Search Results (1,298)

The Philippine Sea Basin is one of the world’s largest marginal sea basins, and the spatiotemporal variation characteristics of its sound speed field hold significant importance for deep-sea navigation and positioning as well as underwater acoustic detection. This study investigates the sound speed field in the central Philippine Basin (130.0–134.0° E, 17.5–20.5° N) using the Global Ocean Physics Analysis and Forecast product from the European Union’s Copernicus Marine Environment Monitoring Service (CMEMS), cross-validated with the U.S. HYCOM (Hybrid Coordinate Ocean Model), and independent verified against 69 Argo profiles. We systematically investigate the spatiotemporal variation characteristics of the sound speed field in this region. Temperature and salinity consistency between the two products is established (deviations of <0.5 °C and <0.05 ppt below 400 m), with CMEMS selected as the primary data source for its higher accuracy and greater temporal stability. Three sound speed formulae—Del Grosso, Chen–Millero, and TEOS-10—are intercompared, with TEOS-10 yielding the highest accuracy in cross-validation; it is therefore recommended for its rigorous thermodynamic consistency. Vertical sound speed profiles are evaluated using bi-exponential, Munk canonical, and fourth-order polynomial models. Among them, the bi-exponential model achieves the optimal balance between physical interpretability and fitting accuracy (RMSE = 2.77 m/s, inter-monthly correlation coefficient = 0.857). Its two exponential decay scales characterize the upper-ocean thermocline and the deep stratification, respectively, avoiding the physically unrealistic deep-water fluctuations exhibited by the polynomial model (RMSE = 2.68 m/s) and the poorer generalization of the Munk model (RMSE = 2.97 m/s). Horizontal gradient analysis reveals a cross-directional correlation of approximately 0.5 between sound speed gradients and ocean currents, reflecting the combined modulation of sound speed gradients by Kuroshio advection and thermodynamic stratification. The general gradient control scale is estimated at approximately 100 km × 100 km, confirmed by cross-method consistency between K-means and Gaussian mixture model clustering. Temporal analysis demonstrates that sound speed peak-to-peak variation attenuates rapidly with depth (from ~8.9 m/s at 50 m to <0.1 m/s at 4000 m), and EOF (empirical orthogonal function) analysis reveals that the first four modes explain over 99% of the total variance, with harmonic fitting identifying annual and semi-annual cycles as the dominant periodic components. Sound channel axis depth varies seasonally between 900 and 1125 m (deeper in winter, shallower in spring), with axis sound speed stable at 1480–1484 m/s (slightly higher in winter, slightly lower in spring) and axis thickness ranging from 225 to 450 m (wider in winter, narrower in spring). These results provide prior critical constraints for underwater acoustic positioning, AUV navigation, and long-range sound channel communication and navigation in the central Philippine Sea region. Full article

Over the past few decades, South Africa has experienced recurrent drought events of varying intensity. The Musina Local Municipality is particularly vulnerable due to a semi-arid climate, erratic rainfall, and rising temperatures which collectively contribute to heightened environmental stress. This study analyzed drought spatiotemporal patterns, livelihood impacts, and associated coping strategies in Musina from 1991 to 2023. Adopting a mixed-method approach, the research integrated quantitative climate data from the Copernicus Climate Data Store with qualitative insights gathered through semi-structured questionnaires administered to local community members. Drought frequency and distribution were examined using rainfall anomalies, temperature trends, and the Standardized Precipitation Evapotranspiration Index (SPEI). With more than 15 drought events recorded between 1991 and 2023, the findings indicate that Musina experiences recurrent drought episodes including major drought events in 1991–1992 and 2015–2016, characterized by rising temperatures that intensify evapotranspiration and exacerbate water scarcity. SPEI results highlight repeated drought conditions over the study period, with over 14 identified episodes and variable recovery periods, while notably negative values during major events (1991–1992 and 2015–2016) reflect severe moisture deficits associated with elevated temperatures. These climatic shifts have significantly undermined agricultural productivity, livestock health, and household water security, thereby threatening overall livelihood stability. More than 50% of the households experienced water shortages, with 17% reporting crop losses and 17% livestock mortality. While the study identified various coping mechanisms—such as reliance on boreholes, government water aid, conservation agriculture, and livelihood diversification—community vulnerability remains high due to limited water infrastructure and ongoing climate variability. These results contribute to efforts to reduce localized drought risk by providing a detailed understanding of drought dynamics and the effectiveness of community-based adaptation strategies in Musina. Full article

Climate change and the increasing frequency of extreme temperatures pose severe threats to global agricultural productivity, making the breeding of water-efficient crops a critical imperative. Originating from arid regions, foxtail millet serves as an ideal C4 model crop for elucidating plant adaptations to water deficits. Unlike previous reviews that often isolate genomic features from physiological responses, this review constructs an explicit conceptual framework integrating cross-scale defense mechanisms—mechanistically linking molecular signal transduction and post-transcriptional regulation to cellular homeostasis and field-scale yield stability. We first detail the developmental stage-specific physiological penalties of water stress and dissect proactive water-conservation strategies, including stomatal anatomical optimization, root-carbon reallocation, and dynamic rhizosphere remodeling. At the genetic level, we highlight the application of dynamic quantitative trait loci (QTL) mapping, which transcends the static limitations of conventional QTLs by capturing the spatiotemporal evolution of drought-tolerance traits across distinct developmental nodes. To bridge the gap between intrinsic genetic potential and field application, we spotlight the emerging integration of machine learning-assisted breeding and genomic prediction for the efficient evaluation of superior germplasms. Across this framework, several persistent gaps emerge: most drought-responsive genes identified in foxtail millet remain at the level of expression association without functional validation; dynamic QTL analysis remains underutilized relative to its capacity to resolve reproductive-stage drought tolerance; and ML-based genomic prediction, though demonstrated in this species, has not been integrated into operational breeding. Closing these gaps will require connecting high-throughput field phenotyping to genomic selection and deploying functionally validated editing targets in genetic backgrounds relevant to dryland production. Full article

Water use efficiency (WUE) is a key indicator of carbon–water coupling in arid and semi-arid ecosystems. This study focuses on Ningxia and its grape-growing areas (2001–2024), integrating multi-source remote sensing with trend analysis, coefficient of variation, Hurst exponent, and geographic detector to examine WUE spatiotemporal evolution, stability, sustainability, and driving mechanisms: (1) The spatial distributions of high-value gross primary productivity (GPP), evapotranspiration (ET), and WUE were highly consistent across both Ningxia and its grape-growing regions; annual mean WUE increased at rates of 0.0059 and 0.0058 g C·m⁻²·mm⁻¹·a⁻¹, with a decrease-then-increase pattern covering 53.52% and 41.67% of the areas; (2) In Ningxia, ecosystem WUE is predominantly characterized by low-to-moderate volatility, accounting for approximately 89.37% of the area, and its sustainability is mainly represented by sustainability and decrease then increase pattern, covering about 53.06%. In the grape-growing regions, low-to-moderate volatility also dominates, accounting for about 84.25%, while the sustainability level is slightly lower than the average level of the whole Ningxia region; (3) The Normalized difference vegetation index (NDVI) and leaf area index (LAI) constitute the primary factors governing the spatial heterogeneity of WUE across Ningxia and grape-growing zones, with respective q-values of 0.73 and 0.91, whereas the human footprint (PopuFopr) and temperature (Tem) exert the lowest explanatory capacity for WUE spatial differentiation in both regions, with q-values of 0.14 and 0.18; (4) The interaction between NDVI and Tem exerted the strongest influence on WUE in Ningxia, with a q-value of 0.86, whereas the interaction between LAI and precipitation (Pre) played the dominant role in the grape-growing regions, achieving a q-value of 0.93. This study clarifies the synergistic impacts of climate, vegetation, and human activities on regional ecosystem WUE, which delivers theoretical support for refined water resource regulation and climate adaptation frameworks in arid regions, while also supporting the development of targeted ecological restoration and irrigation strategies. Full article

Non-grain production of cultivated land has increasingly affected regional food security and the stability of agricultural ecosystems. In traditional rice-producing regions, changes associated with non-rice cultivation, fallow land, rice-fishery integrated farming, and intensive agricultural management are reshaping the spatial structure of paddy landscapes. To identify the long-term spatiotemporal evolution of paddy systems, this study investigated Northern Jiangxi, China, using Landsat surface reflectance imagery from 2000, 2005, 2010, 2015, and 2020 on the Google Earth Engine (GEE) platform. The Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) were used to construct a phenology-based Flooding Frequency (FF) indicator. Based on the annual frequency with which pixels satisfied the condition LSWI > EVI, cultivated land was classified into three categories: non-flooded cropland, standard rice paddy, and high-frequency flooded cropland. In this study, non-flooded cropland was used as an indicator of potential non-rice cultivation rather than as direct evidence of confirmed non-grain production. Landscape metrics, transition matrices, gravity center migration, standard deviation ellipses, and geographically weighted regression (GWR) were then used to examine paddy landscape dynamics, fragmentation patterns, and county-level spatial associations with socioeconomic factors. The results suggest that the paddy system in Northern Jiangxi experienced marked stage-based fluctuations between 2000 and 2020. Standard rice paddy recovered during 2005–2010, whereas non-flooded cropland expanded considerably during 2010–2015, accompanied by intensified paddy landscape fragmentation. Non-flooded cropland was mainly distributed around urban fringes, transport corridors, and some hilly margins. Standard rice paddy was concentrated in traditional grain-producing areas, including the Poyang Lake Plain and the Gan-Fu Plain. High-frequency flooded cropland was primarily located in low-lying lake areas, where its dynamics were likely associated with rice-fishery integrated farming, continuous irrigation, and hydrological fluctuations. Landscape metrics showed that the largest patch index and mean patch size of standard rice paddy declined after 2010, indicating reduced spatial continuity of core paddy fields. The GWR analysis provided auxiliary evidence that total population, per capita gross domestic product (GDP), and urbanization rate were spatially associated with changes in non-flooded cropland at the county level; however, the results should be interpreted as exploratory associations rather than causal mechanisms. Overall, paddy landscape change in Northern Jiangxi was expressed not only through changes in cultivated land area, but also through the reorganization of paddy function, spatial continuity, and land-use intensity. Future cropland protection should therefore move beyond area-based control toward integrated management of quantity, quality, function, and spatial configuration. Future research should further verify these findings using dynamic cropland boundaries, higher-resolution imagery, and more detailed socioeconomic data. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline models, which limits their generalization capability. To address these challenges, we propose a novel spatial semantic-guided online learning framework. Specifically, we first compute the spatial semantic similarity between urban regions using points of interest. Based on this, we then introduce a contrastive learning objective guided by this similarity during training. This design aims to enhance the model’s ability to capture both the similarities and discrepancies among regions. During the prediction process, an iterative online learning strategy is employed to adapt to dynamically changing crime patterns. By continuously fine-tuning the model with streaming data, the proposed framework improves robustness and generalization under non-stationary crime spatiotemporal distributions. Finally, extensive experiments on real-world crime datasets indicate the effectiveness and stability of our proposed approach. Full article

The Dongping Lake Basin (DLB) serves as a critical water regulation and supply zone for the South-to-North Water Diversion Project in China. Understanding the coupling effects and influence mechanisms between ecosystem services is essential for regional ecological restoration and sustainable development. This study employed the Coupling Coordination Degree (CCD) model, Random Forest, and Geodetector. We analyzed the spatiotemporal characteristics and driving factors of the relationship between carbon storage and water yield in the DLB from 1990 to 2020. The results showed that: (1) Carbon storage and water yield exhibited a pronounced spatial mismatch. This was generally characterized by a pattern of high in the eastern/northeastern regions and low in the west/southwest. (2) The overall coordination between carbon storage and water yield remained at a medium-to-low level. Temporally, the CCD followed a trajectory of initial stability, abrupt decline post-2000, and subsequent low-level stagnation. Spatially, the CCD presented an agglomeration gradient of “high in the northeast and low in the southwest”. It also exhibited a significant positive correlation with rising elevation, peaking in mid-to-high altitude zones. Furthermore, the overall coupling relationship showed a continuous degradation trend, heavily concentrated in the southwestern region. (3) Land use type and topographic slope were the primary driving factors shaping the CCD pattern. However, the synergistic interaction between precipitation and soil sand content demonstrated the strongest spatial explanatory power. This underscores the necessity of adapting localized management to specific environmental conditions. This study provides scientific support for carbon sink enhancement and water resource management in lake basins, thereby mitigating potential negative impacts on human well-being. Full article

Posture transition behavior in sows approaching parturition provides an important physiological cue for farrowing prediction. However, manual monitoring is time-consuming, labor-intensive and difficult to sustain under nighttime production conditions, while existing machine vision approaches remain limited in their ability to represent continuous posture transitions in complex farm environments. Here, we propose an event-level posture transition recognition framework that integrates YOLOv11n with an RGB–Flow cross-modal temporal network. YOLOv11n is first used to detect basic sow postures at the frame level, after which candidate transition events are automatically generated and refined according to temporal state changes. For each event segment, RGB appearance features and optical-flow motion features are extracted to construct dual-branch spatio-temporal representations. We further develop a multi-scale cross-modal attention temporal network (MS-CMATNet) for event-level behavior classification. The network captures local temporal dynamics through a multi-scale module, enhances interactions between RGB and Flow representations through cross-modal attention, and improves feature discriminability and stability by incorporating temporal–channel attention blocks (TCBAM) and an auxiliary cross-modal consistency loss (AuxCross). Experiments show that MS-CMATNet achieves an Accuracy of 88.14%, a Macro-Recall of 84.04%, and a Weighted-F1 score of 87.66% under the fixed training/validation split, outperforming the compared machine learning models, deep temporal models, and representative temporal and cross-modal baselines. Repeated stratified cross-validation and paired t-tests further confirm that MS-CMATNet achieves statistically reliable improvements over most compared baselines, particularly in Macro-F1 and Weighted-F1. These findings demonstrate the potential of the proposed framework for automated farrowing prediction in smart livestock farming. Full article

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of Statistics compiled using mobile phone data. The objective is to identify processes of growth, persistence, and spatial intensification using a geospatial methodology based on the Space–Time Cube (STC) and Emerging Hot Spot Analysis (EHSA). The analysis covers the 1000 municipalities with the highest cumulative volume of European tourists, which account for most of the flows recorded during the period. The results show positive and statistically significant temporal trends in 911 municipalities, although the formation of persistent spatial clusters is considerably less widespread. EHSA identified 48 municipalities classified as hot spots when applying a one-month temporal neighborhood and 77 when using a three-month configuration. The two classifications showed an observed agreement of 96.0% and, for the four shared categories, a Cohen’s kappa coefficient of 0.660. The post-pandemic recovery in tourism did not, therefore, result in a homogeneous territorial consolidation of stable spatial patterns. We identify persistent hubs, areas undergoing intensification, and destinations with episodic behavior, located primarily in metropolitan, coastal, and island areas. The main contribution of the study lies in the development of a reproducible workflow based on the STC–EHSA integration, capable of distinguishing between temporal growth, persistence, intensification, and spatial intermittency, and of evaluating the stability of the results under different temporal neighborhood configurations. Full article

To address the engineering challenges of asymmetric large surrounding rock deformation and roadway support failure of deep gob-side entry retaining (GER) in the top-coal roadway, the progressive surrounding rock instability mechanism and fracture spatiotemporal evolution characteristics are revealed via theoretical analysis and universal distinct element code (UDEC) Trigon discrete element simulation. Results show that the top coal first undergoes bed separation and tensile failure, followed by backfill corner crushing and bearing capacity loss, which ultimately induces roadway support failure. Using UDEC simulation and mechanical tests, the influences of top-coal thickness, key block B length, backfill performance, and roadway support mode on roadway support stability are systematically clarified. Results indicate that keeping full top coal within the reinforcement zone, reducing key block B length, adopting a backfill width-to-height ratio of 0.45–0.8, a water–cement ratio of 1.5:1, and combining synergistic anchoring with delayed reinforced support can reduce the risk of roadway support failure. An optimized support scheme for the entry is proposed and field-implemented. Monitoring shows that the backfill has a smooth surface; reinforcement ladder beams and steel mesh have no fracture; coal pillar peak stress reaches 5.95 MPa; coal rib bolt load (178 kN) is significantly higher than that in the backfill section (115 kN); and the backfill adapts well to roof rotation and subsidence. The results support the feasibility of the proposed control scheme under the studied geological and engineering conditions and may provide a useful reference for similar GER projects. Full article

Forest transition is a defining feature of global terrestrial ecosystem change. Systematically identifying and quantitatively assessing its eco-environmental effects provides a critical scientific foundation for regional ecological conservation and territorial spatial governance. Here, we take the terrestrial area of Hainan Island from 1988 to 2020 as the study area. We examine the spatiotemporal heterogeneity of the eco-environmental effects of forest transition and decompose the associated contributions. The principal findings are as follows: The ERI displays a persistent concentric pattern of low values in the interior and high values in the periphery. High-ESV areas largely coincide with the concentrated distribution of natural forests in the central and southern parts of the island. The per-unit-area ESV of natural forests is significantly higher than that of plantations. GTWR results demonstrate that forest quantity, structure and quality all exert stable inhibitory effects on ecological risk. The structural factor exhibits the strongest effect and the greatest temporal stability. This study provides a quantitative basis for the refined management and conservation of the Hainan Tropical Rainforest National Park and for province-wide ecological restoration planning, and also offers a framework for assessing the eco-environmental effects of forest transition in tropical regions. Full article

The water supply–demand pattern directly affects the stability of ecosystems and the level of agricultural sustainable development. Based on the theoretical framework of water supply–demand ecosystem services, this study took 1990–2020 as the research period, and integrated the water yield module of the InVEST model, urban stormwater retention module, standard deviational ellipse method, and spatial interpolation to accurately calculate regional water yield, water demand, water supply–demand ratio, and gravity center shift. The aim was to reveal the spatiotemporal evolution and mechanism of water budget in the Sanjiang Plain under large-scale agricultural expansion. The results showed the following: (1) From 1990 to 2020, the annual water yield in the Sanjiang Plain fluctuated from 12.16 billion m³/yr to 23.28 billion m³/yr, with a spatial pattern of “high in the northwest and southeast, low in the central Songhua River”, and high-value areas were highly coincident with natural ecological land. (2) The multi-year average water demand was approximately 13.0 billion m³/yr, experiencing three phases, “slow growth–rapid rise–stable slowdown”, showing a distribution characteristic of “concentrated along rivers and high in urban areas”. (3) Water surplus and deficit areas maintained a rigid characteristic of “highland supply, lowland consumption” for a long time. The gravity center of deficit areas rotated counterclockwise from 132.35° E, 46.55° N to 132.58° E, 46.78° N, with an annual migration rate of 10–15 km/a. The gravity center of surplus areas rotated clockwise from 133.22° E, 47.45° N to 132.85° E, 46.92° N, with an annual migration rate of 15–20 km/a. (4) From 1990 to 2015, the regional water supply–demand pattern remained relatively stable. However, a pronounced transition occurred during 2015–2020, when surplus areas sharply decreased from 49% to 27%, while conflict zones expanded rapidly, indicating an evident deterioration of regional water security. Full article

Through-going faults represent critical geological hazards that threaten the long-term operational safety of arch dams. Conventional studies predominantly rely on homogeneous material assumptions and static analysis, neglecting two essential characteristics of natural faults: (1) the discrete, localized distribution of intact rock blocks within fractured zones, and (2) degradation of the mechanical properties of faults with time during the service life of arch dams. These limitations will unavoidably introduce systematic errors into the safety state judgment of operating arch dams. To address these limitations, this paper develops an enhanced constitutive model that couples three key mechanisms: confining pressure strengthening with burial depth, local reinforcement from discrete random rock blocks, and fatigue damage accumulation under cyclic water level fluctuations. The model is implemented via ABAQUS UMAT subroutine development, enabling three-dimensional spatiotemporal evolution simulation of fault mechanical parameters. Furthermore, a multi-index comprehensive evaluation framework is established by integrating normalized dam stress state and abutment strength reduction stability, providing a holistic assessment of arch dam performance throughout its service life. Applied to a practical pumped storage arch dam project, the results demonstrate that: (1) Fault damage evolution is characterized by prominent spatial heterogeneity. The results reveal that the fault damage coefficient at a burial depth of 0 m after 40,000 days of service is nearly twice that of the fault at a burial depth of 270 m. (2) The abutment safety factor decreases from 2.36 to 1.17 after 40,000 days of cyclic operation, entering a critical warning state at approximately 28,000 days. This study provides refined characterization methods and quantitative assessment tools for the long-term safety evaluation of fault-controlled arch dams, with direct implications for engineering risk prevention and reinforcement design. Full article

Accurate wind power forecasting is imperative for ensuring grid stability and facilitating the large-scale integration of renewable energy—both central pillars of the global energy transition and the Dual Carbon strategic goals. However, existing methods often fail to fully capture the spatial heterogeneity and interdependencies among individual turbines, limiting their effectiveness for sustainable grid operation. To address this gap, this paper proposes an ultra-short-term wind power forecasting framework that incorporates explicit multi-dimensional spatial features. At the feature level, a 12-dimensional spatial feature system is constructed to quantify the microscale topology of wind farms. These static spatial attributes are seamlessly fused with dynamic temporal data using a dimensionality-balance factor strategy. Finally, a hybrid deep learning network comprising a multi-scale CNN, a multi-layer BiLSTM, and a multi-head self-attention mechanism is developed to capture complex spatiotemporal patterns. Experimental results on three real-world datasets show that the proposed method significantly outperforms baseline models, reducing the Mean Absolute Percentage Error by up to 11.09% and improving the coefficient of determination R² up to 0.9120. By improving forecast accuracy and robustness, the method directly supports more reliable grid dispatching, reduces curtailment of wind energy, and thus contributes to the sustainable utilization of renewable resources. These findings demonstrate that incorporating explicit spatial correlation effectively enhances the accuracy and robustness of ultra-short-term wind power forecasting, providing robust decision support for power grid dispatching and advancing the sustainability of modern power systems. Full article