Search Results (595)

This study investigates the risk of developing musculoskeletal disorders (MSDs) in individuals performing standing tasks, with a focus on real-time posture assessment using motion capture technology. Improper body posture and repetitive movements during daily work activities can impose strain on the musculoskeletal system, increasing the likelihood of discomfort and long-term injury. Data were collected from five male and female participants using the Perception Neuron motion capture system, with body-mounted sensors tracking posture and movement. Joint angles were calculated to distinguish between correct and incorrect postures based on ISO 11226:2000 ergonomic guidelines. Key physical risk factors identified included prolonged forward trunk inclination, elevated arm positions, and repetitive actions. The analysis revealed that participants frequently adopted moderate- to high-risk postures, especially when working at non-ergonomic desk heights, suggesting a heightened risk of MSDs such as back and upper limb pain. These findings underscore the importance of real-time ergonomic monitoring and adaptive workstation design to reduce musculoskeletal risks in standing work environments. Full article

Cascading link failures continue to imperil power grids, transport networks, and cyber-physical systems, yet the relationship between a network’s robustness at the moment of attack and its subsequent resiliency remains poorly understood. We introduce a dynamic framework in which connectivity-based cascades and distributed self-healing act concurrently within each time-step. Failure is triggered when a node’s active-neighbor ratio falls below a threshold $\phi$; healing activates once the global fraction of inactive nodes exceeds trigger $T$ and is limited by budget $B$. Two real data sets—a 332-node U.S. airport graph and a 1133-node university e-mail graph—serve as testbeds. For each graph we sweep the parameter quartet ($\phi ,\hspace{0.17em}B,\hspace{0.17em}T,attackmode$) and record (i) immediate robustness $R$, (ii) 90% recovery time T₉₀, and (iii) cumulative average damage. Results show that targeted hub removal is up to three times more damaging than random failure, but that prompt healing with $B\ge 0.12$ can halve T₉₀. Scatter-plot analysis reveals a non-monotonic correlation: high-$R$ states recover quickly only when $B$ and $T$ are favorable, whereas low-$R$ states can rebound rapidly under ample budgets. A multiplicative fit $T_{90}\propto B^{-\beta }g\left(T\right)h\left(R\right)$ (with $\beta \approx 1$) captures these interactions. The findings demonstrate that structural hardening alone cannot guarantee fast recovery; resource-aware, early-triggered self-healing is the decisive factor. The proposed model and data-driven insights provide a quantitative basis for designing infrastructure that is both robust to failure and resilient in restoration. Full article

Indoor sports facilities face distinctive indoor air quality (IAQ) challenges due to high occupant density, elevated metabolic emissions, and diverse pollutant sources associated with physical activity. This review presents a narrative synthesis of multidisciplinary evidence concerning IAQ in sports environments. It explores major pollutant categories, including carbon dioxide (CO₂), particulate matter (PM), volatile organic compounds (VOCs), and airborne microbial agents, highlighting their sources, behavior during exercise, and associated health risks. Research shows that physical activity can increase PM concentrations by up to 300%, and CO₂ levels frequently exceed 1000 ppm in inadequately ventilated spaces. The presence of semi-volatile organics and bioaerosols further complicates pollutant dynamics, especially in humid and densely occupied areas. Measurement technologies such as optical sensors, chromatographic methods, and molecular techniques are reviewed and compared for their applicability to dynamic indoor settings. Existing IAQ standards across China, the USA, the EU, the UK, and WHO are examined, revealing a lack of activity-specific thresholds and insufficient responsiveness to real-time conditions. Mitigation strategies (e.g., including demand-controlled ventilation, use of low-emission materials, liquid chalk substitutes, and integrated HEPA-UVGI purification systems) are evaluated, many demonstrating pollutant removal efficiencies over 80%. The integration of intelligent building management systems is emphasized for enabling real-time monitoring and adaptive control. This review concludes by identifying research priorities, including the development of activity-sensitive IAQ control frameworks and long-term health impact assessments for athletes and vulnerable users. Full article

Electric vehicle (EV) charging has become a key factor in grid integration, impact analysis, and the development of intelligent charging strategies. However, the rapid rise in EV adoption poses challenges for charging infrastructure and grid stability due to the inherently variable and uncertain charging behavior. Limited access to high-resolution, location-specific data further hinders accurate modeling, emphasizing the need for reliable, privacy-preserving tools to forecast EV-related grid impacts. This study introduces a comprehensive methodology to emulate real-world EV charging behavior using a real-time simulation environment. A physics-based EV charger model was developed on the Typhoon HIL platform, incorporating detailed electrical dynamics and control logic representative of commercial chargers. Simulation outputs, including active power consumption and state-of-charge evolution, were validated against field data captured via phasor measurement units, showing strong alignment across all charging phases, including SOC-dependent current transitions. Quantitative validation yielded an MAE of 0.14 and an RMSE of 0.36, confirming the model’s high accuracy. The study also reflects practical BMS strategies, such as early charging termination near 97% SOC to preserve battery health. Overall, the proposed real-time framework provides a high-fidelity platform for analyzing grid-integrated EV behavior, testing smart charging controls, and enabling digital twin development for next-generation electric mobility. Full article

Virtual reality (VR) is widely used across various areas of human life. One field where its application is rapidly growing is sport and physical activity (PA). Training applications are being developed that support various sports disciplines, motor skill acquisition, and the development of motor abilities. Immersive technologies are increasingly being used to assess motor and cognitive capabilities. As such, validation studies of these diagnostic tools are essential. The aim of this study was to estimate the validity and reliability of reaction speed (RS) measurements using the Rezzil Player application (“Reaction” module) in immersive VR compared to results obtained with the SMARTFit device in a real environment (RE). The study involved 43 university students (17 women and 26 men). Both tests required participants to strike light targets on a panel with their hands. Two indicators of response were analyzed in both tests: the number of hits on illuminated targets within a specified time frame and the average RS in response to visual stimuli. Statistically significant and relatively strong correlations were observed between the two measurement methods: number of hits (r_S = 0.610; p < 0.001) and average RS (r_S = 0.535; p < 0.001). High intraclass correlation coefficients (ICCs) were also found for both test environments: number of hits in VR (ICC = 0.851), average RS in VR (0.844), number of hits in RE (ICC = 0.881), and average RS in RE (0.878). The findings indicate that the Rezzil Player application can be considered a valid and reliable tool for measuring reaction speed in VR. The correlation with conventional methods and the high ICC values attest to the psychometric quality of the tool. Full article

Enzyme-based biosensors have emerged as a transformative technology, leveraging the specificity and catalytic efficiency of enzymes across various domains, including medical diagnostics, environmental monitoring, food safety, and industrial processes. These biosensors integrate biological recognition elements with advanced transduction mechanisms to provide highly sensitive, selective, and portable solutions for real-time analysis. This review explores the key components, detection mechanisms, applications, and future trends in enzyme-based biosensors. Artificial enzymes, such as nanozymes, play a crucial role in enhancing enzyme-based biosensors by mimicking natural enzyme activity while offering improved stability, cost-effectiveness, and scalability. Their integration can significantly boost sensor performance by increasing the catalytic efficiency and durability. Additionally, lab-on-a-chip and microfluidic devices enable the miniaturization of biosensors, allowing for the development of compact, portable devices that require minimal sample volumes for complex diagnostic tests. The functionality of enzyme-based biosensors is built on three essential components: enzymes as biocatalysts, transducers, and immobilization techniques. Enzymes serve as the biological recognition elements, catalyzing specific reactions with target molecules to produce detectable signals. Transducers, including electrochemical, optical, thermal, and mass-sensitive types, convert these biochemical reactions into measurable outputs. Effective immobilization strategies, such as physical adsorption, covalent bonding, and entrapment, enhance the enzyme stability and reusability, enabling consistent performance. In medical diagnostics, they are widely used for glucose monitoring, cholesterol detection, and biomarker identification. Environmental monitoring benefits from these biosensors by detecting pollutants like pesticides, heavy metals, and nerve agents. The food industry employs them for quality control and contamination monitoring. Their advantages include high sensitivity, rapid response times, cost-effectiveness, and adaptability to field applications. Enzyme-based biosensors face challenges such as enzyme instability, interference from biological matrices, and limited operational lifespans. Addressing these issues involves innovations like the use of synthetic enzymes, advanced immobilization techniques, and the integration of nanomaterials, such as graphene and carbon nanotubes. These advancements enhance the enzyme stability, improve sensitivity, and reduce detection limits, making the technology more robust and scalable. Full article

The behavior of gas emissions at the heading face of the coal mine is a key indicator of potentially harmful gas disaster risk, necessitating in-depth study via analytical and statistical methods. However, conventional prediction and evaluation methods depend on long-interval statistical data, which are too coarse for and lack the immediacy required for real-time applications. Based on the physical laws of gas storage and flow, a refined computational model has been developed to compute dynamic gas emission rates that vary with geology and excavating process. Furthermore, by comparing the computed outputs with actual monitoring data, it becomes possible to assess whether abnormal gas emissions are occurring. Methodologically, this model first applies the finite difference method to compute the dynamic gas flux and the dynamic residual gas content. It then determines the exposure duration of each segment of the roadway wall at any given moment, as well as the mass of newly dislodged coal. The total gas emission rate at a specific sensor location is obtained by aggregating the contributions from all of the exposed wall and the freshly dislodged coal. Owing to some simplifications, the model’s applicability is currently restricted to single, medium-thick coal seams. The model was preliminarily implemented in Python (3.13.2) and validated against a case study of an active heading face. The results demonstrate a strong concordance between model predictions and field measurements. The model notably captures the significant variance in emission rates resulting from different mining activities, the characteristic emission surges from dislodged coal and newly exposed coal walls, and the influence of sensor placement on monitoring outcomes. Full article

The assessment of physical quantity values, especially in case of sports-related activities, is critical to evaluate the performance and fitness level of athletes. In real-world applications, motion analysis tools are often employed to assess motor performance in subjects. In case the methods used to calculate a specific quantity of interest differ from each other, different values may be provided as output. Therefore, there is the need to get a coherent final measurement, giving the possibility to compare results homogeneously, combining the different methodologies used by the instruments. These tools vary in measurement capabilities and the physical principles underlying the measurement procedures. Emerging differences in results could lead to non-uniform evaluation metrics, thus making a fair comparison unpracticable. A possible solution to this problem is provided in this paper by implementing an iterative approach, working on two measurement time series acquired by two different instruments, specifically focused on jump height estimation. In the analyzed case study, two instruments estimate the jump height exploiting two different technologies: the inertial and the vision-based ones. In the first case, the measurement value depends on the movement of the center of gravity during jump activity, while, in the second case, the jump height is derived by estimating the maximum distance ground–foot during the jump action. These approaches clearly could lead to different values, also considering the same jump test, due to their observation point. The developed methodology can provide three different ways out: (i) mapping the inertial values towards the vision-based reference system; (ii) mapping the vision-based values towards the inertial reference system; (iii) determining a comprehensive measurement, incorporating both contributions, thus making measurements comparable in time (performance progression) and space (comparison among subjects), eventually adopting only one of the analyzed instruments and applying the transformation algorithm to get the final measurement value. Full article

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools. Full article

Online laboratories have emerged as a viable alternative for providing hands-on experience to engineering students, especially in fields related to computer, software, and electrical engineering. In particular, remote laboratories enable users to interact in real time with physical hardware via the internet. However, current remote laboratory systems often restrict access to a single user per session, limiting broader participation. Embedded systems laboratory activities have traditionally relied on in-person instruction and direct interaction with hardware, requiring significant time for code development, compilation, and hardware testing. Students typically spend an important portion of each session coding and compiling programs, with the remaining time dedicated to hardware implementation, data collection, and report preparation. This paper proposes a remote laboratory implementation that optimizes remote laboratory stations’ availability, allowing users to lock the system only during the project debugging and testing phases while freeing the remote laboratory station for other users during the code development phase. The implementation presented here was developed for a microprocessor laboratory course. It enables users to code the solution in their preferred local or remote environments, then upload the resulting source code to the remote laboratory hardware for cross-compiling, execution, and testing. This approach enhances usability, scalability, and accessibility while preserving the core benefits of hands-on experimentation and collaboration in online embedded systems education. Full article

Erythrina edulis, commonly known as pajuro, is a large leguminous plant native to the Amazon region of Peru. Its seeds are valued for their high protein content and their potential to enhance food security in rural communities. However, the current methods of harvesting and sorting are entirely manual, making the process labor-intensive, time-consuming, and subject to high variability, particularly in industrial contexts. A custom lightweight convolutional neural network (CNN) was developed from scratch and optimized specifically for real-time execution on embedded hardware. The model employs ReLU activation, Adam optimization, and a SoftMax output layer to enable efficient and accurate classification. The system employs a fixed-region segmentation strategy to prevent overcounting and utilizes GPIO-based control on a Raspberry Pi 5 to synchronize seed classification with physical sorting in real time. Seeds identified as defective are automatically removed via a servo-controlled ejection mechanism. The integrated system combines object detection, image processing, and real-time actuation, achieving a classification accuracy exceeding 99.6% and an average processing time of 12.4 milliseconds per seed. The proposed solution contributes to the industrial automation of pajuro sorting and provides a scalable framework for color-based grain classification applicable to a wide range of agricultural products. Full article

Hydrogels are water-rich polymeric networks mimicking the body’s extracellular matrix, making them highly biocompatible and ideal for precision medicine. Their “tunable” and “smart” properties enable the precise adjustment of mechanical, chemical, and physical characteristics, allowing responses to specific stimuli such as pH or temperature. These versatile materials offer significant advantages over traditional drug delivery by facilitating targeted, localized, and on-demand therapies. Applications range from diagnostics and wound healing to tissue engineering and, notably, cancer therapy, where they deliver anti-cancer agents directly to tumors, minimizing systemic toxicity. Hydrogels’ design involves careful material selection and crosslinking techniques, which dictate properties like swelling, degradation, and porosity—all crucial for their effectiveness. The development of self-healing, tough, and bio-functional hydrogels represents a significant step forward, promising advanced biomaterials that can actively sense, react to, and engage in complex biological processes for a tailored therapeutic approach. Beyond their mechanical resilience and adaptability, these hydrogels open avenues for next-generation therapies, such as dynamic wound dressings that adapt to healing stages, injectable scaffolds that remodel with growing tissue, or smart drug delivery systems that respond to real-time biochemical cues. Full article

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps. Full article

The advancement of laser-induced graphene (LIG) has significantly enhanced the development of wearable and flexible electronic devices. Due to its exceptional physical, chemical, and electronic properties, LIG has emerged as a highly effective active material for wearable sensors. However, despite the wide range of materials suitable as precursors for LIG, the scarcity of stretchable and biocompatible polymers amenable to laser graphenization has remained a persistent challenge. In this study, laser-induced graphene (LIG) was fabricated directly on biocompatible and flexible cross-linked PDMS/PEG (with M_n (PEG) = 400 g/mol) composites for the first time, enabling their application in wearable sensors. The addition of PEG compensates for the low carbon content in PDMS, enabling efficient laser graphenization. Laser parameters were systematically optimized to achieve high-quality graphene, and a comprehensive characterization with varying PEG content (10–40 wt.%) was conducted using multiple analytical techniques. Tensile tests revealed that incorporating PEG significantly enhanced elongation at break, reaching 237% for PDMS/40 wt.% PEG while reducing Young’s modulus to 0.25 MPa, highlighting the excellent flexibility of the substrate material. Surface analysis using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Raman spectroscopy demonstrated the formation of high-quality few-layer graphene with the fewest defects in PDMS/40 wt.% PEG composites. Nevertheless, the adhesion of electrical contacts to LIG that was directly induced on PDMS/PEG proved to be challenging. To overcome this challenge, we produced devices by means of laser induction on polyimide and transfer to PDMS/PEG. We demonstrate the practical utility of such devices by applying them to monitor limb motion in real time. The sensor showed a stable and repeatable piezoresistive response under multiple bending cycles. These results provide valuable insights into the fabrication of biocompatible LIG-based flexible sensors, paving the way for their broader implementation in medical and sports technologies. Full article

Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data. Full article