Search Results (170)

The global navigation satellite system (GNSS), the main time synchronization method for phasor measurement units (PMUs) in smart grids, is highly vulnerable to time synchronization attacks (TSAs). This affects the timing of results and poses a serious threat to the safe and stable operation of power systems. To quickly detect TSAs and minimize the impact of time errors on PMU sensor networks, a TSA detection method based on carrier Doppler Pearson correlation coefficient estimation is proposed. This method can be directly implemented on existing commercial receivers without modifications. The method leverages the fact that carrier Doppler shifts in each satellite channel exhibit consistent changes when subjected to a TSA; therefore, if there is a correlation between channels, a consistent change in carrier Doppler shift caused by the TSA can be quickly detected through Pearson correlation coefficient estimation. In the TSA detection experiment, the proposed method was compared against four existing TSA detection methods on a self-developed experimental platform. The experimental results show that compared with the other four methods, the proposed method responds 4–22 s faster and has better detection speed, with more significant changes in the detection statistics. Notably, these advantages become more pronounced as the spoofing speed decreases and the spoofing stealthiness increases, indicating that this method has robust detection capability against sophisticated attacks. Meanwhile, it offers a lightweight computational overhead suitable for embedded PMU implementations, enhancing sensor-layer security in critical infrastructure. This work provides reliable synchronized measurements for power system monitoring and control over a wide area. Full article

Biodegradable metals represent a paradigm shift in orthopedic fixation by providing temporary mechanical support synchronized with bone healing while eliminating long-term complications associated with permanent implants. Conventional bioinert alloys, including stainless steels, Ti-based alloys, and Co-Cr alloys, exhibit high elastic moduli that induce stress shielding and often require secondary removal surgeries. In response, resorbable metallic systems based on Mg, Zn, and Fe have emerged as promising alternatives. Among these, Fe-Mn-C alloys stand out for load-bearing applications due to their exceptional strength-ductility balance governed by twinning-induced plasticity mechanisms, tunable degradation behavior, and intrinsic magnetic resonance imaging compatibility through austenitic phase stabilization. Focusing on Fe-Mn-C alloys, this review critically examines the metallurgical design principles underlying stacking fault energy optimization, phase stability, and Mn-controlled electrochemical behavior. Processing innovations, such as additive manufacturing, are discussed as tools to architecture porosity, refine microstructure, and accelerate degradation by graded designs while preserving mechanical structural support during healing. Hybrid metallic-bioactive systems, surface functionalization strategies, and functionally graded porous architectures were evaluated as advanced approaches to enhance osteointegration and modulate degradability. Despite these advances, significant barriers remain for clinical translation. Persistent discrepancies between in vitro and in vivo degradation rates, often attributed to biological encapsulation and degradation product accumulation, complicate lifetime prediction. Localized corrosion at microstructural heterogeneities such as twin boundaries and phase interfaces can undermine structural reliability under load-bearing conditions. Moreover, predictive multi-physics modeling frameworks capable of coupling electrochemical kinetics, mechanical loading, microstructural evolution, and bone remodeling remain underdeveloped, limiting reliable safety-margin estimation. Regulatory progress is further hindered by the absence of standardized testing protocols specifically tailored to Fe-based biodegradable alloys, including harmonized degradation rate windows, validated corrosion-mechanics coupling methodologies, and clinically defined Mn ion release thresholds. This review aims to discuss whether Fe-based alloys, especially Fe-Mn-C alloys, can transition from promising laboratory materials to clinically viable next-generation orthopedic implants capable of delivering patient-specific, mechanically compatible, and biologically synchronized temporary fixation. Full article

Targeting the long-term goal of synchronous acquisition of underwater terrain and material composition information, this study establishes a radiative transfer model for underwater hyperspectral LiDAR (UDHSL) and systematically verifies the effects of target reflectance, detection distance, and laser wavelength on backscattering echo intensity through controlled laboratory experiments. A wavelength-dependent water attenuation correction term incorporating absorption and scattering was introduced into the conventional LiDAR equation to derive a hyperspectral LiDAR radiative transfer equation applicable to underwater environments, and a normalized echo intensity processing method using window glass reflection as a reference was proposed. This study uses a custom-built UDHSL system (wavelength range: 450; detection range approximately 5–6 m). The echo intensity exhibits pronounced wavelength selectivity, peaking at 450–550 nm in clear water and shifting to 530–570 nm in turbid water. These experimental results are consistent with theoretical predictions of the radiative transfer model, validating its fundamental correctness and providing an experimental basis for radiometric calibration and underwater target reflectance retrieval of UDHSL systems. Full article

Light-responsive hydrogel-based oscillators typically exhibit small oscillation amplitudes because solvent diffusion is intrinsically slow, and their dependence on external periodic light modulation further results in limited amplitude, poor stability, and insufficient autonomy. Inspired by the trigger and sliding mechanism of the ancient crossbow, this study introduces an innovative system that integrates a sliding-block mechanism with time-delay feedback, breaking from conventional approaches that rely on hydrogel inertia or external modulation, within a purely theoretical and simulation-based framework. By establishing a nonlinear dynamic model coupling solvent diffusion, photoisomerization, and optical attenuation, this research shows through numerical simulations that the system can exhibit two distinct modes under constant illumination: a stable state and a self-sustained oscillatory state. The model predicts that the oscillation frequency can be flexibly tuned by varying key parameters, including the crosslinking density, Flory–Huggins interaction parameters of the spiropyran and hydrophilic polymer, ring-opening reaction rate, light intensity, fraction of light-sensitive molecules, and sliding displacement, whereas the initial absorption coefficient has only a minor influence. The slider displacement is also identified as an effective means to regulate the oscillation amplitude. Furthermore, the expansion force at the container bottom is predicted to oscillate synchronously with the hydrogel’s volume change. This theoretical framework represents a paradigm shift from “static small deformation” to “dynamic large-amplitude oscillation”, significantly enhancing the mechanical responsiveness of the material. This work provides a novel and controllable strategy for the conceptual design of autonomous light-driven micromechanical systems. Full article

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation. Full article

Vegetation resilience plays a key role in ecosystem stability as climate change and human disturbance intensify. We quantified resilience via AR(1) from kNDVI data over mainland China (2000–2024), and assessed its spatiotemporal patterns, long-term causal drivers (Causal Forest), and breakpoint-related mechanism shifts (non-stationary causal networks). Resilience varied strongly across space, with higher AR(1) values concentrated in northern transition belts and inland regions. Breakpoints clustered in 2010–2018 and showed broad synchronicity nationwide. Long-term effects were dominated by environmental background states: mean variables generally outweighed variability (CV) and memory terms, suggesting that persistent climate–environment conditions primarily shaped resilience gradients. Temperature emerged as the strongest national-scale control and acted asymmetrically across metrics—TMX strongly suppressed resilience, whereas TMN tended to enhance it—while precipitation and CO₂ gained importance regionally. Driver networks reorganized markedly across breakpoints, exhibiting high edge turnover and heterogeneous lag shifts—pointing to stage-dependent restructuring that goes beyond changes in driver strength. This framework links net effects with mechanism reorganization to help diagnose vegetation resilience under non-stationary conditions. Full article

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance electric vehicles due to their power density; however, conventional field-oriented control (FOC) relies on simplified models in which electromagnetic torque is described as a function of the quadrature current component, together with constant parameters and idealized trajectories in the $i_d-i_q$ plane, limiting adaptability and reducing efficiency and operating range under real conditions. This work introduces a flux linkage vector decomposition approach for SPMSMs, in which the permanent-magnet flux is decomposed into d- and q-axis components under core saturation and integrated into an extended field-oriented control framework. An extended FOC strategy is proposed that incorporates flux linkage vector decomposition, nonlinear magnetic saturation, cross-coupling effects, and nonlinear dependencies of electrical parameters, along with resolver angle correction and dynamic modulation index management. These enhancements modify torque and voltage trajectories by shifting the voltage-limit center and improving the definition of the MTPA, FW, and MTPV regions to better match real motor behavior, enabling performance improvements. Experimental validation on an automotive powertrain using a vehicle control unit (VCU) and precalculated lookup tables (LUTs) demonstrates improvements of up to 13.5% in low-speed torque, 13.7% in high-speed power, and efficiency gains of 4–8% across operating conditions. Full article

Evolutionary reinforcement learning (ERL) offers a compelling alternative for continuous control by combining the population-level exploration of evolutionary algorithms with the gradient-based exploitation of reinforcement learning. However, applying conventional genetic operators to deep networks can be highly destructive, often inducing abrupt behavioral shifts that erase previously learned skills. Proximal distilled evolutionary reinforcement learning (PDERL) addresses this issue with phenotype-aware operators, leveraging proximal mutation and distillation crossover to produce safer and more constructive variations. Despite these advances, PDERL and many ERL frameworks still exhibit a fundamental evaluation asymmetry: an evolving actor population is guided by a single, centralized critic for fitness evaluation and action filtering. This single-critic dependence creates a bottleneck and a potential single point of failure, where bias or instability in value estimation can misdirect the evolutionary search. To overcome this limitation, we propose co-evolutionary proximal distilled evolutionary reinforcement learning (Co-PDERL), a heterogeneous dual-population framework that co-evolves both actor and critic populations. Co-PDERL extends phenotype-aware evolution to the value-function landscape via a loss-filtered distillation crossover and a Jacobian-based proximal mutation tailored for critics, and employs a condition-gated synchronization mechanism to enable robust bidirectional knowledge transfer between the evolutionary populations and the reinforcement learning agent. Experiments on MuJoCo continuous control benchmarks show that Co-PDERL outperforms competitive baselines on most tasks, including standard ERL and PDERL, improving both sample efficiency and asymptotic performance by effectively alleviating the single-critic bottleneck. Full article

Nitrogen (N) availability is a critical determinant of grain yield and protein quality in wheat (Triticum aestivum L.). To elucidate the molecular mechanisms underlying nitrogen response associated with nitrogen use efficiency (NUE), a comparative transcriptomic analysis of high grain protein content (HP) and low grain protein content (LP) wheat lines during N resupply at the seedling stage is conducted in this study, with sampling conducted at T1 (one day after treatment) and T3 (three days after treatment). Our results reveal that the HP line exhibits an early-responsive and well-coordinated metabolic pattern, whereas the LP line shows a distinct temporal response characterized by delayed adjustments. Integrated GSEA and KEGG analyses demonstrated that the HP line prioritized protein processing in the endoplasmic reticulum and diterpenoid biosynthesis, potentially associated with enhanced protein quality control and early signaling efficacy. This allows the HP line to synchronize its N assimilation machinery with the transient peak of N availability at T1 and establishes a robust foundation for protein accumulation. Conversely, the LP line redirected its metabolic resources toward glutathione metabolism and flavonoid biosynthesis to mitigate N-induced oxidative instability. This metabolic shift increases the energetic usage required for antioxidant defense and subsequently deviates resources away from productive N assimilation. These divergent metabolic landscapes were orchestrated by a hierarchical network of transcription factors (TFs). In leaves, the MYB and NAC families showed a more disciplined and immediate increase in the HP line, whereas the LP line demonstrated a delayed peak at T3. In root tissues, while Dof and NAC families were rapidly induced and concluded in the HP line, the LP line exhibited a sluggish sensing-to-response mechanism with prolonged or specific late-stage activation at T3. These results indicate that the capacity for rapid metabolic synchronization and disciplined transcriptomic mobilization is a key physiological indicator of high-protein potential in wheat. This insight provides essential molecular targets for breeding programs aimed at improving NUE and grain quality. Full article

Electrochemical discharge machining (ECDM) with low machining reaction forces is useful for machining hard and brittle materials, which are required in precision equipment. Lathe-type ECD machines have been proposed to machine axisymmetric shapes while reducing cracks caused by thermal expansion, and they are suitable for thin workpiece machining due to the small reaction force. This paper demonstrates the micromachining of non-circular cross-sections using a lathe-type ECD machine equipped with an improved force-controlled tool electrode holder. The tool electrode holder combining a voice coil motor (VCM) with leaf springs arranged in parallel was built. This holder achieves both flexibility in the longitudinal direction of the tool electrode and high rigidity in the lateral direction. The relationship between the VCM current, tool electrode shift within the tool electrode holder, and thrust force was approximated using a polynomial. Consequently, this device allows for the stable, small contact force required in micromachining. An on-machine shape measurement method was also carried out by combining the tool electrode shift with the motion of an XZ stage. As a demonstration for non-circular cross-section machining, a square cross-section was grooved from a cylindrical glass rod. The removal and measurement processes were alternately repeated to achieve precision. During ECDM, the on/off of the DC power supply for ECDM was synchronized with the rotation of the workpiece. The measurement results indicated some dimensional errors, including bulging at the middle of sides and excessive removal at corners. The bulging was mainly caused by drift due to thermal expansion of the stage, as well as tool electrode wear. Since the tool electrode comes into close proximity to with the machined surface, the discharge from the side surface of the tool electrode caused excessive removal at the corners. Full article

The transition from conventional synchronous generators to inverter-based power systems has introduced significant challenges in stability, reliability, and protection coordination. Grid-forming inverters (GFMs) have emerged as a promising solution by emulating inertia and voltage regulation functions while enabling grid-supportive operation in weak or islanded networks. This study presents a structured qualitative review of the recent literature on GFM technologies. The selection process focused on control strategies, advanced semiconductor materials, protection frameworks, and cyber–physical security considerations. A thematic synthesis and comparative analysis were conducted to identify emerging trends and technical gaps. Among established approaches, virtual synchronous machine (VSM) and droop control remain widely adopted. More advanced strategies, including virtual oscillator control (VOC) and model predictive control (MPC), demonstrate improved dynamic performance in weak-grid conditions. Advances in semiconductor technologies, particularly Silicon Carbide (SiC) and Gallium Nitride (GaN), enable faster switching, higher efficiency, and enhanced thermal performance. The findings indicate a growing shift toward decentralized control architectures, fault-resilient converter topologies, and integrated protection–control co-design. Emerging solutions include grid-forming synchronization techniques that replace conventional phase-locked loop (PLL) structures, intrusion-tolerant inverter firmware with embedded anomaly detection, and predictive fault-clearing schemes tailored for low-inertia networks. Despite these advancements, several research gaps remain. These include limited large-scale validation of VOC and MPC strategies under high renewable penetration, insufficient interoperability metrics for legacy system integration, and a lack of standardized cybersecurity benchmarks across platforms. Future research should prioritize real-time experimental validation, robust protection co-design methodologies, and the development of regulatory and dynamic performance standards tailored to inverter-dominated grids. Strengthening protection coordination and interoperability frameworks will be essential to ensure the secure and stable deployment of GFMs in modern power systems. Full article

The deployment of artificial intelligence in safety-critical industrial systems is hindered by a core trust deficit, as models trained via empirical risk minimization often fail catastrophically in out-of-distribution (OOD) scenarios. We address this challenge by developing a physics-informed hybrid ensemble that achieves state-of-the-art accuracy and robustness for Permanent Magnet Synchronous Motor (PMSM) temperature forecasting. Our methodology first calibrates a Lumped-Parameter Thermal Network (LPTN) to serve as a physics engine for generating physically consistent data augmentations, which then pre-trains a Temporal Convolutional Network (TCN) encoder via self-supervision, with the final prediction assembled from the physics model’s baseline guess and a correction learned by an ensemble of gradient boosting models on a rich, multi-modal feature set. Evaluated against a suite of strong baselines, our hybrid ensemble achieves a state-of-the-art Root Mean Squared Error of 5.24 °C on a challenging OOD stress test composed of the most chaotic operational profiles. Most compellingly, our model’s performance improved by an unprecedented −10.68% under these extreme stress conditions where standard, purely data-driven models collapsed. This demonstrated robustness, combined with a statistically valid Coverage Under Shift (CUS) Gap of only 1.43%, provides a complete blueprint for building high-performance, trustworthy AI, enabling safer and more efficient control of critical cyber-physical systems and motivating future research into physics-guided pre-training for other industrial assets. Full article

Differential light shift (DLS) is an important error term that limits the atom interferometer’s measurement precision, especially for the case of the electro-optic modulator (EOM)-based scheme, where multiple laser sidebands exist, and their ratios are hard to control synchronously. This article carried out an experimental and theoretical study on this subject. By conducting long-term gravity measurement, we find that the gravity exhibits drifts of about 13.13 μGal, and is strongly correlated to the Raman laser’s sidebands. A model of the DLS-induced gravity error is established and a DLS compensation method is proposed to suppress the gravity drift to 2.54 μGal. Besides the compensation method, we propose a Dual-Sideband Ratio Locking scheme to more robustly eliminate the gravity measurement drift. By feeding back to both the EOM microwave power and the tapered amplifier’s temperature, this method locks both the ±1 order sideband to a stability level of ${10}^{-5}$, which corresponds to a gravity error of less than 0.1 μGal. Long-term gravity measurement is carried out after the locking method, showing a long-term stability of 1.6 μGal. The proposed methods will benefit the suppression of the DLS effect for high-precision atom interference measurement. Full article

Understanding the ecological behavior of invasive species is essential for assessing their impacts on native biodiversity. This study examines the basking dynamics of the invasive freshwater turtle Trachemys scripta in a Mediterranean wetland within Mesir Nature Park, Türkiye. Data were collected between March and October 2024 using camera traps, yielding 72,456 cumulative basking observations. Principal Component Analysis (PCA) revealed a high degree of environmental synchronization (PC1 = 97.24%), indicating that basking activity is strictly governed by ambient thermal availability. Furthermore, Negative Binomial Regression (NBR) was employed to evaluate temporal shifts and behavioral plasticity. The basking intensity exhibited distinct seasonal transitions, characterized by afternoon peaks during the spring and autumn and an opportunistic shift toward early morning activity during the summer to mitigate thermal constraints. The peak basking duration recorded in May (696.00 ± 10.25 min) and the bimodal activity observed in summer reflect a significant adaptive capacity. These patterns suggest that Mediterranean wetlands provide optimal conditions for the persistence of Trachemys scripta. The species’ ability to effectively track environmental cues and monopolize thermal resources implies a high potential for the ecological displacement of native turtles, particularly Mauremys rivulata. This study provides critical quantitative baseline data in order to inform evidence-based management and control strategies in the Mediterranean region. Full article

TMR-based magnetic encoders provide sensitive SIN/COS signals, but practical accuracy is degraded by channel mismatch and decoder dynamics. This study evaluates an end-to-end embedded implementation on a PMSM (Permanent Magnet Synchronous Motor) bench. We consider amplitude mismatch, quadrature non-orthogonality, and harmonic/noise disturbances in the measured differential channels. We implement a lightweight compensation chain, including fixed-window moving-average filtering, min–max amplitude normalization, and correlation-based quadrature identification with sample-shift correction. We then compare four demodulation configurations under identical sampling and reference alignment to a 24-bit encoder: (A0) conventional second-order PLL (phase locked loop), (A1) compensation + open-loop atan2, (A2) compensation + fixed-${\omega }_n$ third-order PLL, and (A3) compensation + adaptive-${\omega }_n$ third-order PLL. Experiments with a TMR3081 sensor and an STM32 controller show clear differences among A0–A3. In steady operation, A3 removes the DC bias observed with A0 and keeps the angle error within approximately ±0.3° in the evaluated steady window. During commutation and ramp-like segments, PLL-based tracking (A0/A2/A3) is more robust than open-loop atan2 (A1), and bandwidth adaptation in A3 improves the acquisition–noise trade-off within the preset ${\omega }_n$ bounds. These results are reported for this prototype and the tested parameter settings. Full article