Search Results (1,657)

To address the high costs and inefficiencies of blind prospecting in deep geothermal exploration, this study develops a three-dimensional heat transfer model for quantitative prediction of geothermal enrichment targets. Unlike traditional qualitative or single-mechanism analyses, this research utilizes a finite element forward modeling approach based on step-faulted depressions (sedimentary basins/grabens) and uplifts (domes/uplift belts). We simulate temperature fields and heat flux distributions in multilayered systems incorporating four thermal conductivity types (A, K, H, Q). By systematically comparing the geometric heat flow convergence in depressions with the lateral diffusion in uplifts, this work reveals mirror and anti-mirror relationships between temperature fields and structural morphology at middle and deep levels, as well as local “hot spot” and “cold zone” effects. The results indicate that, in depressional structures, shallow high-temperature reservoirs (<2 km) are mainly concentrated in A- and K-types, while deeper reservoirs (>3 km) are enriched in Q- and H-types. In contrast, uplift structures are characterized by mid- to shallow-depth (<3 km) reservoirs predominantly in A- and K-types, with high temperatures at depth preferentially hosted in A- and H-types, and the highest temperatures observed in the A-type. Thermal conductivity contrasts, layer thicknesses, and structural morphology collectively control the spatial distribution of heat flux. A strong positive correlation between thermal conductivity and heat flux is observed at the central target area, significantly stronger than at the margins, whereas this relationship is notably weakened in Q-type. Crucially, low-conductivity zones display high geothermal gradients coupled with low terrestrial heat flow, disproving the axiom that “elevated geothermal gradients imply high heat flow,” thus establishing “high-gradient/low-heat-flow coupling zones” as strategic exploration targets. The model developed in this study demonstrates high simulation accuracy and computational efficiency. The findings provide a robust theoretical basis for reconstructing geothermal geological evolution and precise geothermal target localization, thereby reducing the risk of “blind heat exploration” and promoting the cost-effective and refined development of deep concealed geothermal resources. Full article

The southwestern region of China is highly susceptible to landslides due to steep terrain, fractured geology, and intense rainfall. This study focuses on the Xiluodu Reservoir area in Yunnan Province and applies Geographic Information System (GIS) techniques together with ten key spatial factors—such as slope, lithology, elevation, and distance to rivers—to perform a quantitative landslide risk assessment. In addition to the individual Certainty Factor (CF) and Logistic Regression (LR) models, we developed an integrated CF–LR coupled model to overcome their respective limitations: the CF model’s sensitivity to specific factor attributes but neglect of factor interactions, and the LR model’s robust weight estimation but weak representation of attribute heterogeneity. By combining these strengths, the CF–LR model achieved superior predictive performance (AUC = 0.804), successfully capturing 92.5% of historical landslide events within moderate-to-high risk zones. The results show that lithology, slope angle, and proximity to rivers and roads are dominant controls on susceptibility, with landslides concentrated on soft rock slopes of 30–40° and within 600–900 m of rivers. Compared with previous coupled approaches in similar mountainous reservoir settings, our CF–LR model provides a more balanced and interpretable framework, enhancing both classification accuracy and practical applicability. These findings demonstrate that GIS-based CF–LR integration is a novel and reliable tool for landslide susceptibility mapping, offering important technical support for disaster prevention and risk management in large reservoir regions. Full article

This article presents the results of an experimental study on the hydrodynamics of the coolant at the inlet of the fuel assembly in the RITM reactor core. The importance of these studies stems from the significant impact that inlet flow conditions have on the flow structure within a fuel assembly. A significant variation in axial velocity and local flow rates can greatly affect the heat exchange processes within the fuel assembly, potentially compromising the safety of the core operation. The aim of this work was to investigate the effect of different designs of orifice inlet devices and integrated absorber grids on the flow pattern of the coolant in the rod bundle of the fuel assembly. To achieve this goal, experiments were conducted on a scaled model of the inlet section of the fuel assembly, which included all the structural components of the actual fuel assembly, from the orifice inlet device to the second spacer grids. The test model was scaled down by a factor of 5.8 from the original fuel assembly. Two methods were used to study the hydrodynamics: dynamic pressure probe measurements and the tracer injection technique. The studies were conducted in several sections along the length of the test model, covering its entire cross-section. The choice of measurement locations was determined by the design features of the test model. The loss coefficient (K) of the orifice inlet device in fully open and maximally closed positions was experimentally determined. The features of the coolant flow at the inlet of the fuel assembly were visualized using axial velocity plots in cross-sections, as well as concentration distribution plots for the injected tracer. The geometry of the inlet orifice device at the fuel assembly has a significant impact on the pattern of axial flow velocity up to the center of the fuel bundle, between the first and second spacing grids. Two zones of low axial velocity are created at the edges of the fuel element cover, parallel to the mounting plates, at the entrance to the fuel bundle. These unevennesses in the axial speed are evened out before reaching the second grid. The attachment plates of the fuel elements to the diffuser greatly influence the intensity and direction of flow mixing. A comparative analysis of the effectiveness of two types of integrated absorber grids was performed. The experimental results were used to justify design modifications of individual elements of the fuel assembly and to validate the hydraulic performance of new core designs. Additionally, the experimental data can be used to validate CFD codes. Full article

The blending of hydrogen significantly impacts the diffusion and safety characteristics of natural gas within indoor environments. This study employs ANSYS Fluent 2021 R1 to numerically investigate the diffusion and ventilation characteristics of hydrogen-blended natural gas (HBNG) leakage in indoor spaces. A physical and mathematical model of gas leakage from pipelines is established to study hazardous areas, flammable regions, ventilation characteristics, alarm response times, safe ventilation rates, and the concentration distribution of leaked gas. The effects of hydrogen blending ratio (HBR), ventilation conditions, and space dimensions on leakage diffusion and safety are analyzed. Results indicate that HBNG leakage forms vertical concentration stratification in indoor spaces, with ventilation height being negatively correlated with gas concentration and flammable regions. In the indoor space conditions of this study, by improving ventilation conditions, the hazardous area can be reduced by up to 92.67%. Increasing HBR substantially expands risk zones—with pure hydrogen producing risk volumes over five times greater than natural gas. Mechanical ventilation significantly enhances indoor safety. Safe ventilation rates escalate with hydrogen content, providing quantitative safety criteria for HBNG implementation. The results underscore the critical influence of HBR and ventilation strategy on risk assessment, providing essential insights for the safe indoor deployment of HBNG. Full article

The vulnerability of ecologically important fish species to climate change underscores the need to predict shifts in their distributions and habitat suitability under future climate scenarios. In this study, we modeled the potential distribution ranges of three ecologically important fish species (Collichthys lucidus, Konosirus punctatus, and Clupanodon thrissa) across East Asia using a species distribution modeling framework under both current and projected future climate scenarios. Occurrence data were obtained from the Global Biodiversity Information Facility (GBIF) and the Ocean Biodiversity Information System (OBIS), while environmental data were retrieved from the Bio-ORACLE database. Our models demonstrated high predictive performance (AUC > 0.88). Results showed that dissolved oxygen and salinity were the strongest bioclimatic predictors for C. lucidus, whereas chlorophyll and phosphate primarily shaped the distributions of K. punctatus and C. thrissa. Model projections indicated a decline in suitable habitats for C. lucidus, particularly under high-emission scenarios, and range expansions for K. punctatus and C. thrissa toward higher latitudes and nutrient-enriched waters. Highly suitable habitats were concentrated along coastlines within exclusive economic zones, exposing these species to significant anthropogenic pressures. Conservation gap analysis revealed that only 7%, 2%, and 6% of the distributional ranges of C. lucidus, C. thrissa, and K. punctatus, respectively, are currently encompassed by marine protected areas (MPAs). Our study further identified climatically stable regions that may act as climate refugia, particularly for C. lucidus in the Yellow and East China seas. Our findings highlight the urgent need for adaptive management, including the expansion and reconfiguration of MPAs, transboundary conservation initiatives, stronger regulation of exploitation, and increased public awareness to ensure the resilience of fisheries under future climate change. Full article

Laser processing of heterogeneous composites requires a clear understanding of coupled thermal and mechanical responses to ensure structural integrity and patterning precision. In this study, a thermal–mechanical coupling model based on the finite element method was developed to investigate laser–material interactions in Al–Tedlar–Kevlar composite films. The effects of key parameters—including pulse energy, spot size, pulse duration, and repetition frequency—on the evolution of temperature and stress fields were systematically examined. The simulations reveal that pulse energy leads to a linear rise in peak temperature, while pulse duration exerts a nonlinear influence on energy density and thermal uniformity. Increasing repetition frequency promotes thermal accumulation, enlarging the heat-affected zone. Coupled analyses further indicate significant stress concentrations at material interfaces, which may trigger delamination and compromise film reliability. Through comprehensive parameter evaluation, the optimal processing conditions were identified as 0.5 mJ pulse energy, 20 kHz repetition rate, 45 μm spot diameter, and 120 ns pulse duration. These findings clarify the governing mechanisms of thermal–mechanical interactions in multilayer composites and provide theoretical guidance for optimizing laser micropatterning processes while enhancing interfacial stability and manufacturing quality. Full article

Proactive management of groundwater resources is hindered by the static nature of conventional vulnerability assessments, which provide only a single temporal snapshot and lack predictive capability. To address this limitation, we developed a coupled dynamic–spatial modeling framework to forecast the spatio-temporal evolution of groundwater vulnerability. The framework integrates a βSARMA time-series model for precipitation forecasting with an enhanced M-DRASTIC-LAaRd model, which incorporates Land use, Anthropogenic activity, and River network density, weighted via the Analytical Hierarchy Process (AHP) to better capture hydrogeological complexity. The βSARMA model consistently outperformed conventional SARIMA models across the five subregions of Beijing, achieving the lowest RMSE values (0.0832–0.1617) and MAE values (0.0922–0.1372), with an average RMSE reduction of 15.3% relative to the best SARIMA baseline. These results ensure highly reliable dynamic precipitation inputs for the time-varying Net Recharge (R) parameter. Model validation against historical observations yielded a coefficient of determination (R²) of 0.87, confirming the framework’s robustness and predictive accuracy. Applied to the Beijing metropolitan area (1980–2027), the model projects a marked spatial restructuring of groundwater vulnerability: high-vulnerability zones are expected to expand from 38.65% to 46.18%, while low-vulnerability areas will decline from 42.53% to 34.63%. Emerging “hotspots” are concentrated in the southern urban plains, where urbanization and reduced recharge converge. Overall, 27.9% of the region is predicted to experience intensified vulnerability, whereas only 11.5% will show improvement. This study advances groundwater vulnerability assessment from static mapping toward dynamic forecasting, providing a quantitatively validated and spatially explicit framework that supports more informed groundwater management under future environmental change. Full article

Silver nanoparticles (AgNPs) are valued for their antimicrobial properties, but conventional synthesis often involves toxic chemicals. Eco-friendly biosynthesis using silver-tolerant microbes from contaminated sites offers a sustainable alternative. This study biosynthesized and characterized AgNPs using a native Bacillus sp. from contaminated soil in the Jazan region, Saudi Arabia, and developed predictive models for optimizing synthesis and antimicrobial activity. AgNPs were synthesized under optimized conditions (1.0 mM AgNO₃, 4.0 mL supernatant, pH 8, 85 °C). Characterization using UV–Vis, SEM, TEM, XRD, and FTIR assessed size, shape, structure, and chemistry. Gaussian and second models evaluated yield and inhibition zones based on AgNP concentration, microorganism type, and MIC. The AgNPs were spherical with diameters of 5–10 nm. The optimal nanoparticle yield occurs when the parameters are at their optimal values; C₀ = 1.0 mM, V₀ = 4.0 mL, pH₀ = 8, T₀ = 85 °C. XRD confirmed their crystalline nature, and FTIR showed biomolecular capping agents for stabilization. The Gaussian model accurately predicted synthesis efficiency, validated by 3D plots matching experimental data. The AgNPs showed strong antimicrobial activity against Gram-positive (Bacillus subtilis) (ATCC6051), Staphylococcus aureus (ATCC12600), Gram-negative bacteria Escherichia coli (ATCC11775) and fungi Candida albicans (ATCC10231); with E. coli having the lowest MIC (1.87 μg/mL). The inhibition zone model closely matched observed data. Biosynthesized AgNPs using silver-tolerant Bacillus sp. demonstrated potent antimicrobial effects and provide a green alternative to chemical synthesis. Integrating modeling optimizes biosynthesis and predicts biological performance, supporting future nanobiotechnology and antimicrobial applications. Full article

The construction industry is a major contributor to global resource consumption and waste. This sector extracts over two billion tons of raw materials each year and contributes over 30% of all solid waste generated annually through construction and demolition debris. The movement toward circularity in the built environment aims to replace linear processes of extraction and disposal by promoting policies favoring building preservation and adaptive reuse, as well as the salvage and reuse of building materials. Few North American cities have implemented explicit policies that incentivize circularity to decouple urban growth from resource consumption, and there remain substantial hurdles to adoption. Nonetheless, existing regulatory and planning tools, such as zoning codes and historic preservation policies, may already influence redevelopment in ways that could align with circularity. This article examines spatial patterns in these indirect pathways through a case study of a college town in New York State, assessing how commonly used local planning tools shape urban redevelopment trajectories. Using a three-stage spatial analysis protocol, including exploratory analysis, Geographically Weighted Regressions (GWRs), and Geographic Random Forest (GRF) modeling, the study evaluates the impact of zoning regulations and historic preservation designations on patterns of demolition, reinvestment, and incremental change in the building stock. National historic districts were strongly associated with more building adaptation permits indicating reinvestment in existing buildings. Mixed-use zoning was positively correlated with new construction, while special overlay districts and low-density zoning were mostly negatively correlated with concentrations of building adaptation permits. A key contribution of this paper is a replicable protocol for urban building stock analysis and insights into how land use policies can support or hinder incremental urban change in moves toward the circular city. Further, we provide recommendations for data management strategies in small cities that could help strengthen analysis-driven policies. Full article

Microplastic pollution in marine environments represents a significant ecological threat due to its persistence and harmful effects on biodiversity and human health. In Colombia, coastal ecosystems (particularly in La Guajira) have exhibited increasing microplastic concentrations, but systematic monitoring remains limited. This study explored the application of remote sensing, including multispectral satellite imagery (Sentinel-2) and machine learning algorithms, to detect and monitor microplastics in the coastal zone of Riohacha, La Guajira. To inform the model selection and ensure methodological relevance, a focused systematic literature review was conducted, serving as a foundational step in identifying effective remote sensing strategies and machine learning algorithms previously applied to microplastic detection in aquatic environments. Moreover, microplastic samples were collected from four coastal sites on Riohacha’s coast and analyzed via Fourier transform infrared spectroscopy (FTIR), while environmental parameters were recorded in situ. The remote sensing data were processed and integrated with field observations to train linear regression, random forest, and artificial neural network (ANN) models. The ANN model achieved the highest accuracy (MAE = 0.040; RMSE = 0.071), outperforming the other models in estimating the microplastic concentrations. Based on these results, environmental risk maps were generated, identifying critical zones of pollution. The findings support the integration of remote sensing tools and field data for scalable, cost-efficient microplastic monitoring, offering a methodological framework for marine pollution assessment in Colombia and other developing coastal regions. Full article

This article demonstrates how the non-intrusive techniques PLIF (Planar Laser-Induced Fluorescence) and PIV (Particle Image Velocimetry) are used to study fluid flow and mixing in a water model of a continuous casting tundish. These techniques validate CFD models by providing hydrodynamic data and by testing the models’ ability to predict mixing through simulated concentration field evolution under defined process conditions. Using PIV and PLIF yields more accurate information on turbulent mixing and impurity transport than traditional methods. Access to flow and concentration field evolution enables more precise mathematical model refinement and clarifies the impact of tundish design or operational changes on hydrodynamics and mixing. Relative errors in chemical evolution are approximately 20%, whereas velocity errors vary depending on the measurement plane, being lower for longitudinal planes and higher for transversal planes. This suggests that the turbulence model does not fully capture all low- and high-velocity zones. This approach supports reliable flow and mixing predictions in metallurgy and related fields. Full article

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research. Full article

Over the past three decades, the Yangtze River Delta has undergone a rapid urbanization phenomenon, resulting in pronounced urban sprawl that has significantly impacted regional sustainable development and air quality. This study constructs an urban sprawl index based on nighttime light data spanning 2000–2020 and employs exploratory spatio-temporal analysis, panel data models, and spatial econometric models to examine the evolution of urban sprawl and its effects on PM_2.5 concentrations. The results reveal four key findings: (1) Urban sprawl is spatially heterogeneous, exhibiting a ‘high in the centre-east, low in the north-west’ pattern, with high-intensity sprawl expanding from the central region towards the north-west and south-west; (2) The dominant growth pattern is characterized by relatively rapid expansion. The global Moran’s I index fluctuates between 0.428 and 0.214, indicating a gradual decline in the global clustering effect of urban sprawl. Meanwhile, the share of local high–high agglomeration zones decreases to 21.9%, whereas low–low zones increase to 24.3%; (3) Spatio-temporal transitions of urban sprawl show strong spatial dependence while overall relocation exhibits inertia; (4) Before the implementation of the Ten Key Measures for Air Pollution Prevention and Control in 2013, urban sprawl significantly intensified PM_2.5 pollution. Following the policy, this relationship notably reversed, with sprawl exhibiting pollution-mitigating effects in certain regions. The spatial diffusion of pollution is evident, as urban sprawl influences air quality through both local development and inter-regional interactions. This study provides an in-depth analysis of the spatio-temporal evolution of urban sprawl and establishes a framework to examine the interactive mechanisms between urban expansion and air pollution, thereby broadening perspectives on atmospheric pollution research and offering scientific and policy guidance for sustainable land use and air quality management in the Yangtze River Delta. Full article

Marine shale is highly prone to wellbore collapse due to its high pore pressure, propensity for hydration and swelling, distinct bedding planes, and low tensile strength. Horizontal in situ stress serves as a critical parameter for wellbore stability analysis; however, its accurate prediction is extremely challenging in complex geological environments. Conventional studies often simplify the wellbore as a circular shape, neglecting its natural elliptical deformation under non-uniform in situ stress, which leads to reduced predictive accuracy. To address this limitation, this study establishes an elliptical wellbore model that incorporates the strain-softening characteristics of shale. Theoretical models for stress distribution in both elastic and plastic zones were derived. The strain-softening behavior was validated through triaxial compression tests, providing a foundation for analytical solutions of stress distributions around circular and elliptical wellbores. Furthermore, an elliptical wellbore-based model was developed to derive a new prediction equation for horizontal in situ stress. Numerical programming was employed to compute stress distributions, and finite element simulations under various aspect ratios corroborated the theoretical results, showing excellent agreement. Results demonstrate that the elliptical wellbore model captures the near-wellbore stress state more accurately. As the aspect ratio increases, the extreme values of radial and tangential stresses increase significantly, with pronounced stress concentrations observed around the 180° and 360° positions. Predictions of horizontal in situ stress based on the proposed model achieved over 89% accuracy when verified against field data, confirming its reliability. This study overcomes the limitations inherent in the traditional circular wellbore assumption, providing a more precise analytical method for wellbore stability assessment in Marine shale under complex geological conditions. The findings offer a valuable theoretical basis for wellbore stability management and drilling engineering design. Full article

The quality of groundwater, a crucial freshwater resource in cold regions, directly affects human health. This study used groundwater quality monitoring data collected in the central Songnen Plain in 2014 and 2022 as a case study. The improved DRASTICL model was used to assess the vulnerability index, while water quality indicators were selected using a random forest algorithm and combined with the entropy-weighted groundwater quality index (E-GQI) approach to realize water quality assessment. Furthermore, self-organizing maps (SOM) were used for pollutant source analysis. Finally, the study identified the synergistic migration mechanism of NH₄⁺ and Cl⁻, as well as the activation trend of As in reducing environments. The uncertainty inherent to health risk assessment was considered by developing a kernel density estimation–trapezoidal fuzzy number–Monte Carlo simulation (KDE-TFN-MCSS) model that reduced the distribution mis-specification risks and high-risk misjudgment rates associated with conventional assessment methods. The results indicated that: (1) The water chemistry type in the study area was predominantly HCO₃⁻–Ca²⁺ with moderately to weakly alkaline water, and the primary and nitrogen pollution indicators were elevated, with the average NH₄⁺ concentration significantly increasing from 0.06 mg/L in 2014 to 1.26 mg/L in 2022, exceeding the Class III limit of 1.0 mg/L. (2) The groundwater quality in the central Songnen Plain was poor in 2014, comprising predominantly Classes IV and V; by 2022, it comprised mostly Classes I–IV following a banded distribution, but declined in some central and northern areas. (3) The results of the SOM analysis revealed that the principal hardness component shifted from Ca²⁺ in 2014 to Ca²⁺–Mg²⁺ synergy in 2022. Local high values of As and NH₄⁺ were determined to reflect geogenic origin and diffuse agricultural pollution, whereas the Cl⁻ distribution reflected the influence of de-icing agents and urbanization. (4) Through drinking water exposure, a deterministic evaluation conducted using the conventional four-step method indicated that the non-carcinogenic risk (HI) in the central and eastern areas significantly exceeded the threshold (HI > 1) in 2014, with the high-HI area expanding westward to the central and western regions in 2022; local areas in the north also exhibited carcinogenic risk (CR) values exceeding the threshold (CR > 0.0001). The results of a probabilistic evaluation conducted using the proposed simulation model indicated that, except for children’s CR in 2022, both HI and CR exceeded acceptable thresholds with 95% probability. Therefore, the proposed assessment method can provide a basis for improved groundwater pollution zoning and control decisions in cold regions. Full article