Biomedical Engineering, Healthcare and Sustainability, 2nd Edition

Topic Information

Dear Colleagues,

The 2025 IEEE 7th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (IEEE ECBIOS 2025) will be held in Kaohsiung, Taiwan on October 24–October 26, 2025, and it will provide a unified communication platform for researchers in the topics of biomedical engineering, healthcare, and sustainability. Recently, healthcare is undergoing a sector-wide transformation thanks to advances in computing, networking technologies, big data, and artificial intelligence. Healthcare is not only changing from reactive and hospital-centered to preventive and personalized care but is also changing from disease-focused to well-being-centered. Healthcare systems, as well as fundamental medicine research, are becoming smarter and more accessible in biomedical engineering. Furthermore, with cutting-edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality, and lower cost. However, these innovations often do not incorporate sustainability, health, and happiness for all people. Science and technology should be complemented by arts, humanities, social sciences, and indigenous knowledge and wisdom in order to increase the accessibility of the benefits for those in need across all regions and classes of people. We need an ethically aligned and driven healthcare system integrated with sustainability. This topic, “Biomedical Engineering, Healthcare and Sustainability, 2nd Edition”, includes four journals, Bioengineering, Healthcare, ASI, and C, to publish excellent papers in related fields. It enables interdisciplinary collaboration of science and engineering technologists in the academic and industrial fields, as well as networking internationally.

Topics of interest include the following:

• Smart healthcare system analysis and design.
• Computer and human–machine interactions of healthcare system.
• Application of IoT (Internet of Things) in healthcare system.
• Big data and artificial intelligence-enabled healthcare systems.
• Health-related aspects of sustainability.
• Environmental education and public health.
• Environmental engineering and biotechnology, rehabilitation medicine, and physiotherapy.
• Sports medicine.
• Pediatric and geriatric emergency care.
• Leisure recreation.
• Health promotion.
• Nourishment and healthcare.
• Disaster and health.
• Health and environment.
• Health services.
• Occupational health.
• Impact of safety, security, and disaster management on sustainability.
• Sustainability science.
• Medical electronics.
• Biomedical materials.
• Biomedical diagnostic techniques.
• Medical information and rehabilitation technology.
• Other related topics in healthcare, sustainability, and biomedical engineering.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Prof. Dr. Charles Tijus
Prof. Dr. Po-Lei Lee
Prof. Dr. Yi-Chun Du
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied System Innovation asi	3.7	9.9	2018	22 Days	CHF 1600	Submit
Bioengineering bioengineering	3.7	5.3	2014	17 Days	CHF 2700	Submit
Biomimetics biomimetics	3.9	4.2	2016	17 Days	CHF 2200	Submit
C carbon	2.9	3.4	2015	22.5 Days	CHF 1600	Submit
Healthcare healthcare	2.7	4.7	2013	22.4 Days	CHF 2700	Submit
Processes processes	2.8	5.5	2013	14.9 Days	CHF 2400	Submit

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Published Papers (7 papers)

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All ASI Bioengineering C Healthcare Biomimetics Processes

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13 pages, 1063 KB  

Open AccessReview

Ketamine as a Bridge Therapy: Reducing Acute Suicidality in Hospital Settings

by Paul E. Lie, Titus Y. Lie, Madeleine Nguyen and Donald Y. C. Lie

Healthcare 2026, 14(5), 634; https://doi.org/10.3390/healthcare14050634 - 3 Mar 2026

Viewed by 398

Abstract

This narrative literature review explores the clinical use of Ketamine as part of an untested hypothetical model framework for bridge therapy for acute suicidality. Long-term suicide rates continue to increase in the United States and in many other countries, creating a pressing public [...] Read more.

This narrative literature review explores the clinical use of Ketamine as part of an untested hypothetical model framework for bridge therapy for acute suicidality. Long-term suicide rates continue to increase in the United States and in many other countries, creating a pressing public health challenge with a variety of treatment considerations. Existing standard-of-care SSRI therapeutics have a delay between administration and symptom relief at 2–6 weeks, leaving a so-called danger zone of about 1–3 months of risk for suicidal follow-through behaviors. Ketamine, a potent NMDA (N-methyl-D-aspartate) receptor antagonist, has recently seen widespread interest in both regulatory and clinical settings for increasing neuroplasticity and alleviating depressive symptoms. Ketamine’s mechanism-of-action through mTORC1 is much faster than SSRI’s downstream transcriptional regulation, leading to quicker relief of suicidal symptoms and the removal of the danger zone lag period. The current literature suggests that a controlled, supervised subanesthetic dose of Ketamine in a clinical setting has low risks of addiction or abuse, distinguishing therapeutic uses of Ketamine from recreational uses. While the biological efficacy of Ketamine is established, this conceptual review focuses on a possible initial hypothetical framework of a “Bridge Protocol.” We searched PubMed, Google Scholar, The Cochrane Library, and PsycINFO (January 2000–December 2025) to synthesize evidence regarding SSRI latency, acute Ketamine protocols, and post-discharge safety. We conclude that while promising, the proposed Ketamine Bridge Therapy requires rigorous longitudinal validation and sustained clinical studies before it can be safely used and experience widespread adoption. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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12 pages, 2968 KB  

Open AccessArticle

A Machine Learning-Based Decoder Framework for the Cortical Voltage-Sensitive Dye Responses to Retinal Neuromorphic Microstimulation: A Proof-of-Concept Simulation Study

by Keisuke Yamada, Yuina Terakura, Santa Fukuda and Yuki Hayashida

Bioengineering 2026, 13(2), 231; https://doi.org/10.3390/bioengineering13020231 - 16 Feb 2026

Viewed by 600

Abstract

Intracortical microstimulation (ICMS) is a promising approach for visual prostheses. We recently proposed using retinal neuromorphic spike trains derived from visual images as ICMS pulse sequences, and preliminarily recorded cortical voltage-sensitive dye (VSD) responses to such stimulation. To examine whether these cortical responses [...] Read more.

Intracortical microstimulation (ICMS) is a promising approach for visual prostheses. We recently proposed using retinal neuromorphic spike trains derived from visual images as ICMS pulse sequences, and preliminarily recorded cortical voltage-sensitive dye (VSD) responses to such stimulation. To examine whether these cortical responses contain image information, we explore the feasibility of machine-learning–based decoding. However, constructing such a decoder requires large-scale datasets linking visual images, spike trains, and cortical responses, which are not yet experimentally available. Therefore, we generated surrogate data with a Wiener-system model that simulates VSD responses of the visual cortex to ICMS pulse trains. A convolutional neural network trained on these synthetic datasets successfully reconstructed images from the simulated cortical responses. This simulation work serves as a proof-of-concept study, demonstrating the computational feasibility of estimating visual information contained in neuromorphic ICMS-evoked cortical activity and providing a foundation for future physiological validation. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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17 pages, 1116 KB  

Open AccessArticle

Deep Learning for Emergency Department Sustainability: Interpretable Prediction of Revisit

by Wang-Chuan Juang, Zheng-Xun Cai, Chia-Mei Chen and Zhi-Hong You

Healthcare 2026, 14(4), 464; https://doi.org/10.3390/healthcare14040464 - 12 Feb 2026

Viewed by 445

Abstract

Background: Emergency department (ED) overcrowding strains clinicians and potentially compromises urgent care quality. Unscheduled return visits (URVs), also known as readmissions, contribute to this cycle, motivating tools that identify high-risk patients at discharge. Methods: This study performed a retrospective study using ED electronic [...] Read more.

Background: Emergency department (ED) overcrowding strains clinicians and potentially compromises urgent care quality. Unscheduled return visits (URVs), also known as readmissions, contribute to this cycle, motivating tools that identify high-risk patients at discharge. Methods: This study performed a retrospective study using ED electronic health records (EHRs) from Kaohsiung Veterans General Hospital from January 2018 to December 2022 (n = 184,653). The model integrates structured variables, such as vital signs, medication and laboratory counts, and ICD-10–based comorbidity measures, with unstructured physician notes. Key physiologic measurements were transformed into binary form using clinical reference intervals, and random under-sampling addressed class imbalance. A multimodal, CNN was proposed and evaluated with an 8:2 train–test split and 10-fold Monte Carlo cross-validation. Results: The proposed model achieved a sensitivity of 0.717 (CI: [0.695, 0.738]), accuracy of 0.846 (CI: [0.842, 0.850]), and AUROC of 0.853. Binary transformation improved recall and AUROC relative to the original numeric representations. SHAP analysis showed that unstructured features dominated prediction, while structured variables added complementary value. In a small-scale pilot evaluation using the SHAP-enabled interface, participating physicians reported the system helped surface high-risk cohorts and reduced cognitive workload by consolidating relevant patient information for rapid cross-checking. Conclusions: An interpretable CNN-based clinical decision support system can predict ED revisit risk from multimodal EHR data and demonstrates practical usability in a real-world clinical setting, supporting targeted discharge planning and follow-up as a near-term approach to mitigate overcrowding. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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19 pages, 4373 KB  

Open AccessArticle

Exploring Problem-Solving Strategies in Gifted and Regular Students: Education Insights from Eye-Tracking Analysis

by Po-Lei Lee, Shih-Ting Hung, Pao-Hsin Chang, Chun-Yen Chang, Lei Bao, Ting-Kuang Yeh and Li-Ching Lee

Appl. Syst. Innov. 2026, 9(2), 38; https://doi.org/10.3390/asi9020038 - 1 Feb 2026

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Abstract

This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State [...] Read more.

This study investigated how gifted and regular high school students employ different cognitive strategies and integrate information during scientific problem solving, using eye-tracking techniques. Eighteen multiple-choice items were selected from the Investigating Scientific Thinking and Reasoning (iSTAR) assessment developed at The Ohio State University, including nine text-only questions (tMCQs) and nine picture-embedded questions (pMCQs). The items were chosen to ensure clear spatial separation among text, image, and answer areas, allowing reliable region-based eye-movement analysis. Eye-tracking data were analyzed using two indices: fixation time ratio (FTR), reflecting relative attention allocation, and saccade count ratio (SCR), capturing cross-region information integration. The results revealed clear group differences. Gifted students devoted a larger proportion of attention to pictorial information (0.38 vs. 0.32) and showed more frequent transitions between picture and answer regions (0.15 vs. 0.12), indicating more integrative processing and mental model construction. In contrast, regular students spent more time focusing on textual regions and exhibited higher within-text saccade activity, consistent with a direct translation strategy. Furthermore, SCR-based machine learning classification using a Random Forest model demonstrated meaningful discriminative capability between the two groups, particularly for picture-embedded questions, achieving an accuracy of 77.5%. Overall, the findings provide empirical evidence that question format influences students’ cognitive strategies during scientific reasoning. Methodologically, this study combines a validated reasoning assessment, a carefully defined ROI-based eye-tracking design, and interpretable behavioral indicators, offering practical implications for differentiated science instruction. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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30 pages, 1826 KB  

Open AccessArticle

Unveiling the Scientific Knowledge Evolution: Carbon Capture (2007–2025)

by Kuei-Kuei Lai, Yu-Jin Hsu and Chih-Wen Hsiao

Appl. Syst. Innov. 2025, 8(6), 187; https://doi.org/10.3390/asi8060187 - 30 Nov 2025

Viewed by 748

Abstract

This study explores how research on carbon capture technologies (CCTs) has developed over time and shows how semantic text mining can improve the analysis of technology trajectories. Although CCTs are widely viewed as essential for net-zero transitions, the literature is still scattered across [...] Read more.

This study explores how research on carbon capture technologies (CCTs) has developed over time and shows how semantic text mining can improve the analysis of technology trajectories. Although CCTs are widely viewed as essential for net-zero transitions, the literature is still scattered across many subthemes, and links between engineering advances, infrastructure deployment, and policy design are often weak. Methods that rely mainly on citations or keyword frequencies tend to overlook contextual meaning and the subtle diffusion of ideas across these strands, making it difficult to reconstruct clear developmental pathways. To address this problem, we ask the following: How do CCT topics change over time? What evolutionary mechanisms drive these transitions? And which themes act as bridges between technical lineages? We first build a curated corpus using a PRISMA-based screening process. We then apply BERTopic, integrating Sentence-BERT embeddings with UMAP, HDBSCAN, and class-based TF-IDF, to identify and label coherent semantic topics. Topic evolution is modeled through a PCC-weighted, top-K filtered network, where cross-year connections are categorized as inheritance, convergence, differentiation, or extinction. These patterns are further interpreted with a Fish-Scale Multiscience mapping to clarify underlying theoretical and disciplinary lineages. Our results point to a two-stage trajectory: an early formation phase followed by a period of rapid expansion. Long-standing research lines persist in amine absorption, membrane separation, and metal–organic frameworks (MOFs), while direct air capture emerges later and becomes increasingly stable. Across the full period, five evolutionary mechanisms operate in parallel. We also find that techno-economic assessment, life-cycle and carbon accounting, and regulation–infrastructure coordination serve as key “weak-tie” bridges that connect otherwise separated subfields. Overall, the study reconstructs the core–periphery structure and maturity of CCT research and demonstrates that combining semantic topic modeling with theory-aware mapping complements strong-tie bibliometric approaches and offers a clearer, more transferable framework for understanding technology evolution. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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21 pages, 4271 KB  

Open AccessArticle

Real-Time Attention Measurement Using Wearable Brain–Computer Interfaces in Serious Games

by Manuella Kadar

Appl. Syst. Innov. 2025, 8(6), 166; https://doi.org/10.3390/asi8060166 - 29 Oct 2025

Viewed by 2574

Abstract

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated [...] Read more.

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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10 pages, 1225 KB  

Open AccessArticle

Stress Distribution in Immature Incisors with Regenerative Endodontic Treatment: Which Coronal Restoration Performs Best? An FEA Study

by Öznur Eraslan, Mukadder İnci Başer Kolcu, Oğuz Eraslan and Sema Belli

Biomimetics 2025, 10(10), 674; https://doi.org/10.3390/biomimetics10100674 - 7 Oct 2025

Viewed by 1012

Abstract

Purpose: This study aimed to evaluate the effect of different coronal restoration methods on stresses in immature central incisors with regenerative endodontic treatment and excessive loss of coronal structure. Methods: A three-dimensional (3D) Finite Element Analysis (FEA) model of a maxillary central incisor [...] Read more.

Purpose: This study aimed to evaluate the effect of different coronal restoration methods on stresses in immature central incisors with regenerative endodontic treatment and excessive loss of coronal structure. Methods: A three-dimensional (3D) Finite Element Analysis (FEA) model of a maxillary central incisor treated with a 3 mm MTA coronal plug after regenerative endodontic treatment was created. Six different models were simulated: (1) intact immature tooth (control), (2) direct composite resin build-up, (3) fibre-reinforced composite build-up, (4) hybrid ceramic endocrown, (5) LiSi ceramic endocrown, and (6) endocore and ceramic crown restoration. Analyses were performed with SolidWorks/CosmosWorks, and a 150 N load was applied at a 135° angle. Results: Maximum tensile stresses were concentrated in the cervical region (4.577 MPa). Direct composite and fibre-reinforced restorations showed high stress in root dentin (3.891 and 3.841 MPa, respectively). The endocore/ceramic crown restoration (1.578 MPa) provided the closest stress distribution to the natural tooth (1.322 MPa). Conclusions: The biomechanical performance of the restoration–tooth complex depends on both the restorative material and the restoration design. In immature teeth undergoing regenerative endodontic treatment, the most biomechanically favourable restoration option was an endocore/ceramic crown. Full article

(This article belongs to the Topic Biomedical Engineering, Healthcare and Sustainability, 2nd Edition)

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