Search Results (175,382)

Large language models (LLMs) can turn a flood of cross-layer industrial logs into a fluent incident narrative, but a narrative that cites only real, resolvable events can still be wrong in every relation that matters: the login came from a different workstation, the write command occurred after the physical change it supposedly caused, the action fell inside a planned maintenance window, and the controller does not even actuate the affected process. A cited event is not necessarily supporting evidence. When such a narrative drives automated response, the error propagates into isolating the wrong controller or revoking a legitimate operator. We present EviGuard, a system that decides when an LLM’s understanding is trustworthy enough to act on. EviGuard stores auditable cross-layer evidence in a provenance graph, lets the LLM propose only hypotheses, compiles each hypothesis into atomic machine-checkable claims in an Incident Claim Language, and has an ensemble of deterministic verifiers label every claim supported, contradicted, or unknown against the graph—honoring interval time, event-time policy and credential versions, network reachability, and physical control dependencies. A response gate forbids any high-impact action whose critical preconditions are not all supported. On EviCPS-Bench (42 hardware-in-the-loop attack chains, 9600 claim-level labels, $\kappa =0.87$), EviGuard cuts the unsupported-claim rate from 12.6% to 1.7%, raises relation-edge $F_1$ from 0.64 to 0.89, holds prompt-injection success to 0.4%, and executes zero unverified high-impact actions across 3200 response decisions, at a median end-to-end latency of 0.44 s. Full article

Fluvial sand bodies represent one of the most significant reservoir types in hydrocarbon exploration. Restricted by climatic conditions, sedimentary environments and the properties of provenance parent rocks, sedimentary characteristics and sand-body architectures exhibit substantial spatial variations across different river systems and along individual river segments. Research on modern sedimentary processes of the Weihe River provides critical insights for advancing continental fluvial sedimentology theories and reconstructing paleoriver sedimentary models for analogous basins. Integrating high-resolution satellite image interpretation combined with systematic field geological surveys across typical river reaches, this study systematically characterizes the spatial differentiation of river patterns, sedimentary signatures, sand-body architectures and their primary controlling factors within the Weihe Basin. The results reveal a distinct three-segment spatial differentiation pattern of fluvial styles in the Weihe Basin: braided rivers dominate the Baoji–Zhouzhi reach; low-sinuosity meandering rivers occur in the reach from Zhouzhi to Lintong; and high-sinuosity meandering rivers prevail in the downstream segments below Lintong. Unique hydrodynamic regimes associated with each river type control sedimentary partitioning and sand-body development. Braided rivers feature intense hydrodynamic force and coarse-grained sediments, with sedimentary assemblages consisting of gravelly channel deposits, mid-channel bars and floodplain deposits, which form thick stacked sand bodies via multi-stage sedimentary superimposition. Low-sinuosity meandering rivers possess moderate hydrodynamic energy and are dominated by sandy-gravel deposits, yielding a complete sedimentary succession composed of channel fills, point bars, natural levees and floodplains. High-sinuosity meandering rivers are characterized by weak hydrodynamic conditions and fine grain sizes dominated by sandstone and mudstone units, developing diverse sedimentary facies including channels, crevasse splays and oxbow lake deposits. The spatial heterogeneity of the sedimentary system across the Weihe Basin is synergistically controlled by the channel gradient, provenance attributes, sediment grain size and sediment concentration. This study clarifies the sedimentary evolutionary laws of modern rivers under semi-arid and semi-humid climatic conditions, enriches fundamental fluvial sedimentology theories, and supplies a modern sedimentary analog for paleoriver identification, paleoenvironmental reconstruction and hydrocarbon exploration targeting fluvial reservoir systems. Full article

The development of temporally consistent long-term normalized difference vegetation index (NDVI)climate data records is essential for global change and ecosystem research. The Medium Resolution Spectral Imager-II (MERSI-II) sensor aboard China’s Fengyun-3D (FY-3D) satellite shares similar spectral characteristics with Moderate Resolution Imaging Spectroradiometer (MODIS), offering potential for synergistic applications. However, systematic biases arising from differences in sensor design, radiometric calibration, and atmospheric correction hinder their direct combination. This study established a full-chain framework that integrated cross-calibration of surface reflectance using quasi-synchronous FY-3D/MODIS observations and a MERSI-II-specific atmospheric correction scheme based on the 6S radiative transfer model. After correction, the FY-3D NDVI shows substantially improved consistency with MODIS, achieving a reduction in root mean square error of over 25.9%, an increase in correlation coefficient of approximately 5%, and a decrease in mean absolute error of about 40%. Spatial biases are within ±0.1 over most global land areas, with robust performance across vegetation types and climate zones. Based on this technical framework, a fused FY-3D and MODIS NDVI climate data record was established, which has been operationalized at the Beijing Climate Center for global vegetation monitoring. This work provides a transferable framework for integrating Chinese Fengyun satellite data with international datasets like MODIS/VIIRS. Full article

This study addresses the challenge of temperature non-uniformity during carbon fibre-reinforced polymer (CFRP) composite patch repair, which compromises curing quality and process efficiency. A coupled heat transfer–curing kinetics finite element model was developed and experimentally validated to investigate the heat sink effect of support structures. Key findings reveal that temperature differences concentrate near aluminum components and increase with curing temperature. For the present scarf-repair configuration, global sensitivity analysis identified the second-stage holding temperature (T₂) and heating rate (r₂) as the dominant factors governing temperature uniformity, whereas the holding times (dt₁ and dt₂) primarily determine the total curing time (t_total). A novel multi-objective optimization framework combining optimal Latin hypercube sampling, radial basis functions, and NSGA-II was established. The optimized curing profile achieves a surrogate-predicted reduction of 22.5% in maximum temperature difference (22.0% when confirmed by high-fidelity finite element verification) and 36% in total curing time, while maintaining a minimum degree of cure above 0.98. These results provide a validated, surrogate-based framework for designing curing protocols that resolve metal-induced thermal non-uniformity in composite repairs without sacrificing cure quality. Full article

Climate-related and environmental hazards affect cultural heritage sites in markedly different inland, coastal, lacustrine and underwater settings, creating documentation requirements that cannot be addressed by a single sensing approach. This study presents the multi-sensor geometric documentation of eight cultural heritage sites. Unmanned aerial vehicle (UAV) photogrammetry was applied to six inland and coastal sites, while underwater photogrammetry, unmanned surface vehicles (USVs), acoustic sounding and a prototype green-wavelength flash LiDAR were used at three shallow-water sites. The campaigns produced orthomosaics, elevation models, dense point clouds, textured meshes, bathymetric maps and underwater LiDAR point clouds at scales appropriate to the conservation problem of each site. The resulting products document exposed architectural remains, excavation areas, cliffs and unstable slopes, lake-margin changes, submerged masonry, wooden structures and lakebed morphology. Their main contribution is the establishment of spatially explicit, site-specific baselines that provide measurable geometric and visual evidence for condition assessment, future repeat-survey comparisons and the spatial integration of environmental, archaeological and conservation information. The study demonstrates the operational and information complementarity of optical, acoustic and active ranging approaches, which address different documentation scales, environmental constraints and heritage targets, and provide distinct spatial evidence that can serve as potential inputs to subsequent digital twin and decision support applications. Full article

Commercial format diversity serves as a key indicator indicative of the health and resilience of the commercial structure in rail transit station areas. This study focuses on the rail transit station areas in Shanghai, integrates multi-source geospatial data with Partial Least Squares Structural Equation Modeling (PLS-SEM), and systematically examines the differential influence paths of six categories of factors—urban form, location, facility configuration, passenger flow, land rent, and commercial spatial form—on commercial format diversity between urban core and suburban areas. Multi-group analysis (MGA) is further used to test whether path coefficients differ across concentric zones around stations. The findings are as follows: (1) Passenger flow characteristics are the primary contributing factors to commercial format diversity and serve as a key mediator through which most other factors influence diversity. Location characteristics, particularly the distance to the rail transit station, primarily play a moderating role to commercial format richness. Urban form characteristics function as the “spatial framework” providing a fundamental regulatory influence, with significant interaction effects with station distance. (2) In urban core areas, the temporal rhythm of passenger flow dominates: weekday passenger flow is positively associated with commercial format richness, whereas weekend passenger flow shows a more complex pattern—associated with an increase in format count but declines in both diversity and dominance concentration, indicating a structural reorganization rather than a simple directional effect. In suburban areas, an initial increase followed by a decrease is observed, with an “optimal synergistic zone” existing approximately 600–1000 m from stations where rail transit and other public service facilities jointly promote commercial format diversity. (3) Land rent is mainly associated with the number of format categories rather than their evenness or dominance. These findings reveal systematic differences in the influence paths of commercial format diversity between urban core and suburban rail transit station areas, as well as the distance-dependent zonal variation patterns of these effects, providing empirical evidence for differentiated spatial design in the context of station-city integration. Full article

Background: PADS combines two neural networks predicting ICU mortality and discharge within 48 h, placing critically ill patients into one of four clinically meaningful states. Developed on MIMIC-IV alone, it left open whether it generalizes to other ICUs, whether its models transfer across hospitals, and whether its predictions can be explained at the bedside. Methods: We evaluated PADS on four ICU databases from different hospitals and countries (MIMIC-IV, AmsterdamUMCdb, eICU-CRD, and HiRID), using the same routinely collected variables. Mortality is scored on the final 48-h window (terminal-window, not early-warning, discrimination). For each external database, we compared the MIMIC model used as-is, retrained from scratch, and retrained from the MIMIC weights, and added an explainability layer. Results: For mortality, reusing and retraining the MIMIC model gave the highest discrimination on every database (AUROC 0.955–0.986; terminal-window (near-outcome) discrimination) and stabilized training; used as-is, it ranged from chance (Amsterdam) to good (eICU, HiRID). For discharge, training fresh on local data matched or beat reusing MIMIC on every external database, consistent with discharge timing depending on local organization rather than physiology. The explainability layer produced clinically coherent, cross-checked explanations. Conclusions: Transportability was task-dependent: mortality transferred between hospitals, discharge did not. PADS demonstrated promising external transportability across heterogeneous ICU databases, particularly after local adaptation. Reusing and adapting the MIMIC-IV mortality model across hospitals improves accuracy. This approach also stabilizes training, providing a basis for potential federated deployment, whereas discharge is better trained locally. The mortality results reported here are terminal-window discrimination and do not support use of the framework as an early-warning model. A transparent explainability layer provides an interpretable representation of model predictions, addressing a key barrier to clinical adoption. Full article

The price index of daily commodities is an important indicator of inflation in a country. Adequate modeling of the price index is useful for efficient economic decision-making. Since the exact modeling of the price index is impossible, probabilistic modeling is a suitable alternative for this. The change in the price index of various commodities at different time points is usually dependent, and hence joint modeling is a suitable solution. In this paper, a new family of distributions is proposed for joint modeling of the price index of various Saudi commodities at two different time points and is named as Darwish Bivariate Family of Distributions (DBFDs). Some necessary properties of the family are presented, and some dependence measures are also computed. Conditional distributions for the proposed family are investigated alongside the method for random data generation from any member of the proposed family. The parameter estimation for the proposed family is discussed in general. A specific member of the family, namely the Darwish Bivariate Log-logistic (DBLL) distribution, is studied in detail. Some important properties of the DBLL distribution are presented. The DBLL distribution is used to model the price index of various Saudi commodities. It is found that the proposed DBLL distribution provides a better fit to model the price index in comparison with the other models used in the study. Full article

Exchange rate forecasting is a core issue in empirical finance, but in the majority of studies, the impact of target construction, feature representation, and model structure is not disaggregated, and the evaluation is confined to one prediction problem. The three tasks are: (1) prediction of $E{R}_{t+1}$; (2) prediction of $r_{t+1}=E{R}_{t+1}-E{R}_t$; and (3) prediction of future volatility defined as the sample standard deviation of $r_{t+1},\dots ,r_{t+10}$, where the horizon is the next 10 recorded observations. The seven model families are evaluated with a strict chronological 70%/15%/15% split; $ER$ is excluded as a direct raw predictor, deep models use seeds 7, 42, and 123, and task-specific financial benchmarks are included. The findings reveal markedly different out-of-sample forecasting behavior across the three tasks. For level prediction, the no-change random walk gives RMSE = 0.0005923 and $R^2=0.99815$, while Linear Regression gives RMSE = 0.0005939 and $R^2=0.99814$; the difference is not significant (DM = 0.480, $p=0.631$). For return prediction, Linear Regression gives RMSE = 0.0005920 and $R^2=-0.00061$ and does not significantly outperform the zero-return benchmark (DM = $-0.083$, $p=0.934$). For future volatility, the best average finance-aware learned model is FeatureAttention_Only (RMSE = $0.0004407\pm 0.0000180$; $R^2=-0.027\pm 0.085$), and its three-seed ensemble gives RMSE = 0.0004298 and $R^2=0.024$. FeatureAttention_Only improves squared-error loss relative to EWMA and GARCH in the fixed hold-out, but is not significantly superior to GARCH under QLIKE; rankings also vary across forecast origins. Full article

Forecasting power consumption is essential for intelligent power management in IoT-enabled smart environments, where heterogeneous behaviors appear from diverse building usages. University campuses are considered environments that share similarities with smart cities, making them suitable for power dynamics analysis. We build upon an IoT-based Advanced Metering Infrastructure (AMI) we deployed at our Engineering School’s Campus (INPT, Morocco), and an optimized XGBoost pipeline enhanced via Genetic Algorithms. Limited modeling granularity is addressed in heterogeneous consumption patterns. We propose and justify a cluster-aware approach partitioning data $\left(D\right)$ into K regimes such that $D={\bigcup }_{c=1}^K{C}_c$. Each cluster is treated as a homogeneous behavioral profile and modeled using a GA-XGBoost model, enabling an intermediate granularity between global and meter-level learning. Experiments on real-world campus AMI data show that our proposed GA-XGBoost model consistently outperforms SVR and LSTM baselines across all clusters. In addition, cluster-specific models further improve performance compared to a single GA-XGBoost model trained without clustering, achieving a 48.42% improvement in MASE. Overall, beyond improving forecasting accuracy, cross-cluster generalization shows performance degradation and distributional shift when models are transferred across clusters, while residual diagnostics reveal differences in variance, temporal dependence, and non-Gaussianity. Full article

Objectives: Exchange characteristics describe and evaluate the state of collaboration and the collaborative counterpart from the perspectives of both parties. They reflect the interaction tendencies between collaborators and are important factors influencing the effectiveness of interprofessional teamwork. This study aimed to assess the current exchange characteristics between clinical pharmacists and physicians and identify between-group differences. It also explored approaches to enhancing professional respect, trust, and clarity of Role Recognition among physicians and clinical pharmacists. Method: Separate questionnaire versions were developed for clinical pharmacists and physicians, and a nationwide cross-sectional survey was conducted among both groups. First, descriptive statistics were used to characterize the current status of exchange characteristics in interprofessional collaboration. Second, paired-samples t-tests were conducted to compare clinical pharmacists and physicians across 774 matched pairs. Finally, subgroup analyses were performed using data from 448 clinical pharmacist questionnaires and 446 physician questionnaires. Kruskal–Wallis tests, Mann–Whitney U tests, and chi-square tests were used to examine differences in exchange characteristics across healthcare professionals with different characteristics. Results: A total of 448 clinical pharmacist and 446 physician questionnaires were analyzed. Compared with clinical pharmacists, physicians reported greater professional respect (p < 0.001), clearer role recognition (p < 0.001), better team communication ability (p < 0.001), and a stronger sense of fairness (p < 0.001) in interprofessional collaboration. In subgroup analyses, clinical pharmacists who graduated from universities and those with higher educational attainment reported greater professional respect. Younger clinical pharmacists reported lower levels of fairness and trust, while physicians in central regions and those in non-permanent employment experienced more severe job burnout. Conclusions: Exchange characteristics differed significantly across healthcare professionals by training pathway, region, hospital grade, employment status, gender, age, and education. To strengthen interprofessional collaboration, efforts should focus on optimizing training models, enhancing early-career professional education, improving remuneration systems, and clarifying role boundaries. Full article

The sex ratio of the rice stem borer, Chilo suppressalis (Walker), is important for assessing reproductive potential and outbreak risk, but automated sex classification in field trap images is hindered by small targets, variable postures, subtle morphological differences and complex backgrounds. An edge–cloud collaborative system was developed for fine-grained sex classification of the rice stem borer. The edge terminal performs image acquisition, target detection, ROI extraction, and foreground enhancement, while the cloud platform conducts sex classification and result management. To improve small-target localization, YOLO-CSNet was constructed by integrating a global attention mechanism and adaptive spatial feature fusion into YOLO11n. Within detection-constrained ROIs, Haar-like features and an AdaBoost discriminator were used to suppress tray textures, shadows and non-target regions. For sex classification, DRS-ViT combines convolutional patch embedding, a discriminative-region soft-weighting module and a KANsformer encoder to represent weak sex-related cues and model global structural relationships. YOLO-CSNet achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 94.36%, 93.19%, 95.32%, and 73.95%, respectively. DRS-ViT achieved accuracy, precision, recall, and F1-score values of 93.93%, 94.68%, 93.29%, and 93.91%, respectively. In a temporally independent field deployment conducted at one Shandong site from May to June 2026, the system achieved an image-upload success rate of 91.7% and an end-to-end accuracy of 87.9%. The field results support the feasibility of the workflow under the tested conditions. Future work will extend field validation across multiple sites and seasons to evaluate system generalizability. Full article

The present study aims to investigate the cumulative effects of multi-pulse femtosecond laser etching at the graphene/aluminium interface. In addition, the study seeks to achieve accurate prediction of the ablation threshold and process optimisation at this interface. In this study, a 1.5 μm graphene coating was first deposited onto the surface of a pure aluminium substrate. In the ensuing experiment, the graphene/aluminum interface was subjected to femtosecond laser etching, with the laser parameters varying in both frequency and energy. The present study investigated the ablation process of the graphene/aluminum interface under the action of repeated laser pulses at different frequencies. A model of the cumulative effect of femtosecond lasers at the graphene/aluminum interface was established, and the influence of the cumulative effect on the ablation threshold of the graphene/aluminum interface was analysed. The results indicated that with increasing laser fluence and frequency, the dimensions of the ablation grooves increased significantly and the edge sharpness improved. The square of the ablation diameter showed a linear correlation with the logarithm of the laser fluence. The ablation threshold of graphene decreased gradually with an increase in the effective number of pulses, with a pronounced reduction when the effective pulse number was below 80, and a gentler decline when it exceeded 80. A fitting analysis based on the cumulative model indicates that the cumulative effect coefficient, designated as δ, which was intended to illustrate how the graphene damage threshold changes with the number of pulses, was as low as 0.16. This finding suggested that there was a significant cumulative effect. Through this investigation of the femtosecond laser cumulative effect, an accurate description of the multi-pulse ablation threshold for graphene/aluminum was achieved, providing a theoretical basis and process reference for high-precision femtosecond laser processing of graphene. Full article

Traditional brick masonry buildings in China are commonly constructed using fired clay grey bricks bonded with glutinous rice mortar, forming a unique historical masonry system with significant cultural value. During long-term service, these structures are vulnerable to environmental deterioration, material aging, and seismic actions, resulting in cracking, deformation, and degradation of structural integrity and load-carrying capacity. Carbon fiber-reinforced polymer (CFRP) sheets have been increasingly applied for strengthening masonry structures due to their high strength-to-weight ratio, corrosion resistance, and convenient installation. However, most existing studies on Fiber-reinforced polymer (FRP)–masonry interfaces have focused on conventional masonry systems, while the interfacial bond behavior and load-transfer characteristics between CFRP sheets and traditional grey brick masonry bonded with glutinous rice mortar remain insufficiently investigated. This study investigates the interfacial bond behavior of CFRP-strengthened traditional grey brick masonry through combined experimental testing and numerical analysis. First, uniaxial compression tests were conducted to determine the mechanical properties of glutinous rice mortar and fired clay grey bricks. Subsequently, double-shear tests considering different CFRP bond widths, bond lengths, and interface integrity conditions were performed to characterize the failure modes, force–displacement responses, and interfacial load-carrying behavior. The effects of interface geometric and integrity conditions were considered to evaluate the load-transfer characteristics of the strengthened interface. Based on the experimental results, a finite element model considering interface behavior was established and verified through comparison with the experimental results, which was subsequently employed to investigate the influence of bond width on interfacial stress transfer behavior beyond the experimental conditions. The results show that interfacial debonding accompanied by near-surface masonry damage dominates the failure process of CFRP–glutinous rice mortar masonry interfaces. Increasing the CFRP bond width enhances the interfacial load-carrying capacity and initial stiffness, while the ultimate capacity exhibits an approximately linear relationship with bond width within the investigated range. Numerical analyses further demonstrate that increasing bond width expands the effective load-transfer region, redistributes interfacial stresses, and delays stiffness degradation. These findings improve the understanding of interfacial bond behavior and load-transfer characteristics in CFRP-strengthened traditional masonry systems and provide references for the design and performance evaluation of strengthening applications in historic masonry structures. Full article