Search Results (29,611)

As a critical hydraulic component of pumped storage power stations, the side inlet/outlet directly affects unit efficiency, flow stability, and system safety. This study investigates the side inlet/outlet of a pumped storage power station using three-dimensional numerical simulations, focusing on the influence of the diffuser length L on hydraulic performance, and further analyzes the underlying mechanisms of energy loss based on entropy production theory. The results indicate that, with increasing diffuser length L, the flow rates in individual channels gradually deviate from the design values, leading to an aggravated imbalance in flow distribution. In contrast, the velocity non-uniformity coefficient C_V at the trash rack decreases, accompanied by a pronounced attenuation of recirculation and local flow separation, resulting in a more uniform and stable flow field. Moreover, increasing L improves the streamwise velocity uniformity within each channel, while the extent and intensity of the top recirculation zone are reduced, suppressing local flow separation. Quantitative analysis shows that when L increases from 65 m to 85 m, the total turbulent dissipation entropy production rate in the diffuser section increases linearly from 2732.32 W/K to 2842.32 W/K, whereas the direct dissipation entropy production rate increases from 0.41 W/K to 0.59 W/K. This indicates that turbulent dissipation entropy production plays a dominant role in the overall energy loss. Shorter diffusers tend to induce high-intensity local dissipation, whereas longer diffusers reduce local peak dissipation but increase the overall entropy production within the diffuser, reflecting a trade-off between local optimization and global energy loss. This study reveals the sensitivity and governing effects of diffuser length on the hydraulic characteristics of side inlet/outlets, providing a reference for geometry optimization and engineering design of similar hydraulic components. Full article

Implantable artificial kidneys represent a promising alternative for patients with end-stage renal disease (ESRD), aiming to overcome the limitations of conventional dialysis through the integration of microfluidic and electrokinetic technologies. In this study, we present a sawtooth electrode microfluidic chamber that achieves blood cell separation via negative dielectrophoresis at a record-low operating voltage of 1.4 V, representing a fivefold reduction compared with rectangular electrode designs and supporting potential integration into implantable artificial kidney systems. A microfluidic chip incorporating an asymmetric sawtooth electrode geometry was developed to enhance local electric field gradients while reducing power consumption. Device performance was investigated using COMSOL Multiphysics simulations. Response Surface Methodology (RSM) based on a Box–Behnken design was employed to optimize the number of teeth per unit length (N), sawtooth height (H), and applied voltage (V), while excitation frequency was fixed at 1 MHz and flow velocity was maintained constant at 0.1 µL·min⁻¹. Statistical analysis was conducted using analysis of variance (ANOVA) in Minitab (Version 27; Minitab, LLC, State College, PA, USA, 2024) . The optimization model showed strong predictive capability (R² = 95.8%) and identified applied voltage (59.45% contribution) and sawtooth height (33%) as the dominant factors affecting separation efficiency, with a significant H × V interaction (p = 0.023). Comprehensive voltage-response mapping over the range of 0.8–4.0 V revealed four operational regimes, including a previously unreported high-voltage failure zone above 2.8 V, where electrothermal flow and electroporation degrade performance. Under physiological conductivity conditions, the optimized design maintained a separation efficiency of 78.3% at 1.4 V with a tip temperature rise of only 1.2 °C, while full recovery of performance was achieved at 2.2 V. Cell-specific separation efficiencies reached 97.3% for white blood cells, 95.8% for red blood cells, and 84.7% for platelets, reducing the downstream cellular load by 92.6%. These findings demonstrate that the proposed low-voltage, high-efficiency separation platform has strong potential as a cellular pre-filtration module in implantable artificial kidney systems and other lab-on-chip biomedical devices. Full article

Snow accumulation and ice formation can significantly reduce pavement friction, posing a serious threat to traffic safety during winter. Traditional snow-removal methods, including mechanical removal, chemical de-icing agents, and heated pavement systems, suffer from several limitations such as low efficiency, environmental impacts, and high operational costs. Electrically conductive asphalt concrete (ECAC) has therefore emerged as a promising active snow-melting technology. When an electric current passes through the conductive network formed within the asphalt mixture, heat is generated through the Joule heating effect. After incorporating conductive fillers, the electrical resistivity of ECAC mixtures can be reduced from approximately 10⁶–10⁸ Ω·cm for conventional asphalt mixtures to about 10⁻¹–10² Ω·cm. Under an applied voltage typically ranging from 30 to 60 V, ECAC pavements can increase the surface temperature by 10–30 °C within 10–30 min, thereby enabling rapid snow melting and ice removal. Meanwhile, an optimized conductive network can maintain sufficient mechanical performance, with dynamic stability generally exceeding 3000 cycles/mm. When the conductive filler content is reasonably controlled, only a limited reduction in fatigue resistance is observed. This paper presents a comprehensive review of electrically conductive asphalt concrete technologies for snow-melting pavements. The background, underlying mechanisms, material development, system configuration, and field applications of ECAC are systematically summarized. Finally, the current challenges are discussed, including the stability of conductive networks, the trade-off between electrical conductivity and pavement performance, and electrical safety. Future research directions focusing on material optimization, intelligent power control, and long-term field performance evaluation are proposed to support the practical application of ECAC pavements in sustainable winter road maintenance. Full article

Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN ×4) and a commercial solution (Topaz Video AI ×4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications. Full article

Unmanned Aerial Vehicles (UAVs) are gradually emerging as key platforms for Integrated Sensing, Communication, and Computation (ISCC) systems in next-generation wireless networks. However, strict resource constraints and task coupling make static allocation inefficient in dynamic environments. This paper studies a UAV-driven ISCC system in which a single UAV dynamically allocates communication bandwidth, sensing resources, and computing power. Considering that sensing data in mission-critical applications is highly time-sensitive, minimizing the response time is paramount. To reduce system latency while maintaining sensing quality and energy efficiency, we propose D3PG-Light, a deployment oriented and stability-enhanced refinement of the deep reinforcement learning framework, specifically tailored for real-time resource scheduling under UAV hardware constraints. D3PG-Light incorporates an adaptive gradient stabilization mechanism, Long Short-Term Memory (LSTM), and feature fusion to enhance training stability. Simulation results based on real air–ground channel measurements show that D3PG-Light converges faster and achieves more stable learning behavior than DDPG, TD3, and the original D3PG. In particular, the proposed method reduces the 95th-percentile latency from over 100 ms to approximately 24 ms, achieves higher converged reward values, and requires fewer than 50 k model parameters. These results demonstrate the effectiveness of D3PG-Light for latency-sensitive UAV-ISCC applications. Full article

To address the operational challenges of industrial microgrids under different weather conditions, this study proposes an optimal dispatch strategy for energy storage systems under representative weather scenarios. Photovoltaic (PV) power generation is first forecast using a Light Gradient Boosting Machine (LightGBM) model, while the load input is prepared based on recent historical demand patterns, and the forecasting performance is evaluated under representative sunny and cloudy scenarios. A mathematical microgrid model incorporating PV generation, battery energy storage, load demand, and grid interaction is then established, in which the total operating cost is minimized subject to time-of-use electricity pricing, battery degradation, and state-of-charge (SOC) constraints. Based on this formulation, an optimization-based day-ahead scheduling strategy is implemented. Under the selected representative sunny and cloudy conditions, the proposed method reduced the daily operating cost by 19.93% and 4.44%, respectively. Over seven representative days, the average cost reduction rate reached 12.54%, thereby confirming its economic effectiveness under representative weather scenarios. Full article

As distributed terminals are increasingly integrated into modern power systems with high penetration of renewable energy and decentralized resources, access control mechanisms must support continuous and highly detailed trust assessment. Existing approaches based on machine learning primarily rely on network traffic features from a single source and analyze terminals in isolation, which limits their ability to capture complex device states and correlated attack behaviors. This paper presents a trust assessment framework for distributed power grid terminals that combines multidimensional behavioral modeling with dual domain graph neural networks. Behavioral features are collected from network traffic, runtime environment, and hardware or kernel events and are fused into compact representations through a variational autoencoder to mitigate redundancy and reduce computational overhead. Based on the fused features and observed communication relationships, two graphs are constructed in parallel: a feature domain graph reflecting behavioral similarity and a topological domain graph capturing communication structure between terminals. Graph convolution is performed in both domains to jointly model individual behavioral risk and correlation across terminals. A fusion mechanism based on attention is further introduced to adaptively integrate embeddings specific to each domain, together with a loss function that enforces both shared and complementary representations across domains. Experiments conducted on the CIC EV Charger Attack Dataset 2024 show that the proposed framework achieves a classification accuracy of 96.84%, while maintaining a recall rate above 95% for the low trust category. These results indicate that incorporating multidimensional behavior perception and dual domain relational modeling improves trust assessment performance for distributed power grid terminals under complex attack scenarios. Full article

Human milk is the optimal standard for neonatal nutrition, particularly for preterm infants. Several conditions associated with oxidative stress (OS) may be transmitted from mother to infant through milk, making the preservation of milk quality essential. When maternal milk is unavailable, donor human milk (DM) is commonly used and treated with Holder pasteurization (HoP) to ensure microbiological safety, although this process may affect bioactive components. This study aimed to evaluate the impact of HoP on OS biomarkers, specifically total antioxidant power (TAP) and 15-F2t-isoprostane, using colorimetric and ELISA methods as cost-effective alternatives to analytical gold standards. Twenty paired DM and HoP samples from the Human Milk Bank of Sant’Anna Hospital (Turin, Italy) were analyzed. No significant differences were observed in TAP levels between DM and HoP samples. In contrast, 15-F2t-isoprostane concentrations were significantly lower in DM compared to pasteurized milk (3.16 (1.59–5.27) vs. 0.76 (0.62–1.54), p-value < 0.001). This reduction remained consistent after stratification by sampling day. These findings suggest that HoP may reduce oxidative stress markers in donor milk, potentially limiting neonatal exposure to maternal oxidative imbalance. Although this effect could offer protective benefits for vulnerable preterm infants, further studies are needed to clarify the clinical implications of HoP on redox status and neonatal outcomes. Full article

Background/Objectives: Hand loss or severe impairment significantly reduces quality of life by restricting essential daily activities and professional tasks. Despite advances in prosthetics, challenges remain in affordability, accessibility, and usability. This study aimed to design and develop a low-cost, ergonomic bionic hand prototype that integrates sustainable fabrication, intuitive control, and modular electronics. Methods: A user-centred design process guided by iterative prototyping, anatomical modelling, and functional validation. The prototype was manufactured using 3D printing techniques and assembled with modular electronic components. The design included segmented fingers, independent thumb articulation, and a tendon-like actuation system driven by micro-motors. Control was implemented through an ESP32-based board and a Bluetooth-enabled mobile application. Durability was preliminarily assessed through 500 grasp–release cycles. Results: Experimental validation confirmed the feasibility of both precision and power grips. The pinch grip successfully lifted objects to 120 g, and the power grip up to 85 g, corresponding to effective output forces of approximately 1.2 N and 0.83 N, respectively. The final prototype weighed ~350 g and maintained reliable performance during 500 grasp–release cycles. Conclusions: The developed bionic hand demonstrates that an affordable, ergonomic, and functional prosthetic can be achieved through sustainable 3D printing and accessible electronics. Future work will focus on enhancing actuation strength, long-term durability, and integration of sensory feedback, with the long-term objective of clinical testing and scalable production. Full article

This work investigates the geometric design and optimisation of a dynamic inductive power transfer coupler for two-wheeled electric vehicles under misalignment and magnetic-field exposure constraints. A computational three-dimensional finite-element model of a shielded rectangular coupler is developed to characterise coupling coefficients and magnetic flux density levels on control planes along the longitudinal travel range and under lateral and angular misalignments. Two simulation datasets are generated: one varying only geometric parameters at a nominal position for surrogate construction and global sensitivity analysis, and a second jointly sampling geometry, the travel range and misalignments for optimisation. Sparse Polynomial Chaos Expansions and Canonical Low-Rank Approximation surrogates are built to quantify Sobol’ indices, revealing that a small subset of primary-side geometric variables dominates both coupling efficiency and magnetic field levels. Random forest regressors are then trained on the extended dataset and embedded in the Non-dominated Sorting Genetic Algorithm II to solve a multi-objective optimisation problem that maximises worst-case coupling, improves robustness to misalignment, and enforces magnetic-field leakage limits. Optimal designs were obtained, and a subset was selected for re-evaluation using the finite-element method. The results confirm that the proposed surrogate-assisted framework yields coupler geometries with enhanced coupling and reduced magnetic field leakage while respecting the mechanical constraints for the electric motorcycle system. Full article

This paper proposes a SIM-compatible hardware coordination architecture for secure radio-frequency (RF)-triggered activation in mobile devices. The proposed concept functions as a passive coordination layer rather than as an additional wireless transceiver, enabling controlled interaction between external low-frequency RFID or high-frequency NFC fields and wireless subsystems already available in the host device. The architecture assumes a flexible nano-SIM-compatible form factor integrating passive RF detection structures, a trusted decision component, and a trigger-generation interface aligned with standard SIM/UICC electrical and logical interaction models. Upon detection of an external electromagnetic field, the coordination layer evaluates predefined authorization conditions and produces a controlled trigger event intended to propagate through existing telephony and system-service pathways. In contrast to architectures that embed active wireless transmitters, the proposed approach seeks to minimize hardware redundancy and reduce potential attack surfaces by relying on the host device’s native Bluetooth Low Energy (BLE) capabilities. Rather than directly controlling wireless modules, the interface operates as a hardware-originated coordination mechanism that may support low-power and context-aware activation scenarios in mobile and embedded environments. This paper focuses on the architectural model, system assumptions, security rationale, and implementation constraints of such a SIM-compatible interface. Particular attention is given to integration considerations related to smartphone baseband architectures, operating-system mediation, and secure-element isolation. The presented concept establishes a foundation for future prototype implementation and platform-specific validation of SIM-compatible RF-triggered coordination mechanisms. Full article

The rapid expansion of wind energy into complex and extreme environments has renewed interest in vertical-axis wind turbines (VAWTs) due to their omnidirectional operation, compact footprint, and potential resilience under harsh operating conditions. However, the current understanding of VAWT performance remains fragmented across aerodynamic, structural, operational, and application-specific studies. This systematic review aims to synthesize and critically evaluate VAWT research with environmental stressors as the central organizing framework, addressing performance behavior, adaptation challenges, and future research pathways. Literature searches were conducted in the Web of Science Core Collection, Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases, with Google Scholar used as a supplementary source, covering publications from 2000 to January 2026. Eligible studies focused on VAWTs operating under non-standard or extreme conditions, including icing, offshore, desert, high-turbulence, and thermally severe environments. A systematic quality assessment was applied to evaluate methodological rigor and environmental characterization, and the findings were synthesized using a qualitative–quantitative hybrid approach; no formal meta-analysis was performed. The review reveals substantial advances in unsteady aerodynamics, numerical modeling, and control strategies, but also identifies persistent discrepancies between high-fidelity simulations and real-world performance due to simplified modeling assumptions and limited full-scale experimental validation. Quantitative findings indicate that high turbulence can decrease the power output of large VAWTs by 23–42%, dust and sand in arid environments can reduce torque and power by ~25%, and air temperature increases from 15 °C to 60 °C can reduce the power coefficient of VAWTs by about 38%. Emerging approaches, including artificial intelligence-assisted design, adaptive turbine architectures, and climate-aware methodologies, show promise in addressing these limitations. The findings highlight the urgent need for coordinated long-term field measurements, improved multi-physics modeling, and interdisciplinary research to enhance the reliability and scalability of VAWTs in extreme environments. This review was not registered. Full article

Single-Event Burnout (SEB) is one of the most critical failure mechanisms in silicon power MOSFETs operating in radiation environments, particularly under heavy-ion irradiation, and often limits device operation through excessive voltage derating. In this work, SEB robustness of a silicon VDMOS power device is investigated using detailed electro-thermal transient simulations. The study evaluates two complementary device-level modifications: the introduction of a buffer layer between the epitaxial layer and the substrate, which has been reported in the past, and a new approach considering the incorporation of a novel highly doped boron BOX implant within the P-body region. Heavy-ion impacts are simulated using a physically based model implemented in SENTAURUS TCAD, accounting for ion energy deposition, impact position, and thermal effects. The results show that the buffer layer increases the second breakdown voltage and can suppress high-current operating points, while the BOX implant raises the parasitic BJT activation threshold by reducing the P-body resistance. When combined, both modifications lead to a significant reduction in the peak temperature reached during after-impact transients, without introducing measurable degradation of static electrical characteristics. These results demonstrate that combining buffer layer engineering with localized P-body resistance reduction is an effective strategy to improve SEB robustness in silicon VDMOS power devices without relying on excessive derating. Full article

Gears are one of the most important machine elements widely used to transmit motion and power in various machines. The gear tooth stiffness has a significant impact on the load distribution, vibration characteristics, and overall efficiency of gear systems. Therefore, accurate analysis of tooth stiffness is crucial for optimizing gear performance and ensuring reliable operation. In this study, the effects of geometric parameters on single tooth stiffness (STS) and time-varying mesh stiffness (TVMS) of involute spur gears are investigated numerically. The gear design parameters, such as drive side pressure angle (DSPA) (20°, 25°, 30°), addendum (1–1.5 × module), and dedendum (1.25–1.7 × module), are varied. Gear configurations with both low contact ratio (LCR) and high contact ratio (HCR) are evaluated. Parametric models are first developed using MATLAB, and then 3D CAD models are created in CATIA for static structural analysis in ANSYS Workbench. The results indicate that increasing the pressure angle enhances stiffness in the tooth root region, whereas the effect is less significant near the tooth tip. Increasing the addendum length generally reduces stiffness. In some cases, a rise in contact ratio results in up to a 25% increase in mesh stiffness. These findings demonstrate that single tooth and mesh stiffness can be optimized through precise control of gear geometry. Ultimately, the study provides valuable insights for improving gear performance and durability through informed design choices. Full article

Prostate cancer (PCa) remains a major global health challenge, yet conventional diagnostic methods are often limited by suboptimal accuracy and efficiency. Artificial intelligence (AI) has emerged as a rapidly developing technology capable of integrating multi-source data to enhance clinical decision-making. This narrative review synthesizes current evidence regarding AI applications across key diagnostic domains, including medical imaging, digital pathology, liquid biopsy, and multi-omics integration. Findings indicate that AI models for magnetic resonance imaging (MRI) can improve risk stratification and may reduce unnecessary biopsies in some cohorts, particularly when evaluated alongside structured radiology assessment and clinical variables. In digital pathology, deep learning algorithms have shown high agreement with expert genitourinary pathologists for automated Gleason grading in controlled and externally validated settings, with potential to reduce reporting time for high-volume workflows. Additionally, AI-powered liquid biopsy models may support non-invasive risk stratification, particularly for patients with prostate-specific antigen (PSA) levels in the diagnostic gray zone, while multi-omics integration is being investigated to enhance personalized assessment. Despite advances, challenges regarding data heterogeneity, algorithm interpretability, and workflow integration persist. Future research should prioritize multimodal data fusion, explainable AI development, robust calibration and decision-analytic evaluation, and large-scale prospective validation to standardize protocols and fully realize the potential of AI in precision prostate cancer care. Full article