Search Results (3,589)

The Navel Orangeworm (Amyelois transitella Walker, 1863), a primary pest of nut crops native to North America, poses a significant potential threat to China’s agricultural biosecurity, yet its potential distribution dynamics under climate change remain unquantified. This study utilized the Biomod2 ensemble model platform to predict habitat suitability under current and future climate scenarios (SSP1-2.6 and SSP5-8.5). We evaluated the prediction accuracy of the ensemble model using calibration data, with TSS = 0.898 and AUC = 0.978, while spatially stratified cross-validation confirmed moderate spatial transferability to novel environments (median validation AUC = 0.60–0.75). The model identified thermal factors—Temperature Seasonality (Bio4) and the Mean Temperature of the Wettest Quarter (Bio8)—as the dominant drivers of distribution. While currently climatically suitable habitats are primarily confined to the tropical and subtropical regions of southern China, projections indicate a complex spatial shift driven by future warming: optimal southern habitats will undergo a net contraction due to heat stress, whereas low and moderately suitable areas will expand northward into key temperate agricultural areas. These results highlight that climate change will substantially alter the spatial topology of the pest’s climatic envelope, providing a critical scientific baseline of climatic suitability. These projections do not equate to realized invasion risk, which is further constrained by host availability, land use, irrigation, and human transport, offering a conservative framework for prioritizing early surveillance and optimizing quarantine measures. Full article

Silicene, an atomically thin monolayer allotrope of silicon, had emerged as a prominent topic in condensed matter physics and material science due to its novel properties and promising potential applications. Although challenges exist in fabricating freestanding silicene because of its sensitivity to the conventional environment, its theoretical study continues to develop intensively. This short review highlights the progress made in the ab initio simulations of silicene, such as geometry optimization of silicene and its electrical structure and physical characteristics including optical properties, topological properties and mechanical behavior. The theories and methods used for the theoretical studies of silicene could provide a framework for investigating other one-atom-thick two-dimensional materials with Archimedean lattice structures. Full article

Forest fires occur frequently in China; however, the complex terrain and incomplete road networks severely constrain ground rescue efficiency. Accurate forest road information is essential for the optimization of emergency response and rescue force deployment. Existing road extraction algorithms are primarily designed for urban environments and exhibit limited efficacy in forest scenarios due to dense canopy, complex background interference and specific forest road features. To address this gap, this study proposes a forest road extraction method based on an enhanced DeepLabv3+ model using multi-temporal, high-resolution satellite imagery. Specifically, a Multi-Scale Channel Attention (MCSA) mechanism is embedded in skip connections to suppress background interference, while strip pooling is integrated into the Atrous Spatial Pyramid Pooling (ASPP) module to better capture slender road features. A composite Focal-Dice loss function is also constructed to mitigate sample imbalance. Finally, by applying the model in multi-temporal remote sensing images, a fusion strategy is introduced to integrate multi-seasonal road masks to enhance overall accuracy and topological integrity. Experimental results show that the proposed method achieves a precision of 54.1%, an F1-Score of 59.3%, and an IoU of 41.8%, effectively enhancing road continuity and providing robust technical support for fire-rescue decision-making. Full article

This paper presents a novel dual-module Permanent Magnet Linear Synchronous Motor (PMLSM) featuring an unequal tooth-shoe topology, alongside a highly efficient surrogate-assisted framework to maximize average thrust and minimize thrust ripple. To overcome the computational bottleneck of expensive Finite Element Analysis (FEA), we propose a Constraint-Preserving Maximin Latin Hypercube Design (CP-MmLHD) coupled with an ARD-Kriging model and the Expected Hypervolume Improvement (EHVI) criterion. This closed-loop framework expertly handles strict geometric constraints and anisotropic parameter sensitivities. Within a strict budget of only 150 FEA evaluations, the framework successfully identifies a high-quality Pareto front. Notably, a representative optimal design reduces thrust ripple by over 80% without compromising average thrust. Furthermore, comparative experiments demonstrate superior computational efficiency over conventional algorithms, while multi-run statistical benchmarking and stochastic Monte Carlo analysis rigorously confirm the framework’s algorithmic robustness and manufacturing reliability. Full article

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

Space-deployable antennas have development requirements of an ultra-large aperture, high stiffness, and multi-frequency multiplexing. To address the challenge of stiffness characterization in the multi-closed-loop complex systems of deployable mechanisms, this paper proposes a parametric stiffness modeling method and a static stiffness model is established, ranging from components and limbs to the overall mechanism. The motion/force mapping model of the deployable mechanism is obtained using screw theory, and the stiffness mapping from joint space to workspace is achieved via the Jacobian matrix. A comprehensive stiffness model of the deployable mechanism incorporating joint effects is established based on the principle of virtual work and the superposition principle of deformations, and its validity is verified through finite element simulation. Building on this, stiffness characteristics based on structural configuration are investigated, and structural forms with excellent stiffness performance are selected through comprehensive evaluation. Six configurations of the deployable mechanism are derived topologically from this structure, and the optimal configuration is selected based on stiffness performance. The parametric stiffness modeling method proposed in this study can effectively characterize the contribution of each component to the overall system stiffness. It lays a theoretical foundation for establishing a quantitative relationship between stiffness performance and configuration, enabling performance-based configuration optimization and dimensional optimization. Full article

High-order Poincaré sphere (HOPS) beams have attracted tremendous interest due to their complex polarization and phase characteristics. However, manipulating the second harmonics generation (SHG) of HOPS beams is still challenging. Here, we developed a vector-coupled wave model to predict petal-shaped intensity patterns and reveal a linear correlation between petal number and topological order (n = 2 → 4). Moreover, we experimentally investigated the multidimensional regulation of SHG in HOPS beams through tailored phase-matching strategies. By employing three distinct configurations—(i) type-I phase matching, (ii) type-II phase matching, and (iii) orthogonally arranged BBO crystals based on Type-I phase matching—we establish a comprehensive framework for controlling the spatial and polarization properties of SHG in n = 2 HOPS beams. These results advance the manipulation of structured light in nonlinear optics, providing insights for optimizing applications in optical communication and polarization imaging. Full article

Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling geographic predictors for whether emergency vehicles successfully arrived at incidents in the city of Dallas within the city’s eight-minute benchmark. Using 250,647 incidents and 56 million GPS points along emergency dispatch routes in 2016, we compiled fourteen spatial and operational variables for every incident to train a Bayesian-optimized random forest classifier. The fourteen variables characterized street network topology, roadway attributes, land use, and socioeconomic status, and the model achieved an accuracy of 77.26% in predicting whether emergency response arrived at an incident within eight minutes. A longer distance to dispatch stations, dispatching from non-nearest stations, and low street–network integration were the strongest predictors of unsuccessful responses. Higher-income areas showed slightly elevated unsuccessful rates linked to frequent construction-related disruptions. These findings highlight emergency response as a coupled spatial–operational–temporal process and underscore the need for context-sensitive dispatch strategies and coordinated urban planning. Full article

Zonal ship central cooling systems, which are primarily implemented in naval platforms and advanced specialized vessels to ensure high survivability, exhibit complex fluid–thermal interactions and multi-level valve networks, challenging conventional resilience analysis, especially under large-scale fault scenarios and dynamic topology reconfiguration. This paper presents a physics-informed dynamic resilience assessment and reconfiguration optimization method tailored for such systems. To address the high-dimensional reconfiguration search space, a physics-informed pruning mechanism combining topological reachability filtering and nodal continuity-based feasible-flow verification is introduced, eliminating 42.6% of invalid topologies and reducing optimization time by approximately 38%. Additionally, a cumulative thermal severity (CTS) metric is developed to capture transient thermal shock risks, quantitatively assessing deviation from the 50 °C system safety boundary at the most critical node. Simulation results for a main seawater pump failure scenario demonstrate that the proposed reconfiguration strategy, which coordinates cross-zone tie valves and leverages healthy zones’ pressure margins, shortens recovery time by 47%, suppresses peak temperature from 51.5 °C to 50.2 °C, reduces maximum over-temperature from 1.5 °C to 0.2 °C, and decreases CTS from 8.5 °C·s to 0.1 °C·s (a 98.8% reduction). These findings demonstrate that physics-informed pruning substantially reduces the computational burden of high-dimensional reconfiguration, while the proposed CTS metric enables quantitative assessment of transient thermal-shock risk. Together, they offer robust methodological guidance for resilience-oriented decision support and fault-tolerant design in complex shipboard fluid–thermal systems. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Codon usage bias (CUB) in mitochondrial genomes reflects evolutionary forces such as mutation, selection, and genetic drift, yet its dynamics in early-diverging fungal lineages like Conidiobolus (Zoopagomycota) remain unclear. This study systematically analyzed mitochondrial core protein-coding genes (PCGs) from eight Conidiobolus species to elucidate the drivers of CUB and phylogenomic patterns. Nucleotide composition revealed pronounced AT richness (73.32% ± 3.38%) and low GC3 (13.40% ± 5.11%), indicating a preference for A/T-ending codons. Neutrality and ENC-GC3s plots demonstrated that natural selection, rather than mutation pressure, predominantly shaped codon bias, supported by weak GC12-GC3 correlations (slopes: 0.037–0.335) and significant ENC deviations from mutation-driven expectations. PR2-bias analysis further highlighted a strong bias toward A over T and C over G. Correspondence analysis linked major codon usage variations to GC3s, CAI, and FOP indices. Phylogenetic reconstructions based on relative synonymous codon usage (RSCU) and concatenated mitochondrial sequences revealed discordant topologies, particularly in the placement of C. polytocus and C. polyspermus, suggesting divergent evolutionary trajectories. Optimal codon analysis identified species-specific preferences dominated by A/T termini. These findings underscore natural selection as the primary force driving AT-biased mitochondrial CUB in Conidiobolus, while phylogenomic discordance highlights complex evolutionary pressures in this ecologically diverse fungal genus. This study provides foundational insights into mitochondrial genome evolution and codon adaptation mechanisms in early-diverging fungi. Full article

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and fairness, targeting three core objectives: minimizing total costs, minimizing differences in service quality among demand points, and minimizing material shortage gaps between demand points. To address the issue of limited facility service capacity induced by material shortages, we establish a multi-objective optimization model for the reliable location of emergency facilities. By combining the model’s characteristics with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and an elite retention strategy, the Pareto frontier solution set of the multi-objective model is obtained, and the model’s feasibility is verified through various examples of different scales. Finally, sensitivity analysis was conducted on the reliability location model of emergency facilities under different disruption risks using the control variable method, and the topology structure of the reliability location allocation network for emergency facilities under different disruption situations is obtained. The research findings provide decision-makers with actionable references and technical support for selecting reliable locations for emergency facilities amid disruption risks. Full article

Cogeneration system optimization in refineries confronts the challenge of simultaneously integrating design parameter selection and topological configuration. The literature typically addresses these aspects separately: parametric optimization with fixed topology or configuration optimization for specific nominal conditions. This work develops a comprehensive methodology integrating exhaustive parametric exploration with superstructure-based optimization through mixed-integer nonlinear programming (MINLP), applied to the Miguel Hidalgo refinery in Tula, Mexico. The systematic procedure generates superstructures considering all viable expansion and tempering routes under steam quality restrictions ($x\ge 0.88$), evaluating 84–105 combinations of generation pressure ($P_{\mathrm{HRSG}}=70$–140 bar) and superheater outlet temperature ($T_{s_4}=500$–560 °C). The analysis reveals three topologically distinct configurations identified as generating maximum power under different operating conditions and characterizes how transitions between high-performing configurations occur at discrete thermodynamic thresholds that correlate with constraint activation contradicting the conventional assumption of continuous parameter-configuration relationships. Multi-criteria evaluation positions Configuration 1 as the recommended design, generating 25% increase in electric generation, 11% improvement in utilization factor ($UF$: $0.640\to 0.710$) and 20% reduction in specific fuel consumption ($SFC$: $0.259\to 0.207$ kg/kWh). The methodology is directly generalizable to other refineries through universal thermodynamic principles, with a systematic five-step procedure applicable to any multi-pressure steam demand profile. The characterization of discrete transition phenomena and the associated methodology for their thermodynamic explanation challenges the conventional assumption of continuous parameter–configuration relationships in optimization approaches, with immediate implications for the design of flexible cogeneration systems in refineries worldwide. Full article

Load forecasting for Integrated Energy Systems (IESs) is critical to enabling multi-energy coordinated optimization and low-carbon scheduling. Facing multi-load types and multi-site high-dimensional heterogeneous data, there remains a global learning challenge stemming from insufficient representation of spatiotemporal coupling features. In response to the multi-source heterogeneous characteristics of IES loads, this paper designs a Spatiotemporal Topology Encoder that maps load data into a tensorized multi-energy spatiotemporal topological representation via fuzzy classification and multi-scale ranking. In parallel, we construct a MultiScale Hybrid Convolver to extract multi-scale, multi-level global spatiotemporal features of multi-energy load representations. We further develop a Temporal Segmentation Transformer and a Steady-State Exponentially Gated Memory Unit, and design a jointly optimized forecasting model that enforces global dynamic correlations and local, steady-state preservation. Altogether, we propose a multi-scale spatiotemporal fusion and steady-state memory-driven load forecasting method for integrated energy systems (MSTF-SMDN). Extensive experiments on a public real-world dataset from Arizona State University demonstrate the superiority of the proposed approach: compared to the strongest baseline, MSTF-SMDN reduces cooling load RMSE by 16.09%, heating load RMSE by 12.97%, and electric load RMSE by 6.14%, while achieving R² values of 0.99435, 0.98701, and 0.96722, respectively, confirming its feasibility, efficiency, and promising potential for multi-energy load forecasting in IES. Full article

Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs) is justified by performance gains over established feature engineering paradigms (e.g., XGBoost) under the constraints of sparse altimetry supervision. We established a rigorous comparative framework across four mainstream products—ALOS World 3D, Copernicus DEM, SRTM GL1, and TanDEM-X—using Sichuan Province, China, as a representative natural laboratory. Our results reveal a fundamental scale mismatch (where the ~485 m average spacing of sampled altimetry footprints dwarfs the local terrain resolution): despite their topological complexity, Hybrid GNN models fail to establish a statistically significant accuracy advantage over the systematically optimized XGBoost baseline, demonstrating RMSE parity. Mechanistically, we uncover a critical divergence in decision logic: XGBoost relies on a stable “Physics Skeleton” consistently dominated by deterministic features (terrain aspect and vegetation density), whereas GNNs exhibit severe “Attribution Stochasticity” ($\rho \approx$ 0.63–0.77). The GNN component acts as a residual-dependent latent feature learner rather than discovering universal topological laws. We conclude that for geospatial regression tasks relying on sparse supervision, “Physics Trumps Geometry.” A “Feature-First” paradigm that prioritizes robust, domain-knowledge-based physical descriptors outweighs the indeterminate complexity of “Black Box” architectures. This study underscores the imperative of prioritizing explanatory stability over marginal accuracy gains to foster trusted Geo-AI. Full article