Search Results (105)

Ice lenses in natural frozen soils commonly exhibit inclined and heterogeneous distributions, and their spatial morphology significantly influences the mechanical behavior of frozen soils. To investigate the coupled regulatory mechanism of ice lens inclination angle and thickness on the mechanical properties of frozen silty clay, specimens containing artificial single-layer ice lenses with varying inclination angles (0°, 10°, 20°, 30°) and thicknesses (5 mm, 15 mm) were prepared under constant temperature, water content, and loading rate conditions. Low-temperature uniaxial compression tests were conducted, and the results were systematically analyzed in conjunction with discrete element method (DEM) simulations and a modified Duncan–Chang model. The results indicate that increasing the ice lens inclination angle leads to a nonlinear reduction in the deviatoric stress at 15% axial strain, with the failure mode transitioning from compression-induced bulging to shear sliding dominance. When the ice lens thickness increased from 5 mm to 15 mm, the deviatoric stress at 15% axial strain further decreased across all inclination angles, accompanied by a reduction in the composite modulus. The response surface prediction formulas for parameters a and b, established based on experimental data, effectively describe the stress–strain relationships. DEM simulations reveal, at the mesoscale, the asymmetric displacement field and shear band evolution mechanisms governed by inclined ice layers, with bond breakage accelerating as the inclination angle increases. This study clarifies the coupled effects of ice lens spatial configuration and confining pressure on the mechanical response of frozen soils, providing a theoretical reference for bearing capacity assessment of frozen ground containing inclined ice lenses. Full article

In complex network analysis, the identification of influential nodes is a fundamental issue, which is closely related to the structural robustness of the network and the dynamics of propagation processes. Current research primarily focuses on mesoscale features based on the smallest cycles or local features derived from star-shaped structures. However, the role of neighboring nodes that are connected to a given node but do not participate in its smallest cycles remains underexplored in network analysis. To address this issue, this paper proposes a hybrid centrality measure that integrates information from both smallest-cycle structures and non-smallest-cycle structures associated with each target node. The smallest-cycle structures considered in this method are identified only within the imposed local search range and do not necessarily correspond to the true smallest cycles in the full graph. Specifically, the extent of a node’s involvement in mesoscale structures is characterized by the number of the smallest cycles it participates in, while its local structural heterogeneity is represented by the number of neighboring nodes connected to it that do not belong to any smallest cycles. These two aspects are then unified into a single node importance metric through a weighted integration strategy. This paper evaluates node importance from multiple perspectives, including propagation capability analysis based on the SI model, network robustness testing through node attack simulations, and ranking accuracy assessment using Kendall correlation coefficient. The experimental results demonstrate that the proposed method achieves competitive or superior performance compared with the selected baseline methods under the experimental settings considered in this work. The findings indicate that integrating smallest-cycle and non-smallest-cycle features provides a more comprehensive characterization of a node’s role in complex networks. This study offers a novel perspective on the integration of multi-scale structural information in complex networks and presents an effective new approach for the identification of important nodes. Full article

The purpleback flying squid (Sthenoteuthis oualaniensis) is an important commercially exploited cephalopod resource in tropical open oceans. The formation and spatial variability of its fishing grounds are jointly regulated by local marine environmental conditions, ocean dynamic processes, and multiscale climate variability. In recent years, substantial progress has been achieved in understanding the environmental characteristics and formation mechanisms of S. oualaniensis fishing grounds with the development of satellite remote sensing, multisource oceanographic observations, and ecological statistical models. However, due to differences in study regions, data sources, and modeling approaches, considerable discrepancies remain among studies regarding the mechanisms through which environmental factors and climate modes influence the distribution and abundance variations in S. oualaniensis resources. This review systematically summarizes recent advances in the application of remote sensing and multisource marine data in S. oualaniensis fishing ground studies and highlights the ecological significance of major environmental drivers. Current evidence indicates that the formation of S. oualaniensis fishing grounds is not controlled by a single environmental factor but results from the integrated effects of SST, Chl-a, oceanographic dynamic processes (e.g., mesoscale eddies, fronts, and upwelling), and subsurface environmental conditions such as thermocline structure and dissolved oxygen. Suitable thermal conditions and moderate productivity levels provide fundamental environmental prerequisites for resource aggregation, whereas mesoscale physical processes and three-dimensional ecological structures further regulate prey transport, habitat availability, and squid aggregation processes. Moreover, substantial regional differences exist in fishing ground formation mechanisms. In the northwestern Indian Ocean, fishing grounds are primarily controlled by monsoon-driven upwelling and large-scale circulation systems, exhibiting strong continuity and pronounced climate-related variability. In contrast, fishing grounds in the South China Sea are more strongly influenced by Kuroshio intrusion, island–reef topography, and regional circulation processes, resulting in greater spatial heterogeneity. With advances in analytical approaches, fishing ground prediction methods for S. oualaniensis have gradually evolved from empirical statistical analyses toward multifactor statistical modeling and machine-learning-based prediction frameworks. Nevertheless, resolving three-dimensional ecological processes, developing physical–biological coupled models, and improving model interpretability remain key challenges for future research. This review provides theoretical insights for remote sensing-based investigations of S. oualaniensis fishing ground formation mechanisms, resource assessment, and sustainable exploitation. Full article

Aerospace die forgings are safety-critical structural products whose service performance is governed by coupled microstructural evolution across multiple length scales rather than by any single descriptor. This review critically synthesizes recent progress in multiscale characterization and process–structure–property analysis of aerospace die forgings, with emphasis on forged titanium alloys, wrought nickel-based superalloys, and high-strength aluminum alloys. A practical framework is first established by linking macroscale metal-flow integrity and defect control with mesoscale gradients, microscale grain-boundary and texture evolution, and nanoscale precipitation, segregation, and interface states. The principal characterization routes are then discussed, including X-ray diffraction, EBSD/3D-EBSD, TEM/STEM, atom probe tomography, tomography-based defect evaluation, and correlative workflows. The alloy-specific sections are organized around mechanisms and property consequences rather than isolated micrographs. Finally, the review discusses how multiscale descriptors can support crystal-plasticity, phase-field, cellular-automata, and ICME-oriented modeling, and identifies future priorities in three-dimensional characterization, quantitative descriptor extraction, uncertainty-aware modeling, environmental degradation assessment, and closed-loop process optimization. Overall, the performance of aerospace die forgings is shown to depend on coordinated control of phase stability, grain-boundary network evolution, precipitation state, defect population, and location-dependent heterogeneity across the full manufacturing route. Full article

A multiphase high-strength steel austempered at 260 °C for 24 h was investigated by quasi-in-situ tensile characterization and EBSD-based crystal plasticity finite element modeling. The experimental observations reveal that local plastic deformation is strongly heterogeneous: von Mises strain concentrates preferentially near bainitic-ferrite packets, phase boundaries, and retained-austenite/martensite–austenite regions, whereas blocky retained austenite contributes to strain accommodation at the early deformation stage. To quantify the underlying stress–strain partitioning, a quasi-two-dimensional representative volume element was reconstructed from EBSD data and implemented in ABAQUS through a user-defined material subroutine. The model contained the real grain morphology, phase distribution, and crystal orientation information of the 24 h austempered specimen. A rate-dependent crystal plasticity constitutive framework with BCC matrix, FCC retained austenite, and transformed martensite branches was calibrated against the macroscopic tensile curve. The simulated tensile response agrees well with the experimental curve before macroscopic instability, and the predicted local fields are consistent with the quasi-in-situ strain maps. The results show that local plastic strain first accumulates in M/A-related regions and phase-boundary-neighboring zones, while high Mises stress migrates dynamically with slip activity and stress-induced martensitic transformation. Retained-austenite transformation increases the local load-bearing capacity, modifies interphase load transfer, and delays the direct linkage of strain-localization bands. The present work clarifies the coupling among retained-austenite stability, TRIP-assisted load redistribution, and microstructural strain partitioning in multiphase high-strength steel, providing a mesoscale basis for microstructure-guided strength–ductility optimization. Full article

Existing research on the axial compressive performance and size effect of carbon fiber-reinforced polymer (CFRP)-confined recycled aggregate concrete (RAC) short columns mainly relies on macroscopic experimental analysis, lacking research methods capable of reflecting the heterogeneous characteristics of materials and mesoscopic damage evolution mechanisms. Accordingly, a three-dimensional mesoscale finite element method was adopted in this study to establish a five-phase RAC mesoscopic model, including natural aggregates, old mortar, old interfacial transition zones (ITZs), new mortar, and new interfacial transition zones. Different from existing studies, predominantly based on macroscopic experiments or empirical models, this paper focuses on revealing the coupled effects of the recycled aggregate replacement ratio, the number of CFRP confinement layers, and specimen size. A total of 48 specimens were designed, covering four specimen sizes, four recycled coarse aggregate replacement ratios, and three CFRP confinement layers. The effects of these parameters on failure modes, stress–strain relationships, and size effect were systematically analyzed. The results indicate that the peak stress decreases significantly with the increase in the recycled coarse aggregate replacement ratio; the increase in CFRP layers markedly improves both the bearing capacity and post-peak bearing capacity retention rate; the ultimate stress generally declines as the specimen size increases, which highlights the pronounced size effect of CFRP-confined RAC short columns. Based on peak parameters and normalization analysis, a simplified stress–strain model was established: the goodness of fit R² of the ascending branch is 0.98565, and the goodness of fit for the descending branch parameters are ${\mathrm{R}}_{\mathsf{\beta }}^2$= 0.9655 and ${\mathrm{R}}_{\mathsf{\gamma }}^2$= 0.9350. Compared with existing models, the proposed model achieves a low prediction error of only 1.5–6.9%, demonstrating superior prediction accuracy. It can accurately describe the complete compressive process of CFRP-confined RAC short columns and provide a mesoscopic mechanistic basis for engineering design. Full article

Heap bioleaching is widely used to extract copper from low-grade sulfide ores thanks to its operational simplicity, low cost, and environmental sustainability. However, current control strategies rely primarily on single-factor optimization and often overlook the synergistic interactions of multiple key parameters, such as ore particle size, pore structure, pH, temperature, microbial activity, and oxygen transfer efficiency. As a result, issues such as low recovery rates, extended leaching periods, and high operational costs persist. Moreover, the “gray-box” nature of heap systems impedes real-time monitoring of internal physical, chemical, and biological processes. In addition, empirical multi-parameter optimization is time-consuming and inadequate for capturing complex interdependencies. This review was conducted to systematically examine the key factors influencing heap bioleaching efficiency and critically evaluate recent advances in numerical simulation and intelligent control strategies. As a result, we identified a major research gap: the existing models—including microscale shrinking core models (SCMs), mesoscale pore-network models based on CT reconstruction, and macroscale continuum models—have inherent limitations. SCMs assume idealized spherical particles with uniform mineral distribution while neglecting pore structure evolution and biofilm dynamics. Mesoscale models offer detailed pore characterization but lack robust multi-physics coupling (thermal–hydro–mechanical–chemical–biological, or THMCB). Macroscale models rely on homogenization assumptions that oversimplify spatial heterogeneity and temporal variations in permeability. This analysis covers the relevant literature from 1985 to 2025, with a focus on three methodological scales (micro, meso, and macro) and their integration with machine learning approaches. A notable finding is that hybrid neural network models (e.g., BP and RBF architectures) outperform purely physics-based models in predicting leaching kinetics under varying operational conditions. However, their accuracy depends heavily on high-quality field data—a limitation rarely addressed in prior reviews. By clearly delineating these model-specific limitations and scale-dependent trade-offs, this review makes two unique contributions: a structured framework for selecting and coupling numerical methods according to process requirements and a roadmap for integrating artificial neural networks with multi-physics simulations to achieve real-time intelligent control of heap bioleaching. The findings offer both theoretical guidance and practical references for optimizing the processing of low-grade copper sulfide ores. Full article

To address the poor quality of early-stage wind measurement data and the limited representativeness of short-term observations for long-term climatic conditions in mountainous wind farms, this study takes a 150 MW wind power project in Guangxi, China, as a case study and proposes an integrated framework of “stepwise data fusion-key parameter refinement-life-cycle techno-economic optimization”. For wind resource assessment, a two-stage fusion strategy combining same-mast correlation-based infilling and mesoscale data extrapolation was developed, effectively resolving the heterogeneous data quality among six meteorological masts and revealing significant spatial variations in the wind shear exponent (0.058–0.348). Based on a conservative criterion, the 50-year return-period maximum wind speed was determined to be 31.4 m/s. For turbine selection, the levelized cost of energy was adopted as the core evaluation metric to compare six turbine models rated at 6.0–6.25 MW. The results show that WTG5-200-6.25 is the optimal option, with a levelized cost of energy (LCOE) of 0.321 CNY/kWh, an annual grid-connected electricity generation of 269.915 GWh, and 1799 equivalent full-load hours. In addition, the project can save 82.9 thousand tons of standard coal annually and yield approximately CNY 311 million in carbon-trading revenue over 25 years. The proposed framework provides a useful reference for wind power projects in complex terrain. Full article

With global warming and the increasing frequency of extreme heat events, heat health risk assessment (HHRA) has become a critical topic in climate change studies. However, the study themes, methods, and governance orientation of HHRA vary significantly across spatial scales, limiting the comparability and practical integration of assessment outcomes. This study conducts a review of the HHRA literature from 2007 to 2025, analyzing publication trends and evolving research paradigms. The results indicate the following: (1) rapid growth in the field with a notable shift from identifying static vulnerabilities to adopting “Hazard–Exposure–Vulnerability–Adaptability” (HEVA) frameworks, particularly at the micro-scale; (2) a clear scale-dependent hierarchy in assessment focus, where macro-scale studies identify regional trends, meso-scale research targets urban spatial heterogeneity and green–blue infrastructure, and micro-scale assessments emphasize housing conditions and individual perceptions; and (3) machine learning has been widely applied to capture complex nonlinear mechanisms and threshold effects. Finally, this study further emphasizes the importance of establishing a full-process feedback mechanism from macro-level early warning to meso-scale planning and micro-scale intervention, bridging the gap between regional policy and community-level action and providing a theoretical foundation for building climate-resilient cities. Full article

Magnesium (Mg) alloys are promising for automotive lightweighting and the low-altitude economy, yet their reliability is challenged by stress corrosion cracking (SCC). To realize a quantitative and physics-based evaluation of SCC resistance, this study develops a mesoscale simulation framework coupling dislocation density-based crystal plasticity with an anodic dissolution phase-field model. A 2D representative volume element is constructed for randomly textured polycrystalline Mg to investigate the synergistic acceleration of corrosion by dislocation slip and hydrostatic stress. Results show that heterogeneous dislocation multiplication induced by pre-deformation is the decisive factor in corrosion path selection. In soft-oriented grains, high dislocation densities elevate the interface kinetic coefficient to levels substantially higher than those in hard-oriented regions. Notably, within such soft grains, the contribution of dislocation density to the interface kinetic coefficient can be up to 7.7 times that of hydrostatic stress, establishing dislocation-induced lattice disorder as the primary accelerator for transgranular corrosion. Hard-oriented grains effectively impede corrosion propagation due to restricted dislocation proliferation. This study elucidates how grain orientation-dependent dislocation evolution regulates corrosion morphology, revealing that the random texture delays overall structural failure based on a “weakest-link” mechanism. Full article

Accurate multi-day sea surface temperature (SST) prediction at sub-mesoscale resolution is challenging due to nonlinear ocean dynamics, heterogeneous multi-source observations, and error accumulation during autoregressive rollout. This paper proposes a hybrid Mamba–ConvLSTM framework that combines recurrent local spatiotemporal encoding with selective state-space long-range spatial modeling. The ConvLSTM branch captures local spatial patterns and short-range temporal dependencies through convolutional gating, while the Mamba branch captures long-range spatial dependencies across each frame through cross-direction window scanning and maintains temporal coherence via persistent hidden states across successive time steps. A physically informed preprocessing stage aligns 0.083° reanalysis variables to the 0.05° OSTIA target grid via a Grow-and-Cut strategy and extracts gradient-based advection and diffusion proxy features under boundary-aware finite differencing. During autoregressive rollout, auxiliary variables are held at their last observed values and physical proxies are recomputed from the predicted SST, following a clearly specified protocol. Experiments on a South China Sea benchmark compare the proposed model against nine baselines—including persistence, daily climatology, ConvLSTM, PredRNN, ConvGRU, TCTN, PANN, Swin-UNet, and ViT-ST—under an identical data-split, normalization, and rollout protocol. Evaluation with RMSE, MAE, SSIM, $R^2$, and anomaly correlation coefficient (ACC) shows that the proposed model achieves a 10-day average RMSE of 0.512 °C, outperforming the strongest learning-based baseline ViT-ST by 5.0% and the persistence forecast by 21.0%. Ablation studies, sensitivity analyses, seasonal evaluation, and statistical significance testing verify the contribution of each component and the robustness of the results. Full article

Understanding how the street built environment (SBE) relates to street vitality is critical for promoting livable and sustainable cities, yet its multiscale and spatially heterogeneous patterns remain insufficiently understood, particularly in polycentric urban contexts. Focusing on the core urban area of Chongqing, this study adopts 7951 street segments as the analytical unit to capture street-level spatial processes. A street vitality index was constructed using multi-source data integrating population, social, and economic activities. The SBE was quantified across three dimensions: macroscale street-network structure derived from spatial design network analysis, mesoscale functional characteristics measured using point-of-interest data, and microscale streetscape perception extracted from street-view imagery. The multiscale geographically weighted regression (MGWR) model was employed to examine spatially varying associations between the SBE and street vitality. Results reveal clear spatial non-stationarity in these associations. Closeness, functional density, and functional mix show positive associations with street vitality, whereas connectivity, betweenness, and greenness exhibit mainly negative associations. Transit stop density and enclosure demonstrate bidirectional spatial associations. These findings provide empirical evidence of spatially differentiated associations between the SBE and street vitality in polycentric cities and offer a data-driven basis for differentiated street planning and urban spatial optimization. Full article

The electrochemical reduction of carbon dioxide (CO₂RR) into value-added chemicals using renewable electricity is a pivotal strategy for achieving a sustainable carbon cycle. However, this process is plagued by intrinsic challenges, including poor product selectivity, competing hydrogen evolution, and catalyst instability. Metal–organic frameworks (MOFs), with their highly designable periodic structures, atomically dispersed active sites, and tunable pore microenvironments, have emerged as a uniquely versatile platform to address these issues. This review articulates a multi-scale design philosophy that enables precise steering of the CO₂RR pathway. We systematically elaborate on hierarchical tuning strategies, beginning with molecular-scale engineering of active sites (metal nodes and organic ligands) to define intrinsic activity and intermediate binding. This is synergistically integrated with the optimization of electronic structure and charge transport to overcome conductivity bottlenecks, meso-scale modulation of crystal morphology and defects to enhance mass transport and site accessibility, and the construction of heterogeneous interfaces for tandem catalysis and synergistic effects. Through this coherent, cross-scale design framework, MOF-based catalysts demonstrate exceptional capability in the precise control of reaction pathways, leading to remarkably selective synthesis of target high-value products, from C₁ compounds (CO, HCOOH, CH₄, CH₃OH) to C₂⁺ species (C₂H₄, C₂H₅OH) and urea. Finally, we outline future directions centered on dynamic mechanistic understanding, electrode engineering for industrial current densities, and stability enhancement, thereby providing a comprehensive material design guideline to advance CO₂RR technology. This work positions MOFs as a quintessential tunable catalytic platform for the sustainable conversion of CO₂. Full article

The Copernicus Sentinel-2 (S2) data are a crucial resource for urban policymakers in land-cover classification, offering a freely accessible alternative to expensive commercial data sources. While medium spatial resolution often limits the applicability of data-intensive machine learning approaches, the Geographic Object-Based Image Analysis (GEOBIA) framework could be an effective, operational alternative for urban land-cover classification using S2 data. This study applies the Geographic Object-Based Image Analysis (GEOBIA) approach to classify land cover in Hue, Vietnam, using Sentinel-2 data processed through the eCognition interface. The study’s findings emphasize the potential of GEOBIA and S2 data in enhancing decision-making processes for city authorities, ensuring better resource allocation, environmental protection, and infrastructure development. The results indicate that the method performs reliably for mesoscale and spatially continuous classes, such as vegetation and built-up surfaces, while accuracy is lower for small or spectrally heterogeneous features, particularly shallow water bodies and fragmented rice paddies, due to mixed-pixel effects inherent in 10–20 m resolution imagery. The results demonstrate an Overall Accuracy (OA) of 91%, highlighting the method’s effectiveness in extracting and classifying urban land-cover classes. This study demonstrates a replicable model for urban land monitoring that can be adapted across various geographic contexts. Furthermore, this approach fosters a more data-driven governance model, where urban expansion and land-use changes can be monitored in real time, allowing for proactive interventions. With urbanization accelerating worldwide, particularly in rapidly developing regions, such a cost-effective and accessible classification method can significantly aid in achieving long-term urban sustainability. The findings illustrate the relevance of GEOBIA as a feasible tool for supporting data-driven urban governance, enabling systematic tracking of land-use change, informed infrastructure planning, and sustainable urban management in both developed and rapidly urbanizing regions. Full article

Vegetation indices are used to analyze vegetation dynamics and primary productivity. However, most studies rely on short time series and peak or integral metrics, which limit the understanding of long-term vegetation dynamics in heterogeneous areas. This study aimed to classify a subarea of northeastern Mexico using a 25-year EVI time series and to characterize the resulting groups using growth parameters derived from temporal analysis. MODIS EVI mosaics from 2000 to 2024 were averaged and classified using the ISODATA algorithm, resulting in 16 groups. Smoothed EVI time series were analyzed with TIMESAT to extract growth parameters, which were compared among groups using Discriminant Function Analysis with cross-validation. Minimum primary productivity expressed as EVI base value (BVAL) explained most of the observed variance among groups (70.7%). The classification exhibited robust statistical separability, achieving a cross-validated accuracy of 75.1% (κ = 0.73), and showed mesoscale spatial structure (~12.5 km). The groups had moderate but significant associations (Cramer’s V = 0.33) with existing vegetation and climate cartography. The results suggest that long-term BVAL is a stable and ecologically meaningful descriptor of landscape functioning. Overall, the proposed classification captures gradients and transition zones not represented in static cartographic products, revealing vegetation dynamics across heterogeneous landscapes. Full article