Search Results (1,290)

Island settlement landscapes exhibit distinctive characteristics, and investigating their spatio–temporal differentiation features and formation mechanisms is crucial for effective landscape conservation. This study selected Qida Village, Beigang Village, and Jingsha Village in Fuzhou City, Fujian Province, China, as representative cases. It constructed an integrated evaluation framework termed “livelihood transformation–two dimensional expansion–three dimensional form” and systematically analyzed the spatio–temporal differentiation characteristics and driving mechanisms of island settlement landscapes under the context of livelihood transformation by integrating multi-source data. Research findings indicate that livelihood transformation significantly affects both the horizontal expansion and vertical evolution of settlement landscapes. Aquaculture-based villages demonstrate a high expansion rate (15.10%) and pronounced vertical differentiation (building height difference ratio of 13.30) due to industrial agglomeration. Tourism service-oriented villages, influenced by policy regulation, exhibit low architectural style heterogeneity (0.35) and a harmonized skyline. Villages experiencing significant out-migration show a high housing vacancy rate (64.70%) and reduced spatial compactness (0.13) due to population decline. The livelihood model drives landscape differentiation through the “population mobility–economic investment–land use” pathway, where capital accumulation and policy constraints emerge as key determinants of spatial form heterogeneity. This study provides a solid theoretical foundation and methodological support for the differentiated governance of island settlement landscapes. Full article

A methane-air premixed gas explosion is one of the most destructive disasters in the process of coal mining, and the dynamic coupling between the shock wave triggered by the explosion and the surrounding rock of the roadway can lead to the destabilization of the surrounding rock structure, the destruction of equipment, and casualties. The aim of this study is to systematically reveal the propagation characteristics of the blast wave, the spatial and temporal evolution of the wall load, and the damage mechanism of the surrounding rock by establishing a two-way fluid-solid coupling numerical model. Based on the Ansys Fluent fluid solver and Transient Structure module, a framework for the co-simulation of the fluid and solid domains has been constructed by adopting the standard $k-\epsilon$ turbulence model, finite-rate/eddy-dissipation (FR/ED) reaction model, and nonlinear finite-element theory, and by introducing a dynamic damage threshold criterion based on the Drucker–Prager and Mohr–Coulomb criteria. It is shown that methane concentration significantly affects the kinetic behavior of explosive shock wave propagation. Under chemical equivalence ratio conditions (9.5% methane), an ideal Chapman–Jouguet blast wave structure was formed, exhibiting the highest energy release efficiency. In contrast, lean ignition (7%) and rich ignition (12%) conditions resulted in lower efficiencies due to incomplete combustion or complex combustion patterns. In addition, the pressure time-history evolution of the tunnel enclosure wall after ignition triggering exhibits significant nonlinear dynamics, which can be divided into three phases: the initiation and turbulence development phase, the quasi-steady propagation phase, and the expansion and dissipation phase. Further analysis reveals that the closed end produces significant stress aggregation due to the interference of multiple reflected waves, while the open end increases the stress fluctuation due to turbulence effects. The spatial and temporal evolution of the strain field also follows a three-stage dynamic pattern: an initial strain-induced stage, a strain accumulation propagation stage, and a residual strain stabilization stage and the displacement is characterized by an initial phase of concentration followed by gradual expansion. This study not only deepens the understanding of methane-air premixed gas explosion and its interaction with the roadway’s surrounding rock, but also provides an important scientific basis and technical support for coal mine safety production. Full article

With the rapid development of urbanization and the economy in recent years, increased human activities along the Yinghe River in Fuyang City and industrial expansion have degraded the water quality. Various sewage discharges have elevated nitrogen and phosphorus levels in the water body, disrupting its original ecological balance and exacerbating environmental issues. Therefore, studying the water purification capacity of the Fuyang region is particularly important. Using the InVEST model, this paper analyzes temporal changes and spatial differences in water purification capacity by quantifying nitrogen and phosphorus retention. The results show the following: The water purification capacity of Fuyang exhibits a spatial pattern of higher effectiveness in the north and lower effectiveness in the southwest. This study represents the strength of water purification capacity as the sum of regional output of nitrogen and phosphorus nutrients, based on which different types of areas are divided into water purification capacity deficit areas and water purification capacity control areas, and then combined with the different impacts of different land use types on the regional water purification capacity, corresponding countermeasures are proposed to optimize the water purification capacity of Fuyang City. Full article

To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications. Full article

Jilin Province, an important commodity grain base in China, relies on groundwater resources for its agricultural development. The implementation of a series of policies, including agricultural subsidies and food security policies, has led to a rapid expansion of the sowing area in recent decades, resulting in an increase in agricultural water demand. This has had a significant impact on the groundwater system. It is therefore imperative to understand the dynamics of the groundwater to ensure the security of water resources, ecological security, and food security. An evaluation of the sustainability of groundwater resources in Jilin Province was conducted through a quantitative analysis of the reliability, resilience, and vulnerability of groundwater. This analysis was informed by the inversion of changes in groundwater reserves over a period of 249 months, commencing from 2002-04 to 2022-12. The inversion process utilized data from the Gravity Recovery and Climate Experiment (GRACE) gravity satellite and Global Land Data Assimilation System (GLDAS), offering a comprehensive view of the temporal dynamics of groundwater reserves in the region. The results indicated the following: (1) Groundwater storage (total amount of water below the surface) in Jilin Province exhibited an overall decreasing trend, with the highest groundwater level recorded in June and the lowest in September on a monthly basis. (2) Prior to September 2010, groundwater reserves were in surplus most of the time. From October 2010 to August 2018, however, they began to fluctuate between surplus and deficit states. Since September 2018, the reserves have been in a long-term deficit, showing an overall downward trend. (3) Prior to 2005, the groundwater system was at a high/extremely high level of sustainability. However, following 2011, it fell to a very low level of sustainability and has continued to deteriorate. (4) The maximum information coefficient and correlation analysis indicate that the sown area is the most significant factor contributing to the decline in the sustainability of the groundwater system. This study reveals the spatial and temporal distribution pattern and evolution trend of groundwater resources sustainability in Jilin Province, and provides theoretical and data support for regional groundwater resources protection and management. Full article

Ecotourism, as a resilient and sustainable form of tourism, plays an increasingly vital role in regional economic growth and ecological conservation, particularly in the face of challenges such as climate change and rapid urbanization. This study employs spatial-temporal analysis tools including GIS, Fragstats, and GeoDa to examine the dynamic evolution of ecotourism suitability levels (ESL) and landscape patterns (LP) in the Yangtze River Delta (YRD) from 2002 to 2022. By incorporating spatial autocorrelation analysis, the relationship between ESL and LP is investigated to assess the adaptive capacity of the regional ecotourism system. The results reveal the following: (1) Overall Trends: ESL in the YRD has generally increased over the past two decades, with expansions observed in both high and very low suitability areas, while areas of low suitability have contracted. (2) Spatial Patterns: Core cities such as Shanghai, Hangzhou, Nanjing, and Hefei exhibit high ESL; however, these areas also face intensified landscape fragmentation and decreased ecological connectivity. (3) Landscape Patterns: The region has experienced increasing landscape fragmentation and diversity, particularly in economically advanced zones, posing significant challenges to ecological resilience. (4) Spatial Clustering: Notable spatial clustering of ESL and LP indices is identified in highly urbanized areas, underscoring the necessity for adaptive landscape planning and flexible policy frameworks. This study provides empirical evidence and strategic recommendations to enhance the resilience and sustainability of ecotourism in rapidly urbanizing regions, supporting adaptive responses to crises and informed long-term decision-making. Full article

Citrus tristeza virus (CTV) is an important pathogen threatening the global citrus industry, but its evolution and transmission mechanism in wild citrus has not been clarified. Most of the existing studies are based on CTV-specific gene fragments, lacking genome-wide analysis. There is especially a lack of understanding of CTV transmission dynamics in wild citrus, which needs further investigation. In this study, wild citrus samples from three provinces of China were collected, virus genome data were obtained by high-throughput sequencing (HTS) technology and combined with public database data, and Bayesian phylogeographic inference was used to analyze virus composition characteristics in wild citrus, as well as the population genetic structure, temporal dynamic evolution, and spatial transmission mode of CTV. The results showed that Yunnan wild citrus samples contained the most abundant virus components, including CTV, Citrus Exocortis Viroid (CEVd), Citrus associated Ampelovirus 1 (CaAV-1), and Citrus Virus B (CiVB), while Jiangxi and Hunan samples only contained CTV and CEVd, with all samples showing mixed infection. Phylogenetic analysis showed that nine wild citrus CTV isolates were scattered in different evolutionary clades, and only 9.27% of genetic variation existed between the populations, while 90.72% of genetic variation existed within the populations, indicating little effect of geographic isolation on gene flow. The time to the most recent common ancestor (tMRCA) of CTV was estimated at 1360 CE, with subsequent divergence into two lineages, with population size stabilizing after a rapid increase in 1980–1990. Asia has been identified as the central source of CTV’s global spread, with key migration events including Asia to North America (1746), Asia to Oceania (1829), and Asia to South America (1965), coinciding with global maritime trade and the expansion of the citrus industry. Full article

Assessing habitat quality and quantifying human disturbance are fundamental prerequisites for ecological conservation. However, existing studies predominantly focus on single dimensions. There is an urgent need to integrate habitat quality and human disturbance, and quantify their spatially coupled coordination relationships to address the disconnect between them in current research. As a critical ecological reserve in southeastern China, Fujian Province faces threats of ecological degradation. This study employed the InVEST model to evaluate habitat quality in Fujian from 1980 to 2020, utilized a human disturbance index to quantify spatiotemporal patterns of anthropogenic activities, analyzed their changes using landscape indices, and applied coupling coordination analysis to examine their interrelationships. Machine learning and geographically weighted regression were further integrated to identify driving factors of coupling coordination patterns. The results showed that: (1) Habitat quality remained relatively high while human disturbance levels stayed low throughout 1980–2020, though both showed gradual deterioration over time, particularly during 2010–2020, with riverine and coastal eastern regions exhibiting the lowest habitat quality and highest disturbance levels. (2) Coupling coordination relationships predominantly exhibited synergy, with moderate imbalance zones concentrated in the eastern coastal areas. Temporally, regions with lower imbalance expanded significantly during 2010–2020. (3) Landscape metric analysis revealed declining dominance of high-quality habitat/low-disturbance/synergistic zones, contrasted by increased fragmentation of low-quality habitat/high-disturbance/imbalanced zones. (4) Socioeconomic factors exerted stronger influence on coupling coordination patterns than natural environmental variables, proximity to urban areas, road density, and nighttime light indices. Each driver displayed spatially variable positive/negative effects. The results enhance our understanding of human–nature sustainable development dynamics, urban expansion–ecological conservation trade-offs, and provide methodological insights for regional ecological management and achieving sustainable development goals. Full article

The impact of invasive alien species (IAS) is one of the direct factors causing global biodiversity decline and economic losses, and predicting the potential invasion risks of invasive species is crucial for developing prevention and control strategies. In recent years, an increasing number of studies have shown that invasive species undergo rapid shifts in climate niche in invaded areas. Accurately quantifying the dynamic shifts in the climate niche of invasive species in invaded areas is crucial for developing a more accurate framework for early warning of invasive species risks. Pomacea canaliculata is a freshwater snail found in South America and has become one of the most aggressive aquatic species in the world. Since its introduction to China in 1981, it has rapidly spread and caused multiple serious damages to agriculture, ecology, and public health. Therefore, based on multi-source distribution data of P. canaliculata, this study calculated the climate niche overlap by Schoener’ s D, quantified the niche shifts between the P. canaliculata in native and invaded areas (China) via the COUE scheme (a unified terminology representing niche centroid shift, overlap, unfilling, and expansion), and analyzed their changes on a time scale. The results revealed that there have been significant climate niche shifts (Schoener’s D < 0.2, niche similarity tests p > 0.01, niche equivalence tests p < 0.01) between the native and invaded areas (China) of P. canaliculata, which does not support the climate niche conservation hypothesis. The minimum temperature of the coldest month (Bio 6) and precipitation seasonality (Bio 15) were the key climate variables driving the climatic niche shift, and P. canaliculata can survive in colder and more arid regions than their native counterparts. The changes in the niche shifts in P. canaliculata on a time scale show significant temporal heterogeneity, and its invasion behavior in China presents a discontinuous and phased expansion pattern, with strong adaptability to new environments. The results are of great significance for the future development of more accurate ecological niche model (ENM), the formulation of more targeted prevention and control strategies, and the study of adaptive evolution mechanisms of invasive species. Full article

Urban heat exposure has become an increasingly critical environmental issue under the dual pressures of global climate warming and rapid urbanization, posing significant threats to public health and urban sustainability. However, conventional linear regression models often fail to capture the complex, nonlinear interactions among multiple environmental factors, and studies confined to single LCZ types lack a comprehensive understanding of urban thermal mechanisms. This study takes the central urban area of Zhengzhou as a case and proposes an integrated “Local Climate Zone (LCZ) framework + random forest-based multi-factor contribution analysis” approach. By incorporating multi-temporal Landsat imagery, this method effectively identifies nonlinear drivers of heat exposure across different urban morphological units. Compared to traditional approaches, the proposed model retains spatial heterogeneity while uncovering intricate regulatory pathways among contributing factors, demonstrating superior adaptability and explanatory power. Results indicate that (1) high-density built-up zones (LCZ1 and E) constitute the core of heat exposure, with land surface temperatures (LSTs) 6–12 °C higher than those of natural surfaces and LCZ3 reaching a peak LST of 49.15 °C during extreme heat events; (2) NDVI plays a dominant cooling role, contributing 50.5% to LST mitigation in LCZ3, with the expansion of low-NDVI areas significantly enhancing cooling potential (up to 185.39 °C·km²); (3) LCZ5 exhibits an anomalous spatial pattern with low-temperature patches embedded within high-temperature surroundings, reflecting the nonlinear impacts of urban form and anthropogenic heat sources. The findings demonstrate that the LCZ framework, combined with random forest modeling, effectively overcomes the limitations of traditional linear models, offering a robust analytical tool for decoding urban heat exposure mechanisms and informing targeted climate adaptation strategies. Full article

Identification of temporal relations among suicide mortality and economic/political implementations provides important information for not only planning suicide prevention but also socioeconomic/psychosocial measures. This cross-sectional observation study analyzed temporal fluctuations and causalities of suicide mortalities of working-age individuals, disaggregated by age/gender/social standing (employed/unemployed), in Japan from 2009 to 2024, using government databases, by joinpoint and vector-autoregressive analyses. Suicide mortality among total and employed females decreased until the COVID-19 pandemic outbreak but sharply increased, synchronized with the pandemic outbreak, before resuming a downward trend. Among males, the decreasing trends attenuated from 2016, followed by a transient increase in 2022. Unemployed males aged 40–69 exhibited four joinpoints: 2016 (decreasing–increasing), 2018 (increasing–decreasing), 2022 (decreasing–increasing), and 2023 (increasing–stable). In contrast, suicide mortality among unemployed females aged 40–69 sharply increased in 2022 and maintained the high level. Among individuals aged 30–39, suicide mortality reversed from decreasing to increasing in 2016 (males) and 2018 (unemployed females). Economic expansion was protective for employed individuals but had no significant effect on unemployed populations. The government management instability (AENROP) index was positively associated with suicide mortality among employed and unemployed males and employed females. Unemployed females aged 30–39 were sensitive to AENROP but not economic conditions, while those aged 40–69 were largely unaffected by either. Increasing employment of individuals with psychiatric disabilities was positively associated with suicide mortality among unemployed males (30–69) and females under 40. Positive impacts of the employment rates of individuals with psychiatric disabilities and unemployment enhanced from 2016 and 2022, respectively, whereas the impacts were inconstantly affected by political rather than economic factors. Suicide mortality among unemployed individuals has emerged as a critical public health concern in Japan, with rates more than doubling among males and tripling among females in the 2020s. These findings underscore the need for integrated suicide prevention policies that address both labor market vulnerabilities and psychosocial determinants. Full article

The rapid expansion of bike-sharing systems has introduced significant management challenges related to spatial-temporal demand fluctuations and inefficient e-fence capacity allocation. This study proposes a Spatio-Temporal Graph Attention Transformer Network (STGATN), a novel hybrid deep learning framework for dynamic demand forecasting in bike-sharing e-fence systems. The model integrates Graph Convolutional Networks to capture complex spatial dependencies among urban functional zones, Bi-LSTM networks to model temporal patterns with periodic variations, and attention mechanisms to dynamically incorporate weather impacts. By constructing a city-level graph based on POI-derived e-fences and implementing multi-source feature fusion through Transformer architecture, the STGATN effectively addresses the limitations of static capacity allocation strategies. The experimental results from Shenzhen’s Nanshan District demonstrate the performance, with the STGATN model achieving an overall Mean Absolute Error (MAE) of 0.0992 and a Coefficient of Determination (R²) of 0.8426. This significantly outperforms baseline models such as LSTM (R²: 0.6215) and a GCN (R²: 0.5488). Ablation studies confirm the model’s key components are critical; removing the GCN module decreased R² by 12 percentage points to 0.7411, while removing the weather attention mechanism reduced R² by nearly 5 percentage points to 0.8034. The framework provides a scientific basis for dynamic e-fence capacity management, advancing spatio-temporal prediction methodologies for sustainable transportation. Full article

While technologies like renewable energy and low-carbon transportation are known to mitigate carbon emissions from urban expansion, achieving carbon neutrality during this process remains a critical unresolved challenge. This issue is particularly pressing for developing countries striving to balance urbanization with carbon reduction. Taking Qionglai City as a case study, this study simulated the territorial spatial functional patterns (TSFPs) and carbon emission distribution for 2025 and 2030. Based on the key drivers of carbon emissions from urban expansion identified through the Geographical and Temporal Weighted Regression (GTWR) model, carbon-neutral pathways were designed for two scenarios: urban expansion scenarios under historical evolution patterns (Scenario I) and urban expansion scenarios optimized under carbon neutrality targets (Scenario II). The results indicate that (1) urban space is projected to expand from 6094.73 hm² in 2020 to 6249.77 hm² in 2025 and 6385.75 hm² in 2030; (2) total carbon emissions are forecasted to reach 1.25 × 10⁶ t (metric tons) and 1.40 × 10⁶ t in 2025 and 2030, respectively, exhibiting a spatial pattern of “high in the central-eastern regions, low in the west”; (3) GDP, Net Primary Productivity (NPP), and the number of fuel vehicles are the dominant drivers of carbon emissions from urban expansion; and (4) a four-pronged strategy, optimizing urban green space vegetation types, replacing fuel vehicles with new energy vehicles, controlling carbon emissions per GDP, and purchasing carbon credits, proves effective. Scenario II presents the optimal pathway: carbon neutrality in the expansion zone can be achieved by 2025 using the first three measures (e.g., optimizing 66.73 hm² of green space, replacing 800 fuel vehicles, and maintaining emissions at 0.21 t/10⁴ CNY per GDP). By 2030, carbon neutrality can be achieved by implementing all four measures (e.g., optimizing 67.57 hm² of green space, replacing 1470 fuel vehicles, and achieving 0.15 t/10⁴ CNY per GDP). This study provides a methodological basis for local governments to promote low-carbon urban development and offers practical insights for developing nations to reconcile urban expansion with carbon neutrality goals. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

Biodiversity is crucial for humankind. It encompasses three main levels: ecosystem, species, and intraspecific genetic diversity. Species consist of populations that exhibit deoxyribonucleic acid (DNA) variability, which is a key component of intraspecific genetic diversity. In turn, intraspecific genetic diversity is directly linked with the term population genetic structure (PGS). There is a great deal of uncertainty and confusion surrounding the concept of the PGS of species in the scientific literature, yet the term PGS is central to population genetics, and future research is expected to focus on the evolutionary continuum from populations to species. Therefore, it is necessary for current biologists and the next generation of scientists to acquire a better understanding of a PGS, both as a term and a concept, as well as the various roles PGSs play within a biodiversity context. This knowledge can then be applied to the expansion of both practical and theoretical science. Finding answers and reaching a consensus among the scientific community on certain questions regarding PGSs could expand the horizons of population genetics and related research disciplines. The major areas of interest and research are PGSs’ roles in the processes of microevolution and speciation, the sustainable use of natural resources, and the conservation of genetic diversity. Other important aspects of this perspective review include proposals for scientific definitions of some terms and concepts, as well as new perspectives and explanations that could be used as a basis for future theoretical models and applied research on PGSs. In conclusion, a PGS should be viewed as a fragile genetic mosaic encompassing at least three spatial dimensions and one temporal dimension. Full article