Search Results (1,784)

In the era of information explosion, the effective configuration of labels on maps is crucial for the rapid comprehension of information. The point-feature label placement problem, particularly in large-scale and high-density scenarios with spatial mutual-exclusion constraints, is a classic NP-hard discrete optimization challenge. Existing metaheuristic algorithms (e.g., Simulated Annealing and Genetic Algorithm) often struggle to achieve high-quality global layouts due to their propensity to become trapped in local optima, inefficient random point-selection processes, and inadequate modeling of the spatial mutual-exclusion and blocking constraints between labels. To address these limitations, this paper proposes a Progressive Reinforcement Learning (PRL) algorithm specifically tailored for the point-feature label placement problem. The algorithm models the label placement process as a sequential decision-making problem within the Reinforcement Learning framework, optimized through agent–environment interaction. Its core design comprises the following: (1) a staircase-like policy learning mechanism that shifts from “broad exploration in the early stage to precise exploitation in the later stage” to balance global search and local optimization; (2) a data mining-based Intelligent Action Screening (IAS) mechanism, which dynamically identifies and prioritizes “high-value action points” critical for improving layout quality by constructing the “Contribution Decline Degree” and “Contribution Support Degree” metrics. Experiments on large-scale real-world POI datasets (10,000, 20,000, and 32,312 points) demonstrate that the proposed algorithm significantly outperforms 13 state-of-the-art comparative algorithms, including Simulated Annealing, Genetic Algorithm, Differential Evolution, POPMUSIC, and DBSCAN, in terms of both placement quality and the number of successfully placed labels. It exhibits remarkable adaptability and competitiveness in handling high-density and complex scenarios. Full article

Local lattice distortion (LLD) arising from atomic size mismatch is an important structural feature of body-centered cubic (BCC) refractory high-entropy alloys (RHEAs). Reported LLDs are often difficult to compare across alloys because studies use different definitions and reference lattices. In this paper, we computed a consistent static DFT baseline for width-based LLD descriptors for three equimolar quaternary BCC RHEAs: NbTaTiV, TiZrNbMo, and the sparsely reported HfZrNbMo. The alloys were modeled as 128-atom special quasi-random structures and fully relaxed using density functional theory (DFT). Two complementary descriptors were evaluated from the relaxed geometries using a consistently defined reference lattice: a displacement-based metric derived from atomic off-site displacements and a shell-resolved bond length broadening metric for the first and second coordination shells. The resulting LLD descriptors have the lowest values for NbTaTiV, intermediate values for TiZrNbMo, and the highest for HfZrNbMo. Element-resolved analysis shows that individual species contribute differently to the overall distortion, information that is not captured by global descriptors alone. The pretrained MACE machine learning interatomic potential is assessed as a pre-relaxation step prior to DFT relaxation, as well as for screening candidate lattice parameters for HfZrNbMo. Full article

Considering escalating global climate change, flood disaster risk assessment for architectural heritage must evolve from static models toward dynamic adaptive systems. This paper proposes a dynamic evaluation model based on the Full-Life-Cycle perspective, dividing disaster progression into three phases: pre-disaster, during-disaster, and post-disaster. This system constructs a dual-track indicator system encompassing Exposure and Vulnerability. By integrating the CRITIC objective weighting method with the G1 subjective ranking approach, the model enables dynamic weight adjustment according to disaster phase. A case study of 392 cultural heritage sites in Beijing reveals that during the disaster phase, 20 sites experienced a risk level increase in two or more tiers, with 13.7% directly entering high-risk status. This finding demonstrates the spatiotemporal evolution of flood risks. The weight for Road Network Density exhibited a substantial increase from 0.046 pre-disaster to 0.153 post-disaster, a 169.5% rise, underscoring its dynamic responsiveness. The findings demonstrate that the proposed model is effective in identifying high-risk heritage sites and dynamically capturing key targets experiencing rapid risk escalation within the disaster chain. These results provide quantitative evidence to support the implementation of phased targeted protection measures and emergency decision-making. Full article

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

The high number of deaths and serious injuries in traffic crashes can be considered a silent global epidemic, as it is still understood by part of society as an inherent consequence of road traffic. There are several risk factors that can increase the occurrence or severity of crashes on roads, acting alone or in combination. Road safety diagnoses based on facts and evidence are essential for improving public policies to reduce victims. With the aim of assisting in these diagnoses and since the official database on these victims is not made available in detail to the public, this work investigates the relationship between seven indicators, collected in field research and in public databases, and the occurrence and fatality of traffic victims in the City of Rio de Janeiro. Linear regression models are developed for each approach and the one with the best statistical parameters is chosen. The model with greater robustness demonstrated that helmet non-use, the density of traffic enforcement cameras, and illiteracy together explain a significant portion of the variation in the fatality rate. The results are considered satisfactory, since a limited number of existing risk factors for road safety were used. Full article

Background/Objectives: This paper investigates the application of radial basis function (RBF) interpolation to adapt the Toyota Human Model for Safety (THUMS) version 6 finite element (FE) models to diverse anthropometric profiles using ANSUR II data. The research focuses on generating personalized human body models (HBMs) across 50th, 80th, and 98th percentiles for both sexes in standing and seated postures, evaluating mesh quality with quantitative metrics, and assessing posture-dependent transformations. Methods: The geometric accuracy for the standing configuration was quantified using DICE similarity coefficients and the 95th percentile Hausdorff distance (HD95). Results: While global whole-body DICE similarity averaged approximately 0.40 due to an inherent variability in distal limb positioning, regional analysis demonstrated strong volumetric overlap in the critical chest and torso regions with DICE values ranging from 0.80 to 0.88. Regional HD95 values were within 20–30 mm across most of the surface area. Surfaces distance analyses showed that more than 95% of the nodes were within $\pm 20$ mm of the target surfaces with the distribution centered near zero across all the percentiles. The mesh quality for both standing and seated morphs demonstrated low violation rates with the aspect ratio being 28% to 30%, while warpage, skewness and, Jacobian determinants were less than 15%. The seated morphs preserved anatomical alignment and posture despite mesh density differences between the postures. Conclusions: These findings indicate that the morphing process preserves anatomical fidelity while highlighting the need for further optimization to mitigate localized distortions in dynamic simulations. Full article

With the acceleration of the global energy transition, the photovoltaic industry has become a significant force in the promotion of green development, and photovoltaic raw materials play a crucial role in this process. In this paper, 177 countries during the period of 2001 to 2024 were taken as the research subjects, with a focus on polysilicon and silicon wafers as components of upstream photovoltaic raw materials. Through a combination of the evolutionary analysis of nodes, the overall structure, and the three-dimensional structure with an exponential random graph model, the evolution and dynamic mechanisms of the global photovoltaic raw material trade network are explored. The study reveals the following: (1) The global PV raw material trade volume tended to increase from 2001 to 2024. (2) The global photovoltaic raw material trade network showed a tendency towards the “enhanced dominance of core countries and denser trade connections,” with the trade volume between core countries continuously expanding and the network density, average clustering coefficient, and connection efficiency increasing annually, which is a reflection of the globalization and regional cooperation of the global photovoltaic industry. (3) From the weighted out-degree and in-degree ranking evolution of the global photovoltaic raw materials trade network, it can be seen that China consolidated its core position, while Southeast Asian countries tended to transfer their processing and manufacturing links. The status of the United States and traditional industrial powers gradually declined, which is a reflection of the restructuring of the global industrial chain along with regional geopolitical agglomeration effects. (4) Internal attributes such as the national economic level, population size, and urbanization rate, as well as external network effects such as common language and geographical proximity, significantly influence the formation path of the photovoltaic raw material trade network. Moreover, the network exhibits distinct heterogeneous complementarity mechanisms and path dependence characteristics, with a structural evolution that tends toward stability and cooperative relationships showing significant time inertia. Overall, the global trade volume of photovoltaic raw materials continues to grow, and the core positions of major countries such as China, the United States, and Germany remain prominent but show a transitional trend towards Southeast Asian countries. The strengthening of the level of coordination and cooperation among global photovoltaic raw material producers to ensure supply chain stability, promote resource sharing and technological progress, and achieve the sustainable development of green energy policies is necessary. Full article

The plain river network region is faced with ecological and environmental challenges such as insufficient hydrological connectivity and degradation of ecosystem services under the influence of urbanization and human activities, and therefore attention needs to be paid to river network changes in this region and the synergistic benefits of natural–social–economic multidimensional factors. This study took the Lixiahe region, a typical plain river network region, as the research object, using Mann–Kendall, spatial autocorrelation analysis, random forest, multiple validation and Granger causality test of key drivers to analyze the spatiotemporal evolution of its river network from 2013 to 2025 and quantify driving mechanisms from natural, social and economic factors. The results showed that: (1) From 2013 to 2025, the Lixiahe Plain river network region tended to be trunk and artificial, with the number and connectivity of river networks showing an upward trend while the curvature of river network decreased significantly. (2) The Global Moran’s I index of the Lixiahe Plain river network decreased from 0.612 to 0.534, indicating a continued weakening of spatial agglomeration in the water area and exhibiting characteristics of edge fragmentation. (3) Random forest analysis showed that socioeconomic factors dominated recent river network change in the Lixiahe Plain. Economic factors mainly influenced quantity-related indicators, while social factors were more important for meander degree and connectivity in several ecologically sensitive counties. Multilevel validation demonstrated the robustness and generalization ability of the model. Granger causality analysis further indicated that GDP, road network density, freshwater aquaculture area, and agricultural output statistically preceded changes in key hydrological indicators. These findings suggest that river network management in plain river network regions should move beyond quantity-based engineering expansion and adopt a multi-indicator, spatially differentiated approach. Integrating river quantity, morphology, and connectivity into management can better support the balance between socioeconomic development and ecological protection and promote the sustainable optimization of river network. Full article

To meet the need for global coverage, space–air–ground integrated networks (SAGINs) are crucial, but the openness of wireless links makes communications vulnerable to eavesdropping. This paper investigates the physical layer security (PLS) of uplink transmissions in a cooperative dual-hop SAGIN. The system comprises a ground source with a directional antenna, an unmanned aerial vehicle (UAV) relay cluster, and a low Earth orbit (LEO) satellite. Utilizing stochastic geometry, we model the spatial randomness of terrestrial eavesdroppers and the multi-layered dual-orbital LEO destination. To combat mixed radio-frequency (RF) and free-space optical (FSO) fading, multiple relay selection and maximum ratio combining (MRC) are integrated into the UAV cluster. We analytically derive the piecewise probability density function for the FSO link distance, obtaining exact closed-form expressions for the end-to-end secrecy outage probability (SOP). Monte Carlo simulations strictly validate the derivations. The results demonstrate that while increasing available relays and antennas enhances PLS via spatial diversity, a security bottleneck restricts the RF-FSO architecture under high-transmit power regimes, generating asymptotic secrecy floors. These findings provide explicit theoretical guidelines for the secure design and parameter optimization of future SAGINs. Full article

In the context of the global dual-carbon goals and China’s DP strategy, strengthening the coupling between digital productivity (DP) and the high-quality development of the energy industry (HQDEI) is essential for resource-based regions. Doing so can help these regions overcome transition constraints and advance green, low-carbon development. Using panel data for nine prefecture-level cities in Guizhou Province from 2014 to 2023, we construct composite indices for DP and HQDEI with an improved entropy-weight TOPSIS approach. We then characterize their spatiotemporal evolution using a coupling coordination degree (CCD) model and kernel density estimation. Finally, we examine the determinants of coupling coordination through panel regression and threshold models. The results show that: (1) The CCD between DP and HQDEI efficiency continues to increase, with regional differences displaying a periodic convergence–divergence pattern and a spatial structure characterized by core agglomeration and outward diffusion. Gradient disparities in coordinated development are evident between central and peripheral areas. (2) Consumption upgrading and fiscal self-sufficiency significantly promote CC, whereas a traditional resource-dependent growth model significantly suppresses it. Constrained by short-term adaptation and integration costs, digital innovation currently exerts a negative effect, and its enabling potential has not yet been fully realized. (3) Nonlinear tests identify a single digital-infrastructure threshold: the enabling effect of digital innovation turns positive only once infrastructure surpasses a critical level, revealing pronounced interval heterogeneity. This study advances the theoretical understanding of the bidirectional coupling between DP and HQDEI, provides empirical guidance for energy digital transformation and high-quality development in resource-based regions of western China, and offers transferable insights for green, low-carbon transitions in traditional energy regions worldwide. Full article

Climate-induced migration is increasingly recognized as a major demographic consequence of environmental change, yet projections vary widely due to differences in spatial scale, hazard coverage, and modeling approaches. This study introduces the First Street Global Climate Migration Model (FS-GCMM), a globally consistent, multi-hazard framework that estimates climate-driven population redistribution at a 12.5 km resolution across all countries through 2100. The model integrates high-resolution global climate hazard datasets, including flood (GloFAS), wind (IBTrACS and ERA5), drought (ERA5), wildfire (Global Fire Atlas), and extreme heat and cold (ERA5-LAND) datasets, with gridded population data from NASA SEDAC’s Gridded Population of the World (GPWv4) and Shared Socioeconomic Pathway (SSP) projections. To identify climate-related migration effects, we applied within-country propensity score matching to construct balanced samples of exposed and unexposed grid cells with similar socioeconomic, demographic, geographic, and governance characteristics. Hazard-specific impacts on annualized population change from 2000 to 2020 were then estimated using mixed-effects ridge regression with country-level random effects to account for cross-national heterogeneity and multicollinearity. These empirically derived coefficients were applied to SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios to project future climate-driven outmigration, which was subsequently redistributed using a spatial attractiveness framework incorporating economic opportunity, population density, climate safety, and geographic proximity. Results indicate statistically significant negative effects of all modeled hazards on population retention globally, with approximately 199.5 million people projected to experience climate-driven displacement by 2055 under SSP2-4.5. Full article

In this investigation, the nonlinear dust-acoustic waves in the lunar terminator region are studied in a three-component complex plasma comprising Boltzmann-distributed electrons and ions and inertial, cold, negatively charged dust grains. The fluid model is reduced, via the reductive perturbation technique, to a planar Korteweg–de Vries (KdV) equation that governs the evolution of small-amplitude dust-acoustic structures in this environment. Hirota’s direct method is then employed to derive exact multiple-soliton solutions, which allow us to examine the parameter dependence of dust-acoustic solitons and to characterize their overtaking collisions. The analysis shows that the soliton polarity and amplitude are controlled by the equilibrium electron–ion density ratio and the electron-to-ion temperature ratio, and that multi-soliton interactions remain elastic, with only finite phase shifts after collision. In the second part of the study, the planar integer KdV model is generalized to a time-fractional KdV (FKdV) equation to incorporate nonlocal temporal memory effects in the dust-acoustic dynamics. This FKdV equation is analyzed using two analytical approximation schemes: the Tantawy technique, recently proposed as a direct and rapidly convergent approach to fractional evolution equations, and the new iterative method, a widely used high-accuracy scheme in the fractional literature. For both methods, higher-order approximations are constructed, and their absolute and global maximum residual errors are quantified. The results demonstrate that the Tantawy technique provides compact approximations with superior accuracy and stability compared with the new iterative method for the present FKdV-soliton problem. The combined integer- and fractional-analytic framework provides a physically transparent framework for understanding how nonlinearity, dispersion, and fractional memory jointly shape dust-acoustic solitary structures in the electrostatically complex lunar terminator plasma, which is of paramount interest for future lunar missions like Luna-25 and Luna-27. Full article

In this work, the electronic, thermochemical, and vibrational characterization of the drug delivery system formed by clusters of selenium (Se₆ allotrope) and 5-fluorouracil (5-FU) are studied, based on density functional theory (DFT) calculations. Computational calculations were performed using the B3LYP functional and the 6-31G(d,p) base set, considering an aqueous medium through the CPCM solvation model. We propose evaluating two different interaction modes based on experimental observations: Se–H(N) (through the amino groups of 5-FU) and Se–O(C) (through the carbonyl oxygen of 5-FU). All complexes proved to be energetically stable, exhibiting chemisorption as their adsorption process. Analysis of adsorption energy and thermodynamic parameters indicates that both interaction pathways are equally viable, which agrees with previous experimental findings. The theoretical FT-IR spectra of these complexes also coincide with the experimental results. Furthermore, global molecular descriptors show that the stability of the selenium carrier is not affected by post-functionalization, which is desirable for more controlled drug delivery systems. Full article

Shaped charge is widely used in petroleum drilling, yet the inherent parametric uncertainty of oil shale introduces significant uncertainties that affect perforation outcomes. The complex coupling of oil shale constitutive parameters under extreme strains poses challenges for uncertainty quantification. A coupled algorithm integrating an improved material point method (MPM) and polynomial chaos expansion (PCE) is presented, and polynomial chaos expansion (PCE) is used to systematically analyze the uncertainty and sensitivity of shaped charge jet penetration depth. Mechanical parameters from oil shale samples at Checun Coal Mine well No. 1 were tested to define key parameter ranges and establish a reliable uncertainty space. A benchmark simulation of a single isolated shaped charge jet validated the algorithm, and Sobol’ global sensitivity analysis identified internal friction angle, density, and Poisson’s ratio as strongly sensitive parameters, while tensile strength, Young’s modulus, and cohesion showed weak sensitivity, supporting surrogate model dimensionality reduction. Composite detonation models of three and five charges further revealed the effects of multi-projectile blast wave coupling on jet dynamics, providing new theoretical insights into cluster effects under high-energy, high-pressure, and extreme-strain conditions. Sensitivity and uncertainty analyses based on surrogate models emphasized the critical influence of internal friction angle alongside Poisson’s ratio and density. A reliable numerical framework is established for multi-physics coupled simulations of geomechanical responses under complex multi-source explosive loading. Full article

Background/Objectives: Osteoporosis is a progressive systemic skeletal disease, with male osteoporosis emerging as a critical global concern due to high morbidity and mortality from fractures. This study investigated the anti-osteoporotic potential of CGAC—a herbal mixture of Cervus elaphus Linnaeus, Glycine max (L.) Merr., Angelica gigas Nakai, and Cnidium officinale Makino—and its underlying mechanisms in an orchiectomized (ORX) mouse model. Methods: C57BL/6J mice underwent ORX for 8 weeks, followed by CGAC administration (250 and 500 mg/kg) for an additional 8 weeks. Molecular mechanisms were further validated using MG63 osteoblastic and RAW 264.7 osteoclast assays. Results: ORX induced severe osteoporotic phenotypes, including significant reductions in bone mineral density (BMD) and trabecular microarchitecture. Notably, at the time point examined, ORX was associated with a suppressed bone remodeling state, reflected by reductions in both TRAP-positive osteoclasts and ALP-positive osteoblasts, together with lower serum BALP, CTX-1, and Gla/Glu-OC ratio. Conversely, CGAC normalized this stagnant state and restored physiological remodeling. This was accompanied by reduced marrow fat accumulation through the AMPK signaling axis, which upregulated Runx2 and downregulated PPAR-γ. In vitro results confirmed that CGAC promoted osteoblast differentiation and mineralization while suppressing RANKL-induced osteoclastogenesis. These actions suggest that CGAC may be involved in regulating Wnt/β-catenin signaling. Conclusions: Overall, CGAC is a promising therapeutic candidate for male osteoporosis, offering pharmacological benefits particularly relevant to aging populations. Full article