Journal Description
Engineering Proceedings
Engineering Proceedings
is an open access journal dedicated to publishing findings resulting from conferences, workshops, and similar events, in all areas of engineering. The conference organizers and proceedings editors are responsible for managing the peer-review process and selecting papers for conference proceedings.
Latest Articles
Dynamic Modelling of a Metal Hydride Reactor During Discharge Through Artificial Neural Network Regression
Eng. Proc. 2025, 117(1), 70; https://doi.org/10.3390/engproc2025117070 (registering DOI) - 20 Mar 2026
Abstract
With hydrogen as a clean but hazardous energy carrier, solid-state hydrogen storage in the form of a metal hydride has come forth as a safe and low-pressure storage solution with competitive volumetric energy density. This paper reports the modelling of a metal hydride
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With hydrogen as a clean but hazardous energy carrier, solid-state hydrogen storage in the form of a metal hydride has come forth as a safe and low-pressure storage solution with competitive volumetric energy density. This paper reports the modelling of a metal hydride reactor during its discharge state using neural network regression. This was done by generating a validated finite element model of the reactor, which was then used to generate dynamic operational data based on the desired pressure outlet and heating fluid temperature as independent variables. The best-performing neural network model validation using the experimentally observed data achieved a regression coefficient of 0.99 and a mean squared error of less than 10−4. This predictive model, with further refinement, can be implemented to allow for predictive control, which has always been a challenge through conventional means due to the batch nature of the system. Moreover, the hydrogen concentration as stored in a solid-state measurement would be too expensive for industrial applications.
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(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)
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Open AccessProceeding Paper
Biometrics and Cybersecurity: Beyond Passwords for Digital Protection
by
José Portillo-Portillo, Aldo Hernández Suárez, Gabriel Sánchez Pérez, Linda Karina Toscano Medina and Jesús Olivares Mercado
Eng. Proc. 2026, 123(1), 41; https://doi.org/10.3390/engproc2026123041 - 20 Mar 2026
Abstract
During the early years of interaction between humans and computer systems, user authentication and identification was carried out with the support of knowledge-based factors (something the user knows: passwords, PINs, etc.) and tokens (something the user possesses: credentials, RFID cards, etc.) or a
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During the early years of interaction between humans and computer systems, user authentication and identification was carried out with the support of knowledge-based factors (something the user knows: passwords, PINs, etc.) and tokens (something the user possesses: credentials, RFID cards, etc.) or a combination of both. In other words, the user presents a token and a password to the system in order to gain access. These solutions pose major challenges: Knowledge-based systems, which rely on secrets like passwords, are vulnerable to those secrets being guessed, shared, or forgotten. On the other hand, tokens are also vulnerable; some, despite implementing encryption, attract cyber attackers who can forge them, and users can share or lose them. In the search for more robust methods, the use of biometrics has been considered.
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(This article belongs to the Proceedings of First Summer School on Artificial Intelligence in Cybersecurity)
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Open AccessProceeding Paper
Physicochemical Characterization of Emerging Contaminants: A Conductance-Based Determination of Diffusion Coefficients for Butylparaben and Triclosan in Aqueous Solution
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Jesse Louise Javier, Karl Steven Narte, Mohammad Naif Sali, Rolex Villaflor, Janine Renz Villegas, Rugi Vicente Rubi, Allan Soriano and Rich Jhon Paul Latiza
Eng. Proc. 2026, 124(1), 84; https://doi.org/10.3390/engproc2026124084 - 19 Mar 2026
Abstract
The escalating accumulation of pharmaceutical micropollutants in global water systems represents a significant challenge to current circular economy frameworks, highlighting a critical gap in the management of environmental persistence. Although advanced remediation technologies are often proposed to mitigate this crisis, their engineering optimization
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The escalating accumulation of pharmaceutical micropollutants in global water systems represents a significant challenge to current circular economy frameworks, highlighting a critical gap in the management of environmental persistence. Although advanced remediation technologies are often proposed to mitigate this crisis, their engineering optimization is frequently compromised by a reliance on empirical approximations rather than precise physicochemical constants. Addressing this fundamental deficit, this study executes a rigorous determination of mass transfer properties for two ubiquitous contaminants: Butylparaben and Triclosan. Utilizing a high-precision electrolytic conductance method under infinite dilution, we investigated transport dynamics across varying temperature gradients (305.15–319.15 K). Experimental data were subjected to advanced mathematical modeling, where the Modified Robinson–Stokes (MRS) quadratic model significantly outperformed classical linear approaches ( ), accurately capturing non-ideal solute–solvent interactions. The derived limiting molar conductivities facilitated the calculation of infinite dilution diffusion coefficients via the Nernst–Haskell equation, yielding values of m2/s for Butylparaben and m2/s for Triclosan. Furthermore, Stokes–Einstein analysis quantified the hydrodynamic radii, elucidating the steric mechanisms governing the sluggish migration of bulky chlorinated ethers compared to single-ring esters. These precise transport parameters are not merely theoretical values; they are essential inputs for developing accurate computational fate models and designing regenerable separation processes, thereby providing the hard physics required to engineer solutions for the perpetual pollution era.
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(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)
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Open AccessProceeding Paper
Intelligent Drone Patrolling with Real-Time Object Detection and GPS-Based Path Adaptation
by
Gurugubelli V. S. Narayana, Shiba Prasad Swain, Debabrata Pattnayak, Manas Ranjan Pradhan and P. Ankit Krishna
Eng. Proc. 2026, 124(1), 82; https://doi.org/10.3390/engproc2026124082 - 18 Mar 2026
Abstract
Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we
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Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we aim at designing and validating experimentally a low-cost drone-based unmanned autonomous mission patrolling system with waypoint navigation, real-time video backhauling, AI-based human/object detection and GPS path re-planning when an event occurs to ensure the safety of patrol missions under battery constraints. Methods: The proposed architecture combines autonomous navigation and embedded flight-control with online analog video streaming and ground-station-based computer vision processing. Object detection based on deep learning for live aerial video is used, and the proposed system’s performance is tested at different altitudes, lighting states and GPS patrol plans. Results: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system is able to adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Conclusions: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system can adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment.
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(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)
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Open AccessProceeding Paper
Improving Preventive Maintenance Efficiency in University Laboratories Using Radio Frequency Identification-Based Decision Support System and Rapid Application Development Method
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Rizky Fajar Ahmad Gurnita, Rayinda Pramuditya Soesanto, Amelia Kurniawati and Fahmy Habib Hasanudin
Eng. Proc. 2026, 128(1), 41; https://doi.org/10.3390/engproc2026128041 - 18 Mar 2026
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Laboratory asset maintenance in higher education institutions often suffers from inefficiencies due to incomplete data and reactive maintenance practices. We designed a radio frequency identification (RFID)-based information system that supports preventive maintenance and decision-making for laboratory asset management. Utilizing the rapid application development
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Laboratory asset maintenance in higher education institutions often suffers from inefficiencies due to incomplete data and reactive maintenance practices. We designed a radio frequency identification (RFID)-based information system that supports preventive maintenance and decision-making for laboratory asset management. Utilizing the rapid application development method, the system was developed through iterative prototyping and stakeholder engagement. The system integrates RFID-based asset identification with a web-based interface for real-time monitoring and log management. A decision-support module was also implemented, allowing stakeholders to prioritize maintenance tasks based on asset age, repair frequency, and usage patterns. Evaluation results of user acceptance testing showed an average score of 82%, indicating strong usability and relevance. The results demonstrate that integrating RFID with decision-support features significantly improve maintenance planning, reduce operational risk, and optimize resource allocation in academic laboratory environments.
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Open AccessProceeding Paper
Emerging Environmental Pollutants and Diabetes: A Pilot Study on PFASs in a South African Mixed-Ancestry Population
by
John Baptist Nzukizi Mudumbi, Seteno Karabo Obed Ntwampe, Adegbenro Peter Daso, Okechukwu Jonathan Okonkwo, Thomas Joel Farrar, Didier Mugisho Nyambwe and Tandi Edith Matsha
Eng. Proc. 2026, 124(1), 83; https://doi.org/10.3390/engproc2026124083 - 17 Mar 2026
Abstract
Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and consumer products due to their thermal stability and resistance to degradation. These properties also contribute to their environmental persistence and potential adverse health effects. Despite increasing global concern regarding
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Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals widely used in industrial applications and consumer products due to their thermal stability and resistance to degradation. These properties also contribute to their environmental persistence and potential adverse health effects. Despite increasing global concern regarding PFAS exposure, biomonitoring data from African populations remain limited. This study investigated the serum concentrations of perfluorooctanoic acid (PFOA), perfluorooctanesulfonate (PFOS), and perfluorobutane sulfonate (PFBS), and their association with diabetes mellitus (DM) in a mixed-ancestry population from Bellville South, Cape Town, South Africa. Serum samples (n = 179) were analysed using liquid chromatography–tandem mass spectrometry (LC–MS-8030), and statistical analyses were performed using STATISTICA 13.5 software. All three PFASs were detected in 100% of samples, with PFOA exhibiting the highest mean concentration (9.43 ± 13.16 ng/mL), followed by PFBS and PFOS. PFAS concentrations were generally higher in females than in males, with significantly elevated PFOA levels observed among women (p = 0.0116). No statistically significant associations were identified between PFAS concentrations and glycemic status, obesity, or related metabolic indicators (p > 0.05). However, PFOS showed a modest positive correlation with HbA1c in females, suggesting potential gender-specific interactions. These findings confirm measurable PFAS exposure in the South African population and highlight the need for larger longitudinal studies implications.
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(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)
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Open AccessProceeding Paper
Solvent-Based Simulation and Techno-Economic Evaluation of CO2/H2S Separation at Shurtan Gas Chemical Complex
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Adham Norkobilov, Rakhmatullo Muradov, Sanjar Ergashev, Zafar Turakulov, Yulduz Safarova and Noilakhon Yakubova
Eng. Proc. 2026, 124(1), 81; https://doi.org/10.3390/engproc2026124081 - 17 Mar 2026
Abstract
The separation of carbon dioxide (CO2) and hydrogen sulfide (H2S) from sour natural gas is an important step in gas processing and emission control. This study applies a rate-based Aspen Plus simulation to examine solvent-based CO2/H2
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The separation of carbon dioxide (CO2) and hydrogen sulfide (H2S) from sour natural gas is an important step in gas processing and emission control. This study applies a rate-based Aspen Plus simulation to examine solvent-based CO2/H2S removal under conditions representative of the Shurtan Gas Chemical Complex in Uzbekistan. Monoethanolamine (MEA) and methyldiethanolamine (MDEA) are evaluated as reference solvents with respect to separation performance and energy demand. The rate-based modeling framework accounts for reaction kinetics and mass transfer effects in the absorber–regenerator system. Simulation results indicate that both solvents achieve high acid gas removal efficiencies. From an engineering perspective, the results provide practical guidance for solvent selection and energy optimization in existing acid gas removal units, supporting pilot-scale deployment under industrial operating conditions. Sensitivity analysis suggests that increasing gas throughput beyond 30 t/h leads to a gradual reduction in CO2 capture efficiency, primarily due to mass transfer limitations. From a techno-economic perspective, the lower energy demand of the MDEA-based system may imply reduced operating costs. The captured CO2 stream reaches a purity of around 99.5%, which is compatible with downstream soda ash production. Overall, the results provide a screening-level assessment supporting further detailed evaluation.
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(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)
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Open AccessProceeding Paper
Microstructural and Spectral Characterization of ZrO2-Doped PEO/PMMA Nanocomposite Polymer Electrolytes
by
Amudha Subramanian, Rajalakshmi Kumaraiah and Mohammed Tasleem Tahira
Eng. Proc. 2026, 124(1), 80; https://doi.org/10.3390/engproc2026124080 - 17 Mar 2026
Abstract
Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer
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Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO2) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO2 nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behavior of the PEO/PMMA blend system. The inclusion of ZrO2 nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making them promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO2 nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behavior of the solid polymer electrolytes.
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(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)
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Open AccessProceeding Paper
Modeling and Control of Nonlinear Fermentation Dynamics in Brewing Industry
by
Mirjalol Yusupov, Jaloliddin Eshbobaev, Zafar Turakulov, Komil Usmanov, Dilafruz Kadirova and Azizbek Yusupbekov
Eng. Proc. 2025, 117(1), 67; https://doi.org/10.3390/engproc2025117067 - 17 Mar 2026
Abstract
This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The
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This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state-space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 h under optimized input profiles. The combined FLC–MPC scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies.
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(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)
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Open AccessProceeding Paper
Enhancement of Dynamic Microgrid Stability Under Climatic Changes Using Multiple Energy Storage Systems
by
Amel Brik, Nour El Yakine Kouba and Ahmed Amine Ladjici
Eng. Proc. 2025, 117(1), 66; https://doi.org/10.3390/engproc2025117066 - 17 Mar 2026
Abstract
The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems
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The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions.
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(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)
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Open AccessProceeding Paper
Scenario-Based Simulation for Evaluating Trade-Offs Among Efficiency, Effectiveness, and Equity in Emergency Response Routing: A Monte Carlo Approach and MATLAB
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Charmine Sheena Saflor, Anton Luis Martin Espina, Marlon Era, Samantha Louise Jarder, Francisco Emmanuel Munsayac Jr. III and Ronnel Agulto
Eng. Proc. 2026, 128(1), 40; https://doi.org/10.3390/engproc2026128040 - 17 Mar 2026
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In disaster response logistics, it is critical to evaluate strategies for operational speed and efficiency and fairness in aid distribution. Therefore, we developed a simulation-based framework for assessing emergency delivery performance using the efficiency, effectiveness, and equity (3E) model under uncertainty. Using the
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In disaster response logistics, it is critical to evaluate strategies for operational speed and efficiency and fairness in aid distribution. Therefore, we developed a simulation-based framework for assessing emergency delivery performance using the efficiency, effectiveness, and equity (3E) model under uncertainty. Using the Monte Carlo simulation v4.4.9 and MATLAB v4.4.9, the model tests a greedy resource allocation strategy across 100 randomized scenarios involving variable regional demand and travel times. Each scenario is evaluated based on total fulfillment, distribution balance, and delivery effort. The results indicate that under ideal conditions with sufficient supply and no logistical constraints, the strategy achieves full effectiveness and perfect equity, with consistent efficiency outcomes. While the system performs optimally in the base case, the model also highlights the importance of testing strategies under more constrained or disrupted environments. The proposed approach enables planners to assess performance trade-offs, providing a robust foundation for future extensions involving optimization, real-time data integration, or prioritization schemes.
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Open AccessProceeding Paper
Towards Predictive Models of Mechanical Properties in 3D-Printed Polymers: An Exploratory Study
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Bruno A. G. Sousa, César M. A. Vasques and Adélio M. S. Cavadas
Eng. Proc. 2026, 124(1), 79; https://doi.org/10.3390/engproc2026124079 - 16 Mar 2026
Abstract
Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed
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Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed to provide the experimental basis for the development and calibration of predictive models of mechanical properties in 3D-printed components. Standard ISO 527-2 Type 1A specimens were fabricated using thermoplastic PLA (polylactic acid) with systematic variations in layer orientation, infill overlap, and printing velocity. Mechanical characterization was carried out through uniaxial tensile testing to determine tensile strength and stiffness of the material specimens, while scanning electron microscopy (SEM) provided complementary insights into interlayer bonding, filament alignment, porosity, and fracture morphology. Results showed that material type and processing strategies strongly influenced mechanical response, with SEM highlighting microstructural features that govern interlayer adhesion and failure mechanisms. These findings contribute to a deeper understanding of process–structure–property relationships in additive manufacturing and establish the groundwork for predictive model development. Ongoing efforts will integrate these experimental insights into numerical simulations employing homogenized material models, thereby enhancing design optimization and reliability of 3D-printed structural components.
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(This article belongs to the Proceedings of The 6th International Electronic Conference on Applied Sciences)
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Open AccessProceeding Paper
Development of Wingbeat-Based Acoustic Health Monitoring System for Bee Colonies
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Li-Hao Chen, Shi-You Zhou, Jia-Wen He and Chau-Chung Song
Eng. Proc. 2025, 120(1), 73; https://doi.org/10.3390/engproc2025120073 - 16 Mar 2026
Abstract
We developed an intelligent acoustic health monitoring system for honeybee colonies based on wingbeat frequency analysis, offering a practical solution for modernizing apicultural practices. The system employs a three-layer architecture—the Internet of Things, fog, and cloud—to achieve real-time, non-invasive hive condition assessment. At
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We developed an intelligent acoustic health monitoring system for honeybee colonies based on wingbeat frequency analysis, offering a practical solution for modernizing apicultural practices. The system employs a three-layer architecture—the Internet of Things, fog, and cloud—to achieve real-time, non-invasive hive condition assessment. At the edge level, a Raspberry Pi and low-noise microphone continuously capture in-hive audio, which is converted into spectrograms using short-time Fourier transform (STFT). These are analyzed by a deep learning classification model deployed on the fog layer to distinguish four critical queen-related states: original queen present, queen absent, new queen rejected, and new queen accepted. The cloud layer supports data storage, visualization, and model refinement through manual annotations. Our results show that both the vision Transformer and CNN models perform effectively in classifying complex hive states, each contributing to the overall classification task, demonstrating the system’s potential for improving colony management and early intervention. This work contributes to precision apiculture by enabling scalable, real-time queen status monitoring through acoustic sensing and deep learning.
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(This article belongs to the Proceedings of 8th International Conference on Knowledge Innovation and Invention)
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Open AccessProceeding Paper
Measuring Air Pollution in Populated Areas Using Sensors Installed on Vehicles and Drones
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András Molnár, Saidumarkhon Saidakhmadov, Azizbek Kamolov and Botir Usmonov
Eng. Proc. 2025, 117(1), 68; https://doi.org/10.3390/engproc2025117068 - 16 Mar 2026
Abstract
Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal
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Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m3 in forested areas to as high as 160 μg/m3 in industrial zones, while CO2 levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement.
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(This article belongs to the Proceedings of The 4th International Electronic Conference on Processes)
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Open AccessProceeding Paper
Prototyping Galileo Signal Authentication Service: Current Status and Plans
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Ignacio Fernandez-Hernandez, Jon Winkel, Cillian O’Driscoll, Tom Willems, Simon Cancela, Miguel Alejandro Ramirez, Rafael Terris-Gallego, Jose A. Lopez-Salcedo, Gonzalo Seco-Granados, Florian Fuchs, Gianluca Caparra, Daniel Blonski, Beatrice Motella, Aleix Galan and Javier Simon
Eng. Proc. 2026, 126(1), 40; https://doi.org/10.3390/engproc2026126040 - 16 Mar 2026
Abstract
The Galileo Signal Authentication Service (SAS) is the next new feature to be offered by Galileo, the European GNSS. Its signal-in-space initial capability is expected already in the next months of 2025, starting with the L3 (Launch 3) Galileo elliptical-orbit satellites. It is
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The Galileo Signal Authentication Service (SAS) is the next new feature to be offered by Galileo, the European GNSS. Its signal-in-space initial capability is expected already in the next months of 2025, starting with the L3 (Launch 3) Galileo elliptical-orbit satellites. It is the first-ever navigation signal authentication feature offered globally and openly. Galileo SAS uses the existing Galileo E6-C signal to be encrypted, in combination with OSNMA (Open Service Navigation Message Authentication), through the so-called semi-assisted authentication concept. In this concept, portions of the E6-C are re-encrypted with OSNMA future keys and published in a server. The concept allows signal authentication openly and for free, and without private key management by users. In exchange, the time between authentications is 30 s, inherited from OSNMA, and it introduces a latency between the E6-C signal reception and its authentication down to a few seconds. This work presents the status of Galileo SAS. It outlines its latest technical definition, already shared in previous publications. It will also present the MMARIO (Message and Measurement Authentication Receiver for Initial Operations) project, developing the first SAS server, receiver and testing platform. The paper also outlines the Galileo SAS plans for the near future, up to the Initial Service Declaration.
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(This article belongs to the Proceedings of European Navigation Conference 2025)
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Open AccessProceeding Paper
Automated Vulnerability Repair Using Prototype-Based Deep Metric Learning with Normative Compliance Constraints
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Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Hector Perez-Meana, Jesús Olivares Mercado, Andrew Wilson and Marco Perez-Cisneros
Eng. Proc. 2026, 123(1), 40; https://doi.org/10.3390/engproc2026123040 - 16 Mar 2026
Abstract
Automated Program Repair (APR) is increasingly used for vulnerability patching, yet many existing methods focus primarily on syntactic similarity between vulnerable and fixed code, with limited guarantees of semantic correctness and limited alignment with security frameworks. This work presents a prototype-based deep metric
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Automated Program Repair (APR) is increasingly used for vulnerability patching, yet many existing methods focus primarily on syntactic similarity between vulnerable and fixed code, with limited guarantees of semantic correctness and limited alignment with security frameworks. This work presents a prototype-based deep metric learning method for vulnerability repair that integrates normative constraints from OWASP and NIST SSDF. The method combines embeddings of vulnerable code and CWE descriptions, refines category prototypes to improve separation among CWE types, and validates repairs against statement-level control requirements derived from the normative mapping. Experiments on 959 vulnerable–fixed pairs across Python, Java, C, and C++ covering 15 CWE categories achieved a Match Ratio of 88.95%, 0.81 compliance, and 0.84 consistency.
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(This article belongs to the Proceedings of First Summer School on Artificial Intelligence in Cybersecurity)
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Open AccessProceeding Paper
Design of an STM32 Coaxial Cable Length and Terminal Load Monitoring System
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Chuan Yang, Wenge Huang and Shulin Yu
Eng. Proc. 2026, 128(1), 39; https://doi.org/10.3390/engproc2026128039 - 16 Mar 2026
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Coaxial cable plays a vital role in the wide application of telecommunications, network, and television broadcasting and other fields, with its transmission performance directly affecting signal quality and transmission efficiency. In practical applications, the length of the cable and the terminal load state
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Coaxial cable plays a vital role in the wide application of telecommunications, network, and television broadcasting and other fields, with its transmission performance directly affecting signal quality and transmission efficiency. In practical applications, the length of the cable and the terminal load state of the connection often affect the stability of the signal. In order to solve this problem, we used STMicroelectronics STM32F407VET6 (STMicroelectronics, Geneva, Switzerland) as the master controller in this system, and deduced the length of the cable by analyzing the functional relationship between the length of the cable and the open circuit frequency. An open cable is regarded as a capacitor, and any two core wires are regarded as two plates of a flat capacitor. The linear relationship between open frequency and length is used to detect the length of the coaxial cable. The system then determines whether the terminal load is capacitance or resistance based on the detected frequency. If no frequency is detected, then the load is considered resistance. The system detects the resistance value of the resistor through series voltage division. If a frequency is detected, this indicates that the load is capacitance. At this time, the system uses an RC oscillation circuit composed of HGSEMI ICL8038 (Huagao Semiconductor Co., Ltd., Wuxi, China) for testing, and provides the phase shift required by the corresponding signal through the RC network, so as to detect the capacitance value. Finally, we successfully designed a coaxial cable length and terminal load detection system based on STM32F407VET6. Through this system, the user can accurately understand the length of the coaxial cable and the load of the connection terminal, which provides a reliable guarantee for the stability of signal transmission.
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Open AccessProceeding Paper
Comparison and Optimization of Intelligent Control for a Two-Link Robot Manipulator
by
Chia-Chen Fang and Shuo-Feng Chiu
Eng. Proc. 2026, 128(1), 38; https://doi.org/10.3390/engproc2026128038 - 16 Mar 2026
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We investigate the control of a two-link robot manipulator through the application of sliding mode control (SMC), proportional–integral–derivative (PID) control, and their hybrid control strategy. Firstly, a mathematical model incorporating nonlinear coupling effects is derived based on the Lagrangian method. Then, SMC, PID,
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We investigate the control of a two-link robot manipulator through the application of sliding mode control (SMC), proportional–integral–derivative (PID) control, and their hybrid control strategy. Firstly, a mathematical model incorporating nonlinear coupling effects is derived based on the Lagrangian method. Then, SMC, PID, and hybrid controllers are compared based on disturbance rejection, stability, and time-domain responses. In addition, a genetic algorithm (GA) is employed for PID parameter optimization, improving system performance and efficiency. Overall, the PID-SMC controller achieves an effective balance between stability and response tracking accuracy. The results of this study provide a reference for control strategy development in robotic systems, aligning with smart manufacturing applications.
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Open AccessProceeding Paper
AI Facial Acupuncture Point Interactive Voice Health Care Teaching System
by
Wen-Cheng Chen, Yu-Hsuan Chen, Yu-Hsing Chen, Jiu-Wen Wang, Hung-Jen Chen and Jr-Wei Tsai
Eng. Proc. 2026, 128(1), 37; https://doi.org/10.3390/engproc2026128037 - 16 Mar 2026
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We developed an AI-based system for facial acupoint recognition and healthcare support, integrating MediaPipe facial and hand tracking technologies to address the problems of inaccurate and non-standardized acupoint identification in traditional Chinese medicine (TCM). By leveraging facial landmark detection and fingertip tracking, the
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We developed an AI-based system for facial acupoint recognition and healthcare support, integrating MediaPipe facial and hand tracking technologies to address the problems of inaccurate and non-standardized acupoint identification in traditional Chinese medicine (TCM). By leveraging facial landmark detection and fingertip tracking, the system enables accurate localization of facial acupoints to ensure precise stimulation. The system contributes to the standardization of acupoint recognition, intelligent health consultation, and the digital transformation of TCM practices. Further enhancements are necessary by expanding acupoint recognition to other body parts (e.g., ears, hands, feet, and back) and integrating with wearable devices to further promote personalized and precise TCM healthcare.
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Open AccessProceeding Paper
Application of Analytic Hierarchy Process for Evaluating Service Quality of Subscription Video on Demand Services Based on Weighted Values in the Philippines
by
Maria Sabrina Cantos, Nathan Tyler Quach, Sean Bradley Ruy, Patricia Santiago, Richard Li and Madeline Tee
Eng. Proc. 2026, 128(1), 36; https://doi.org/10.3390/engproc2026128036 - 16 Mar 2026
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Streaming services have gained popularity due to their vast content library and convenience. To ensure continued patronage, service quality measurement is important. In this study, we ranked and determined how to maintain the competitiveness of streaming services using the analytic hierarchy process. Using
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Streaming services have gained popularity due to their vast content library and convenience. To ensure continued patronage, service quality measurement is important. In this study, we ranked and determined how to maintain the competitiveness of streaming services using the analytic hierarchy process. Using focus group discussions and questionnaire administration, Netflix was found to have the highest perceived service quality, as measured by the consistency ratio and rating scales. Content library, quality of experience, and system availability were the top three service quality dimensions, while the top three sub-dimensions were quality of content, frequency of video freezing, and picture quality. These results allow companies to adjust their service strategies to suit the Philippine market.
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