Search Results (20,333)

The Logistics Performance Index (LPI) is a widely used benchmarking tool for assessing national logistics capabilities. However, its role in sustainability-oriented research remains unclear. This study reconceptualizes the LPI as a multidimensional analytical framework for examining the structural associations between logistics performance and sustainability outcomes. Using cross-country data from 2023, the analysis evaluates the alignment of the six disaggregated LPI dimensions with economic (GDP per capita), social (Human Development Index), and environmental (CO₂ emissions) indicators across approximately 120 countries. The analysis applies an integrated framework combining linear models, ensemble learning techniques, explainable artificial intelligence (SHAP), and clustering analysis to assess the consistency and interpretability of these relationships. The results indicate that logistics performance is more strongly aligned with economic and social outcomes than with environmental indicators. Infrastructure quality, tracking and tracing, and timeliness emerge as key logistics dimensions associated with higher income levels and human development. In contrast, the moderate alignment observed for CO₂-related outcomes highlights the influence of broader structural factors, such as energy systems and industrial composition, beyond logistics performance. Clustering analysis further reveals distinct logistics–environmental configurations, underscoring substantial heterogeneity in sustainability trajectories among countries with similar logistics capabilities. Overall, these findings establish the LPI as a system-level lens for diagnosing logistics–sustainability relationships and for designing context-sensitive policies aligned with the Sustainable Development Goals (SDGs), particularly SDGs 8, 9, 11, and 13. Full article

This paper studies the supervisory control of discrete event systems (DESs) from an event observation perspective and addresses the problem of supervisor design with minimal observation. In supervisory control, a supervisor enables or disables controllable events based on its observation of the system trajectory to guarantee controllability and nonblocking behavior with respect to a given specification, while the number of observed events critically affects the implementation complexity and cost of the control logic. Rather than minimizing the state space of the supervisor, which is the focus of classical supervisor reduction, this paper is dedicated to the minimization of observable events. Specifically, it aims to reduce the observation alphabet while preserving control equivalence with the original supremal supervisor. By analyzing the consistency of disabling decisions between event-enabled and event-disabled states, necessary and sufficient distinguishability conditions are derived and represented using Parikh vectors, which enables their formulation as linear separation constraints. In addition, event-enabled circles are introduced to capture intrinsic structural observability requirements induced by cyclic behaviors of the supervisor. These results lead to a mixed-integer linear programming (MILP) formulation that minimizes the observation alphabet while preserving control equivalence with the original supremal supervisor, together with an E-closure-based construction that synthesizes an executable event-minimal supervisor. Illustrative examples demonstrate that the proposed method can significantly reduce observation requirements even when state-minimal supervisors are already available, thereby improving implementation efficiency in resource-constrained DES applications. Full article

In order to evade interception, advanced aircraft often adopt jump-gliding trajectories to efficiently utilize aerodynamics and achieve complex maneuvers. Precise guidance of UAVs for intercepting such targets is critically challenged due to their high speed and uncertain maneuvers. For terminal guidance scenarios, the extremely short engagement window necessitates strict convergence within the predefined finite time. While PPC offers a promising framework to ensure such convergence with guaranteed transient performance, it suffers from singularity when target uncertainties drive tracking errors beyond performance bounds. To address these challenges, this paper proposes an adaptive prescribed-performance guidance law with predefined-time convergence for UAVs. Built upon the analysis that jump-gliding targets exhibit predominantly longitudinal oscillatory maneuvers, we first establish a velocity model to characterize their motion uncertainties. Using the derived uncertainty bounds and estimated parameters, a predefined-time performance function (PPF) is then developed and robustly modified to eliminate the singularity risk. By integrating this modified PPC with an adaptive law, the proposed framework achieves robust predefined-time convergence of the line-of-sight angle while simultaneously compensating for unknown target maneuvers. Theoretical analysis verifies the framework’s stability, and simulation results demonstrate its effectiveness in intercepting highly maneuverable targets. Full article

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article

Bundle recommendation has gained significant attention, but it faces two key challenges: sparse interaction data and complex UB, UI, and BI relations. Recent work uses multi-view contrastive learning, yet current frameworks rely on fixed-weight fusion that ignores view-specific importance and suffers from gradient suppression on sparse data. We propose TriadCBR, a tri-view adaptive contrastive learning architecture for bundle recommendation. It uses a simplified GCN to learn view-specific representations and a Mixture-of-Experts (MoE) module to generate personalized fusion weights, addressing the limitations of fixed-weight fusion. TriadCBR further incorporates a fine-grained contrastive module integrating InfoNCE, DCL, and Barlow Twins. This combination effectively mitigates gradient vanishing from invalid negatives and minimizes cross-view feature redundancy. To handle data sparsity, we design a Difficulty-Aware BPR (DA-BPR) with curriculum augmentation to dynamically refine the ranking trajectory. Extensive experiments on Youshu, iFashion, and NetEase demonstrate that TriadCBR achieves statistically significant improvements, boosting Recall and NDCG by an average of 3.61%, with 9 of 12 metric–dataset combinations reaching statistical significance, over state-of-the-art baselines, validating the robustness of its dynamic fusion and adaptive optimization. Full article

Voice user interfaces (VUIs) have evolved from simple command-based systems to more advanced platforms capable of engaging in complex, multi-turn conversations. While current VUIs primarily perform routine tasks, their future trajectory is poised to be significantly shaped by advancements in large language models (LLMs), enhancing their language understanding and human-like interaction capabilities. This study explores user perceptions of next-generation VUIs using a design fiction approach. We crafted five plausible future scenarios, depicted in comic-style formats, showcasing diverse VUI use-cases. Results from the focus group discussions reveal valuable insights highlighting the potential and challenges of integrating advanced VUIs into everyday interactions. Our results highlight the importance of building trust, factors influencing trust, social aspects and implications of technology, preferences for interaction techniques, and various ethical considerations associated with technology. We conclude by providing design guidelines for future VUIs, emphasizing the need for designing to build trust, the importance of domain specificity, the importance of enabling social experiences mediated via VUIs, and more. Full article

The risks associated with hazardous materials (HazMat) transportation exhibit typical characteristics of chain-like distribution, spatiotemporal regularity, and individual heterogeneity. A personalized trip-chain feature spectra recognition framework for HazMat vehicles is proposed to enhance the capability to assess and analyze individual risks using vehicle positioning data. The proposed framework addresses the challenges of deriving personalized risk feature maps arising from missing real-time trajectory data, complex sub-trip-chain segmentation, and the extraction of personalized risk feature representations. An improved conditional Wasserstein Generative Adversarial Network (WGAN) model is initially developed to impute trajectories with missing positional data, and it can robustly reconstruct trajectories with large-scale missing segments by integrating a multi-head self-attention mechanism and a gradient penalty. A two-layer clustering algorithm, K-Means-multiplE-THreshOlds-adaptive-DBSCAN (KMETHOD), which combines an adaptive mechanism with threshold rules, is subsequently designed to identify the dwell time and related spatial attributes of dwell points along vehicle trips. A BERT-based model is incorporated to filter Points of Interest (POIs) around dwell points, which enables the extraction of their detailed location semantics and trip characteristics and thus supports trip chain identification and segmentation. A threshold-activated multilayer trajectory feature-map method (TAFEM) is constructed to generate feature maps for each trip chain. The Liquefied Natural Gas (LNG) transportation trajectory data from Guangdong Province is selected to evaluate the effectiveness of the proposed methods. The experimental results demonstrate that the proposed framework can effectively identify trip chains and generate their corresponding feature maps. The trajectory imputation model achieved the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Dynamic Time Warping (DTW) of 2.34–3.33, 6.05–7.74, and 0.74–1.21, respectively, across different missing-rate scenarios, outperforming other benchmark models. The identification accuracy of dwell-point duration and location reaches 98.35%. The BERT-based method achieves a maximum accuracy of 92.83% in origin–destination (OD) point recognition, effectively capturing comprehensive trip-chain information. TAFEM accurately characterizes the spatiotemporal distribution and potential causal factors of personalized HazMat transportation safety risks, providing a reliable foundation for risk identification and safety management strategies. Full article

This study investigates the effects of roll, pitch, and heave on the motion characteristics of a fully wind-powered ship equipped with two rigid wing sails. While previous research by the authors demonstrated that L-shaped and T-shaped sail arrangements improve thrust generation and maneuverability, the importance of six-degree-of-freedom (6-DOF) motion modeling has not been fully clarified. To clarify this open question, the present work provides a systematic comparison between the 6-DOF model and a simplified three-degree-of-freedom (3-DOF) model in which roll, pitch, and heave are constrained. Four sail configurations are analyzed under true wind directions of 150° and 180°. The comparison reveals that the 3-DOF model cannot accurately reproduce key features of the ship’s trajectory, drift angle, and speed, particularly for cases where aerodynamic–hydrodynamic coupling strongly affects yaw stability. In contrast, the 6-DOF simulations reveal substantially different steady-state behavior and demonstrate that roll, pitch, and heave play an essential role in predicting maneuvering performance. The results clarify how sail arrangement and motion modeling interact to shape the maneuvering characteristics of fully wind-powered vessels, providing fundamental insights for the development of reliable 6-DOF simulation frameworks and for the design assessment of next-generation wind-propelled ships. Full article

To tackle the degraded stability and non-smooth mode transitions under wide-range grid-strength variations with high renewable penetration, an adaptive fusion and disturbance-free switching control strategy is proposed, where parameter stability regions are analyzed using the D-partition method, thereby improving robustness over single-mode grid-following/grid-forming operation and reducing transients from conventional switching. First, a unified frequency-domain characteristic equation that incorporates the fusion weight is derived based on the sequence-impedance stability criterion, providing a consistent theoretical basis for stability modeling and assessment across operating conditions. Next, under wide-range grid-strength conditions, the controller-parameter stability region is computed subject to multiple constraints, including phase margin, gain margin, and short-circuit ratio, and the resulting robust feasible set is geometrically characterized on the parameter plane. Furthermore, to suppress transient disturbances induced by variations of the fusion weight with grid strength near the switching threshold, a D-zone-based multi-partition, stage-by-stage smoothing adaptive fusion strategy is developed. A nonlinear weight mapping yields a continuous transition trajectory, enabling seamless, disturbance-free transitions from weak to strong grids. Finally, simulation and experimental results quantitatively validate the superiority of the proposed method. Under severe weak-grid conditions with a short-circuit ratio of 1, the fusion strategy enlarges the parameter-stability feasible region by approximately 11.5% compared to single-mode operations. Moreover, the proposed D-zone strategy achieves a peak fusion advantage ratio of 43.11%, ensuring robust and disturbance-free switching across a wide range of grid-strength scenarios where the short-circuit ratio varies from 1 to 30. Full article

As a key spatial platform for implementing China’s Northeast Revitalization Strategy, coordinated development of production–living–ecological (PLE) functions in the Changchun Metropolitan Area is crucial for high-quality regional development. This study uses 24 counties (districts) in the metropolitan area as analytical units and develops a quantitative indicator system to evaluate PLE functions. We integrate the entropy-weighted TOPSIS method, social network analysis (SNA), and geographically and temporally weighted regression (GTWR) to examine the spatiotemporal dynamics, spatial correlation networks, and driving mechanisms of the three functions from 2013 to 2023. Temporally, the production function follows a growth–decline–recovery trajectory, the living function increases overall despite fluctuations, and the ecological function strengthens continuously. Overall, the three functions increasingly exhibit coupling and synergy. Spatially, the production function concentrates in core areas and diffuses along major axes. The living function is led by the core and followed by county-level catch-up. The ecological function is higher in the east, relatively stable in the west, and connected by corridors, together forming a multi-center, axis-based synergistic pattern. In the spatial correlation networks, densities of the production and ecological networks remain largely stable, whereas the living network becomes markedly denser. The three networks display distinct topologies and continue to evolve structurally. For driving mechanisms, the GTWR model provides the best fit. Geographic proximity positively contributes to the formation of all three functional networks, while the eight explanatory factors show pronounced spatiotemporal heterogeneity. These findings provide an evidence base for optimizing functional coordination and implementing differentiated spatial governance in metropolitan areas. Full article

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation. Full article

Traffic calming measures are widely used to reduce operating speeds and mitigate crash risk in urban corridors; however, the way drivers adapt their control strategy when traversing Berlin speed cushions is still poorly described from a multivariate behavioral perspective. This study proposes a behavior-oriented analysis to identify recurring speed-cushion traversal strategies using driving simulator telemetry. A fixed-base simulator reproduced a real urban corridor, and trajectories were segmented in device-centered spatial windows capturing approach, traversal, and immediate recovery. Each segment was summarized by three indicators describing longitudinal and lateral control: mean speed, peak braking demand, and average lane position deviation. Features were standardized and clustered using k-means. The number of clusters was selected primarily through mean silhouette evaluation, while resampling-based checks and a Gaussian mixture modeling comparison were used as supportive evidence rather than competing decision rules. Three traversal profiles emerged: smooth cautious, reactive cautious, and unmoderated fast. The introduction of speed cushions shifted the distribution of segments towards cautious profiles, while driver-level concentration within a single profile was moderate. Overall, results indicate that speed cushions influence the whole vehicle control strategy, offering a quantitative basis for behavior-oriented evaluation of local traffic calming interventions in smart-city contexts. Full article

Floodplain disconnection caused by channel incision and/or levee construction has led to widespread loss of riparian habitats and ecosystem functions globally. Restoring full stream–floodplain connectivity is increasingly promoted, yet evidence of ecological outcomes remains limited. This study evaluates the initial performance of two Stage 0 restoration projects on Whychus Creek, Oregon, which reconnected incised channels to their historical floodplains in 2012 and 2016. We combined pre- and post-restoration vegetation surveys along fixed transects with hydrogeomorphic-based riparian and wetland function assessments and applied quantitative analyses, including Kruskal–Wallis tests, Jaccard correlations, Sorensen similarity indices, and factor analysis, to compare changes in plant assemblages and ecosystem functions across restored, transitional, and unrestored reaches. Our research results indicate that two years post-restoration, the active riparian area expanded 2.5-fold, species richness and structural diversity increased significantly, and riparian and wetland functions such as water storage, sediment retention, and habitat support for fish and amphibians improved markedly. Numbers of anadromous salmonids also increased markedly. This is important as salmon recovery is a regional stream restoration goal. Comparisons with a reach restored six years earlier suggest a positive trajectory toward mature, resilient ecosystems. These findings demonstrate that Stage 0 restoration can rapidly reestablish complex habitat mosaics and enhance ecosystem services critical for biodiversity, water quality, and flood resilience. Practically, this evidence supports process-based restoration strategies that prioritize full floodplain reconnection as a cost-effective approach to reversing long-term ecological degradation. Continued monitoring is essential to guide adaptive management and strengthen the evidence base for the wide-scale implementation of valley-floor wide stream restoration. Full article

Background/Objectives: Impella support is increasingly utilized as a crucial bridge to durable left ventricular assist device (LVAD) in patients with refractory cardiogenic shock. However, the transvalvular path of the Impella catheter raises concerns regarding mechanical trauma, potentially precipitating or accelerating aortic regurgitation (AR). We aimed to characterize the complete longitudinal trajectory of AR following Impella bridge-to-LVAD and to determine its association with clinical and hemodynamic sequelae. Methods: We conducted a single-center retrospective cohort study including all patients bridged from Impella to durable LVAD between 2013 and 2024 (n = 19). At Impella initiation, all patients met the retrospective SCAI shock stage D or worse criteria. At LVAD implantation, all patients were classified as INTERMACS 1–2 (INTERMACS 2, n = 13). The Impella models were 5.0 in 11 (axillary access), 2.5 in 5 (femoral access), and CP in 3 (femoral access); no periprocedural Impella complications were recorded. The implanted LVAD systems were HeartMate II (n = 7), HVAD (n = 3), and HeartMate III (n = 9). Patients undergoing concomitant aortic valve intervention were excluded. Transthoracic/TEE echocardiography was performed at prespecified time points (pre-Impella, pre-LVAD, post-LVAD discharge, 12 months, and 24 months) with standardized aortic regurgitation (AR) grading. Right ventricular (RV) function was assessed qualitatively when quantitative indices (TAPSE) were unavailable. Primary endpoints were new or progressive AR and AR severity at LVAD implantation. Secondary endpoints included survival, renal dysfunction, biomarkers, and rehospitalization. Univariate analyses were used to compare outcomes according to AR severity. Results: Nineteen patients (68% male, median age 57 years, IQR 47–60) underwent Impella support for 13.3 ± 9.9 days before HeartMate 3 (84%) or HVAD (16%) implantation. All patients had competent aortic valves (grade 0 AR) at the time of LVAD implantation. AR ≥ mild developed in 9/18 (50%) at discharge, 12/15 (80%) at 12 months, and 13/15 (87%) at 24 months, and 8/15 (53%) progressed to ≥ moderate AR by 24 months. Patients with moderate-to-severe AR had higher NT-proBNP levels at 12 months (median 6318 vs. 2336 pg/mL, p = 0.137). Thirty-day and 24-month survival rates were 95% and 79%, respectively. Conclusions: Aortic regurgitation frequently develops or progresses from the pre-LVAD period to follow-up in patients bridged from Impella to durable LVAD. Although limited by a small sample size and incomplete quantitative RV metrics, these observations support structured echocardiographic surveillance after Impella use and management strategies—routine valve inspection at LVAD implantation and post-LVAD speed/blood pressure targets that encourage aortic valve opening—to mitigate the risk and clinical impact of aortic regurgitation. Full article

Ensuring the rights of involuntary resettlers is fundamental to a law-based state and essential for achieving social equity and sustainable development. However, institutional improvement depends not only on the intent of top-level design but also on the capacity for dynamic adaptation amid evolving social contexts. Moving beyond the predominant research focus on policy design principles, this study investigates the dynamic evolution of China’s reservoir resettlement rights protection policies from 1949 to 2025. We first constructed a corpus of 32 core policy documents. Employing a bibliometric analysis within a multi-dimensional framework, we statically examined the developmental patterns of these policies. Subsequently, we applied the Punctuated Equilibrium Theory (PET) to dynamically analyze their policy changes, identifying a trajectory marked by both long-term stability and significant punctuations. Our findings reveal that over 76 years, the policy process has undergone two major equilibrium periods and two critical punctuation nodes, demonstrating a clear pattern of “protracted stability interspersed with short bursts of rapid transformation.” The policy image has correspondingly evolved through four distinct stages: “Administratively Mobilized Resettlement,” “Development-Oriented Resettlement,” “Harmonious Society for Resettlers,” and “Common Prosperity.” The study argues that this evolution is driven by the interplay of shifting central government attention, the occurrence of focusing events, and the reinforcement of evolving Policy Images, which collectively broadened the policy venue and led to non-linear institutional change. Based on these findings, the paper recommends: first, adopting a dynamic approach to policy formulation; second, maintaining sustained political commitment and robust institutional safeguards; and third, fostering multi-stakeholder consultation and collaborative governance mechanisms. These strategies are essential to more effectively secure the multifaceted rights of reservoir resettlers. Full article