Search Results (58,546)

Identifying the heterogeneous characteristics of habitat quality (HQ) trajectories is a key prerequisite for refined ecological spatial management. We used kernel Normalized Difference Vegetation Index (kNDVI) to correct the highly sensitive parameters, validated the correction results based on their consistency with the prior study findings, developed a framework for the evolution of HQ using Sen+MK and Pettitt’s tests, and utilized XGBoost and partial correlation analysis to identify the primary drivers of dynamic changes in HQ from both spatiotemporal perspectives. Our findings include the following: (1) between 2000 and 2023, the average annual rate of change in the HQ index was 0.0037 per year, indicating a continuous improvement in HQ. Compared with the period from 2011 to 2023 (0.0026 per year), the rate of improvement in HQ was faster during 2000–2011 (0.0047 per year). (2) Mutational improvement and progressive improvement were the main evolutionary trajectories, accounting for over 50.33% of the total. (3) Precipitation, land-use intensity (LUI), temperature, and elevation show a strong correlation with HQ distribution. The magnitude of HQ variation is related to HQ status, LUI, precipitation, and elevation. This study establishes a scientific foundation for developing differentiated regulatory strategies for YRB. Full article

Red mud (RM), a waste residue from alumina extraction, poses serious environmental impacts on water resources, land resources, and ecological systems due to its large production, high alkalinity, and low resource utilization. To enhance the overall utilization rate of RM solid-waste materials, this study focuses on RM, blast furnace slag (BFS), and fly ash (FA) cementitious materials as the research objects. Through mechanical tests and microstructural analysis, the optimal mix ratio of the ternary RM-based cementitious material is determined, and a systematic study of its microstructural evolution is conducted. Concurrently, fractal theory was used to quantify the microstructure of the material, revealing the evolution laws of the mechanical properties of ternary red-mud-based cementitious materials from a mesoscopic perspective. The results indicate that reducing the proportion of RM or slag alone to increase the FA content yields inferior modification effects compared to simultaneously reducing the proportions of both RM and BFS to increase FA content. Compared with the binary RM-based cementitious material made of RM and BFS, the 28-day compressive strength increases by approximately 25%, reaching 50 MPa. The incorporation of FA can reduce the volume of harmful pores in the cementitious matrix, providing ample reactive material for subsequent hydration reactions, promoting later hydration products, and improving the distribution of the internal pore structure. This leads to increases in both fractal dimensions, and a rational mix proportion can effectively improve the microstructure and mechanical properties of the ternary RM-based cementitious material. Full article

Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), is transforming the modeling of complex spatiotemporal urban processes such as urban growth, sprawl, shrinkage, redevelopment, and Land Use/Land Cover Change (LULCC). However, despite rapid methodological innovation, applications remain fragmented, and there is limited synthesis of how AI-based models complement, extend, or supersede conventional approaches. This study addresses this gap through a systematic review of 6356 records, from which 120 articles were selected for detailed analysis. It investigates: (i) how ML/DL techniques are embedded within spatiotemporal modeling frameworks; (ii) their use in simulating urbanization dynamics and land-use (LU) transitions; (iii) methodological and performance gains relative to traditional statistical and rule-based models; and (iv) emerging research frontiers and limitations. The review shows that LULCC dominates current applications, with Artificial Neural Networks (ANNs) as the most prevalent ML method, increasingly complemented by DL architectures. Across cases, AI is primarily used to learn non-linear transition dynamics, represent spatial and temporal dependencies, identify influential drivers, and improve classification performance and computational efficiency. Building on these insights, the paper synthesizes the roles of AI in spatiotemporal urban modeling and outlines forward-looking research directions to support more robust, transparent, and policy-relevant applications for urban sustainability. Full article

The growing volume of construction and demolition waste has made discarded concrete a major source of urban solid waste, placing increasing pressure on land resources and the environment. Recycling waste concrete into recycled aggregate concrete (RAC) offers an effective solution for resource conservation and carbon reduction, aligning with the goals of sustainable development. However, due to the residual mortar, high porosity, and microcracks of recycled aggregates, RAC generally exhibits lower compactness, strength, and durability than conventional concrete, particularly under freeze–thaw conditions where degradation accelerates and service life decreases. To address these challenges, this study investigates the optimization of RAC mix design and its frost resistance performance for pavement base applications. An orthogonal experimental design was employed, with the water-to-binder ratio, recycled aggregate replacement ratio, and air-entraining agent dosage as key variables, while 7-day compressive strength, permeability coefficient, and rebound modulus served as evaluation indices. The influence and interaction of these factors were analyzed to determine an optimal mix meeting both mechanical and durability requirements. Rapid freeze–thaw cycling tests were then conducted to examine the variations in mass loss, relative dynamic modulus, and compressive strength retention, followed by exponential and damage variable modeling to characterize the degradation process. Results show that the water-to-binder ratio primarily governs strength, the replacement ratio affects stiffness and permeability, and the air-entraining agent significantly enhances frost resistance by improving pore structure. The optimized mix retained over 70% of its relative dynamic modulus after 300 freeze–thaw cycles, exhibiting superior durability. This work establishes a systematic framework for multi-factor optimization and durability evaluation of RAC, providing theoretical and practical guidance for its application in cold-region pavement bases. Full article

The evaluation of potentially toxic element concentrations (PTEs) in soils and plants is essential for understanding environmental quality and potential human exposure in areas affected by intense anthropogenic activity. This study addresses a research gap in the Valencian Region, focusing on soil–plant interactions of PTEs in urban and industrial environments. We assess the status of the soil–plant system in a region of the Valencian Community (eastern Spain) subjected to strong urban, industrial and agricultural pressure. A total of 55 soil samples and 47 plant samples were collected from agricultural, urban and industrial sites and analysed for soil properties, major elements (Al, Mg, Fe) and PTEs (As, Cd, Co, Cr, Cu, Li, Mn, Ni, Sr, V and Zn). Land use significantly influenced soil physicochemical characteristics, with clear differentiation among environments. Soil texture and organic matter were the main factors controlling element retention, while Al, Fe and Mg dominated the geochemical composition, consistent with Mediterranean calcareous soils. Correlation analyses revealed strong co-occurrence patterns among lithogenic elements (e.g., Fe-Al, r = 0.917 p < 0.01), soil texture and chemical properties, indicating a shared origin and preferential retention in the fine fraction and soil organic matter. Contamination indices identified potential environmental risk mainly associated with Cu, Pb, Sr and Zn, particularly in densely populated areas. Mean concentrations of Cd, Cr, Cu, Pb and Zn were, respectively, 0.63 mg kg⁻¹, 42.25 mg kg⁻¹, 31.49 mg kg⁻¹, 56.91 mg kg⁻¹ and 76.08 mg kg⁻¹. These elements exceeded Spanish regulatory reference values in several soils. Bioaccumulation indices indicated notable plant uptake of As, Sr and Zn, highlighting their potential for trophic transfer. Full article

This study analyses the influence of three-dimensional (3D) urban form on intra-urban thermal variability and its long-term evolution in Zagreb, Croatia. The research focuses on four residential districts (Špansko sjever, Dugave, Lanište, and Novi Jelkovec) representing different development periods. The central hypothesis is that differences in the development period and urban compactness are associated with differences in summer thermal patterns, with more open spatial configurations generally exhibiting weaker thermal responses than more compact developments. The methodology integrates LiDAR-derived building morphology with a decade-long Landsat time series (2015–2024), including land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The results indicate a consistent increase in summer LST across all analysed neighbourhoods, with warming rates ranging from approximately 2.00 to 2.83 °C per decade. Built-up intensity shows a positive association with temperature, while vegetation trends are generally weak. A multiple linear regression model explains 47% of the variance in LST (R² = 0.47), with NDBI identified as a significant predictor (p < 0.01), whereas NDVI and volumetric building density are not statistically significant. Despite this, neighbourhoods with higher volumetric building density (up to ≈2.96 m³/m²) tend to exhibit stronger warming trends compared to lower-density areas (≈1.69 m³/m²), indicating the additional explanatory value of three-dimensional urban morphology. These findings support the concept of a volumetric expression of urban thermal processes, while highlighting that 3D urban morphology contributes to the interpretation of the long-term thermal patterns when considered alongside other factors. They also emphasize the importance of integrating 3D spatial metrics into climate-sensitive urban planning and mitigation strategies. Full article

Leaf water content (LWC) is a key physiological indicator for assessing crop water status. However, its spectral response may vary under different irrigation practices, which limits the general applicability of existing models. This study aims to develop irrigation-specific LWC estimation models for spring wheat based on UAV multispectral imagery. Field experiments were conducted during two growing seasons (2023–2024) under three irrigation methods, with five water treatments and three replicates, resulting in a total of 45 experimental plots. Multispectral data and in situ measurements were collected at key growth stages. Irrigation-dependent sensitive vegetation indices were identified through correlation analysis, and machine learning models, including Random Forest (RF), Multiple Linear Regression (MLR), and Backpropagation Neural Network (BPNN), were constructed and evaluated using a five-fold cross-validation framework. The results showed that spectral sensitivity to LWC varied significantly across irrigation methods, with different dominant indicators under FD, ND, and MD. Model performance also exhibited irrigation-dependent differences. Among the three models, RF showed the most stable performance, achieving mean R² values of 0.70, 0.74, and 0.62 and corresponding RMSE values of 0.04, 0.06, and 0.08 under FD, ND, and MD, respectively. In contrast, MLR showed lower predictive accuracy, while BPNN exhibited limited robustness under the current dataset, particularly under ND. These findings highlight the importance of irrigation-specific modeling strategies for improving LWC estimation reliability. Full article

Rapid urbanization has led to a significant increase in land use carbon emission (LCE), putting great pressure on ecological security. The coupling relationship between LCE and the ecological security index (ESI) is the key to sustainable development. Based on land use/cover change (LUCC) and Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) data, the LCE of the Jilin Border Cities (JLBCs) from 2013 to 2023 was estimated. Twenty-seven indicators were selected from both natural and socioeconomic aspects to evaluate the ESI using the Driving forces–Pressure–State–Impact–Response–Management (DPSIRM) model. The spatial interaction between LCE and ESI was analyzed using the coupling degree model and spatial autocorrelation. The results show that from 2013 to 2023, the main LCE areas in the JLBCs were concentrated in central urban districts, while the total LCE remained negative but exhibited a clear upward trend. The ESIs in Tonghua City and Baishan City have continued to improve, but those in Yanbian Autonomous Prefecture have gradually deteriorated, with ecological security warnings intensifying progressively toward the east. The spatial variation in the LCE–ESI coupling degree is significant, predominantly exhibiting low coupling with differences across scales. Within the study area, coupling degree shows a strong positive correlation, revealing distinct spatial clustering patterns dominated by low clusters and cold spots. Future efforts should focus on promoting low-carbon development models, strengthening protection and restoration, while implementing targeted measures to enhance the overall ecology of JLBCs. Full article

Two-dimensional (2D) materials exhibit electronic and collective excitation properties that are highly sensitive to surface chemistry and thickness, requiring surface-sensitive characterization at low electron energies. Here, we investigate graphene, hexagonal boron nitride (h-BN), molybdenum disulfide (MoS₂), and titanium carbide (Ti₃C₂) MXene using an advanced home-built scanning low-energy electron microscopy system combined with time-of-flight electron spectroscopy (SLEEM/ToF). The system uniquely records electron energy-loss spectra (EELS) from transmitted electrons rather than from the reflected electrons used in conventional SLEEM. Compared with high-energy EELS, our low-energy ToF-EELS approach offers enhanced surface sensitivity and reduced beam-induced damage, enabling direct probing of π and π + σ plasmon excitations. Additionally, complementary techniques, including scanning transmission electron microscopy (STEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), were employed to characterize structural and chemical properties. EELS were acquired for all investigated 2D materials at electron landing energies of 500–1500 eV, and in the 5–50 eV range for selected materials, including graphene and MoS₂. Analysis of these spectra enabled determination of the average plasmon positions across the measured energy range for all studied materials. Furthermore, a quantitative determination of the inelastic mean free path (IMFP) was achieved for graphene in the 10–50 eV range, yielding a value of 1.9 ± 0.2 nm. These results demonstrate the potential of SLEEM–ToF for surface-sensitive analysis of 2D materials. Full article

The primary aim of this study is to validate the utility of Social Media Data (SMD) as a scientifically grounded tool for quantifying the spatial mismatch between objective ecological supply and subjective social demand. Assessing the spatial coupling and mismatch between Habitat Quality (HQ)—representing objective ecological supply—and Ecological Perception (EP)—representing subjective social demand—is essential for developing targeted urban management and development strategies. Focusing on the core urban area of Hangzhou, this study quantified ecological supply using the InVEST HQ model. To reflect social demand, 4958 geolocated Weibo posts were processed using contextual sentiment analysis. A Coupling Coordination Degree model served as a diagnostic tool to evaluate the synergy between these two dimensions. Additionally, a Geodetector model was employed to investigate the factors driving spatial differentiation in this coupling. The findings indicate that: (1) The regional average HQ is 0.56, reflecting a moderate overall level of degradation, while EP shows a preference for natural environments and exhibits a distinct “strip-like” spatial distribution. (2) The overall CCD value is 0.384; high-coupling areas are primarily concentrated in regions with superior natural conditions and dense vegetation, whereas low-coupling areas correspond to zones with intensive urban functions. (3) Driving factor analysis reveals that land-use type exerts the most significant influence on the overall degree of coupling. This study demonstrates that the HQ-EP coupling framework provides a reliable spatial diagnostic tool for urban planners to identify socio-ecological vulnerabilities. The results suggest that an appropriate integration of natural elements enhances coupling outcomes, with the highest synergy observed in environments characterized by high HQ and minimal anthropogenic disturbance. Full article

Apolygus lucorum (Meyer-Dür) is a phytozoophagous crop pest. While the effects of plant-based diets on its development and reproduction have been extensively studied, the combined effects of plant- and prey-based diets on these traits remain poorly understood. This study systematically evaluated the effects of plant-only, prey-only, and mixed plant–prey diets on A. lucorum nymphal survival and development, as well as adult longevity and fecundity, under controlled laboratory conditions. The results demonstrate that diet composition significantly affected nymphal survival and developmental progression. Nymphs fed exclusively on prey (Aphis gossypii Glover or Bemisia tabaci (Gennadius) nymphs) failed to complete juvenile development. Although a diet of Helicoverpa armigera (Hübner) eggs alone enabled some individuals to reach adulthood, survival rates were significantly lower than those in mixed-diet treatments. Mixed feeding markedly improved nymphal survival, with the highest rates observed in groups fed green beans + H. armigera eggs and cotton leaves + B. tabaci nymph combinations (both 64.45%). The developmental duration was also influenced. Mixed diets, particularly green beans + H. armigera eggs, significantly shortened each instar and the total developmental time (11.04 ± 0.17 d), whereas a diet of cotton leaves alone prolonged development (19.45 ± 0.24 d). Adult longevity and reproductive output were likewise diet-dependent. The longest lifespans were recorded in adults fed green beans alone or green beans + H. armigera eggs, while the shortest lifespan was observed for those fed only cotton leaves. Successful oviposition was only achieved following four dietary treatments: green beans alone, green beans + H. armigera eggs, H. armigera eggs alone, and cotton leaves + H. armigera eggs. Among these, the green bean + H. armigera egg diet yielded the best reproductive performance, featuring the shortest pre-oviposition period (5.82 ± 0.60 d), the longest oviposition period (19.41 ± 1.68 d), and the highest mean fecundity per female (238.35 ± 25.51 eggs). This underscores the reproductive advantage of a mixed plant–prey diet. This study clarifies how dietary conditions shape the survival, development, and reproduction of A. lucorum, highlighting its strong reliance on nutritional quality for key life-history traits. These findings offer valuable insights into the ecological adaptations underlying the feeding behavior of this insect. Full article

During the preliminary design of flight vehicles, i.e., missiles or guided rockets, propulsion system performance serves as a critical determinant of both maximum range and terminal velocity. However, complex grain configurations in solid rocket motors (SRMs) typically require geometric modeling software to obtain burning surface area, which severely constrains efficiency. To address this challenge, this study presents a neural network-enhanced rapid performance prediction and matching optimization framework for solid rocket motors (NN-SRM). In NN-SRM, neural networks are employed to simulate the evolution of key parameters during grain combustion, including burning surface area, grain volume, and moment of inertia. The zero-dimensional internal ballistics equations coupled with one-dimensional steady isentropic flow relations are incorporated into the framework to rapidly obtain thrust curves. A discrete–continuous mixed differential evolution algorithm is further employed to identify the optimal grain configuration that satisfies specific thrust requirements. Results demonstrate that, as for cylindrical, star, and finocyl grains, the neural network achieves R² exceeding 0.95. Finally, thrust matching optimization is conducted on three grains and achieves promising thrust solutions for the conditions of large thrust with short time and small thrust with long time, which demonstrates the effectiveness and practicality of the constructed NN-SRM. Full article

A multi-task network for runway lines and runway markings based on deep learning was designed to address the issue of prior information dependence on runway width in unmanned aerial vehicle visual autonomous landing application scenarios. By detecting runway images captured at different positions during flight, the parameters of the runway start line, left and right boundary lines, and runway markings were obtained. On this basis, a runway width estimation model and visual positioning algorithm based on line features were designed. In standard runway scenarios, the recognition of runway signs provides valuable prior information about the runway width. For simplified runways or cases where signs are missing, we have devised a width estimation model based on the left/right boundary lines. Furthermore, considering the variation in pitch angle during the UAV’s landing process, we have analyzed and refined the width estimation model to ensure its applicability throughout the entire landing process. Additionally, we have developed a visual positioning algorithm that utilizes the runway width and runway line parameters to calculate the relative position between the UAV and the runway. Considering the limitations of a single visual positioning algorithm, we adopt a visual and inertial navigation fusion positioning algorithm to enhance the reliability of landing positioning. To validate our algorithms, we have constructed a visual simulation platform and flight test. These tests confirm the effectiveness and accuracy of our detection algorithm and width estimation model. Furthermore, by utilizing the estimated runway width and the detected runway line parameters, we have successfully calculated the relative position, further validating the effectiveness of our positioning algorithm. Full article

Understanding the development characteristics and controlling factors of organic-rich shales in carbonate platform settings is essential for predicting their distribution and assessing their natural gas exploration potential. However, the mechanisms governing the accumulation of such shales in these specific sedimentary environments remain poorly constrained, and the lack of integrated petrological and geochemical studies limits accurate evaluation of their resource potential. The key objective of this study is to investigate the development characteristics and formation mechanisms of organic-rich shales within intraplatform depressions. To address this objective, we conducted a comprehensive petrological and geochemical analysis of the Cambrian Shuijingtuo Formation organic-rich shale deposits deposited in a carbonate platform setting, particularly from Well EYY3 in Western Hubei, Central Yangtze region. The obtained results indicate that total organic carbon (TOC) contents in the Shuijingtuo Formation can reach up to 4.77%, with a thickness of approximately 9.5 m for shales containing over 2% TOC. Vertically, TOC content exhibits a rapid increase at the base, followed by a gradual decline toward the top, reflecting the evolution of depositional environments. The characteristics of organic-rich shale indicate a significant presence of carbonate minerals, which increase in concentration, alongside tuff lenticular bodies and lithological transition surfaces between tuff and shale. While the longitudinal variation of SiO₂ content in shale is subtle, there is a slight increase in land-sourced clasts and excess silica, and TOC has a significant positive correlation. At the base of the Shuijingtuo Formation, redox parameters, including U-EF and Mo-EF, display a rapid increase followed by a gradual decrease. Conversely, changes in Ni-EF, which indicate paleoproductivity, are less pronounced, and their correlation with TOC is relatively poor. These findings suggest that rapid sea-level rise associated with Cambrian transgressions was the main factor influencing organic matter enrichment in the carbonate platform depressions. This rise supplied nutrients and silica-rich organisms, altering the biological landscape and fostering anoxic conditions in the intraplatform depressions, promoting organic-rich shale formation. As sea levels declined, water circulation became restricted, leading to oxidation of shallow water bodies, decreased paleoproductivity, and shale deposits transitioned to tuff. Therefore, organic-rich shale can also be developed on carbonate platforms, with its formation primarily controlled by fluctuations in sea level. During highstand periods, intraplatform depressions may serve as favorable zones for shale gas exploration. Full article

As a key link between short-distance urban mobility and public transport, dockless bike-sharing (DBS) systems have expanded rapidly in recent years. However, existing studies are limited by insufficient factor coverage, incomplete temporal analysis, and inadequate assessment of spatial-scale effects. To address these gaps, this study uses Shenzhen as a case study, integrating 40 million DBS trip records from August 2021 with multi-source geospatial data to develop a spatiotemporal analytical framework. First, it examines differences in riding patterns between weekdays and weekends, further segmenting trips into six time periods to capture intra-day temporal variations. Through multicollinearity and spatial autocorrelation tests, a 700-m grid was identified as the optimal analysis unit. Subsequently, a Multi-scale Geographically Weighted Regression (MGWR) model quantified how multiple sources of factors collectively shape DBS usage behavior. Results indicate that higher frequency, faster speeds, and longer distances during peak periods characterize weekday trips. Office POIs and transit accessibility positively affect DBS usage during weekday peaks, whereas Residential POIs and Convenience Service POIs have a greater influence on weekend trips. Population density and land-use mix consistently promote DBS use across all periods. Younger residents (<30 years) were the main users, especially during weekday peak and weekend no-peak periods, whereas gender and education had limited impact. These findings provide empirical evidence to optimize bike-sharing deployment, enhance multimodal transport integration, and support sustainable urban mobility planning. Full article