Search Results (1,035)

Wireless Body Area Networks (WBANs) enable real-time data collection for medical monitoring, sports tracking, and environmental sensing, driven by Internet of Things advancements. Their layered architecture supports efficient sensing, aggregation, and analysis, but energy constraints from transmission (over 60% of consumption), idle listening, and dynamic conditions like body motion hinder adoption. Challenges include minimizing energy waste while ensuring data reliability, Quality of Service (QoS), and adaptation to channel variations, alongside algorithm complexity and privacy concerns. This paper reviews recent power control mechanisms in WBANs, encompassing feedback control, dynamic and convex optimization, graph theory-based path optimization, game theory, reinforcement learning, deep reinforcement learning, hybrid frameworks, and emerging architectures such as federated learning and cell-free massive MIMO, adopting a systematic review approach with a focus on healthcare and IoT application scenarios. Achieving energy savings ranging from 6% (simple feedback control) to 50% (hybrid frameworks with emerging architectures), depending on method complexity and application scenario, with prolonged network lifetime and improved reliability while preserving QoS requirements in healthcare and IoT applications. Full article

Battery energy storage systems (BESSs) are increasingly required to provide grid-support services under weak-grid conditions, where the stability of virtual synchronous generator (VSG) control largely depends on the health status and dynamic characteristics of the battery unit. However, existing VSG strategies typically assume fixed parameters and neglect the intrinsic coupling between battery aging, DC-link energy variations, and converter dynamic performance, resulting in reduced damping, degraded transient regulation, and accelerated lifetime degradation. This paper proposes a health-responsive VSG control strategy enabled by real-time energy-dynamics sensing. By reconstructing the DC-link energy state from voltage and current measurements, an intrinsic indicator of battery health and instantaneous power capability is established. This energy-dynamics indicator is then embedded into the VSG inertia and damping loops, allowing the control parameters to adapt to battery health evolution and operating conditions. The proposed method achieves coordinated enhancement of transient stability, weak-grid robustness, and lifetime management. Simulation studies on a multi-unit BESS demonstrate that the proposed strategy effectively suppresses low-frequency oscillations, accelerates transient convergence, and maintains stability across different aging stages. Full article

Efficient clustering and cluster head (CH) selection are the critical parameters of wireless sensor networks (WSNs) for their prolonged network lifetime. However, the performances of the traditional clustering algorithms like LEACH and HEED are not satisfactory when they are implemented on a dense WSN due to their unbalanced load distribution and high contention nature. In the traditional methods, the cluster heads are selected with respect to the residual energy criteria, and often create a circular cluster shape boundary with a uniform node distribution. This causes the cluster heads to become overloaded in the high-density regions and the unutilized cluster heads gather in the sparse regions. Therefore, frequent cluster head changes occur, which is not suitable for a real-time dynamic environment. In order to avoid these issues, this proposed work develops a density-aware adaptive clustering (DAAC) protocol for optimizing the CH selection and cluster formation in a dense wireless sensor network. The residual energy information, together with the local node density and link quality, is utilized as a single cluster head detection metric in this work. The local node density information assists the proposed work to estimate the sparse and dense area in the network that results in frequent cluster head congestion. DAAC is also included with a minimum inter-CH distance constraint for CH crowding, and a multi-factor cost function is used for making the clusters by inviting the nodes by their distance and an expected transmission energy. DAAC triggers re-clustering in a dynamic manner when it finds a response in the CH energy depletion or a significant change in the load density. Unlike the traditional circular cluster boundaries, DAAC utilizes dynamic Voronoi cells (VCs) for making an interference-aware coverage in the network. This makes dense WSNs operate efficiently, by providing a hierarchical extension, on making secondary CHs in an extremely dense scenario. The proposed model is implemented in MATLAB simulation, to determine and compare its efficiency over the traditional algorithms such as LEACH and HEED, which shows a satisfactory network lifetime improvement of 20.53% and 32.51%, an average increase in packet delivery ratio by 8.14% and 25.68%, and an enhancement in total throughput packet by 140.15% and 883.51%, respectively. Full article

Carpal tunnel syndrome (CTS) is the most common peripheral nerve entrapment disorder, with a lifetime prevalence estimated at approximately 10%. This narrative review explores the historical evolution, current management strategies, and emerging trends in CTS diagnosis and treatment. Early recognition of CTS led to the development of conservative interventions, including splinting, corticosteroid injections, and physical therapy, aimed at alleviating median nerve compression and associated symptoms. The advent of open carpal tunnel release established surgery as the definitive treatment for moderate-to-severe CTS, with subsequent refinements—such as mini-open and endoscopic techniques—focused on minimizing tissue trauma and expediting recovery. Comparative studies demonstrate similar long-term efficacy between surgical modalities, though endoscopic approaches often provide faster short-term recovery. Advances in diagnostic imaging, including high-resolution ultrasound, have improved early detection and dynamic assessment of median nerve compression. Emerging therapies, such as regenerative biologics, neuromobilization, and minimally invasive surgical innovations, offer promising adjuncts to current care. Despite substantial progress, further research is needed to clarify optimal patient selection, refine minimally invasive techniques, and explore regenerative interventions. This review underscores the importance of individualized, evidence-based, and patient-centered approaches to CTS management, integrating both established and emerging strategies to optimize functional outcomes and quality of life. Full article

This study presents an experimental 3E (energy–exergy–environmental) assessment of a PLC-controlled solar air heater (SAH) equipped with adjustable internal baffles. Unlike conventional passive systems, the proposed design enables active airflow regulation to maintain stable outlet temperatures of 54 °C and 60 °C, achieving rapid stabilization within 3–10 s under outdoor conditions. Experimental results show that increasing the baffle inclination significantly enhances convective heat transfer and thermal efficiency, while the friction factor remains primarily governed by the Reynolds number and exhibits minimal sensitivity to baffle angle. Exergy efficiency values remain relatively low (1.24–2.69%), and the sustainability index stays close to unity, reflecting the inherent thermodynamic limitations of low-temperature solar air heaters rather than deficiencies in system design. A regression-based airflow velocity model is developed to support fan-speed optimization and to clarify the trade-off between thermal enhancement and auxiliary power demand. Long-term projections based on regional solar data indicate that the proposed SAH can deliver approximately 20–22 MWh of useful heat and mitigate nearly 9 tons of CO₂ emissions over a 20-year operational lifetime. Overall, the results demonstrate that PLC-assisted dynamic baffle control provides a flexible and effective approach for improving the performance and operational stability of solar air heaters for low-temperature drying applications. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

Currently, wireless sensor networks (WSNs) have been mutually applied to environmental monitoring and industrial control due to their low-cost and low-energy sensor nodes. However, WSNs are composed of a large number of energy-limited sensor nodes, which requires balancing the relationship among energy consumption, transmission delay, and network lifetime simultaneously to avoid the formation of energy holes. In nature, gregarious herbivores, such as the white-bearded wildebeest on the African savanna, employ a “fast-transit and selective-dwell” strategy when searching for water; they cross low-value regions quickly and prolong their stay in nutrient-rich pastures, thereby minimizing energy cost while maximizing nutrient gain. Ants, meanwhile, dynamically evaluate the “energy-to-reward” ratio of a path through pheromone concentration and its evaporation rate, achieving globally optimal foraging. Inspired by these two complementary biological mechanisms, our study proposes a novel ACO-conceptualized optimization model formulated via mixedinteger linear programming (MILP). By mapping the pheromone intensity and evaporation rate into the MILP energy constraints and cost functions, the model integrates discrete decision-making (path selection) and continuous variables (dwell time) by dynamic path planning and energy optimization of mobile sink, constituting multi-objective optimization. Firstly, we can achieve flexible trade-offs between multiple objectives such as data transmission delay and energy consumption balance through adjustable weight coefficients of the MILP model. Secondly, the method transforms complex path planning and scheduling problems into deterministic optimization models with theoretical global optimality guarantees. Finally, experimental results show that the model can effectively optimize network performance, significantly improve energy efficiency, while ensuring real-time performance and extended network lifetime. Full article

To address the energy limitations, long-term operation demands, and load imbalance in fragment velocity measurement wireless sensor networks, this paper proposes an Energy-Balanced and Stability-Oriented Grey Wolf Optimization (EBSIGWO) algorithm. The algorithm employs a multi-objective fitness function that jointly considers residual energy, intra-cluster load balance, and long-term communication cost, ensuring both energy efficiency and clustering stability. A dynamic elite ratio strategy is further introduced to adaptively balance global exploration and local exploitation, thereby mitigating cluster-head overload and slowing energy depletion. Simulation results show that EBSIGWO significantly extends network lifetime compared with LEACH, HEED, GWO, and FIGWO, improving the half-node-death (HND) round by 518.0%, 200.1%, 111.2%, and 30.5%, respectively. Moreover, EBSIGWO reduces energy variance and slows energy consumption, demonstrating superior energy balance and overall efficiency. These results indicate that EBSIGWO provides an effective solution for reliable fragment velocity measurement applications. Full article

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability. Full article

This paper proposes a unified state-of-health (SoH) predictive energy management and adaptive grid-forming (GFM) control framework for battery energy storage systems, addressing the conflict between battery lifetime degradation and dynamic stability under grid-support operation. A composite degradation model is incorporated into a multi-timescale EMS to anticipate aging trends, while real-time virtual inertia, damping, and impedance are adjusted according to instantaneous SoH. Simulation results demonstrate that, compared with conventional non-SoH-aware control, the proposed method reduces transient overshoot by up to 32%, shortens settling time by 25–40%, and lowers peak battery current stress by 12–23% under aged (60% SoH) conditions. Moreover, the unified framework maintains consistent damping performance across different aging stages, whereas traditional approaches exhibit significant degradation. These quantitative improvements confirm that jointly embedding SoH prediction into both dispatch scheduling and GFM control can effectively enhance transient performance while extending battery lifetime. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

To address the challenges of improving energy balance and extending the operational lifetime of wireless sensor networks for Automated Railway Track Fastener Maintenance Robots (ARTFMR) along railways, this paper proposes an enhanced LEACH protocol incorporating Particle Swarm Optimization (PSO). Initially, network nodes are deployed, and their energy consumption is calculated to formulate a non-uniform deployment model aimed at improving energy balance, followed by network clustering. Subsequently, a routing protocol is designed, where the cluster head election mechanism integrates two critical factors—dynamic residual energy and distance to the base station—to facilitate dynamic and distributed cluster head rotation. During the communication phase, a Time Division Multiple Access (TDMA) scheduling mechanism is employed in conjunction with an inter-cluster multi-hop routing scheme. Additionally, a joint data-volume and energy optimization strategy is implemented to dynamically adjust the transmission data volume based on the residual energy of each node. Finally, simulations were conducted using MATLAB, and the results indicate that the proposed energy-balanced non-uniform deployment optimization strategy improves network energy utilization, effectively extends network lifetime, and exhibits favorable scalability. Full article

This study formulates predictive maintenance of complex technical systems as a constrained multi-layer probabilistic optimization problem that unifies four interdependent analytical paradigms. The mathematical framework integrates: (i) Weibull reliability modelling with parametric lifetime estimation; (ii) Bayesian posterior updating for dynamic adaptation under uncertainty; (iii) nonlinear machine-learning inference for data-driven pattern recognition; and (iv) ontology-based semantic reasoning governed by logical axioms and domain-specific constraints. The four layers are synthesized through a formal orchestration operator, defined as a sequential composition, where each sub-operator is governed by explicit mathematical constraints: Weibull cumulative distribution functions, Bayesian likelihood-posterior relationships, gradient-based loss minimization, and description logic entailment. The system operates within a cognitive digital twin architecture, with orchestration convergence formalized through iterative parameter refinement until consistency between numerical predictions and semantic validation is achieved. The framework is validated through a case study on aircraft wheel-hub crack prediction. The mathematical formulation establishes a rigorous analytical foundation for cognitive predictive maintenance systems applicable to safety-critical technical systems including aerospace, energy infrastructure, transportation networks, and industrial machinery. Full article

Silicon is commonly used in micro/nano-electromechanical system (MEMS/NEMS) devices. Because detailed information about the friction interface in these systems is lacking, the relationship between texture shape and friction remains unclear. In this study, molecular dynamics simulations were performed to investigate the dry-friction tribological behavior of crystalline silicon, focusing on the effects of surface roughness, normal load, and sliding speed. The results show that between normal loads of 4 GPa and 8 GPa, the average frictional force exhibits significant nonlinear behavior under a sliding speed of 0.2 Å/ps. The approximate steady value of the friction coefficient is 0.39, which is in good agreement with the experimental result of 0.37. Under a normal load of 5 GPa, the friction force increases linearly from 110 nN at 0.05 Å/ps to 311 nN at 2 Å/ps. In addition, in systems with sinusoidal surface roughness, the amplitude has a greater effect on the frictional properties than the period. Among the four rough surfaces studied, A10T32 exhibits the lowest friction force and friction coefficient. This provides theoretical support for the further design of MEMS/NEMS devices with long operational lifetimes. Full article

The dynamic characteristics of flotation foam, such as velocity and breakage rate, are critical factors that influence mineral separation efficiency. However, challenges inherent in foam images, including weak textures, severe deformations, and motion blur, present significant technical hurdles for dynamic monitoring. These issues lead to a fundamental conflict between the efficiency and accuracy of traditional feature matching algorithms. This paper introduces a novel progressive framework for dynamic feature matching in flotation foam images, termed “stable extraction, efficient coarse screening, and precise matching.” This framework first employs the Accelerated-KAZE (AKAZE) algorithm to extract robust, scale- and rotation-invariant feature points from a non-linear scale-space, effectively addressing the challenge of weak textures. Subsequently, it innovatively incorporates the Grid-based Motion Statistics (GMS) algorithm to perform efficient coarse screening based on motion consistency, rapidly filtering out a large number of obvious mismatches. Finally, the Progressive Sample and Consensus (PROSAC) algorithm is used for precise matching, eliminating the remaining subtle mismatches through progressive sampling and geometric constraints. This framework enables the precise analysis of dynamic foam characteristics, including displacement, velocity, and breakage rate (enhanced by a robust “foam lifetime” mechanism). Comparative experimental results demonstrate that, compared to ORB-GMS-RANSAC (with a Mean Absolute Error, MAE of 1.20 pixels and a Mean Relative Error, MRE of 9.10%) and ORB-RANSAC (MAE: 3.53 pixels, MRE: 27.36%), the proposed framework achieves significantly lower error rates (MAE: 0.23 pixels, MRE: 2.13%). It exhibits exceptional stability and accuracy, particularly in complex scenarios involving low texture and minor displacements. This research provides a high-precision, high-robustness technical solution for the dynamic monitoring and intelligent control of the flotation process. Full article