Topic Editors

Department of Electronic Engineering, National Formosa University, Yunlin City 632, Taiwan
The Graduate Institute of Science Education and the Department of Earth Sciences, National Taiwan Normal University (NTNU), Taipei, Taiwan
Director of the Cognitions Humaine et Artificielle Laboratory, University Paris 8, 93526 Saint-Denis, France
Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan
Department of Recreation and Health Care Management, Chia Nan University of Pharmacy & Science, Tainan City 71710, Taiwan

Applied System on Biomedical Engineering, Healthcare and Sustainability 2024

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
Viewed by
4486

Topic Information

Dear Colleagues,

Recently, the healthcare sector is undergoing a transformation due 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, but is also changing from disease focused to focusing on well-being. Healthcare systems, as well as fundamental medicinal research, are becoming smarter and more enabled in Biomedical Engineering. Furthermore, with cutting-edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality, and lower costs. However, these innovations often do not result in sustainability, health, and happiness for all people. Science and technology need to be complemented by arts, humanities, social sciences, as well indigenous knowledge and wisdom if we are to increase the accessibility of the benefits for those in need across all regions and classes of people. We need ethically aligned and driven health care systems and sustainability. This topic “Applied System on Biomedical Engineering, Healthcare, and Sustainability 2024” includes the five following journals: Applied Sciences, ASI, Bioengineering, Electronics, and Healthcare. This enables the interdisciplinary collaboration of science and engineering technologists in the academic and industrial fields, as well as international networking.

Topics of interest include as followings:

  • Smart healthcare system analysis and design
  • Computer and human–machine interactions of healthcare system
  • Application of IoT (Internet of Things) on 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 health care
  • 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, Biomedical Engineering.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Prof. Dr. Charles Tijus
Prof. Dr. Po-Lei Lee
Prof. Dr. Kuei-Shu Hsu
Topic Editors

Keywords

  • biomedical engineering
  • healthcare
  • sustainability
  • smart healthcare system
  • medical electronics
  • biomedical materials
  • environmental engineering
  • public health

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.3 2011 17.8 Days CHF 2400 Submit
Applied System Innovation
asi
3.8 7.9 2018 27.7 Days CHF 1400 Submit
Bioengineering
bioengineering
3.8 4.0 2014 15.6 Days CHF 2700 Submit
Electronics
electronics
2.6 5.3 2012 16.8 Days CHF 2400 Submit
Healthcare
healthcare
2.4 3.5 2013 20.5 Days CHF 2700 Submit

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Published Papers (7 papers)

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14 pages, 7338 KiB  
Article
pH-Dependent Morphology of Copper (II) Oxide in Hydrothermal Process and Their Photoelectrochemical Application for Non-Enzymatic Glucose Biosensor
by Trung Tin Tran, Anh Hao Huynh Vo, Thien Trang Nguyen, Anh Duong Nguyen, My Hoa Huynh Tran, Viet Cuong Tran and Trung Nghia Tran
Appl. Sci. 2024, 14(13), 5688; https://doi.org/10.3390/app14135688 - 29 Jun 2024
Viewed by 425
Abstract
In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies [...] Read more.
In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies including nanoparticles, nanorods, and nanosheets according to the pH levels of 4, 7, and 12, respectively. The observed variations in surface morphology are attributed to fluctuations in growth rates across different crystal facets, which are influenced by the presence of intermediate species within the reaction. This report also compared the structural and optical properties of the synthesized CuO nanostructures and explored their potential for photoelectrochemical glucose sensing. Notably, CuO nanoparticles and nanorods displayed exceptional performance with calculated limits of detection of 0.69 nM and 0.61 nM, respectively. Both of these morphologies exhibited a linear response to glucose within their corresponding concentration ranges (3–20 nM and 20–150 nM). As a result, CuO nanorods appear to be a more favorable photoelectrochemical sensing method because of the large surface area as well as the lowest solution resistance in electroimpedance analysis compared to CuO nanoparticles and nanosheets forms. These findings strongly suggest the promising application of hydrothermal-synthesized CuO nanostructures for ultrasensitive photoelectrochemical glucose biosensors. Full article
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12 pages, 1503 KiB  
Article
Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network
by Wei Chien, Chien-Ching Chiu, Po-Hsiang Chen, Hung-Yu Wu and Eng Hock Lim
Appl. Sci. 2024, 14(13), 5624; https://doi.org/10.3390/app14135624 - 27 Jun 2024
Viewed by 301
Abstract
The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which [...] Read more.
The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which can be used in the Internet of Things. We incident TM and TE waves to unknown objects and receive the scattered fields. In order to reduce the training difficulty, we first input the gathered scattered field information into a deep convolutional neural network (DCNN) to obtain the preliminary guess. In the second step, we feed the guessed image into the convolutional neural network (CNN) to reconstruct high-resolution images. Our numerical results demonstrate the real-time imaging capability of our proposed two-step method in reconstructing high-contrast scatterers. Full article
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16 pages, 4054 KiB  
Article
Investigating the Effect of Cyclodextrin Nanosponges and Cyclodextrin-Based Hydrophilic Polymers on the Chemical Pharmaceutical and Toxicological Profile of Al(III) and Ga(III) Complexes with 5-Hydroxyflavone
by Claudiu Radu, Andreea Alexandra Olteanu, Corina Cristina Aramă, Mirela Mihăilă and Valentina Uivaroși
Appl. Sci. 2024, 14(13), 5441; https://doi.org/10.3390/app14135441 - 23 Jun 2024
Viewed by 521
Abstract
In the present study, the complexes of aluminum and gallium with 5-hydroxyflavone were evaluated for their interaction with cyclodextrin polymers, as well as for the pharmacological effect of their inclusion. The cyclodextrin polymers were synthesized using diphenylcarbonate as a crosslinking agent, resulting in [...] Read more.
In the present study, the complexes of aluminum and gallium with 5-hydroxyflavone were evaluated for their interaction with cyclodextrin polymers, as well as for the pharmacological effect of their inclusion. The cyclodextrin polymers were synthesized using diphenylcarbonate as a crosslinking agent, resulting in a lipophilic nanosponge (DPCNS), and pyromellitic dianhydride, resulting in a hydrophilic polymer (PMDACD). The inclusion complexes were synthesized and characterized via IR spectrometry and thermal analysis. The effect on the solubility of the metal complexes was also studied, where the hydrophobic nanosponge did not lead to an increase in solubility, but on the contrary, in the case of Al, it decreased; meanwhile, in the case of the hydrophilic polymer, the solubility of the metal complexes increased with the amount of polymer added. The cytostatic effect of inclusion complexes was investigated on two cell lines with different localizations, human colon adenocarcinoma (LoVo) and human ovarian adenocarcinoma (SKOV-3). The cytostatic efficacy is increased compared to simple complexes with efficacy on LoVo cells. Compared between the two metals, gallium complexes proved to be more active, with the efficacy of gallium complexes with the PMDACD being approximately the same as that of cisplatin, an antitumor agent used in therapy. Full article
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18 pages, 3164 KiB  
Article
Cough Detection Using Acceleration Signals and Deep Learning Techniques
by Daniel Sanchez-Morillo, Diego Sales-Lerida, Blanca Priego-Torres and Antonio León-Jiménez
Electronics 2024, 13(12), 2410; https://doi.org/10.3390/electronics13122410 - 20 Jun 2024
Viewed by 452
Abstract
Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective [...] Read more.
Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions. Full article
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46 pages, 4394 KiB  
Article
Empowering Healthcare: A Comprehensive Guide to Implementing a Robust Medical Information System—Components, Benefits, Objectives, Evaluation Criteria, and Seamless Deployment Strategies
by Ana-Maria Ștefan, Nicu-Răzvan Rusu, Elena Ovreiu and Mihai Ciuc
Appl. Syst. Innov. 2024, 7(3), 51; https://doi.org/10.3390/asi7030051 - 14 Jun 2024
Viewed by 1130
Abstract
In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components [...] Read more.
In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components that constitute this innovative system, we explore its fundamental architecture and how each element contributes to seamless information flow. The benefits of adopting a medical information system are highlighted, emphasizing improved patient care, streamlined processes, and enhanced decision making for healthcare professionals. Full article
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17 pages, 7019 KiB  
Article
Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO
by Shaofang Wang, Jun Xie, Yanrong Cui and Zhongju Chen
Electronics 2024, 13(12), 2298; https://doi.org/10.3390/electronics13122298 - 12 Jun 2024
Viewed by 528
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning [...] Read more.
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning techniques have been extensively employed in the detection of colorectal polyps. However, these algorithms have not yet addressed the issue of detection in low-resolution images. In this study, we propose a novel YOLO-SRPD model by integrating SRGAN and YOLO to address the issue of low-resolution colonoscopy images. Firstly, the SRGAN with integrated ACmix is used to convert low-resolution images to high-resolution images. The generated high-resolution images are then used as the training set for polyp detection. Then, the C3_Res2Net is integrated into the YOLOv5 backbone to enhance multiscale feature extraction. Finally, CBAM modules are added before the prediction head to enhance attention to polyp information. The experimental results indicate that YOLO-SRPD achieves a mean average precision (mAP) of 94.2% and a precision of 95.2%. Compared to the original model (YOLOv5), the average accuracy increased by 1.8% and the recall rate increased by 5.6%. These experimental results confirm that YOLO-SRPD can address the low-resolution problem during colorectal polyp detection and exhibit exceptional robustness. Full article
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22 pages, 18402 KiB  
Article
Preliminary Failure Analyses of Loaded Hot Water Bottles
by Joseph Towler, Mohamed Baraya, Ziying Ran, Adel Alshammari, Syead Arif, Mohammad Desai, Sasidharan Palanivel, Rosti Readioff and Ahmed Abass
Appl. Sci. 2024, 14(11), 4427; https://doi.org/10.3390/app14114427 - 23 May 2024
Viewed by 513
Abstract
Hot water bottles are widely utilised for their therapeutic advantages, such as relieving muscle tension and imparting warmth. However, the increasing frequency and potential risks associated with bursting or failure necessitate a detailed examination of the contributing factors as their failure is not [...] Read more.
Hot water bottles are widely utilised for their therapeutic advantages, such as relieving muscle tension and imparting warmth. However, the increasing frequency and potential risks associated with bursting or failure necessitate a detailed examination of the contributing factors as their failure is not fully understood in a scientific manner. With the apparent lack of analysis of hot water bottles in the literature, this study employs, for the first time, a dual methodology involving finite-element (FE) analysis conducted in ABAQUS and experimental validation to systematically investigate the underlying mechanisms leading to failure incidents. Through FE modelling and analysis, the stress and strain distribution within typical hot water bottles is modelled under compression loading conditions, facilitating the identification of vulnerable areas prone to failure. Experimental validation encompasses uniaxial loading compression tests on distinct specimens, generating load–displacement curves that elucidate material responses to compressive forces and highlight variations in load-bearing capacities. The study explores diverse failure modes, attributing them to stress concentration at geometric transitions and contact regions. Stress–strain curves contribute valuable insights into material characteristics, with ultimate stress values as crucial indicators of resistance to deformation and rupture. The FE analysis simulation results visualise deformation patterns and stress concentration zones. The findings illustrate that the highest stress concentration areas exist in the internal boundary of hot water bottles near the neck and cap region. This is experimentally confirmed through the bursting failures of four samples, with three failures occurring in this specific region. The findings support the guidance that users should avoid sleeping with a hot water bottle as it may fail under compression if they lay on top of it. Meanwhile, this result guides manufacturers to strengthen the weak areas of hot water bottles around the nicks and edges. This study significantly enhances our understanding of hot water bottle mechanics, thereby guiding design practice to improve overall performance and user safety. In summary, hot water bottles are commonly used but have not been investigated scientifically regarding external loading conditions and their related failure, as the current study has achieved. Identifying the weak points through experiment and simulation directs manufacturers towards required improvements in particular regions, such as the bottleneck and edge reinforcement during the design and manufacturing phases. Full article
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