Search Results (4,386)

Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. Full article

Perovskite manganites (AMnO₃) and perovskite-like manganites (A’_1−xA_xMnO₃) are complex oxide materials that have attracted significant attention from the scientific community in recent years due to their structural flexibility, mixed-valence state, tunable electronic configuration, and multifunctional properties. This review systematically analyzes the synthesis methods, structural classification, and physicochemical characterization of perovskite manganites, as well as their magnetic, optical, electrical, dielectric, and catalytic properties. The influence of solid-state reactions, sol–gel, Pechini, hydrothermal, co-precipitation, microwave, and other mild chemical approaches on phase purity, morphology, particle size, and oxygen stoichiometry was examined. The structural diversity of perovskite and perovskite-like manganites, including simple ABO₃, double perovskites, multilayer, and low-dimensional systems, was characterized in relation to their functional properties. The review discussed the capabilities of methods for synthesizing and analyzing morphological properties, demonstrating the role of doping, cation substitution, oxygen vacancies, and Jahn–Teller distortions in controlling material properties. Prospects for the application of perovskite manganites in spintronics, magnetocaloric cooling, photocatalysis, gas-sensing devices, and energy conversion and storage systems were analyzed. This review highlights the structure–property–application relationship in perovskite manganites. Full article

Against the backdrop of the global energy transition towards clean, low-carbon sources and China’s “carbon peak, carbon neutrality” strategic goals, distributed photovoltaic (PV) power generation is being integrated into distribution networks at large scale and with a high penetration level. This trend profoundly changes the configuration and operational characteristics of traditional distribution networks, posing challenges in system planning, operation control, power quality, and economics. This paper innovatively treats the step-up transformers of multiple distributed PV stations as a “distributed generation collection network” that requires coordinated optimization and constructs an integer linear programming (ILP) model aimed at minimizing the total life-cycle cost. The model deeply integrates engineering practice, incorporates nonlinear construction, installation, operation, and maintenance costs related to cluster size, as well as power transmission costs proportional to distance, and it employs piecewise cost functions to accurately capture economies of scale. This research achieves a system-level coordination framework that moves beyond single-device optimization, reducing system costs for step-up transformer deployment in distributed PV stations under complex terrain conditions. Full article

Smart labels are emerging as a key enabling technology for product traceability, environmental monitoring, and user interaction within Internet of Things (IoT) ecosystems. This work presents the design and experimental validation of a low-power smart label platform integrating Bluetooth Low Energy (BLE) communication, temperature sensing, and dynamic e-paper visualization based on the HY0020 System-on-Chip (SoC). This platform was implemented on a custom Printed Circuit Board (PCB) designed around a 1.02-inch monochrome e-paper display and incorporates a TXS0108E interface to support reliable display communication. The developed prototype enables wireless user interaction, dynamic QR code rendering, and ambient temperature monitoring while maintaining low average power consumption. Experimental evaluation included BLE communication testing, display operation validation, temperature monitoring assessment using the integrated HY0020 sensor, and energy consumption characterization. Experimental results confirmed reliable BLE connectivity, stable temperature monitoring performance under normal environmental conditions, and an estimated battery lifetime of approximately 54 days under the evaluated operating profile. The presented platform demonstrates the feasibility of integrating sensing, wireless communication, and electrophoretic display technology within a compact battery-powered smart label device. The proposed architecture provides a practical proof-of-concept foundation for future applications involving product traceability, digital information management, and Digital Product Passport (DPP)-oriented services. Full article

Industrial waste heat recovery is an important approach for improving energy utilization efficiency and reducing environmental impacts. Thermoelectric devices can directly convert waste heat into electricity, but their practical application is limited by relatively low output power. Active water cooling can enhance the power generation performance of thermoelectric devices, but the pumping power may reduce the net output power. In this study, a water-cooling thermoelectric device is investigated under constant heat input conditions using three-dimensional numerical simulations and a semi-analytical prediction model. The effects of cooling water inlet temperature and flow rate on the thermal response, electrical output, heat transfer behavior, and net output power are systematically analyzed. The results show that increasing the cooling water flow rate increases the gross electrical power but also increases pumping power, resulting in an optimal flow rate of approximately 3 m/s to maximize the net output power. At inlet temperatures of 24 °C, 28 °C, and 32 °C, the maximum net output powers are 51.46 W, 49.89 W, and 48.68 W, respectively. A prediction model for cooling water input conditions is further developed based on energy balance and convective heat transfer correlations, and the predicted velocities agree with the numerical results with relative errors below 2%. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

This study presents an energy-based assessment of a battery electric sport utility vehicle (SUV) tested under high-altitude and high-gradient real-world conditions in Ambato, Ecuador, at approximately 2500 m above sea level. A low-cost instrumentation setup composed of a Global Navigation Satellite System (GNSS) device, a Fluke 393 FC clamp meter, and an On-Board Diagnostics II (OBD-II) interface was used to evaluate zero, positive, and negative road-gradient conditions in Normal and Sport driving modes. The results show that positive gradients increased the acceleration energy from 0.0454 to 0.0658 kWh in Normal mode and from 0.0351 to 0.0535 kWh in Sport mode. In contrast, negative gradients favored regenerative braking, with Normal mode reaching a net energy balance of $-0.0249$ kWh and a segment-level recovery ratio of 194.38%. This value reflects the contribution of gravitational potential energy. Sport mode showed lower regenerative performance, particularly during uphill operation, where the recovery ratio decreased to 8.96%. These findings demonstrate that low-cost instrumentation can capture representative route-level energy trends and support real-world electric vehicle (EV) energy assessment in topographically complex high-altitude environments. Full article

The integration of wind energy into urban environments is constrained by low wind speeds, high turbulence, and the recurrent negative torque experienced by lift-driven vertical-axis wind turbines (VAWTs). This study specifically evaluates a straight-bladed H-Darrieus rotor equipped with a single upstream passive flat-plate deflector for the wind regime measured on the campus of the Universidad Nacional Mayor de San Marcos (Lima, Peru). A three-dimensional transient CFD model using the SST k–ω turbulence model was applied to compare the baseline rotor and the deflector-assisted configuration under identical operating conditions; DMST calculations were used only as a low-order cross-check for the bare rotor performance trend, not as a substitute for experimental validation. The deflector was selected after a geometric sensitivity assessment and positioned at 30° relative to the incoming flow, with a span equal to the rotor height and a length comparable to the rotor diameter. At TSR = 2.5, the maximum power coefficient increased from 0.4459 for the bare rotor to 0.6153 with the deflector, equivalent to an improvement of approximately 38%. Velocity and pressure fields show that the deflector accelerates the flow toward the advancing blade while shielding the returning blade, thereby reducing adverse torque and smoothing cyclic torque fluctuations. The results define the applicability of the proposed passive device for low-to-moderate urban wind environments with a dominant wind sector and provide a reproducible numerical basis for subsequent wind-tunnel and field validation. Full article

Accelerometer sensors and artificial intelligence (AI) are reshaping automated behavior monitoring in precision livestock management, yet their joint deployment on extensive rangelands is constrained by energy and bandwidth budgets. Low-Power Long-Range Wide-Area Network (LoRaWAN) collars address these constraints by compressing the raw tri-axial signal on the device into a single scalar per reporting interval, the Motion Index (MI). This onboard compression preserves enough signal to separate active behaviors but discards the per-axis and frequency content that fine-grained classification typically relies on. On a dataset of 9222 labeled observations from 24 cows across four breeds, MI distinguishes walking from grazing reliably but fails to separate ruminating from resting; both correspond to a stationary animal and yield near-zero, statistically indistinguishable distributions. Earlier MI-only models reached only about 65% four-class accuracy, and ruminating was commonly merged into resting. We show that much of this loss can be recovered by treating the MI stream as a time series. Session-aware lag features, rolling statistics, and an autoregressive previous-behavior feature lift four-class macro-F1 from 0.647 to 0.94, with per-class F1 of 0.95 for ruminating and 0.92 for resting (and at least 0.92 for every behavior). In autonomous deployment the previous behavior must be predicted rather than observed; for this setting we add a Viterbi sequence-decoding step that combines the classifier’s per-step outputs with a learned behavior-transition model, recovering a substantial part of the ruminating signal from the activity stream alone while keeping walking and grazing reliable. The gain is consistent across seven classifiers and four genetically distinct breeds, indicating that it is driven by the features rather than by a specific model. Full article

Hydrogen is a promising clean-energy carrier, but its low ignition energy, high diffusivity, and wide flammability range demand reliable leak detection. Chemiresistive sensors based on n-type metal oxide semiconductors are attractive owing to their simple architecture, low cost, large resistance modulation, thermal robustness, and compatibility with miniaturized devices. This review focuses on n-type metal oxide semiconductor nanomaterials for hydrogen sensing, particularly ZnO, SnO₂, In₂O₃, WO₃, TiO₂, and related mixed oxides. The fundamental sensing mechanisms are examined, including oxygen chemisorption, electron-depletion-layer modulation, grain-boundary barrier control, catalytic hydrogen spillover, and hydrogen-induced surface reduction or metallization, together with the way these mechanisms compete and cooperate under different operating conditions. Recent performance-enhancement strategies are organized around morphology and porosity control, noble-metal sensitization, defect and dopant engineering, n–n heterojunctions, molecular sieving, and low-temperature activation. Density functional theory is discussed as a design tool for evaluating adsorption energetics, vacancy formation, work-function shifts, band alignment, and interfacial charge transfer, along with its current limitations for modeling humid surfaces. Finally, key challenges and future directions, including humidity tolerance, standardized reporting, device integration, and emerging materials, are summarized to guide the development of high-performance hydrogen sensors. Full article

The integration of photovoltaic (PV) and wind turbine (WT) systems into modern power grids demands not only accurate component-level models but also a holistic understanding of their coordinated operation. This review bridges the gap between low-level device physics and high-level system coordination, offering a dual perspective often overlooked in existing surveys that treat generation and management separately. We systematically analyze PV models, from single-diode equivalent circuits to data-driven approaches, and WT models, ranging from aerodynamic and mechanical representations to simplified electrical equivalents suitable for stability studies. Critically, we then shift focus to the system level by examining energy management systems (EMS) that enable hybrid PV–WT coordination. Unlike prior reviews that emphasize either component accuracy or dispatch strategies alone, this paper highlights the emerging synergy between hybrid PV–WT modeling and EMS architectures. By identifying mismatches between model fidelity and EMS requirements, this review maps a pathway towards more integrated hybrid renewable systems. The discussion synthesizes key trade-offs in scalability, uncertainty handling, and real-time feasibility, underscoring that true potential is unlocked only through intelligent integration of component models and control architectures. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

Permalloy (Py) is a crucial component in spin nano-oscillators due to its excellent soft magnetic properties. Due to orbital angular momentum quenching, Py exhibits very low magnetic damping. It reduces intrinsic energy dissipation during precession, which is beneficial for lowering operational power consumption and enhancing the thermal stability of certain memory devices. But lower magnetic damping limits its application in fast-switching spintronic devices. Thus, in this work, the rare earth element Gd is introduced into Py to further enhance the spintronic performance of Py_100−xGd_x alloys. Through spin-torque ferromagnetic resonance measurements (ST-FMRs), the maximum spin Hall angle of the system was calculated to be 0.149 when x = 20, significantly exceeding that of 0.042 in the pure Py sample. Additionally, Gd doping significantly enhances the ability to modulate the magnitude of the linewidth. Also, as the Gd content in the alloy increased, the magnetic damping coefficient of the device gradually rose, reaching a peak in the sample with 17% Gd content. The maximum magnetic damping coefficient of the Py-Gd alloy was 0.051, representing an approximate 2.4-fold increase compared to that of pure Py. The findings of this study confirm that the use of rare-earth elements is highly effective in tuning the performance of spintronic devices and provide support for the development of highly efficient SOT devices. It is noted that the regulation of magnetic damping by Py-Gd holds significant implications for enhancing the magnetization switching speed of SOT devices and reducing the drive current density for microwave emission in spin nano-oscillators. Full article

Phosphoric acid-doped polybenzimidazole membranes are a leading fluorine-free electrolyte platform for high-temperature proton exchange membrane fuel cells, enabling proton transport under anhydrous conditions. However, recent evidence shows that conductivity, mechanical stability, and acid retention are intrinsically coupled, preventing independent optimization of these properties. This review establishes a unified framework in which membrane performance is governed by a multidimensional design space defined by acid doping level, activation energy (E_a), hydrogen-bond network topology, and mechanical confinement. Conductivity is shown to scale with both carrier density and hopping energetics, while mechanical stability decays with increasing ADL due to acid-induced plasticization, described through a semi-empirical relationship. Analysis across molecular architectures, including molecular weight control, crosslinking, backbone modification, topological design, and free-volume engineering, demonstrates that performance emerges from a balance between transport efficiency and structural stability. Device-level benchmarking further reveals that similar conductivity values can correspond to orders-of-magnitude differences in voltage decay rate, confirming that durability is governed primarily by mechanical confinement and acid mobility rather than σ alone. A multivariate stability corridor is identified, within which phosphoric acid-doped polybenzimidazole membranes achieve σ ≈ 0.14–0.20 S·cm⁻¹ while maintaining low degradation rates under realistic high temperature proton exchange membrane conditions. Based on this framework, quantitative design rules are derived linking acid doping level, activation, topology, and mechanical properties. This work shifts membrane design from conductivity-driven optimization toward predictive structure–property–durability engineering, providing a basis for the development of next-generation HT-PEM fuel cells with sustained long-term performance. Full article

Self-powered integrated electrochemical systems require electrode materials that can simultaneously provide energy storage and sensing functions. Boron-doped diamond (BDD) electrodes have good chemical stability and a wide potential window, but their small specific surface area and slow interfacial charge transfer limit their use in such bifunctional applications. In this work, we prepared a three-dimensional porous BDD scaffold on titanium foam by hot-filament chemical vapor deposition, and then grew polypyrrole (PPy) layers on the scaffold by in situ oxidative polymerization. The polymerization time was varied from 8 to 20 h. The BDD/PPy composite obtained after 12 h showed an areal capacitance of 398.6 ± 15.2 mF/cm² at 1 mA/cm², which is about 5.8 times that of the porous BDD alone (67.9 mF/cm²). Its charge transfer resistance (R_ct) was as low as 1.3 ± 0.1 Ω, among the lowest reported for BDD-based electrodes. The porous BDD framework provides ion diffusion pathways, while the PPy layer introduces pseudocapacitance. X-ray photoelectron spectroscopy reveals that the PPy layer contains pyrrolic –NH– groups, which are known to chelate various water pollutants (e.g., heavy metal ions and organic molecules). Based on these surface properties and the low R_ct, we suggest that this composite may have theoretical potential for preconcentrating and detecting multiple pollutants. This work demonstrates a way to improve the capacitance of BDD-based electrodes and may serve as a starting point for future exploration in integrated energy-sensing devices after experimental validation. Full article