Search Results (1,270)

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. Full article

Background/Objectives: Short-term low-fermentable oligo-, di-, and monosaccharide and polyol (FODMAP) diets can reduce exercise-associated gastrointestinal symptoms (Ex-GIS); however, their effects on the gut microbiome, short-chain fatty acids (SCFAs), and gastrointestinal biomarkers remain unclear. This study explored the effects of 48 h dietary FODMAP manipulation within a high-carbohydrate diet on faecal bacterial and SCFA profiles, and their relationships with exercise-induced gastrointestinal syndrome (EIGS) biomarkers, Ex-GIS, and performance. Methods: Twelve endurance athletes experiencing Ex-GIS were randomly allocated to a 48 h high-carbohydrate (mean ± SD: 12.1 ± 1.8 g∙d⁻¹)–high-FODMAP (HC-HFOD) (54.8 ± 10.5 g∙d⁻¹) and a 48 h high-carbohydrate–low-FODMAP (HC-LFOD) (3.0 ± 0.2 g∙d⁻¹) diet before 2 h of running at 60% $\dot{V}$O_2max, followed by a 1 h distance test (22.9 ± 1.2 °C, 46 ± 8% RH). Baseline faecal samples were collected before exercise trials to determine faecal bacterial and SCFA profiles. Blood samples were collected pre- and post-exercise to determine plasma I-FABP, sCD14, and CRP concentrations. Ex-GIS were recorded every 15 min throughout exercise. Results: Faecal bacterial α-diversity and relative abundance (RA%) at the phylum level were unchanged following both diets, while several family- and genus-level taxa RA% values were changed (p < 0.05), with greater shifts after HC-HFOD. HC-HFOD significantly increased faecal total-SCFA (p = 0.004), acetic (p = 0.002), and butyric (p = 0.028) acid concentrations. Strong positive and negative correlations between bacterial RA% and EIGS biomarkers and Ex-GIS were observed. Strong negative correlations with bacterial RA% and performance were observed. Conclusions: The 48 h HC-HFOD resulted in greater increases in bacterial RA% and SCFA concentrations compared with baseline. Bacterial RA% correlated bidirectionally with EIGS biomarkers and Ex-GIS, alongside strong negative associations with performance. Full article

Reliable prediction of post-initiation run-out distance of rainfall-induced landslides is essential for hazard assessment, evacuation planning, and disaster-risk mitigation. However, most existing data-driven approaches formulate run-out prediction as a deterministic regression problem and therefore provide limited information on predictive uncertainty, rare long-runout events, and explainable decision support. To address these limitations, this study proposes CQR-EVT-XAI, a trustworthy AI framework that integrates Quantile LightGBM, Conformalized Quantile Regression (CQR), Extreme Value Theory (EVT), and Explainable Artificial Intelligence (XAI) for uncertainty-aware and explainable landslide run-out risk prediction. Based on 10,158 rainfall-induced landslide samples, physics-informed features are constructed from elevation difference H, source area A, source volume V, and mean slope angle $\theta$. The proposed framework generates calibrated prediction intervals, threshold-based exceedance probabilities, upper-tail risk indicators, and interpretable risk levels. The CQR-LightGBM median model achieves high point-prediction accuracy, with $R^2$ = 0.939, RMSE = 18.03 m, and MAE = 6.55 m. Conformal calibration improves the empirical coverage of the nominal 90% and 95% prediction intervals from 0.813 to 0.903 and from 0.876 to 0.953, respectively. Tail-risk analysis shows that the upper prediction bound ${\widehat{L}}_{95}$ effectively identifies extreme long-runout events, achieving recall values of 0.974 and 0.900 for L > 300 m and L > 500 m, respectively. SHAP analysis reveals that elevation difference H, source volume V, and energy-related derived features dominate both median run-out prediction and upper-tail risk behavior, while slope-related variables mainly influence predictive uncertainty and exceedance-risk levels. These results demonstrate that the proposed CQR-EVT-XAI framework provides a practical workflow for calibrated uncertainty quantification, tail-risk identification, and explainable decision support in rainfall-induced landslide run-out risk assessment. Full article

Early wildfire monitoring in power transmission corridors requires reliable detection of weak fire and smoke cues under complex field conditions and strict edge-computing constraints. To address these issues, this paper proposes an edge-deployable RGB–thermal framework based on visible and thermal infrared (TIR) imaging for unmanned aerial vehicle (UAV)-based corridor monitoring, including a spatial detector, YOLO-MMSC, and a temporal-enhanced version, YOLO-MMSC-T. The study also establishes a self-collected corridor-oriented RGB–thermal (RGB–T) dataset to complement public wildfire data. Unlike existing RGB–thermal wildfire datasets that mainly focus on forest or wildland fire scenes, the proposed dataset is specifically organized for complex-background power transmission-corridor monitoring, including continuous UAV sequences, nighttime conditions, smoke/vegetation occlusion, long-range small targets, and hard-negative interference. To the best of our knowledge, this is the first self-collected RGB–thermal wildfire dataset designed for this specific application scenario. The framework integrates a mobile inverted bottleneck convolution (MBConv) lightweight backbone, a Shallow Detail Fusion Module (SDFM) for shallow cross-modal alignment and denoising, a Content-Guided Attention (CGA) module for adaptive fusion, and normalized Wasserstein distance (NWD)-based box regression for long-range small-target localization. Experiments on public and self-collected datasets show that YOLO-MMSC achieves 94.6% mAP@0.5, 95.0% precision, and 93.9% recall while running at 60 FPS on Jetson Orin NX. With temporal fine-tuning, YOLO-MMSC-T reaches a continuous detection rate (CDR) of 95.6% with a jitter index of $2.8\times {10}^{-3}$. Field experiments using a DJI Matrice 4T further indicate a practical operating altitude of 120–180 m. These results support lightweight RGB–thermal remote sensing for real-time wildfire monitoring in complex transmission-corridor environments. Full article

Background/Objectives: Insufficient physical activity remains a major public health concern among adult women, highlighting the need to identify structured activity contexts that can contribute meaningfully to recommended weekly physical activity levels. Official masters basketball may represent one such context; however, the amount of physical activity accumulated during female masters basketball match play remains insufficiently quantified. This study quantified the physical activity profile of official tournament match play among female masters basketball athletes and described the associated external physical demands. Methods: This observational study included 52 female master basketball athletes aged 37–63 years who competed in a three-day national masters tournament. Match demands were monitored using tri-axial microsensors. Physical activity was classified from processed raw tri-axial acceleration data into intensity zones, and differences in time spent across zones were examined using one-way repeated-measures ANOVA. External load during active play was quantified using total distance, distance across speed zones, accumulated acceleration load (AAL), mechanical load (ML), jump load (JL), and Physio Load. Results: Significant differences were observed across physical-activity intensity zones, with more time accumulated in light physical activity (LPA) and vigorous physical activity (VPA) than in moderate physical activity (MPA), whereas MPA accounted for the least time overall [F (1.98, 101.16) = 47.57, p < 0.001, ηp² = 0.48]. Descriptively, moderate-to-vigorous physical activity (MVPA) amounted to 42.78 min, calculated as the sum of MPA (9.41 ± 3.82 min) and VPA (33.37 ± 14.49 min). During active play, athletes covered 59.19 ± 17.26 m·min⁻¹, with most distance accumulated in the low- and medium-speed zones and limited very-high-speed running; AAL, ML, and JL averaged 8.32 ± 2.31 AU·min⁻¹, 22.35 ± 5.53 AU·min⁻¹, and 31.26 ± 28.35 J·min⁻¹, respectively. Conclusions: Official female masters basketball appears to provide a meaningful intermittent physical-activity stimulus within a single monitored match exposure and may contribute substantially to weekly aerobic physical-activity accumulation in adult women. Full article

This study addresses a modified continuous multi-reservoir scheduling problem characterized by a high-dimensional continuous decision space, strong time-varying storage constraints, strict terminal storage closure requirements, and a highly nonconvex composite objective. To solve this challenging problem, a multi-stage collaborative memetic algorithm based on CVT-MAP-Elites and clustering gradient (MCMA-CCG) is proposed. The framework consists of three tightly coupled stages: an exploration stage based on CVT-MAP-Elites to preserve diverse high-potential elites, a clustering stage using DBSCAN with a customized noise-retention strategy, and a refinement stage that combines DE with gradient-enhanced SLSQP to perform accurate exploitation. Under a unified experimental setting, MCMA-CCG was evaluated against several representative optimization algorithms, including DE, GA, SAPHTLR, HBMO, MFA, and MBWOHHO, over 30 independent runs. The updated results show that MCMA-CCG consistently achieves the best overall performance in both the four-cycle and five-cycle reservoir scheduling scenarios while also exhibiting superior empirical runtime and feasibility behavior. In the four-cycle case, it attained a best value of 6.08 × 10³, an average of 5.97 × 10³, and a standard deviation of 4.62 × 10¹; meanwhile, it produced feasible solutions in 26 of 30 runs, achieved a mean feasibility distance of 1.20 × 10⁻³, and required only 54.91 s on average under the 30,000-function-evaluation budget. In the more challenging five-cycle case, it attained a best value of 7.60 × 10³, an average of 7.44 × 10³, and a standard deviation of 7.55 × 10¹; it still generated feasible solutions in 19 of 30 runs, with a mean feasibility distance of 4.84 × 10⁻³ and an average runtime of 93.80 s under the 50,000-function-evaluation budget. By contrast, all baseline algorithms produced no fully feasible runs under the same feasibility criterion and generally required longer wall-clock time. Ablation studies further demonstrate that the superior performance of MCMA-CCG does not arise from any single module, but from the effective synergy among quality-diversity exploration, cluster-guided seed extraction, and gradient-assisted local refinement. These results confirm both the numerical superiority and the physical interpretability of the proposed framework for complex continuous multi-reservoir scheduling problems. Full article

Biomimetic optimizers are increasingly used to solve complex engineering problems, yet their performance depends strongly on how effectively they preserve diversity while maintaining selection pressure toward promising regions. In this study, the Starfish Optimization Algorithm (SFOA) is enhanced through fitness–distance-aware selection control, leading to two improved variants: Fitness–Distance Balance Starfish Optimization Algorithm (FDBSFOA) and Dynamic Fitness–Distance Balance Starfish Optimization Algorithm (dFDBSFOA). The proposed framework guides candidate selection using both solution quality and spatial diversity relative to the current best solution, while the dynamic variant further adapts this balance over the course of the search to improve exploration in early iterations and exploitation near convergence. The proposed methods are evaluated on the IEEE CEC2017, CEC2020, and CEC2022 benchmark suites under a unified maximum function evaluation budget, MaxFEs = 10,000 × D, with 21 independent runs, and are further validated on constrained engineering design problems. Performance is assessed using convergence behavior, robustness indicators, computational overhead, and nonparametric statistical tests. The results show that the proposed variants improve the robustness and search efficiency of baseline SFOA, with dFDBSFOA providing the most consistent overall performance while introducing a controlled and interpretable computational overhead. These findings suggest that diversity-aware selection can serve as an effective design principle for strengthening biomimetic optimization frameworks. The current study focuses mainly on continuous, single-objective, and stationary benchmark problems, while the engineering-design validation also includes constrained and discrete/integer-coded cases. Extending the proposed strategy to dynamic, noisy, large-scale mixed-integer, or multi-objective settings remains future work. Full article

Trustworthy deployment of artificial intelligence in safety-critical systems requires accurate diagnosis of anticipated scenarios and reliable rejection of out-of-distribution (OOD) inputs that fall outside the modeled operational scope. Existing data-driven diagnostic models typically assume that test inputs are drawn from the training distribution or rely on heuristically tuned thresholds that lack enforceable safety guarantees. This article presents SCOPE (Safety-Calibrated Out-of-distribution Prediction via Contrastive Embeddings), a framework integrating supervised contrastive learning with split-conformal prediction to provide statistically grounded OOD rejection with finite-sample false-alarm control. SCOPE employs a causal residual convolutional encoder to map multivariate sensor streams into a hyperspherical embedding space with a compact, class-specific structure. A k-nearest-neighbor density nonconformity score, computed in the encoder embedding space, flags transients that occupy low-density regions relative to known accident manifolds; an ablation shows that this density score outperforms prototype distance, entropy, and conservative maximum fusion as well as a panel of standard OOD baselines (MSP, ODIN, energy, Mahalanobis, OpenMax, MC-dropout, and a reconstruction autoencoder). To support temporally evolving trajectories, SCOPE aggregates window-level scores under a monotone decision policy and performs trajectory-level conformal calibration, yielding distribution-free guarantees that bound the probability of falsely rejecting a known accident run. SCOPE is evaluated on the Nuclear Power Plant Accident Data (NPPAD) benchmark using high-openness splits that withhold entire accident families as unknowns, and all metrics are reported as mean ± standard deviation across multiple random seeds. Results demonstrate strong diagnostic accuracy on accepted trajectories, conservative false-alarm rates satisfying user-specified safety constraints across multiple operating points, and timely rejection of unseen accident mechanisms, making SCOPE suitable for deployment in safety-critical monitoring applications. Full article

This study develops a mobile-supported system that municipalities can use in their irregular waste collection services within the scope of smart cities. Irregular waste refers to waste that individuals or organizations produce non-periodically, which arises unexpectedly or in an unusual manner. Unlike small-volume household waste collected at routine times, irregular waste is generally large-volume waste such as construction rubble, vegetable oil, mineral oil, and garden waste. In the irregular waste collection system developed in this study, waste locations are marked on the map of an application running on mobile devices, and notifications are sent to the municipality. The Google Distance Matrix API was used for processing and visualizing the notification locations on the map. Daily or 4 h planning is carried out using this data. In this study, a genetic algorithm and a differential evolution algorithm were used for vehicle routing and vehicle type optimization. To compare the efficiency of both methods, four different scenarios were designed with different numbers of waste locations and different types and amounts of waste, and the successes of the methods were compared. Differential evolution is found to be on average 0.8% better. Optimizations performed with actual road distances were found to be 8.0% more successful than optimizations performed with Euclidean distances. Full article

For enhancing the level of refinement of short-horizon wind-storage power prediction, this paper introduces an advanced BiLSTM prediction model integrating data preprocessing based on the density-based clustering technique known as DBSCAN, partial least squares regression (PLSR), and particle swarm optimization (PSO). In this paper, “wind-storage power” refers to the net power output of a wind farm integrated with a battery energy storage system (BESS), where the measured data already embed the effects of charge/discharge operations. First, outage and missing data are removed from the historical dataset. DBSCAN is then employed to identify abnormal samples in wind-storage power and meteorological variables, such as wind speed, wind direction, atmospheric pressure, temperature, and humidity, and linear regression is used to correct the detected noise points. Correlation analysis is further conducted to identify the most relevant meteorological inputs, namely wind speed, wind direction, and atmospheric pressure. Next, the PLSR model is applied to generate the preliminary prediction of wind-storage output. On this basis, the BiLSTM network is employed to predict the residual error, which mainly reflects the nonlinear characteristics not captured by the preliminary prediction. Meanwhile, PSO is implemented to determine the most suitable core hyperparameters for the BiLSTM architecture. Ultimately, the preliminary PLSR result is corrected by the predicted residual to obtain the final wind-storage power prediction. The DBSCAN parameters are systematically selected via a k-distance plot (ε = 0.9, MinPts = 2.5), and the PLSR number of components is set to A = 3 based on five-fold cross-validation. Case studies show that, for the 24 h prediction horizon, the proposed method improves prediction accuracy by 2.29%, 11.47%, and 5.54% compared with the BP, Wavelet-LSTM, and standard LSTM models, respectively. Furthermore, statistical significance is confirmed by Diebold–Mariano tests and 10-run confidence intervals. Full article

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions. Full article

Objectives: The aim of this paper is to determine whether leg kinematics and the normal force generated during the stance phase of running differed between (i) long-distance runners with medial tibial stress syndrome (MTSS) or (ii) long-distance runners who were asymptomatic at baseline testing but developed MTSS compared to asymptomatic control participants. Methods: Lower-limb kinematics, normalised stance-phase forces and spatiotemporal outcome variables were compared between the limbs of MTSS symptomatic long-distance runners (n = 11) and matched asymptomatic controls (n = 11). Outcome variables were also compared between the limbs of long-distance runners who were asymptomatic at baseline but developed MTSS (n = 4) compared to asymptomatic control limbs. Results: In the case-control comparison, MTSS symptomatic participants demonstrated slower running speeds but no differences in stance-phase normal forces or kinematics compared to asymptomatic controls. In the case study, participants who developed MTSS during the study displayed substantially lower normal forces, less plantar flexion and a more vertical tibia than the asymptomatic controls. Conclusions: The slower running speeds observed among the MTSS symptomatic participants may be pain-related or reflect reduced plantar flexor propulsive capacity. The development of MTSS by Participants 1 and 2, despite lower normal forces and plantar flexion compared with asymptomatic controls, suggests that the tibial load tolerance may vary among individuals. Furthermore, the peak stance-phase force appears to have limited utility as a standalone screening tool for MTSS injury risk. Finally, further prospective research is required to investigate plantar flexor function, propulsive force capacity and the risk of MTSS development to substantiate these findings. Full article

Accurate LiDAR point cloud registration on resource-constrained edge platforms is a prerequisite for intelligent robotics and industrial automation, yet it remains challenging because low-overlap matching, false correspondences, and fine alignment must be handled under limited computing budgets without GPU acceleration. While learning-based methods have advanced the field, their heavy hardware dependency and training requirements often hinder their practical deployment on mobile edge devices. To bridge this gap, this paper proposes GeoRescue, a training-free geometric registration framework designed for high-precision perception under stringent hardware limits. The method consists of three modular stages: Asymmetric Correspondence Expansion (ACE), which enlarges the candidate correspondence set to reduce the loss of true matches; Dynamic Geometric Topology Gating (DGTG), which suppresses false matches through distance-consistency-based hypothesis filtering; and Uncertainty-Aware Manifold Refinement (UAMR), which improves fine alignment by explicitly modeling local anisotropic noise via covariance-guided optimization. Experiments on 3DMatch, 3DLoMatch, and KITTI show that GeoRescue achieves registration recall rates of 84.84% and 41.27%, respectively, and a 94.95% success rate on KITTI. Remarkably, the framework matches the accuracy of high-capacity learning models while running on a GPU-free, 15 W edge CPU platform (Intel Core i5-8265U). These results indicate that GeoRescue provides a deployment-ready solution with an optimal efficiency–accuracy trade-off for LiDAR sensing and robotics perception in complex, real-world scenarios. Full article

Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article

The Shymkent–Saryagash–Abay (A-15) international highway is a major Kazakhstan–Uzbekistan freight corridor that runs through the irrigated horticultural belt of the Turkestan Region in South Kazakhstan, where adjacent fields supply vegetables and cucurbits to the regional market. Composite soil samples (n = 18) were taken at six distances (2–300 m) from the road edge across three locations during 2022–2023, along with edible fruits of tomato, cucumber, watermelon, and melon (n = 12) from the adjoining fields. Pb, Zn, and Cd were measured via flame atomic absorption spectrometry after HNO₃/H₂O₂ digestion. Soil concentrations decreased sharply with distance (Pb: 26.3 → 5.98 mg kg⁻¹; Zn: 21.29 → 4.16; Cd: 0.47 → 0.01 mg kg⁻¹), exceeding the national soil MPCs by 1.5–3 times within 2–10 m. Pb and Zn exceeded the Kazakhstani food-safety MPCs in all four crops, and Cd in three of four (tomato, cucumber, and melon). Transfer factors followed the order of Cd (2.90–4.40) > Zn (1.99–3.00) > Pb (0.16–0.30), and the Cd geo-accumulation index ranged from 1.05 to 1.65 at 2–5 m. Adult dietary risk was acceptable (HI = 0.029–0.052; CR < 1.7 × 10⁻⁶), yet food-safety exceedances support a precautionary sanitary buffer and combined soil-and-crop monitoring along the corridor. Full article