Search Results (1,317)

Effective anomaly detection in vehicle trajectories is crucial for developing sustainable and safe urban transportation systems. However, current research faces three main challenges including scarce anomaly data, inadequate spatial feature extraction in complex road networks, and limited capability in identifying complex behaviors. To address these issues, this paper proposes a Multi-scale Temporal and Road Network Interaction Anomaly Detection model (MTRI). Our framework leverages a Contrastive Learning-based Conditional Diffusion Model (CL-CD) to generate synthetic anomalous trajectories across diverse scenarios. It then employs an Urban road Network Interaction Modeling model (UNIM) to capture the profound interactions between trajectories and the road network. Finally, a Long-Short Temporal Anomaly Detection model (LSTAD) is designed to learn multi-scale temporal features for detecting sophisticated anomalies. Extensive experiments on real-world datasets from various urban scenarios demonstrate the superiority of our approach, which achieves high accuracy and adaptability (AUC-ROC > 0.85). This work contributes to sustainable urban mobility by providing a reliable solution for enhancing road safety through proactive anomaly detection. Full article

This study proposes an integrated agility assessment system that combines Millimeter-Wave (MMW) radar, Ultra-Wideband (UWB) ranging, and Mixed Reality (MR) technologies to quantitatively evaluate athlete performance with high accuracy. The system utilizes the fine motion-tracking capability of MMW radar and the immersive real-time visualization provided by MR to ensure reliable operation under low-light conditions and multi-object occlusion, thereby enabling precise measurement of mobility, reaction time, and movement distance. To address the challenge of player identification during doubles testing, a one-to-one UWB configuration was adopted, in which each base station was paired with a wearable tag to distinguish individual athletes. UWB identification was not required during single-player tests. The experimental protocol included three specialized agility assessments—Table Tennis Agility Test I (TTAT I), Table Tennis Doubles Agility Test II (TTAT II), and the Agility T-Test (ATT)—conducted with more than 80 table tennis players of different technical levels (80% male and 20% female). Each athlete completed two sets of two trials to ensure measurement consistency and data stability. Experimental results demonstrated that the proposed system effectively captured displacement trajectories, movement speed, and reaction time. The MMW radar achieved an average measurement error of less than 10%, and the overall classification model attained an accuracy of 91%, confirming the reliability and robustness of the integrated sensing pipeline. Beyond local storage and MR-based live visualization, the system also supports cloud-based data uploading for graphical analysis and enables MR content to be mirrored on connected computer displays. This feature allows coaches to monitor performance in real time and provide immediate feedback. By integrating the environmental adaptability of MMW radar, the real-time visualization capability of MR, UWB-assisted athlete identification, and cloud-based data management, the proposed system demonstrates strong potential for professional sports training, technical diagnostics, and tactical optimization. It delivers timely and accurate performance metrics and contributes to the advancement of data-driven sports science applications. Full article

Deploying public EV charging infrastructure while balancing efficiency, equity, and implementation feasibility remains a key challenge for sustainable urban mobility. This study develops an integrated, grid-based planning framework for Wuhan that combines attention-enhanced ConvLSTM demand forecasting with a trajectory-derived, rank-based accessibility index to support equitable network expansion. Using large-scale charging-platform status observations and citywide ride-hailing mobility traces, we generate grid-level demand surfaces and an accessibility layer that helps reveal structurally connected yet underserved areas, including demand-sparse zones that may be overlooked by utilization-only planning. We screen feasible grid cells to construct a new-station candidate set and formulate expansion as a constrained three-objective optimization problem solved by NSGA-II: maximizing demand-weighted neighborhood service coverage, minimizing the Group Parity Gap between low-accessibility populations and the citywide population, and minimizing grid-connection friction proxied by road-network distance to the nearest power substation. Practical deployment plans for 15 and 30 sites are selected from the Pareto set using TOPSIS under an explicit weighting scheme. Benchmarking against random selection and single-objective greedy baselines under identical candidate pools, constraints, and evaluation metrics demonstrates a persistent coverage–equity–cost tension: coverage-driven heuristics improve demand capture but worsen parity, whereas equity-prioritizing strategies reduce gaps at the expense of coverage and feasibility. Full article

The development of street-based outdoor physical activities plays a vital role in improving public health issues and advancing the goals of the “Healthy China” initiative, and the built environment is widely considered a key factor in promoting such activities and urban sustainability. Existing studies have paid limited attention to the nonlinear relationships between the built environment and outdoor physical activity and have mostly focused on a single type of activity (such as walking or cycling), with few comparative analyses across different activity types. With the purpose of addressing these limitations and providing cross-sectional empirical evidence for sustainable street design and active-transport policy, this study examines streets within the Second Ring Road of Changsha and uses large-scale street-level outdoor activity trajectory data to investigate associations between built environment indicators and outdoor activity flows. A Random Forest model, followed by the application of SHapley Additive exPlanations (SHAP), is used to characterize the nonlinear associations and interactions among variables, capturing patterns relevant to sustainable mobility, public health and urban form. The results indicate the following: (1) The built environment indicators are associated with walking, jogging, and cycling in distinctly different patterns—walking shows stronger associations with population density and access to bus stops; jogging demonstrates stronger associations with the accessibility of large open spaces; and cycling is more associated with land use mix and road intersection density. (2) Nonlinear associations and threshold-like patterns commonly exist between built environment variables and activity flows, with indicators such as bus stop density and walking continuity exhibiting pronounced effects within specific intervals. (3) Interaction effects among variables contribute importantly to model predictions, especially for jogging where their influence can even exceed the main effects of individual factors. These results underscore the potential value of implementing tailored street design strategies for different activity types and provide empirical evidence relevant to health-oriented urban planning. Full article

In the dynamic marine environment, the high mobility of intrusion targets, complex interference, and insufficient multi-vessel coordination accuracy pose significant challenges to the cooperative interception mission of multiple unmanned surface vehicles (USVs). This paper proposes an adaptive dynamic prediction-based cooperative interception control algorithm and establishes a “mission planning—anti-interference control—phased coordination” system. Specifically, it ensures interception accuracy through threat-level-oriented target assignment and extended Kalman filter multi-step prediction, offsets environmental interference by separating the cooperative encirclement and anti-interference modules using an improved Two-stage architecture, and optimizes the movement of nodes to form a stable blockade through the “target navigation—cooperative encirclement” strategy. Simulation results show that in a 1000 m × 1000 m mission area, the node trajectory deviation is reduced by 40% and the heading angle fluctuation is decreased by 50, compared with the limit cycle encirclement algorithm, the average interception time is shortened by 15% and the average final distance between the intrusion target and the guarded target is increased by 20%, when the target attempts to escape, the relevant collision rates are all below 0.3%. The TFMUSV framework ensures the stable optimization of the algorithm and significantly improves the efficiency and reliability of multi-USV cooperative interception in complex scenarios. This paper provides a highly adaptable technical solution for practical tasks such as maritime security and anti-smuggling. Full article

Equipped with one degree of freedom in one-dimensional translation of the base, a mobile dual-arm robot (MDAR) is proposed in this paper, and the two arms and the base move simultaneously. As a result, the motion of the base has a significant influence on the motion of both end-effectors at the same time, and the relative positions of the two end-effectors change all the time. Therefore, this paper focuses on the main issues related to the presented MDAR in two key areas: the relative dynamics and relative force/position hybrid control. First, based on the D-H parametric method, the relative kinematics of the proposed MDAR is established, and the relative Jacobian matrix of the robot is derived. Secondly, the dynamic model of the proposed MDAR is constructed using the Lagrangian method. Furthermore, a closed-loop control strategy for relative force/position hybrid control of the MDAR based on the relative dynamics is proposed to enable the two end-effectors of the MDAR to track the planned trajectory accurately. Finally, a simulation is carried out on a dual-arm cutting robot (DACR) for a coal mine to prove the effectiveness of the proposed relative dynamics and the proposed relative force/position hybrid control law in terms of the absolute error (AE) and root mean square error (RMSE). The results show that the proposed relative dynamic model and relative force/position hybrid control can significantly reduce error of the DACR, effectively improve the adaptability and operation accuracy of the system to complex environment, and verify the feasibility and superiority of the method in practical application. Full article

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions. Full article

The paper focuses on the goal point following an algorithm design based on the exact Global Positioning System (GPS) points. In order to achieve that, the first GPS point and initial heading angle are previously calculated by recursively adopting GPS points from the Naver Application Programming Interface (API) map. The GPS points are designated as a goal point in order to follow the mobile platform to the generated path. Simulation and experimental results demonstrate that goal point following logic can be implemented based on the generated path achieved from the map. Furthermore, the goal-point-following method is extended to trajectory tracking by defining the vector rather than the designated goal point. The result is demonstrated through simulation and an experiment with the real mobile platform. Full article

Locomotor disorders involving the spine are a major cause of impaired performance and early retirement in sport horses. Swimming is increasingly incorporated into rehabilitation protocols, but its effects on spinal biomechanics remain poorly understood. This prospective study evaluated changes in thoracolumbar mobility in sixteen sport horses diagnosed with cervical or thoracolumbar axial musculoskeletal lesions over a 12-week rehabilitation program comprising 4 weeks of land-based training followed by 8 weeks during which swimming sessions were incorporated three times per week. Weekly measurements of thoracolumbar flexion–extension range of motion (ROM) were performed during straight-line trot on a hard surface using inertial measurement units attached to the withers, T18, and tubera sacrale. Group-level analyses revealed minimal changes across training phases: in horses with thoracolumbar lesions, mean ROM decreased slightly during the second month of aquatic training (−0.1° [95% CI −0.1; 0], Cohen’s d = 0.2), whereas no significant variation was detected in horses with cervical lesions. As the study did not include a control group, these temporal changes cannot be specifically attributed to swimming and should be interpreted as descriptive rather than causal. Individual trajectories showed heterogeneous patterns, but these were not consistent enough to alter the group-level interpretation. Overall, the findings suggest that thoracolumbar mobility remains relatively stable throughout this type of rehabilitation program, highlighting the importance of individualized monitoring rather than the expectation of a uniform biomechanical response. Full article

In this paper, we propose a mobile target tracking method for airport movement areas based on an adaptive interacting multiple model framework combined with a strong tracking unscented Kalman filter, referred to as the AIMM-STUKF algorithm. The objective is to enhance real-time tracking accuracy, improve model adaptability, and strengthen robustness against abrupt disturbances in complex airport environments. The proposed AIMM-STUKF adopts a standard STUKF formulation within the overall tracking framework, thereby enhancing responsiveness to maneuvering targets. An exponential correction factor is further constructed based on posterior model probability differences to adaptively adjust the Markov transition matrix, enabling self-adaptive mode switching. In addition, airport map information is incorporated to impose constraints on the position components of the filtered state estimates, enhancing the adaptability of the algorithm to the airport operational environment. Experimental validation is conducted through Monte Carlo simulations using representative trajectories that reflect realistic airport operational characteristics. Comparative results with the standard IMM-UKF and two existing AIMM-UKF algorithms demonstrate that the proposed AIMM-STUKF achieves superior performance in terms of tracking accuracy, model matching consistency, mode-switching responsiveness, and robustness against sudden disturbances. Full article

Sodium-ion batteries (SIBs) have attracted growing attention as an alternative to lithium-ion technologies for electric mobility and stationary energy-storage applications, owing to the wide availability of sodium resources, cost advantages, and comparatively favorable safety characteristics. Accurate state-of-health (SOH) estimation is essential for safe and reliable SIB deployment, yet existing data-driven methods still suffer from limited accuracy and interpretability, as well as a lack of dedicated aging datasets. This study proposes an explainable SOH estimation methodology based on a long short-term memory (LSTM) network combined with model-agnostic KernelSHAP analysis. Thirteen health indicators (HIs) are extracted from charge/discharge data and post-charge relaxation segments, and the most relevant indicators are selected via Pearson correlation screening as model inputs. Built on these HIs, an LSTM-based multi-step framework is developed to take HI sequences as input and forecast the SOH trajectory over the subsequent 20 cycles. Experimental results show that the proposed method achieves high accuracy and robust cross-cell generalization, with mean absolute error (MAE) below 1.0%, root-mean-square error (RMSE) below 1.2% across all cells, and an average RMSE of about 0.75% in the main cross-cell setting. KernelSHAP-based global and temporal analyses further clarify how different HIs and time positions influence SOH estimates, enhancing model transparency and physical interpretability. Full article

Urban health, well-being, and equity—core objectives of Sustainable Development Goals (SDGs 3, 10, and 11)—have become key themes in contemporary urban planning research and landscape research. While existing studies focus predominantly on quantitative assessment, environmental exposure, and human mobility, the historical origins of health-oriented urban green space planning remain insufficiently explored. Focusing on Lingnan area as a representative case, this research investigates the emergence of public green space in late Qing cities and its early contributions to urban health and spatial governance. Through a systematic examination of American and British Gardens at the Thirteen Factories in Guangzhou, the planned public green space system of the Shameen concession, and the municipal greening practices of neighboring Hong Kong and Macao, the study further analyzes Zhang Zhidong’s tree-lined boulevard project along Changdi avenue as a key instance of localized institutional adaptation. Drawing on late-Qing and Republican newspapers, nineteenth-century Western travelogs and reports, historical and contemporary studies and photo albums, the study finds the following: (1) the American and British Gardens marked the earliest emergence of health-oriented urban green space in Lingnan area; (2) the systematically planned green space network of the Shameen concession constituted a prototypical form of health-oriented urban green space planning; (3) the botanical gardens, street-tree systems, public parks, and institutionalized management practices in Hong Kong and Macao exerted a strong regional demonstrative influence on Guangzhou; (4) the street-tree planting along Changdi Avenue represented a localized absorption of foreign planning paradigms and marked the institutionalization of municipal greening in Guangzhou. Although these early practices did not yet form a modern healthy city planning framework at that time, they played a crucial role in improving urban sanitation, enhancing public space quality, and shaping urban order. By tracing the historical trajectory from transnational demonstration to local adaptation and institutional consolihdation, this study provides new insights into the historical foundations of health-oriented urban planning in China and contributes a long-term perspective to contemporary debates on healthy cities and nature-based urban interventions. Full article

Background: Parkinson’s disease (PD) and frailty frequently co-occur and may interact bidirectionally through shared mechanisms of aging biology, mitochondrial dysfunction, inflammation, and reduced physiological reserve. Objective: We aimed to synthesize current evidence on prevalence, directionality, clinical overlap, adverse outcomes, and management implications of the PD–frailty nexus. Methods: A narrative review of epidemiologic, cohort, and interventional studies was performed, examining frailty in PD and PD risk in prefrail/frail populations, plus trials of multimodal interventions. Results: Frailty is common in PD, affecting approximately one-third of patients overall and becoming more prevalent as the disease advances. It independently predicts falls, cognitive decline, hospitalization, institutionalization, and mortality. Large cohorts suggest prefrailty/frailty is associated with incident PD risk, supporting a potential bidirectional association rather than direct causation. Diagnostic complexity arises because PD motor and non-motor features overlap with frailty constructs, risking misclassification. Management based on Comprehensive Geriatric Assessment (CGA) enhances personalized, multidisciplinary care. Exercise, particularly combined aerobic and resistance training reduces frailty and improves mobility, postural control, and quality of life. Complementary nutritional strategies, including muscle-targeted supplementation, can further strengthen rehabilitation outcomes, while careful attention to social determinants and polypharmacy remains essential to optimizing overall health and functional independence. Conclusions: Frailty is best understood as a clinical marker of vulnerability within PD and a correlate of more adverse trajectories rather than a proven causal determinant. Systematic frailty assessment integrated into PD care may help refine prognosis, individualize treatment, and support efforts to preserve independence. Priorities include PD-adapted frailty tools, CGA implementation, and rigorous trials of combined exercise–nutrition programs. Full article

Background/Objectives: People with visual impairments can participate in orientation and mobility (O&M) training to learn how to navigate to their desired destinations. Instructors adapt their approach to each individual client. However, assessments of client characteristics and resulting instructional adaptations are not standardised and may therefore vary. This study aimed to identify which individual differences instructors consider during O&M training and why. Methods: We conducted semi-structured qualitative interviews with 10 O&M instructors. Participants were asked to describe how they prepare for a training trajectory, and to describe a route they taught a specific client. Thematic analysis was used to determine instructional choices and the relevant client-specific factors. Results: We observed a common four-step instructional process in which clients are taught to notice, interpret, act upon, and anticipate relevant sensory cues until a destination is reached. Four main themes captured the individual differences impacting this process: Sensory modalities, Capacities and limits, Personal contextual characteristics, and Training approach. Conclusions: Instructors perceive route learning to be shaped by clients’ sensory abilities (even fluctuating within sensory modalities), mental and physical capacities (especially concentration and energy), and personal characteristics (especially age and anxiety). The dynamic social context in which training takes place (e.g., the instructor–client relationship) is shaped by individual differences between both clients and instructors. We speculate that trust-related themes (e.g., building confidence) may explain why certain client characteristics are emphasised by instructors, as they are associated with training outcomes. Full article

This paper investigates a load-balanced pickup strategy for heterogeneous multi-UAV systems, where unmanned aerial vehicles (UAVs) with different flight speeds and payload capacities cooperatively collect spatially distributed parcels while avoiding no-fly zones. The goal is to minimize the maximum mission completion time among UAVs while ensuring balanced workload distribution according to their heterogeneous capabilities. The formulated problem is a mixed-integer nonlinear program that jointly optimizes pickup assignment, trajectory planning, and slot duration allocation under mobility, safety, and payload constraints. To address the nonconvexity of the optimization problem, the successive convex approximation and penalty convex–concave procedure are applied, leading to a two-stage iterative algorithm that efficiently derives practical UAV strategies for load-balanced parcel pickup. The first stage minimizes the maximum completion time, and the second stage further refines the trajectories to reduce the total travel distance. Simulation results demonstrate that the proposed scheme effectively adapts to UAV capability asymmetry and achieves superior time efficiency compared to benchmark schemes. The results also point to future research opportunities, such as incorporating energy models, communication constraints, or stochastic task dynamics to extend the applicability of the proposed framework. Full article