Applied System on Biomedical Engineering, Healthcare and Sustainability 2024

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

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 (11 papers)

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All Applied Sciences ASI Bioengineering Electronics Healthcare

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10 pages, 2470 KiB  

Open AccessArticle

Improving Workplace Safety and Health Through a Rapid Ergonomic Risk Assessment Methodology Enhanced by an Artificial Intelligence System

by Adrian Ispășoiu, Ioan Milosan and Camelia Gabor

Appl. Syst. Innov. 2024, 7(6), 103; https://doi.org/10.3390/asi7060103 - 28 Oct 2024

Viewed by 517

Abstract

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these [...] Read more.

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these conditions can become chronic diseases and can even be the cause of workplace accidents. In this research, a methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace. This methodology uses artificial intelligence through a specific algorithm and takes into account a number of factors that, when combined, can have a significant impact on workers. To achieve a more accurate simulation of a work situation or to evaluate an ongoing work situation, and to significantly correlate these parameters, we used logarithmic calculation formulas. To streamline the process, we developed software that performs these calculations, conducts a rapid assessment of ergonomic risks, estimates a comfort level, and proposes possible measures to mitigate the risks and effects on workers. To assist in diagnosing the work situation, we used a neural network with five neurons in the input layer, one hidden layer, and two neurons in the output layer. As a result, most work situations, in any industrial field, can be quickly analyzed and evaluated using this methodology. The use of this new analysis and diagnosis tool, implemented through this new research technology, is beneficial for employers and workers. Moreover, through further developments of this methodology, achieved by increasing the number of relevant input parameters for ergonomics and integrating advanced artificial intelligence systems, we aim to provide high precision in assessing ergonomic risk and calculating the level of ergonomic comfort. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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13 pages, 1315 KiB  

Open AccessArticle

An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis

by Cing-Han Yang, Jhen-Li Huang, Li-Kai Tsai, David Taniar and Tun-Wen Pai

Bioengineering 2024, 11(10), 1020; https://doi.org/10.3390/bioengineering11101020 - 12 Oct 2024

Viewed by 814

Abstract

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. [...] Read more.

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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18 pages, 3454 KiB  

Open AccessArticle

“BrainHeart”: Pilot Study on a Novel Application for Elderly Well-Being Based on Mindfulness Acceptance and Commitment Therapy

by Roberta Bruschetta, Desiree Latella, Caterina Formica, Simona Campisi, Chiara Failla, Flavia Marino, Serena Iacono Isidoro, Fabio Mauro Giambò, Lilla Bonanno, Antonio Cerasa, Angelo Quartarone, Silvia Marino, Giovanni Pioggia, Rocco Salvatore Calabrò and Gennaro Tartarisco

Bioengineering 2024, 11(8), 787; https://doi.org/10.3390/bioengineering11080787 - 3 Aug 2024

Viewed by 1278

Abstract

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and [...] Read more.

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = −0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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12 pages, 1266 KiB  

Open AccessArticle

Stratification of Older Adults According to Frailty Status and Falls Using Gait Parameters Explored Using an Inertial System

by Marta Neira Álvarez, Elisabet Huertas-Hoyas, Robert Novak, Ana Elizabeth Sipols, Guillermo García-Villamil-Neira, M. Cristina Rodríguez-Sánchez, Antonio J. Del-Ama, Luisa Ruiz-Ruiz, Sara García De Villa and Antonio R. Jiménez-Ruiz

Appl. Sci. 2024, 14(15), 6704; https://doi.org/10.3390/app14156704 - 1 Aug 2024

Viewed by 763

Abstract

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without [...] Read more.

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2—To identify variables that stratify participants according to frailty status and falls. 3—To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls. Methods: Observational, multicenter case-control study. Participants, adults over 70 years with and without falls were recruited from two outpatient clinics and three nursing homes from September 2021 to March 2022. Clinical variables and gait parameters were gathered using the G-STRIDE inertial sensor. Random Forest regression was applied to stratify participants. Results: 163 participants with a mean age of 82.6 ± 6.2 years, of which 118 (72%) were women, were included. Significant differences were found in all gait parameters (both conventional assessment and G-STRIDE evaluation). A hierarchy of factors contributed to the risk of frailty and falls. The confusion matrix and the performance metrics demonstrated high accuracy in classifying participants. Conclusions: Gait parameters, particularly those assessed by G-STRIDE, are effective in stratifying individuals by frailty status and falls. These findings underscore the importance of gait analysis in early intervention strategies. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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14 pages, 7338 KiB  

Open AccessArticle

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

Cited by 1 | Viewed by 1064

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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12 pages, 1503 KiB  

Open AccessArticle

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 569

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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16 pages, 4054 KiB  

Open AccessArticle

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 906

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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18 pages, 3164 KiB  

Open AccessArticle

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 1139

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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46 pages, 4394 KiB  

Open AccessArticle

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 5058

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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17 pages, 7019 KiB  

Open AccessArticle

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

Cited by 2 | Viewed by 1146

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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22 pages, 18402 KiB  

Open AccessArticle

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 964

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

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

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