Search Results (2,141)

Upper-limb motor dysfunction resulting from neurological disorders severely limits patients’ activities of daily living and social participation. Pneumatic upper-limb rehabilitation robots have emerged as a promising intervention owing to their inherent compliance, lightweight design, and high power-to-weight ratio, which facilitate safe, repetitive, and home-based training. Despite these advantages, extensive clinical translation remains hindered by challenges including actuator hysteresis, nonlinear dynamics, limited accuracy in intention recognition, and inconsistent clinical evaluation metrics. This review systematically examines recent advancements in pneumatic upper-limb rehabilitation robots across four critical dimensions: structural design, human–robot interaction, control strategies, and clinical translation. We comparatively analyze rigid exoskeletons, soft wearable devices, and rigid–soft hybrid configurations based on output capability, motion accuracy, comfort, and clinical applicability. The findings suggest that while rigid systems offer high precision and soft systems maximize safety, rigid–soft hybrid architectures represent a critical developmental trend for balancing motion accuracy with interaction compliance. Furthermore, the review evaluates multimodal sensing techniques (e.g., EMG, EEG, and IMUs) for motion intention decoding and training state monitoring, alongside conventional, adaptive, and artificial intelligence-driven control methods aimed at compensating for pneumatic nonlinearity and improving real-time response. Current clinical evidence indicates that these systems effectively enhance upper-limb function and muscle strength, particularly in post-stroke rehabilitation; however, existing trials are frequently constrained by small sample sizes, short interventions, and heterogeneous protocols. Future research must prioritize rigid–soft hybrid architectures, robust multimodal sensor fusion, digital twin-assisted assessment, adaptive intelligent control, and standardized home-based rehabilitation platforms. Ultimately, this comprehensive review provides a concise reference for the design optimization and clinical deployment of next-generation pneumatic rehabilitation systems. Full article

The very fast transient overvoltage (VFTO) generated by the operation of disconnectors in gas-insulated switchgears (GIS) features a wide frequency band and a steep rising edge, posing a severe threat to the safe and stable operation of power grids. Accurate measurement of VFTO is a core prerequisite for the insulation protection design of power equipment. Aimed at solving the problem that ultra-high frequency (UHF) sensors widely installed in existing GIS are limited by—the built-in inductance and inability to fully reconstruct the VFTO waveform—this paper proposes a broadband VFTO measurement method based on modified UHF sensors. The VFTO measuring system based on UHF sensors was constructed and tested by modifying the structure of UHF sensors and a broadband pulse source capable of outputting equivalent frequencies from 1 kHz to 10 MHz. The results showed that the measuring system exhibited excellent high-frequency (100 kHz~1 MHz) response performance but poor low-frequency (~1 kHz) response. By introducing a voltage-follower impedance transformation module based on a high-speed operational amplifier, the UHF measuring system could accurately reconstruct the VFTO waveform during the opening and closing of the 750 kV GIS disconnector. This non-contact measurement scheme provided a highly promising and reliable technical path for on-site VFTO detection for in-service equipment without altering the existing structure of GIS. Full article

During coal mine roof fracturing drilling, vibration signals from the drilling tool can reflect both the drilling state and the structural response of the roof. However, traditional vibration sensors generally depend on batteries or wired power delivery, which hinders their long-term deployment in underground monitoring environments. To overcome this limitation, a self-powered vibration sensor featuring a wedge-shaped column array structure was developed, enabling vibration-induced electrical signal generation through the triboelectric effect. The sensor utilizes drilling-induced vibration to trigger cyclic contact–separation between the nanolayers, thereby converting vibration energy into electrical signals associated with the vibration frequency. In this way, the sensor can achieve both vibration frequency measurement and energy harvesting. The sensor was experimentally verified to enable reliable frequency detection across the 0–9 Hz range, with a measurement error of less than 3%. It also retained stable operational performance under temperatures up to 100 °C and relative humidity below 90%. Moreover, the output power reached a maximum value of 8 × 10⁻⁷ W with an external load of 10⁸ Ω. The developed sensor enables self-powered vibration frequency measurement, while its redundant vibration structure enhances operational reliability. These features make it suitable for underground coal mine drilling environments characterized by limited space and strong mechanical vibration. Full article

Agricultural expert forums serve as important platforms for knowledge exchange, containing large volumes of text that embody professional expertise and practical experience. However, because experts often focus on different agricultural subfields and category semantics may overlap, traditional text classification methods that rely solely on textual content often show limited discriminative power. To address this issue, this paper proposes a text classification model for agricultural expert forums that integrates expert domain preferences with deep semantic representations. Specifically, the model incorporates an expert domain preference matrix and an expert semantic vector matrix into the deep semantic encoding of forum texts to construct interaction representations between experts and categories, thereby enabling collaborative modeling of textual semantics and expert domain preferences. To further improve classification performance, a category-level enhancement strategy based on domain attention is introduced at the output stage. This strategy uses candidate category masking and probability constraints to guide the model in adaptively aligning its predictions with experts’ long-term domain focus patterns. Experimental results on over 110,000 text sentences show that the proposed model improves average precision by approximately 12.7% and the overall F1 score by around 10.1%, with stable gains in both accuracy and recall. Compared with mainstream encoder-based classification models, the proposed model achieves an average accuracy improvement of 11.67%, demonstrating significant and robust performance advantages. Overall, the proposed model enhances both the accuracy and robustness of text classification in agricultural expert forums while improving interpretability, providing effective technical support for agricultural knowledge management and intelligent information retrieval. Full article

The rapid development of wearable electronics has created a demand for flexible, lightweight, and sustainable power-supply technologies. The persistent temperature difference between the human body and the environment provides a low-grade thermal source for thermoelectric energy harvesting. However, traditional flexible thermoelectric devices still face limited self-supporting capabilities and difficulties in maintaining sufficiently low cold-side temperatures. Here, we designed a passively radiative-cooled thermoelectric film (PRT film) by integrating a PAM/PEDOT:PSS self-supporting thermoelectric composite layer with a polymer metamaterial radiative cooling (PMRC) film. The PAM/PEDOT:PSS layer serves as a self-supporting thermoelectric conversion component for harvesting low-grade heat, while the PMRC film layer acts as a passive cold-side regulator without energy input to lower the cold-side temperature and enhance the temperature gradient. By optimizing the PAM content, the PAM/PEDOT:PSS composite material with 85 wt% PAM achieved the highest power factor of 72.3 μW m⁻¹ K⁻². Under a temperature difference of 39 °C, the optimized PAM/PEDOT:PSS sample provided an open-circuit voltage of 0.47 V, a maximum output power of 1.1 μW, and a power density of 11.2 μW cm⁻². According to the temperature-difference enhancement measured in experiments and the independently obtained load characteristics, the integration of PMRC films is expected to increase the maximum output power from 1.1 to 1.4 μW, with the corresponding power density rising from 11.2 to 14.25 μW cm⁻², representing a 27.2% enhancement. This work demonstrates the feasibility of passive radiative cold-side regulation in enhancing low-level thermoelectric energy harvesting for wearable applications. Full article

The growing share of photovoltaic generation in power grids intensifies the need for converter architectures capable of combining efficient energy conversion, DC-bus stability, and ancillary service provision at the grid coupling point. This paper presents the modeling, implementation, and simulation-based evaluation of a 91 kWp three-phase photovoltaic (PV) system integrated with a battery energy storage system (BESS), developed in the PLECS environment. The proposed architecture comprises three interleaved Boost stages for maximum power point tracking (MPPT), a DC bus regulated at 600 V, three independent bidirectional buck–boost converters for LiFePO${}_4$ bank management, and a two-level three-phase voltage source inverter (VSI) with an LC output filter. The control is organized in cascade voltage–current loops for the DC–DC stages and in vector control within the synchronous reference frame (SRF) for the inverter, with synchronization via SRF-PLL. A C-Script supervisory block integrates the Perturb and Observe (P&O) MPPT algorithm, independent state of charge (SOC) estimation per bank via coulomb counting, and Volt–VAR reactive power reference generation with a dead band of 0.90–1.10 pu. Five scenarios are analyzed for validation: DC-bus regulation under irradiance transients; reactive power support during undervoltage and overvoltage events (0.80–0.85 pu and 1.15–1.20 pu); BESS operation as an active DC-link support element; and PV curtailment with fully charged banks. All five scenarios were additionally corroborated on a Typhoon HIL402 Pro 2 hardware-in-the-loop platform, reproducing the PLECS waveforms within the amplitude and timing resolution of the oscilloscope captures. Across all scenarios, the DC bus is held within $\pm 15$ V (2.5%) of the 600 V reference, with the worst-case transient recovering in 80–100 ms; under a sustained 9 s bidirectional disturbance, redirecting PV surplus to BESS charging in both the undervoltage and overvoltage segments—with no externally imposed active-current limit—keeps the current-vector magnitude $\sqrt{i_d^2+i_q^2}$ below the 335 A rating throughout (≈271 A and ≈242 A, respectively), while the available reactive margin $Q_{disp}$ reaches ≈78– 80 kVAr in both segments and the bank SOC advances by ≈$0.03$ pu; and supervisory curtailment under a sustained overvoltage ride-through with a saturated bank keeps the per-bank SOC dispersion within $4\times {10}^{-5}$ pu while expanding the available reactive margin $Q_{disp}$ from ≈50 to ≈90 kVAr. Full article

With the large-scale integration of distributed photovoltaic generation (PV) into modern distribution networks, the inherent stochasticity and volatility of renewable energy outputs have imposed non-negligible impacts on the secure and economic operation of power systems. Conventional probabilistic power flow (PPF) methods are limited in accurately modeling source–load uncertainty and, more importantly, in capturing complex nonlinear and time-varying dependence among multiple renewable energy sources. To address these issues, this paper proposes a novel PPF calculation framework based on advanced source-load modeling and time-varying D-vine Copula. Firstly, an enhanced finite mixture Beta model and a Gaussian cluster mixture model are developed to characterize the uncertainty of PV output and load demand, respectively. Secondly, a time-varying D-vine Copula model based on the generalized autoregressive score framework is constructed. And a two-stage regularized profile likelihood estimation method is proposed to estimate correlation parameters, capturing the dynamic nonlinear dependence among multiple PV generators. Finally, the de-randomized Sobol sequence-based Quasi-Monte Carlo method is adopted to perform stochastic power flow calculation. Simulation results on a real-world 129-bus distribution system in East China verify the accuracy and effectiveness of the proposed method. Full article

Existing long-wave infrared (LWIR) BaGa₄Se₇ optical parametric oscillators (BGSe-OPOs) adopt linear-cavity configurations yet struggle to balance high beam quality and high output energy. To overcome this limitation, we report a compact Type I phase-matched ring cavity BGSe-OPO pumped by a 1.06 μm laser. Operating at 8.5 μm, the OPO generates 1.2 mJ single pulses with a peak power of 0.25 MW and an optical-to-optical conversion efficiency of 2%. The estimated beam quality factor M² is 9, representing a threefold enhancement relative to linear-cavity under identical pump conditions. The system features a compact footprint of 400 × 200 mm² and a far-field divergence angle of 4 mrad after 6× beam expansion, enabling practical applications in far-field monitoring. Full article

Electromagnetic wave manipulation underpins radar detection, imaging, and electronic countermeasures. Conventional phased-array and radio-frequency-chain-based radar architectures provide mature and high-performance operation but can face practical constraints related to aperture profile, power and thermal management, calibration, bandwidth, and multifunctional integration. Electromagnetic metasurfaces provide a complementary approach by controlling the phase, amplitude, polarization, and frequency content of scattered or radiated fields through subwavelength surface elements. This article presents a radar-system-oriented review of metasurface-enabled wave manipulation. We first summarize the relevant physical mechanisms, including generalized scattering, digital and information metasurfaces, time-varying modulation, and polarization and geometric-phase control. We then review experimentally reported applications in radar-cross-section control, programmable beam steering, computational imaging, time-modulated radar, multiple-input multiple-output systems, and integrated sensing and communication. Particular attention is given to the level of experimental validation and to the distinction between measured device- or subsystem-level performance and anticipated system-level benefits. Potential applications in stealth, radar deception, low-probability-of-intercept-oriented operation, and cognitive sensing are discussed together with limitations in bandwidth, efficiency, power handling, biasing, calibration, thermal management, and scalability. Overall, the available literature indicates that metasurfaces can support selected aperture-level radar functions, whereas general system-level advantages in SWaP, cost, latency, and energy efficiency remain to be established through controlled comparative experiments. Full article

Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy harvesting (EH) offers a promising approach toward low-maintenance and partly autonomous sensing, but its practical value in building automation depends on more than the output of individual transducers. This article presents a structured review of EH for IoT/WSN and edge-enabled building automation, focusing on smart-building, Building Management System (BMS) and Building Automation and Control System (BACS) contexts. Light-based, thermoelectric, mechanical, RF/wireless-power-transfer and hybrid harvesting technologies are interpreted through a system-oriented chain linking energy sources, power management, storage, communication, adaptive operation, gateways, diagnostics and edge intelligence. The synthesis shows that EH is most promising for low-duty-cycle environmental monitoring, envelope and façade sensing, occupancy and human–building interaction, airflow-related sensing, technical monitoring and retrofit automation. The main challenges concern the transition from device autonomy to sensing-service autonomy, complete-node evaluation under real building conditions, interoperability with supervisory systems and diagnostic interpretation of intermittent operation. Further research is also needed on lifecycle value assessment and safe transferability toward remote, temporary, resilient and closed ecological infrastructure applications. Full article

Satellite battery subsystems are fundamental for reliable operation during periods when solar power is unavailable. Their performance is strongly influenced by the harsh and variable thermal conditions of the space environment. Pre-launch thermal simulations are routinely employed to evaluate subsystem behaviour; however, their ability to fully represent in-orbit dynamics remains limited. Meanwhile, machine learning (ML) approaches have emerged for satellite health monitoring, yet their practical role alongside conventional thermal analysis is not clearly established. This study investigates satellite battery temperature prediction by integrating pre-launch thermal simulation, real in-orbit telemetry, and data-driven ML forecasting within a unified framework. A dataset of 27,552 temperature measurements from an operational low-Earth-orbit satellite was analysed. A sliding-window regression approach was used to predict one-hour-ahead minimum and maximum battery temperatures, consistent with operational thermal margins. Three models—linear regression, Random Forest, and Extreme Gradient Boosting—were trained using historical temperature data and evaluated against telemetry and simulation outputs using MAE, RMSE, and R² metrics. Results indicate that linear regression achieved the highest accuracy (R² up to 0.98, MAE ≈ 0.20 °C), outperforming more complex models. ML-based predictions captured thermal behaviour more effectively than static simulation outputs under nominal conditions, while all predicted values remained within the acceptable operational range (10–30 °C). Rather than replacing physics-based methods, this work demonstrates that interpretable ML models can serve as an effective real-time complement to thermal simulations, offering practical insights into model selection and enhancing satellite battery thermal monitoring. Full article

The existing basic control system of fractional-order modular multilevel converters (FO-MMCs) is mainly designed for ideal operating conditions and exhibits limited adaptability under grid unbalanced conditions. To address this issue, this paper proposes a fractional-order composite control system for a FO-MMC. The proposed system consists of fractional-order positive-sequence and negative-sequence control loops. The positive-sequence control loop includes a power outer-loop fractional-order proportional-integral (FOPI) controller and a fractional-order decoupled FOPI positive-sequence current inner-loop controller. The negative-sequence control loop adopts a fractional-order decoupled FOPI negative-sequence current inner-loop controller. Combined with three sequence current reference calculation strategies, the proposed system achieves three control objectives: active power oscillation elimination (APOE), reactive power oscillation elimination (RPOE), and balanced positive-sequence current output (BPSC). A FO-MMC simulation model was established based on the MATLAB/Simulink platform, and the proposed control system was verified. The results demonstrate that the proposed control system achieves accurate regulation of positive- and negative-sequence currents. Under different control objectives, the system exhibits excellent dynamic response and steady-state performance, providing a theoretical basis for stable FO-MMC operation under unbalanced grid conditions. Full article

To address the transient power surges and DC-link voltage fluctuations arising from fast frequency response demands in new power systems, this paper proposes a Natural Switching Surface (NSS) control strategy for active neutral-point clamped (ANPC) converters. First, the operating modes and working principles of the ANPC converter are analyzed, and the phase trajectory relationship between the inductor current and DC-side voltage under diverse operating conditions is mathematically derived. On this basis, a systematic NSS control law is established according to the piecewise mathematical model of the converter. Furthermore, a current-limited NSS control scheme is developed to suppress transient current spikes, which realizes smooth voltage and current output regulation and effectively mitigates power transients and DC voltage fluctuations induced by fast frequency response operations and external power disturbances. Comprehensive simulation and prototype experimental results validate the superior performance of the proposed method. Quantitative comparisons demonstrate that, compared with the conventional PI control, the proposed strategy shortens the converter startup time by 1.5 s, restricts the DC voltage drop within 15 V under power disturbance conditions (in contrast to over 60 V with PI control), and achieves faster dynamic recovery and higher operation stability. The proposed method provides an effective solution for high-performance fast frequency response and stable grid integration of renewable energy and energy storage systems. Full article

The rapid dissemination of misinformation through online social networks creates challenges for information reliability and network stability. We evaluate a multimodal framework that produces a credibility classification and a propagation-level prediction within a single processing pipeline. The framework combines frozen RoBERTa features, engineered interaction statistics, a standard GCN, and Transformer self-attention over modality representations. These are established components; the purpose of the framework is to integrate the available modalities and return both outputs from one model, rather than to introduce a new encoder or attention mechanism. On 12,701 MCFEND news items, the framework obtains 94.77% accuracy and 95.79% F1-score for credibility classification, together with an MAE of 0.34, RMSE of 0.44, and R² of 0.96 for propagation-level prediction. The small variation across five matched seeds supports the stability of these means under the fixed protocol. The credibility F1 difference from modality-matched MLP-Fusion is not significant after Holm correction (adjusted p = 0.0810), and five pairs provide limited power for detecting small differences; the two implementations are therefore interpreted as having close performance. RandomForest also obtains lower propagation errors than the evaluated framework. The implemented retrospective fractional-observation protocol observes a fraction defined by final cascade size and uses snapshot engagement values with a fixed random split. In one diagnostic, replacing the fractional-observation rule with fixed K = 15 preserves classification F1 at a similar level but reduces the framework’s propagation R² from 0.963 to 0.896. In a separate target-definition diagnostic conducted with the original observation setting, predicting residual future interactions yields an R² of 0.870. The findings therefore characterize retrospective within-dataset prediction and do not establish leakage-free early forecasting. The two outputs are interpreted separately because the present experiments evaluated one shared dual-output configuration rather than comparing it with two independently optimized systems. Full article

In industrialized countries, existing regulations generally require the use of renewable energy to the greatest feasible extent. A major difficulty with renewable sources is the inherent fluctuation in their power output due to the main source character. By now, one of the best technologies capable of providing rapid compensation for these fluctuations is hydroelectric power. Hydropower plants, those equipped with hydraulic turbines with fixed blades (e.g., Francis, propeller) are typically designed to operate close to their best efficiency point (BEP) with acceptable load limits in the vicinity due to vibrations and pressure pulsations. Usually, the swirling flow exiting the runner is tailored for peak overall efficiency, which minimizes energy losses in the draft tube cone. When operating away from the design point, draft tube cone losses increase abruptly, and pronounce flow instabilities arise (e.g., vortex rope). This study proposes a new method to control such instabilities that inject a radial-axial water jet into the draft tube cone. Compared with conventional axial water jet injection, the radial-axial jet requires a lower additional flow rate while still effectively suppressing hydraulic instabilities in the draft tube cone. The carried-out analysis was done numerically by using Ansys Fluent 2023 R2. The performed 3D unsteady numerical simulations were carried out to examine the internal flow behavior and evaluate the effect of the radial-axial water jet injection on the unsteady behavior of the flow unsteadiness. Finally, the paper quantifies the relationship between the draft tube pressure fluctuation amplitude and the auxiliary flow rate needed to mitigate these instabilities. Full article