Search Results (11,830)

With the rapid development of intelligent connected vehicles and autonomous driving, real-time and accurate road condition perception has become increasingly critical. Aiming at the limitations of traditional direct and indirect detection methods, this paper proposes an ultrasonic-based real-time detection system for road roughness. Most urban roads today feature asphalt pavements; therefore, this system focuses its research on asphalt pavements. Under the same pavement type (asphalt roads), there is a strong correlation between pavement roughness and the friction coefficient. By measuring the roughness of different pavements, the friction coefficient is estimated using the fuzzy processing method. Then the system through measuring ultrasonic echo amplitude and sensor–road distance, combined with software digital filtering, dual-parameter compensation (distance and temperature–humidity), probabilistic statistical analysis, and fuzzy inference, the mapping relationship among echo signals, road roughness and friction coefficient is established. The system mainly includes an ultrasonic transceiver module, a hardware signal conditioning module, and an MCU-based data processing, display and transmission module. Both simulated experiments and real asphalt pavement tests are carried out for verification. The results show that the system can effectively suppress noise, compensate distance attenuation and environmental interference, and achieve accurate real-time detection of road roughness, with a relative error less than 10% compared with the reference value. The proposed system can provide reliable data support for vehicle active safety systems and autonomous driving applications. Full article

Background: Psychological adaptation in high-demand contexts such as the military depends on the interaction of multiple emotional and psychological resources. Previous research has mainly examined emotional intelligence, resilience, and self-esteem using latent variable approaches, limiting understanding of how these variables dynamically interact within a broader network of interacting psychological resources. Objective: The present study aimed to analyze the network of relationships among emotional intelligence dimensions, resilience, and self-esteem, identifying the most central variables, their degree of clustering, and the strength of their associations. Methods: A cross-sectional study was conducted with 739 Spanish military personnel (M = 33.29; SD = 7.48). A regularized partial correlation network was estimated using the EBICglasso method (γ = 0.5). Centrality indices (strength and expected influence), clustering coefficients, and node predictability were analyzed. Network accuracy and stability were assessed through bootstrap procedures. Results: The estimated network showed moderate connectivity, indicating meaningful interrelations among emotional intelligence dimensions, resilience, and self-esteem. General mood and adaptability emerged as the most central nodes within the network. Resilience showed strong positive associations with adaptability and general mood, whereas self-esteem occupied a more peripheral position. Clustering analyses revealed a cohesive organization among adaptive emotional resources. Conclusions: Emotional intelligence dimensions and related psychological resources can be conceptualized as a dynamically interacting system associated with emotional adaptation in military personnel. The identification of central components may contribute to the development of targeted interventions aimed at strengthening emotional regulation and psychological adaptation in high-demand environments. Full article

Motor bearings are key transmission components in drive motors and they play an important role in the operational safety and stability of electric vehicles (EVs). The contact stiffness and rotational speed are key factors governing the frequency and vibration responses of bearing systems. Identifying these two parameters provides critical information for a better characterization of the dynamic behavior of bearings. In this study, we developed an efficient hybrid parameter identification method to estimate rotational speed and contact stiffness. An EV motor-bearing test rig with wireless data transmission was established, along with a four-degree-of-freedom dynamic bearing model. The rotational speed was estimated from vibration signals by time-frequency ridge tracking without a physical tachometer. The evolution of the effective equivalent contact stiffness was inversely identified by scaling the Hertzian stiffness coefficient using the artificial fish swarm algorithm. The proposed method is a potential technique for sequential updating of rotational speed and contact stiffness, which is suitable for assisting in the construction of a virtual–physical parameter-identification framework for bearing systems. Full article

This study presents the quantitative estimation of the effect of temperature on the mechanical properties of 5Cr-0.5Mo steels using an artificial neural network (ANN) model. The developed ANN model predicts yield strength (YS, MPa), ultimate tensile strength (UTS, MPa), elongation (El, %), and reduction in area (RA, %) at different service temperatures. Predictions were validated against experimental data at critical temperatures of 450 °C and 700 °C and found to show high accuracy. Predicted results show minimal errors of 3.84%, 2.3%, 2.2%, and 0.42% for YS, UTS, El, and RA, respectively at 450 °C, and 3.7%, 0.45%, 1.88%, and 0.19%, respectively at 700 °C. Furthermore, ten-fold cross-validation confirmed the generalization capability of the developed model, yielding high coefficients of determination and correlation coefficients together with low normalized prediction errors across all output variables. Despite the absence of explicit metallurgical descriptors, the ANN model successfully quantified the influence of temperature from 25 to 700 °C, demonstrating its effectiveness as a predictive tool for high-temperature Cr–Mo steels. Furthermore, a user-friendly graphical interface was developed to facilitate rapid property estimation, demonstrating the potential of the framework as a supportive tool for the preliminary assessment of high-temperature Cr–Mo steels. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

Objective, continuous assessment of real-world mobility using wearables has significant potential to transform clinical research and practice, yet the field lacks standardised, open-source tools that enable reproducible algorithm real-world validation, across multiple clinical cohorts. This would improve transparency around definitions and performance, thereby enhancing interpretation and more meaningful comparison across studies. The Mobilise-D consortium validated a comprehensive analytical pipeline for estimating digital mobility outcomes from wearables, originally implemented in a combination of MATLAB, R, and Python codes. To overcome the licencing, reproducibility, and accessibility limitations of this implementation, the pipeline has been re-implemented and re-validated, against gold standards, as the open-source mobgap Python package. Here, we describe the mobgap ecosystem, detail how algorithms can be integrated and benchmarked in a reproducible way and present a re-validation of the pipeline against reference data across six clinical cohorts under real-world conditions. Validation results showed that across all cohorts, walking speed was estimated with an absolute error of 0.10 m/s and an intraclass correlation coefficient (ICC) of 0.81, demonstrating comparable or superior performance to the original implementation. Mobgap (v1.2) is openly available and is intended to serve as a reproducible reference implementation and benchmarking platform for researchers developing or validating mobility analysis algorithms using wearable data. Full article

While systematic video analysis is widely used to understand anterior cruciate ligament (ACL) injury mechanisms and contexts, human observation reliability remains a source of concern. This study evaluates the intra- and inter-rater reliability of a systematic video-analysis checklist for assessing ACL injuries in elite men’s football. Twenty-five match-related injuries from the top six European leagues (2020–2024) were randomly selected. Independent observers assessed contextual and situational (sunny weather, match minute, playing phase, field location, injury side, dominant leg, and situational pattern), biomechanical (player contact and anatomical area of player contact), and neurocognitive (attentional inhibition and motor response inhibition) variables. Reliability was calculated using Cohen’s kappa ($\kappa$) and Intraclass Correlation Coefficients (ICC). Quantitative variables and macro-contextual factors, including injury side, playing phase, and situational pattern (0.810 < $\kappa$ < 1.000) revealed near-perfect to perfect agreement. Biomechanical details exhibited substantial agreement (0.601 < $\kappa$ < 0.784). Neurocognitive variables only reached moderate to substantial agreement (0.503 < $\kappa$ < 0.752), while visual speed estimations proved highly unreliable (−0.106 < $\kappa$ < 0.412). The checklist is a highly reliable tool for evaluating the contextual and situational patterns of ACL injuries, but visual speed estimation should be removed or replaced by objective tracking technologies. Full article

Autonomous driving sensor fusion housings (SFHs) integrate LiDAR, radar, camera, and computing modules within a shared mechanical and thermal enclosure. This review examines how coefficient of thermal expansion (CTE) mismatch among housing polymers, aluminum heat spreaders, substrates, and solder joints can contribute to interfacial delamination, solder joint fatigue, optical misalignment, and Thermomechanical Coupling Interference (TMCI). Using a structured narrative review of 99 publications and authoritative standards from primarily 2009 to 2026, the article organizes the evidence into a 4 × 4 taxonomy linking four failure mechanisms with experimental, computational, AI/ML, and qualification-oriented approaches. The review explicitly distinguishes direct literature evidence, transferred package-level evidence, model-based extrapolation, and author-derived conceptual estimates. Accordingly, TMCI temperature increments, sensor spacing values, optical drift estimates, and lifetime projections are discussed only as case-specific screening-level hypotheses unless directly validated in the cited literature. Five research gaps are identified: standardized multi-sensor TMCI validation, aging-corrected material and solder fatigue databases, long-term qualification of thermally conductive nanocomposites, SFH-specific validation of physics-informed digital twins, and integrated multi-failure testing. The contribution of this article is therefore primarily structural and agenda setting: it clarifies what is supported by direct evidence, what is transferred from adjacent domains, and what remains to be validated before robust SFH-level reliability guidance can be established. Full article

Interpretable feature-group fusion for tabular regression remains challenging because strong predictive models often lack explicit group-level attribution, complexity control, and attribution-stability guarantees. This paper proposes SHAP-Weighted Feature Fusion with Residual Mixing (SWFF-R), a closed-form convex fusion framework that combines a full-feature predictor with a SHAP-weighted blend of group-specific expert models. Group weights are obtained from temperature-controlled softmax transformations of group-level TreeSHAP importances, and the residual mixing coefficient is selected on a validation set to preserve predictive robustness. To avoid terminological confusion, we stress that these weights are derived from aggregated TreeSHAP importance scores of the full-feature model, not from group-level Shapley values recomputed by treating each feature group as a single player; the construction is therefore best described as TreeSHAP-importance-weighted feature-group fusion. Under fixed fusion weights and explicit centering, the proposed attribution satisfies Shapley-consistency properties for the induced centered additive group decomposition. We derive a Rademacher complexity upper bound for SWFF-R, provide a complementary minimax lower-bound calculation on a simplified linear subclass, and establish guarantees for temperature search and fixed-coefficient Lipschitz stability. Experiments on seven real-world tabular regression datasets and a separate synthetic 500K scalability stress test show that SWFF-R preserves predictive performance, yields point-estimate RMSE improvements on several datasets, and provides stable group-level attribution. Overall, SWFF-R offers a theoretically grounded framework for interpretable feature-group fusion in tabular regression. Full article

Composite environmental risks accumulate unevenly along urban–rural gradients, yet the conditional and nonlinear interaction between built environment quality (BEQ) and urban–rural functional connectivity (URFC) remains poorly quantified at fine resolution. This study aims to determine whether, and under what conditions, BEQ moderates the relationship between URFC and a population-weighted composite risk index (CRI), and to translate the result into spatia targeted green-infrastructure priorities. We use 744,714 grid cells at 100 m resolution over Guangzhou, China. The framework couples entropy-weighted BEQ from satellite and street-view imagery, gravity-model URFC computed on the real road network, and a two-stage population-weighted CRI of heat and air hazards. We apply nested ordinary least squares with incremental F-tests, spatial-lag and spatial-error models, generalised additive models with B-spline bases, gradient-boosted trees with SHAP interaction values, and Baron–Kenny mediation analysis. The main BEQ × URFC estimates are negative across the parametric and machine-learning specifications. The interaction is, however, small: a spatial-lag model on a 10,000-cell subsample returns β = −5.4 × 10⁻⁴, but a scalable generalised-method-of-moments spatial regression on the full grid—where the spatial autoregressive coefficient reaches ρ ≈ 0.99—shows the coefficient to be negative yet not statistically significant, and a five-seed re-estimation confirms that the subsample-based significance is draw-dependent. We therefore interpret the buffering as directionally supported but small and not robustly significant once spatial autocorrelation is fully modelled. The buffering response is nonlinear in the GAM main effects, and BEQ buffers across the entire observed connectivity range rather than switching sign at an interior threshold; URFC functions predominantly as a moderator rather than a mediator. Population-stratified estimation shows that the buffering is exposure-conditional: it is strongest where population exposure is high and weakens or reverses in sparsely populated cells, consistent with the risk = hazard × exposure structure of CRI. Sensitivity tests across values of the distance-decay parameter, 100 entropy perturbations and spatial scales corroborate the buffering direction. The framework provides an evidentiary basis for prioritising green infrastructure in functionally connected, populated but environmentally degraded transition zones. Full article

This paper aims to assess the relationship between foreign direct investment (FDI) and economic development/employment rate over the period 2013–2023 for Romania, as well as for other European Union country groups (Central and Eastern Europe, Northern and Western Europe and Peripheral Europe). In this respect, we used the Panel FEGLS method adjusted with cross-section SUR and found a positive relationship between FDI and Gross Domestic Product (GDP) per capita for all panels, the strongest estimated relationship being identified for Romania (followed by the one specific to Central and Eastern European states), considering the important role of the level of economic development in shaping these differences. Regarding the relationship between FDI and employment rate, we also found positive coefficients, the highest ones being identified for Central and Eastern Europe and Romania. However, the weakest estimated relationship between FDI and GDP per capita/employment was identified for the Peripheral Europe countries. Full article

Military conflict generates a qualitatively distinct category of market shock that is sudden, geographically concentrated, and channeled directly through physical energy supply routes. This paper examines investor herding and cross-sectional return dispersion across five commodity markets (Brent crude, WTI crude, Henry Hub natural gas, spot gold, and the Baltic Dry Index) using 475 daily observations from January 2024 through April 2026, covering the sustained escalation phase of the Iran–Israel conflict. The empirical analysis incorporates eight complementary specifications: (1) baseline CSAD regression; (2) GARCH(1,1) conditional volatility augmentation; (3) volatility regime partitioning (high versus low); (4) quantile regression across the CSAD distribution; (5) asset-level disaggregation; (6) interaction with the geopolitical risk (GPR) index; (7) asymmetric analysis distinguishing between up- and down-market conditions; and (8) rolling 240-day estimation to capture time-varying dynamics. The results tend to reject the herding hypothesis and provide suggestive evidence of positive cross-commodity dispersion. The baseline model shows that large market movements significantly increase cross-sectional dispersion. At the asset level, natural gas exhibits the highest dispersion coefficient, reflecting its structural independence from oil-related geopolitical fundamentals. Moreover, gold provides evidence consistent with a positive but comparatively smaller coefficient, consistent with its role as a stabilizing safe-haven asset. Dispersion effects are broadly symmetric across market conditions. Furthermore, the geopolitical risk index does not exert a significant marginal effect. However, the analysis is restricted to five commodity assets and a single geopolitical conflict episode (the Iran–Israel conflict), which may limit the generalizability of the findings to other markets or conflict contexts. Full article

Accurate change detection in high-resolution remote sensing imagery is essential for urban planning, land-use monitoring, and disaster response. This study introduces CTA-Net, a Cross-Temporal Attention Network for binary change detection in bi-temporal optical imagery, designed to improve robustness against pseudo-changes caused by illumination variation, seasonal effects, and sensor noise. The proposed method employs a shared Siamese encoder with multi-scale Cross-Temporal Attention modules that derive spatial and channel attention from L2 feature differences, along with a lightweight confidence estimation head for per-pixel uncertainty modelling. A hybrid loss function combining confidence-weighted binary cross-entropy and focal loss is used to address class imbalance. Experiments on the LEVIR-CD dataset demonstrate that CTA-Net achieves an overall accuracy of 98.99%, an F1-score of 87.68%, an Intersection over Union of 78.06%, a Cohen’s kappa of 0.8715, and a Matthews Correlation Coefficient of 0.8721, with stable convergence and minimal overfitting. Qualitative and calibration analyses further indicate that the model produces interpretable attention maps and reliable probabilistic outputs. To evaluate cross-domain generalization, we conduct a transfer learning case study on multispectral Sentinel-2 agricultural imagery. The model is adapted to 11-channel input and fine-tuned on automatically generated change masks derived from NDVI-delta thresholding. Under this supervision protocol, CTA-Net achieves an F1-score of 95.18% and an IoU of 90.81% on a held-out test region, with balanced precision and recall. While these results demonstrate effective adaptation across sensor modality, spatial resolution, and semantic domain, the evaluation reflects agreement with the mask generation procedure rather than independently annotated ground truth. While CTA-Net shows strong performance and reasonable interpretability, its cross-domain evaluation is limited by the use of automatically generated labels. As a result, the reported transferability should be interpreted cautiously until validated on human-annotated datasets. Full article

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure. Full article

Precipitation is central to the global hydrological cycle, and its accurate monitoring is vital for preventing meteorological disasters. Traditional satellite retrieval methods fail to model nonlinear relationships and adapt to regional heterogeneity. Using China’s new-generation geostationary satellite FY-4B/AGRI, this study develops a two-step machine learning model—separating precipitation identification from intensity estimation—for the complex terrain of Anhui Province and further conducts experiments in the Huaibei Plain, Jianghuai Hills, and Jiangnan mountainous areas. This design separately addresses precipitation occurrence and rainfall intensity, which represent distinct classification and regression tasks. The model takes 37 features as input, including multispectral brightness temperatures, brightness temperature differences, spatiotemporal cloud-top temperature dynamics, secondary cloud parameters, and terrain. For identification, XGBoost at a 1:4 precipitation/non-precipitation ratio performed best, with POD, FAR, CSI, and ETS of 0.6961, 0.3676, 0.4956, and 0.4422, outperforming the FY-4B QPE product (0.5876, 0.5703, 0.3301, 0.2607). Subregional modeling further improved CSI to 0.4716, 0.5186, and 0.5210 for the three areas. For rainfall estimation, XGBoost trained with the original precipitation class ratio was optimal in all subregions, markedly surpassing the QPE product. Spatial aggregation of the three regional models yielded a correlation coefficient of 0.5304 and RMSE of 0.6274 mm, outperforming the unified model and QPE during the study period. This study provides a useful machine learning approach for precipitation retrieval, and the results demonstrate the efficacy of incorporating regional heterogeneity into machine-learning-based precipitation retrieval, leading to enhanced precipitation estimation during the June–July 2024 Meiyu period over Anhui Province. Full article