Search Results (2,863)

To reduce the conservatism of local trajectory planning in dynamic road scenarios caused by redundant projection of predicted trajectories, this paper proposes a hierarchical hybrid trajectory-planning framework with a time-to-arrival (TTA)-driven dynamic risk-filtering mechanism. In the Frenet coordinate system, road boundaries, ego states, and static and dynamic obstacles are represented uniformly to construct an S–L fused risk field and an S–T spatiotemporal interaction graph, enabling the filtering of temporally irrelevant conflict regions based on TTA relationships. At the path-planning layer, risk-guided adaptive sampling is integrated with dynamic programming and quadratic programming to improve search efficiency and trajectory quality. At the speed-planning layer, spatiotemporal coordination is achieved through non-uniform discretization, safe-corridor extraction, and speed-profile optimization. Simulation results show that the proposed method generates safe, smooth, continuous, and executable local trajectories in scenarios involving static-obstacle avoidance, adjacent-vehicle cut-ins, non-motorized road-user crossings, and mixed multi-obstacle interactions, while reducing unnecessary deceleration and detours. Ablation results further indicate that adaptive sampling reduces the number of DP search nodes by approximately 50% and the average planning time by about 30%, while maintaining a nearly unchanged minimum safety distance. These findings demonstrate that the proposed framework effectively suppresses redundant conflict regions and improves planning efficiency, solution feasibility, and motion continuity without compromising safety. Full article

Background: Gastric cancer survivors frequently encounter a “care gap” after discharge because of complex postgastrectomy syndromes. We evaluated “WECARE,” a bidirectional digital health platform designed to provide real-time symptom monitoring and multidisciplinary support. The primary goal of this study was to assess the efficacy of the platform by measuring the change in the Korean Quality of Life Questionnaire for Gastric Cancer Survivors (KOQUSS-40) total score over a six-month recovery period. Methods: This nationwide, multicenter pilot randomized controlled trial was conducted by the Korean Quality of Life in Stomach Cancer Patients Study Group (KOQUSS) across nine tertiary centers in Korea. A total of 88 patients who underwent curative gastrectomy were enrolled. Following an initial optimization phase involving 22 patients, the remaining 66 patients were randomized at a 1:1 ratio to the WECARE group or the control group. The WECARE group used a platform integrating the KOQUSS-40 algorithm for structured symptom reporting, real-time feedback on nutrition and exercise, and educational content on meal planning, symptom coping, and recovery. Assessments were performed at baseline and at 1, 3, and 6 months after discharge. Results: The WECARE group showed high feasibility and acceptability, with an adherence rate of 86.7% and an 82% satisfaction rate. At 6 months, the KOQUSS-40 total score (primary endpoint) did not differ significantly between the WECARE and control groups (85.3 ± 1.6 vs. 83.8 ± 1.6, p = 0.603). However, the WECARE group showed a numerically favorable recovery trajectory from the acute postoperative phase. Subgroup analysis revealed a positive trend in reflux symptom management in the WECARE group (p = 0.0856). In addition, more than 77% of users reported that the platform improved their self-management capabilities. Conclusions: The WECARE platform is a feasible and acceptable digital intervention for gastric cancer survivors. Although the primary endpoint was not significantly different, the favorable recovery trajectory, high adherence, and patient engagement support further evaluation in larger studies with longer follow-up and broader healthcare settings. Full article

Background/Objectives: Visuomotor interception requires aligning action with the future state of moving targets under sensory and motor delays. This constraint provides a tractable framework to examine how predictive and feedback-driven processes interact. This narrative review evaluates theoretical and empirical accounts of interception, with emphasis on how prediction and online control are integrated across behavioral and neural levels. Methods: We conducted a narrative synthesis of behavioral, eye-tracking, computational, and neurophysiological studies on visuomotor interception. Literature was identified through searches of PubMed, Web of Science, and Google Scholar using search terms including “visuomotor interception,” “predictive motor control,” “eye–hand coordination,” “time-to-contact,” “sensorimotor delay,” and related combinations. Studies published between 1986 and 2026 were considered, with emphasis on peer-reviewed empirical and theoretical work. Preprints were included only when directly relevant and are identified as such. The review compares internal model, ecological, and hybrid frameworks, and organizes evidence around spatial (“where”) and temporal (“when”) components of control. Results: Across paradigms, interception behavior is not well accounted for by purely predictive or reactive mechanisms. Instead, trajectories reflect a continuous interaction between anticipatory guidance and online correction. Spatial and temporal components show partial dissociation across tasks and manipulations. Available evidence supports the involvement of distributed circuits, including parietal, frontal, cerebellar, and subcortical systems, while indicating that eye movements play an active role in both information sampling and motor planning. Conclusions: Interception is best understood as the product of interacting biological, environmental, and learned constraints. Similar behavioral signatures can arise from distinct mechanisms, arguing against a unitary account. Progress requires integrating behavioral analyses with model-based and neural approaches to dissociate underlying computations. Full article

Urban roundabouts present significant design challenges for the integration of micro-mobility, yet comparative evidence regarding user behavior remains limited. As cities transition toward sustainable transport networks, understanding the operational needs of different micromobility modes is essential for urban planning. This study investigates the dynamic strategies of micromobility users through a controlled field experiment at a mini-roundabout in Gravina di Catania, Italy. Twenty experienced riders executed crossings using conventional bicycles and electric scooters. Utilizing drone recordings and open-source tracking, the analysis extracted speed, longitudinal acceleration, and path radius across 80 maneuvers. The findings reveal that behavior is highly dependent on vehicle type and geometric deflection. On highly deflected trajectories, e-scooters selected wider radii and achieved up to 15% higher speeds and accelerations than bicycles, whereas on gentler trajectories, they adopted more conservative, tighter lines with intense braking. Bicycles exhibited smaller, less systematic adjustments. These significant kinematic differences indicate that bicycles and e-scooters possess distinct performance envelopes. Treating them as a single legal or design class obscures stability disparities influencing conflict risk. Ultimately, this research provides empirical insights to guide urban planners in redesigning intersections, emphasizing that tailored infrastructure and targeted speed management are critical steps toward safer, truly sustainable urban mobility. Full article

Uncertainty in vessel arrival times can substantially reduce the efficiency of berth planning in port operations. To address this issue, this study proposes a unified, data-driven, predict-then-optimize framework that explicitly links vessel arrival time (VAT) prediction with downstream continuous berth allocation optimization. In the prediction stage, heterogeneous maritime data, including port call records, AIS trajectories, and vessel physical characteristics, are integrated to construct VAT prediction models. In the optimization stage, the predicted VAT is embedded into a continuous berth allocation problem (BAP) model to support berth scheduling decisions. To better reflect real operations, a two-stage evaluation framework is further developed, in which berth plans generated from estimated arrival times (ETAs) or predicted VATs are re-evaluated under realized actual arrival times while preserving the original temporal and spatial service order. Experimental results show that the proposed framework improves VAT prediction accuracy substantially, reducing the MAE and RMSE from 4.795 h and 7.255 h for the vessel-reported ETAs to 2.844 h and 4.934 h, respectively. More importantly, the predicted-VAT-based BAP consistently outperforms the ETA-based benchmark, yielding an overall 35.96% reduction in objective value across tested scenarios. These findings demonstrate that improved VAT prediction can be effectively translated into meaningful operational gains in berth allocation. Full article

Aim: Percutaneous liver ablation is a challenging procedure and operator-dependent. During the time when transarterial liver oncological therapies are favored over percutaneous liver ablation, we discuss the challenges of liver ablation with bowel interposition within the needle tract. Materials and Methods: In this IRB-approved retrospective review, we analyzed 481 cases of percutaneous microwave ablation performed between 2012 and 2025 using the NeuWave microwave ablation system with 15 or 20 mm probes under non-contrast CT guidance, with needle trajectories planned based on ultrasound. Dissection techniques were not performed, as intraprocedural ultrasound and CT assessment suggested that the ablation zone would remain confined to hepatic parenchyma. Cases of bowel transgression or close proximity were identified on post-procedural CT imaging, with a follow-up duration of 3 months performed consistently across all cases. Results: Three cases (0.6%) of bowel transgression or close proximity to bowel loops during needle placement were identified. There was no evidence of transmural bowel perforation or clinically significant bowel injury on clinical or radiologic follow-up. Post-procedural imaging demonstrated no free intraperitoneal air or fluid collections. Conclusions: In cases where the ablation zone is confined to hepatic parenchyma, bowel proximity to or inadvertent traversal by the cooled antenna shaft may not result in clinically significant injury and can be managed conservatively in selected patients. Full article

Background: Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition for which non-pharmacological interventions remain the primary therapeutic approach. Although research output in this field has increased substantially, a comprehensive synthesis of its developmental trajectory and emerging directions is still lacking. Methods: We conducted a bibliometric analysis of publications on non-pharmacological interventions for ASD indexed in the Web of Science Core Collection between 2001 and 2025. Knowledge structures, research hotspots, and temporal trends were visualized and analyzed using CiteSpace. Results: The field has transitioned from an early focus on behavioral interventions in children to a diversified and interdisciplinary research ecosystem spanning the lifespan. Recent growth has been driven by the integration of neuroscience-based approaches, particularly neuromodulation techniques, alongside continued refinement of behavioral, sensorimotor, and complementary therapies. Increasing attention has been paid to individual heterogeneity, methodological rigor, and mechanism-oriented research. Current frontiers emphasize multimodal intervention strategies, neural plasticity-based mechanisms, and the development of personalized precision intervention frameworks. Conclusions: This bibliometric analysis delineates the intellectual evolution of non-pharmacological intervention research for ASD and identifies key research gaps, particularly the need for longitudinal and pragmatic studies targeting individualized treatment response. The findings provide an evidence-informed overview of current concepts and emerging research directions in non-pharmacological care for ASD, with important implications for future clinical research, intervention design, and strategic research planning. Full article

The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of $8\pm 1.2$, which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios. Full article

Multi-Robot Systems (MRS) demand optimal spatial resource configuration to ensure systemic efficiency in mission-critical applications. Conventional paradigms rely on rigid coverage-first principles, prioritizing exhaustive spatial scanning over rapid target discovery, thereby compromising systemic responsiveness. To bridge this gap, this study proposes the Attraction of Unknown area Centroid for Exploration (AUCE) architecture, a centralized framework designed to simultaneously optimize global exploration efficiency and early-stage target discovery rates. The control framework incorporates a dynamic region planning strategy that adaptively modulates the systemic search focus based on the specific field of view of autonomous agents, alongside an optimized S-shaped trajectory pattern to establish a rigorous balance between localized path simplicity and global coverage. A versatile profit function synthesizing constant and time-varying coefficient strategies explicitly regulates the systemic trade-off between accelerated early-stage target discovery and global path cost minimization. Quantitative simulations demonstrate that AUCE significantly outperforms established methods by mitigating redundant path costs and generating a distinct front-loading effect to accelerate target localization. Subsequent evaluations confirm the framework’s computational scalability in expanded swarms and its systemic adaptability when navigating static obstacles. Full article

Background: Length of stay (LOS) reflects healthcare utilization but may also capture patient clinical trajectories. We investigated the relationship between LOS categories, organ support requirements, and in-hospital mortality. Methods: This retrospective observational study included 1332 consecutive adult ICU patients in a tertiary-care center. ICU LOS patterns were categorized using median-based and predefined cutoffs. Multivariable logistic regression was used to identify independent predictors of in-hospital mortality. Results: Prolonged ICU LOS was associated with higher crude mortality (61.0% vs. 43.5%, p < 0.001). However, in LOS-adjusted models, mortality was independently associated with mechanical ventilation (aOR 29.89, 95% CI 17.92–49.86), inotropic support (aOR 4.94, 95% CI 3.50–6.97), hemodialysis (aOR 5.43, 95% CI 2.52–11.72), older age, and diabetes mellitus. Prolonged LOS was not independently associated with mortality (aOR 0.93, p = 0.630). Conclusions: LOS reflects underlying disease severity rather than acting as an independent driver of mortality. Integrating LOS pattern assessment with markers of organ dysfunction may improve risk stratification and resource planning in hospitalized populations. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

Mobile crane repositioning and on-site logistics coordination constitute a highly coupled, nonlinear decision problem in constrained construction environments. Existing approaches largely decouple these tasks, limiting achievable system-level efficiency. This study introduces EVCrane, a kinematics-informed evolutionary optimization framework that simultaneously optimizes crane stopping positions, stockpile deployment, and task allocation within a unified mixed continuous–binary formulation. Unlike distance-based approximations, the proposed model propagates geometric decisions through coordinated crane motion components—including radial boom adjustment, slewing rotation, and vertical hoisting—ensuring physically consistent cycle-time estimation. A real industrial case study was used to benchmark five optimization algorithms under identical MATLAB R2026a implementations. The Genetic Algorithm (GA) achieved the lowest total crane engaged time (34.516 h), reducing operational duration by 6.45% and utilization cost by 6.32% compared with a deterministic nonlinear programming baseline. Comparative analysis reveals that recombination-based evolutionary search exhibits superior compatibility with assignment-driven non-convex landscapes, outperforming swarm-based and trajectory-based alternatives. Sensitivity analysis confirms structural robustness of optimal spatial configurations under parametric perturbations. The proposed framework advances crane planning from decoupled geometric heuristics toward integrated, physics-consistent, and computationally robust optimization, supporting intelligent and sustainable construction site management. Full article

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications. Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. Full article