Search Results (3,605)

3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexities. While existing methods rely on fixed geometric or appearance-based associations, they struggle to handle ambiguous cases and detection failures. We present an adaptive multi-level 3D MOT framework that achieves robust tracking through three key innovations: (1) multi-granularity temporal modeling that captures both fine-grained short-term motion and coarse long-term trends via dual-scale spatio-temporal attention, enabling accurate motion prediction across different object dynamics; (2) Transformer-based Appearance Association that employs cross-attention to model global inter-object relationships, resolving ambiguous associations in crowded scenarios where geometric cues alone fail; and (3) scene-adaptive learned thresholds that automatically adjust association strictness based on object density, motion complexity, and occlusion levels, avoiding the one-size-fits-all limitations of fixed thresholds. Our hierarchical four-level tracking strategy progressively handles cases from easy geometric matching (Level 1) to complex interval-frame recovery (Level 4), with SOT-based virtual detection generation bridging detector failures. Extensive experiments on the nuScenes benchmark demonstrate state-of-the-art performance. Full article

Rapid population growth challenges governance systems, housing markets, infrastructure capacity, and social cohesion, yet it is often treated as a predictable and uniform process. This structured comparative review synthesizes four distinct rapid-growth literatures: energy boomtowns, amenity-migration destinations, gateway communities, and mega-event host towns, to examine how different growth drivers shape community resilience. Using systematic forward and backward citation tracking grounded in community theory, the review identifies recurring patterns across otherwise separate research traditions. The analysis shows that outcomes are shaped less by growth itself than by institutional and spatial conditions. Extractive boomtowns and mega-event hosts experience compressed cycles of disruption and recovery that test adaptive capacity, while amenity-migration destinations and gateway communities face sustained pressures related to housing affordability, land-use conflict, and social boundary formation. Across contexts, three interrelated dimensions of adaptive capacity consistently structure trajectories: multilevel governance coordination, housing and land-use elasticity, and the management of social equity and cohesion. The findings advance a conceptual resilience framework that interprets rapid population change as a socio-spatial shock filtered through institutional and spatial conditions, with implications for sustainable urban design, flexible infrastructure planning, and inclusive governance. Full article

Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuations and limited robustness due to the non-stationary nature of sEMG signals. To address this issue, we propose an uncertainty-aware probabilistic fusion post-processing framework for continuous wrist motion estimation. The proposed approach decouples regression and uncertainty modeling, enabling plug-in compatibility with feature-based regression models. A local Gaussian process regression (LGPR) model is employed to estimate predictive uncertainty from a sliding feature window. The instantaneous regression output is then fused with the LGPR prediction through a Bayesian-inspired Gaussian formulation, resulting in a closed-form adaptive gain that dynamically adjusts smoothing strength according to predictive variance. Experimental results from both open-loop wrist joint motion estimation and closed-loop myoelectric control tasks demonstrate that our method outperforms existing methods in key performance indicators, including task completion time, trajectory smoothness, and trajectory tracking error. Full article

This paper investigates the trajectory planning problem for multiple unmanned aerial vehicles (UAVs) tasked with monitoring ground targets in complex, uneven terrains. The key challenge lies in maintaining continuous Line-of-Sight (LoS) while satisfying non-holonomic motion constraints and handling terrain-induced occlusions. To address this problem, we propose a Beam-search Model Predictive Control (BMPC) framework. The method integrates a first-order kinematic predictor for target motion estimation and a proactive safety altitude margin to guide UAVs toward favorable viewpoints before occlusions occur. The proposed approach is validated through extensive simulations based on high-resolution Digital Elevation Models (DEMs). Monte Carlo results demonstrate a significant reduction in LoS occlusion, decreasing the average occlusion rate from $38.75\pm 26.12\%$ to near zero in the noise-free case, compared with conventional reactive MPC methods. Under perception noise with a standard deviation of $1.5$ m, the LoS retention rate remains above $99\%$, indicating strong robustness to sensing uncertainty. In addition, the algorithm maintains stable computational performance, with an average execution time of approximately $1.68$ s per step in a non-optimized simulation environment. The proposed framework provides an effective solution for autonomous aerial surveillance in environments with substantial elevation variations, such as mountainous regions and urban canyons, by achieving a balance between tracking continuity and computational tractability. Full article

Corporate risk prediction is a central problem in financial analysis and corporate risk management. This study proposes a functional approach in which firms are represented through multivariate financial trajectories constructed from retrospective windows of accounting indicators, over which a similarity measure is defined and incorporated into a k-nearest neighbors classifier. The target variable is derived from administrative records, combining reporting discontinuity and firm administrative status as a proxy for financial distress. The empirical application is conducted using data from firms in the tourism sector in Colombia and is evaluated through stratified cross-validation. The results show that the trajectory-based representation captures gradual patterns of financial deterioration and improves the performance of k-NN relative to its static variable counterpart. In addition, the approach enhances interpretability by enabling the identification of historically comparable firms and the analysis of the financial dimensions that explain their similarity. Overall, the model provides a complementary perspective for corporate risk analysis based on the comparison of financial trajectories. Full article

IgA nephropathy is the most common primary glomerulonephritis worldwide and a leading cause of chronic kidney disease and kidney failure, with geographic and ancestral variation and a course ranging from asymptomatic urinary abnormalities to progressive loss of kidney function. This narrative review links the multi-hit model to risk stratification, biomarkers, current management, and emerging therapies, and highlights implementation gaps. Risk assessment is longitudinal, prioritizing proteinuria and estimated glomerular filtration rate trajectories and integrating Oxford MEST-C, prediction tools, and biomarker and multi-omics approaches, while recognizing limitations in histologic reproducibility and model calibration. Current management is anchored in optimized supportive care aimed at sustained proteinuria reduction and kidney protection, including intensive blood pressure control with maximal tolerated renin–angiotensin system blockade, dietary sodium restriction and lifestyle measures, and sodium–glucose co-transporter 2 inhibitors for eligible patients. For selected higher-risk patients with persistent proteinuria despite optimized supportive care, immunomodulatory strategies are discussed, including systemic corticosteroids and targeted-release budesonide (Nefecon), emphasizing structured toxicity risk mitigation and cautioning against assuming interchangeability among alternative oral budesonide formulations. Emerging therapies are organized around mechanism-aligned targets across the BAFF/APRIL axis, complement pathways, and endothelin-based approaches, with growing interest in sequencing and combination regimens layered on supportive care. Key gaps include reliance on surrogate endpoints, limited long-term durability and safety data, and uneven evidence for special populations. Full article

Background/Objectives: Hypoalbuminaemia is a consistent predictor of mortality in sepsis; however, the temporal dynamics of albumin decline and its relationship with fluid exposure and early norepinephrine therapy remain incompletely characterised. Determining whether early norepinephrine use is associated with attenuation of albumin loss could inform fluid management strategies and identify therapeutic windows for combined vasopressor–albumin interventions. The study aimed to assess whether serum albumin trajectories in sepsis are associated with fluid exposure, modulated by early norepinephrine therapy, and related to 30-day mortality. Methods: We conducted a prospective longitudinal study of patients admitted to an intermediate care unit (IMCU) with community-acquired sepsis. Serum albumin concentrations, cumulative fluid balance (CFB), and vasopressor use were recorded during the first 5 days of hospitalisation. Longitudinal mixed-effects and segmented linear models assessed the association of CFB and vasopressor therapy with albumin trajectories. Lagged mediation modelling explored the potential mediating role of albumin in the association between fluid exposure and 30-day mortality. Results: A total of 389 patients with community-acquired sepsis were included. Thirty-day mortality was 18%. Mean serum albumin at baseline was 2.58 g/dL and declined early to 2.24 g/dL at 72 h. Serum albumin was inversely correlated with cumulative fluid balance over time (r ranging from −0.235 to −0.348; p < 0.001). In longitudinal models, each 1% increase in ΔCFB was associated with a −0.029 g/dL decrease in serum albumin (p < 0.001), supporting an independent effect of fluid exposure. Before norepinephrine initiation, the albumin slope was −0.043 g/dL per interval and was −0.008 g/dL after vasopressor initiation (interaction p = 0.012). Lower albumin concentrations at 72 h predicted 30-day mortality (OR 1.49 per 0.5 g/dL decrease), and serum albumin mediated 18.6% of the association between fluid exposure and mortality. Conclusions: Cumulative fluid exposure was associated with a progressive decline in serum albumin in patients with community-acquired sepsis. Early norepinephrine initiation was associated with attenuation of this trajectory, consistent with the hypothesis that vasopressor-guided haemodynamic stabilisation may limit fluid-associated albumin loss. Full article

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

The squat is a fundamental multi-joint movement widely studied in strength training and biomechanics. While numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanisms governing how squat technique adapts to increasing external load remain insufficiently understood. In particular, inverse-dynamics-based approaches often overlook explicit constraints imposed by limited joint moment capacity. This study presents a computational framework for predicting load-dependent adaptations of squat posture. The human body was represented as a multi-segment rigid-body system, with joints modeled as nonlinear rotational elements with bounded moment capacity. A reference squat trajectory was first generated kinematically, and a constrained optimization procedure was then applied at each motion frame to determine a mechanically admissible posture under increasing barbell load. The results show that higher loads lead to systematic posture adaptations, including increased torso inclination and redistribution of rotational demand from the knee toward the hip joint. For the highest load, peak torso pitch increased from 30° to over 40°, while joint utilization exceeded unity, indicating the onset of yielding. These findings identify joint moment capacity as a key constraint governing squat technique and demonstrate the potential of the proposed framework for predictive biomechanical analysis. 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

Background: Psychological determinants are increasingly recognized as central contributors to both performance decline and recovery in competitive sport; however, contemporary evidence integrating injury-related and non-injury performance contexts remains fragmented. Objective: This systematic review synthesized empirical evidence (2016–2025) examining psychological determinants associated with return to sport (RTS), reinjury risk, burnout, injury incidence, and performance decline among competitive athletes. Methods: Conducted in accordance with PRISMA 2020 guidelines, a systematic search of PubMed, Scopus, Web of Science, and SPORTDiscus identified peer-reviewed studies published between January 2016 and December 2025. Eligibility criteria were defined using a PICO framework. Prospective cohort studies, longitudinal multi-wave investigations, one randomized controlled trial, matched cohort studies, diary-based designs, and injury-related observational studies were included. Due to heterogeneity in constructs and outcomes, findings were synthesized narratively. Results: Fourteen studies met the inclusion criteria, including prospective cohort studies, multi-wave longitudinal designs, one randomized controlled trial, one matched cohort study, and a diary-based investigation. Seven independent cohorts examined psychological readiness using the Anterior Cruciate Ligament—Return to Sport after Injury scale (ACL-RSI) in athletes with anterior cruciate ligament (ACL) injuries (sample sizes ranging from n = 39 to n = 384), consistently demonstrating that higher readiness predicted successful RTS at 6–24 months, while two prospective studies reported contrasting associations with second ACL injury risk. Four longitudinal studies (n = 93–491) showed that increased burnout and controlled motivation predicted performance decline and dropout trajectories, whereas higher resilience and mental toughness reduced burnout progression. One seasonal longitudinal study (n = 21) linked elevated cognitive anxiety and mood disturbance to increased injury incidence. Conclusion: Psychological determinants operate across deterioration and restoration pathways. Psychological readiness shows the strongest predictive consistency for RTS, while burnout, motivational climate, and resilience significantly shape long-term performance sustainability and injury-related outcomes. Full article

One of the most puzzling and unsolved challenges in molecular biology is understanding how proteins fold. Despite having advanced predictive tools that can accurately estimate the native structures of proteins, we still lack a comprehensive model that explains how amino acid sequences dictate folding pathways and trajectories. This manuscript introduces a novel treatment for the issue by employing the “principle of least action.” This approach enables us to explore an intriguing question: how does a protein achieve its native state at a constant folding rate and within a biologically plausible time frame? A response to this inquiry will help us understand why proteins must fold along specific pathways and identify the boundary conditions that limit their availability. Furthermore, the principle of least action—together with the effective trajectory conjecture—enables us to explain why different proteins could exhibit the same folding rate. Finally, it will enable us to provide an in-depth description of the genesis and solution of Levinthal’s paradox. Our results are expected to pave the way for a more profound understanding of how proteins fold, shedding light on how the amino acid sequence and its surrounding environment encode the protein’s folding pathways and, consequently, the protein’s three-dimensional structure. 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

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

Modern standards and requirements for ensuring the reliability and safety of transport infrastructure are aimed at shifting from routine maintenance to preventive maintenance, focused on predicting technical conditions and lifecycle management. Modern engineering approaches are based on the logic of state assessment and ensuring structural strength and dimensional stability. Therefore, they focus on recording defects or deviations from acceptable values without revealing the failure mechanism, which limits the ability to identify degradation processes and predict failures. The purpose of this article is to develop a formal conceptual framework for operationalizing process-oriented reliability analysis. Within this methodological framework, state is viewed as a snapshot of a dynamic process, while process stability is defined as the ability of a system to maintain its key behavioral characteristics under changing operating conditions and the geometric and physical–mechanical properties of system elements. The proposed framework expands on classical state-based diagnostics by introducing process invariants as prognostic indicators. The transition to trajectory-based behavior analysis allows monitoring systems to evolve into lifecycle management tools. Full article