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V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the “stop–go” state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle’s active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems.

With the increasing deployment of unmanned systems in maritime patrol, coastal monitoring, and environmental mapping, achieving effective UAV-USV collaboration in dynamic environments remains challenging. This paper proposes an edge-intelligence-driven collaborative control framework that integrates unified data modeling, multi-objective task scheduling, lightweight fault-tolerant middleware, and multi-sensor fusion. A Weighted Kalman Filter combines UAV imaging and USV sonar data to enhance perception accuracy, while NSGA-II optimizes task allocation considering completion time, energy consumption, and sensing reliability. The framework was validated through representative maritime scenarios, including patrol and coastal sediment mapping, on a virtual simulation platform. Results show improved task efficiency, energy utilization, communication latency, and robustness compared with single-platform and centralized scheduling approaches. The proposed method provides a balanced optimization of execution efficiency, energy consumption, data accuracy, and resilience, offering a reliable solution for large-scale maritime applications.

Apparent power and power factor are crucial metrics for evaluating the energy transmission efficiency and reactive power management in power systems. The increasing complexity of power load structures, driven by evolving energy production and consumption models, has intensified the nonlinear and unbalanced characteristics of circuits, presenting significant challenges to accurate apparent power measurement. The IEEE 1459-2010 standard introduces the concept of effective apparent power to enhance the assessment of energy transmission efficiency under non-sinusoidal and unbalanced conditions. However, the absence of a physical standard and a standardized traceability method for effective apparent power results in inconsistent measurement outcomes across instruments. This study proposes a novel method to trace effective apparent power measurements to the International System of Units (SI) benchmarks, based on the loss characteristics of transmission lines. The method includes a comprehensive analysis of measurement uncertainty. Simulation and experimental validation confirm that the proposed traceability circuit can achieve a measurement uncertainty of 0.0110% (coverage factor k = 2), satisfying the engineering requirement of expanded uncertainty U approximately 0.02% (k = 2). These results demonstrate the method’s practical suitability for engineering applications.

Background Currently, resistance training (RT) programs for sarcopenic older adults are numerous they remain controversial, and there is a lack of standardized programs specifically tailored for this population. Objectives This study aims to investigate the dose-response effects between RT on grip strength, skeletal muscle mass index (SMI), and short physical performance battery (SPPB) in sarcopenic older adults, to identify key training parameters in RT. Methods A comprehensive search was conducted in databases including PubMed, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), and WANFANG for randomized controlled trials published up to March 2025. Studies were selected based on the inclusion and exclusion criteria outlined in this study, resulting in 12 studies. The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) system was used to assess the quality of evidence, while Cochrane’s Risk of Bias Tool evaluated the methodological quality of the included studies. Meta-analysis of baseline data and outcomes was performed using Review Manager version 5.3.4 and R version 3.5 software. Results 12 studies involving 538 participants met the inclusion criteria. Compared to the control group, RT had a moderate effect on grip strength (SMD = 0.63, 95% CI 0.43–0.83; I² = 32%, X² = 11.73, df = 8, p  < 0.00001) and SPPB (SMD = 0.56, 95% CI 0.18–0.94; I² = 53%, X² = 8.48, df = 4, p  = 0.004) with in sarcopenic older adults, but it did not significantly improve SMI (SMD = 0.24, 95% CI -0.05-0.53; I² = 0%, X² = 2.85, df = 4, p  = 0.10). Meta-regression analysis revealed that training frequency ( p  < 0.0001) and training intensity ( p  = 0.0019) were significant predictors of grip strength improvement, while training frequency ( p  = 0.052) may be a potential predictor for SPPB improvement. Additionally, subgroup analysis showed that incorporating pulling exercises into the training program significantly improved grip strength compared to studies that did not include such exercises (SMD = 0.90, 95% CI 0.61–1.19; I² = 0%, X² = 0.75, df = 2, p  < 0.00001). Conclusions Our meta-analysis suggests that RT is associated with improvements in grip strength and SPPB scores in sarcopenic older adults, whereas its effect on SMI was not statistically significant. The findings indicate that performing moderate-intensity resistance training twice a week is a promising strategy for improving grip strength, while a similar frequency may also benefit SPPB scores. Furthermore, incorporating pulling exercises into the RT regimen may positively influence grip strength outcomes.

The Transformer-based target detection model, DETR, has powerful feature extraction and recognition capabilities, but its high computational and storage requirements limit its deployment on resource-constrained devices. To solve this problem, we first replace the ResNet-50 backbone network in DETR with Swin-T, which realizes the unification of the backbone network with the Transformer encoder and decoder under the same Transformer processing paradigm. On this basis, we propose a quantized inference scheme based entirely on integers, which effectively serves as a data compression method for reducing memory occupation and computational complexity. Unlike previous approaches that only quantize the linear layer of DETR, we further apply integer approximation to all non-linear operational layers (e.g., Sigmoid, Softmax, LayerNorm, GELU), thus realizing the execution of the entire inference process in the integer domain. Experimental results show that our method reduces the computation and storage to 6.3% and 25% of the original model, respectively, while the average accuracy decreases by only 1.1%, which validates the effectiveness of the method as an efficient and hardware-friendly solution for target detection.

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.

This paper proposes a novel polar coding scheme tailored for indoor visible light communication (VLC) systems. Simulation results demonstrate a significant reduction in bit error rate (BER) compared to uncoded transmission, with a coding gain of at least 5 dB. Furthermore, the reliable communication area of the VLC system is substantially extended. Building on this foundation, this study explores the joint design of polar codes and physical-layer network coding (PNC) for VLC systems. Simulation results illustrate that the BER of our scheme closely approaches that of the conventional VLC relay scheme. Moreover, our approach doubles the throughput, cuts equipment expenses in half, and boosts effective bit rates per unit time-slot twofold. This proposed design noticeably advances the performance of VLC systems and is particularly well-suited for scenarios with low-latency demands.

In an intelligent driving environment, monitoring the physiological state of drivers is crucial for ensuring driving safety. This paper proposes a method for monitoring and analyzing driver physiological characteristics by combining electronic vehicle identification (EVI) with multimodal biometric recognition. The method aims to efficiently monitor the driver’s heart rate, breathing frequency, emotional state, and fatigue level, providing real-time feedback to intelligent driving systems to enhance driving safety. First, considering the precision, adaptability, and real-time capabilities of current physiological signal monitoring devices, an intelligent cushion integrating MEMSs (Micro-Electro-Mechanical Systems) and optical sensors is designed. This cushion collects heart rate and breathing frequency data in real time without disrupting the driver, while an electrodermal activity monitoring system captures electromyography data. The sensor layout is optimized to accommodate various driving postures, ensuring accurate data collection. The EVI system assigns a unique identifier to each vehicle, linking it to the physiological data of different drivers. By combining the driver physiological data with the vehicle’s operational environment data, a comprehensive multi-source data fusion system is established for a driving state evaluation. Secondly, a deep learning model is employed to analyze physiological signals, specifically combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The CNN extracts spatial features from the input signals, while the LSTM processes time-series data to capture the temporal characteristics. This combined model effectively identifies and analyzes the driver’s physiological state, enabling timely anomaly detection. The method was validated through real-vehicle tests involving multiple drivers, where extensive physiological and driving behavior data were collected. Experimental results show that the proposed method significantly enhances the accuracy and real-time performance of physiological state monitoring. These findings highlight the effectiveness of combining EVI with multimodal biometric recognition, offering a reliable means for assessing driver states in intelligent driving systems. Furthermore, the results emphasize the importance of personalizing adjustments based on individual driver differences for more effective monitoring.

This study examined developmental toxicity of different mercury compounds, including some used in traditional medicines. Medaka ( Oryzias latipes ) embryos were exposed to 0.001–10 µM concentrations of MeHg, HgCl2, α -HgS ( Zhu Sha ), and β -HgS ( Zuotai ) from stage 10 (6–7 hpf) to 10 days post fertilization (dpf). Of the forms of mercury in this study, the organic form (MeHg) proved the most toxic followed by inorganic mercury (HgCl 2 ), both producing embryo developmental toxicity. Altered phenotypes included pericardial edema with elongated or tube heart, reduction of eye pigmentation, and failure of swim bladder inflation. Both α -HgS and β -HgS were less toxic than MeHg and HgCl 2 . Total RNA was extracted from survivors three days after exposure to MeHg (0.1 µM), HgCl 2 (1 µM), α -HgS (10 µM), or β -HgS (10 µM) to examine toxicity-related gene expression. MeHg and HgCl 2 markedly induced metallothionein ( MT ) and heme oxygenase-1 ( Ho-1 ), while α -HgS and β -HgS failed to induce either gene. Chemical forms of mercury compounds proved to be a major determinant in their developmental toxicity.