Search Results (20,040)

Net primary productivity (NPP) and its response to global climate change are one of the hot topics in global change research. Based on Net primary productivity remote sensing data and meteorological data, this study analyzed the spatiotemporal variation in vegetation NPP in Maowusu sandy land by using Sen trend analysis, Mann–Kendall significance test, coefficient of variation stability analysis, partial correlation and complex correlation analysis, and quantitatively analyzed the response of vegetation NPP to climate factors. The results showed that from 2001 to 2023, the overall vegetation NPP showed a significant upward trend, and the annual average increased from 124.28 g·(m⁻²·a)⁻¹ to 221.41 g·(m⁻²·a)⁻¹. The Theil–Sen median slope of NPP was +3.87 g·(m⁻²·a)⁻¹ with a coefficient of variation (CV) of 0.19, suggesting a robust but spatially variable greening trend. In total, 98.53% of the area showed an upward trend, with a very significant and significant increase area. The overall stability of vegetation NPP was strong, with an average coefficient of variation (CV) of 0.19 and a CV< of 0.30 in 97.96% of the regions, but the local area from southwest to east was highly volatile and there was a risk of secondary desertification. The influence of climate factors on vegetation NPP had significant spatial heterogeneity: precipitation was the key driving factor, and most areas were positively correlated. Potential evapotranspiration was positively correlated in the central and northern regions, and negatively correlated in some southern areas. The overall temperature has a negative effect, and only the local area has a weak promoting effect. Multi-correlation analysis shows that vegetation NPP is the result of the synergy of multiple climatic factors, and the hydrothermal coupling mechanism plays a decisive role in its spatial pattern. This study can provide a scientific basis for the restoration of vegetation ecosystems, environmental protection policy formulation, ecological protection and high-quality development of the Yellow River Basin in Maowusu Sandy Land. Full article

Biomass, composed of natural polymers such as cellulose, hemicellulose, and lignin, can be converted into circular chemical feedstocks through thermochemical conversion processes like pyrolysis. Char conversion is the rate-limiting step in the thermochemical conversion process, and thus, char reactivity is essential for determining the overall efficiency of pellet-based thermochemical processes. Pyrolysis experiments were conducted on rice straw pellets of different sizes (i.e., 8, 10, and 12 mm) in a vertical quartz tube reactor at 700 °C, and then the chemical structure of chars sampled at different stages and locations within a 10 mm pellet was analyzed using Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). The results indicate that increasing the pellet size facilitates the growth of polycyclic aromatic structures, as evidenced by the observed variations in the abundance of typical aromatic compounds in bio-oil. This also promotes volatile–char interactions, leading to greater deposition of large aromatic structures on the char surface, thereby enhancing char aromatization. Analogous to the spatial scale effect of pellet size on char structure, the evolution of the char structure within a single pellet exhibits distinct spatial heterogeneity during the initial devolatilization and subsequent char aromatization stages due to the location-dependent coupling of heat/mass transfer limitations and aromatization reactions during pyrolysis. Furthermore, the spatiotemporal evolution of the char structure leads to differences in the specific reactivity: during the devolatilization stage at 75 s, the center exhibits the highest reactivity, whereas the outer surface becomes the most reactive in the subsequent char aromatization stage at 300 s. Full article

This study addresses the high-risk scenario of dual-sided deep foundation pit construction adjacent to operational metro lines, a complex urban underground engineering context with significant safety implications. A multi-level dynamic safety risk assessment model is proposed by integrating the Analytic Hierarchy Process (AHP) with a risk matrix. Existing approaches generally lack the capability to dynamically incorporate spatiotemporal variations and real-time construction management information, limiting their applicability under complex working conditions. To overcome these limitations, the Tianjin Shouchuang Beiyunhe Metro Complex project is adopted as a case study to develop a concise and efficient risk assessment framework. The framework introduces spatiotemporal effect and safety management coefficients to dynamically adjust risk values and conducts risk identification and integrated evaluation across four dimensions—geology, environment, design, and construction—using 25 indicators. The model enables quantitative, real-time identification and dynamic control of safety risks during metro foundation pit construction. The assessment results indicate that the overall project risk is classified as Level I (highest), with the western pit exhibiting slightly higher risk. Targeted mitigation measures include the use of diaphragm walls with internal buttresses and grouting reinforcement. Compared with conventional methods, the proposed model demonstrates significant advantages in adapting to dynamic construction conditions, enhancing engineering applicability, and strengthening early-warning capability. These improvements provide a scientific, practical, and scalable technical solution for the accurate identification of critical risks and proactive safety management in complex metro foundation pit projects. Full article

Agricultural ecosystems in northeastern Bangladesh are increasingly vulnerable to climate-induced stressors, particularly rising temperatures and seasonal droughts. While previous research has examined the climate’s impact on agriculture in broader contexts, no study has specifically investigated long-term seasonal vegetation and thermal dynamics in Sylhet. This study addresses this gap by assessing spatio-temporal variations in vegetation health under climate stress in the Sylhet region from 2005 to 2025 using remote sensing techniques. To investigate this problem, the study derived the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from Landsat satellite imagery and evaluated their seasonal behavior across the major cropping periods Rabi, Kharif I, and Kharif II. The relationship between vegetation health and surface temperature was examined using Pearson’s correlation matrix along with a statistical comparison to identify change patterns, transitions among vegetation and thermal stress classes, and the seasonal intensity of climate stress. The findings indicate that increased LST generally corresponds with reduced vegetation cover in lowland agricultural zones, whereas elevated areas with forest or tree covers show an opposite response. Distinct spatial hotspots of thermal stress and drought-prone zones were also identified, particularly during the dry Rabi season. These results highlight the idea that rising LST corresponds with declining NDVI values, indicating that increasing thermal stress and potential reductions in agricultural vegetation productivity and climate stress across Sylhet’s agricultural landscape have intensified markedly from 2005 to 2025, with clear seasonal differences in vulnerability. NDVI analysis reveals a consistent decline in vegetation health, while LST patterns show widespread transitions from moderate to high and severe thermal stress, particularly during the Kharif seasons. The observed NDVI decline under elevated LST conditions indicates reduced vegetation vigor and potential productivity within agricultural lands, rather than a direct reduction in cultivated areas, since NDVI primarily captures vegetation density and physiological condition. The strongest NDVI–LST inverse relationship occurs in Rabi and Kharif I, indicating vegetation’s cooling role, whereas this linkage weakens in Kharif II due to dominant monsoon-driven atmospheric controls. Full article

This study examines the 3D attributes of submesoscale eddies identified over a 12-month period within the Western Pacific Ocean. Composite parameters of cyclonic submesoscale eddies (CSMEs) occurring within and away from the Kuroshio Current system are compared and analyzed for their surface and subsurface features, as well as the seasonality of their core properties. Within the Kuroshio Current (KC) region, CSMEs are faster, stronger and deeper than in the open water (OW) region, with composite eddy depths of 97.5 m and 77.5 m, or 2.8 and 2.0 times the mixed layer depth, respectively. Prominent dipolar divergence patterns both at the surface and at depth reveal the presence of ageostrophic influence, with KC CSME cores deviating 48% and OW CSMEs deviating 40% from geostrophic balance at the surface. This imbalance drives strong vertical motion with maximum upward velocities of 19.2 m day⁻¹ at 57.7 m and 9.3 m day⁻¹ at 157.1 m within the KC and OW region CSME cores, respectively. Subsurface extrema analysis reveals structural differences in CSMEs between dynamic regions. These results provide a useful model-based estimate for subsurface CSME features which are difficult to quantify with observations. Full article

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article

The advancement of on-orbit servicing and space debris removal missions has established high-precision visual perception for non-cooperative spacecraft as a key research focus. However, the availability of high-quality, diverse spacecraft image datasets is severely limited due to extreme on-orbit imaging conditions, data confidentiality, and morphological diversity of targets, significantly constraining the advancement of data-driven algorithms in this domain. To address this challenge, we propose a relative orbital motion-guided framework for generating multimodal visual data of spacecraft. The proposed method integrates an orbital dynamics model into the synthetic data generation pipeline to simulate typical relative motion patterns between the camera and the target in a realistic orbital environment, thereby generating image sequences characterized by continuous spatiotemporal evolution. Targeting four representative spacecraft—Tiangong, Spacedragon, ICESat, and Cassini—this work simultaneously generates a dataset comprising 8000 samples, each containing four strictly aligned modalities: RGB images, instance segmentation masks, depth maps, and surface normal maps, along with precise 6-degree-of-freedom (6-DoF) pose ground truth. Furthermore, an end-to-end physical image degradation model is developed to accurately simulate the complete imaging chain—from optical diffraction and aberrations to sensor sampling and noise—thereby effectively narrowing the domain gap between synthetic and real data. By addressing three key aspects—physical motion modeling, synchronous multimodal ground truth, and imaging degradation simulation—this work provides a crucial data foundation for training, testing, and validating data-driven on-orbit perception algorithms. Full article

In power systems with a high penetration level of wind power, wind turbines are required to temporarily extract stored rotor kinetic energy to provide primary frequency support, thereby compensating for the limited frequency regulation (FR) capability of the overall system. However, wind turbine participation may lead to underresponse (insufficient frequency support) or overresponse, potentially causing a secondary frequency dip (SFD). Electrolytic aluminum load (EAL), as an industrial load with FR potential, can rapidly adjust its active power input by controlling the electrolytic cell voltage, equivalently increasing the system’s FR capacity and thereby enhancing the load disturbance resistance of power systems with high wind power penetration. This paper first analyzes the causes and mechanisms of the SFD induced by wind turbine overresponse based on a Unified transfer function structure (UTFS) model and introduces the concept of a frequency stability region. Within this region, the virtual droop and virtual inertia coefficients for wind turbines are tuned to prevent SFD during FR. Simultaneously, by involving EAL in system FR, the analysis reveals that its participation essentially equivalently expands the system’s frequency stability region. Building on this analysis and considering spatiotemporal influencing factors, a coordinated wind-aluminum FR control strategy across multiple timescales is proposed to avoid SFD. Finally, a 39-bus simulation system built in DIgSILENT is used for validation. The simulation results indicate that the proposed control strategy effectively suppresses SFD under high wind power penetration conditions, and that the incorporation of EAL significantly expands the frequency stability region of the power system. Full article

Due to ongoing global warming, the frequency and intensity of extreme weather events have increased substantially. Compared to individual extremes, compound drought and high temperature (CDHT) events represent a major climate risk in China. However, their spatiotemporal characteristics remain insufficiently understood, particularly at fine temporal scales. To address this gap, this study systematically investigated CDHT events across China from 1982 to 2023. Methodologically, CDHT events were identified at the raster level by combining an improved daily Standardized Precipitation Evapotranspiration Index (SPEI) with daily maximum temperature using a quantile relative dynamic threshold. The results show strong spatial heterogeneity: the longest event durations are primarily observed in Xizang, while higher event severity is concentrated in regions south of 30° N. Trend analysis reveals a widespread increase in the duration, frequency, and severity of CDHT events across most of China, with the most pronounced intensification detected in Xinjiang, Inner Mongolia, and Yunnan. Overall, these findings highlight a clear climate-driven intensification of CDHT events, offering new insights into their spatiotemporal dynamics. The results offer a robust scientific basis for improving risk assessment and developing targeted adaptation strategies to mitigate the impacts of compound climate extremes in China. Full article

Grain and cultivated land resources constitute the most fundamental means of human subsistence, and their spatial mismatch can directly reveal issues related to the rationality of regional resource utilization and urban–rural development patterns. The downstream region of the Yangtze River Economic Belt, as a major grain-producing area in China, holds significant importance for optimizing regional arable land utilization patterns, achieving sustainable use of cultivated land resources, and ensuring national food security through the investigation of the spatiotemporal mismatch characteristics between grain production and arable land resources and their influencing factors. This study focuses on the downstream region of the Yangtze River Economic Belt, employing the Center of Gravity Transfer Model, Spatial Mismatch Model, and Geographical and Temporal Weighted Regression Model to analyze the spatiotemporal variation characteristics of grain production and cultivated land area, as well as their mismatch patterns. It further investigates the factors that influence such mismatches and their spatial heterogeneity. The research findings indicate that, in terms of temporal characteristics, grain production in the downstream region of the Yangtze River Economic Belt exhibited an upward, fluctuating trend from 2000 to 2023. The cultivated land area initially decreased, then gradually increased, while the overall quantity showed a net reduction. From the perspective of spatial changes, the migration rate of grain production was significantly higher than that of cultivated land. The center of gravity of grain production shifted 78.85 km northwestward, while the center of gravity of cultivated land moved 4.16 km in the same direction. The overall mismatch between grain production and cultivated land shows fluctuating changes, while its spatial characteristics show an increasing trend toward polarization. The average intensity order of influencing factors is as follows: effective irrigated area > fertilizer’s equivalent weight > the proportion of agricultural output value > total power of agricultural machinery > urbanization rate > the proportion of people employed in the primary industry. Meanwhile, these influencing factors exhibit significant spatial heterogeneity characteristics, with their impact directions and intensities varying across different development stages in distinct regions. From a spatiotemporal perspective, the research findings provide differentiated policy recommendations for the efficient utilization of cultivated land resources and grain production in the downstream region of the Yangtze River Economic Belt. Full article

As a major ecological safeguard in northwestern China and an important corridor for the Belt and Road Initiative, the Hexi Corridor holds strategic significance for improving landscape structure and enhancing regional ecological security. Focusing on the Hexi Corridor, this study develops a landscape ecological risk (LER) index based on land use (LU) data from 2000, 2010, and 2020. The study employs ArcGIS spatial analysis and XGBoost-SHAP, an interpretable machine learning method, to analyze the spatiotemporal evolution of LU and LERs, as well as their driving factors. Furthermore, the Markov-FLUS model is utilized to simulate and predict LU and LER spatial patterns under multiple scenarios for 2030. The results show that: (1) The dominant land type in the Hexi Corridor is unused land, accounting for 67.33%. During the research period, the extents of unused land, grassland, and forestland showed a steady decline, while built-up land and cropland increased. (2) LERs are categorized into five types, with high risk being the most prevalent, accounting for 52.02%. Between 2000 and 2020, the total area of higher and high risks decreased by 4312 km², indicating an overall decrease in LER across the region. (3) LER is primarily influenced by annual rainfall, population density, distance to main roads, and distance to rivers. (4) Marked variations in LU patterns and LER are observed across different development scenarios projected for 2030. Full article

This study examines the spatio-temporal evolution and restricting mechanisms of agricultural supply chain resilience in the Yangtze River Basin from a gradient perspective. An evaluation index system encompassing the dimensions of the supply side, demand side, circulation side, and support side was developed. The Entropy-Weighted TOPSIS method, kernel density estimation, and obstacle degree model were comprehensively applied to measure and dynamically analyze supply chain resilience across 11 provinces from 2013 to 2023. The findings reveal distinct spatio-temporal evolution patterns: while the overall resilience shows an upward trend, significant gradient disparities exist, with downstream areas exhibiting markedly higher resilience than the mid- and upstream regions. Regarding the restricting mechanisms, the circulation and support sides exhibit higher levels of obstacles, representing key constraints to resilience enhancement. Among these, express delivery volume, freight turnover, and local R&D personnel full-time equivalents are the core obstacle factors affecting resilience. Based on these findings, this study proposes targeted recommendations, including optimizing rural last-mile logistics, upgrading inter-provincial freight hubs, improving rail–water intermodal transport, and strengthening cold-chain infrastructure, as well as implementing differentiated regional strategies and establishing cross-regional coordination mechanisms. These recommendations aim to provide decision-making guidance for enhancing the risk-response capabilities of agricultural supply chains in the Yangtze River Basin and to promote balanced regional development. Full article

Nitrogen metabolism is fundamental to all organisms, with ammonium transporters (Amt) playing a pivotal role in transmembrane ammonium transport. Brachiopods, as “living fossils”, offer unique insights into the evolutionary adaptation of marine invertebrates. This study systematically identified and characterized the Amt gene family in the brachiopod Lingula anatina. Five canonical Amt genes were identified, with nonrandom chromosomal distribution and evidence of lineage-specific duplication events. Phylogenetic analysis revealed that these Amt proteins cluster into three well-supported clades, showing closer affinity to Caenorhabditis elegans, reflecting conserved ancestral features predating protostome radiation. Structural predictions showed that LanAmtA and LanAmtB retain the canonical 11-transmembrane helix (TMH) topology with an extracellular N-terminus, while LanAmtC features a unique 12-TMH architecture with an intracellular N-terminus, resembling certain vertebrate Amt-related proteins. Critical functional residues involved in ammonium selectivity and transport were preserved across all paralogs. Expression profiling revealed non-redundant spatiotemporal patterns: LanAmtA1 and LanAmtB2 dominate early embryogenesis, with LanAmtB2 becoming the major isoform in late developmental stages; LanAmtC exhibits constitutive high expression across adult tissues. Collectively, our findings demonstrate that the L. anatina Amt family expanded via local duplications, evolving structural stability, regulatory diversity, and functional specificity. This study provides a comprehensive molecular framework for understanding the evolutionary adaptation of nitrogen-handling mechanisms in basal lophotrochozoans and sheds light on how intertidal organisms cope with dynamic environmental conditions. Full article

Grazing is a widespread land use practice with significant implications for biodiversity and ecosystem functioning. Wild ungulates, as key components of terrestrial ecosystems, play a vital role in maintaining ecosystem health. Dietary overlap exposes wild ungulates to intense competition with livestock, yet the effects of grazing on them remain underexplored. Using camera-trapping data from Xinglong County in the mountains of Southeast China, we investigated the spatiotemporal responses of eight wild ungulates to livestock (cattle and horses). Our results showed the following: (1) The potential distribution of suitable habitats for ungulates was primarily shaped by elevation (ELE), distance to temple (DTT), precipitation of the coldest quarter (bio19), and distance to water (DTW). Among the species, blue sheep (Pseudois nayaur) showed the strongest response to livestock presence. (2) Spatially, most ungulates appeared to avoid livestock; forest musk deer (Moschus berezovskii) showed spatial avoidance of horses. (3) Temporally, the daily activity patterns of most ungulates generally showed low to moderate overlap with those of cattle. These findings suggest that livestock grazing may be associated with negative effects on wild ungulates in Xinlong County. Given the ecological importance of ungulates, the need to maintain wildlife community integrity, and the long history of grazing in the region, further research is warranted to understand the role of livestock in shaping wildlife communities. Full article