Search Results (6,561)

Soil organic carbon (SOC) is a major terrestrial carbon pool, and its maintenance is important for climate change mitigation. Straw retention is widely used in croplands, but the association between crop residue cover (CRC) and soil organic carbon density (SOCD) may be nonlinear and may vary under peri-urban thermal conditions. We integrated multi-source spatial datasets for Mollisol croplands in Northeast China to compare recent mean SOCD in the upper 20 cm between stable rural and rural-to-urban transition zones and to examine the associations among CRC, urban heat island (UHI) intensity, and SOCD. XGBoost combined with SHapley Additive exPlanations (SHAP) was used to quantify predictor importance and characterize the nonlinear contribution of CRC to predicted SOCD. Structural equation modeling and partial least squares path modeling were further used to assess statistical associations and interaction terms among CRC, daytime and nighttime UHIs, and SOCD. Mean SOCD was slightly higher in the transition zone than in the stable zone, whereas mean CRC did not differ significantly between the two zones. The contribution of CRC to predicted SOCD showed nonlinear dependence patterns in both zones, with a similar turning region at approximately 48–50% CRC. The lower-CRC patterns differed between zones, whereas CRC was strongly positively correlated with its SHAP value across the higher-CRC interval in both zones. Daytime and nighttime UHIs were associated with contrasting CRC-SHAP patterns, and the estimated interaction effects differed between the stable and transition zones. These results reveal nonlinear and spatially heterogeneous associations between CRC and SOCD under peri-urban thermal conditions and provide a basis for further temporal and process-based investigation. Full article

Aggregated epidemics, characterized by rapid transmission over short periods, pose severe threats to public health security, necessitating the development of precise source tracing and simulation methods to support efficient prevention and control. However, existing studies suffer from three major gaps: (1) predominant focus on national/regional scales with limited urban-scale analysis; (2) reliance on proprietary mobile data that are often inaccessible; and (3) NP-hard computational complexity in traditional source tracing methods. To bridge these gaps, this paper proposes an integrated spatiotemporal diffusion model comprising two core components: outbreak point estimation and spatial diffusion simulation. The model first uses the SEAIR infectious disease dynamics model to predict trends, combines the 3-Sigma criterion and viral incubation period to screen early epidemiological survey data, employs a road network-constrained DBSCAN algorithm for spatial clustering, and locates the outbreak point via an improved inverse distance weighting method incorporating time and POI density weights. Subsequently, using the estimated outbreak point as the initial transmission center, and based on the “cell-type” living structure hypothesis of populations, it fuses multi-source geographic data to quantify regional attractiveness and simulate viral diffusion in grid space. The model is validated using COVID-19 epidemic data from Xi’an, Shanghai, and the Hong Kong Special Administrative Region of China. Results show that the proposed outbreak point estimation method effectively estimates the initial transmission center, with distances between estimated points and officially announced points of 0.786 km, 1.676 km, and 5.441 km, respectively—shortened by 179 m, 1091 m, and 711 m compared to related studies. The spatiotemporal diffusion model effectively simulates daily incidence patterns, achieving average coverage rates of 67.32% and 72.9% and average precision rates of 60.67% and 78.84% in Shanghai and Hong Kong, respectively, with significantly better simulation accuracy than traditional methods in the early and middle stages of the epidemic. This study provides a scientifically robust, data-parsimonious framework for precise source tracing, early warning, and resource allocation in urban epidemics, with strong generalizability to other resource-limited settings. Full article

Pavement texture contributes to friction only where it engages the tire, limiting the interpretability of whole-surface texture indices. This study develops a contact-informed framework for predicting skid resistance at RIOHTrack, Beijing Highway Traffic Test Field. Pressure-sensitive film and three-dimensional laser scanning characterized contact stress and surface morphology under a 25 kN tire load and 830 kPa inflation pressure. British Pendulum Number, peak contact stress, and effective texture density were related to the side-force coefficient at 60 km/h using seven asphalt-section means. Asphalt surfaces exhibited partial, or semi-interlocking, contact, with effective depths mainly of 2–3 mm and effective-to-maximum depth ratios of 0.3–0.4; concrete surfaces showed different ranges. The reported regression achieved adjusted R² = 0.92 and an overall F-test p-value of 0.013, but two individual predictors were not significant at the 0.05 level. Reanalysis of the tabulated data gave a leave-one-section-out root-mean-square error of 4.56 SFC units, exceeding the in-sample error of 0.97. Two-year lane comparisons associated the PG82-22 binder with lower attenuation of excess effective texture density than PG76-22. The framework links texture selection to partial tire engagement, while the small calibration sample requires independent validation before engineering deployment. Full article

Field crops change continuously across growth stages, exhibit substantial spatial heterogeneity, and must be managed within short operational windows. This study presents a structured narrative review of information sensing and intelligent monitoring from pre-sowing conditions to stand establishment, growth and yield formation, biotic stress, maturity, lodging, and harvest readiness. The literature is organized by growth stage and analyzed through a common chain of agricultural need, observable variable, sensing platform, data processing method, validation design, state interpretation, and management or equipment output. Satellite remote sensing, unmanned aerial vehicle sensing, ground and proximal sensing, field Internet of Things, machinery-mounted sensors, multisource fusion, crop models, and machine learning methods are compared according to spatial support, temporal continuity, scale matching, field robustness, transfer conditions, uncertainty, and operational applicability. The reviewed studies report crop-phenotype retrieval, field-environment characterization, and biotic-stress identification under specified conditions, whereas cross-stage state inheritance, consistent reference measurements, independent validation, and conversion of monitoring results into executable tasks remain insufficiently established. The review therefore develops a lifecycle-oriented information-processing perspective in which multisource observations are quality-marked, interpreted as stage states, linked across time and scale, and checked against management and equipment records. Future work should strengthen cross-crop and cross-region validation, mechanistic and data-driven model coordination, uncertainty reporting, interoperability, and field feedback without presuming universally autonomous decision-making. Full article

The return oil filter in an excavator hydraulic system plays a critical role in maintaining oil cleanliness, and its blockage fault directly affects the thermal state and operational reliability of the machine. Owing to the progressive evolution and concealed characteristics of this fault, accurate identification remains challenging. To address this issue, this study proposes a return oil filter blockage fault identification model combining the whale optimization algorithm (WOA), multilayer perceptron (MLP), and gated recurrent unit (GRU). First, the hydraulic system configuration, operating process, and blockage mechanism of the return oil filter are analyzed. Then, multi-source operational variables are selected according to the fault propagation mechanism, while ACF and FFT analyses are employed to support signal denoising. In addition, variational mode decomposition (VMD) is introduced for representative pressure signals to provide supplementary evidence for fault evolution and the rationality of variable selection. On this basis, WOA is used to optimize the key hyperparameters of the hybrid model. Finally, the proposed scheme is systematically validated. Experimental results show that WOA-MLP-GRU achieves the best overall performance among the compared models, with an identification accuracy of 98.3%. Moreover, the proposed model exhibits lower validation loss, better feature separability, and a more concentrated error distribution, demonstrating superior training stability, robustness, and generalization capability. Full article

Global climate change and rapid urbanization have intensified ecological security risks in arid oasis regions, creating an urgent need for reliable ecological security assessment and spatial zoning to support regional sustainable development. Taking the Kashgar Oasis as the study area, this study combined the pressure-state-response (PSR) framework with the Mazziotta–Pareto Index (MPI) to construct a Multivariate Ecological Security Index (MESI), with the aim of reducing excessive compensation among different ecological dimensions and highlighting ecological weaknesses. Based on multisource spatial data from 2010 to 2025, we further examined the spatiotemporal evolution of ecological security, spatial relationships among ecological dimensions, and ecological security zoning. The main results were as follows: (1) Ecological security exhibited a pronounced spatial gradient, with higher levels in mountainous areas, intermediate levels in oasis areas, and lower levels in desert areas. High ecological security areas were concentrated mainly in mountainous regions, river corridors, and natural oasis areas, whereas low ecological security areas occurred primarily in urban expansion areas, agricultural development zones, and desert margins. (2) Ecological security changed substantially over the study period, following three successive stages: expansion of moderate-security areas, increasing spatial differentiation, and overall recovery, with the recovery trend becoming increasingly apparent after 2020. (3) The ecological dimensions exhibited marked spatial heterogeneity. Mountainous areas and river corridors were generally characterized by lower human disturbance, lower land desertification sensitivity, and higher ecosystem service capacity, whereas oasis margins and urban expansion fronts showed more complex spatial mismatches and potential trade-offs. (4) Six ecological security functional zones were delineated based on multidimensional ecological characteristics and the overall ecological security state, and differentiated management strategies were proposed for the respective zones. Overall, by jointly considering multiple ecological dimensions and their imbalances, this study links ecological security assessment, spatial relationship identification, and spatially continuous zoning, thereby providing an integrated analytical framework for identifying ecological weaknesses and supporting differentiated ecological management in arid oasis regions. Full article

The unified expression of the mixed, dynamic and scattered navigation situation information in complex port area environments in a computable mathematical model is the foundation for ships to achieve intelligence and unmanned capability. Existing studies remain insufficient in terms of characterizing complex navigation situations involving multiple interacting factors, such as ship attributes, dynamic encounter relationships, spatial constraints, environmental conditions and navigation rules. To address this issue, a mathematical expression method for navigation situations is proposed. The ship interest perception area is first defined and discretized to characterize the directional anisotropy of surrounding situation constraints. Then, the ship interaction field, encounter conflict field, and navigation restriction field are constructed and integrated through normalization and weighted coupling. Environmental impacts, navigation-rule compliance, direction weights, and direction consistency are further incorporated to map the comprehensive situation constraint distribution into a virtual guidance direction, and a computable relationship between situation space and short-term behavioral preference space is established. Finally, the model is calibrated and validated using real ship data. The results demonstrate that the proposed model can effectively characterize directional multi-source constraints in typical complex port scenarios and generate directional preferences that are reasonably consistent with actual short-term ship motion, providing a structured information basis for navigation situation understanding and upper-level autonomous decision-making of unmanned ships. Full article

Bridge infrastructure worldwide is under increasing pressure from ageing assets, growing traffic demand, environmental deterioration, and the need for more effective lifecycle management. Although bridge-monitoring technologies have developed rapidly, much of the existing research has focused on individual sensing techniques, while the integration of heterogeneous information across different spatial scales has received comparatively less attention. This review takes an information-centric perspective on intelligent bridge monitoring and focuses on three representative technologies that provide complementary information at the material, structural, and network scales: Ground-Penetrating Radar (GPR) for assessing material condition, distributed optical-fibre sensing (DOFS) for monitoring structural response, and satellite remote sensing for providing network-scale information. Instead of comparing these technologies solely for their sensing principles, the review considers their complementary roles in bridge engineering and proposes a Material–Structural–Network (M–S–N) framework to organise monitoring information across scales. Recent developments in multi-source information fusion, AI-based interpretation, connected monitoring systems, autonomous monitoring, and self-evolving Digital Twins are also reviewed. Together, these advances indicate a shift from simply collecting monitoring data towards integrating information, extracting engineering knowledge, and supporting intelligent decisions. Based on this perspective, a Multi-Scale Integration Framework is proposed to illustrate how observations at different scales can be combined to provide a more complete understanding of bridge condition, structural performance, and infrastructure-network context. Future bridge monitoring should therefore move beyond isolated sensing and data collection towards multi-scale information generation and integration, supporting predictive maintenance, resilient asset management, and intelligent lifecycle decision-making across bridge networks. Full article

Early-warning research for approaching small unmanned aerial vehicles (sUAVs) requires more than isolated images, sounds, or radio-frequency traces. A reusable resource must connect event context, synchronized observations, evidence strength, media quality, provenance, privacy treatment, and leakage-resistant evaluation units. This Data Descriptor presents uavews 0.1.0-rehearsal, an openly deposited software-and-data record that implements such a model and exercises it end-to-end on a fixed-seed synthetic corpus. The record is a rehearsal of dataset formation and validation, not a measurement campaign. It contains 180 synthetic events—90 controlled-flight simulations, 26 verified observations, 14 weak observations, and 50 hard negatives—together with 4119 analysis windows, 102 source profiles, 971 observations, 391 media objects (214 audio, 121 image, and 56 video objects), and 7242 released labels. The nominal synthetic coverage spans 30 generalized locations in three site groups from 1 April to 15 May 2025. Six canonical Parquet tables, five evaluation manifests, a machine-readable data dictionary, DataCite and PROV-O metadata, an RO-Crate description, validation outputs, source media bytes, and SHA-256 manifests are supplied. The versioned Python pipeline implements ten ordered stages, five reported equations, configurable quality gates, de-identification checks, near-duplicate grouping, and event-, location-, time-, source-, and hard-negative-aware splits. Validation of the rehearsal produced 100% schema and checksum pass rates, median and fifth-percentile completeness of 1.0, synchronization median/p95/max of 3.671/42.184/340.344 ms, exact and near-duplicate rates of 0.512% and 9.463%, cross-modal consistency of 89.88%, and Krippendorff’s alpha of 0.324 for vehicle presence. Seven of eleven release gates passed; the four failures deliberately expose properties that a real release would have to repair or adjudicate. The deposit includes 54 test functions but this article does not claim that the suite was independently executed during manuscript preparation. The accompanying field-trial calculations are planning assumptions, not observations. One of them, the reduced-design sortie count of 7200, omits the configured altitude and background factors and is reported as a known defect of the deposited code rather than as a usable experimental design. The record therefore provides a transparent, executable instrument for designing and auditing a future empirical dataset without representing synthetic values as measured performance. Full article

Organizations face increasing regulatory and stakeholder pressure to integrate environmental, social, and governance commitments into strategic decision-making, yet sustainability reporting often remains disconnected from planning, operational control, and performance measurement. This study develops MISE as a theoretically informed, design-oriented organizational architecture that couples double materiality, strategic ESG programs, governed indicators, documentary evidence, and a Balanced Scorecard–Global Reporting Initiative (BSC–GRI) dashboard. An integrated multi-source single-case study was conducted in a Colombian industrial company using documentary, perceptual, technical, performance, and expert-appraisal evidence. The evidence base comprised 62 study records: 48 internal organizational participants, 8 external stakeholders, and 6 internal expert evaluators with a separate methodological role. The structured consultation obtained 51 responses from 56 eligible participants (91.1%). The case application formalized nine strategic sustainability programs and expanded and governed the measurement architecture from 6 to 18 KPIs. Between the 2024 reference measurement and the 2025 follow-up measurement, complete governance attributes for the 18 KPIs increased from 38/72 (52.78%) to 56/72 (77.78%), equivalent to +25.00 percentage points and +47.37% relative change. Combined Scope 1 and Scope 2 emissions decreased from approximately 1000 to 880 tCO₂e (−12.0%). Six experts appraised usefulness, feasibility, traceability, governance, and scalability potential. These observed changes describe the application period and do not constitute causal effects attributable exclusively to MISE. The contribution is the explicit coupling logic that links prioritization, execution, governed measurement, evidence, review, and reassessment within one case-based governance architecture. Full article

Postharvest fruit quality differs among various fruits and alters during sorting, packaging, transportation and storage. Therefore, it is important to have an efficient and objective grading method that causes little mechanical damage to ensure the uniformity of products and decrease market price and losses in the supply chain. This paper describes the grading norms, detection techniques, processing algorithms, structural designs and practical uses in different types of fruits. It examines the external features, internal quality and hidden faults by using machine vision, visible-near-infrared spectroscopy, hyperspectral and X-ray imaging, acoustic and mechanical detection, electronic noses and the integration of multiple sensors. In addition, it also investigates conventional machine learning, deep learning, transfer learning and lightweight implementations. The research has developed from grading according to size, weight and color to a complete evaluation of ripeness, juice volume, hardness, internal defects and shelf life. Moreover, single detection devices are joined together to construct integrated systems including feeding, separation, inspection, classification, redirection, packaging and data management. However, the application is restricted by the discrepancy between the grading criteria and measurable results, the lack of cross-species and batch generalization ability, and the difficulty in coordinating multiple sensors in real time. The systems should maintain a balance between production speed, mechanical damage and costs. Some matters needing attention are to standardize the quality description, choose multi-source fusion, develop adaptive lightweight models, design modular structures and guarantee end-to-end traceability. Solving these problems will facilitate the transition from accurate laboratory identification to reliable, economic and extensive commercial grading. Full article

Compressor rotor blades in marine engineering applications are exposed to harsh, corrosive environments and complex aerodynamic loads, making them prone to premature failure. This study investigates the fracture of 12th-stage 2Cr13 martensitic stainless-steel blades following a maintenance overhaul. Despite the replacement of several cracked blades, five blades fractured shortly after restart, accompanied by abnormal vibration. A comprehensive failure analysis was conducted, including macroscopic inspection, fractographic observation, energy-dispersive spectroscopy, metallographic examination, and mechanical property testing. The results indicate that the fractures are multi-source high-cycle fatigue. Crack initiation in the new blade originated from pre-existing transverse mechanical damage, while in the old blades, it initiated from sharp pits and microcracks introduced by sandblasting, which compromised surface integrity. The material exhibited a normal tempered sorbite structure and adequate mechanical properties, with slight strengthening due to service-induced precipitation and dislocation accumulation. The failure followed a typical evolution of multi-source initiation, propagation, crack coalescence, and final overload ductile fracture. These findings highlight the critical role of surface integrity in blade reliability. Full article

Reservoir-bank landslides in canyon-type reservoirs commonly show slow creep and episodic acceleration driven by rainfall infiltration, groundwater fluctuation, reservoir regulation, and engineering disturbance. Sparse coherent targets and the high computational burden of long synthetic aperture radar image stacks limit operational time-series interferometric synthetic aperture radar monitoring (TS-InSAR). Moreover, effectively linking long-term deformation monitoring with mechanism interpretation and short-term prediction remains challenging. This study develops an integrated framework for the Xiaolangdi Reservoir, China, combining Ministack-InSAR, interpretable machine learning, and multi-source deep learning. Sentinel-1A images acquired from 2018 to 2024 were processed using Ministack-InSAR, while random forest (RF) and extreme gradient boosting (XGBoost) combined with Shapley additive explanations (SHAP) were employed to identify the dominant conditioning factors controlling deformation. Based on the identified factors and historical deformation information, multi-source deep learning models were further developed for short-term deformation prediction. Ministack-InSAR improved the spatial continuity of monitoring points (MPs) and preserved phase quality in vegetated reservoir-bank slopes. The RF/XGBoost–SHAP results identified groundwater storage, rainfall, distance to rivers, overburden thickness, and road density as the dominant controls on the spatial variability of deformation. Among the tested prediction models, the multi-source-factor convolutional neural network–long short-term memory (MSF-CNN-LSTM) model achieved the best overall performance, with a mean absolute error (MAE) of 3.0 mm, a root mean square error (RMSE) of 4.5 mm, and a coefficient of determination (R²) of 0.885. These results demonstrate that the proposed framework can effectively integrate deformation monitoring, mechanism interpretation, and short-term prediction, providing practical support for active-zone identification and early warning of reservoir-bank landslides. Full article

To address the reduced motion control accuracy of underwater cleaning remotely operated vehicles (ROVs) caused by strongly coupled nonlinear dynamics and multisource time-varying disturbances, this study proposes a backstepping adaptive sliding mode control (B-ASMC) strategy. The proposed method combines the systematic design framework of backstepping control with the strong robustness of sliding mode control. An adaptive law is introduced to estimate the lumped system disturbance online and to compensate for it in real time, thereby avoiding dependence on an accurate disturbance observer or an exact system model. In addition, a hybrid thrust allocation strategy that integrates the pseudo-inverse method with the active-set method is developed to improve computational efficiency while suppressing thrust saturation. The global asymptotic stability of the closed-loop system is rigorously proved using Lyapunov stability theory. Simulations and tank experiments are conducted using a self-developed ROV. Simulation analyses under two representative operating conditions, namely depth holding and bow heading holding, show that B-ASMC achieves higher tracking accuracy, faster dynamic response, and stronger robustness than PID under time-varying disturbances. The tank experiments further verify that the proposed method exhibits control accuracy, disturbance rejection, and engineering applicability. Full article

This study aimed to identify and characterise major wildfire events in South Africa (SA) between 2004 and 2023 by integrating long-term multi-source datasets with anomaly detection techniques. Biomass-burning emissions, such as black carbon from biomass burning (BCBB), organic carbon from biomass burning (OCBB), and carbon monoxide (CO), were retrieved from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis dataset, while burned area data were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Isolation Forest (IF; contamination = 0.05) and the Generalised Extreme Studentised Deviate (GESD) test were applied independently and integrated at the decision level: single-method detections were classified as candidate anomalies, while agreement between the methods identified high-confidence anomalies. Calendar-month standardisation, Spearman rank correlation, Benjamini–Hochberg false-discovery-rate correction, and calendar-month-matched event composites were used to assess meteorological relationships. IF-selected 12 candidate months were identified throughout, whereas GESD identified a smaller subset. July 2007 was the only high-confidence burned area anomaly. High-confidence emission anomalies occurred in 2010, 2018, 2021, and 2023, predominantly between September and November, while the only high-confidence precipitation anomaly occurred in August 2006. After false-discovery-rate correction, the burned area was weakly associated with higher wind speed and lower daytime relative humidity. CO, BCBB, and OCBB were weakly associated with lower precipitation, lower daytime relative humidity, and higher wind speed, while surface air temperature (SAT) showed no significant relationships. Event composites displayed similar patterns, but none of the 16 comparisons remained statistically significant after correction. Spatial analyses showed that the July 2007 burned area anomaly was concentrated in eastern SA; CO and BCBB anomalies were prominent across the northern, central, and eastern interior, and the 2018 OCBB anomalies were concentrated in the southern Western Cape. Percentile-selected spatial composites demonstrated additional regional heterogeneity but were distinct from the IF-GESD consensus anomalies and were interpreted descriptively. The framework, therefore, provides transparent confidence stratification rather than evidence of superior predictive accuracy, while highlighting limitations arising from national monthly aggregation and differences in product resolution. Full article