Search Results (2,463)

Transcutaneous electrical stimulation (TES) is a non-invasive technique that delivers electrical currents through the skin to modulate peripheral neural and muscular activity. However, conventional surface stimulation often exhibits limited spatial selectivity because current spreads across superficial and deep tissues. To investigate physical principles that may inform future transcutaneous applications without assuming anatomical or physiological fidelity, this study developed a finite-element framework based on an idealized multilayer cylindrical limb model. The model represented skin, adipose tissue, muscle, cortical bone, and bone marrow using concentric tissue domains parameterized with averaged forearm dimensions and literature-derived electrical properties. Two-dimensional and three-dimensional surface-electrode montages were evaluated by varying return-electrode positions, axial electrode spacing, and inter-channel current ratios. The simulations showed that electrode configuration influenced the location, volume, and compactness of high-maximum-envelope-modulation-amplitude (MEMA) regions. Within the investigated idealized geometry, intermediate return-electrode angles (approximately θ₂ = 130–150°) produced more centrally distributed intramuscular high-MEMA regions, whereas current-ratio modulation shifted the MEMA_max-defined field maximum under fixed electrodes. These results demonstrate the computational feasibility of regulating TI electric-field distributions in a controlled virtual model rather than physiological selectivity or clinical efficacy. Translation to actual transcutaneous stimulation will require staged validation using anatomically realistic models, physical phantoms, physiological experiments, and ultimately human studies. Full article

This work focuses on studying the magnetohydrodynamic flow and heat transfer mechanism of a Casson–Maxwell nanofluid due to a stretching surface through a porous medium, using a physics-informed neural network (PINNs) approach as the main tool for the solution of the physical problem. The mathematical model describes the phenomena of viscosity variation with temperature, viscous dissipation, thermal slip, Brownian motion, thermophoresis, and drag force due to a porous medium, which give a realistic physical scenario of the coupled transport phenomena of momentum, heat, and nanoparticles. First, the nonlinear partial differential equations are converted into a dimensionless boundary layer model using similarity transformations. Then, the yielded system is solved via the PINNs approach, which integrates physical law within the optimization procedure. The proposed technique does not require a significant number of labeled datasets and provides accurate and stable predictions of the strongly nonlinear flow. A comprehensive parametric analysis was performed to explore the impact of the dimensionless controlling factors on the velocity, temperature, and nanoparticle concentration distributions. It is found that the interaction of magnetic field effects, porous media resistivity, thermal and concentration slip, viscosity variation, and viscous heating significantly modifies the transport features for the studied model of the Casson–Maxwell nanofluid, which can be used effectively to control the rate of heat and mass transfer. This study proves the efficiency of the PINN technique in solving this type of model, and it also provides useful insights for designing thermal systems, energy conversion devices, and electrically conducting viscoelastic nanofluid transport problems. The close concordance between the present findings and established data from the literature validates the precision and dependability of the developed PINN-based framework. Full article

Continuous monitoring of permafrost dynamics remains challenging in remote high-mountain environments due to logistical constraints, harsh climatic conditions, and the limited availability of spatially distributed observations. In addition to direct temperature observations in boreholes, Autonomous Electrical Resistivity Tomography (A-ERT) offers significant potential for long-term monitoring by providing high temporal resolution observations of subsurface electrical properties, which are highly sensitive to freeze/thaw processes. This study presents the field validation of a low-power A-ERT system designed for long-term autonomous operation in extreme environments. The system was deployed at a high-altitude permafrost site near the Kumtor gold mine in the Central Tien Shan, Kyrgyzstan, representing the first application of continuous A-ERT monitoring in the Central Asian mountain ranges. The system operated continuously under harsh environmental conditions with air temperatures as low as −30 °C. Data quality remained consistently high throughout the monitoring period, with less than 1% of measurements removed during filtering, and inversion results with root-mean-square errors generally ranging between 3% and 4%. Time-lapse resistivity observations revealed strong seasonal freeze–thaw dynamics within the active layer and continued seasonal resistivity variations within the underlying permafrost despite permanently frozen conditions. Analysis of depth-dependent resistivity–temperature relationships revealed increasingly pronounced hysteresis behavior below the active layer, indicating that subsurface electrical properties were not controlled solely by temperature. This behavior likely reflects variations in unfrozen water content and pore connectivity within the fine-grained permafrost, where liquid water can persist at sub-zero temperatures. The results demonstrate the capability of the A-ERT system for reliable long-term autonomous monitoring in remote permafrost environments and investigation of coupled thermal and hydrological processes in permafrost systems. Full article

The increasing demand for real-time monitoring systems has accelerated the adoption of distributed sensors operating in the ultra-low-power regime. However, in retrofittable and hard-to-access applications, providing a continuous external power supply is challenging, while batteries are limited by their lifetime and maintenance requirements. Energy harvesting represents a promising approach to enable autonomous sensor operation by exploiting ambient energy sources, including photovoltaic, thermal, mechanical, and electromagnetic sources. In this work, different circuit topologies for near-field electromagnetic energy harvesting are investigated through simulation and experimental validation. The proposed approach exploits the electromagnetic interference generated by shielded commercial power electronics, such as drivers and power supplies, as an available energy source. The results demonstrate the capability of compact and easily deployable circuits to capture and convert near-field electromagnetic energy into usable electrical power. The proposed methodology provides a flexible solution for powering low-power monitoring systems and can be adapted to different environments by tailoring the harvesting circuit parameters to the available electromagnetic source. Full article

Fruit drying transforms a living, water-rich tissue into a stable food through coupled changes in moisture distribution, structure, color, nutrients, and aroma. Although drying technologies and non-destructive sensing have advanced rapidly, these fields have largely developed in parallel, leaving the relationship between quality formation and measurable process signals insufficiently resolved. Here, physical and chemical changes during drying are connected to the signals that can support quality prediction. Current evidence shows that moisture loss and surface appearance are the most tractable real-time targets. Texture, bioactive retention, and flavor remain less accessible because their signals depend more strongly on internal structure, reference chemistry, or sensory response. Optical, magnetic-resonance, thermal, volatile-sensing, and electrical approaches consequently provide complementary rather than interchangeable views of the product. Multimodal models improve prediction when the added signals resolve different aspects of drying, but redundant inputs can increase complexity without improving transferability. Progress toward intelligent fruit drying therefore depends on matching sensors to the evolving product state, validating models beyond individual batches and instruments, and linking predictions to practical process decisions. This process–quality perspective provides a basis for moving from retrospective quality assessment toward reliable monitoring and controlled drying. Full article

Electric vertical takeoff and landing (eVTOL) represents a novel mode of transportation emerging within the low-altitude economy framework, exhibiting extensive development prospects. As a widely adopted incentive policy, government subsidy constitutes a crucial method for supporting the development of this strategic emerging industry. This paper constructs a differential game model of a supply chain composed of a manufacturer and retailer capable of simultaneously producing and selling eVTOL, considering three scenarios: centralized decision-making and decentralized decision-making with or without cost-sharing. Based on optimal control and differential game theory, the decision-making processes of supply chain members are investigated, and equilibrium strategies under different scenarios are compared and analyzed, with the model’s validity confirmed through numerical simulations. The findings indicate that the subsidy policy exerts a positive impact on the eVTOL supply chain: as the subsidy rates increase, supply chain members are better resourced to invest in endurance and marketing, thereby fostering eVTOL development. Concurrently, a reduction in wholesale and retail prices is observed, rendering eVTOL more affordable and of higher quality for consumers. The competitive structure of the upstream market does not alter this fundamental conclusion. The optimal endurance effort, marketing effort, eVTOL brand goodwill, and demand under the centralized decision-making model are higher than those under the decentralized one. In a certain feasible region, the two-way cost-sharing contract enables the eVTOL supply chain to achieve Pareto improvement, but it does not reach the level of centralized decision-making. This research expands the application of differential game theory in the field of the low-altitude economy, providing a scientific basis for the government to formulate policies and enterprises to distribute products. Full article

Conductive polymer gel materials are inherently susceptible to localized overheating during electrical heating owing to spatially nonuniform conductivity distributions, while their internal three-dimensional temperature fields remain challenging to monitor in real time through noncontact means. Traditional inversion methods, such as Tikhonov-LSQR and TV-ADMM, perform frame-by-frame spatial regularization. The former enforces global smoothness, while the latter preserves sharp edges—but neither exploits the temporal evolution of the temperature field governed by the heat conduction equation, leading to unstable reconstructions in deep regions. To address this limitation, we propose a comprehensive methodology for the calibration, reconstruction, and overheating warning of three-dimensional dynamic temperature fields based on the focused light-field infrared camera. A forward electro-thermal coupled heat conduction model is established to characterize the transient temperature evolution within the gel throughout the heating process. Concurrently, a forward imaging model and its corresponding linear system matrix are constructed for focused light-field infrared imaging, enabling the acquisition of infrared light-field images and subsequent reconstruction of the three-dimensional dynamic temperature field. Furthermore, we develop an ETP-Causal LSQR online inversion algorithm tailored for real-time overheating warning during gel heating. Unlike conventional spatial regularization methods, we incorporate a temporal physical prior: the previous reconstruction is propagated through the heat conduction equation to predict the current temperature field, and this prediction is introduced as a soft constraint into the LSQR solver. The algorithm strictly respects causality, using only current measurements and historical reconstructions. Comparative results demonstrate that the proposed method consistently outperforms conventional Tikhonov-LSQR and TV-ADMM algorithms across multiple aspects, including reconstruction accuracy, noise robustness, physical consistency, cross-operating condition generalization, and computational efficiency, thereby validating the effectiveness and broad applicability of the physically constrained causal inversion framework. Full article

X2CrNiMo18.12 (nitrogen-controlled) stainless steel is prone to slag-shell erosion during electroslag remelting (ESR), which may lead to steel breakout and mold leakage. To investigate the evolution behavior of the slag shell and molten pool, a transient multiphysics model coupling electromagnetic, flow, and thermal fields was developed based on the volume of fluid (VOF) method and dynamic mesh technique. The distributions of the coupled physical fields and the effects of electrical parameters and cooling intensity on slag-shell stability and molten-pool morphology were systematically analyzed. The results show that a pronounced edge effect exists at the lower electrode corner, resulting in concentrated current density, Joule heating, and Lorentz force. Under the combined effects of electromagnetic force and thermal buoyancy, a dominant circulation vortex is formed in the slag pool, which governs heat transfer and slag-shell evolution. The slag-shell thickness is determined by the competition among vortex-induced erosion near the mold wall, erosion by molten steel at the slag–metal interface, and mold cooling. Among these factors, erosion by molten steel is the primary cause of steel breakout and mold leakage. Increasing the current from 2.5 to 3.5 kA significantly increases the melt superheat, transforms the molten pool from a shallow U-shape to a deep V-shape, and reduces the slag-shell thickness to approximately 0.95 mm, leading to leakage failure. In contrast, a moderate current of 2.5–3.0 kA combined with a cooling intensity above 2400 W·m⁻²·K⁻¹ maintains the slag-shell thickness at approximately 2 mm and effectively suppresses steel breakout and mold leakage. These findings provide guidance for process optimization and operational safety in the ESR of X2CrNiMo18.12 steel. Full article

Buildings account for roughly 37% of energy-related CO₂ emissions, and space cooling already consumes nearly 10% of global electricity. Cooling demand is rising fastest in tropical cities, where air-conditioning could reach 45% of peak load, especially in India by 2050. This review critically examines thermal energy storage (TES) as a flexibility resource across three distinct scales: individual buildings, district heating and cooling networks, and city-level multi-energy systems. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based search of Scopus, Web of Science, and IEEE Xplore with primary and supplementary strings, 4447 records were identified, of which 174 were included. Each quantitative study was classified by validation level (simulation, laboratory, pilot, or operational) and by the centrality of thermal storage. Sensible, latent, and thermochemical storage technologies are compared using energy density (10–500 kWh/m³), efficiency (40–95%), cycle stability, and technology readiness. The review then evaluates the role of artificial intelligence (AI), machine learning, and Internet of Things platforms in forecasting, predictive control, and operational optimization of TES networks. Thirteen method families, grouped into AI and machine learning methods, optimization methods, control methods, and digital enabling technologies, are assessed against six explicitly defined criteria with evidence-coded scores. Among 47 quantitative studies, 37 (78.7%) are simulation-only, and only four (8.5%) report operational data. Direct TES-AI studies report simulated energy savings of 8–64% and peak load reductions of about 35%, whereas field-validated intelligent control reports 17% energy savings in a single real building experiment. The review also identifies inherent drawbacks of artificial intelligence-based operations, including limited interpretability, high data and computational demands, concept drift, and cyber vulnerabilities that increased peak electric load by 17.4% in a simulated attack. A structural imbalance in the literature is evident: most validated deployments remain at the building-scale, whereas urban-scale evidence is confined to district cooling, aquifer and pit storage, and multi-energy hub studies; no study reports the coordinated operation of distributed TES assets across multiple districts. A conceptual framework and a staged roadmap linking building, district, and urban scales are proposed. Priority research needs include urban-scale pilots in tropical climates, techno-economic assessment, interpretable and drift-robust AI, and interoperability standards that support United Nations’ Sustainable Development Goals 7, 11, and 13. Full article

Microfluidic Hall-effect biosensors detect superparamagnetic bead labels as they flow past a thin-film Hall element in a microchannel. Designing one couples bead magnetization, stray-field distribution, Hall transport, and channel flow across 14 parameters that finite-element solvers explore only at minutes to hours per configuration. We present a coupled analytical–numerical framework for this signal chain: Clausius–Mossotti bead magnetization with a volume fraction correction, a point-dipole stray field, a volume-averaged Hall voltage, Poiseuille transport, and a Johnson–Nyquist and Hooge $1/f$ noise model, evaluated across 12 sensor presets compiled from the literature, spanning graphene, III–V semiconductors, Si CMOS, bismuth, and topological insulators; any other platform can be defined from user-supplied transport parameters. Benchmarked against a companion COMSOL Multiphysics 6.0 study, the framework reproduces the Hall voltage to within 4.8% at a favorable bead-to-sensor area ratio and deviates by 22% and 15% at off-optimum geometries, consistent with the point-dipole near-field limit at $h/r_b=1$. Three design rules follow: a signal-to-noise ridge at sensor widths comparable to the bead diameter ($w^{*}\approx d_b$; area ratios 0.4–1.0 at constant voltage, 0.5–2.6 at constant current), matching reported single-bead geometries; a material choice that must be made under an explicit electrical drive constraint; and a sampling-limited flow-velocity window. Predicted signals agree at the order-of-magnitude level with published InAs and Si CMOS experiments. We release the model as a freely accessible, no-install browser implementation with a built-in 2D axisymmetric magnetostatic finite-element (FEM) solver that maps where the dipole approximation degrades. Full article

Power quality degradation in low-voltage (LV) distribution networks remains insufficiently documented in many rapidly urbanizing African cities despite its significant impact on electrical equipment, service reliability, and network operation. This study investigates the following research question: To what extent does the power quality of an urban low-voltage distribution network comply with international standards, and which network characteristics are most strongly associated with the observed disturbances? To address this question, an extensive field measurement campaign was conducted from October 2024 to February 2025 on five radial feeders supplied by the Babemba medium-voltage/low-voltage (MV/LV) substation in Lubumbashi, Democratic Republic of the Congo. Electrical parameters were monitored using a Class B Chauvin Arnoux Qualistar C.A. 8331 power quality analyzer and evaluated against internationally recognized power quality standards. The measurements revealed persistent power quality degradation characterized by chronic under-voltage, with prolonged voltage levels below 207 V, typical deviations ranging from −20% to −30%, and voltage dips reaching 70–80% of the nominal voltage during peak loading conditions. Power supply continuity was also severely affected, with a System Average Interruption Frequency Index (SAIFI) of 7.85 interruptions/year and a System Average Interruption Duration Index (SAIDI) of 491 min/year, while a medium voltage outage lasting approximately 48 h highlighted the limited resilience of the distribution system. Additional disturbances included phase voltage imbalance reaching 18%, neutral currents up to 327 A, and short-term flicker values (Pst) approaching 1.5, exceeding the recommended comfort threshold. Overall, the observed disturbances were associated with heterogeneous feeder loading conditions, network configuration, non-standard electrical connections, and documented physical deterioration of the infrastructure. This study provides a comprehensive field-based assessment of power quality and service continuity in an urban LV distribution network in the Democratic Republic of the Congo and offers a quantitative basis for prioritizing feeder reinforcement, phase balancing, infrastructure rehabilitation, and the establishment of continuous local power quality monitoring. Full article

This paper proposes a novel axially parallel salient pole hybrid excitation synchronous generator (PSPHESG) for small and medium hydropower (SMHP). To prevent irreversible demagnetization of the permanent magnets (PMs), while improving the power density and reducing the volume compared with those of a conventional electrically excited synchronous generator equipped with an AC exciter, a salient pole series hybrid excitation machine is axially integrated with an electrically excited machine, with the latter serving as the power compensation section. The basic structure and operating principles of the proposed PSPHESG are introduced. Finite-element analysis (FEA) is used to investigate the magnetic field distribution and no-load characteristics. Moreover, the phase angle deviation characteristics and output performances under load are analyzed, showing favorable voltage output capability over a wide load range during steady-state operation. Finally, the anti-demagnetization capability of the PM is studied under field forcing (FF) and de-excitation (DE). The results confirm that the PSPHESG not only provides good constant-voltage capability but also effectively avoids irreversible PM demagnetization during FF and DE, indicating its promising applicability to SMHP systems. Full article

Due to the complex pore structure and strong heterogeneity of carbonate reservoirs, accurately characterizing reservoir properties remains a major challenge. Electrical imaging logs and nuclear magnetic resonance (NMR) logging are the essential techniques for analyzing reservoir pore structures and assessing fluid distribution. However, the high cost of NMR logging often limits its field application, resulting in sparse data coverage. To overcome this limitation, a pore spectrum model was constructed from electrical imaging logs, and corresponding pseudo-echo signals were generated. Through an optimized inversion algorithm, key parameters such as pseudo-NMR total porosity, fracture porosity, and other pore-structure-related parameters were extracted. These parameters, together with acoustic and density logs, as well as other derived logging features, were integrated to develop a comprehensive porosity prediction model based on support vector machines (SVMs). The model was applied to carbonate reservoirs in the Tarim Basin. For Well AT01, the average relative errors of the four-parameter SVM model and the full-parameter SVM model on the independent test set were 43.49% and 9.70%, respectively. The relative error of the full-parameter SVM model ranged from 4.40% to 15.30%, indicating improved prediction accuracy compared with direct use of pseudo-NMR-derived porosity. In addition, the full-parameter SVM model provided porosity estimates that were generally consistent with core-measured porosity in the locally calibrated test interval. These results suggest that integrating pseudo-NMR-derived pore-structure parameters with conventional logging features can improve porosity evaluation in complex carbonate reservoirs, especially when measured NMR logging data are limited. Full article

Shield tunnels are the primary structural form of subway tunnels. Under the influence of sulfate ions and stray currents, the internal bars in shield tunnels will corrode rapidly, affecting the safe operation of the subway. In this paper, a numerical calculation model was developed to simulate rebar corrosion in shield tunnels under coupled electric and chemical field effects, the accuracy of the numerical calculation model was verified using a model test. Based on this model, the migration patterns of sulfate ions within the tunnel under stray current conditions are investigated, as well as the corrosion mechanisms of tunnel rebars. Results show that under the coupled effects of stray currents and sulfate ions, the corrosion area in a segment rebar gradually spreads from the arch waist area of a tunnel. Compared to bilateral leakage, both the migration rate of sulfate ions and the corrosion rate of rebar were higher than those in the unilateral leakage condition. Changes in the input voltage at the top of the ballast did not alter the shape of the distribution curves for the voltage and corrosion current density of segmental rebar. When the input voltage increased from 1 V to 5 V, the voltage at the mid-section of the rebar increased by 5.2 times under a bilateral leakage condition, and the voltage at the mid-section of the rebar increased by 8.2 times under a unilateral leakage condition. Compared to the bilateral leakage case, the amplitude of fluctuations in the curve was significantly reduced under unilateral leakage, but the corrosion current density increased proportionally with increasing leakage voltage in both cases. The research conclusions provide a theoretical basis for the safety assessment and disease treatment of shield tunnel structures. Full article

A complementary metal oxide semiconductor (CMOS)-compatible z-axis magnetic field sensor incorporating an additional collector is proposed to enhance magnetic sensing performance. The sensor consists of four identical magnetic sensing elements arranged in a cross-shaped configuration, while shallow trench isolation (STI) and a post-CMOS cavity structure are employed to suppress substrate leakage current and improve electrical isolation. The sensing characteristics were investigated using three-dimensional TCAD simulations to analyze carrier transport and current density distributions under different magnetic fields. The simulation results confirmed that the structure effectively enhances the differential output response and magnetic sensitivity. The device was fabricated using a commercial CMOS process followed by a simple post-CMOS micromachining process. Optical microscope and scanning electron microscope observations verified the successful formation of the sensing structure and the cavity beneath the sensing elements. The sensor was experimentally characterized under magnetic fields ranging from −300 to 300 mT. The measured results exhibited excellent linearity over the entire measurement range. The sensor achieved a measured sensitivity of 120 mV/T. Owing to its high sensitivity, simple fabrication process, and full compatibility with standard CMOS technology, the magnetic field sensor is promising for integrated microsystems, industrial monitoring, and intelligent sensing applications. Full article