast (BSY) as an adsorbent for tannins from a chestnut shell extract (CS tannin extract). This extract was derived from an alkaline treatment (5% NaOH (v/v)) to recover cellulosic material from [...] Read more.

This study evaluated the use of brewer’s spent yeast (BSY) as an adsorbent for tannins from a chestnut shell extract (CS tannin extract). This extract was derived from an alkaline treatment (5% NaOH (v/v)) to recover cellulosic material from chestnut shells and needed valorization. Various BSY treatments, including lyophilization, immobilization in calcium alginate beads, and alkaline and acid treatments, were tested to identify which had the best tannin adsorption capacity. The results highlight BSY’s potential as a system to valorize tannins from this treatment solution. Full article

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9 pages, 2407 KiB

Open AccessProceeding Paper

Investigation of Structural, Optical, and Frequency-Dependent Dielectric Properties of Barium Zirconate (BaZrO₃) Ceramic Prepared via Wet Chemical Auto-Combustion Technique

by Anitha Gnanasekar, Pavithra Gurusamy and Geetha Deivasigamani

Eng. Proc. 2025, 87(1), 22; https://doi.org/10.3390/engproc2025087022 - 19 Mar 2025

Viewed by 209

Abstract

The wet chemical auto-combustion technique was used to synthesize barium zirconate ceramic (BaZrO₃). Many strategies were applied to regulate the functional properties of the perovskite-structured sample which was calcinated at 800 °C for 9 h. A Fourier-transform IR spectrometer, an X-ray [...] Read more.

The wet chemical auto-combustion technique was used to synthesize barium zirconate ceramic (BaZrO₃). Many strategies were applied to regulate the functional properties of the perovskite-structured sample which was calcinated at 800 °C for 9 h. A Fourier-transform IR spectrometer, an X-ray diffractometer, a scanning electron microscope (SEM)-EDAX, an LCR meter, and a UV–visible spectrometer were employed to study the structural, morphological, optical, and electrical properties of the prepared barium zirconate sample. Using data derived from XRD, the perovskite phase was confirmed, and the average value of the crystallite size was found to be 17.68 nm. The lattice constant, crystallinity, unit cell volume, tolerance factor, and X-ray density were also calculated. SEM-EDAX confirmed the elemental composition of the product and verified that it contained only the major constituents (Ba, Zr, and O). The vibrational modes of the prepared sample were investigated using FTIR in wavelengths ranging from 400 to 4000 cm⁻¹. Energy bandgap was observed using Tauc’s plot, where a graph was prepared for photon energy (hυ) and (αhυ)². The powder sample was blended with PVA and made into pellets of 13 mm diameter using a pelletizer to explore dielectric parameters like the dielectric constant, while the loss factor was recorded at a frequency ranging from 100 Hz to 4 MHz at room temperature. With its high dielectric constant and low dielectric loss factor, barium zirconate ceramic stands as an excellent material for several microwave applications. Full article

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9 pages, 1985 KiB

Open AccessProceeding Paper

Strength Behavior of Internally Reinforced Beams Subjected to Structural Optimization Under Simple Bending Loading

by Hugo Miguel Silva, César M. A. Vasques and Jerzy Wojewoda

Eng. Proc. 2025, 87(1), 23; https://doi.org/10.3390/engproc2025087023 - 20 Mar 2025

Viewed by 192

Abstract

In this study, we analyzed novel internally reinforced hollow-box beams to evaluate their strength using the finite element method (FEM) in ANSYS Mechanical APDL 18.1. Twelve different FEM models were subjected to static bending loads, and their performance was assessed based on Huber–Mises [...] Read more.

In this study, we analyzed novel internally reinforced hollow-box beams to evaluate their strength using the finite element method (FEM) in ANSYS Mechanical APDL 18.1. Twelve different FEM models were subjected to static bending loads, and their performance was assessed based on Huber–Mises equivalent strength values. The results show that most optimized models exhibited improved strength compared to their initial versions, with some configurations achieving up to a 470% increase. These findings highlight the effectiveness of structural optimization in enhancing the strength behavior of hollow-box beams, providing valuable insights for engineering applications. Full article

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7 pages, 1910 KiB

Open AccessProceeding Paper

Green Synthesis and Characterization of Silver Nanoparticles from Aqueous Extract of Harrisonia abyssinica Fruits

by Alinanuswe J. Mwakalesi and Emmy S. Lema

Eng. Proc. 2025, 87(1), 24; https://doi.org/10.3390/engproc2025087024 - 20 Mar 2025

Viewed by 228

Abstract

The synthesis of silver nanoparticles using phytochemical reducing agents is the most preferred technique because of its low cost and environmental friendliness. Consequently, there are several reports published on the synthesis of silver nanoparticles using extracts from leaves, barks, roots, and fruit peels. [...] Read more.

The synthesis of silver nanoparticles using phytochemical reducing agents is the most preferred technique because of its low cost and environmental friendliness. Consequently, there are several reports published on the synthesis of silver nanoparticles using extracts from leaves, barks, roots, and fruit peels. However, information on the use of fruit extracts for the synthesis of silver nanoparticles is limited. Thus, the green synthesis of silver nanoparticles (HAF-AgNPs) using phytochemicals extracted from Harrisonia abyssinica fruit (HAF) is reported in the current study. The silver nanoparticles were synthesized through chemical precipitation and characterized using UV-Vis spectrophotometry, transmission electron microscopy (TEM), energy-dispersive X-ray (EDX), and X-ray diffraction analysis (XRD). The findings showed that fabricated HAF-AgNPs were crystalline and spherical, and exhibited a strong UV-absorption band at 420 nm. The appearance of a peak at 3 keV in the EDX spectrum indicated metallic silver atoms in the fabricated nanoparticles. The fabricated nanoparticles exhibited antibacterial activity against Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacterial strains. The antibacterial activity was stronger for Staphylococcus aureus (MIC = 5 µg/mL) compared to Escherichia coli (MIC = 10 µg/mL). The preliminary findings from the current study suggest that the nanoparticles prepared from the extract could serve as a potential antibacterial agent against Gram-positive and Gram-negative bacteria. Full article

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8 pages, 8967 KiB

Open AccessProceeding Paper

Design and Optimisation of Inverted U-Shaped Patch Antenna for Ultra-Wideband Ground-Penetrating Radar Applications

by Ankur Jyoti Kalita, Nairit Barkataki and Utpal Sarma

Eng. Proc. 2025, 87(1), 25; https://doi.org/10.3390/engproc2025087025 - 24 Mar 2025

Viewed by 253

Abstract

Ground-Penetrating Radar (GPR) systems with ultra-wideband (UWB) antennas introduce the benefits of both high and low frequencies. Higher frequencies offer finer spatial resolution, enabling the detection of small-scale features and details, while lower frequencies improve depth penetration by minimising signal attenuation, allowing the [...] Read more.

Ground-Penetrating Radar (GPR) systems with ultra-wideband (UWB) antennas introduce the benefits of both high and low frequencies. Higher frequencies offer finer spatial resolution, enabling the detection of small-scale features and details, while lower frequencies improve depth penetration by minimising signal attenuation, allowing the system to explore deeper subsurface layers. This combination optimises the performance of GPR systems by balancing the need for detailed imaging with the requirement for deeper penetration. This work presents the design of a wideband inverted U-shaped patch antenna with a wide rectangular slot centred at a frequency of 1.5 GHz. The antenna is fed through a microstrip feed line and employs a partial ground plane. Through simulation, the antenna is optimised by varying the patch dimensions and slot size. Further modifications to the partial ground plane improve the UWB and gain characteristics of the antenna. The optimised antenna is fabricated using a double-sided copper-clad FR4 substrate with a thickness of 1.6 mm and characterised using a Vector Network Analyser (VNA), with final dimensions of 200 mm × 300 mm. The experimental results demonstrate a return loss below −10 dB across the operational band from 1.068 GHz to 4 GHz and a maximum gain of 7.29 dB at 4 GHz. In addition to other bands, the antenna exhibits a return loss consistently below −20 dB in the frequency range of 1.367 GHz to 1.675 GHz. These results confirm the antenna’s UWB performance and its suitability for GPR applications in utility mapping, landmine and artefact detection, and identifying architectural defects. Full article

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10 pages, 53442 KiB

Open AccessProceeding Paper

On the Electrical Resistivity Measurement Methods and Properties of Conductive 3D-Printing PLA Filaments

by César M. A. Vasques, João P. R. Ferreira, Fernando A. V. Figueiredo and João C. C. Abrantes

Eng. Proc. 2025, 87(1), 26; https://doi.org/10.3390/engproc2025087026 - 25 Mar 2025

Viewed by 325

Abstract

In recent years, there has been a growing interest in and research efforts enabling the use of composite conductive 3D-printing filaments in material extrusion additive manufacturing processes, which can bestow novel and distinctive functions onto 3D-printed components. These composite filaments, in general blending [...] Read more.

In recent years, there has been a growing interest in and research efforts enabling the use of composite conductive 3D-printing filaments in material extrusion additive manufacturing processes, which can bestow novel and distinctive functions onto 3D-printed components. These composite filaments, in general blending a thermoplastic with carbon-based materials, open up new research and development avenues in electronics and sensors. Additionally, by exploring the underlying piezoresistivity of conductive filaments, they also enable the creation of novel structural components possessing integrated (intrinsic) self-sensing capabilities that can be effectively employed in structural health monitoring of critical components. However, piezoresistivity features require measuring the electrical resistance of structures made with these conductive filaments, which might be hard, especially when measuring small changes in resistance caused by mechanical loads on the component. The goal of this study is to compare the two- and four-probe methods for measuring the electrical resistance of 3D-printed parts and to look at how different types of electrical contacts and bonding may affect electrical resistivity measurement and self-sensing capabilities. The research is conducted on 3D-printed specimens using a conductive composite PLA (polylactic acid) filament from Protopasta. The efficiency of each method and the influence of the bonding and electrodes on the measurements are experimentally analyzed and discussed. Our experiments reveal that the four-probe method consistently yields resistivity values between 15.35 and 16.38

Ω \cdot

cm, while the two-probe method produces significantly higher values (up to 52.92–62.37

Ω \cdot

cm), underscoring the impact of wire and contact resistances on measurement accuracy. Full article

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12 pages, 3465 KiB

Open AccessProceeding Paper

Design and Implementation IoT-Driven Distribution Transformer Health Monitoring System for the Smart Power Grid

by Abdullah Al Noman, Partha Baidya, Md Aslam Hossain, Pranta Dev, Kaushik Saha and Md. Lokman Hossain

Eng. Proc. 2025, 87(1), 27; https://doi.org/10.3390/engproc2025087027 - 26 Mar 2025

Viewed by 642

Abstract

The increasing demand for reliable power distribution necessitates advanced monitoring solutions for distribution transformers. This paper presents an IoT-driven health monitoring system designed to enhance the reliability and efficiency of smart power grids. The system integrates sensors to measure voltage, current, oil temperature, [...] Read more.

The increasing demand for reliable power distribution necessitates advanced monitoring solutions for distribution transformers. This paper presents an IoT-driven health monitoring system designed to enhance the reliability and efficiency of smart power grids. The system integrates sensors to measure voltage, current, oil temperature, and body temperature, ensuring real-time data acquisition and fault detection. An ESP32 microcontroller processes sensor data and transmits it to the Blynk IoT platform for remote monitoring and predictive maintenance. The system effectively identifies phase failures, earth faults, overheating, and other anomalies, allowing for timely intervention and reduced downtime. Unlike conventional manual inspection methods, this low-cost solution provides continuous monitoring, improving transformer lifespan and operational efficiency. The proposed approach offers a scalable and cost-effective strategy for smart power grid applications, promoting sustainable energy management through data-driven decision-making. Future enhancements may include AI-based fault prediction and expanded integration with smart grid infrastructures. Full article

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12 pages, 583 KiB

Open AccessProceeding Paper

Determination of Properties of Meat Products with Plant Supplements

by Natalia Murlykina

Eng. Proc. 2025, 87(1), 28; https://doi.org/10.3390/engproc2025087028 - 26 Mar 2025

Viewed by 250

Abstract

One approach to improving population nutrition is the development of widely consumed minced meat products (MMPs) enriched with biologically active compounds such as polyunsaturated fatty acids, dietary fibers, and iron. This study investigated the functional–technological properties and chemical composition of MMPs with plant [...] Read more.

One approach to improving population nutrition is the development of widely consumed minced meat products (MMPs) enriched with biologically active compounds such as polyunsaturated fatty acids, dietary fibers, and iron. This study investigated the functional–technological properties and chemical composition of MMPs with plant supplements—fenugreek and dried leaves of blackcurrant (DLBC). The emulsion stability of minced meat was assessed based on the mass fraction of the intact emulsion, which lost a certain amount of moisture and fat after heat treatment. The water-holding capacity (WHC), fat-holding capacity (FHC), energy value as well as a proximate composition, including the total iron content were determined using standard methods. Sensory evaluation was conducted using quantitative descriptive analysis and profile analysis methods based on the descriptors of appearance, consistency, cross-sections appearance, flavour, and taste. The protein content of MMPs with plant supplements ranged from 16.4 to 19.0%. Fenugreek increased iron levels from 1.27 ± 0.03 mg/100 g to 2.14 ± 0.04 mg/100 g. FHC and WHC values in samples with fenugreek or DLBC surpassed control values by 6.3–23.0% and 2.7–5.0%, respectively. Sunflower oil, fenugreek, and DLBC not only enhanced nutritional value, but also improved functional–technological properties, sensory quality, and reduced heat-treatment losses. These MMPs can be classified as health-oriented foods suitable for dietary adjustments. Full article

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14 pages, 2174 KiB

Open AccessProceeding Paper

Investigating the Availability and Key Features of Dental Health Applications in the Google Play Store

by Snehasish Tripathy, Aditi Prasad Tasgaonkar, Ankita Tapkir, Vini Mehta, Srushti Kharat, Mirza Adil Beig, Gopi Patel and Luca Fiorillo

Eng. Proc. 2025, 87(1), 29; https://doi.org/10.3390/engproc2025087029 - 28 Mar 2025

Viewed by 387

Abstract

Amidst the rapid proliferation of mHealth applications, questions persist regarding their efficacy, usability, and integration into oral health practice. This review aims to inform practitioners and stakeholders in the field of oral health about the potential of digital technologies to transform healthcare delivery [...] Read more.

Amidst the rapid proliferation of mHealth applications, questions persist regarding their efficacy, usability, and integration into oral health practice. This review aims to inform practitioners and stakeholders in the field of oral health about the potential of digital technologies to transform healthcare delivery and promote healthy behaviors. Key terms included “dental”, “dentistry”, “oral health”, “dental treatment”, and “tooth care”. The Google Play Store search identified 130 applications, out of which 18 met our study objectives. Most apps (n = 13) focused on providing dental appointments, oral health education, and promotion. The Mobile Application Rating Scale (MARS) quality rating revealed that only 50% of these apps were of high quality. Engagement and information were the lowest scored subscales. This review highlights the many benefits that these digital tools provide, including online appointments, teleconsultations, and access to oral health educational resources. Nevertheless, despite their potential, the current state of dental health applications left substantial room for development, especially in the areas of user involvement and information quality. The lack of reliable and accurate information in many apps may be harmful to users’ health. To fully realize the potential of these digital technologies and to enhance oral health outcomes on a larger scale, coordinated actions involving stakeholders from the technology and dentistry sectors are imperative. Full article

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8 pages, 1148 KiB

Open AccessProceeding Paper

Temporal Dynamics and Sources of Heavy Metals in an Aquatic Ecosystem: An Applied Study

by Olha Biedunkova, Pavlo Kuznietsov and Yuliia Trach

Eng. Proc. 2025, 87(1), 30; https://doi.org/10.3390/engproc2025087030 - 31 Mar 2025

Viewed by 235

Abstract

This study investigates the sources and distribution of heavy metals in the Styr River, particularly in the area influenced by the cooling water blowdown from the Rivne Nuclear Power Plant (Ukraine). The concentrations of eight heavy metals (Zn, Cd, Pb, Cu, Ni, Mn, [...] Read more.

This study investigates the sources and distribution of heavy metals in the Styr River, particularly in the area influenced by the cooling water blowdown from the Rivne Nuclear Power Plant (Ukraine). The concentrations of eight heavy metals (Zn, Cd, Pb, Cu, Ni, Mn, As, and Cr) were measured over a period from 2018 to 2022. Monthly water samples were collected and analyzed using an inductively coupled plasma optical emission spectroscopy (ICAP 7400 Duo, Thermo Fisher Scientific, Waltham, MA, USA). The results show that the average concentrations (M ± SD) of the heavy metals decreased in the following order: Cu (6.43 ± 1.82 ppb), As (5.1 ± 0.2 ppb), Zn (4.67 ± 1.14 ppb), Mn (4.03 ± 2.81 ppb), Ni (3.3 ± 0.8 ppb), Cr (1.06 ± 0.22 ppb), Pb (1.05 ± 0.11 ppb), and Cd (1.01 ± 0.03 ppb). Seasonal and annual variations in metal concentrations were observed, with notable decreases in Zn, Cu, and Mn in 2021, likely due to anthropogenic activities. Pearson correlation analysis and cluster analysis were employed to explore relationships between the metals. The findings suggest that certain metals, such as Pb, Cr, and Ni, share common sources, likely industrial emissions or urban pollution, while others, such as Cd and As, have more isolated sources. This research highlights the complex interplay of natural and anthropogenic factors influencing heavy metal levels in the Styr River. Full article

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10 pages, 1160 KiB

Open AccessProceeding Paper

Determination of Escherichia coli in Raw and Pasteurized Milk Using a Piezoelectric Gas Sensor Array

by Anastasiia Shuba, Ruslan Umarkhanov, Ekaterina Bogdanova, Ekaterina Anokhina and Inna Burakova

Eng. Proc. 2025, 87(1), 31; https://doi.org/10.3390/engproc2025087031 - 1 Apr 2025

Viewed by 279

Abstract

The importance of assessing the microbiological safety of food products is beyond doubt, which is also true for milk and dairy products. The goal of this work was to evaluate the changes in the composition of the gas phase in milk based on [...] Read more.

The importance of assessing the microbiological safety of food products is beyond doubt, which is also true for milk and dairy products. The goal of this work was to evaluate the changes in the composition of the gas phase in milk based on signals from chemical sensors to predict the quantity of the bacteria in the milk samples. The gas phase in raw milk samples and samples during pasteurization, as well as for a standard (a model aqua solution of macronutrients and minerals), was studied using an array of sensors with polycomposite coatings, including those contaminated with E. coli bacteria. Assessment of microbiological indicators was carried out according to GOST in parallel with the gas-phase analysis. The applicability of the results obtained on model systems was assessed using milk samples, including those containing other types of pathogenic microorganisms (Staphylococcus aureus, Klebsiella spp., etc.). It was found that the obtained models can be used to assess the presence and quantity of E. coli in milk at the pasteurization stage. Full article

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8 pages, 2080 KiB

Open AccessProceeding Paper

Video Surveillance and Augmented Reality in Maritime Safety

by Igor Vujović, Mario Miličević and Joško Šoda

Eng. Proc. 2025, 87(1), 32; https://doi.org/10.3390/engproc2025087032 (registering DOI) - 2 Apr 2025

Viewed by 222

Abstract

Recently, augmented reality and machine learning have become integral parts of many developed systems. In the maritime domain, it is particularly interesting to develop a concept that combines augmented reality with the visualization of collision risks, using machine learning for motion prediction as [...] Read more.

Recently, augmented reality and machine learning have become integral parts of many developed systems. In the maritime domain, it is particularly interesting to develop a concept that combines augmented reality with the visualization of collision risks, using machine learning for motion prediction as its foundation. Hence, this research aims to propose a system that visualizes the risk in an augmented reality application. The paper presents a distance estimation method that mainly uses a single stationary camera placed at the harbor entrance. The machine learning component involves training the YOLO algorithm on the Split Port Ship Classification Dataset. This distance estimation is an input for the speed estimation algorithm. Speed is a key parameter for the prediction of collision risk. Preliminary experiments were conducted to provide proof of concept for further research, and the description of a case study is included in this paper. Full article

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6 pages, 209 KiB

Open AccessProceeding Paper

Influence of Dispersant and Surfactant on nZVI Characterization by Dynamic Light Scattering

by Filipe Fernandes, Ana Isabel Oliveira, Cristina Delerue-Matos and Clara Grosso

Eng. Proc. 2025, 87(1), 33; https://doi.org/10.3390/engproc2025087033 - 2 Apr 2025

Viewed by 178

Abstract

The agrifood industries generate tremendous amounts of waste, with the valorization of these wastes being of the utmost importance. The aim of this work was to synthesize green zero-valent iron nanoparticles (nZVI) using hydromethanolic extracts of spent coffee grounds (SCGs) and post-distillation residues [...] Read more.

The agrifood industries generate tremendous amounts of waste, with the valorization of these wastes being of the utmost importance. The aim of this work was to synthesize green zero-valent iron nanoparticles (nZVI) using hydromethanolic extracts of spent coffee grounds (SCGs) and post-distillation residues of Cistus ladanifer L. leaves (CLL). The synthesized nZVI were then analyzed by dynamic light scattering (DLS), and their size, polydispersity index (PDI), and zeta potential (ZP) were determined. Different dispersants (water and methanol) and the impact of a surfactant (Tween^® 20) were tested for DLS analysis. nZVI dispersed in water and added with Tween^® 20 displayed lower agglomeration, particle size, and PDI, but higher ZP than nZVI without the addition of surfactant and methanolic suspension. These results provide further insight into the applicability of surfactants in nZVI characterization. Full article

7 pages, 5282 KiB

Open AccessProceeding Paper

Tuning the Electrical Resistivity of Molecular Liquid Crystals for Electro-Optical Devices

by Michael Gammon, Iyanna Trevino, Michael Burnes, Noah Lee, Abdul Saeed and Yuriy Garbovskiy

Eng. Proc. 2025, 87(1), 34; https://doi.org/10.3390/engproc2025087034 - 2 Apr 2025

Viewed by 308

Abstract

Modern applications of molecular liquid crystals span from high-resolution displays for augmented and virtual reality to miniature tunable lasers, reconfigurable microwave devices for space exploration and communication, and tunable electro-optical elements, including spatial light modulators, waveguides, lenses, light shutters, filters, and waveplates, to [...] Read more.

Modern applications of molecular liquid crystals span from high-resolution displays for augmented and virtual reality to miniature tunable lasers, reconfigurable microwave devices for space exploration and communication, and tunable electro-optical elements, including spatial light modulators, waveguides, lenses, light shutters, filters, and waveplates, to name a few. The tunability of these devices is achieved through electric-field-induced reorientation of liquid crystals. Because the reorientation of the liquid crystals can be altered by ions normally present in mesogenic materials in minute quantities, resulting in their electrical resistivity having finite values, the development of new ways to control the concentration of the ions in liquid crystals is very important. A promising way to enhance the electrical resistivity of molecular liquid crystals is the addition of nano-dopants to low-resistivity liquid crystals. When nanoparticles capture certain ions, they immobilize them and increase their resistivity. If properly implemented, this method can convert low-resistivity liquid crystals into high-resistivity liquid crystals. However, uncontrolled ionic contamination of the nanoparticles can significantly alter this process. In this paper, building on our previous work, we explore how physical parameters such as the size of the nanoparticles, their concentration, and their level of ionic contamination can affect the process of both enhancing and lowering the resistivity of the molecular liquid crystals. Additionally, we analyze the use of two types of nano-dopants to achieve better control over the electrical resistivity of molecular liquid crystals. Full article

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9 pages, 204 KiB

Open AccessProceeding Paper

Computational Drug-Likeness Studies of Selected Thiosemicarbazones: A Sustainable Approach for Drug Designing

by Ekhlakh Veg, Kulsum Hashmi, Satya, Seema Joshi and Tahmeena Khan

Eng. Proc. 2025, 87(1), 35; https://doi.org/10.3390/engproc2025087035 - 2 Apr 2025

Viewed by 243

Abstract

Drug intake, its absorption in the body, removal, and various side effects are factors considered when designing the drugs. Here, the in silico tools act as virtual shortcuts, assisting in the prediction of several important physicochemical properties like polar surface area (PSA), molecular [...] Read more.

Drug intake, its absorption in the body, removal, and various side effects are factors considered when designing the drugs. Here, the in silico tools act as virtual shortcuts, assisting in the prediction of several important physicochemical properties like polar surface area (PSA), molecular weight, and molecular flexibility, etc., to evaluate probable drug leads as potential drug candidates. These tools also play a vital role in the prediction of the bioactivity score of probable drug leads against various human receptors. This paper presents a virtual combinatorial library of selected thiosemicarbazones (TSCs) and their metal complexes. Different properties like bioactivity score, physicochemical, distribution, absorption, excretion, metabolism, and toxicity (ADMET) parameters were assessed. By using ChemDraw Ultra 12.0, the structures of ligands and complexes were drawn and downloaded in PDB format. Physicochemical parameters were calculated using online softwares viz. Molinspiration and SwissADME, and ADMET properties were calculated using admetSAR (2.0). Molecular docking was performed using PyRx Python Prescription 0.8. with Janus Kinase and Transforming Growth Factor Beta (Tgf-β). Janus Kinase and Tgf-β are some cytokines involved in cell development, proliferation, and cell death. Three important TSCs, i.e., salicyldehyde thiosemicarbazone, acenaphthenequinone thiosemicarbazone, 2-chloronicotinic thiosemicarbazone, and their virtually designed complexes exhibited appreciable in silico results. Most ligands and complexes had good bioactivity values against all the biological targets. Full article

11 pages, 2526 KiB

Open AccessProceeding Paper

Practical Evaluation and Performance Analysis for Deepfake Detection Using Advanced AI Models

by Bikash Ranjan Barik, Ankush Nayak, Adyasha Biswal and Neelamadhab Padhy

Eng. Proc. 2025, 87(1), 36; https://doi.org/10.3390/engproc2025087036 - 1 Apr 2025

Viewed by 941

Abstract

In the 21st century of digital technology, deepfakes are increasingly becoming a serious issue across the globe. We have many machine learning and deep learning algorithms that are meant to serve humanity, but nowadays, these algorithms are the main cause of deepfake media, [...] Read more.

In the 21st century of digital technology, deepfakes are increasingly becoming a serious issue across the globe. We have many machine learning and deep learning algorithms that are meant to serve humanity, but nowadays, these algorithms are the main cause of deepfake media, which can affect human life. This study aimed to create a model for recognizing deepfake media or manipulated media using deep learning and machine learning algorithms. The dataset we required for training the model was collected from online sources, and we created some GAN-generated images. Then, we created a model by using the MTCNN, InceptionResNetV1, and FaceNet_PyTorch. All the algorithms gave an excellent result, with an accuracy of 95% by the MTCNN, 98% by InceptionResNetV1, and 98% by Facenet_pytorch. Full article

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14 pages, 2889 KiB

Open AccessProceeding Paper

Comparative Analysis of Machine Learning Algorithms for Flow Rate Prediction in Optimizing Pipeline Maintenance Strategies

by Adamu Abubakar Sani, Mohamed Mubarak Abdul Wahab and Nasir Shafiq

Eng. Proc. 2025, 87(1), 37; https://doi.org/10.3390/engproc2025087037 - 3 Apr 2025

Viewed by 312

Abstract

Using machine learning to predict maintenance schedules for crude oil pipelines is crucial for enhancing efficiency and minimizing disruptions in the oil and gas sector. Our research explores the effectiveness of machine learning algorithms in this context, with a specific focus on using [...] Read more.

Using machine learning to predict maintenance schedules for crude oil pipelines is crucial for enhancing efficiency and minimizing disruptions in the oil and gas sector. Our research explores the effectiveness of machine learning algorithms in this context, with a specific focus on using oil flow rate as a primary predictor. When trained with a variety of inspection data, machine learning models can accurately predict flow rates, thus improving maintenance planning. Several pipeline scenarios were analyzed, and the Python library was used for dataset augmentation. The study shows a correlation between variations in the buildup deposits and the oil flow rate in the pipeline, indicating that the oil flow rate gives an indication for determining the need for maintenance. The flow rate was categorized into three efficiency levels: High Efficiency (flow rate > 90% of the allowable rate), Moderate Efficiency (flow rate between 70% and 90%), and Low Efficiency (flow rate < 70%). Each efficiency level was linked to specific maintenance intervals: Specifically, a higher flow rate allowed longer intervals between maintenance activities, while a lower flow rate could indicate there is an accumulation of deposits that necessitates urgent intervention. Several machine learning models were trained, and variations in performance were observed. Gradient Boosting and XGBoost Regressor show the best performers with lower values for MSE, RMSE, and MAE and higher R² scores compared to the Support Vector Regressor. The result shows Gradient Boosting has an MSE of 0.000005, RMSE of 0.002259, MAE of 0.000968, and an R² of 0.997259, followed by XGBoost Regressor with MSE of 0.000005, an RMSE of 0.002269, an MAE of 0.000922, and an R² of 0.997234. Support Vector Regressor indicates the least performance, with an MSE of 0.002868, RMSE of 0.053554, MAE of 0.046311, and an R² of −0.540765. The findings of the study emphasize the necessity of choosing machine learning algorithms that are appropriately suited to the features of the dataset and the task. The findings highlight the importance of selecting machine learning algorithms that are more suitable to the features of the dataset and the task. Full article

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8 pages, 4501 KiB

Open AccessProceeding Paper

Parametric Investigation of Fatigue-Cracked Tubular T-Joint Repair Using Composite Reinforcement

by Muhammad Hazim, Saravanan Karuppanan and Mohsin Iqbal

Eng. Proc. 2025, 87(1), 38; https://doi.org/10.3390/engproc2025087038 - 8 Apr 2025

Viewed by 171

Abstract

Circular hollow sections (CHSs) are widely used in offshore jacket structures due to their excellent compressive strength, torsional resistance, and direction-independent stiffness. However, CHS joints are prone to fatigue-induced cracking caused by complex geometries, environmental loading, and aging. Fatigue crack propagation, governed by [...] Read more.

Circular hollow sections (CHSs) are widely used in offshore jacket structures due to their excellent compressive strength, torsional resistance, and direction-independent stiffness. However, CHS joints are prone to fatigue-induced cracking caused by complex geometries, environmental loading, and aging. Fatigue crack propagation, governed by the stress intensity factor (SIF), threatens structural integrity if the SIF exceeds fracture toughness. Composite reinforcement has emerged as a promising solution for mitigating crack propagation and enhancing joint performance. This study presents a numerical parametric investigation of fatigue-cracked tubular T-joints, focusing on the effects of crack size, crack location, and composite reinforcement on the SIF under various loading conditions. The highest SIF was consistently observed at the saddle point in T-joints under axial and out-of-plane bending (OPB) loads. However, in T-joints subjected to in-plane bending (IPB) loads, the highest SIF was found between the crown and saddle points. The SIF increased with the size and diameter of the cracks. The application of CFRP wrapping was found to reduce the SIF by more than 50% across all loading conditions, with the most significant reductions observed when the reinforcement was oriented along the chord axis. Full article

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10 pages, 1684 KiB

Open AccessProceeding Paper

Design and Implementation of Novel Dynamic Voltage Restorer Configuration for Electric Vehicle Charging Applications

by Kesav Sanadhan Saikumar, Thenmozhi Mutharasan, Vijayaraja Loganathan, Dhanasekar Ravikumar, Vishal Thirumalai Nambi and Sudhesh Kumar Ezhilarasan

Eng. Proc. 2025, 87(1), 39; https://doi.org/10.3390/engproc2025087039 - 8 Apr 2025

Viewed by 177

Abstract

Electric vehicles are replacing conventional vehicles in today’s world due to their eco-friendly operation and reduced maintenance. Although EVs offer advantages over conventional vehicles, there is a limited number of charging stations, and numerous power quality issues have emerged at these locations. This [...] Read more.

Electric vehicles are replacing conventional vehicles in today’s world due to their eco-friendly operation and reduced maintenance. Although EVs offer advantages over conventional vehicles, there is a limited number of charging stations, and numerous power quality issues have emerged at these locations. This is due to the voltage, current, or frequencies being abnormal, which leads to sudden voltage drops, voltage swells, long interruptions, and short interruptions occurring at the charging stations. To address issues arising from client-side anomalies, we attach conventional FACTS devices closer to the load end. One such dependable custom power gadget for dealing with voltage sag is the one developed in this article, and it is called an enhanced dynamic voltage restorer (DVR). The proposed device continuously monitors the load voltage waveform and injects (or absorbs) the balance (or surplus) voltage into (or away from) the load voltage whenever a sag occurs. We develop a reference voltage waveform to achieve the aforementioned capabilities. In this paper, the methods of compensation for these problems at charging stations are discussed. Furthermore, the power quality problems are compensated for by the proposed system using an SVPWM controller. Simulation and real-time implementation are carried out, and the results are discussed. The inclusion of SVPWM control significantly improves voltage regulation and reduces THD by 60–70% compared to conventional PWM methods, which achieve only 40–50% reduction. The proposed DVR is designed for single-phase applications, making it suitable for low-voltage distribution systems and sensitive industrial loads. The proposed model provides a rapid response time (<10 ms), and the efficiency of the proposed DVR is found to be 92%, which is greater than that of conventional designs (86%). Full article

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9 pages, 1186 KiB

Open AccessProceeding Paper

Innovative Drug Delivery Systems: The Comprehensive Role of Natural Polymers in Fast-Dissolving Tablets

by Meet V. Naliyadhara, Riya B. Chovatiya, Shyam R. Vekariya, Deep D. Undhad and Sheetal S. Buddhadev

Eng. Proc. 2025, 87(1), 40; https://doi.org/10.3390/engproc2025087040 - 8 Apr 2025

Viewed by 297

Abstract

Fast-dissolving tablets (FDTs) have arisen as a novel way to tackle issues encountered by patients with dysphagia, including youngsters, older people, and those with neurodegenerative or developmental disabilities. This review emphasises the crucial function of natural polymers as super disintegrants in improving fast-disintegrating [...] Read more.

Fast-dissolving tablets (FDTs) have arisen as a novel way to tackle issues encountered by patients with dysphagia, including youngsters, older people, and those with neurodegenerative or developmental disabilities. This review emphasises the crucial function of natural polymers as super disintegrants in improving fast-disintegrating tablet formulation. Natural polymers, such as chitosan, guar gum, xanthan gum, and fenugreek seed mucilage, are biocompatible, biodegradable, and offer better affordability than synthetics. Natural polymers can quickly break down and disintegrate oral tablets. They also help accelerate drug release bioavailability and patient compliance. This article discusses the benefits of natural polymers, such as environmentally sustainable processing, cost effectiveness, and patient engagement, as well as challenges and limitations. The comprehensive comparison between natural polymers and synthetic polymers emphasises the benefits of natural substances to overcome challenges in the production and promotion of sustainable pharmaceutical practices. Spray drying, freezing, and nanotechnology are advancements in FDT production technology. Apart from its ownership, like Zydis and Durasolv, the combination of these techniques aids in the creation of a medicine system that may be adjusted. They prioritize patients and are also effective. Prospective studies should focus on the expansion of natural polymer procurement and distillation processes to improve the use of FDT. Full article

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9 pages, 566 KiB

Open AccessProceeding Paper

Comparative Analysis of Multicarrier Waveforms for Terahertz-Band Communications

by Srinivas Ramavath, Umesh Chandra Samal, Prasanta Kumar Patra, Sunil Pattepu, Nageswara Rao Budipi and Amitkumar Vidyakant Jha

Eng. Proc. 2025, 87(1), 41; https://doi.org/10.3390/engproc2025087041 - 8 Apr 2025

Viewed by 218

Abstract

The terahertz (THz) band, ranging from 0.1 to 10 THz, offers substantial bandwidths that are essential for meeting the ever-increasing demands for high data rates in future wireless communication systems. This paper presents a comprehensive comparative analysis of various multicarrier waveforms suitable for [...] Read more.

The terahertz (THz) band, ranging from 0.1 to 10 THz, offers substantial bandwidths that are essential for meeting the ever-increasing demands for high data rates in future wireless communication systems. This paper presents a comprehensive comparative analysis of various multicarrier waveforms suitable for THz-band communications. We explore the performance, advantages, and limitations of several waveforms, including Orthogonal Frequency Division Multiplexing (OFDM), Filter Bank Multicarrier (FBMC), Universal Filtered Multicarrier (UFMC), and Generalized Frequency Division Multiplexing (GFDM). The analysis covers key parameters such as spectral efficiency, the peak-to-average power ratio (PAPR), robustness to phase noise, and computational complexity. The simulation results demonstrate that while OFDM offers simplicity and robustness to multipath fading, it suffers from high PAPR and phase noise sensitivity. FBMC and UFMC, with their enhanced spectral efficiency and reduced out-of-band emissions, show promise for THz-band applications but come at the cost of increased computational complexity. GFDM presents a flexible framework with a trade-off between complexity and performance, making it a potential candidate for diverse THz communication scenarios. Our study concludes that no single waveform universally outperforms the others across all metrics. Therefore, the choice of multicarrier waveform for THz communications should be tailored to the specific requirements of the application, balancing performance criteria and implementation feasibility. Future research directions include the development of hybrid waveforms and adaptive techniques to dynamically optimize performance in varying THz communication environments. Full article

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7 pages, 1429 KiB

Open AccessProceeding Paper

Digital Semantics for Enterprise Information System Development

by Gaetanino Paolone, Francesco Pilotti and Romolo Paesani

Eng. Proc. 2025, 87(1), 42; https://doi.org/10.3390/engproc2025087042 - 11 Apr 2025

Viewed by 289

Abstract

This position paper discusses the use of Digital Semantics, ontologies, and automata in the era of Artificial Intelligence (AI). Digital Semantics represents a potential new definition and paradigm for simulating human intelligence within a machine. Integrating this paradigm with other AI research approaches [...] Read more.

This position paper discusses the use of Digital Semantics, ontologies, and automata in the era of Artificial Intelligence (AI). Digital Semantics represents a potential new definition and paradigm for simulating human intelligence within a machine. Integrating this paradigm with other AI research approaches can significantly enhance the future of AI and its relevance for Enterprise Information System (EIS) automation. Our proposal is based on three Research Questions (RQs). The ultimate goal of our research is to define a method that fosters the use of AI in EISs for business modeling, system modeling, design, and implementation. Full article

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18 pages, 1983 KiB

Open AccessProceeding Paper

HauBERT: A Transformer Model for Aspect-Based Sentiment Analysis of Hausa-Language Movie Reviews

by Aminu Musa, Fatima Muhammad Adam, Umar Ibrahim and Abubakar Yakubu Zandam

Eng. Proc. 2025, 87(1), 43; https://doi.org/10.3390/engproc2025087043 - 9 Apr 2025

Viewed by 531

Abstract

In this study, we present a groundbreaking approach to aspect-based sentiment analysis (ABSA) using transformer-based models. ABSA is essential for understanding the intricate nuances of sentiment expressed in text, particularly across diverse linguistic and cultural contexts. Focusing on movie reviews in Hausa, a [...] Read more.

In this study, we present a groundbreaking approach to aspect-based sentiment analysis (ABSA) using transformer-based models. ABSA is essential for understanding the intricate nuances of sentiment expressed in text, particularly across diverse linguistic and cultural contexts. Focusing on movie reviews in Hausa, a language under-represented in sentiment analysis research, we propose HauBERT, a bidirectional transformer-based approach tailored for aspect and polarity classification, by fine-tuning a pre-trained mBERT model. Our work addresses the scarcity of resources for sentiment analysis in under-represented languages by creating a comprehensive Hausa ABSA dataset. Leveraging this dataset, we preprocess the text using state-of-the-art techniques for feature extraction, enhancing the model’s ability to capture nuanced aspects of sentiment. Furthermore, we manually annotate aspect-level feature ontology words and sentiment polarity assignments within the reviewed text, enriching the dataset with valuable semantic information. Our proposed transformer-based model utilizes self-attention mechanisms to capture long-range dependencies and contextual information, enabling it to effectively analyze sentiment in Hausa movie reviews. The proposed model achieves significant accuracy in aspect term extraction and sentiment polarity classification, with scores of 99% and 92% respectively, outperforming traditional machine models. This demonstrates the transformer’s ability to capture complex linguistic patterns and nuances of sentiment. Our study advances ABSA research and contributes to a more inclusive sentiment analysis landscape by providing resources and models tailored for under-represented languages. Full article

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14 pages, 3266 KiB

Open AccessProceeding Paper

Early-Stage Research to Characterize the Electrical Signal of Optically Stimulated Hydroponic Strawberries Using Machine Learning Techniques

by Levi Garcia-Menchaca, Carlos Guerra-Sánchez, Néji Tarchoun, Raouia Lebbihi, Oscar Cruz-Dominguez, Claudia Sifuentes-Gallardo, Juan Gerardo Peréz-Martínez, Mario Cleva, José Ortega-Sigala and Héctor Durán-Muñoz

Eng. Proc. 2025, 87(1), 44; https://doi.org/10.3390/engproc2025087044 - 14 Apr 2025

Viewed by 378

Abstract

Through the electrical signal generated by a plant, it is possible to identify water stress, pests on its roots, a sick plant, or even identify the optimal growing conditions. In particular, the optimal growing conditions in the strawberry plant can be identified by [...] Read more.

Through the electrical signal generated by a plant, it is possible to identify water stress, pests on its roots, a sick plant, or even identify the optimal growing conditions. In particular, the optimal growing conditions in the strawberry plant can be identified by its electrical signal, which can be useful to increase its production, since its fruits are in high demand for human consumption. Therefore, the aim of this pilot study is to use machine learning techniques to characterize the electrical signal of optically stimulated hydroponic strawberries, in order to identify the optimal growing conditions. The electrical signal was monitored using a home-made electronic system, based on Arduino. The principal result obtained shows that red light is the most informative feature in the random forest (RF) model, demonstrating superior performance in minimizing misclassification rates. In contrast, the support vector machine (SVM) model exhibited increased sensitivity to data variations, resulting in elevated misclassification rates. The feature importance analysis shows that the variable red light contributes 35% to the predictive capability of the model. Natural light and green light follow with approximately 25% each, while the contribution of yellow light is negligible at 15%. Finally, in this exploratory study, it would appear that the electrical signal from the plant is sensitive to specific light conditions, with red light being most impactful. Full article

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7 pages, 4821 KiB

Open AccessProceeding Paper

Electrospinning Poly(acrylonitrile) (PAN) Nanofiber Mats with Mushroom Mycelium Powder

by Nonsikelelo Sheron Mpofu, Elzbieta Stepula, Uwe Güth, Andrea Ehrmann and Lilia Sabantina

Eng. Proc. 2025, 87(1), 45; https://doi.org/10.3390/engproc2025087045 - 11 Apr 2025

Viewed by 223

Abstract

Electrospinning is a technique to produce nanofiber mats for diverse applications. In biomedicine in particular, the addition of an antibacterial agent can be advantageous. Here, we report on the needleless electrospinning of nanofiber mats using poly(acrylonitrile) (PAN) blended with different mushroom mycelium powders, [...] Read more.

Electrospinning is a technique to produce nanofiber mats for diverse applications. In biomedicine in particular, the addition of an antibacterial agent can be advantageous. Here, we report on the needleless electrospinning of nanofiber mats using poly(acrylonitrile) (PAN) blended with different mushroom mycelium powders, which have antibacterial and other functional properties. While PAN blended with Pleurotus ostreatus (oyster mushroom) powder could be electrospun well, PAN blended with Ganoderma lucidum (reishi mushroom) powder was nearly impossible to spin. The PAN/P. ostreatus nanofiber mats showed a morphology after electrospinning similiar to pure PAN; however, the carbon yield was lower. This indicates the possibility of embedding P. ostreatus powder in PAN nanofiber mats for biotechnological or biomedical applications. Full article

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9 pages, 2816 KiB

Open AccessProceeding Paper

Mechanical Characterization of Triply Periodic Minimal Surface Structures Fabricated via SLA 3D Printing Using Tough Resin: Influence of Geometry on Performance

by Sofia Kavafaki and Georgios Maliaris

Eng. Proc. 2025, 87(1), 46; https://doi.org/10.3390/engproc2025087046 - 14 Apr 2025

Viewed by 450

Abstract

Triply periodic minimal surfaces (TPMSs) structures are particularly suited for energy-absorbing, light-weight applications in fields such as medicine and engineering due to their ability to achieve maximum stress distribution with minimum density. Advances in stereolithography apparatus (SLA) three-dimensional (3D) printing and hard resins [...] Read more.

Triply periodic minimal surfaces (TPMSs) structures are particularly suited for energy-absorbing, light-weight applications in fields such as medicine and engineering due to their ability to achieve maximum stress distribution with minimum density. Advances in stereolithography apparatus (SLA) three-dimensional (3D) printing and hard resins make it possible to fabricate such complex geometries precisely. The present study contrasts the mechanical performance of six geometries of TPMS under com-pressive loading with particular focus on how wall geometry and thickness affect it but at a fixed porosity of 75% and size of 70 × 70 × 70 mm³. Among the structures, the peak compressive stress was the maximum in Gyroid (2.1 MPa), while Neovius demonstrated remarkably good performance (0.9 MPa) despite its low wall thickness. The objective is to analyze geometry-dependent performance trends in order to inform future structural design. These results imply that TPMS geometry can have a significant effect on mechanical response, regardless of wall thickness alone. Full article

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15 pages, 4254 KiB

Open AccessProceeding Paper

A Custom Convolutional Neural Network Model-Based Bioimaging Technique for Enhanced Accuracy of Alzheimer’s Disease Detection

by Gogulamudi Pradeep Reddy, Duppala Rohan, Shaik Mohammed Abdul Kareem, Yellapragada Venkata Pavan Kumar, Kasaraneni Purna Prakash and Malathi Janapati

Eng. Proc. 2025, 87(1), 47; https://doi.org/10.3390/engproc2025087047 - 14 Apr 2025

Viewed by 306

Abstract

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behavior, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures. However, current diagnostic methods are often inaccurate in identifying [...] Read more.

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behavior, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures. However, current diagnostic methods are often inaccurate in identifying the disease in its early stages. Although deep learning-based bioimaging has shown promising results in medical image classification, challenges remain in achieving the highest accuracy for detecting AD. Existing approaches, such as ResNet50, VGG19, InceptionV3, and AlexNet have shown potential, but they often lack reliability and accuracy due to several issues. To address these gaps, this paper suggests a novel bioimaging technique by developing a custom Convolutional Neural Network (CNN) model for detecting AD. This model is designed with optimized layers to enhance feature extraction from medical images. The experiment’s first phase involved the construction of the custom CNN structure with three max-pooling layers, three convolutional layers, two dense layers, and one flattened layer. The Adam optimizer and categorical cross-entropy were adopted to compile the model. The model’s training was carried out on 100 epochs with the patience set to 10 epochs. The second phase involved augmentation of the dataset images and adding a dropout layer to the custom CNN model. Moreover, fine-tuned hyperparameters and advanced regularization methods were integrated to prevent overfitting. A comparative analysis of the proposed model with conventional models was performed on the dataset both before and after the data augmentation. The results validate that the proposed custom CNN model significantly overtakes pre-existing models, achieving the highest validation accuracy of 99.53% after data augmentation while maintaining the lowest validation loss of 0.0238. Its precision, recall, and F1 score remained consistently high across all classes, with perfect scores for the Moderate Demented and Non-Demented categories after augmentation, indicating superior classification capability. Full article

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10 pages, 2316 KiB

Open AccessProceeding Paper

Recent Advancements in Bismuth Complexes: Computational Strategies for Biological Activities

by Satya, Kulsum Hashmi, Sakshi Gupta, Priya Mishra, Ekhlakh Veg, Tahmeena Khan and Seema Joshi

Eng. Proc. 2025, 87(1), 48; https://doi.org/10.3390/engproc2025087048 - 15 Apr 2025

Viewed by 158

Abstract

Bismuth (Bi) and its compounds are generally recognized for their biological safety and non-toxicity, making them highly valuable for the large-scale synthesis of various Bi-based complexes for their use in diverse biological applications. Bi drugs are among the few antimicrobial agents that have [...] Read more.

Bismuth (Bi) and its compounds are generally recognized for their biological safety and non-toxicity, making them highly valuable for the large-scale synthesis of various Bi-based complexes for their use in diverse biological applications. Bi drugs are among the few antimicrobial agents that have not developed drug resistance and have a synergistic effect with antibiotics. Studies have established that the biological activities of Bi complexes are influenced by the properties and positions of the substituted groups on ligand. Even slight modifications have profound effects on their efficacy. Computational methods, such as Density Functional Theory (DFT) and Molecular Docking (MD) offer a greener approach and provide detailed information into the structure, stability, and reactivity of compounds. This review presents insights into the factors influencing the biological activity of Bi complexes through computational techniques. Full article

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10 pages, 1379 KiB

Open AccessProceeding Paper

Recognizing Human Emotions Through Body Posture Dynamics Using Deep Neural Networks

by Arunnehru Jawaharlalnehru, Thalapathiraj Sambandham and Dhanasekar Ravikumar

Eng. Proc. 2025, 87(1), 49; https://doi.org/10.3390/engproc2025087049 - 16 Apr 2025

Viewed by 398

Abstract

Body posture dynamics have garnered significant attention in recent years due to their critical role in understanding the emotional states conveyed through human movements during social interactions. Emotions are typically expressed through facial expressions, voice, gait, posture, and overall body dynamics. Among these, [...] Read more.

Body posture dynamics have garnered significant attention in recent years due to their critical role in understanding the emotional states conveyed through human movements during social interactions. Emotions are typically expressed through facial expressions, voice, gait, posture, and overall body dynamics. Among these, body posture provides subtle yet essential cues about emotional states. However, predicting an individual’s gait and posture dynamics poses challenges, given the complexity of human body movement, which involves numerous degrees of freedom compared to facial expressions. Moreover, unlike static facial expressions, body dynamics are inherently fluid and continuously evolving. This paper presents an effective method for recognizing 17 micro-emotions by analyzing kinematic features from the GEMEP dataset using video-based motion capture. We specifically focus on upper body posture dynamics (skeleton points and angle), capturing movement patterns and their dynamic range over time. Our approach addresses the complexity of recognizing emotions from posture and gait by focusing on key elements of kinematic gesture analysis. The experimental results demonstrate the effectiveness of the proposed model, achieving a high accuracy rate of 91.48% for angle metric + DNN and 93.89% for distance + DNN on the GEMEP dataset using a deep neural network (DNN). These findings highlight the potential for our model to advance posture-based emotion recognition, particularly in applications where human body dynamics distance and angle are key indicators of emotional states. Full article

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9 pages, 1118 KiB

Open AccessProceeding Paper

Color Stability of PET-G in Clear Aligners: Impact of Prolonged Exposure to Everyday Substances and Its Psychological and Social Implications

by Fabiana Nicita, Joseph Lipari, Frank Lipari and Arianna Nicita

Eng. Proc. 2025, 87(1), 50; https://doi.org/10.3390/engproc2025087050 - 21 Apr 2025

Viewed by 236

Abstract

The aesthetics of clear aligners is a critical factor that can influence patient satisfaction and psychological and social well-being. However, their transparency can be compromised by exposure to staining agents. This study aimed to evaluate the color stability of PET-G aligners following prolonged [...] Read more.

The aesthetics of clear aligners is a critical factor that can influence patient satisfaction and psychological and social well-being. However, their transparency can be compromised by exposure to staining agents. This study aimed to evaluate the color stability of PET-G aligners following prolonged exposure to common daily substances, including food, tobacco products, and cleaning agents. Flat samples of PET-G (n = 220) were immersed in various solutions, including coffee, tea, Coca-Cola, red wine, a colloidal silver-based disinfectant, nicotine, artificial saliva, cigarette smoke, and mixtures of saliva with smooth, coffee, and nicotine. Immersion times of 10 (n = 110) and 15 days (n = 110) were randomly assigned. Colorimetric assessments were conducted by measuring L*a*b* parameters before and after immersion, and total color change (ΔE) was calculated. Non-parametric statistical tests revealed significant color changes in PET-G samples after both immersion durations, with pairwise comparisons indicating notable differences in ΔE values among groups exposed to different substances, particularly coffee, tea, and Coca-Cola. The findings highlight the psychological and social impact of aligner staining on patient confidence and compliance. Understanding these effects highlights the need for enhanced patient education to improve aligner aesthetics and satisfaction. Full article

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12 pages, 707 KiB

Open AccessProceeding Paper

A Rule-Based Model for Stemming Hausa Words

by Mustapha Ashiru Bari, Hadiza Ali Umar, Bello Shehu Bello and Ibrahim Said Ahmed

Eng. Proc. 2025, 87(1), 51; https://doi.org/10.3390/engproc2025087051 - 21 Apr 2025

Viewed by 462

Abstract

The increasing number of online communities has led to the significant growth in digital data in multiple languages on the Internet. Consequently, language processing and information retrieval have become important fields in the era of the Internet. Stemming, a crucial preprocessing tool in [...] Read more.

The increasing number of online communities has led to the significant growth in digital data in multiple languages on the Internet. Consequently, language processing and information retrieval have become important fields in the era of the Internet. Stemming, a crucial preprocessing tool in natural language processing and information retrieval, has been extensively explored for high-resource languages like English, German, and French. However, more extensive studies regarding stemming in the context of the Hausa language, an international language that is widely spoken in West Africa and one of the fastest-growing languages globally, are required. This paper presents a rule-based model for stemming Hausa words. The proposed model relies on a set of rules derived from the analysis of Hausa word morphology and the rules for extracting stem forms. The rules consider the syntactic constraints, e.g., affixation rules, and performs a morphological analysis of the properties of the Hausa language, such as word formation and distribution. The proposed model’s performance is evaluated against existing models using standard evaluation metrics. The evaluation method employed Sirstat’s approach, and a language expert assessed the system’s results. The model is evaluated using the manual annotation of a set of 5077 total words used in the algorithm, including 2630 unique words and 3766 correctly stemmed Hausa words. The model achieves an overall accuracy of 98.66%, demonstrating its suitability for use in applications such as natural language processing and information retrieval. Full article

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9 pages, 1886 KiB

Open AccessProceeding Paper

Modeling the Quantitative Structure–Activity Relationships of 1,2,4-Triazolo[1,5-a]pyrimidin-7-amine Analogs in the Inhibition of Plasmodium falciparum

by Inalegwu S. Apeh, Thecla O. Ayoka, Charles O. Nnadi and Wilfred O. Obonga

Eng. Proc. 2025, 87(1), 52; https://doi.org/10.3390/engproc2025087052 - 21 Apr 2025

Viewed by 176

Abstract

Triazolopyrimidine and its analogs represent an important scaffold in medicinal chemistry research. The heterocycle of 1,2,4-triazolo[1,5-a] pyrimidine (1,2,4-TAP) serves as a bioisostere candidate for purine scaffolds, N-acetylated lysine, and carboxylic acid. This study modeled the quantitative structure–activity relationship (QSAR) of 125 congeners of [...] Read more.

Triazolopyrimidine and its analogs represent an important scaffold in medicinal chemistry research. The heterocycle of 1,2,4-triazolo[1,5-a] pyrimidine (1,2,4-TAP) serves as a bioisostere candidate for purine scaffolds, N-acetylated lysine, and carboxylic acid. This study modeled the quantitative structure–activity relationship (QSAR) of 125 congeners of 1,2,4-TAP from the ChEMBL database in the inhibition of Plasmodium falciparum using six machine learning algorithms. The most significant features among 306 molecular descriptors, including one molecular outlier, were selected using recursive feature elimination. A ratio of 20% was used to split the x- and y-matrices into 99 training and 24 test compounds. The regression models were built using machine learning sci-kit-learn algorithms (multiple linear regression (MLR), k-nearest neighbours (kNN), support vector regressor (SVR), random forest regressor (RFR) RIDGE regression, and LASSO). Model performance was evaluated using the coefficient of determination (R²), mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), p-values, F-statistic, and variance inflation factor (VIF). Five significant variables were considered in constructing the model (p < 0.05) with the following regression equation: pIC₅₀ = 5.90 − 0.71npr1 − 1.52pmi3 + 0.88slogP − 0.57vsurf-CW2 + 1.11vsurf-W2. On five-fold cross-validation, three algorithms—kNN (MSE = 0.46, R² = 0.54, MAE = 0.54, RMSE = 0.68), SVR (MSE = 0.33, R² = 0.67, MAE = 0.46, RMSE = 0.57), and RFR (MSE = 0.43, R² = 0.58, MAE = 0.51, RMSE = 0.66)—showed strong robustness, efficiency, and reliability in predicting the pIC₅₀ of 1,2,4-triazolo[1,5-a]pyrimidine. The models provided useful data on the functionalities necessary for developing more potent 1,2,4-TAP analogs as anti-malarial agents. Full article

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11 pages, 4877 KiB

Open AccessProceeding Paper

Leveraging RFID for Road Safety Sign Detection to Enhance Efficiency and Notify Drivers

by Dhanasekar Ravikumar, Vijayaraja Loganathan, Pranav Ponnovian, Vignesh Loganathan and Bharanidharan Sivalingam

Eng. Proc. 2025, 87(1), 53; https://doi.org/10.3390/engproc2025087053 - 15 Apr 2025

Viewed by 140

Abstract

Road safety signboards are now difficult to see due to pollution and harsh weather elements such as snow and fog, which has resulted in more accidents. The problem is especially common in Western countries where snow can block these critical signs. An approach [...] Read more.

Road safety signboards are now difficult to see due to pollution and harsh weather elements such as snow and fog, which has resulted in more accidents. The problem is especially common in Western countries where snow can block these critical signs. An approach addressing this issue involves a system that uses Radio Frequency Identification (RFID) and Internet of Things (IoT). The real-time alerts that this system sends to drivers improve driver safety in complex environments. For this purpose, an RFID reader is placed in the vehicle, and passive RFID tags are attached to road safety signboards. The reader picks up the signal as a vehicle comes within range, and the warning for the vehicle is sent to the driver. It helps to reduce the number of accidents resulting from poor visibility. In addition, because its multi-lingual audio alerts the drive through speakers and visual warnings displayed on a display screen, the system is accessible to drivers from various regions. To make the system more sustainable, we added some solar panels to the system to cut costs as far as energy efficiency is concerned. The system combines GPS and GSM modules to provide the vehicle position in real time in the cloud. It gives better warnings and helps avoid accidents. In addition to improving road safety, the system offers support for the environment, by limiting emissions and waste of resources caused by accidents. Traffic patterns can thus be studied with the data, creating more efficient and ecofriendly transportation systems. This solution enables a smarter vehicle network that is safer and more sustainable with quick, accurate alerts. Full article

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7 pages, 1734 KiB

Open AccessProceeding Paper

The Thermo-Optic Discrimination of an Aqueous Solution Composition Using a Multimodal Interference Fiber Optic Sensor

by Ruth K. Delgadillo-González, Nailea Mar-Abundis, René F. Domínguez-Cruz, Federico Ampudia-Ramírez, Yadira A. Fuentes-Rubio and José R. Guzmán-Sepúlveda

Eng. Proc. 2025, 87(1), 54; https://doi.org/10.3390/engproc2025087054 - 25 Apr 2025

Viewed by 164

Abstract

Fiber optics sensors based on multimodal interference (MMI) have proven effective for refractometry of liquid samples. Here, we extend these capabilities to demonstrate that aqueous solutions with a similar refractive index (RI), which at room temperature are indistinguishable at the same concentration, can [...] Read more.

Fiber optics sensors based on multimodal interference (MMI) have proven effective for refractometry of liquid samples. Here, we extend these capabilities to demonstrate that aqueous solutions with a similar refractive index (RI), which at room temperature are indistinguishable at the same concentration, can be discriminated against based on their thermo-optical response. We used an MMI sensor with the standard singlemode–multimode–singlemode architecture, where a section of no-core multimode fiber provides environmental sensitivity to the fiber surroundings. The proposed idea has been tested on aqueous solutions of tris and fructose, whose RI has a similar dependence on concentration. Indeed, we verified that they produce indistinguishable wavelength shifts as a function of concentration, measuring 0.2179 nm/% for tris and 0.2264 nm/% for fructose. Then, by varying the temperature in a controlled manner, from 25 °C to 45 °C in 2.5 °C increments, the distinct thermo-optic response can be unveiled for the two samples, which now permits differentiating them. Thermal sensitivities of 0.14433 nm/°C for tris and 0.1852 nm/°C for fructose were observed. This optical sensor requires no specific preparation or specialized equipment because the temperature range needed to achieve thermo-optical discrimination is accessible. Therefore, the measurement protocol can be incorporated into commercial refractometers equipped with temperature control. Full article

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7 pages, 2079 KiB

Open AccessProceeding Paper

Antioxidant Activity of Ablated CeO₂ Nanoparticles with Narrow-Size Distribution

by Vladimir Mamontov, Maksim Pugachevskii, Petr Snetkov and Ratneshwar Kumar Ratnesh

Eng. Proc. 2025, 87(1), 55; https://doi.org/10.3390/engproc2025087055 - 27 Apr 2025

Viewed by 155

Abstract

A method for the synthesis of nanodispersed aqueous solutions based on ablated cerium dioxide nanoparticles with a narrow-size distribution has been developed, and their physicochemical properties have been investigated. The nanoparticle sizes of CeO₂ were analyzed using atomic force microscopy and small-angle [...] Read more.

A method for the synthesis of nanodispersed aqueous solutions based on ablated cerium dioxide nanoparticles with a narrow-size distribution has been developed, and their physicochemical properties have been investigated. The nanoparticle sizes of CeO₂ were analyzed using atomic force microscopy and small-angle X-ray scattering. The dependence of the electronic structure of ablated cerium dioxide nanoparticles on their size was established. The influence of the size factor on the antioxidant properties of the obtained particle size groups was investigated. Full article

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9 pages, 3313 KiB

Open AccessProceeding Paper

Fuzzy Logic-Based Adaptive Droop Control Designed with Feasible Range of Droop Coefficients for Enhanced Power Delivery in Microgrids

by Mandarapu Srikanth, Yellapragada Venkata Pavan Kumar and Sivakavi Naga Venkata Bramareswara Rao

Eng. Proc. 2025, 87(1), 56; https://doi.org/10.3390/engproc2025087056 - 27 Apr 2025

Viewed by 220

Abstract

Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to the lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive [...] Read more.

Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to the lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive droop control to provide compensation during transient conditions, thereby improving the power delivery capability. In this context, fuzzy logic-based adaptive droop control is a state-of-the-art technique that was developed based on empirical knowledge of the system. However, this way of designing the droop coefficient values without considering the mathematical knowledge of the system leads to instability during transient conditions. This problem further aggravates when dominant inductive load changes occur in the system. To address this limitation, this paper proposes an improved fuzzy logic-based adaptive droop control method. In the proposed methodology, the values of droop coefficients that are assigned for different membership functions are designed based on the stability analysis of the microgrid. In this analysis, the feasible range of active power–frequency droop values that could avoid instability during large inductive load changes is identified. Accordingly, the infeasible values are avoided in the design of the fuzzy controller. The performance of the proposed and the conventional fuzzy logic methods is verified through simulation in MATLAB/Simulink. From the results, it is identified that the proposed method has improved the power delivery capability of the microgrid by 14% compared to the conventional method. Full article

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10 pages, 2018 KiB

Open AccessProceeding Paper

Data Protection in Brazil: Applying Text Mining in Court Documents

by Arnaldo Lucas Santos Duarte, Everton Reis de Souza, Marcos Paulo de Oliveira Silva, Madson Bruno da Silva Monte, Nathaly Oliveira de Almeida Correia, Victor Diogho Heuer de Carvalho and Fernando Henrique Taques

Eng. Proc. 2025, 87(1), 57; https://doi.org/10.3390/engproc2025087057 - 29 Apr 2025

Viewed by 820

Abstract

The rise of information technology and artificial intelligence has sparked debates on data protection in various fields. Data protection has been addressed in court rulings long before Brazil’s General Data Protection Law (LGPD). This study analyzes jurisprudence related to data protection by examining [...] Read more.

The rise of information technology and artificial intelligence has sparked debates on data protection in various fields. Data protection has been addressed in court rulings long before Brazil’s General Data Protection Law (LGPD). This study analyzes jurisprudence related to data protection by examining 10,009 documents from the Brazilian States’ courts collected through a web scraping process in an online juridical platform without restricting the period of publication. This analysis reveals document distribution among state courts, with the southeast and southern regions being the most productive, and identifies key terms in each state court. This provides a deeper understanding of the legal processes surrounding data protection issues in each Brazilian region. Full article

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15 pages, 1545 KiB

Open AccessProceeding Paper

In Vitro Antibacterial Activity of Pure and Encapsulated Mangiferin Against ESKAPE Bacteria

by Polina Serbun, Roman Shaikenov, Vladislava Klimshina, Svetlana Morozkina and Petr Snetkov

Eng. Proc. 2025, 87(1), 58; https://doi.org/10.3390/engproc2025087058 - 29 Apr 2025

Viewed by 264

Abstract

Currently, multidrug-resistant (MDR) bacteria are a global problem, which requires modern approaches and effective pharmaceutical agents. Substances isolated from nature sources have a strong potential in combating highly virulent and antibiotic-resistant microorganisms, including within the ESKAPE group (Enterococcus faecium, Staphylococcus aureus [...] Read more.

Currently, multidrug-resistant (MDR) bacteria are a global problem, which requires modern approaches and effective pharmaceutical agents. Substances isolated from nature sources have a strong potential in combating highly virulent and antibiotic-resistant microorganisms, including within the ESKAPE group (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.). One of these substances is mangiferin, the bioactive compound obtained from parts of Mangiferin indica L. Mangiferin has a low aqueous solubility, which causes low activity and requires additional modification or delivery system to increase its concentration in the cell. Many studies show that it has multiple biological effects and can be used as an antioxidant or an anticancer agent, and it can exhibit antibacterial properties. Extracts obtained from plant parts show high efficacy in low doses against the ESKAPE group and other strains. This makes mangiferin a possible candidate as a strong agent against bacterial infections. The mechanisms underlying the action of mangiferin on bacterial cells are poorly understood. This review summarizes studies confirming the antibacterial properties of mangiferin both in its native form and using delivery systems. Full article

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10 pages, 1504 KiB

Open AccessProceeding Paper

Air Quality Health Index and Discomfort Conditions in a Heatwave Episode During July 2024 in Rhodes Island

by Ioannis Logothetis, Adamantios Mitsotakis and Panagiotis Grammelis

Eng. Proc. 2025, 87(1), 59; https://doi.org/10.3390/engproc2025087059 - 29 Apr 2025

Viewed by 237

Abstract

Climate conditions in combination with the concentration of pollutants increase the human health stress and exacerbate systemic diseases. The city of Rhodes is a desirable tourist destination that is located in a sensitive climate region of the southeastern Aegean Sea in the Mediterranean [...] Read more.

Climate conditions in combination with the concentration of pollutants increase the human health stress and exacerbate systemic diseases. The city of Rhodes is a desirable tourist destination that is located in a sensitive climate region of the southeastern Aegean Sea in the Mediterranean region. In this work, hourly recordings from a mobile air quality monitoring system, which is located in an urban area of Rhodes city, are employed in order to measure the concentration of regulated pollutants (

{S O}_{2}, {N O}_{2}, O_{3}, {P M}_{10} a n d {P M}_{2.5})

and meteorological factors (pressure, temperature, and relative humidity). The air quality health index (AQHI) and the discomfort index (DI) are calculated to study the impact of air quality and meteorological conditions on human health. The analysis is conducted during a hot summer period, from 29 June to 14 July 2024. During the second half of the studied period, a heatwave episode occurred that affected the bioclimatic conditions over the city. The results show that despite the fact that the concentration of pollutants is lower than the pollutant thresholds (according to Directive 2008/50/EC), the AQHI and DI conditions degrade significantly over the heatwave days. In particular, the AQHI is classified in the “Moderate” class, and the DI indicates that most of the population suffers discomfort. The AQHI and DI simultaneously increase during the days of the heat episode, showing a possible negative synergy for the health risk. Finally, both the day maximum and night minimum temperature are increased (about 0.8 and 0.6 °C, respectively) during the heatwave days as compared to the whole studied period. Full article

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10 pages, 3253 KiB

Open AccessProceeding Paper

Advanced Virtual Synchronous Generator Control Scheme for Improved Power Delivery in Renewable Energy Microgrids

by Mandarapu Srikanth, Yellapragada Venkata Pavan Kumar and Sivakavi Naga Venkata Bramareswara Rao

Eng. Proc. 2025, 87(1), 60; https://doi.org/10.3390/engproc2025087060 - 30 Apr 2025

Viewed by 289

Abstract

Renewable energy and voltage source inverter-driven microgrids generally lack natural inertia to provide transient energy support during sudden load demands. To address this, the virtual synchronous generator (VSG) is a state-of-the-art control technique applied in power controllers to emulate virtual inertia during sudden [...] Read more.

Renewable energy and voltage source inverter-driven microgrids generally lack natural inertia to provide transient energy support during sudden load demands. To address this, the virtual synchronous generator (VSG) is a state-of-the-art control technique applied in power controllers to emulate virtual inertia during sudden load changes. This allows for stable power delivery from the source to the loads during sudden active power load demands. However, in systems with large inductively dominant load demands, conventional VSG-based power controllers may exhibit a delayed reactive power response due to their inertia-emulating characteristics, potentially affecting the overall power-sharing performance. To address this limitation of VSG control, this paper proposes an advanced control scheme in which the VSG is supported by appropriately designed voltage and current controllers. Conventionally, classical tuning techniques are used to design the controllers in the forward paths of the voltage and current controllers (CVAs). Thus, the conventional control scheme is a combination of a VSG and CVAs. Recently, a hybrid modified pole-zero cancellation technique has been discussed in the literature for the design of voltage and current controllers (HVAs) to improve the vector control of the inverter. This method supports better tuning for controllers of both forward and cross-coupling paths. Therefore, to improve the power delivery with VSG-based control when subjected to inductive load changes, this paper proposes an advanced control scheme that is based on the combination of VSG and HVA. The performance of both conventional and proposed control schemes is verified through simulation in MATLAB/Simulink under two different test load conditions, namely good and poor power factor loadings. Based on the results obtained during these test cases, the response and power delivery capability of the proposed control scheme is comparable with that of the conventional control scheme. The results verify that the power delivery capability of the microgrid with the proposed control scheme is improved by 25% compared to the conventional control scheme. Full article

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8 pages, 383 KiB

Open AccessProceeding Paper

Methods for Processing Signal Conversion in Velocity and Acceleration Measurement Considering Transducer Characteristics

by Sergii Filonenko and Anzhelika Stakhova

Eng. Proc. 2025, 87(1), 61; https://doi.org/10.3390/engproc2025087061 - 6 May 2025

Viewed by 156

Abstract

This study presents an innovative approach to processing vibration signals in bridge structures, with a focus on enhancing the accuracy of dynamic response measurements and structural health assessments. It addresses key challenges in signal processing, particularly the uncertainties in selecting filtering parameters for [...] Read more.

This study presents an innovative approach to processing vibration signals in bridge structures, with a focus on enhancing the accuracy of dynamic response measurements and structural health assessments. It addresses key challenges in signal processing, particularly the uncertainties in selecting filtering parameters for isolating dynamic components from static displacements. A novel method for adaptive filter parameter selection is proposed, which considers variations in resonant frequencies and the non-linearity of quasi-static displacements caused by moving loads. This approach significantly reduces errors in determining forced and natural vibration parameters, leading to more accurate assessments of the bridge’s mechanical characteristics. The study introduces an optimized algorithm for processing acceleration and velocity signals, improving the resolution of natural frequency identification. This method combines traditional Fast Fourier Transform (FFT) techniques with an innovative spectral analysis approach, enabling precise identification of resonant frequencies and damping coefficients. A comprehensive evaluation framework is developed, integrating vibration amplitude, frequency, and damping ratio analyses. This framework enhances structural health assessments, improving the detection and characterization of potential defects and changes in load-bearing capacity. The practical significance of this research lies in its real-world application to bridge diagnostics. The study provides guidelines for sensor selection and configuration, adapted for various bridge types and sizes. The proposed methods demonstrate notable improvements in dynamic coefficient determination and overall structural assessments, offering the potential to reduce maintenance costs and enhance bridge safety. Full article

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8 pages, 1340 KiB

Open AccessProceeding Paper

Correlation Between Nutrient Concentration and Leaf Optical Attenuation Coefficient of Brassica Rapa (Pechay) as Measured by Time-Domain Optical Coherence Tomography System

by Tristan Dave Taeza, Mark Emmanuel Witongco, Maria Cecilia Galvez, Edgar Vallar, Mark Nickole Tabafa, James Roy Lesidan, Jumar Cadondon, Jejomar Bulan and Tatsuo Shiina

Eng. Proc. 2025, 87(1), 62; https://doi.org/10.3390/engproc2025087062 - 9 May 2025

Viewed by 232

Abstract

This study explores the relationship between nutrient concentration (NC) and epidermal thickness (d) of the leaves of hydroponically grown Brassica rapa and its attenuation coefficients (m) using portable Time-Domain Optical Coherence Tomography (TD-OCT), which is a non-invasive [...] Read more.

This study explores the relationship between nutrient concentration (NC) and epidermal thickness (d) of the leaves of hydroponically grown Brassica rapa and its attenuation coefficients (m) using portable Time-Domain Optical Coherence Tomography (TD-OCT), which is a non-invasive imaging technique that uses low-coherence interferometry to generate axial scans of plants’ leaves by measuring the time delay and intensity of backscattered light. The portable TD-OCT system in this study has an axial and lateral resolution of 7 m and 3 m, respectively, a scanning depth of 12 mm, and a 1310 nm Super Luminescent Diode (SLD). Several studies suggest that the differences in d and m are related to nutritional, physiological, and anatomical status. The study used the Kratky method, a simple non-circulating hydroponic system, to cultivate Brassica rapa with varying NC (25%, 50%, 75%, 100% (control), and 125%). Each treatment group used two plants. The TD-OCT sample probe was placed on a fixed holder and was oriented vertically so that light was directed downward onto the leaf’s surface to obtain the depth profile (A-scan). The distance between the probe and the leaf was adjusted to obtain the optimum interference signal. Five averaged A-scans were obtained per leaf on the 7th, 18th, and 21st days post nutrient exposure. The logarithm of the averaged A-scan is linearly fitted to extract m. The results showed a positive correlation between NC and m, which suggests that plants produce more chlorophyll and develop denser cells and increase m. There was no correlation obtained between NC and d. The study demonstrates the potential of TD-OCT as a non-destructive tool for assessing plant health and monitoring growth dynamics in hydroponic systems and m as a sensitive indicator of plant health as compared to d. The continued exploration of TD-OCT applications in agriculture can contribute to improving crop management strategies and promoting sustainable food production practices. Full article

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8 pages, 1651 KiB

Open AccessProceeding Paper

Examination of New Fused Deposition Modeling (FDM) Filaments for Applications with Large Temperature Variations

by Ömer Balandi, Uwe Güth, Leon Diel, Sinan Kiremit and Andrea Ehrmann

Eng. Proc. 2025, 87(1), 63; https://doi.org/10.3390/engproc2025087063 - 9 May 2025

Viewed by 139

Abstract

Today, 3D printing is no longer only used for rapid prototyping, but also for the production of customized objects, spare parts, etc. However, printed parts often exhibit mechanical and thermal inadequacies. Here, we investigate novel filaments for fused deposition modeling (FDM) with and [...] Read more.

Today, 3D printing is no longer only used for rapid prototyping, but also for the production of customized objects, spare parts, etc. However, printed parts often exhibit mechanical and thermal inadequacies. Here, we investigate novel filaments for fused deposition modeling (FDM) with and without fibrous fillers before and after cyclic temperature variations between −40 °C and +80 °C, similar to the situation of a microsatellite in the low Earth orbit (LEO). Maximum bending forces, deflection at maximum force, and tensile strengths remained nearly unchanged for most materials after heat treatment, suggesting that most materials investigated here can be used in environments with strongly varying temperatures. Full article

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10 pages, 322 KiB

Open AccessProceeding Paper

Optimizing Brain Tumor Classification: Integrating Deep Learning and Machine Learning with Hyperparameter Tuning

by Vijaya Kumar Velpula, Kamireddy Rasool Reddy, K. Naga Prakash, K. Prasanthi Jasmine and Vadlamudi Jyothi Sri

Eng. Proc. 2025, 87(1), 64; https://doi.org/10.3390/engproc2025087064 - 12 May 2025

Viewed by 238

Abstract

Brain tumors significantly impact global health and pose serious challenges for accurate diagnosis due to their diverse nature and complex characteristics. Effective diagnosis and classification are essential for selecting the best treatment strategies and forecasting patient outcomes. Currently, histopathological examination of biopsy samples [...] Read more.

Brain tumors significantly impact global health and pose serious challenges for accurate diagnosis due to their diverse nature and complex characteristics. Effective diagnosis and classification are essential for selecting the best treatment strategies and forecasting patient outcomes. Currently, histopathological examination of biopsy samples is the standard method for brain tumor identification and classification. However, this method is invasive, time-consuming, and prone to human error. To address these limitations, a fully automated approach is proposed for brain tumor classification. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in improving the accuracy and efficiency of tumor detection from magnetic resonance imaging (MRI) scans. In response, a model was developed that integrates machine learning (ML) and deep learning (DL) techniques. The process began by splitting the data into training, testing, and validation sets. Images were then resized and cropped to enhance model quality and efficiency. Relevant texture features were extracted using a modified Visual Geometry Group (VGG) architecture. These features were fed into various supervised ML models, including support vector machine (SVM), k-nearest neighbors (KNN), logistic regression (LR), stochastic gradient descent (SGD), random forest (RF), and AdaBoost, with GridSearchCV used for hyperparameter tuning. The model’s performance was evaluated using key metrics such as accuracy, precision, recall, F1-score, and specificity. Experimental results demonstrate that the proposed approach offers a robust and automated solution for brain tumor classification, achieving the highest accuracy of 94.02% with VGG19 and 96.30% with VGG16. This model can significantly assist healthcare professionals in early tumor detection and in improving diagnostic accuracy. Full article

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13 pages, 3176 KiB

Open AccessProceeding Paper

Enhancing Predictive Accuracy in IoT-Based Smart Irrigation Systems: A Comparative Analysis of Advanced Ensemble Learning Models and Traditional Techniques for Soil Fertility Assessment

by Satyajit Puajpanda, Debasish Mahapatra, Sriya Mishra, Neelamadhab Padhy and Rasmita Panigrahi

Eng. Proc. 2025, 87(1), 65; https://doi.org/10.3390/engproc2025087065 - 12 May 2025

Viewed by 224

Abstract

Unpredictable climate patterns and mounting groundwater depletion are major challenges to sustainable agriculture. The purpose of this research is to improve predictive accuracy in IoT-based smart irrigation systems using machine learning models for soil fertility estimation and water optimization. In contrast to existing [...] Read more.

Unpredictable climate patterns and mounting groundwater depletion are major challenges to sustainable agriculture. The purpose of this research is to improve predictive accuracy in IoT-based smart irrigation systems using machine learning models for soil fertility estimation and water optimization. In contrast to existing research, this paper compares state-of-the-art ensemble learning models (LRBoost, LR+RF) with conventional methods to ascertain their real-time effectiveness in water usage prediction. Training and testing data were derived from open access agricultural data repositories, including soil moisture, temperature, humidity, and rainfall. Feature selection was performed through correlation analysis and model performance was evaluated using R² score, mean squared error (MSE), and root mean squared error (RMSE). Our results indicate that the hybrid ensemble model LR+RF performed better than others with an R² measure of 96.34%, an MSE of 0.0016, and an RMSE of 0.040. The findings confirm the capability of the system in minimizing water wastage and maximizing crop production. Full article

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7 pages, 858 KiB

Open AccessProceeding Paper

A Model-Based Analysis of Direct Methanol Production from CO₂ and Renewable Hydrogen

by Azizbek Kamolov, Zafar Turakulov, Botir Shukurillaevich Usmonov, Khayrulla Pulatov, Abdulaziz Bakhtiyorov, Bekjon Urunov and Adham Norkobilov

Eng. Proc. 2025, 87(1), 66; https://doi.org/10.3390/engproc2025087066 - 14 May 2025

Viewed by 93

Abstract

Methanol synthesis from CO₂ is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. The direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of [...] Read more.

Methanol synthesis from CO₂ is a key strategy for carbon capture and utilization, offering a viable solution to mitigate climate change. The direct synthesis of methanol not only reduces greenhouse gases but also produces valuable chemicals for industrial applications. The aim of this study is to model and optimize the methanol synthesis process from CO₂, focusing on maximizing methanol yield while minimizing CO₂ content in the product stream. In this work, a detailed methanol synthesis process simulation was developed using the Soave–Redlich–Kwong equation of state in the Aspen Plus V11 commercial software environment. Pure CO₂ streams, which are produced from the post-combustion carbon capture process, and renewable hydrogen streams were used. The results are compared with open literature sources. In addition, a sensitivity analysis was employed to evaluate the effects of the pressure, temperature, and recirculation fraction on process efficiency. The results showed that the highest methanol yield of 76,838 kg/h was obtained at 80 bar, 276 °C, and a recirculation fraction of 0.9. The lowest CO₂ content in the final product (73 kg/h) occurred at 80 bar, 220 °C, and a recirculation fraction of 0.6. These findings demonstrate the trade-off between maximizing methanol output and reducing unreacted CO₂. In conclusion, optimal operating conditions for both the high yield and low CO₂ content were identified, providing a foundation for further process refinement. Future work will involve developing a more complex multi-reactor model and conducting economic assessments for large-scale industrial implementation. Full article

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7 pages, 5843 KiB

Open AccessProceeding Paper

Solvothermal Synthesis of Nanomagnetite-Coated Biochar for Efficient Arsenic and Fluoride Adsorption

by Diego-Antonio Corona-Martinez, Lourdes Díaz-Jiménez, Audberto Reyes-Rosas, Alejandro Zermeño-González, Luis Samaniego-Moreno and Sasirot Khamkure

Eng. Proc. 2025, 87(1), 67; https://doi.org/10.3390/engproc2025087067 - 16 May 2025

Viewed by 97

Abstract

Arsenic contamination in water demands effective, low-cost removal methods. This study introduces nanomagnetite-coated biochar derived from pecan nutshells for efficient arsenic adsorption. Utilizing a solvothermal method, uniform magnetite crystals were grown on biochar in a controlled process at 200 °C. The resulting bioadsorbent, [...] Read more.

Arsenic contamination in water demands effective, low-cost removal methods. This study introduces nanomagnetite-coated biochar derived from pecan nutshells for efficient arsenic adsorption. Utilizing a solvothermal method, uniform magnetite crystals were grown on biochar in a controlled process at 200 °C. The resulting bioadsorbent, characterized by XRD, SEM, and FTIR, exhibited a narrow size distribution and consistently high arsenic removal rates (97.30–98.76%). Biochar with varied particle sizes, synthesized at a short reaction time (6 h), showed the highest removal efficiency of arsenic (98.76%) and adsorption capacity (7.974 mg/g). This approach offers a sustainable for arsenic remediation, and ease of magnetic separation. Full article

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10 pages, 989 KiB

Open AccessProceeding Paper

Application of Quantum Computing Algorithms in the Synthesis of Control Systems for Dynamic Objects

by Noilakhon Yakubova, Komil Usmanov, Zafar Turakulov and Jaloliddin Eshbobaev

Eng. Proc. 2025, 87(1), 68; https://doi.org/10.3390/engproc2025087068 - 20 May 2025

Viewed by 70

Abstract

Currently, the main focus in the automation of technological processes is on developing control systems that enhance the quality of the control process. Because the systems being controlled are often complex, multidimensional, and nonlinear, quantum computing algorithms offer an effective solution. Although there [...] Read more.

Currently, the main focus in the automation of technological processes is on developing control systems that enhance the quality of the control process. Because the systems being controlled are often complex, multidimensional, and nonlinear, quantum computing algorithms offer an effective solution. Although there are several intelligent control methods available to improve the quality of technological processes, each has certain drawbacks. Quantum algorithms, which rely on the principles of quantum correlation and superposition, are designed to optimize control while minimizing energy and resource consumption. This article discusses the diesel fuel hydrotreating process, a critical step in oil refining. The primary goal of hydrotreating is to enhance fuel quality by removing sulfur, nitrogen, and oxygen compounds. To accurately model this process, it is essential to consider not only the external factors affecting it but also its physical characteristics. By doing so, the mathematical model becomes more precise. Based on this approach, a quantum fuzzy control system for the diesel fuel hydrotreating process was developed using quantum algorithms. These algorithms can rapidly analyze large amounts of data and make decisions. At the same time, a computer model of a fuzzy quantum control system for the process of hydrotreating diesel fuel was constructed, and a number of computational experiments were carried out. As a result, a 1.8% reduction in energy costs for the diesel fuel hydrotreating process was achieved. Full article

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8 pages, 1387 KiB

Open AccessProceeding Paper

Polymeric Membranes in Water Treatment: Insights into Contaminant Removal Mechanisms and Advanced Processes

by Bishnu Kant Shukla, Bhupender Parashar, Tanu Patel, Yashasvi Gupta, Shreshth Verma and Shrishti Singh

Eng. Proc. 2025, 87(1), 69; https://doi.org/10.3390/engproc2025087069 - 29 May 2025

Abstract

Accelerated urbanization and industrialization have significantly heightened water contamination risks, posing severe threats to public health and ecological balance. Polymeric membranes stand at the forefront of addressing this challenge, revolutionizing water and wastewater treatment. These membranes adeptly remove a broad spectrum of contaminants, [...] Read more.

Accelerated urbanization and industrialization have significantly heightened water contamination risks, posing severe threats to public health and ecological balance. Polymeric membranes stand at the forefront of addressing this challenge, revolutionizing water and wastewater treatment. These membranes adeptly remove a broad spectrum of contaminants, including organic compounds and heavy metals, thereby playing a crucial role in mitigating environmental pollution. This research delves into the sophisticated mechanisms of polymeric membranes in filtering out pollutants, with a spotlight on the enhancements brought about by nanotechnology. This includes a detailed examination of their inherent antibacterial properties, showcasing their innovative design and potential for extensive application. The study further investigates advanced techniques like electrochemical processes and membrane distillation, particularly focusing on desalination. These methods are central to the advancement of water purification, emphasizing efficiency and environmental sustainability. However, challenges such as membrane fouling pose significant hurdles, necessitating ongoing research into surface modifications and antifouling strategies. This paper offers a comparative analysis of various membrane technologies, highlighting their manufacturing complexities and efficiency benchmarks. In summation, the paper underscores the importance of continuous innovation in membrane technology, aiming to develop sustainable and effective water treatment solutions. By bridging the gap between basic science and technological advancements, this review aims to guide practitioners and researchers towards a future where clean water is universally accessible, ensuring the preservation of our ecosystems. Full article

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