Search Results (908)

The prosperity of the Maritime Spice Route in China during the Song–Yuan dynasties (960–1368) propelled Quanzhou into a global hub for maritime trade and cultural integration. A large number of Muslims settled in Quanzhou via maritime routes, living and multiplying over generations—their journey fully documenting the localization trajectory of the immigrant group. To explore the relationship between the evolution of their traditional residence types and immigration localization, this study takes 185 “one bright hall and two dark rooms” traditional residences of the Ding’s Hui ethnic group in Chendai as an example, constructing a “4 × 6” matrix framework via the spatiotemporal biaxial coordinate classification method, with an integrated application of statistics, field surveying and mapping, Space Syntax, and genealogical document analysis. Results reveal that 15 of the 24 theoretical residence types are effectively preserved, forming a “prototype + combined type” evolutionary chain. Residence-type acceptance presents distinct traits, Class A as the foundational form, Class D as the mainstream, and Classes B and C as transitional types, a pattern reflecting the comprehensive influence of construction land conditions, living patterns, and local construction concepts on residence-type selection. Significant disparities in average connectivity between the central courtyard and various core public spaces embody the multi-branch small-family cohabitation mode and verify the localization development trajectory of residential space. The evolution of this residence-type system is confirmed to feature three core characteristics—nonlinearity, integrated and diversified fusion, and spatial constraint—and proposes preservation strategies for double-standard dimensional, multicultural and identifiability qualities, which provide a scientific reference for the protection and renewal of architectural heritage in Hui ethnic communities and similar immigrant settlements on China’s southeast coast. Full article

By integrating multi-source data, this study systematically analyzes the evolution of land use structure, spatiotemporal differentiation characteristics of Ecosystem Service Value (ESV), and core driving mechanisms in the Huaihe River Ecological Economic Belt (HREEB) in eastern China from 2000 to 2020, based on the ESV equivalent accounting model and XGBoost-SHAP coupled framework. The main results are as follows: (1) The land use structure is dominated by cropland, construction land, and forest land. Over the 20-year period, cropland was continuously converted out, primarily transforming into construction land and forest land, while other land types remained relatively stable. (2) Temporally, the total ESV showed a fluctuating downward trend, first increasing and then decreasing from 2000 to 2020. Spatially, the ESV exhibited a corridor effect of “decreasing from the river channel center to both banks”. High-value areas were concentrated in the eastern river–sea linkage zone and the central-western inland rising zone, while extremely low-value areas in 2020 were located in the northern Huaihai Economic Zone (with dense construction land), indicating an overall medium service level. (3) The evolution of ESV was driven by both natural and human factors: among natural factors, water coverage, elevation, and slope had positive effects, while high temperature had an inhibitory effect; among human–economic factors, population density showed an “increase first and then decrease” effect, and urban expansion significantly weakened ESV in the later period. The spatial differentiation presented a pattern of “natural background support in the upper reaches and socioeconomic intervention in the lower reaches”. This study provides a scientific basis for the optimization of territorial space and ecological protection and restoration in the Huaihe River Ecological Economic Belt, and also offers a replicable research paradigm for ecosystem service management in similar river basin-type regions. Full article

Soil drought impact on irrigation areas is not merely a single reduction in crop yields, but rather a chain reaction that occurs from multiple dimensions including crop growth, water resource allocation, soil environment, operation of irrigation area projects, agricultural economy and ecosystems. The changing trend and mutation characteristics of soil drought are unclear in the People’s Victory Canal Irrigation District (PVCID). The Standardized Soil Moisture Index (SSMI) and the breaks for additive seasons and trend (BFAST) decomposition algorithm were adopted, combined with the eXtreme Gradient Boosting (XGBoost) model, to explore spatio-temporal evolution characteristics, driving factors and response to meteorological drought of soil drought. During the research period, the area percentage of SSMI showing a downward trend was 97.30%. The most severe soil drought occurred in 2019. In addition, the optimal trivariate combination is precipitation, evapotranspiration, and air temperature. This study has clarified the spatio-temporal evolution laws and driving mechanisms of soil drought in the PVCID, providing an important theoretical basis for the early warning, prevention and control of soil drought and the adaptive management of the ecosystem. Full article

Rotating machinery serves as a critical component in various engineering systems, making accurate prediction of its Remaining Useful Life (RUL) essential for ensuring operational stability. To address the technical limitations of mainstream RUL prediction models comprehensively capturing spatial correlations among multiple sensors, this paper proposes a multi-graph-structured spatiotemporal feature fusion model for RUL prediction of rotating machinery. Breaking through the constraints of constructing a single correlation graph, the model first builds two distinct graphs—a prior correlation graph based on the structural mechanism of the rotating machinery and a similarity correlation graph derived from monitoring data distribution characteristics. These dual-perspective graphs collectively characterize the potential spatial dependencies among multiple sensors. Subsequently, a Graph Attention Network (GAT) is introduced to aggregate spatial features from both graphs, and a feature concatenation fusion strategy is adopted to achieve a comprehensive representation of the inter-sensor spatial dependencies. Finally, a Long Short-Term Memory (LSTM) network is employed to extract temporal evolution features from the operational data. The effective fusion of these spatial and temporal features enhances the model’s RUL prediction performance. Simulation experiments conducted on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset validated the robustness of the proposed method. Full article

Against the background of global climate change and intensified human activities, the Black Soil Region of Northeast China (BSRNC)—an ecologically fragile zone and critical grain-producing area—faces mounting pressures on ecosystem stability, productivity sustainability, and black soil conservation. Clarifying the spatiotemporal evolution characteristics of vegetation net primary productivity (NPP) and its associative patterns is crucial for ecological protection and sustainable land management in this region. Based on remote sensing, meteorological, topographic, soil and human activity data, this study employed the improved Carnegie–Ames–Stanford Approach (CASA) model to quantify vegetation NPP—an analytical approach that integrates the CASA model with tree-based machine learning and SHapley Additive exPlanations (SHAP) interpretation. By further combining multiple spatial analysis methods, it characterizes the spatiotemporal dynamics of NPP in the black soil region and innovatively compares seven machine learning algorithms to select the optimal Light Gradient Boosting Machine (LightGBM) model for quantifying the contributions of drivers in this region with high spatial heterogeneity. The results showed that the average annual vegetation NPP in the BSRNC was 301.18 g C·m⁻², exhibiting a fluctuating upward trend at a rate of 1.55 g C·m⁻²·a⁻¹ over the 24-year period. Spatially, NPP displayed significant heterogeneity, climbing gradually from the region’s southwest to its northeast quadrant, with over 90% of the territory showing an upward trajectory. Overall NPP reached a high stability level, though the western and southern regions faced higher degradation risks, and the entire region presented a weak anti-persistent trend. Precipitation was the dominant factor associated with NPP variations, followed by soil moisture, while soil pH had the smallest correlative contribution (0.38). Land-use changes were positively associated with NPP growth, and the interaction of multiple factors showed a significant associative pattern with NPP variations. This study clarifies the spatiotemporal patterns and associative patterns of vegetation NPP in the BSRNC with a 24-year-long time series, and its incremental findings on the coupling of land-use change and multi-factor interaction provide a targeted scientific basis for ecological protection, restoration policies and sustainable management of black soil resources. Full article

Situated at the core of the Asian arid zone, the Tarim River Basin (TRB) serves as a critical indicator of regional hydroclimatic responses to global warming. Utilizing 27 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices, this study analyzes the spatiotemporal evolution of climate extremes in the TRB from 1960 to 2022 and explores their correlations with primary large-scale atmospheric circulation factors. The results indicate that, at the temporal scale, extreme warm indices (TX90P, TN90P, SU25, TR20) and most extreme precipitation indices (except for CDD) exhibited increasing trends, accompanied by pronounced abrupt changes and periodic characteristics. The changes were characterized by stronger warming at low temperatures than at high temperatures, greater nighttime warming than daytime warming, and larger increases in warm days than cold days. Extreme temperature and precipitation indices underwent abrupt changes in the mid-to-late 1990s and 1980s, respectively. The former exhibits pronounced “cold-warm” oscillations at 10–15-year and 25–35-year scales, while the latter displays distinct “wet-dry” cyclic alternations at 8–9-year and 30–32-year scales. Spatially, extreme temperature indices showed consistent warming across most stations. In contrast, the change trends of extreme precipitation indices displayed obvious spatial heterogeneity, with growth rates generally decreasing from west to east. Further analyses demonstrate that most extreme climate indices exhibit significant linear correlations with the AMO, PDO, NAO, and NOI. Notably, the AMO emerges as the dominant driver of variations in both extreme temperature and precipitation. In the context of accelerated global warming, these insights are pivotal for enhancing regional climate risk management and water resource adaptability. Full article

As urbanization accelerates in China, the protection and renewal of historical and cultural districts have become key issues. The Beiyuanmen Historical and Cultural District in Xi’an, with its long history and cultural significance, is a prime example. This study uses Beiyuanmen as a case study, employing Baidu heatmap data, Point of Interest (POI) data, and space syntax theory to examine the district’s spatial layout, crowd activity distribution, and functional structure. The purpose is to quantify its vitality and spatial characteristics, providing a basis for scientific planning. The methods involve analyzing spatiotemporal crowd activity intensity via heatmaps, assessing street network configuration through integration and choice values, and comparing POI data from 2014 and 2024 to track functional evolution. The research identifies the distinctive spatiotemporal patterns of crowd activity, revealing not only a southeast concentration correlated with urban functions but also distinct diurnal rhythms—a bimodal pattern on weekdays versus a sustained leisure-oriented pattern on weekends, underscoring a functional shift. It also explores the directed permeability of the spatial structure, identifying streets like Miaohou Street that form a highly integrated “cross-shaped backbone”. Analysis of POI data shows that commercial services dominate and have expanded outward, with the growth rate of POI density in the control area surpassing that of the core, indicating a trend of functional diffusion. Finally, the study highlights Miaohou Street, Beiguangji Street, Damai Market Street, Beiyuanmen, and Sajinqiao as key areas, and it concludes by proposing integrated planning recommendations that focus on four strategic aspects—spatial and crowd activity distribution management, functional zoning guidance, enhancement of public services and cultural displays, and alignment with broader urban policies—for prioritized landscape enhancement and tourism. Full article

Resource-based cities in the Yellow River Basin are important pillars of national energy security and regional coordinated development, and their green transformation is closely related to the overall strategy of ecological protection and high-quality development in the basin. This study takes 34 resource-based cities within the basin as the research objects and employs a combination of methods, including the Super Slacks-Based Measure (SBM) model, the Malmquist–Luenberger index, the standard deviational ellipse, the Dagum Gini coefficient, and the geographical detector, to systematically analyze the spatio-temporal evolution characteristics and driving mechanisms of green development efficiency from 2012 to 2022. The results indicate that: (1) green development efficiency shows an overall upward trend, forming a pattern of leading performance in the lower reaches, lagging development in the middle reaches, and accelerated catching-up in the upper reaches, with efficiency improvements jointly driven by technical efficiency enhancement and technological progress; (2) the gravity center of efficiency shifts southwestward overall, and interregional disparities constitute the main source of overall differences; (3) economic development level, science and technology investment, fiscal expenditure, and energy intensity are the key driving factors, with significantly strengthened interactions among multiple factors. From the dual perspectives of basin location and the urban life cycle, this study constructs a multidimensional analytical framework that provides a reference for categorized regulation and coordinated regional governance of resource-based cities. Full article

Land subsidence (LS) is a major global geo-environmental issue that profoundly affects the suitability and safety of land use planning (LUP). However, existing LUP systems generally neglect the dynamic evolution of LS and lack a systematic framework for assessing conflicts between land use and subsidence. To address this gap, this study develops an integrated evaluation framework that combines SBAS-InSAR, GeoDetector, and a spatial conflict detection model. A total of 166 Sentinel-1A images acquired from 2017 to 2024 were processed using SBAS-InSAR to derive the spatiotemporal characteristics of LS. GeoDetector was subsequently applied to identify the dominant driving factors and their interactions. A sensitivity classification scheme for current land use (CLU) and LUP types with respect to LS hazards was then developed, and a spatial conflict detection model was constructed to delineate conflict zones and quantify conflict intensity. Using Huainan City as a case study, the results show the following: (1) from 2017 to 2024, LS was generally characterized by slight or negligible subsidence, with severe subsidence mainly concentrated in coal mining areas; ongoing and recently suspended mines exhibited pronounced LS, whereas early-closed and unmined areas showed an overall uplift trend. (2) LS in Huainan was primarily driven by soil type, annual rainfall, and mining activities, and two-factor interactions generally exhibited enhancement effects. (3) Compared with CLU, LUP has, to some extent, incorporated LS risk considerations and implemented corresponding mitigation measures, although certain areas still insufficiently account for LS risks. (4) The proposed framework demonstrates strong rationality and applicability in LS monitoring, driving factor identification, and spatial conflict assessment, providing scientific support for LS risk management and land use spatial optimization. Full article

Grasslands, covering over 40% of terrestrial land surfaces, play a critical role in regional water cycling through their greening processes. However, the decoupling mechanisms between grassland greening and water availability (WA) changes across the Northern Hemisphere, along with their future trajectories, remain poorly understood. Here, we integrated multi-source satellite observations with CMIP6 model ensembles to systematically assess the spatiotemporal evolution and trend divergence of leaf area index (LAI) and WA across Northern Hemisphere grasslands from 2000 to 2100. Our results showed that grassland LAI exhibited sustained growth during 2000–2020, with 55.28% of regions showing significant increasing trends. However, 73.67% of grassland regions experienced declining WA during the historical period, revealing widespread decoupling between grassland greening and water deficit. Future scenario projections indicated a reversal to increasing WA trends, with 57.51% of regions showing significant increases under SSP5–8.5. Furthermore, 61.87% of grasslands exhibited greening-driven drying (GDD) characteristics during the historical period, while greening-driven wetting (GDW) regions were projected to expand to 72.44% in the future. Analysis along aridity gradients revealed that humid zones contributed most prominently to LAI and WA changes. Mechanistic decomposition demonstrated that grassland WA changes shifted from precipitation-dominated control (53.60%) in the historical period toward a regime jointly governed by precipitation dominance and coupled precipitation–evapotranspiration drivers in the future. Concurrently, the dominant factor controlling grassland greening transitioned from vapor-pressure deficit (VPD) to temperature (TEM) control. Additionally, driving factors exhibited pronounced differentiation patterns along aridity gradients during the historical phase: arid zones were dominated by soil moisture (SM) and semi-arid zones displayed dual control by SM and VPD, while humid zones were governed by coupled TEM-VPD regulation. This study reveals the divergent trends between grassland greening and WA and unravels their driving mechanisms, offering important scientific evidence for formulating regionally differentiated ecological water resource management strategies. Full article

Strategic Emerging Industries (SEIs) are a critical driver of China’s green transition and high-quality development; however, the Ecological and Environmental Effects of Firm Entry and Exit remain insufficiently explored. Based on micro-level data of Chinese SEI enterprises from 2009 to 2023, this study employs kernel density estimation and a panel fixed-effects model to construct a five-dimensional ecological environment evaluation system under the PSDRP framework and to examine the spatio-temporal evolution characteristics of Firm Entry and Exit and their Ecological and Environmental Effects. The results indicate that SEI enterprises exhibit agglomeration in the Eastern Region and gradual diffusion toward the Western Region, with exit activities showing higher spatial concentration. Firm Entry generates stage-specific constraining effects on the ecological environment, whereas Firm Exit alleviates ecological Pressure and enhances Resilience. Significant regional heterogeneity is observed, forming a pattern of optimization in the Eastern Region, improvement in the Central and Western Regions, and greater adjustment challenges in the Northeast Region. This study provides empirical evidence for differentiated and coordinated industrial–environmental policy design. Full article

The prevalence of high-rack warehouses and large-space facilities with high ceilings poses significant challenges to traditional automatic sprinkler systems, which often exhibit activation delays and limited suppression efficacy. This study investigates the spatio-temporal evolution and distribution characteristics of fire-induced thermal smoke flow through a hybrid approach combining full-scale fire experiments and numerical simulations. A physical hypothesis is proposed: the ceiling temperature field approximately follows a two-dimensional Gaussian distribution. Through parametric numerical simulations under varied ambient temperatures, fire identification criteria were calibrated, encompassing a sustained increase in the average temperature rise within high-temperature zones, the attainment of a predefined threshold, and the spatial stabilization of the Gaussian distribution center. Subsequently, a precise algorithm for rapid fire identification and source localization was developed. Experimental validation demonstrates that the proposed algorithm significantly outperforms traditional passive-activation closed sprinklers, advancing fire detection by 46–67 s. Furthermore, the fire source localization error is maintained within half of the sprinkler spacing. The algorithm also exhibits robust environmental adaptability and generalizability across a wide ambient temperature range, providing a technical foundation for active-actuation fire suppression. Full article

Tropical cyclones are among the most destructive meteorological systems on Earth. Accurate track forecasting of tropical cyclones remains a core challenge in atmospheric science, and it is of great significance for disaster prevention and mitigation. This study targets the critical limitations of existing tropical cyclone track forecasting models: the insufficient ability to extract non-linear spatiotemporal features from 3D atmospheric circulation fields and the long-standing bottlenecks in multi-source heterogeneous meteorological data fusion. To address these issues, we propose a Dual-Stream Gated Fusion Network (DGF-Net), a high-precision track forecasting method tailored to the Northwest Pacific basin. The proposed framework takes the Best Track dataset and ERA5 Reanalysis Dataset as primary inputs: a Bidirectional Gated Recurrent Unit (Bi-GRU) is adopted to capture the temporal evolution characteristics of 2D tropical cyclone trajectory sequences, and a SpatioTemporal Convolutional Gated Recurrent Unit (STConvGRU) is used to extract complex non-linear features from 3D atmospheric environmental fields. Then, a multimodal fusion module integrating gating and attention mechanism is constructed to achieve deep fusion of cross-dimensional features, which effectively mines the intrinsic physical correlations between tropical cyclone track evolution and environmental driving factors. Comparative experiments based on historical observational datasets of the Northwest Pacific show that DGF-Net achieves superior forecasting performance, with the 6 h, 12 h, and 24 h Great Circle Distance (GCD) errors of 35.62 km, 43.53 km, and 135.49 km, respectively. The results significantly outperform mainstream baseline models, which validates the effectiveness of DGF-Net in feature extraction and multimodal fusion and provides solid technical support for tropical cyclone disaster prevention and operational decision-making. Full article

The construction of high-speed railways (HSRs) is the core engine for promoting the economic integration and spatial structure optimization of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Changes in land use along HSR corridors are inextricably linked to the efficacy of regional coordinated development and ecological protection initiatives, as well as the realization of regional sustainable development. Nevertheless, past relevant studies exhibit prominent limitations. First, the lack of effective methodologies for the intuitive comparison of multiple research subjects makes it difficult to accurately portray the differential characteristics of land use across various HSR routes. Second, the insufficient comprehensive analysis of the dynamic evolution of landscape patterns along routes, coupled with the absence of intuitive spatial visualization expressions, fails to explicitly reveal the spatiotemporal differentiation of landscape fragmentation, which hinders sustainable land resource utilization and ecological protection. To address these gaps, this study introduces the intensity comparison map and the comprehensive index map of landscape fragmentation and takes six typical HSRs in the GBA to conduct an intuitive comparative analysis of land use changes along multiple routes. Results show that land use evolution along HSRs presents distinct phased characteristics, with construction land acting as the core driving factor. Its proportion increases continuously, while the proportions of cultivated land and water bodies decline dramatically. Significant disparities exist in land use evolution across different HSR routes, which are closely associated with the natural and economic conditions of the traversed regions, reflecting the heterogeneous adaptability between individual routes and regional development dynamics. High landscape fragmentation areas are predominantly distributed in the transition zones between construction land and natural landscapes; fragmentation intensifies during the planning and construction phases and stabilizes or even diminishes along certain routes during the operation phase, with human activities identified as the pivotal influencing factor. This research deepens the understanding of the interaction mechanism between transportation infrastructure and land use changes in the GBA and provides a scientific basis for sustainable HSR construction planning, the rational utilization of land resources, and the coordinated advancement of ecological protection in the GBA and other similar regions worldwide, thus facilitating the sustainable development of high-density urban agglomerations globally. Full article

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article