Search Results (8,817)

Coordinated management of drip irrigation water demand and lateral canal supply is a critical strategy for improving water use efficiency in arid irrigation districts; however, under water-deficit conditions, the efficient and equitable allocation of limited canal water among multiple drip irrigation systems remains largely unresolved. This study developed a bi-objective cooperative water allocation and scheduling model for a lateral canal serving 11 subordinate drip irrigation systems. Subject to canal diversion flow balance and total deficit constraints, the model simultaneously minimized (i) the mean coefficient of variation (CV) of water allocation duration within rotation irrigation groups, targeting temporal uniformity, and (ii) the sum of squared deviations of the water supply satisfaction rate across systems, targeting distributional equity. Water demand inputs were derived from a localized FAO-56 Penman–Monteith irrigation schedule for Jinghe County with stage-specific crop coefficients. A hybrid binary–continuous NSGA-II encoding with a dynamic intra-group flow allocation mechanism was employed. For the baseline deficit scenario (Early May, supply-to-demand ratio β = 67.76%), the model partitioned the 11 systems into four rotation groups with a mean CV of 3.15 × 10⁻³, while the sum of squared deviations of the satisfaction rate decreased from 4.366 under the empirical scheme to 1.50 × 10⁻⁴, confining all systems to 67.38–68.45% and eliminating the coexistence of over-supply and complete deprivation (Wilcoxon signed-rank test, p < 0.001; Cohen’s d = −1.698). The Pareto front revealed a significant efficiency–equity trade-off (Spearman’s ρ = −0.9999), and NSGA-II outperformed SPEA2 by approximately 29-fold and 22-fold in the two objectives. Robustness was confirmed across three deficit scenarios and algorithm parameter sensitivity analyses (CV < 2%). The study offers methodological support for refined water allocation management of terminal canal systems in arid regions. Full article

Human–machine interaction (HMI) has become a central issue in autonomous driving because partial automation can degrade, rather than improve, human performance during takeover, handover, and out-of-the-loop transitions. Similar interaction risks are emerging in manned–unmanned vehicle teaming (MUM-T), where operators must supervise multiple autonomous or remotely controlled assets under higher mission complexity and safety-critical constraints. However, existing effectiveness analyses of unmanned and MUM-T systems often treat the level of autonomy (LOA) as a fixed system attribute or assume the highest autonomy level, thereby obscuring the human–automation bottlenecks that arise during partial autonomy. This study proposes a function-specific HMI effectiveness framework in which autonomy is represented as a vector across surveillance, maneuver, fire or neutralization, command and control, and human–machine teaming functions. Measures of performance (MOPs) are modeled as conditional performances jointly shaped by function-specific LOA, operational environment, and intrinsic system capability, and are propagated through a five-layer LOA–MOP–MOE structure to a mission-level measure of effectiveness (MOE). The framework is demonstrated using a notional mine countermeasure scenario in which manned minehunters cooperate with unmanned underwater and surface vehicles. Four autonomy progression stages, from manned-centric operation to advanced cooperative autonomy, are evaluated for timely route opening. The case illustrates a non-monotonic partial-autonomy trap, or LOA-2 valley: remotely controlled unmanned assets may temporarily reduce mission effectiveness when teleoperation workload and HMI bottlenecks outweigh equipment gains, before cooperative and supervisory autonomy restore and exceed baseline performance. The contribution of this study lies not in the notional numerical results but in providing an explicit diagnostic structure for identifying where and why function-specific autonomy, control sharing, and HMI bottlenecks shape mission effectiveness. The framework thereby extends human–machine interaction analysis from autonomous driving to the broader, higher-risk setting of manned–unmanned vehicle teaming. Full article

As a critical component of electric railways, catenary systems are prone to cracks, loosening, corrosion, and wear under long-term vibration, fatigue, and environmental erosion. However, ambiguous fault boundaries, large inter-component variations, and tiny defects severely hinder reliable anomaly detection and condition assessment. To address these challenges, this paper proposes a vision-based intelligent fault assessment framework for railway catenary components based on a novel Railway Diffusion-based Anomaly Detection (Rail-DiffAD) model. Specifically, Rail-DiffAD combines residual feature mapping, a Multi-scale Partial Convolutional Spatial-Channel Attention (MPSCA) module with Log-Barrier Bi-directional Constraint Loss (LBBCL), and conditional diffusion-based distribution modeling to achieve robust anomaly localization in complex industrial scenarios. Furthermore, a severity-aware diffusion representation is introduced to characterize structural defect evolution, and a multi-physics fault assessment framework integrating mechanical response, corrosion evolution, and stress concentration analysis is established for quantitative fault grading and maintenance decision-making. Experiments on a real catenary dataset covering 10 component categories demonstrate that the proposed framework achieves a 0.953 image-level AUROC and a 0.957 pixel-level AUROC, outperforming existing methods while maintaining strong cross-component generalization and providing quantitative fault grading support for intelligent railway catenary maintenance. Full article

Small object detection in real-world scenarios remains challenging due to two coupled factors: severe scale imbalance and progressive degradation of fine-grained cues during backbone downsampling. Under drastic scale variations, conventional detectors still rely on static backbones whose fixed convolutional responses cannot consistently accommodate the divergent spectral characteristics of large and tiny objects, leading to scale-mismatched representations. Meanwhile, repeated strided operations reduce the sampling rate of feature maps and tend to introduce aliasing, eroding the high-frequency details that are critical for tiny objects. To address these issues, we propose Spectra-Net, a novel frequency-aware detection framework that redesigns backbone feature encoding with explicit spectral control. At its core, we introduce Dynamic Fourier Alignment (DFA), which performs content-adaptive yet frequency-controllable modulation to reshape convolutional responses in the spectral domain, aligning representations across scales and amplifying discriminative cues for small objects. In addition, we develop Wavelet-Guided Spectral Downsampling (WGSD), which conducts explicit sub-band decomposition via Haar wavelets to suppress aliasing while selectively preserving informative high-frequency components during resolution reduction. Extensive experiments on VisDrone-2019 and TT100K, together with comprehensive ablations, demonstrate that Spectra-Net consistently improves small object detection performance under severe scale imbalance. Full article

Phased-array radar networks require real-time collaborative scheduling of search, tracking, and identification tasks under coupled resource, beam, time-window, and power constraints. Current decisions affect future residual power, task refresh intervals, and tracking uncertainty, making long-horizon scheduling particularly challenging. This paper proposes CS-Mamba, a long-horizon constraint-aware collaborative scheduling framework for homogeneous multiple phased-array radars. The scheduling problem is formulated as a finite-horizon constrained decision process with structured hybrid actions, where the discrete component represents radar–task matching and the continuous component represents transmit-power allocation. A Mamba-based temporal encoder is introduced to summarize long scheduling histories with linear sequence complexity. Based on the encoded representation, the scheduler predicts task priorities, constructs a masked radar–task bipartite graph, solves a constrained maximum-weight matching problem, and projects raw transmit powers onto the feasible power domain. In addition, an action-dependent radar model is incorporated to link transmit power, effective SNR, detection probability, measurement noise, and tracking covariance. The model is trained using behavioral cloning from constraint-aware heuristic trajectories followed by actor–critic fine-tuning. Experiments on the proposed MRSched-Bench show that CS-Mamba improves the normalized cost-effectiveness score from 0.62 to 0.78 compared with MAPPO in the Medium scenario, while reducing end-to-end decision latency from 24.5 ms to 12.8 ms per step. Additional ablation studies verify the contributions of temporal encoding, structured matching, feasible power projection, and two-stage training. Full article

Real-time monitoring of operator fatigue is critical for ensuring operational safety and reliability in dynamic industrial environments, especially under swing conditions such as offshore floating nuclear power platforms. This study proposes a multimodal fatigue monitoring framework based on surface electromyography (sEMG) and heart rate variability (HRV) sensors for real-time fatigue recognition. Experiments were conducted on a six-degree-of-freedom motion platform with three swing levels, involving 23 participants performing simulated emergency operation tasks. Four machine learning models (Naive Bayes, K-Nearest Neighbor, Multilayer Perceptron, and Random Forest) were employed for fatigue state classification. The results show that the Random Forest model achieves the best performance, with an overall accuracy of 98.2%, 100% true positive rate for the normal state and fatigue, and 66.7% true precision for severe fatigue. The proposed multimodal fusion method effectively suppresses motion artifacts and improves recognition robustness under swing interference. Rigorous subject-level stratified cross-validation eliminates sample leakage risks; bootstrap confidence intervals and pairwise significance tests statistically verify model performance differences; class imbalance mitigation strategies are deployed to quantify uncertainty for the scarce severe-fatigue category; literature-supported Borg CR-10 grading thresholds are validated via retrospective cutoff sensitivity analysis to guarantee reliable fatigue labeling. This sensor-based intelligent monitoring system provides a reliable solution for real-time fatigue detection of operators in dynamic digital industrial scenarios, supporting accident prevention and sustainable operation of high-risk industrial systems. Full article

Expansion joint anchorage zones are prone to premature cracking and fatigue deterioration under repeated wheel impact and interfacial stress concentration. Engineered cementitious composites (ECCs) are promising anchorage materials because of their tensile strain-hardening behavior, multiple fine cracking, and high deformation capacity. However, how ECC strength–ductility characteristics affect vehicle-induced stress redistribution and fatigue damage accumulation remains unclear. This study develops a material–structure–fatigue framework for ECC anchorage zones. Three PVA-ECC mixtures were tested, and their measured constitutive relationships were incorporated into a three-dimensional vehicle–expansion joint coupled finite element model validated using reported field strain data from a C50 concrete anchorage zone. Critical tensile stress histories were extracted for rainflow counting and Miner-based fatigue assessment. Results show that ECC reduced tensile stress concentration and increased tensile safety margins compared with C50 concrete. Under the defined loading scenario, the estimated fatigue life increased from 9.93 years for C50 concrete to 83.15 years for the best-performing ECC scheme. Ten-year comparative field observations supported the predicted durability trend. By linking ECC strength–ductility characteristics with vehicle-induced stress redistribution and cumulative fatigue damage, the proposed framework provides a quantitative basis for fatigue-resistant material selection and durability-oriented design of expansion joint anchorage zones. Full article

In the context of China’s carbon peaking and carbon neutrality goals, clarifying whether the Yellow River Basin can achieve a decoupling of economic growth from carbon emissions and sustain regional development through deep decarbonization is critical to both ecological protection and high-quality development of the region. This study integrates the Tapio decoupling model, the geographically and temporally weighted regression (GTWR) model, and an author-developed LEAP-YRB v5 macro-sectoral hybrid model to examine 95 prefecture-level cities from 2010 to 2022 and to project energy consumption and carbon emissions for the nine YRB provincial-level regions from 2022 to 2060. The results show that: (1) the urban decoupling status fluctuated among expansive coupling, strong decoupling, and weak decoupling, with weak decoupling becoming dominant and increasing to 62 cities in 2022; (2) per capita GDP and urbanization tended to increase the decoupling index and therefore inhibited decoupling, whereas more intensive construction-land use promoted decoupling, and industrial structure upgrading and green patents showed context-dependent effects; and (3) the basin cannot peak its emissions under the business-as-usual scenario, while the policy-driven scenario peaks at approximately 3.357 billion tons of CO₂ around 2030. Under the carbon-neutrality-oriented scenario, net emissions decline substantially to 825 million tons by 2060, indicating deep decarbonization but not full carbon neutrality. Full neutrality would require additional carbon sinks, cross-regional clean-electricity integration, stronger power-sector decarbonization, or negative-emission technologies beyond the endogenous measures represented in the model. Full article

Bio-based origin, biodegradability, and compostability represent distinct concepts, and promising laboratory results do not always translate biopolymer-based food packaging into industrial implementation. This review adopts a value-chain perspective to critically assess the transition of biopolymer-based food packaging from renewable feedstocks to commercial products and end-of-life management. It evaluates key stages of the entire value chain, including polymer production, processing technologies, economic feasibility, regulatory requirements, environmental performance, and waste-management strategies. Major barriers to commercialization include feedstock and material variability, production and purification costs, processing limitations, performance gaps, certification challenges, and insufficient recycling or composting infrastructure. Evidence from techno-economic and life cycle assessments indicate that successful implementation depends on integrated production systems, process optimization, co-product valorization, and realistic end-of-life scenarios. Advancing biopolymer packaging therefore requires coordinated development across the entire value chain rather than isolated improvements in polymer design. Full article

Secondary inorganic aerosols (SIAs) are critical components of regional air pollution, yet uncertainties in bottom–up emission inventories lead to biases in the simulation of nitrate (PNO₃⁻) and ammonium (PNH₄⁺). This study utilizes a joint NO_x−NH₃ inversion inventory constrained by satellite observations to investigate the response characteristics of SIAs to precursor reductions in the Beijing–Tianjin–Hebei (BTH) region during July 2020. Results indicate that the a priori emission inventory significantly underestimated NH₃ emissions in the BTH region, with a posteriori emission in cities such as Shijiazhuang and Handan increasing to 2–3 times the a priori levels, while NO_x emissions were slightly underestimated. Sensitivity analysis conducted via Comprehensive Air Quality Model with extensions (CAMx)—Decoupled Direct Method (DDM) reveals that the sensitivities of PNO₃⁻ and PNH₄⁺ to precursor variations are higher in south–central BTH; specifically, the sensitivity of PNO₃⁻ concentration to NO_x emission abatement under the a posteriori inventory rises substantially compared with a priori estimates, with relative growth rates spanning 33% to 308% across the study domain. Scenario simulations demonstrate that synergistic NO_x and NH₃ control is the most effective strategy, reducing PNO₃⁻ and PNH₄⁺ concentrations by 2.27 ug/m³ and 0.77 ug/m³, respectively. Full article

Satellite remote sensing has become a vital data source for monitoring power infrastructure. As critical infrastructure supporting power line operations, the monitoring and maintenance of power towers have become core elements in ensuring grid reliability. However, power tower detection in satellite remote sensing imagery remains challenging because of the substantial scale differences between distribution and transmission towers, the weak feature representation of small objects, and interference from complex backgrounds. To address these challenges, this paper proposes MS-YOLO, a power tower detection algorithm for satellite remote sensing imagery based on multi-scale feature extraction and small object enhancement. First, the poly kernel inception bottleneck (PKI_Bottleneck) module is introduced into the YOLOv9 backbone, enhancing the extraction of scale-diverse features and contextual cues while limiting interference from complex backgrounds. Second, the dual-branch semantic-spatial synergy attention (DSSA) module is introduced. By decoupling deep semantic and shallow spatial information, it effectively preserves small object features while suppressing environmental noise, enhancing the perception capability for small tower objects. Finally, a dynamic focal-weighted intersection over union (DFW-IoU) loss function is introduced to optimize the balance between easy and difficult samples, compelling the model to prioritize small objects and challenging samples during gradient updates. Experimental results demonstrate that MS-YOLO achieves mAP50 values of 79.1% and 95.7% on the two datasets used in this paper, representing improvements of 4.1% and 3.4% over baseline model. These results validate the effectiveness of the improved model for power tower detection in complex remote sensing scenarios. Full article

Nanoplastics (NPs; <1000 nm) are persistent soil contaminants that suppress extracellular enzyme activity, the biochemical engine of terrestrial nutrient cycling. Despite a rapidly expanding primary literature, no comprehensive meta-analysis has systematically integrated quantitative effect-size synthesis with interpretable machine learning (ML) approaches to identify and rank the physicochemical drivers of NP-induced soil enzymatic toxicity. Following the PRISMA 2020 statement, we systematically searched four databases (Web of Science, Scopus, PubMed, Google Scholar) from database inception through December 2024 and extracted 413 effect sizes from 113 peer-reviewed studies. Hedges’ g was estimated using three-level random-effects models with restricted maximum likelihood (REML) estimation implemented in the metafor package. Three supervised ML algorithms—random forest (RF), gradient boosting machines (GBMs), and support vector regression (SVR)—were trained using 18 study-level predictors derived from the complete meta-analytic dataset, and SHapley Additive exPlanations (SHAP) were applied to quantify and rank the relative importance of individual predictors. The overall meta-analysis demonstrated a significant inhibitory effect of NPs on soil enzyme activity (Hedges’ g = −0.94; 95% CI: −1.14 to −0.73; k = 413; I² = 78.4%; τ² = 0.412). Among the evaluated enzymes, dehydrogenase activity exhibited the greatest inhibition (g = −1.12), whereas polystyrene nanoplastics produced the strongest adverse effects (g = −1.15). Particles smaller than 100 nm caused approximately 2.6-fold greater inhibition than particles larger than 500 nm, and dose–response meta-regression identified a nonlinear increase in toxicity at concentrations exceeding 200 mg kg⁻¹. The RF model demonstrated the highest predictive performance, explaining 73% of the variance in an independent testing dataset (R² = 0.73; test set n = 83). SHAP analysis identified particle diameter as the most influential predictor, revealing an approximate critical threshold of 150 nm, below which inhibitory effects increased markedly. Higher soil organic carbon concentrations partially mitigated enzymatic inhibition, likely through competitive adsorption and reduced nanoplastic bioavailability. Overall, our findings demonstrate that NP-induced inhibition of soil enzymatic activity is widespread and primarily governed by particle size, exposure concentration, and soil properties. The identified 150 nm threshold should be interpreted as a data-driven hypothesis requiring further validation under environmentally realistic exposure scenarios rather than as a universal regulatory limit. Nevertheless, the integration of three-level meta-analysis with interpretable machine learning (SHAP) provides a robust and reproducible framework for identifying key toxicity drivers and supports future ecological risk assessment and evidence-based regulatory decision-making for nanoplastics. Full article

Rapid and accurate identification of mine water inrush sources is critical for hazard control in underground coal mining. Conventional Fisher discriminant analysis is often limited by feature redundancy and multicollinearity when applied to small-sample, high-dimensional hydrochemical data. To address this, we propose GWO–Fisher, a hybrid model integrating the Grey Wolf Optimizer (GWO) with Fisher discriminant analysis. The model employs correlation-based pre-screening followed by global optimization, using a fitness function that combines Fisher accuracy with a feature-size penalty, to achieve a compact and interpretable feature set. Trained on data from the Xiegou Coal Mine (Shanxi, China), it reduced 17 hydrochemical indicators to 12 key features, achieving 92.98% training accuracy and 86.21% test accuracy—an improvement of 10.35 percentage points over conventional Fisher. When independently validated across four mines in three spring domains, the model maintained over 83% accuracy, consistently selecting TDS, K⁺, and HCO₃⁻ as core features. Misclassification patterns were cross-domain consistent and linked to hydrogeological conditions. The proposed GWO–Fisher model balances predictive accuracy with hydrogeological interpretability, demonstrating reliable performance across both single-mine and cross-spring-domain scenarios. Full article

This study focuses on the development of a sensor-informed motion-continuity control method for shared-return electro-hydraulic actuator networks subject to neighboring-branch disturbances. The objective is to reduce the local velocity fluctuations induced by return-line pressure transients while retaining explicit hydraulic and valve constraints. A control-oriented shared-return disturbance model is established to map neighboring-valve action, T-port replenishment, accumulator buffering, common return-line pressure, net driving pressure difference, and local actuator motion. On this basis, sensor-derived motion and pressure states together with neighboring-action prior information are used to reconstruct the objective of a constrained predictive controller according to the disturbance stage. Soft Actor–Critic is restricted to bounded objective-weight inference, whereas the valve command remains generated by locally linearized receding-horizon optimization; bounded mapping, smoothing update, and soft pressure constraints preserve positive weighting matrices and online quadratic programming solvability. Co-simulation, simulation-based ablation and baseline comparisons, timing evaluation, and scaled dual-branch experiments show that the proposed framework improves motion continuity, reduces disturbance-induced pressure-difference excursions, maintains smoother valve execution, and completes each tested online update within the sampling period. These findings support feasibility-preserving sensor-driven objective reconstruction under the investigated shared-return disturbance scenarios. Full article

As cryptocurrency adoption accelerates globally, cities face a critical question: can digital currencies be adapted by different agencies without compromising financial stability? This paper develops an agent-based modeling framework that enables transit authorities to systematically explore cryptocurrency integration strategies. We simulate 1000 heterogeneous agents with varying risk tolerance, technological proficiency, and social influence susceptibility over 365 days, testing five policy regimes across a comprehensive scenario matrix comprising four risk-attitude compositions, four technology-adoption levels, four social-influence intensities, and three market conditions (bullish, neutral, bearish)—creating 192 distinct population-market configurations evaluated across all five policies with 15 independent replications per configuration (14,400 total simulation runs). The framework produces adoption outcomes ranging from near-zero to over 49% depending on scenario assumptions, with technology familiarity emerging as the dominant driver. The framework provides transit authorities with a practical tool for scenario-based planning: testing policy interventions, stress-testing financial stability under various market conditions, identifying potential vulnerabilities before deployment, and comparing alternative strategies across diverse demographic contexts. This simulation-based approach enables data-driven decision-making in the absence of real-world precedent, offering a structured methodology for evaluating cryptocurrency integration while managing financial stability risks. Full article