This study analyzes the safety risk transmission mechanism in urban logistics drone last-mile delivery within the policy-driven low-altitude economy. To address the limitations of traditional risk identification methods, which rely heavily on accident data, this research integrates the Fuzzy Decision Analysis Laboratory Method (Fuzzy-DEMATEL),Interpretive Structural Modeling (ISM), and the Matrix of Cross-Impact Multiplication (MICMAC) to construct a three-dimensional analytical framework based on causal relationships, structural hierarchy, and attribute classification.First, Fuzzy-DEMATEL is employed to quantify the strength of causal relationships among risk factors. Next, ISM is used to deconstruct the multi-level hierarchical network and identify fundamental causes within the risk system. Finally, MICMAC is applied to calculate the dependencies and driving forces of each influencing factor, helping prioritize risk governance measures. The research findings indicate that: (1) The safety risk system of urban logistics drones for last-mile delivery exhibits a “dual-core driven – multi-loop coupled” characteristic. Equipment failures act as the physical carriers of systemic failures, while the root-cause risks stem from institutional factors such as inadequate pre-service training and violations of laws and regulations. (2) The risk hierarchy follows a pyramid-shaped transmission path, with risks propagating from the root layer to the surface in successive layers. Open airspace serves as an accelerator, transforming environmental disturbances into institutional defects, which in turn lead to technical failures. (3) The dependency attributes of each factor indicate the priority order for risk prevention and control: management leverage points serve as the strategic control core, the environment-technology interaction network is central to joint prevention, standardized processes solidify basic operations, and systemic risk levels are reduced.

Accurate and efficient detection of small-scale targets on dynamic water surfaces remains a critical challenge in the deployment of unmanned surface vehicles (USVs) for maritime applications. Complex background interference—such as wave motion, sunlight reflections, and low contrast—often leads to missed or false detections, particularly when using conventional convolutional neural networks. To address these issues, this study introduces LMS-YOLO, a lightweight detection framework built upon the YOLOv8n architecture and optimized for real-time marine object recognition. The proposed network integrates three key components: (1) a C2f-SBS module incorporating StarNet-based Star Blocks, which streamlines multi-scale feature extraction while reducing parameter overhead; (2) a Shared Convolutional Lightweight Detection Head (SCLD), designed to enhance detection precision across scales using a unified convolutional strategy; and (3) a Mixed Local Channel Attention (MLCA) module, which reinforces context-aware representation under complex maritime conditions. Evaluated on the WSODD and FloW-Img datasets, LMS-YOLO achieves a 5.5% improvement in precision and a 2.3% gain in mAP@0.5 compared to YOLOv8n, while reducing parameter count and computational cost by 37.18% and 34.57%, respectively. The model operates at 128 FPS on standard hardware, demonstrating its practical viability for embedded deployment in marine perception systems. These results highlight the potential of LMS-YOLO as a deployable solution for high-speed, high-accuracy marine object detection in real-world environments.

Phenol is a persistent and toxic pollutant in industrial wastewater, demanding efficient and sustainable removal technologies. Conventional treatment methods often suffer from high operational costs, incomplete degradation, and secondary contamination. In this study, ZnO–Fe$_2$O$_3$ nanocomposites were synthesized using pulsed laser ablation in liquid (PLAL)-a clean, surfactant-free, and environmentally benign route—to develop eco-friendly adsorbents for phenol removal. The structural, morphological, and optical characteristics of the as-prepared nanoparticles were examined using X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-visible spectroscopy, and zeta potential analysis. The 50:50 ZnO–Fe$_2$O$_3$ composite demonstrated moderate colloidal stability (-28.54 mV), nanoscale crystallinity, and a heterogeneous surface morphology conducive to adsorption. Batch adsorption experiments at an initial phenol concentration of 100 mg/L revealed a maximum removal efficiency of 68.44% under 600 laser pulses after 50 minutes of contact time. The consistent optical band gap values (2.48-2.50 eV) across all samples indicated structural and electronic stability. The enhanced adsorption efficiency was attributed to synergistic interfacial interactions between ZnO and Fe$_2$O$_3$ within the nanocomposite matrix. Although the present work is limited to batch-scale trials under fixed conditions, future studies will investigate the effects of pH, adsorption kinetics, isotherm behavior, and material reusability. Overall, the findings highlight the potential of PLAL-fabricated ZnO–Fe$_2$O$_3$ nanocomposites as sustainable adsorbents for aqueous phenol remediation.

Real-time face detection in crowded scenes remains challenging due to small-scale facial regions, heavy occlusion, and complex illumination, which often degrade detection accuracy and computational efficiency. This study presents an enhanced detection framework that integrates Slicing-Aided Hyper Inference (SAHI) with the YOLOv11 architecture to improve small-face recognition under diverse visual conditions. While YOLOv11 provides a high-speed single-stage detection backbone, it tends to lose fine spatial information through downsampling, limiting its sensitivity to tiny faces. SAHI addresses this limitation by partitioning high-resolution images into overlapping slices, enabling localized inference that preserves structural detail and strengthens feature representation for small targets. The proposed YOLOv11–SAHI system was trained and evaluated on the WIDER Face dataset across Easy, Medium, and Hard difficulty levels. Experimental results demonstrate that the integrated framework achieves Average Precision (AP) scores of 96.33%, 95.87%, and 90.81% for the respective subsets—outperforming YOLOv7, YOLOv5, and other lightweight detectors, and closely approaching RetinaFace accuracy. Detailed error analysis reveals that the combined model substantially enhances small-face detection in dense crowds but remains sensitive to severe occlusion, motion blur, and extreme pose variations. Overall, YOLOv11 coupled with SAHI offers a robust and computationally efficient solution for real-time face detection in complex environments, establishing a foundation for future work on pose-invariant feature learning and adaptive slicing optimization.

Sediment deposition in Iraqi water channels represents a persistent constraint on agricultural irrigation and industrial water supply systems. Existing predictive models often neglect the unique hydraulic and sedimentological conditions of arid-region channels, limiting their applicability. This study integrates controlled laboratory experiments with statistical modeling to establish an empirical equation that quantifies sediment deposition mass (D) as a function of flow velocity (V), sediment concentration (C), and channel slope (S). A series of 54 experiments were conducted in a recirculating flume under precisely monitored conditions, including triplicate trials to ensure statistical robustness. The resulting power-law model, D=0.024·V-1.32·C0.89·S-0.75, exhibited strong predictive capability with R²=0.93, identifying flow velocity as the dominant governing parameter (56% influence). Optimal channel slopes between 5° and 7° were found to minimize deposition. Field validation within the Al-Diwaniyah irrigation network confirmed the model’s reliability, achieving 89% agreement between predicted and observed deposition values. These findings provide a practical and region-specific framework for improving channel design and maintenance strategies in arid environments. Future extensions will incorporate computational fluid dynamics (CFD) simulations and IoT-based monitoring to support adaptive sediment management.

In order to improve the durability of road structures, this study investigated the influence of temperatures, vehicle speeds, and axle configurations on pavement deflections with the PLAXIS 3D, a three-dimensional finite element modeling specifically developed for analyzing geotechnical engineering projects. A total of 32 models were developed, considering the temperatures of 4°C, 10°C, 20°C, and 30°C, when combined with the moving load velocities of 60, 80, 100, and 120 km/h. The effects of uneven distributions of axle loads were examined to capture the realistic condition of traffic loading. The results indicated that when the axle loads on both wheels were identical, the maximum pavement settlement occurred at the midpoint between them. Under unequal axle loading, the maximum settlement shifted to the wheel carrying the heavier load. This study revealed that a rising temperature reduced the strength of pavement materials, thus leading to a greater deflection. Nevertheless, higher vehicle speeds reduced pavement deflections due to decreased load–pavement interaction time. The findings highlighted the coupled effects of thermal conditions, traffic speeds, and load distributions on pavement performance, thus providing useful insights for the improved design and maintenance of sustainable road structures.

Vehicles comprise several critical systems, including the braking, steering, transmission, and suspension systems, which operate in concert to ensure safe and efficient movement. Research has established that vehicle malfunctions, particularly in the braking system, contribute significantly to road accidents, with technical failures accounting for approximately 15% of crashes and brake system failures responsible for 17.4% of these incidents. In light of this, an investigation was conducted to identify the factors that influence the braking coefficient and the variability of braking force in vehicle service brakes. A total of 1,018 vehicles were involved in the study, with results indicating that variables such as vehicle production year, category, place of registration, engine power and displacement, gross and curb weight, and payload significantly affect the braking coefficient. Furthermore, the analysis revealed that factors such as vehicle production year, category, registration location, gross and curb weight, and payload are prominent in determining the braking force variability. Neural network analysis was employed to further assess these influential factors, highlighting that the year of manufacture, place of registration, and vehicle payload are particularly influential in predicting both compliance with minimum braking coefficient requirements and variations in braking force. The findings underscore the importance of these factors in the development of more precise models for vehicle brake performance, with potential implications for safety standards and regulatory frameworks.