Search Results (163)

Simultaneously enabling wireless charging for multiple electronic devices is a distinctive advantage of wireless power transfer (WPT). Nevertheless, the development of dual-receiver WPT systems is constrained by several challenges, including undesired cross-coupling effects, suboptimal spatial utilization, complex control strategies, and insufficient system stability. To overcome the limitations, this article develops a multipolarity decoupled four-coil WPT system with constant voltage (CV) and constant current (CC). The proposed system suppresses undesired cross-coupling to negligible levels, thereby reducing the system complexity. In addition, the compensation network can be designed in a straightforward manner, providing improved design flexibility. A detailed mathematical derivation is presented to rigorously demonstrate the load-independent CV and CC output characteristics. Meanwhile, the inverter can achieve zero phase angle (ZPA), thereby improving the power factor of the WPT system. In addition, the multipolarity decoupled mechanism of the four-coil magnetic coupler is analyzed in detail theoretically. Finally, an experimental prototype is built and tested. The experimental results demonstrate a strong agreement with the theoretical analysis, ensuring load-independent CV and CC outputs of 68 V and 3.5 A, respectively. The system achieves a measured peak efficiency of 85.97%. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Tor-based hidden services host substantial criminal infrastructure, yet de-anonymization research remains fragmented across heterogeneous techniques. No prior work has organized these techniques into a unified taxonomy oriented toward forensic investigation. This paper proposes a five-layer vulnerability taxonomy for Tor hidden services, distinguishing network-level (L1), application-level (L2), side-channel (L3), operational-security-failure (L4), and ecosystem-level (L5) categories. The taxonomy is derived from a structured review of literature published between 2002 and 2024. We further propose a Traceability Evaluation Framework (TEF) that scores 11 vulnerability types along three dimensions: Applicability, Technical Difficulty, and Legal Admissibility. The TEF dimension weights are derived through Analytic Hierarchy Process elicitation from a five-member expert panel of cybercrime investigators, digital forensics researchers, and a legal scholar. The resulting weights of (0.385, 0.204, 0.412) for Applicability, inverted Technical Difficulty, and Legal Admissibility prove robust to ±0.10 perturbations in sensitivity analysis. Under this framework, four application-layer (L2) and operational-security-failure (L4) vulnerabilities receive the highest traceability scores (TS ≥ 2.80), while two network-level (L1) attacks and one side-channel (L3) technique fall to the lowest tier. The framework integrates technical exploitability with legal admissibility constraints across U.S., EU, and other evidentiary regimes, providing a structured reference for investigators and a methodological foundation for case-based empirical validation in future work. Full article

Fatigue driving causes 20–30% of global traffic accidents. To address limitations in feature fusion and real-time performance, this study proposes an improved You Only Look Once version 8 (YOLOv8)-Long Short-Term Memory (LSTM) model with symmetry-aware spatiotemporal feature learning. In the spatial phase, Group Shuffle Convolution (GSConv) and Slim Neck structures are introduced to enhance facial feature detection while reducing parameters by 32.3%. In the temporal phase, an improved Inverted Transformer(iTransformer) with differential attention is integrated with an LSTM-Feed-Forward Network (FFN) architecture, achieving a 90.1% prediction accuracy and an 84.6% noise suppression rate. A standardized dataset of 13,200 images was constructed using a four-level classification system. By implementing TensorRT acceleration and multi-process parallel frameworks, the system optimizes single-frame latency to 38 ms—a 9.5× efficiency gain—while maintaining an overall detection accuracy of 92.4%. These results demonstrate that the proposed framework effectively balances model lightweighting with high precision, providing a robust and efficient solution for real-time driver monitoring in complex driving scenarios. Full article

Grapevine branch and leaf silage (GBLS), a polyphenol-rich unconventional forage, exhibits antimicrobial and antioxidant properties that can benefit animal health and productivity. A total of 60 healthy six-month-old Kazakh rams (43.29 ± 4.55 kg, p > 0.05 for initial body weight among groups) were randomly assigned to three dietary groups, each consisting of four replicates with five rams per replicate. The control group (CK) was fed a basal diet based on whole-plant corn silage, whereas the experimental groups received diets in which 50% (GBLS50%) or 100% (GBLS100%) of the corn silage was replaced with GBLS. A 10-day adaptation period preceded the 90-day formal feeding trial. Results showed a significant quadratic response for average daily gain (ADG) and average daily feed intake (ADFI) across GBLS substitution rates (p < 0.05), with the 50% level yielding the highest values. Specifically, ADFI at the 50% replacement level was significantly higher than that of the control (p < 0.05), confirming an inverted U-shaped response with 50% as the optimal substitution rate. However, in-depth analysis of serum biochemical parameters revealed that GBLS supplementation significantly reduced serum concentrations of total cholesterol, triglycerides, urea nitrogen, interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and malondialdehyde (MDA), while significantly increasing levels of immunoglobulins (IgA, IgM, IgG), superoxide dismutase (SOD), and catalase (CAT) (p < 0.05). Rumen fermentation analysis showed that the GBLS50% group had significantly lower concentrations of acetate, butyrate, and total volatile fatty acids (VFA) (p < 0.05). In the rumen microbiota study, no significant differences were observed in alpha or beta diversity or at the phylum level between groups (p > 0.05); however, the abundance of Lactobacillus gasseri was significantly reduced in the GBLS50% group (p < 0.05). Metabolomic profiling identified 43 significantly altered metabolites—27 upregulated (e.g., PE (18:1(9Z)/0:0) and 12,14-pentacosadiynoic acid) and 16 downregulated (e.g., deoxyadenosine). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis highlighted purine metabolism as a significantly altered pathway (p < 0.05), providing insight into the potential metabolic mechanisms underlying the physiological effects of GBLS in rams. In conclusion, replacing 50% of whole-plant corn silage with grapevine branch and leaf silage improves growth performance trends and significantly enhances immunity and antioxidant capacity in Kazakh rams. Full article

In all-weather Earth observation and complex unstructured environments, traditional single-modal remote sensing object detection often fails due to low illumination and strong background interference. While RGB–infrared fusion provides complementary information, existing methods are typically computationally intensive and struggle with dense small objects and modality discrepancies, limiting their deployment on resource-constrained platforms. To address these challenges, we propose ISDG-Net, a lightweight and efficient visible-infrared dual-modal object detection framework specifically tailored for edge deployment. ISDG-Net integrates four core components: (1) a channel-separated inverted bottleneck backbone (IBC-Conv) that reduces parameter redundancy while preserving modality-specific semantics; (2) a dynamic sparse attention module (DySparse) based on Bi-Level Routing Attention, enabling long-range dependency modeling with low computational cost; (3) an adaptive spatial fusion detection head (Detect-SASD) that aligns visible and infrared features at the pixel level to resolve semantic inconsistency and scale mismatch; and (4) a geometry-aware IoU selector (GIS) that mitigates over-suppression in crowded scenes by incorporating multi-dimensional geometric constraints into post-processing. Extensive experiments on the VEDAI, M3FD, and LLVIP datasets demonstrate the effectiveness and efficiency of ISDG-Net. It achieves 55.1% and 77.1% mAP@0.5 on VEDAI and M3FD, respectively, and 93.7% mAP@0.5 with 89.7% recall on LLVIP, while maintaining a compact model size of 4.2 M parameters and 11.3 GFLOPs. These results validate that accurate RGB–infrared detection is achievable under strict resource constraints, making ISDG-Net well-suited for deployment in edge-based remote sensing systems. Full article

The continuous combustion of coal releases carbon dioxide emissions, which has disrupted the Earth’s climate system and posed severe challenges to sustainable human development. As the world’s largest consumer of coal, China faces a critical challenge in curbing its dependence on this fuel. The Yangtze River Delta region, characterized by its advanced economy and high level of industrialization, accounts for a substantial share of the nation’s coal consumption. Therefore, identifying the driving factors of coal consumption changes in this region is essential for formulating targeted low-carbon transition policies. Based on panel data of the YRD region covering 2000 to 2022, this paper employs the LMDI method to decompose the changes in coal consumption from both production and residential sectors, with four driving factors for the production sector and three for the residential sector. The results show that the total coal consumption in the four provinces of the Yangtze River Delta region follows an inverted U-shaped trend, peaking in 2011, with an average annual growth rate of 4.75% before the peak and an annual decline rate of 4.64% after the peak. Production coal consumption accounts for an average of 96.2% of the region’s total consumption. The effect of production intensity and the effect of economic scale are respectively the main inhibitory and driving factors. Spatially, Shanghai was the only province with negative cumulative coal consumption growth, and its average gap with Anhui was the largest among all pairs. Finally, this paper puts forward targeted policy recommendations, focusing on improving coal utilization efficiency and strengthening inter-regional coordinated emission reduction. Full article

Power electronics is a cornerstone of modern electrical engineering, underpinning technologies from renewable energy systems to electric vehicles. Traditional lecture-based methods often emphasise rote learning and procedural skills but provide limited opportunities for higher-order thinking or experiential practice. To meet the needs of Generation Z learners and align with industry expectations, new pedagogical frameworks are required that combine cognitive rigour with authentic, technology-enhanced learning. This study introduces a Higher-Order Thinking Skills with Real-Time Simulation pedagogical framework to enhance learning outcomes in diploma-level power electronics. A quasi-experimental mixed-methods design was applied with 40 students divided into control and experimental groups. The control group received lectures, while the experimental group engaged with the HOTS–RTS framework across four topics: rectifiers, converters, inverters, and applications. Pre- and post-tests, Likert-scale surveys, reflections, and instructor observations provided data for both quantitative (t-tests, effect sizes) and qualitative thematic analysis. The experimental group achieved higher post-test gains (20.1 vs 9.5 points), with a large effect size (d = 1.9). Surveys revealed that 65 per cent of respondents rated RTS as highly effective, and Likert scores improved by 1 or more points in HOTS-related skills. Reflections emphasised clarity, confidence, and collaboration. HOTS–RTS effectively integrates cognitive rigour with real-time practice, aligning with STREAMS principles and equipping learners with next-generation industry competencies. Full article

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making. Full article

Amid the coordinated advancement of the digital economy and rural revitalization, Digital Rural Construction (DRC) has increasingly emerged as a critical catalyst for agricultural modernization and sustainable development. Faced with dual challenges of land resource constraints and agricultural green transformation, improving the Agricultural Land Green Utilization Efficiency (ALGUE) has become essential for achieving high-quality agricultural development. Based on panel data from 29 Chinese provinces from 2012 to 2023, this study employs the super-efficiency SBM model to quantify ALGUE. A comprehensive four-dimensional evaluation system—encompassing digital infrastructure, service capacity, human capital quality, and practical application—is constructed, and the entropy method is used to measure the level of digital rural construction. By applying two-way fixed effects models, mediation analysis, and heterogeneity tests, this study systematically examines the impact of digital rural construction on ALGUE and its underlying transmission pathways. The results demonstrate that: (1) Digital rural construction significantly enhances ALGUE, and this finding remains robust under multiple sensitivity checks. (2) Pronounced heterogeneity exists in two dimensions: the promotion effect is stronger in economically developed regions and in regions with higher agricultural mechanization intensity, while it is weaker in less developed and low-mechanization regions. (3) Mechanism analysis reveals that digital rural construction promotes ALGUE through two channels. The first involves accelerating the transition of the primary industry toward intelligent and high-value-added models, thereby optimizing resource allocation and reducing environmental pressure. The second operates by fostering regional economic growth in an inverted U-shaped nonlinear pattern that supports agricultural green transformation. By integrating DRC and ALGUE into a unified framework, this study identifies two mediating channels and reveals heterogeneity across economic development levels and agricultural structures. These findings provide empirical support and policy implications for digitally driven green agricultural development. Full article

The increasing substitution of conventional synchronous generation by photovoltaic resources has introduced significant challenges to voltage stability, reactive power management, and dynamic system performance. This paper compares a STATCOM, an SVC, a synchronous condenser (SC), and a BESS with a grid-forming inverter (BESS-GFM) in the IEEE 9-bus system using DIgSILENT PowerFactory 2023 SP 5. PV generation is ramped up while synchronous output is reduced to effectively emulate the global movement to greater renewable energy generation as part of decarbonization strategies. Performance is assessed using AC load flows, quasi-dynamic time-series load flows, PV curves, and three-phase short-circuit calculations, concentrating on voltage compliance, additional active power headroom, reactive power capability, and LVRT/HVRT tendency. However, existing work is technology-specific or uses inconsistent assumptions and metrics, which prevent a like-for-like comparison of STATCOM, SVC, SC, and BESS-GFM as PV displaces synchronous generation. This paper addresses that gap by applying a single, consistent study framework across all four technologies. The results indicate that the best performing options provide broadly comparable voltage support at the PCC (Point of Common Coupling), the STATCOM and BESS-GFM maintain voltage close to the setpoint through fast, continuous converter-based reactive control, while the synchronous condenser achieves similar regulation with the added benefit of increasing system strength and fault level through synchronous contribution. Overall, the findings support coordinated deployment of continuous VAR control and strength enhancing support to maintain voltage resilience in high-PV networks. Full article

The mechanical properties and in situ stress conditions of coal reservoirs critically control the effectiveness of hydraulic fracturing, yet the continuous acquisition of relevant parameters at the well scale is often limited by logging data availability and quality. To address this, an integrated workflow combining machine learning-based parameter inversion with a fracturing suitability evaluation framework was proposed for coalbed methane (CBM) reservoirs. A supervised neural network model was developed to establish nonlinear relationships between conventional logs and key parameters, including Young’s modulus, Poisson’s ratio, and horizontal principal stresses. Based on these inverted parameters, a dimensionless Fracturing Index (FI) was constructed to comprehensively characterize coal fracturability by integrating brittleness, fracture toughness, and stress conditions, with a density-based constraint introduced to ensure mechanical consistency. Point-scale FI values within coal seams were upscaled to the well scale for inter-well comparison and regional evaluation. Results showed that FI varied relatively little within individual wells but markedly between wells, reflecting systematic inter-well variations in mechanical and stress conditions, consistent with spatial patterns revealed by cross-well profiles. Correlation analysis from over ten wells with both FI and treatment data demonstrated positive relationships between FI and breakdown pressure, injected fluid volume, and proppant volume, confirming its engineering relevance. Consequently, a four-level FI-based classification scheme was established to identify favorable zones across the study area. This FI framework provides a practical, interpretable tool for early-stage CBM development, offering quantitative guidance for well prioritization, stimulation design, and regional planning in unfractured areas. Full article

Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH₃-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination ($R_{adj}^2$) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies. Full article

This study introduces a control strategy that integrates a photovoltaic (PV) array reconfiguration approach into a Three-Level Neutral Point Clamped (NPC) inverter with LCL filtering and Space Vector Pulse Width Modulation (SVPWM) control. The control strategy eliminates multiple local Maximum Power Points (MPP) caused by partial shading in PV systems, thereby reducing mismatch losses and preventing the Maximum Power Point Tracking (MPPT) algorithm from becoming stuck at a local maximum. To achieve this, it utilizes an electrical reconfiguration strategy that dynamically shifts the PV array interconnections. Furthermore, this strategy reduces the system’s Total Harmonic Distortion (THD) by adjusting the DC bus voltage. Consequently, simulation evaluations across four different weather conditions have shown that this control strategy achieves significant power improvements: up to 54.8% in Case 1, 39.4% in Case 2 and 3, 21.3% in Case 4. Furthermore, the proposed approach suppressed DC bus voltage changes (<8.8 V) even under the worst conditions and reduced the THD in the grid current from 10.1% to 3.7%. Full article

Land use transformation directly affects the stability and sustainability of regional ecosystems. Clarification of the trade-off/synergy dynamics among ecosystem services (ESs) provides a theoretical foundation to understand the transition of ES interactions from trade-offs to synergies, thereby facilitating the achievement in ecological sustainability in the ecoregion. This study, taking Jiangxi Province, China, as an example, utilized the InVEST model, Theil–Sen estimator, Mann–Kendall test, bivariate spatial autocorrelation, ecosystem service bundles (ESBs), and Random Forest (RF) models to conduct such an ecosystem-focused integrated analysis. According to land use changes from 1980 to 2020, the time-series spatiotemporal patterns of water yield (WY), soil conservation (SC), habitat quality (HQ), and carbon storage (CS) were analyzed. Differences in ES trade-off/synergy relationships and their underlying motivating factors were examined using a 3 km spatial grid framework. Compared with previous studies that mainly focused on typical subregions and of which driver analyses often remained at the individual ES level, this study introduced an explainable RF-SHAP framework based on the cooperative game theory at the grid scale, to quantitatively characterize the relative contributions of every motivating factor to ES trade-off/synergy relationships. The results indicate that from 1980 to 2020, forests and croplands constituted the predominant land use types, taking up 88% of the studied area. Throughout this period, forests, croplands, and grasslands decreased markedly, while built-up areas expanded notably, with a rise of 2876.65 km². Over the same time span, WY increased on average by 0.50% whereas SC, HQ, and CS declined by 0.50%, 0.98%, and 1.30%, respectively. Overall, these ESs demonstrated a geographical distribution characterized by low levels in SC, HQ and CS in the central area and high levels towards the provincial boundary. At the grid scale, the four ESs demonstrated predominantly a synergistic relationship while WY&HQ and WY&SC pairs were characterized by trade-offs. The constraint effect analysis revealed U-shaped relationships for SC&HQ, WY&HQ, and WY&SC, and inverted U-shaped relationships for SC&CS and HQ&CS, with clear threshold effects among these ES pairs. Based on self-organizing maps, the study area is partitioned into six ESBs, and the trade-off/synergy linkages of ESs are affected by the interplay of natural and societal forces. Elevation, slope, and rainfall emerge as the primary driving variables accompanied by population density and proximity to urban centers. These results are anticipated to offer reference to governments for their sustainable management in environmental resources to achieve United Nations Sustainable Development Goal (SDG) 15 (Life on Land: Protect, restore and promote sustainable use of terrestrial ecosystems). The methods used in this paper provide a replicable framework for exploring ES interactions and driving mechanisms in other ecologically sensitive regions in the world. Full article