Hariharasakthisudhan Ponnarengan^1,*, Sivakumar Rajendran², Vikas Khalkar³, Gunapriya Devarajan⁴, Logesh Kamaraj⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 1-48, 2025, DOI:10.32604/cmes.2024.056605 - 17 December 2024

Abstract The purpose of this review is to explore the intersection of computational engineering and biomedical science, highlighting the transformative potential this convergence holds for innovation in healthcare and medical research. The review covers key topics such as computational modelling, bioinformatics, machine learning in medical diagnostics, and the integration of wearable technology for real-time health monitoring. Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems, while machine learning algorithms have improved the accuracy of disease prediction and diagnosis. The synergy between bioinformatics and computational techniques has led to breakthroughs in More >

Muhammad Faiz Aizamddin^1,2,*, Nazreen Che Roslan², Ayu Natasha Ayub², Awis Sukarni Mohmad Sabere³, Zarif Mohamed Sofian⁴, Yee Hui Robin Chang⁵, Mohd Ifwat Mohd Ghazali^6,7, Kishor Kumar Sadasivuni⁸, Mohamad Arif Kasri⁹, Muhamad Saipul Fakir¹⁰, Mohd Muzamir Mahat^2,*

Journal of Polymer Materials, Vol.41, No.4, pp. 239-261, 2024, DOI:10.32604/jpm.2024.057420 - 16 December 2024

Abstract Poly-3,4-ethylenedioxythiophene: polystyrene sulfonate (PEDOT/PSS) has revolutionized the field of smart textiles as an advanced conductive polymer, offering an unprecedented combination of high electrical conductivity, solution processability, and mechanical conformability. Despite extensive research in PEDOT/PSS-coated fabrics over the past decade, a critical challenge remains in finding the delicate balance between enhanced conductivity and washing durability required for real-world wearable applications. Hence, this study investigates the electrical conductivity and durability properties of PEDOT/PSS-based conductive fabrics for wearable electronics. By carefully controlling the doping concentration of dimethyl sulfoxide (DMSO), an optimal conductivity of 8.44 ± 0.21 × 10⁻³ S… More >

Meixin Zhou¹, Jia Shin Lee², Kun Zhou^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011064

Abstract 3D printing of metamaterials has garnered significant attention in recent years, as metamaterials, especially the triple periodic minimal surface (TPMS) structures, are engineered to exhibit extraordinary properties. However, challenges such as limited structural designs and lack of real-world applications have restrained the development of 3D printed metamaterials. Herein, a series of TPMS structures were designed and printed via selective laser sintering, and their mechanical energy absorption capabilities under the quasi-static compression condition were compared. Novel TPMS structures were then designed by blending the investigated TPMS structures, and their compressive properties and deformation mechanism were explored. More >

Manh-Tuyen Vi¹, Duc-Nghia Tran², Vu Thi Thuong^3,4, Nguyen Ngoc Linh^5,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1729-1756, 2024, DOI:10.32604/cmc.2024.055511 - 15 October 2024

Abstract Analyzing physical activities through wearable devices is a promising research area for improving health assessment. This research focuses on the development of an affordable and real-time Human Activity Recognition (HAR) system designed to operate on low-performance microcontrollers. The system utilizes data from a body-worn accelerometer to recognize and classify human activities, providing a cost-effective, easy-to-use, and highly accurate solution. A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment. The system employs a Random Forest (RF) classifier, which outperforms Gradient Boosting Decision Trees (GBDT), Support Vector Machines… More >

Hamed Taherdoost^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 79-104, 2024, DOI:10.32604/cmc.2024.054378 - 15 October 2024

Abstract Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care, which is essential for independent living, especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common. Recent advances in the Internet of Things (IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition, gaining significant attention in personalized healthcare. This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring. Relevant papers were extracted and analyzed using a systematic numerical review method, covering various More >

GyuHyun Jeon¹, Hojun Jin¹, Ju Hyeon Lee¹, Seungho Jeon², Jung Taek Seo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1575-1622, 2024, DOI:10.32604/cmes.2024.053465 - 27 September 2024

Abstract The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have… More >

Licheng Sun¹, Heping Li^2,3, Liang Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024

Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >

Zerui Xiao, Wei Liu, Zhiwei Ye^*, Jiatang Yuan, Shishi Liu

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475 - 17 July 2024

Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

Peiheng He, Jiye Zhang^*, Lan Zhang, Jiaqi Wang, Yuzhe Ma

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 937-953, 2024, DOI:10.32604/cmes.2023.043700 - 30 December 2023

Abstract The running stability of high-speed train is largely constrained by the wheel-rail coupling relationship, and the continuous wear between the wheel and rail surfaces will profoundly affect the dynamic performance of the train. In recent years, under the background of increasing train speed, some scientific researchers have proposed a new idea of using the lift force generated by the aerodynamic wings (aero-wing) installed on the roof to reduce the sprung load of the carriage in order to alleviate the wear and tear of the wheel and rail. Based on the bidirectional running characteristics of high-speed… More >

Qixin Lan¹, Binqiang Chen^1,*, Bin Yao¹, Wangpeng He²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378 - 15 December 2023

Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when… More >