Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare....
Autonomous driving technology is advancing rapidly, particularly in vision-based approaches that use cameras to perceive the driving environment, which is the most humanlike perception method. However, one of the key challenges that smart vehicles face is adapting to various weather conditions, which can significantly impact visual perception and vehicular control strategies. The ideal design for the latter is to dynamically adjust in real time to ensure safe and efficient driving, taking into account the prevailing weather conditions. In this study, we propose a lightweight weather perception model that incorporates multi-scale feature learning, channel attention mechanisms, and a soft voting ensemble strategy. This enables the model to capture various visual patterns, emphasize critical information, and integrate predictions across multiple modules for improved robustness. Benchmark comparisons are conducted using several well-known deep learning networks, including EfficientNet-B0, ResNet50, SqueezeNet, MobileNetV3-Large, MobileNetV3-Small, and LSKNet. Finally, using both public datasets and real-world video recordings from roads in Taiwan, our model demonstrates superior computational efficiency while maintaining high predictive accuracy. For example, our model achieves 98.07% classification accuracy with only 0.4 million parameters and 0.19 GFLOPs, surpassing several well-known CNNs in computational efficiency. Compared with EfficientNet-B0, which has a similar accuracy (98.37%) but requires over ten times more parameters and four times more FLOPs, our model offers a much lighter and faster alternative....
Solar energy solutions have become increasingly popular worldwide due to the growing need for renewable energy. This article presents a photovoltaic (PV) system connected to a three-phase power grid, modeled under varying climatic conditions. It consists of two conversion stages, a DC-DC Boost converter and a DC-AC inverter. The former uses a variable-step P&O based on fuzzy logic control to maximize the power of the photovoltaic panels, allowing for greater tracking accuracy than traditional P&O techniques. Inverters with phase-locked loop technology improve the performance of grid-connected PV systems by using a conventional PI controller that has a faster response. Using Matlab/Simulink environments, the entire system and control techniques are evaluated and verified. The simulation results confirm the effectiveness and robustness of the proposed system....
Software development has been revolutionized by low-code and no-code platforms, which make it possible for even non-programmers to create and launch apps rapidly. In contrast to traditional coding, they speed up development with drag-and-drop components, pre-built templates, and ready-to-use plugins. Their effects on accelerating innovation, reducing expenses, and facilitating digital transformation are examined in this article, together with issues including scalability, security, and restricted customization. It also investigates how similar platforms might be used in the future to promote cooperation between technical and non-technical teams. While LCNC platforms help close the gap between IT solutions and business needs, thorough integration is necessary for long-term success....
Advances in robotic technology for hand rehabilitation, particularly soft robotic gloves, have significant potential to improve patient outcomes. While vision-based algorithms pave the way for fast and convenient hand pose estimation, most current models struggle to accurately track hand movements when soft robotic gloves are used, primarily due to severe occlusion. This limitation reduces the applicability of soft robotic gloves in digital and remote rehabilitation assessment. Furthermore, traditional clinical assessments like the Fugl-Meyer Assessment (FMA) rely on manual measurements and subjective scoring scales, lacking the efficiency and quantitative accuracy needed to monitor hand function recovery in data-driven personalised rehabilitation. Consequently, few integrated evaluation systems provide reliable quantitative assessments. In this work, we propose an RGB-based evaluation system for soft robotic glove applications, which is aimed at bridging these gaps in assessing hand function. By incorporating the Hand Mesh Reconstruction (HaMeR) model fine-tuned with motion capture data, our hand estimation framework overcomes occlusion and enables accurate continuous tracking of hand movements with reduced errors. The resulting functional metrics include conventional clinical benchmarks such as the mean per joint angle error (MPJAE) and range of motion (ROM), providing quantitative, consistent measures of rehabilitation progress and achieving tracking errors lower than 10◦. In addition, we introduce adapted benchmarks such as the angle percentage of correct keypoints (APCK), mean per joint angular velocity error (MPJAVE) and angular spectral arc length (SPARC) error to characterise movement stability and smoothness. This extensible and adaptable solution demonstrates the potential of vision-based systems for future clinical and home-based rehabilitation assessment....
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