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Open AccessEditorial

Preface: The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025

by Teen-Hang Meen, Cheng-Yi Chen and Cheng-Fu Yang

Eng. Proc. 2026, 129(1), 32; https://doi.org/10.3390/engproc2026129032 - 29 Apr 2026

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Abstract

This volume represents the proceedings of the 7th Eurasia Conference on Biomedical Engineering, Healthcare, and Sustainability 2025 (ECBIOS 2025) [...] Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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2 pages, 348 KB

Open AccessEditorial

Statement of Peer Review

by Teen-Hang Meen, Cheng-Yi Chen and Cheng-Fu Yang

Eng. Proc. 2026, 129(1), 34; https://doi.org/10.3390/engproc2026129034 - 3 Jun 2026

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In submitting conference proceedings of the 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025) to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to [...] Read more.

In submitting conference proceedings of the 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025) to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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12 pages, 1100 KB

Open AccessProceeding Paper

Circular Economy Through Green Additive Manufacturing in Medical Device Manufacturing

by Wai Yie Leong

Eng. Proc. 2026, 129(1), 1; https://doi.org/10.3390/engproc2026129001 - 20 Feb 2026

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Abstract

Circular economy (CE) decouples value creation from virgin resource use and waste in the medical device sector, which faces stringent patient-safety, quality, and regulatory obligations. Green Additive Manufacturing (AM) offers a precise, digitally driven route to implement CE through dematerialization, on-demand localized production, [...] Read more.

Circular economy (CE) decouples value creation from virgin resource use and waste in the medical device sector, which faces stringent patient-safety, quality, and regulatory obligations. Green Additive Manufacturing (AM) offers a precise, digitally driven route to implement CE through dematerialization, on-demand localized production, topology optimization, and material circularity. In this study, a comprehensive CE framework is tailored to medical device manufacturing that integrates eco-design, material circularity, remanufacturing, and regulatory compliance across the product life cycle. Methods include an International Organization for Standardization (ISO) 14040/44-aligned life cycle assessment, process energy metering, sterilization-compatibility studies, mechanical/biocompatibility verification to relevant standards, and a techno-economic/circularity analysis with Monte Carlo uncertainty quantification. Three case studies are explored using bio-based PA11 (selective laser sintering), recycled polyethylene terephthalate glycol (fused deposition modeling), and low-volatile organic carbon biocompatible photopolymer (stereolithography): (1) a patient-specific wrist orthosis, (2) a dental surgical guide, and (3) a single-use catheter Y-connector. Results indicate 38–68% reductions in embodied greenhouse-gas emissions, 22–54% energy savings per functional unit, and up to 80% mass recapture through in-process powder/runner reuse while maintaining clinical performance and regulatory conformity. Design-for-circularity patterns (DfC) were created for DfDisassembly, DfSter, DfTraceability, DfUpgrade, and DfPowder-Loop and provide a governance architecture combining ISO 13485 QMS, ISO 10993 biological evaluation, the European Union’s Medical Device Regulation (Regulation (EU) 2017/745), and the United States Food and Drug Administration’s guidance on Additive Manufactured (3D-printed) medical devices, guidance with unique device identification for closed-loop returns. The paper concludes with an Industry 5.0 roadmap for hospital-proximate micro-factories, materials passports, and digital product passports enabling verified circular flows at scale. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 860 KB

Open AccessProceeding Paper

Game-Theoretic Framework for Coordinating Mixed Traffic for Emergency Vehicle Passage

by Wei-Xiang Li, I-Hsien Liu, Kuan-Ting Lee and Chu-Fen Li

Eng. Proc. 2026, 129(1), 2; https://doi.org/10.3390/engproc2026129002 - 24 Feb 2026

Cited by 1 | Viewed by 329

Abstract

We address the challenge of coordinating traffic at unsignalized intersections, particularly with an emergency vehicle, by proposing a game-theoretic decision model. In this non-cooperative game, each vehicle selects a Go or Yield strategy to maximize a utility function based on efficiency, collision risk, [...] Read more.

We address the challenge of coordinating traffic at unsignalized intersections, particularly with an emergency vehicle, by proposing a game-theoretic decision model. In this non-cooperative game, each vehicle selects a Go or Yield strategy to maximize a utility function based on efficiency, collision risk, and driving style. We use the Iterated Best Response algorithm to find the pure strategy Nash equilibrium. A MATLAB–SUMO co-simulation validates that our model significantly enhances safety and efficiency while substantially reducing travel time. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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9 pages, 2480 KB

Open AccessProceeding Paper

Design and Optimization of Magnetic Circuits in Electric Scooter Motors

by Chun-Chieh Chang, Cheng-Che Yang, Chin-Chung Lin, Ming-Hung Chao, Yi-Kai Chen and Cheng-Yi Chen

Eng. Proc. 2026, 129(1), 3; https://doi.org/10.3390/engproc2026129003 - 25 Feb 2026

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Abstract

We investigated stator–rotor structure optimization for a commercial electric scooter motor through geometric modeling and comparative analysis of various magnet configurations and arrangements. We improved magnetic circuit distribution to enhance output performance, efficiency, and overall motor characteristics. Sensitivity analysis was conducted to identify [...] Read more.

We investigated stator–rotor structure optimization for a commercial electric scooter motor through geometric modeling and comparative analysis of various magnet configurations and arrangements. We improved magnetic circuit distribution to enhance output performance, efficiency, and overall motor characteristics. Sensitivity analysis was conducted to identify the dominant design parameters. Magnetic bridges were then incorporated on both outer sides of the rotor magnets to increase magnetic flux density and reduce leakage flux. The Taguchi method was applied to determine the optimal parameter set. Comparative simulations between the optimized and baseline commercial motor revealed that, at a rated current of 87 A and rated voltage of 96 V, the optimized design achieved an efficiency improvement from 89.14 to 90.28% (+1.28%), a torque increase from 22.84 to 23.29 N·m (+0.45 N·m), and a power output enhancement from 7104.78 to 8053.44 W (+948.65 W). The results confirm that the proposed rotor design yields superior performance across efficiency, torque, and power output compared with the commercial reference motor. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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6 pages, 1483 KB

Open AccessProceeding Paper

Development of an Android-Based Mobile Application for Menstrual Health and Sports Performance Tracking in Female Athletes

by Lee Fan Tan, Xuan Ning Chai, Choon Hian Goh, Kamala Krishnan and Muhammad Noh Zulfikri Mohd Jamali

Eng. Proc. 2026, 129(1), 4; https://doi.org/10.3390/engproc2026129004 - 25 Feb 2026

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Abstract

Female sports science has historically relied on evidence derived largely from male cohorts, despite known menstrual-cycle-related hormonal effects on thermoregulation, metabolism, and performance in women. We developed an Android application to support female athletes in documenting menstrual health alongside self-rated sports performance, addressing [...] Read more.

Female sports science has historically relied on evidence derived largely from male cohorts, despite known menstrual-cycle-related hormonal effects on thermoregulation, metabolism, and performance in women. We developed an Android application to support female athletes in documenting menstrual health alongside self-rated sports performance, addressing an underexplored area in current mobile health tools. The app was built in the Massachusetts Institute of Technology’s App Inventor following a rapid application development process (requirements determination, user design, construction, and implementation). Implemented features include period-date recording and prediction, health and performance logging, record review, basic personalization, and phase-specific, non-personalized training and nutrition tips. Unit test results verified core functions, including date recording, period prediction, navigation, and record retrieval, and a small-sample usability assessment (n = 5) using the system usability scale indicated above-average usability. In conclusion, the application offers a practical tool for period-date and symptom tracking with integrated performance self-logging. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 436 KB

Open AccessProceeding Paper

Designing Projection-Mapped Interview Rooms with Diffusion Models for Sustainable Digital Interaction

by Jongwook Si, Hyeri Jeong, Youngsei Lee and Sungyoung Kim

Eng. Proc. 2026, 129(1), 5; https://doi.org/10.3390/engproc2026129005 - 25 Feb 2026

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We developed an integrated pipeline that combines generative AI and 3D modeling technologies to create a realistic virtual interview environment for sustainable digital interaction. The process begins with generating virtual interview room images using Stable Diffusion. Spatial information is then extracted on the [...] Read more.

We developed an integrated pipeline that combines generative AI and 3D modeling technologies to create a realistic virtual interview environment for sustainable digital interaction. The process begins with generating virtual interview room images using Stable Diffusion. Spatial information is then extracted on the X, Y, Z axes and the camera through FSpy manually. Based on this information, 3D structures are modeled in the Blender environment and a corresponding depth map is generated. This depth information, along with text prompts, serves as input to ControlNet, enabling the generation of additional interview room images under various perspectives and conditions. These images are projected onto the 3D models as textures via projection mapping in Blender. The resulting 3D objects are imported into the Unity engine to construct an interactive virtual interview space. The developed pipeline effectively supports the creation of immersive and realistic environments, demonstrating its applicability not only for interview simulations but also for training, education, and experiential content development. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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5 pages, 314 KB

Open AccessProceeding Paper

Rapid Eye Movement Sleep Detection: A Machine Learning Approach Using Vital Signs

by Tzu-I. Tseng, Chia-Yung Jui and Shu-Hui Hung

Eng. Proc. 2026, 129(1), 6; https://doi.org/10.3390/engproc2026129006 - 25 Feb 2026

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Abstract

Rapid eye movement sleep (REM) is a critical sleep stage associated with several sleep disorders, including sleep apnea and rapid eye movement sleep behavior disorder. Polysomnography (PSG) is the gold standard for identifying REM periods and diagnosing sleep disorders. However, PSG is typically [...] Read more.

Rapid eye movement sleep (REM) is a critical sleep stage associated with several sleep disorders, including sleep apnea and rapid eye movement sleep behavior disorder. Polysomnography (PSG) is the gold standard for identifying REM periods and diagnosing sleep disorders. However, PSG is typically conducted in sleep medicine centers using specialized equipment, where sleep experts assess sleep conditions through measurements such as brain activity, respiration, heart activity, and eye movements. An overnight stay in a sleep laboratory can adversely affect a patient’s natural sleep quality, introducing the risk of iatrogenic sleep disturbances. Recent studies have explored sleep stage detection using lightweight wearable devices, such as smartwatches, which offer lower cost but rely on a limited set of psychological signals. In this study, we propose a machine learning approach for REM sleep staging based solely on breathing rate (BR) and heart rate (HR), without relying on PSG recordings. Experimental evaluations conducted on the Dreamt dataset demonstrate the feasibility of the proposed approach and its potential to provide meaningful information for sleep staging. Future work will focus on developing a fully non-contact REM detection framework by integrating video-based estimation of HR and BR. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 1172 KB

Open AccessProceeding Paper

Explainable Deep Learning for Stress and Performance Analysis in Professional Tennis Matches

by Hsien-Chung Huang, Wei-Hsin Hung and Meng-Hsiun Tsai

Eng. Proc. 2026, 129(1), 7; https://doi.org/10.3390/engproc2026129007 - 2 Mar 2026

Cited by 1 | Viewed by 516

Abstract

Tennis match analysis is a critical component of sports science, offering data on player performance, workload management, and competitive stress. We developed a data-driven framework to classify tennis matches as high-stress or low-stress using the Association of Tennis Professionals’ match statistics. High-stress matches [...] Read more.

Tennis match analysis is a critical component of sports science, offering data on player performance, workload management, and competitive stress. We developed a data-driven framework to classify tennis matches as high-stress or low-stress using the Association of Tennis Professionals’ match statistics. High-stress matches are characterized by extended duration or frequent break points, both representing elevated physical and psychological demands. We implement TabNet and compare its performance with recurrent deep learning models, including long short-term memory (LSTM), bidirectional LSTM, attention-enhanced LSTM, and convolutional LSTM. Experimental results show that TabNet achieves the best accuracy (98%), while the recurrent models maintain accuracies above 93%, demonstrating consistent predictive capability. To enhance interpretability, SHAP analysis identifies break points faced, break points saved, and match duration as the most influential determinants of match stress, with serving and returning features providing secondary contributions. These findings confirm the effectiveness of interpretable deep learning in sports analytics and highlight its potential for guiding training and match preparation. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 980 KB

Open AccessProceeding Paper

Implicitly Empathy Prompting Features to Improve Empathetic Chatbot Performance in Lightweight Language Models

by Yun-Rong Chen, Kun-Ta Chuang and Hung-Yu Kao

Eng. Proc. 2026, 129(1), 8; https://doi.org/10.3390/engproc2026129008 - 26 Feb 2026

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Abstract

An empathetic chatbot is an essential component of intelligent mental healthcare. We adopted implicitly empathy prompting (IEP) by decomposing empathy into supportive dialogue, paraphrased response, emotional understanding, and attitude expression, referred to as the four features of empathy decomposition. IEP is based on [...] Read more.

An empathetic chatbot is an essential component of intelligent mental healthcare. We adopted implicitly empathy prompting (IEP) by decomposing empathy into supportive dialogue, paraphrased response, emotional understanding, and attitude expression, referred to as the four features of empathy decomposition. IEP is based on lightweight language multi-agents (LLM-Agents) to generate empathy dialogue. The approach contrasts with the explicitly defined empathy of simply prompting a model to be empathetic. Three datasets for the four features scenario were generated by using the Generative Pre-Trained Transformer (GPT)-4o model, with cases in finance, family, and health issues. For each dataset, 30 examples were randomly selected and examined as input prompting onto six lightweight language models. These models include Mistral (7B), Phi-4 (14B), StableLM2 (12B), Tulu3 (8B), Neural-chat (7B), and Llama 3.1-Instruct (8B). After that, the output was evaluated by using GPT-4o to calculate empathy perception scores (EP scores). The average EP scores on three datasets for implicit/explicit empathy prompting ranged from 1 to 10. The final evaluation results are as follows: (1) implicitly empathy prompting (IEP): Mistral (8.83), Phi-4 (8.96), StableLM2 (9.03), Tulu3 (7.24), Neural-chat: (8.03), Llama 3.1-Instruct (8.74); (2) Explicitly Empathy Prompting (EEP): Mistral (7.52), Phi-4 (8.55), StableLM2 (7.78), Tulu3 (7.67), Neural-chat (8.35), Llama 3.1-Instruct (8.76). Among these values, three models (Mistral, Phi-4, and StableLM2) achieve higher and stable EP scores obviously. The other models (Tulu3, Neural-chat, and Llama 3.1-Instruct) keep comparable EP scores. Our experiment findings showed that the prompt engineering method with the IEP approach could significantly outperform EEP. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 509 KB

Open AccessProceeding Paper

In-Vehicle Communication Challenges for Urban Emergency Vehicles

by Han-Wen Kuo, I-Hsien Liu, Zhi-Yuan Su and Jung-Shian Li

Eng. Proc. 2026, 129(1), 9; https://doi.org/10.3390/engproc2026129009 - 25 Feb 2026

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Abstract

Ensuring fast, reliable communication for emergency vehicles is vital in a smart-city vehicular ad hoc network. However, conventional technologies such as dedicated short-range communications and radio links often fail to meet strict low-latency, high-reliability requirements in congested, resource-limited environments. We developed a priority-based [...] Read more.

Ensuring fast, reliable communication for emergency vehicles is vital in a smart-city vehicular ad hoc network. However, conventional technologies such as dedicated short-range communications and radio links often fail to meet strict low-latency, high-reliability requirements in congested, resource-limited environments. We developed a priority-based power allocation scheme that reserves sufficient transmission power and bandwidth for emergency vehicles while maintaining acceptable service for regular vehicles. Simulation and performance analysis show that the proposed method achieves lower outage probability and higher sum rate than existing resource allocation strategies under various channel conditions and signal-to-noise ratios, providing an effective communication solution for urban emergency services. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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12 pages, 745 KB

Open AccessProceeding Paper

AI-Enabled Predictive Maintenance of Medical Equipment for Energy and Waste Reduction

by Yuan Zhi Leong and Wai Yie Leong

Eng. Proc. 2026, 129(1), 10; https://doi.org/10.3390/engproc2026129010 - 26 Feb 2026

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Abstract

Hospitals are energy- and waste-intensive systems. Inpatient buildings dominate the sector’s electricity and gas consumption, and healthcare waste streams—especially device-associated disposables—increase environmental burdens. AI-enabled predictive maintenance (PdM) offers a dual lever: (1) reducing energy use by keeping assets operating at efficient points, and [...] Read more.

Hospitals are energy- and waste-intensive systems. Inpatient buildings dominate the sector’s electricity and gas consumption, and healthcare waste streams—especially device-associated disposables—increase environmental burdens. AI-enabled predictive maintenance (PdM) offers a dual lever: (1) reducing energy use by keeping assets operating at efficient points, and (2) preventing avoidable waste by extending component life, reducing emergency spares, and avoiding device-induced clinical workflow disruptions. In this study, an end-to-end architecture is developed by integrating multi-modal sensing (electrical, thermal, acoustic, vibration), computerized maintenance management systems (CMMS), risk-based maintenance under International Electrotechnical Commission (IEC)/International Organization for Standardization standards (ISO 60601, 62353/62304, 81001-5-1), and learning pipelines (self-supervised anomaly detection, remaining useful life estimators, and carbon-aware work order scheduling). Using representative hospital archetypes and equipment classes (imaging, patient monitoring, laboratory analyzers, sterilizers, and pumps), energy, downtime, and waste avoidance are simulated under baseline preventive maintenance (PM) versus PdM with alternate equipment management. Results showed that 10–22% site electricity reduction was achieved, attributable to equipment efficiency and optimized duty-cycling, 18–35% fewer unplanned failures, and a 12–28% reduction in associated consumable waste and emergency part scrappage across scenarios, while maintaining compliance with Joint Commission/Centers for Medicare & Medicaid Services and IEC safety testing intervals. We discuss cybersecurity (IEC 81001-5-1) and the trustworthiness of AI, present a governance model linking CMMS events to carbon telemetry, and provide an implementation roadmap. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 3009 KB

Open AccessProceeding Paper

IoT-Based Anomaly Detection for Long-Term Care Using Principal Component Analysis and Isolation Forest

by Chun-Pin Chang, Hong-Rui Wei, Hung-Wei Chang and Zhi-Yuan Su

Eng. Proc. 2026, 129(1), 11; https://doi.org/10.3390/engproc2026129011 - 27 Feb 2026

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Abstract

Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address this, we propose an IoT-based intelligent environmental monitoring and early-warning [...] Read more.

Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address this, we propose an IoT-based intelligent environmental monitoring and early-warning system designed for care facilities. The three-layer architecture integrates sensors for temperature, humidity, light, air quality, and noise; employs ESP-NOW and wireless fidelity mesh for reliable networking; and supports user interfaces with real-time anomaly alerts. Using PCA and Isolation Forest for efficient anomaly detection, the modular, node-based design enhances safety, reduces manpower burden, and enables scalable smart services. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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12 pages, 1010 KB

Open AccessProceeding Paper

Sustainable Wearable Health Monitoring Using Energy-Harvesting and Biodegradable Electronics

by Wai Yie Leong

Eng. Proc. 2026, 129(1), 12; https://doi.org/10.3390/engproc2026129012 - 27 Feb 2026

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Abstract

Wearable health monitoring systems (WHMS) are recognized as key enablers of continuous real-time physiological sensing in healthcare, eldercare, sports, and occupational safety. However, current devices face critical limitations due to their dependence on non-renewable batteries, rigid substrates, and non-degradable electronic components, which contribute [...] Read more.

Wearable health monitoring systems (WHMS) are recognized as key enablers of continuous real-time physiological sensing in healthcare, eldercare, sports, and occupational safety. However, current devices face critical limitations due to their dependence on non-renewable batteries, rigid substrates, and non-degradable electronic components, which contribute to environmental waste and limit long-term usability. This study aims to explore the development of sustainable, energy-autonomous WHMS that integrate multimodal energy harvesting, including triboelectric, piezoelectric, photovoltaic, thermoelectric, and radio frequency, with biodegradable and bioresorbable electronics using silk fibroin, cellulose nanofibers, poly(lactic-co-glycolic acid), magnesium, and transient silicon. This unified system architecture would further comprise harvesters, power management circuits, energy buffers, low-power sensing front-ends, and tiny machine learning-enabled data processing. The methodology emphasizes energy-neutral operation through duty-cycling, harvest-aware scheduling, and compressive sensing. Simulation and modeling results indicate harvested power densities between 100 and 220 µW·cm⁻², sufficient to sustain electrocardiography, photoplethysmography, and temperature monitoring under realistic daily use profiles. Material degradation studies demonstrate predictable dissolution kinetics over 8–20 weeks in physiological conditions, aligning with safety and environmental goals. By uniting sustainable materials science with energy-efficient circuit design, this work establishes a blueprint for the next generation of eco-friendly, clinically relevant, and ethically responsible wearable health technologies. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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9 pages, 1173 KB

Open AccessProceeding Paper

Schottky Energy Barrier Characteristics of Gadolinium Oxide Thin-Film Resistive Memory Devices with Low-Temperature Supercritical Fluid Technology

by Hsin-Chin Chen, Kai-Huang Chen, Guo-Jau Hung, Ming-Cheng Kao, Yao-Chin Wang, Chin-Chueh Huang Kao and Shen-Feng Lin

Eng. Proc. 2026, 129(1), 13; https://doi.org/10.3390/engproc2026129013 - 27 Feb 2026

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Abstract

In this study, reactive radio frequency magnetron sputtering was used to deposit thin films. Gadolinium oxide was deposited on titanium nitride substrates at different deposition times and oxygen concentrations. Next, rapid thermal annealing and supercritical fluid treatment were performed. The three-dimensional profiler (alpha-step), [...] Read more.

In this study, reactive radio frequency magnetron sputtering was used to deposit thin films. Gadolinium oxide was deposited on titanium nitride substrates at different deposition times and oxygen concentrations. Next, rapid thermal annealing and supercritical fluid treatment were performed. The three-dimensional profiler (alpha-step), X-ray diffractometer, and X-ray photoelectron spectroscopy were used to measure the thickness, surface morphology, crystal structure, and element analysis. Then, indium tin oxide was sputtered and deposited on the gadolinium oxide, which was covered with the metal mask to form a top electrode, thereby manufacturing a metal/insulator/metal resistive memory structure. Finally, a power meter was used to measure the characteristics of the resistive random access memory, including the current–voltage characteristics, and to explore the leakage current conduction mechanism and component durability. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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12 pages, 1153 KB

Open AccessProceeding Paper

Flood-Adaptive Primary Care Clinics with Smart Microgrids and Rapid-Deploy MedTech

by Wai San Leong and Wai Yie Leong

Eng. Proc. 2026, 129(1), 14; https://doi.org/10.3390/engproc2026129014 - 2 Mar 2026

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Abstract

Extreme hydro-meteorological events are intensifying under climate change, disproportionately disrupting last-mile healthcare in flood-prone geographies. In this study, flood-adaptive primary care clinics (FAPCCs) integrated with islandable smart microgrids and a rapid-deploy medical technology stack (MedTech) are developed and evaluated to ensure continuity of [...] Read more.

Extreme hydro-meteorological events are intensifying under climate change, disproportionately disrupting last-mile healthcare in flood-prone geographies. In this study, flood-adaptive primary care clinics (FAPCCs) integrated with islandable smart microgrids and a rapid-deploy medical technology stack (MedTech) are developed and evaluated to ensure continuity of essential services (triage, maternal and child health, vaccination cold-chain, minor procedures, diagnostics, and telemedicine) during fluvial, pluvial, and coastal flooding. Evidence on resilient health facilities, microgrid architectures, distributed energy resources, and modular clinical systems is presented in a multi-layer systems design: (1) a modular, amphibious, and elevatable clinic chassis; (2) a photovoltaic–battery–diesel hybrid system with demand-aware energy management; (3) redundant connectivity long-term evolution/fifth-generation, satellite, and very high frequency; (4) a rapid-deploy MedTech kit including point-of-care diagnostics, low-temperature cold-chain, negative-pressure isolation, and sterilization modules; and (5) flood-aware logistics using unmanned aerial vehicle/unmanned surface vehicle. A mixed-integer linear programming sizing is formulated and dispatched with a continuity-of-care reliability metric that couples energy availability to clinical throughput. Simulation across three archetypal sites (peri-urban delta, inland riverine, coastal estuary) shows that FAPCCs achieve the service availability of higher than 99.5% across 7-day grid outage scenarios while reducing fuel use by 62–81% relative to diesel-only baselines, maintaining vaccine temperatures within 2–8 °C with <0.1% thermal excursion time, and sustaining telemedicine quality of service with <150 ms median uplink latency in hybrid networks. A life-cycle cost analysis indicates a 7.1–9.8 year discounted payback from fuel displacement and avoided service loss. Deployment playbooks and policy guidance are also proposed for Ministries of Health and Disaster Agencies in monsoon-impacted regions. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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10 pages, 378 KB

Open AccessProceeding Paper

Sustainable Cold-Chain Logistics for Vaccine and Blood Supply in East Malaysia

by Yuan Zhi Leong and Wai Yie Leong

Eng. Proc. 2026, 129(1), 15; https://doi.org/10.3390/engproc2026129015 - 2 Mar 2026

Cited by 2 | Viewed by 1218

Abstract

Ensuring product integrity across Malaysia’s East Malaysian states (Sabah and Sarawak) requires a cold chain that is resilient to tropical heat, long multimodal routes, intermittent power, and dispersed rural populations. This paper proposes a sustainability-first architecture for vaccine and blood component logistics that [...] Read more.

Ensuring product integrity across Malaysia’s East Malaysian states (Sabah and Sarawak) requires a cold chain that is resilient to tropical heat, long multimodal routes, intermittent power, and dispersed rural populations. This paper proposes a sustainability-first architecture for vaccine and blood component logistics that combines World Health Organization and the United Nations International Children’s Emergency Fund Effective Vaccine Management (EVM 2.0) criteria with energy-aware transport planning, solar-hybrid edge refrigeration, phase-change materials, and digital temperature monitoring compliant with ISO 23412 for temperature-controlled delivery services. In this study, a mixed-methods methodology was employed, including (1) route and mode optimization under temperature risk and carbon intensity constraints; (2) equipment right-sizing using duty-cycle energy models and IEC 60068 environmental tests as design baselines; (3) governance with real-time earned value management (EVM) and key performance indicators (KPIs); and (4) scenario analysis for riverine, road, air, and drone last-mile segments relevant to remote East Malaysian communities. Results from realistic logistic scenarios indicate a 45–65% reduction in dose-weighted temperature-excursion minutes, 28–41% reduction in CO₂e per successful dose delivered, and 35–52% reduction in product loss compared with status quo planning. For blood components, solar-hybrid storage and mixed-mode routing reduced breach risk by 37% while maintaining red cells (2–6 °C), platelets (20–24 °C, continuous agitation surrogate), and fresh frozen plasma (≤−18 °C) requirements aligned with WHO guidance and Malaysia’s national transfusion policies. We provide a reference architecture, implementation bill of materials, and an EVM-aligned KPI dashboard to guide scale-up. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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9 pages, 2235 KB

Open AccessProceeding Paper

Interpretable Deep Learning Models for Cancer Diagnosis Using Tumor-Educated Platelet Transcriptomes

by Chia-Te Hsu, Wei-Hsin Hung and Meng-Hsiun Tsai

Eng. Proc. 2026, 129(1), 16; https://doi.org/10.3390/engproc2026129016 - 27 Feb 2026

Viewed by 586

Abstract

Tumor-educated platelets (TEPs) have emerged as promising biomarkers for liquid biopsy-4based cancer diagnostics. However, the intrinsic challenges of transcriptomic data, including high dimensionality, small sample size, and strong nonlinearity, hinder robust classification. In this study, we develop a deep learning model that integrates [...] Read more.

Tumor-educated platelets (TEPs) have emerged as promising biomarkers for liquid biopsy-4based cancer diagnostics. However, the intrinsic challenges of transcriptomic data, including high dimensionality, small sample size, and strong nonlinearity, hinder robust classification. In this study, we develop a deep learning model that integrates long short-term memory (LSTM), bidirectional LSTM, convolutional neural network–LSTM (CNN-LSTM), and attention-based LSTM (Attention-LSTM) to classify cancer and healthy samples from the GSE68086 dataset (235 cancer, 50 healthy). Experimental results demonstrate that all models achieve competitive performance, with overall accuracies approaching 0.88, while the Attention-LSTM and CNN-LSTM further enhance interpretability and local feature extraction. Moreover, SHAP analysis confirms that selected genes contributing to classification align with biologically meaningful signals, providing both predictive accuracy and clinical interpretability. These findings highlight the potential of deep sequential models in advancing platelet-based cancer diagnostics. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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13 pages, 1057 KB

Open AccessProceeding Paper

Sustainable Telemedicine: Low-Energy Edge AI and Green Data Center Routing for National Rollout

by Wai San Leong and Wai Yie Leong

Eng. Proc. 2026, 129(1), 17; https://doi.org/10.3390/engproc2026129017 - 28 Feb 2026

Viewed by 1093

Abstract

Telemedicine at the national scale must balance clinical quality, privacy, latency, and sustainability. This study aims to develop a system architecture and methodology for low-energy edge AI combined with green data center routing to reduce energy per consultation while maintaining clinical-grade performance. The [...] Read more.

Telemedicine at the national scale must balance clinical quality, privacy, latency, and sustainability. This study aims to develop a system architecture and methodology for low-energy edge AI combined with green data center routing to reduce energy per consultation while maintaining clinical-grade performance. The results present (1) an energy-aware edge inference stack for physiological sensing and video triage; (2) a carbon-aware, service level agreement (SAL)-constrained routing strategy across regional data centers using software-defined networking and dynamic workload placement; (3) a techno-environmental methodology linking patient-level service key performance indexes to energy neutrality factor, grams CO₂e per encounter, and latency–reliability envelopes; and (4) national rollout playbooks covering network tiers (household/clinic/edge/cloud), facilities upgrades, and governance. Scenarios in urban, peri-urban, and rural/remote environments show 37–62% energy savings and 28–49% carbon reductions relative to cloud-only baselines, with median end-to-end latency ≤120 ms for triage and ≤40 ms for vitals alarms, meeting the World Health Organization and the International Telecommunication Union latency expectations for eHealth. Trade-offs, risks (drift, network volatility), and policy levers (green SLAs, data residency, open standards) are evaluated to scale sustainable telemedicine without compromising safety or equity. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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8 pages, 1857 KB

Open AccessProceeding Paper

Computational Simulation of Corneal Thickness and Ocular Rotation Influences on Intraocular Pressure Accuracy

by Jiehui Zheng, Chih-Chung Cheng, Chia-Wen Lee, Chao-Ming Hsu, Linda Yi-Chieh Poon and Cheng-Fu Yang

Eng. Proc. 2026, 129(1), 18; https://doi.org/10.3390/engproc2026129018 - 25 Feb 2026

Viewed by 529

Abstract

Using simulation methods, we investigated the effects of corneal thickness and ocular rotation on intraocular pressure (IOP) measurement accuracy. In the first part, a 24 mm emmetropic eye model with a titanium-alloy probe was used to evaluate corneal thicknesses of 0.50, 0.55, and [...] Read more.

Using simulation methods, we investigated the effects of corneal thickness and ocular rotation on intraocular pressure (IOP) measurement accuracy. In the first part, a 24 mm emmetropic eye model with a titanium-alloy probe was used to evaluate corneal thicknesses of 0.50, 0.55, and 0.60 mm. Probe force–IOP response curves were analyzed under supine, upright, and prone positions to determine the role of corneal biomechanics in measurement variation. In the second part, ocular rotation was examined using a fixed probe force of 19.62 mN and a corneal thickness of 0.55 mm. Five eye models with different refractive conditions—hyperopic, emmetropic, and myopic eyes of −6.00, −12.00, and −18.00 D—were simulated to assess the influence of rotation and instantaneous acceleration on IOP and shear stress. The findings highlight how both structural and dynamic ocular factors contribute to IOP variability, offering valuable insights for improving the accuracy of clinical tonometry. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 1603 KB

Open AccessProceeding Paper

Evaluation of Absolute and Real Signal Values in Reconstruction of Electrical Impedance Tomography Images

by Minh Quan Cao Dinh, Hoang Nhut Huynh, Tan Loc Huynh, Thanh Ven Huynh, Dinh Tuyen Nguyen and Trung Nghia Tran

Eng. Proc. 2026, 129(1), 19; https://doi.org/10.3390/engproc2026129019 - 25 Feb 2026

Viewed by 328

Abstract

We explore the differences between real and absolute values of signals in Electrical Impedance Tomography image reconstruction, with a focus on their impact on image quality and accuracy. Simulations were conducted using a finite element mesh model containing three inclusions with varying conductivity [...] Read more.

We explore the differences between real and absolute values of signals in Electrical Impedance Tomography image reconstruction, with a focus on their impact on image quality and accuracy. Simulations were conducted using a finite element mesh model containing three inclusions with varying conductivity values. The inclusions representing regions with moderate, poor, and high conductivity were carefully chosen to create sharp contrasts in conductivity. In the experiment, 16 electrodes were placed around a circle, a current injection pattern was applied, and the resulting boundary voltages were recorded. The reconstruction based on absolute signal values, depicted in the center image, tended to smooth out sharp conductivity contrasts, leading to significant artifacts and reduced accuracy in localizing the inclusions. In contrast, the reconstruction based on real signal values provided an accurate representation of the true conductivity distribution, improving the localization of the inclusions. The results underscore the critical role of considering the real component of the signal in electrical impedance tomography image reconstruction to achieve improved accuracy and higher fidelity in the resulting images. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 1885 KB

Open AccessProceeding Paper

Evaluation of Current Injection and Voltage Acquisition Patterns for Electrical Impedance Tomography Image Reconstruction: A Simulation Study

by Minh Quan Cao Dinh, Hai Anh Nguyen Thi, Dang Khoa Trinh Vo, Lin Dan Lieu, Trung Thach Nguyen and Hong Duyen Trinh Tran

Eng. Proc. 2026, 129(1), 20; https://doi.org/10.3390/engproc2026129020 - 27 Feb 2026

Viewed by 495

Abstract

The influence of different voltage measurement and current injection configurations on the quality of image reconstruction in electrical impedance tomography (EIT) was investigated using numerical simulations. Adjacent and opposing techniques were systematically used to examine their effectiveness in voltage acquisition and current delivery. [...] Read more.

The influence of different voltage measurement and current injection configurations on the quality of image reconstruction in electrical impedance tomography (EIT) was investigated using numerical simulations. Adjacent and opposing techniques were systematically used to examine their effectiveness in voltage acquisition and current delivery. The simulation model employed 16 equally spaced electrodes arranged around a circular domain, with an injected alternating current of 1 mA at a frequency of 50 kHz. A circular object with a conductivity of 0.9 units was sequentially positioned at five distinct locations within the imaging domain, each spaced 0.05 units apart. The reconstructed images were analyzed for positional accuracy and contrast resolution. While each configuration offers specific advantages, they exhibit inherent limitations depending on the application. The results of this study enable the understanding of the trade-offs involved in selecting electrode drive and measurement strategies for optimizing image quality in EIT systems. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 5296 KB

Open AccessProceeding Paper

Multi-Step Action Recognition for Long-Term Care Using Temporal Convolutional Network–Dynamic Time Warping–Finite State Machine and MediaPipe

by Feng-Jung Liu, Mei-Jou Lu and Min Chao

Eng. Proc. 2026, 129(1), 21; https://doi.org/10.3390/engproc2026129021 - 28 Feb 2026

Viewed by 761

Abstract

An intelligent multi-step action recognition system was designed for long-term caregiver training and assessment. Leveraging MediaPipe for precise and real-time human pose estimation, the system extracts detailed spatiotemporal body and hand keypoints. Temporal convolutional networks are employed to effectively capture temporal dependencies and [...] Read more.

An intelligent multi-step action recognition system was designed for long-term caregiver training and assessment. Leveraging MediaPipe for precise and real-time human pose estimation, the system extracts detailed spatiotemporal body and hand keypoints. Temporal convolutional networks are employed to effectively capture temporal dependencies and complex features from sequential motion data. Dynamic time warping provides robust sequence alignment, allowing flexible comparison between performed actions and standard templates despite temporal variations in execution speed or style. A finite state machine imposes logical constraints by modeling expected action step sequences, enabling accurate detection of sequence anomalies or deviations. This hybrid architecture supports comprehensive evaluation and real-time feedback, facilitating improved caregiver skill acquisition, process adherence, and quality control within long-term care settings. The system aims to advance digital transformation in healthcare education by providing a scalable, precise, and adaptive training solution. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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8 pages, 2084 KB

Open AccessProceeding Paper

Trainable Multicellular Phase-Field Model for Targeted Pattern Formation via Local Interactions

by Shinji Kotani and Tadashi Nakano

Eng. Proc. 2026, 129(1), 22; https://doi.org/10.3390/engproc2026129022 - 10 Mar 2026

Viewed by 477

Abstract

Multicellular systems form diverse morphologies through chemical and physical interactions among cells. In this study, we propose a trainable multicellular phase-field model that incorporates a neural network. The phase-field model represents cell morphology and position as continuous fields and serves as the basis [...] Read more.

Multicellular systems form diverse morphologies through chemical and physical interactions among cells. In this study, we propose a trainable multicellular phase-field model that incorporates a neural network. The phase-field model represents cell morphology and position as continuous fields and serves as the basis for learning intercellular dynamics. In the proposed model, the neural network takes the local state as input and predicts the subsequent state, adjusting the spatial evolution of the multicellular system to approach a target pattern defined by a loss function. Through simulation experiments, we confirmed that the trained neural network enables cells to aggregate into a cluster and reproduce the spatial pattern of a given image. Moreover, similar results were obtained in a larger simulation domain than that used during training, demonstrating the scalability of the model. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 3419 KB

Open AccessProceeding Paper

Evaluation of Dual-Wavelength LED Light Irradiation of the Skull for Alleviating Neck and Shoulder Pain and Improving Heart-Rate Variability

by Yi-Sheng Wang, Chih-Yu Wang, Chang-Yin Lee, Ke-Nung Huang and Chih-Lung Cheng

Eng. Proc. 2026, 129(1), 23; https://doi.org/10.3390/engproc2026129023 - 11 Mar 2026

Viewed by 543

Abstract

We investigate the use of non-invasive, dual-wavelength (630 nm red/940 nm near-infrared) LED irradiation of the skull for relieving chronic neck and shoulder pain. A low-energy device was applied bilaterally for 15 min, with assessments of pain performed using the numeric rating scale [...] Read more.

We investigate the use of non-invasive, dual-wavelength (630 nm red/940 nm near-infrared) LED irradiation of the skull for relieving chronic neck and shoulder pain. A low-energy device was applied bilaterally for 15 min, with assessments of pain performed using the numeric rating scale (NRS), muscle relaxation assessed via infrared thermography, and autonomic function determined through heart-rate variability (HRV) analysis. The results demonstrated a mean NRS score reduction of 2.4 points, a 0.6 °C increase in cervical skin temperature, and a significant increase in HRV’s root mean square of successive differences, indicating improved autonomic regulation. This technique shows promise for effectively relaxing muscles, alleviating pain, and enhancing autonomic function. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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9 pages, 1196 KB

Open AccessProceeding Paper

Empowering In-Facility Care Safety and Heritage Asset Visualization via Bluetooth Low Energy Indoor Tracking

by Junlin Zhong, Kunta Hsieh, Min Chao, I-Cheng Li, Jinghuang Chen, Jingyi Pan and Cong Gao

Eng. Proc. 2026, 129(1), 24; https://doi.org/10.3390/engproc2026129024 - 13 Mar 2026

Viewed by 360

Abstract

We developed a Bluetooth Low Energy-based indoor asset-tracking system oriented toward elderly care and cultural heritage stewardship. The system stabilizes the noisy received signal strength indicator using a Kalman filter, adapts a logarithmic path loss model to local attenuation via dynamic calibration, and [...] Read more.

We developed a Bluetooth Low Energy-based indoor asset-tracking system oriented toward elderly care and cultural heritage stewardship. The system stabilizes the noisy received signal strength indicator using a Kalman filter, adapts a logarithmic path loss model to local attenuation via dynamic calibration, and estimates positions with an inverse distance weighted centroid. Built on inexpensive beacons and commodity gateways, it supports real-time updates and map-based visualization while remaining easy to deploy and scale across rooms and facilities. We validate the pipeline in a laboratory grid and discuss applicability to workflows such as geofenced reminders, caregiver situational awareness, and collection movement oversight, offering an affordable, interoperable path to reliable indoor tracking for care institutions, museums, and smart buildings. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 204 KB

Open AccessProceeding Paper

Effect of Visual Information Manipulation on Motor Control Indicators in Waiter’s Bow Test

by Genki Adachi, Atsushi Iwashita, Junya Miyazaki and Hayato Shigeto

Eng. Proc. 2026, 129(1), 25; https://doi.org/10.3390/engproc2026129025 - 27 Mar 2026

Viewed by 547

Abstract

We investigated the effects of manipulating visual information on motor control indicators during the Waiter’s Bow Test. The results suggested that visual information occlusion reduced the maximum flexion angles of the lumbar spine and upper lumbar region. Furthermore, subjects who tested negative under [...] Read more.

We investigated the effects of manipulating visual information on motor control indicators during the Waiter’s Bow Test. The results suggested that visual information occlusion reduced the maximum flexion angles of the lumbar spine and upper lumbar region. Furthermore, subjects who tested negative under the open-eye condition tested positive under the closed-eye condition. Regarding muscle activity in the rectus abdominis and erector spinae muscles, it was suggested that this activity was not affected by visual information. These findings indicate that visual sensory feedback is one factor influencing lumbar motor control. The integration of electromyography and accelerometer systems in this study highlights the role of wearable sensor technologies in quantifying neuromuscular function in Bioengineering. By restricting visual information, a model for sensory reweighting can be established for the design of biofeedback systems, rehabilitation robotics, and assistive devices. The results of this study demonstrate how sensor-based evaluation and sensory manipulation can inform the engineering of diagnostic and therapeutic technologies for motor control assessment. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

7 pages, 194 KB

Open AccessProceeding Paper

Muscle Activity of Hip Adductor During Closed Kinetic Chain Movement

by Atsushi Iwashita, Yuto Konishi, Iori Arisue, Genki Adachi and Satoshi Nakanishi

Eng. Proc. 2026, 129(1), 26; https://doi.org/10.3390/engproc2026129026 - 27 Mar 2026

Viewed by 1000

Abstract

The closed kinetic chain is an essential movement method for humans in daily life, and is also important as a training method. However, there have been few studies focusing on the hip adductor muscles. We used electromyography to measure the muscle activity of [...] Read more.

The closed kinetic chain is an essential movement method for humans in daily life, and is also important as a training method. However, there have been few studies focusing on the hip adductor muscles. We used electromyography to measure the muscle activity of the hip adductor muscles during walking and standing movements as part of daily living activities, as well as bicycle ergometer exercise and squats. Concerning the role of the adductor muscles, they are thought to stabilize the pelvis during the unilateral support phase when walking, and to act as hip extension and hip alignment adjustment during cycle ergometer exercise. By using electromyography and inertial sensors, the results of this study showed that wearable technologies can be used to quantify neuromuscular function during closed kinetic chain movements. The results serve as a reference for the development of rehabilitation devices, assistive technologies, and computational models that need the simulation of hip joint mechanics. Linking muscle activity data to engineering-based strategies enables precise musculoskeletal assessment and intervention beyond biological observation. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

13 pages, 2909 KB

Open AccessProceeding Paper

Application of Spatial Information in Traditional Settlement Resource Assessment and Optimization

by Simin Huang, Tongxin Ye, Huiying Liu, Weifeng Li, Tao Zhang and Wei-Ling Hsu

Eng. Proc. 2026, 129(1), 27; https://doi.org/10.3390/engproc2026129027 - 27 Mar 2026

Viewed by 464

Abstract

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, [...] Read more.

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, it systematically evaluates the spatial distribution and socioeconomic conditions of these settlements. A multi-criteria evaluation model is constructed to quantify resource endowment across cultural, historical, and ecological dimensions, with particular emphasis on key factors influencing conservation effectiveness, such as infrastructure and economic vitality. Combining field investigations and literature review, we propose adaptive reuse strategies and policy recommendations to enhance settlement resilience and balance cultural preservation with regional development. Their expected outcomes include the engineering of a multidimensional geographic database for traditional settlements, the establishment of a spatial decision-support framework for heritage infrastructure conservation, and the development of systematic optimization protocols integrated with China’s rural revitalization technical policies. These results provide a computational and methodological foundation for interdisciplinary research in sustainable cultural heritage management and smart rural engineering. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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10 pages, 2003 KB

Open AccessProceeding Paper

Assessment of Working Environment Quality and Solutions for Its Improvement at University Medical Center Ho Chi Minh City Branch 2

by Ngoc An Dang Nguyen, Minh Quan Cao Dinh, Hong Thu Nguyen Thi and Lam Duc Vu Nguyen

Eng. Proc. 2026, 129(1), 28; https://doi.org/10.3390/engproc2026129028 - 1 Apr 2026

Viewed by 401

Abstract

We evaluated the indoor environmental quality of the administrative office at University Medical Center Ho Chi Minh City branch 2 and implemented a multi-stage engineering control strategy to optimize occupational health conditions. A cross-sectional assessment monitored important air quality parameters, including carbon dioxide [...] Read more.

We evaluated the indoor environmental quality of the administrative office at University Medical Center Ho Chi Minh City branch 2 and implemented a multi-stage engineering control strategy to optimize occupational health conditions. A cross-sectional assessment monitored important air quality parameters, including carbon dioxide (CO₂), fine particulate matter (PM2.5 and PM10), humidity, and illumination. Following baseline measurements, an integrated system was deployed to address pollutant mass balance, consisting of High-Efficiency Particulate Air (HEPA) filtration units for mechanical particle scrubbing, ceiling-mounted axial fans to induce forced convection, and ultraviolet-C germicidal lamps for photochemical disinfection. Post-intervention results demonstrated significant gains in system removal efficiency. CO₂ concentrations decreased by over 60% due to enhanced volumetric air exchange, while PM2.5 levels decreased by more than 40% through interception and diffusion mechanisms within the HEPA media. Furthermore, UVC irradiation achieved a 90% reduction in viable airborne microbial colonies. The results of this study show that low-cost, scalable environmental engineering controls and fluid dynamic optimizations effectively mitigate indoor air pollution and enhance workplace stability in healthcare administrative settings. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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8 pages, 409 KB

Open AccessProceeding Paper

Designing an AI Agent System to Execute Biodesign Debate Process

by Ya Chuan Chen, Shih-Huan Lin, Ke-Wei Chen and Hsiang-Wei Hu

Eng. Proc. 2026, 129(1), 29; https://doi.org/10.3390/engproc2026129029 - 16 Apr 2026

Viewed by 823

Abstract

Early-stage healthcare innovation depends on systematic unmet need discovery, a process constrained by time and multidisciplinary coordination. We developed BioDesign Agent, a multi-agent debate framework built on LangGraph to augment the identify phase of design thinking. The system assigns expert roles, clinical, engineering, [...] Read more.

Early-stage healthcare innovation depends on systematic unmet need discovery, a process constrained by time and multidisciplinary coordination. We developed BioDesign Agent, a multi-agent debate framework built on LangGraph to augment the identify phase of design thinking. The system assigns expert roles, clinical, engineering, human factors, regulatory, business, intellectual property, and patient access, to digital agents engaging in structured debate and scoring. Applied to antimicrobial resistance risk prediction, the agent surfaced diverse perspectives, refined need statements, and produced prioritized evaluations. Multi-agent debate yielded more differentiation, richer trade-off analysis, and more actionable insights compared with ChatGPT oss:20b only baselines, demonstrating how structured AI-assisted debate can accelerate healthcare need discovery and complement human-driven biodesign with scalable front-end innovation support. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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6 pages, 1788 KB

Open AccessProceeding Paper

DroneDeep RL (DDR): A Traffic Congestion Control Strategy Using Prioritization LLM Agent and Circular Deep Q-Network

by Md. Mujahid Hasan, Afsana Siddika, Maria Akter Khushi, Salman Md Sultan, Tahira Alam and Shajedul Hasan Arman

Eng. Proc. 2026, 129(1), 30; https://doi.org/10.3390/engproc2026129030 - 16 Apr 2026

Viewed by 902

Abstract

Traffic congestion is a problem in urban traffic that needs to be monitored and managed intelligently. In this study, a hybrid traffic management system is designed based on a combination of drone vision, large language model (LLM) inferences, and deep reinforcement learning (DRL). [...] Read more.

Traffic congestion is a problem in urban traffic that needs to be monitored and managed intelligently. In this study, a hybrid traffic management system is designed based on a combination of drone vision, large language model (LLM) inferences, and deep reinforcement learning (DRL). Using drones videos of real-time traffic, the lightweight You Only Look Once v11 model detects vehicles, and after, traffic flow levels are identified by the proposed LLM agent. A Circular-Deep Q-Networks-based DRL controller is proposed to reduce the average waiting time of vehicles. Simulation experiments validate improved congestion detection, reduced delay, and more effective communication for smart city traffic control. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 622 KB

Open AccessProceeding Paper

Facial Attribute Prediction Using Knowledge Distillation

by Maisha Zaman, Mahfuja Akhter Mohona, Syed Minhaj Ahmad, Md Akhlakuzzaman, Md Shopon, Tahira Alam and Shajedul Hasan Arman

Eng. Proc. 2026, 129(1), 31; https://doi.org/10.3390/engproc2026129031 - 16 Apr 2026

Viewed by 1987

Abstract

The rising need for intelligent applications on resource-limited devices presents a significant challenge. The deployment of big, high-accuracy deep learning models is impractical due to their processing costs. This thesis examines this issue in the realm of face attribute analysis. We present an [...] Read more.

The rising need for intelligent applications on resource-limited devices presents a significant challenge. The deployment of big, high-accuracy deep learning models is impractical due to their processing costs. This thesis examines this issue in the realm of face attribute analysis. We present an approach for multi-attribute prediction (age, gender, ethnicity) using knowledge distillation to develop a model that is both highly accurate and computationally economical. The methodology comprises a two-phase technique utilizing the UTKFace dataset. A substantial, high-capacity “teacher” model, with a ResNet50 architecture, is trained to attain state-of-the-art precision in predicting age, gender, and ethnicity. Consequently, the extensive, intricate understanding from this educator is conveyed to a considerably smaller and more efficient “student” model utilizing a MobileNetV2 architecture. The transfer is accomplished by instructing the learner using both the accurate labels and the softened probability distributions produced by the teacher. The findings indicate that the distilled student model attains prediction accuracy nearly equivalent to that of its larger instructor model across all variables, while markedly decreasing model size and inference duration. The findings also confirm that knowledge distillation is a viable and efficient method for creating high-performance facial analysis systems appropriate for real-world implementation on mobile, edge, and other resource-constrained platforms. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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7 pages, 485 KB

Open AccessProceeding Paper

Development of Real-Time Monitoring System for Cooperative Driving in Vehicle Lanes

by Wei-Hao Li and Feng-Chia Chuang

Eng. Proc. 2026, 129(1), 33; https://doi.org/10.3390/engproc2026129033 - 6 May 2026

Viewed by 465

Abstract

For intelligent transportation systems, monitoring road surface integrity is critical for enhancing vehicle safety and infrastructure longevity. Traditional detection relies on high-cost Light Detection and Ranging (LiDAR) and vehicle-mounted sensors that are often computationally expensive and difficult to deploy at scale. This study [...] Read more.

For intelligent transportation systems, monitoring road surface integrity is critical for enhancing vehicle safety and infrastructure longevity. Traditional detection relies on high-cost Light Detection and Ranging (LiDAR) and vehicle-mounted sensors that are often computationally expensive and difficult to deploy at scale. This study aims to address the challenge of deploying a high-accuracy CNN on a resource-constrained edge device (Raspberry Pi 4B) by optimizing the balance between inference latency and detection sensitivity. By utilizing a depthwise separable convolution architecture, the system shows a 10% increase in vehicle window area identification accuracy while operating within a low-power envelope of less than 15 W. Experimental results demonstrate that the integrated curvature-based mathematical model improves anomaly detection sensitivity by 15% compared to traditional threshold-based triggers. The developed system reduces hardware expenses to 30% of conventional LiDAR-centric systems, maintaining a real-time inference latency of 120 ms and a packet loss rate below 2% at speeds of 60 km/h. These results establish a cost-effective, edge-intelligent solution for vehicle-road collaborative monitoring, increasing overall driver comfort and safety by 15%. Full article

(This article belongs to the Proceedings of The 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2025 (ECBIOS 2025))

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