Search Results (4,311)

Critical-sized bone defects represent a major clinical challenge, as defects of this magnitude do not heal spontaneously without regenerative intervention. This study aimed to evaluate the osteoinductive effects of recombinant human bone morphogenetic protein-2 (rhBMP-2)loaded β-tricalcium phosphate (β-TCP) scaffolds on bone regeneration and vascularization in a rabbit calvarial critical-sized defect model. Eighteen male New Zealand White rabbits were used, and four standardized circular defects (5 mm in diameter) were created in the calvaria of each animal. The defects were assigned to four groups: control (unfilled), β-TCP + 5 µg rhBMP-2, β-TCP + 10 µg rhBMP-2, and β-TCP + 20 µg rhBMP-2. Bone healing was evaluated at 2, 4, and 8 weeks using histological, histomorphometric, and cluster of differentiation 31 (CD31) immunohistochemical analyses. The results demonstrated that rhBMP-2–loaded β-TCP scaffolds significantly enhanced bone regeneration compared with the control group, with a progressive increase in bone formation observed with increasing rhBMP-2 doses. The β-TCP + 20 µg rhBMP-2 group exhibited the highest levels of new bone formation, more advanced bone maturation, improved collagen organization, and increased vascularization. However, no statistically significant differences were observed between the 10 µg and 20 µg groups at later time points (p > 0.05), suggesting a dose-dependent saturation (plateau) effect. In conclusion, rhBMP-2–loaded β-TCP scaffolds promote bone regeneration and angiogenesis in a dose-related manner up to a threshold, beyond which additional increases in dose do not result in proportional improvements. These findings emphasize that optimal rhBMP-2 dosing is critical to maximize regenerative outcomes while avoiding unnecessary dose escalation. Full article

Last-mile delivery companies can face demand surges where large-volume order requests exceed daily courier capacity. In such cases fast and robust feasibility-first planning becomes more practical and valuable than building optimal routes. This paper proposes a hierarchical, computationally feasible decomposition pipeline that produces shift-feasible clusters under a strict shift-duration limit using travel-time-based duration estimates. While decomposition methods for large-scale VRPs are well established, they typically remain oriented toward route-construction quality within a single operational day or toward balancing customer counts, demand, or Euclidean territory partitions. In contrast, the proposed method targets a different decision problem: rapid feasibility-first strategic capacity planning for one-time extreme demand surges, where the primary requirement is to estimate, within seconds, a conservative upper bound on the number of courier shifts under a strict shift-duration limit. When end-to-end latency is evaluated from raw geographic points, including distance-matrix preparation for monolithic baselines, the proposed pipeline becomes 187 to 1315 times faster than matrix-based monolithic optimization on the common benchmark sizes. Methodologically, the contribution lies in combining (i) topology-preserving spatial linearization with a Hilbert Space-Filling Curve, (ii) adaptive greedy microclustering driven by empirical travel-time quantiles, and (iii) lexicographic dynamic-programming merge that minimizes the number of shifts first and total travel time second. This yields a planning-oriented decomposition mechanism that is distinct from classical route-quality-centered hierarchical VRP approaches. Full article

Nabkhas are a common type of biogenic aeolian landform in arid and semi-arid regions. Their morphological characteristics, surface sediment grain size composition, and spatial distribution patterns can, to some extent, be associated with the interactions between vegetation and the aeolian environment. In this study, nabkhas formed around Caragana tibetica shrubs in the desert steppe of Damao Banner, Inner Mongolia, were selected as the research object. Based on field investigations, UAV image identification, grain size analysis, and spatial point pattern analysis, the characteristics of nabkhas were comparatively analyzed among a control plot without shrubs (CK) and three shrub-covered plots: a low coverage plot (LCP), a medium coverage plot (MCP), and a high coverage plot (HCP). The results showed that (1) some morphological parameters of nabkhas varied among plots with different vegetation cover, but the responses of various indicators were not entirely consistent. The MCP exhibited relatively higher values in indicators such as shrub long axis (L_g), short axis (W_g), and windward slope length (L_y). (2) The surface sediments of nabkhas were mainly composed of silt and fine sand, followed by very fine sand. Compared with the CK, the silt content was generally lower in the shrub-covered plots, whereas the contents of fine sand and very fine sand were higher. The mean grain size (Mz, Φ value) tended to decrease, while the skewness (SK_G) and kurtosis (K_G) tended to increase, and the sorting coefficient (σ_G) showed relatively limited variation. (3) In the LCP, MCP, and HCP, the fractal dimension (D) was significantly positively correlated with the Mz and σ_G (p < 0.05), and significantly negatively correlated with the SK_G and K_G (p < 0.01), suggesting that the D may be associated with variations in sediment grain size structure. (4) Overall, the nabkhas around Caragana tibetica shrubs exhibited a spatial distribution pattern characterized by aggregation at small scales and randomness at large scales, with small-scale clustering being more evident in the MCP and HCP. In general, nabkhas around Caragana tibetica shrubs under different vegetation cover conditions showed observable differences in morphological characteristics, surface sediment grain size composition, and spatial distribution patterns, providing a comparative case reference for the study of nabkhas in desert steppe areas. Full article

Virtual vitality has become an important complementary dimension for describing urban vitality; however, the identification and formation mechanisms of its stable, high-vitality state during dynamic change remain insufficiently explored. Taking five urban districts of Harbin as the study area, this study uses TikTok short-video data from July to August 2024 (summer) and December 2024 to January 2025 (winter), together with Gaode Map POI data, as the core dataset. Kernel density differences between adjacent weeks are used to measure the dynamic changes in virtual vitality. Bivariate local spatial autocorrelation is applied to identify high-vitality stable zones, and a Random Forest model is employed to examine the nonlinear influence of physical vitality spatial structures. The results show the following: (1) Dynamic change patterns of virtual vitality differ significantly across seasons, and when online attention content points to specific physical spatial structures, a stable high-vitality state is more likely to be maintained. (2) Bivariate local spatial autocorrelation analysis indicates that high-vitality stable zones (HH zones) exhibit significant spatial clustering, with vitality-enhancing zones (LH zones) distributed around them and showing spillover effects, while vitality-declining zones (HL zones) are more scattered. (3) The Random Forest results show that the stable maintenance of high virtual vitality depends more on combinations of spatial structural characteristics with high recognizability, among which distance to activity center (tourism), functional composition dissimilarity (culture), and functional composition dissimilarity (shopping) have the strongest influence. These findings reveal a nonlinear relationship between the stable high-vitality state and the structure of physical vitality space, providing insights for guiding online attention to support physical spatial development. Full article

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015–December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1–86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42–4.0). Brain–computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05–5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade. Full article

Introduction: Nerve-sparing (NS) during robot-assisted radical prostatectomy (RARP) plays a critical role in postoperative functional recovery, particularly urinary continence and erectile function. Despite the importance of precise neurovascular bundle (NVB) preservation, intraoperative assessment of NS quality remains largely subjective and lacks standardized evaluation tools. The aim of this study was to develop and preliminarily evaluate a structured intraoperative scoring system designed specifically for assessing NS quality during RARP. Methods: A novel 10-point intraoperative NS scoring system (NSQ Score) based on five domains was developed: dissection plane, bleeding control, bundle manipulation, continuity of dissection, and symmetry. Each parameter was rated on a 0–2 scale. Thirty robot-assisted radical prostatectomy (RARP) procedures performed in 2024 were randomly selected from a prospectively maintained institutional surgical video archive. Cases were not pre-filtered based on tumor stage, surgical difficulty, or intraoperative complexity. High-definition video recordings of the nerve-sparing phase were anonymized and independently evaluated by three experienced observers blinded to patient outcomes and to each other’s assessments. Inter-rater agreement was analyzed using weighted Cohen’s kappa statistics with quadratic weights, complemented by exact and near-agreement proportions. Cluster bootstrap resampling was applied to account for bilateral observations. Results: A total of 48 evaluable observations were analyzed. The overall inter-rater agreement demonstrated a weighted kappa of 0.41 (95% CI 0.36–0.48), indicating fair-to-moderate agreement among reviewers. Exact agreement occurred in 43% of observations, while near-agreement (allowing one ordinal level difference) reached 98%. Among individual parameters, symmetry demonstrated the highest reliability with substantial agreement (κ = 0.70; 95% CI 0.58–0.81). Other domains showed fair agreement, including intraoperative bleeding (κ = 0.36), continuity of dissection (κ = 0.39), bundle manipulation (κ = 0.34), and dissection plane (κ = 0.27). Agreement levels were comparable between left- and right-sided dissections. Conclusions: We propose a novel structured intraoperative scoring system for evaluating nerve-sparing quality during RARP. The scale is simple, procedure-specific, and feasible for structured postoperative or video-based assessment. Preliminary results demonstrate fair-to-moderate inter-rater reliability with very high near-agreement, supporting the feasibility of this tool for clinical use. The proposed scoring system may facilitate standardized training, objective performance assessment, and future studies correlating intraoperative NS quality with functional outcomes. Full article

To address the critical challenges of lithology acquisition and low blasting refinement under extreme low temperatures and varying thermal conditions in high-altitude environments, this study develops a real-time dynamic identification method for rock-like interfaces using Measurement While Drilling (MWD) technology. The scope of this research involves the use of a self-developed indoor digital drilling experimental platform to simulate both ambient and freezing (−20 °C) conditions. Procedures included conducting comprehensive comparative drilling experiments on various rock-like materials with distinct strength levels to evaluate their mechanical responses during penetration. The major findings reveal a significant influence of low-temperature hardening effects on MWD parameters; specifically, the frozen state notably increases drilling torque and feed pressure while simultaneously decreasing the stable rotational speed of the drill bit. To resolve the feature parameter drift induced by temperature variations, a novel interface recognition algorithm is proposed that integrates Z-score normalization, change-point detection, and multi-dimensional spatial clustering. Through a dual-detection mechanism involving both single-point and cumulative features, the algorithm effectively captures precise mutation information during rock layer transitions. It further incorporates multi-dimensional indicators, such as consistency, change intensity, and point density, to perform comprehensive weighted scoring. Experimental results demonstrate that the proposed algorithm effectively eliminates the systematic offset of parameters caused by temperature fluctuations. The prediction error at both “strong-weak” and “weak-strong” transition interfaces is maintained within 1.5 mm, which significantly improves the accuracy and robustness of interface recognition under complex and varying working conditions. These key conclusions provide essential technical support for the implementation of differentiated charging and green refined mining operations, ensuring greater energy efficiency and environmental protection in cold-region engineering. Full article

A graph-theoretical approach to the analysis of motion and rest in many-body systems is developed. Point bodies are represented as vertices of a complete bi-colored graph, termed the motion–rest graph (MRG). Two vertices are connected by a rust-colored edge when the corresponding bodies are at rest relative to each other; that is, when their mutual distance remains constant in time, bodies moving relative to each other are connected by a cyan edge. It is shown that the logical structure of the relation “to be at rest relative to each other” determines the combinatorial structure of the graph. For one-dimensional motion in classical mechanics and special relativity, this relation is reflexive, symmetric, and transitive, and therefore defines an equivalence relation. As a result, rust edges form disjoint complete cliques corresponding to rest-clusters, and the MRG becomes a semi-transitive complete bi-colored graph that is completely determined by the partition of the bodies into equivalence classes. It is proven that any such graph on five vertices necessarily contains a monochromatic triangle. For two- and three-dimensional motion, the transitivity of relative rest generally fails because constant mutual distance does not imply an equality of velocities in the presence of rotational degrees of freedom. In this case, the MRG is non-transitive, and the Ramsey threshold becomes the classical value R(3,3) = 6. The approach is extended to mixed sets containing moving bodies and reference points, including the center of mass of the system. Generalizations to general relativity and quantum mechanics are also discussed. In general relativity, transitivity of relative rest is generically lost because global rigid congruences do not generally exist. In quantum mechanics, exact transitivity survives only at the level of idealized delocalized eigenstates, whereas for physically realizable localized states, the notion of mutual rest becomes only approximate. The results demonstrate that the interplay between kinematics, logical properties of relational motion, and Ramsey-type combinatorial constraints gives rise to unavoidable ordered substructures in many-body systems. Full article

This paper considers geometry-aware ground user clustering and Cluster Center Optimization for beam pointing and scheduling in adaptive multibeam Geostationary Earth Orbit (GEO) satellite networks. By grouping ground users, beams can be directed toward user clusters to maximize satellite throughput. We propose GeoClust, a polynomial-time geometric user clustering algorithm for adaptive multibeam GEO satellite networks, using a geometric set-cover approach that explicitly balances link signal-to-interference-plus-noise ratio (SINR) and hopping overhead. The algorithm employs a Boyle–Dykstra projection-based cluster center update within an alternating optimization framework, combined with nearest-center membership updates, to enforce the cluster-radius constraint while ensuring feasibility and provable convergence. It also achieves near-linear throughput scaling with increasing number of RF chains. Numerical evaluations on real-world population data show that, under heavy traffic conditions, our approach more than doubles the zero outage and median user rates compared to benchmark schemes. Full article

This study evaluates the productive efficiency in the agrifood sector of 21 rural Bulgarian districts as a proxy for territorial competitiveness. Output-oriented Data Envelopment Analysis (DEA) was performed using district-level data from 2022 to 2024. The analysis incorporates five inputs related to labor, land, and capital and three economic outputs from agriculture and food processing. Results indicate substantial variation in efficiency among rural districts. Twelve districts form the efficiency frontier, with effective resource use and diverse structures; nine are inefficient due to scale or organizational/technological constraints. Bootstrap bias correction revealed standard DEA underestimates efficiency gaps. Frontier districts include large plains, mountainous regions and smaller, specialized systems, indicating diverse paths to competitiveness. A composite Territorial Competitiveness Index (TCI) showed frontier status does not guarantee efficiency, often due to underused manufacturing capital. Cluster analysis identified four performance groups needing different policy support, ranging from near-frontier territories that need knowledge transfer to deeply underperforming districts that require restructuring. No geographic clustering of efficiency was found, pointing to structural and institutional, rather than geographic, drivers. These results highlight the need for territorially tailored rural policies within the Common Agricultural Policy (CAP) and offer an empirical basis for diagnosing regional agrifood efficiency gaps. Full article

This study illuminates fundamental questions in behavioral science through advanced machine learning methodologies applied to large-scale public opinion data. Drawing on Kahneman and Tversky’s dual-process theory and Sunstein’s nudge architecture, we employ hierarchical unsupervised clustering and supervised predictive models to detect cognitive biases—loss aversion, availability heuristic, and partisan motivated reasoning—embedded within a nationally representative survey of 5022 American respondents. Our primary methodological contribution is a hierarchical two-stage clustering framework that uncovers latent opinion structures without imposing a priori partisan categories, permitting discovery of cross-cutting cleavages invisible to conventional survey analysis. Three principal findings emerge: (1) loss aversion is empirically confirmed in prospective economic perception, with pessimists outnumbering optimists at a 1.14:1 ratio even among respondents rating current conditions positively; (2) partisan motivated reasoning produces a 13.15 percentage-point perception gap among individuals with identical financial circumstances; and (3) multi-platform digital engagement is associated with reduced partisan bias, providing evidence that challenges simple echo chamber assumptions. Crime safety perception emerges as the strongest predictor of economic bias, surpassing party affiliation, and substantiating availability heuristic dominance in political cognition. These findings carry implications for democratic accountability, platform governance, and the ethics of AI-augmented behavioral analysis in an era of affective polarization. Full article

Objective biomarkers for neurodevelopmental disorders remain an unmet clinical need. The electroretinogram (ERG), a non-invasive recording of the retinal response to light, has shown promise as a physiological marker for autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD), yet existing classification approaches based on time-domain and time–frequency features achieve limited accuracy in clinically relevant multi-group scenarios. This study introduces ERG-Graph, a novel graph signal processing (GSP) framework that transforms each ERG waveform into a weighted, undirected graph through amplitude quantization and temporal-adjacency connectivity. Nine topological and spectral features, including total load centrality, clique number, algebraic connectivity, and clustering coefficient, were extracted from each graph to characterize the structural dynamics of the signal. Using light-adapted ERG recordings from 278 participants (ASD = 77, ADHD = 43, ASD + ADHD = 21, Control = 137), we evaluated these features across binary, three-group, and four-group classification scenarios using seven machine learning classifiers with 10-fold subject-wise cross-validation. The proposed ERG-Graph features achieved balanced accuracies of 0.91 (ASD vs. control, males) and 0.88 (ADHD vs. control, females). Critically, fusing ERG-Graph with time-domain features yielded a balanced accuracy of 0.81 for three-group classification (ASD vs. ADHD vs. control), representing an 11-percentage-point improvement over the previous benchmark of 0.70. Statistical analysis confirmed significant topological differences between groups (Kruskal–Wallis, p < 0.001; Cliff’s delta: large effect sizes), and SHAP analysis revealed that graph-theoretic features dominated the top-ranked predictors. These results demonstrate that graph-based topological features capture discriminative information in the ERG waveform that is inaccessible to conventional signal analysis methods, advancing the development of objective biomarkers for neurodevelopmental disorder screening. Full article

Rapid and balanced situational awareness of fire fronts is critical for effective initial response to forest fires, yet suboptimal task planning for Unmanned Aerial Vehicle (UAV) swarms can delay intelligence delivery. This paper presents a cloud–edge collaborative approach that integrates edge-driven rapid task partitioning with cloud-based global workload balancing, explicitly addressing the NP-hard multiple traveling salesman problem underlying multi-UAV reconnaissance. At the edge, a fire-spread-informed line clustering algorithm quickly assigns monitoring points to UAVs, exploiting low-latency processing for initial sectorization. The cloud then refines this allocation through a novel cooperative–competitive task transfer mechanism that minimizes the makespan. Extensive simulations and a real-world case study based on the 2020 Liangshan wildfire show that the proposed method reduces makespan by up to 24.5% compared to conventional centralized and distributed baselines, while remaining robust under severe communication constraints. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article