Search Results (3,071)

Objective: Functional training exercises involve complex multi-joint movements that challenge traditional rule-based or data-driven recognition systems. This paper introduces a Movement Description Language (MDL) designed to formally represent, analyze, and evaluate such exercises using camera-based pose estimation and interpretable, composable structures. Methods: The proposed MDL models each exercise as a finite-state machine defined by pose-derived angle proxy transitions, allowing movements to be described in a modular and reusable way. Demonstrated with MediaPipe landmark extraction from monocular video, while the MDL remains compatible with any pose estimation algorithm, the framework focuses on exercise phase detection and repetition counting. Experimental validation was conducted on a dataset of 1513 videos of 12 functional exercises (squats, deadlifts, lunges, shoulder presses, planks, push-ups, pull-ups, bent-over rows, box jumps, thrusters, overhead squats, and burpees) obtained from public pose datasets, competition footage, and recordings of 9 participants in real-world environments. Results: Automated repetition counts were compared against manually annotated ground truth, showing an overall repetition-counting accuracy of 97.2%, with a mean per-exercise accuracy of 98.8% (range 95–100%). The MDL successfully handled both simple and compound exercises, maintaining reliable phase detection despite variations in execution speed, camera perspective, and environmental conditions. Conclusion: The system was implemented using real-time pose estimation to demonstrate the practical execution of the MDL framework. The proposed MDL provides a transparent, extensible, and computationally efficient framework for functional exercise analysis. By bridging human-readable movement semantics with executable motion logic, it enables interpretable automatic repetition counting and phase detection, offering an alternative to black-box recognition approaches. The results support its potential for scalable deployment in training, monitoring and movement analysis applications. The proposed system is not intended for biomechanical measurement or clinical-grade kinematic analysis, but rather for interpretable modeling of exercise structure and repetition detection using approximate pose-derived signals. Full article

To effectively address the challenge of faulty line selection during high-impedance grounding faults in distribution networks with a flexible grounding system, a novel fault line selection method that integrates both the amplitude and phase characteristics of zero-sequence currents is proposed. The characteristics of zero-sequence currents under single-phase grounding faults in a flexible grounding system are thoroughly investigated, with a particular focus on analyzing the phase relationship and amplitude differences between the zero-sequence currents of each feeder and that of the neutral point. Upon the switching of the parallel low-resistance device, the zero-sequence current of the faulty line is approximately equal in amplitude but opposite in phase to that of the neutral point. In contrast, the zero-sequence current amplitude of a healthy line is significantly smaller than that of the neutral point, and its phase is nearly orthogonal to the neutral point zero-sequence current. To capture these characteristic differences, the projection of each line’s zero-sequence current onto the neutral point zero-sequence current is employed. A projection coefficient criterion is subsequently constructed to enhance the reliability of line selection. Furthermore, by utilizing the neutral point zero-sequence current, the method can effectively extract the weak zero-sequence current of healthy lines, thereby mitigating the risk of misjudgment by the fault line selection device caused by the inability of zero-sequence current transformers (CT) to accurately acquire such faint signals. Simulation results obtained via PSCAD validate that the proposed method remains effective for single-phase grounding faults with transition resistances up to 3000 Ω, even under extreme operating conditions such as reverse polarity of zero-sequence CT or the presence of strong noise interference. Full article

To address the low accuracy of faulty feeder detection methods based on single-fault characteristics, we propose a faulty feeder detection method for resonant grounding systems that fuses multiple transient characteristics. First, we analyze the transient zero-sequence current fault characteristics of both faulty and healthy feeders during single-phase-to-ground (SPG) faults. Then, the transient zero-sequence current of each feeder is decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD), and a new signal was constructed by combining IMF1 and IMF2. Subsequently, transient energy and waveform similarity fault characteristics are extracted from the constructed signal, and a faulty feeder detection criterion based on multiple transient characteristics fusion is developed. Finally, extensive simulations and field data verify the proposed faulty feeder detection method. The results demonstrate that the method is robust against fault resistance, fault inception angle, fault location, and noise, achieving high accuracy in faulty feeder detection. This method can be widely applied to detect faulty feeders in resonant grounding systems. Full article

Based on DFT+DMFT calculations, we explore the interplay between electron correlations, lattice disorder and substrate–film interaction on the Mo-$4d$ spectra ${\mathrm{MoS}}_2$ monolayer. We show that ${\mathrm{MoS}}_2$ serves as an ideal testing ground for the exploration of weakly correlated phenomena with tunable semiconducting-to-metal phase transitions. We also show why our orbital-selective results in the dirty limit are important to understanding the emergence of substrate-induced localized in-gap states and the implication of it for future memristors for memory-based neuromorphic computing. Full article

Accurate time synchronization is essential in distributed electrical signal monitoring, where phase coherence and event correlation depend on precise timing agreement between acquisition nodes. Conventional approaches often rely on a single synchronization source, typically internet-based Network Time Protocol (NTP) or GPS-disciplined clocks, which is impractical in isolated, offline, or cost-sensitive scenarios. This paper introduces an autonomous offline synchronization architecture for multi-node monitoring systems built on Raspberry Pi 5 (RPI5) platforms connected to a private Ethernet network. Instead of depending on one timing method, the system integrates several complementary mechanisms: battery-backed RTC persistence via the J5 interface, deterministic orchestration through systemd services, automated boot time recovery, chrony-managed NTP discipline, and Precision Time Protocol (PTP) hardware timestamping using PTP Hardware Clock (PHC). Synchronization performance is validated through continuous multi-day measurements of long-term stability, inter-node phase coherence, and short-term jitter. Controlled power-loss scenarios are also included to verify recovery behavior. The system maintains sub-microsecond alignment between nodes using only commodity hardware and no external time source. To further confirm inter-node timestamp alignment at the signal level, both hardware-based reference signal injection and software-based synchronized signal emulation are employed, providing ground-truth validation alongside scalable and reproducible evaluation. The results show that low-cost embedded hardware can support reliable, long-duration synchronization in fully offline installations. Full article

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article

The increasing operational variability in distribution networks (e.g., abrupt load changes and distributed generation integration) increases the demands on voltage regulation devices and, in particular, on transformers with on-load tap changers (OLTCs). This paper develops and validates a discrete supervisory control scheme based on Petri nets, implemented in Stateflow and coupled to an electromagnetic model of the OLTC transformer in Simulink/Simscape. The Petri net formalizes the conditional and sequential logic of OLTC operation, enabling state- and time-dependent decisions (e.g., delays between maneuvers) to improve voltage regulation and reduce unnecessary tap operations. The evaluation is performed by simulation under transient scenarios that include sudden load variations anda phase-to-ground fault in the IEEE 13-node standard network, specifically at node 634. In the base case, the controller maintains the voltage within the tolerance band $\pm 1.875\%$ during 96% of the simulated time, with an 88% reduction in RMS error (from 1.92% to 0.23%) and 100% operational efficiency (16 effective maneuvers, with a single hunting event). Subsequently, the scheme is validated on the standard IEEE 13-node network, with four disturbances applied over 600 s (two load increments, photovoltaic injection, and a temporary line disconnection). In this case, regulation remains within a precision zone of $\pm 0.3\%$ for 96.8% of the time, with an average RMS error of 0.23% and 100% efficiency, with no hunting events. The results confirm that a Petri net-based supervisory logic can simultaneously improve the OLTC’s voltage quality and switching efficiency, providing a reproducible alternative for distribution network automation. Full article

This study quantifies how confinement changes the orientational phase space of proteins by comparing hanging-drop (HD) with Langmuir–Blodgett (LB) conditions within a unified probabilistic framework grounded in structural data from the Protein Data Bank (PDB). For each protein, principal moments of inertia are computed from atomic coordinates, trace-normalized, and used to define a geometry-based benchmark for the probability of occupying a predefined productive-orientation set. In parallel, a Hamiltonian-weighted probability is obtained within a classical statistical–mechanical treatment by reconstructing the orientational distribution over the polar–azimuthal domain under a fixed global confinement protocol. The analysis is carried out on a ten-protein panel spanning diverse sizes and anisotropies, and the HD→LB contrast is characterized through probability gains, distributional distances, and an energy-basin decomposition that distinguishes basin depth from basin measure. Under identical parameterization, LB globally produces higher productive-orientation probabilities than HD across all proteins, establishing a uniform direction of the confinement effect while preserving protein-dependent magnitudes. The inertia-based benchmark exhibits broader dispersion in LB/HD amplification, whereas the Hamiltonian construction yields a more regular cross-protein gain, consistent with LB acting as a global reweighting of orientational phase space rather than a protein-specific re-tuning. By integrating PDB-derived structural descriptors with a statistical–mechanical operator, the framework provides a transparent bridge between molecular geometry and confinement-driven ordering and offers a compact basis for comparing crystallization-relevant confinement protocols across structurally heterogeneous proteins. Full article

Background: Evaporative water loss from burn wounds is a major but often neglected component of early fluid requirements. Despite its physiological importance, no dedicated review has quantified acute post-burn evaporative water loss (TEWL) and its interaction with modern resuscitation strategies in over 40 years. Recent mass-casualty burn events in specialized centers have re-emphasized the clinical importance of accurate early fluid balance, which is particularly challenging. Methods: A scoping review (PRISMA-ScR) of historical quantitative studies and 23 contemporary (2015–2025) adult major-burn resuscitation cohorts was conducted. Expected TEWL was derived from Lamke benchmarks; interstitial edema was estimated from the only available regression of simultaneous fluid input and 24 h weight change. A novel TEWL/edema ratio was tested against resuscitation volume (mL/kg/%TBSA) and the established input/output (I/O) ratio. Results: In the acute phase, the median TEWL normalized to total body surface area was 71 mL/m²/h [52–79 mL/m²/h], allowing for calculation of the TEWL/edema ratio. The TEWL/edema ratio was inversely correlated with the resuscitation fluid dose (R² = 0.811) and the I/O ratio as well (R² = 0.86), crossing unity at 2.85 mL/kg/%TBSA. A ratio > 1 signals high evaporative drive and/or possible under-resuscitation; a ratio < 1 alerts to fluid creep before significant weight gain. Conclusions: The TEWL/edema ratio is the first physiology-grounded, easily calculable resuscitation endpoint that complements urine output by providing insight into whether administered fluid is lost as obligatory evaporation or sequestered as edema. Routine estimation of expected TEWL and early monitoring of the TEWL/edema ratio may help guide goal-directed burn resuscitation, especially when early excision is delayed or impossible. Given the substantial inter-individual variability, the ratio derived from aggregate data should not be interpreted as a patient-specific predictor. Full article

The shortage of interdisciplinary talent is a critical bottleneck constraining the development of smart agriculture. Taking Jiangsu Province as a case study, this research constructs and empirically validates an ecosystem model for cultivating interdisciplinary talent oriented toward smart agriculture. In the theoretical construction phase, an initial three-dimensional model covering “core actors,” “supportive environment,” and “resource elements” was proposed based on ecosystem theory and literature review. This model was subsequently refined through in-depth interviews (March–August 2024, 60–120 min each) and thematic analysis with 58 diverse stakeholders across 13 prefecture-level cities in Jiangsu Province, encompassing universities, agribusinesses, government agencies, research institutes, and frontline practitioners. In the empirical testing phase, structural equation modeling was employed to analyze 382 valid questionnaire responses covering six dimensions: policy environment, market environment, university–enterprise collaboration, curriculum resources, platform resources, and talent cultivation effectiveness (20 items in total). The findings indicate that: (1) the ecosystem model demonstrates good fit and strong explanatory power, with a pronounced “university–enterprise” dual-core driving effect; (2) government policy guidance and platform construction play pivotal supportive roles; (3) market demand and industrial policy constitute critical external driving forces; and (4) “industry–education integrated practice platforms” together with “modular interdisciplinary curricula” exert the most direct positive influence on cultivation outcomes. Based on these findings, this study offers systematic recommendations from three perspectives—mechanism coordination, policy optimization, and resource allocation—providing a theoretically grounded and practically referenced solution for cultivating interdisciplinary talent in smart agriculture. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Urban heritage structures are most commonly understood as memorial artifacts, tourism assets, or redevelopment resources. While this common view acknowledges cultural and economic value, it overlooks a deeper function of heritage within the long evolution of human settlements. This paper advances a counter thesis: in addition to its historic contingencies and power relationships—which are real, but only part of the picture—urban heritage embodies valuable but often hidden intelligence that is highly relevant to contemporary urban challenges. Specifically, heritage environments encode useful structured information about spatial configurations that have gained adaptive value over time in a process known as stigmergy. Drawing on complexity science, network theory, the mathematics of symmetry, and theories of extended cognition, the paper argues that enduring urban forms persist not only for symbolic or historical reasons, but because they embed structural properties conducive to resilience, legibility, social interaction, climatic adaptation, and human well-being. Recurring characteristics include fine-grained network connectivity, fractal scaling hierarchies, organized symmetry, articulated thresholds, and biophilic integration. Evidence from environmental psychology, public health, and urban morphology suggests that such properties correlate with reduced stress, increased walkability, stronger social capital, and improved ecological performance. The paper proposes a methodological framework—what we call the Adaptive Patterns Model—for identifying, evaluating, and translating this embedded intelligence into contemporary regeneration practice. The Model is presented as a four-phase, conceptually synthesized framework—integrating insights from complexity science and stigmergy, urban morphological analysis, and pattern-language methodology—comprising documentation, pattern extraction, encoding, and performance correlation. It concludes by challenging a still-prevalent assumption that contemporary conditions invalidate accumulated spatial knowledge. Instead, urban heritage is understood as adaptive capital within an ongoing evolutionary process, offering a structurally grounded foundation for resilient urban transformation. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

The inhibitory effect of vegetation on soil wind erosion along grassland highways in semi-arid regions has not been fully elucidated. In this study, the dry vegetation near S105 provincial highway in the Sangendalai area of Xilingol League, Inner Mongolia was selected for a wind tunnel test, and the vegetation coverage and porosity during the test were determined by using image processing methods. On this basis, a porous medium model of dry vegetation was established, and the two-phase flow of wind and sand was numerically simulated. The results show that: (1) The numerical simulation results are in good agreement with the wind tunnel observations, confirming the feasibility of using CFD to simulate wind erosion affected by vegetation along grassland highways in semi-arid areas. (2) The aerodynamic roughness of the grassland surface increases nonlinearly with the increase of vegetation cover, and the increase of aerodynamic roughness is more obvious when the vegetation cover is more than 16% in the scope of this study. (3) Vegetation changed the typical jump-dominated wind–sand flow structure on the bare ground surface, showing a significant interception and attenuation effect of vegetation, which was manifested by the reduction of sand accumulation at the wind outlet and the increase of deposition within the vegetated area, thus effectively inhibiting the wind erosion process. The results of the study provide methodological references and a theoretical basis for the study of wind erosion along grassland highways in semi-arid regions and help to promote the sustainable development and ecological balance of grassland ecosystems in semi-arid regions. Full article

Creases in origami reflectarray antennas (ORAs) impose layout exclusion zones that invalidate conventional shaped beam synthesis, assuming continuous periodic apertures. A crease-compatible shaped beam synthesis approach is presented, in which crease-intersecting elements are treated as constrained reflectors by removing only their patches while retaining a continuous ground plane, thereby translating geometric restrictions into explicit amplitude/phase constraints. These constraints are incorporated into a modified alternating projection method (MAPM) via an iteration-updated ternary state matrix and a revised inverse projector, where the amplitudes of internal elements are kept prescribed, and only their phases are iteratively optimized. A 15 GHz hexagonal twist ORA using triangular-ring unit cells is designed to generate a sector beam in the xoz plane and a pencil beam in the yoz plane. Full-wave simulations demonstrate a peak gain of 26.4 dBi with sidelobe levels below −16.1 dB, validating the proposed beam shaping synthesis with crease constraints for ORAs. Full article