Search Results (3,819)

As a geographical development mode, can urban agglomeration solve the problem of intensive land use that cannot be solved on the urban scale? What is the degree of balanced development between urbanization and intensive land use? This study constructs the index system of the coupling system between urbanization development and intensive land use, and evaluates the urbanization development subsystem and the intensive land use subsystem using the coupling Comprehensive Gravity–Gram–Schmidt Orthogonalization model (CG-GSO) and the entropy weight method, based on the coupling coordination degree model to explore coordinated development, and, finally, it analyzes the driving factors. The results showed the following: (1) the urbanization development and the intensive land use subsystems were rising in the two urban agglomerations; (2) in the coupling system, the driving factors were the economic development and the land input level dimensions in the Jing-Jin-Ji urban agglomeration, and the economic development and the land output level dimensions in the Yangtze River Delta urban agglomeration; and (3) the Jing-Jin-Ji urban agglomeration was always in the land input stage, while the Yangtze River Delta urban agglomeration had experienced the land utilization stage, the land input stage and the land output stage. In general, urban agglomeration, as a development mode, had indeed solved the imbalance in the coupling system. Although the coordination degree was unbalanced from 2003 to 2020, it increased and had a strong development momentum, approaching the balanced development (the Jing-Jin-JI urban agglomeration was 0.3493 and the Yangtze River Delta was 0.3611) in 2020, and achieving slightly balanced development in 2023, with barely balanced development in 2034 and superiorly balanced development in 2043 (Jing-Jin-Jin urban agglomeration) and in 2044 (Yangtze River Delta urban agglomeration). The research provides ideas for other countries to solve the uncoordinated development between urbanization and intensive land use. Full article

Land economic density (LED) is vital for optimizing industrial structure and promoting intensive resource utilization. However, most existing studies have focused on city or county scales, with limited attention to township-level patterns. To address this research gap, we take 86 townships in Anyang City as research units and develop a four-dimensional evaluation system for LED. The study aims to reveal the spatial patterns and driving mechanisms of township-level LED evolution. This study is based on township-level land use, statistical, and socioeconomic data from 2005 to 2023. Using ArcGIS 10.5 for spatial analysis, spatial autocorrelation, standard deviation ellipse, and geographically weighted regression methods were applied to explore the spatiotemporal evolution and driving mechanisms of LED in Anyang City. The results indicate that (1) high-LED areas form a ring around the central city with dual cores in western Linzhou county and southeastern Huaxian county, while low-LED areas are concentrated at the northwestern and northeastern margins; (2) global spatial autocorrelation is weak, with low–low clusters shrinking from contiguous patches to only three townships by 2023, while high–high clusters expand from isolated points to multi-centered diffusion; (3) the ellipse consistently shows a northwest–southeast orientation, with the rotation angle increasing from 128.24° to 130.35°, the flatness ratio rising from 0.432 to 0.445, and the centroid shifting northwest then southeast; (4) The geographically weighted regression (GWR) results highlight economic foundation, industrial upgrading, and government support as the dominant drivers. Based on these findings, we propose a “One Core–Four Poles, Three Axes–Five Zones” spatial optimization framework to promote coordinated urban–rural development. This study provides a practical and multidimensional evaluation approach at the township level, offering methodological support for regional territorial spatial planning and sustainable development. Full article

Brazil is recognized for its rich biodiversity, including aromatic species of economic importance, among which Baccharis dracunculifolia De Candole (1836) stands out. The essential oil distilled from this species exhibits biological and therapeutic activities. Despite its relevance, studies addressing the chemodiversity of this species on a broad scale remain scarce. This study aimed to map and characterize the chemical and physicochemical profiles of B. dracunculifolia essential oils from different regions of the state of Rio de Janeiro, considering the influence of geographic factors and plant sex. Fifty georeferenced accessions of B. dracunculifolia were collected in 2023 and 2025, and dried leaves were subjected to hydrodistillation. The essential oils were characterized through physicochemical analyses and chemically analyzed by GC-FID and GC-MS. Essential oil yields ranged from 0.34 to 2.17%, relative density from 0.89 to 0.96 g/cm³, refractive index from 1.485 to 1.497 n^D, and specific optical rotation from −12.56° to +6.80°. Sixty-two compounds were identified, predominantly oxygenated sesquiterpenes, with E-nerolidol (16.8–51.0%), spathulenol, bicyclogermacrene, and germacrene D as the main compounds. Multivariate analysis revealed five chemical profiles, all containing E-nerolidol as the major compound, indicating moderate to low chemical diversity. No significant differences were observed between the essential oils from female and male plants. However, variation in the chemical profile of the essential oil was observed as a function of year and altitude. Full article

The development of large-scale, flexible, and safe hydrogen storage is critical for enabling a low-carbon energy system. Deep saline aquifers (DSAs) offer substantial theoretical capacity and broad geographic distribution, making them attractive options for underground hydrogen storage. However, hydrogen storage in DSAs presents complex technical, geochemical, microbial, geomechanical, and economic challenges that must be addressed to ensure efficiency, safety, and recoverability. This study synthesizes current knowledge on hydrogen behavior in DSAs, focusing on multiphase flow dynamics, capillary trapping, fingering phenomena, geochemical reactions, microbial consumption, cushion gas requirements, and operational constraints. Advanced numerical simulations and experimental observations highlight the role of reservoir heterogeneity, relative permeability hysteresis, buoyancy-driven migration, and redox-driven hydrogen loss in shaping storage performance. Economic analysis emphasizes the significant influence of cushion gas volumes and hydrogen recovery efficiency on the levelized cost of storage, while pilot studies reveal strategies for mitigating operational and geochemical risks. The findings underscore the importance of integrated, coupled-process modeling and comprehensive site characterization to optimize hydrogen storage design and operation. This work provides a roadmap for developing scalable, safe, and economically viable hydrogen storage in DSAs, bridging the gap between laboratory research, pilot demonstration, and commercial deployment. Full article

Earthquake predictability remains a central challenge in seismology. Are earthquakes inherently unpredictable phenomena, or can they be forecasted through advances in technology? Contemporary seismological research continues to pursue this scientific milestone, often referred to as the ‘Holy Grail’ of earthquake prediction. In the direction of earthquake prediction based on historical data, the Grammatical Evolution technique of GenClass demonstrated high predictive accuracy for earthquake magnitude. Similarly, our research team follows this line of reasoning, operating under the belief that nature provides a pattern that, with the appropriate tools, can be decoded. What is certain is that, over the past 30 years, scientists and researchers have made significant strides in the field of seismology, largely aided by the development and application of artificial intelligence techniques. Artificial Neural Networks (ANNs) were first applied in the domain of seismology in 1994. The introduction of deep neural networks (DNNs), characterized by architectures incorporating two hidden layers, followed in 2002. Subsequently, recurrent neural networks (RNNs) were implemented within seismological studies as early as 2007. Most recently, grammatical evolution (GE) has been introduced in seismological studies (2025). Despite continuous progress in the field, achieving the so-called “triple prediction”—the precise estimation of the time, location, and magnitude of an earthquake—remains elusive. Nevertheless, machine learning and soft computing approaches have long played a significant role in seismological research. Concerning these approaches, significant advancements have been achieved, both in mapping seismic patterns and in predicting seismic characteristics on a smaller geographical scale. In this way, our research analyzes historical seismic events from 2004 to 2011 within the latitude range of 21°–79° longitude range of 33°–176°. The data is categorized and classified, with the aim of employing grammatical evolution techniques to achieve more accurate and timely predictions of earthquake magnitudes. This paper presents a systematic effort to enhance magnitude prediction accuracy using GE, contributing to the broader goal of reliable earthquake forecasting. Subsequently, this paper presents the superiority of GenClass, a key element of the grammatical evolution techniques, with an average error of 19%, indicating an overall accuracy of 81%. Full article

The increasing demand for decarbonized urban environments has intensified interest in integrating renewable energy systems within cities. This review investigates the potential of urban wind energy as a promising technology in the development of Positive Energy Districts, supporting the transition toward climate-neutral urban areas. A systematic analysis of recent literature is presented, covering methodologies for urban wind resource assessment, including Geographic Information Systems (GIS)-based mapping, wind tunnel experiments, and Computational Fluid Dynamics simulations. The study also reviews available small-scale wind technologies, with emphasis on building-integrated wind turbines, and evaluates their contribution to local energy self-sufficiency. The integration of urban wind systems with energy storage, Power-to-Heat solutions, and smart district networks is discussed within the PED framework. Despite technical, economic, and social challenges, such as low wind speeds, turbulence, and public acceptance, urban wind energy offers temporal complementarity to solar power and can enhance district-level energy resilience. The review identifies key technological and methodological gaps and proposes strategic directions for optimizing urban wind deployment in future sustainable city planning. Full article

Agriculture has been widely practiced for food production, yet food insecurity remains a critical issue, especially in Africa. Due to the significant role played by small-scale farmers, urban agriculture has been acknowledged as a viable strategy for reducing food insecurity in urban areas of Sub-Saharan Africa. This review analyzes urban household food insecurity factors through a systematic literature approach, retrieving data from various online databases. These databases include ScienceDirect, Wiley Online Library, Web of Science, UNIZULU online library, and PubAg. The search process involved the use of keywords to obtain relevant information along with the application of filters such as geographic location, publication period, language, article type, and accessibility. A total of 37 articles was included in this review after the application of the review eligibility criteria. This was achieved following PRISMA guidelines. Findings reveal a growing trend in the publication of articles on urban farming and an increasing acknowledgment of its importance by high-impact journals. It also shows various factors that determine household food insecurity, categorized as socioeconomic (11), institutional (5), and environmental factors (2). This led to the recommendation that urban government structures including policy makers and stakeholders should support food production and ensure an efficient urban food supply system. Full article

Identifying the key drivers of plant community turnover under disturbance is essential for understanding ecological processes and informing conservation efforts. We investigated the Kangxi Grassland in the Yeyahu Wetland Nature Reserve, Beijing, using Generalized Dissimilarity Modeling (GDM) across two spatial scales and three areas, integrating soil properties, remote sensing data, and geographic distance. The models explained 25–49% of the deviance with low cross-validation error, showing a clear nonlinear turnover pattern. Pronounced species replacement occurred at short ecological distances, followed by slower change at greater distances. Although the overall patterns were similar, driver importance varied among areas: available nitrogen (AN) dominated in the Southeast Area, while soil water content (SWC) was the primary driver in the Northwest Area and across the entire Study Area; in all cases, geographic distance consistently ranked second. Texture indices, although weaker than geographic distance, still outperformed most vegetation indices and spectral bands. These results indicate that soil properties, geographic distance, and texture indices jointly shape spatial patterns of species turnover, with their relative importance varying by scale or area. Disturbances, such as drought, grazing, tourism, and fluctuations in inundated areas caused by variations in water levels in a nearby reservoir, influenced species turnover by directly or indirectly altering key drivers. In combination with a comparative analysis of species importance values (IVs) and ecological types, this study further demonstrates that the factors driving species turnover are influenced not only by scale but also by the complex and diverse ecological processes operating at their respective scales. It also shows the applicability of GDM in analyzing fine-scale turnover patterns and the factors driving them in disturbed grasslands. Full article

Healthcare discrimination is an important barrier to accessing services among Latino populations in the United States. However, few validated scales have been developed to systematically examine this issue. In this study, we evaluated the validity and reliability of a bilingual healthcare discrimination scale in a sample of churchgoing Latino adults in Los Angeles, California. The study sample included 336 participants (foreign-born: 250; US-born: 86) who attended 12 Catholic churches in Los Angeles. Psychometric testing of the 7-item healthcare discrimination (HCD) scale included internal consistency; split-half reliability; convergent, discriminant, and predictive validity; and confirmatory factor analyses. The HCD had relatively high internal consistency (full sample Cronbach’s α = 0.92; foreign-born: 0.91; US-born: 0.92) and showed good convergent and discriminant validity, as it was moderately correlated with the depression scale (full sample r = 0.28, p < 0.001) and weakly correlated with the acculturation scale (full sample r = 0.15, p = 0.008). Confirmatory factor analyses yielded further support for a one-factor solution. Our study finds that the HCD is a valid and reliable scale for use among churchgoing Latino adult populations in the United States. Future studies should examine the psychometric properties of the HCD among Latinos of diverse backgrounds, geographic locations, religious beliefs, and languages. Full article

Bangladesh is highly susceptible to land use land cover (LULC) changes due to its geographical location and dense population. These changes have significant effects on food security, urban development, and natural resource management. Policy planning and resource management largely depend on accurate and detailed LULC maps. However, Bangladesh does not have its own national scale detailed high-resolution LULC maps. This study aims to develop high-resolution land use land cover (HRLULC) maps for Bangladesh for the years 2020 and 2023 using a deep learning method based on convolutional neural network (CNN), and to analyze LULC changes between these years. We used an advanced LULC classification algorithm, namely SACLASS2, that was developed by JAXA to work on multi-temporal satellite data from different sensors. Our HRLULC maps with 14 categories achieved an overall accuracy of 94.55 ± 0.41% with Kappa coefficient 0.93 for 2020 and 94.32 ± 0.42% with Kappa coefficient 0.93 for 2023, which is higher than the commonly accepted standard of around 87 overall accuracy for 14 category LULC map. Between 2020 and 2023, the most notable LULC increase were observed in single cropland (17 ± 4%), aquaculture (20 ± 5%), and brickfield (56 ± 25%). Conversely, decrease occurred for salt pans (47 ± 16%), bare land (24 ± 3%), and built-up (13 ± 3%). These findings offer valuable insights into the spatio-temporal patterns of LULC in Bangladesh, which can support policymakers in making informed decisions and developing effective conservation strategies aimed at promoting sustainable land management and urban planning. Full article

This study investigates the efficacy of large language models (LLMs) in supporting responsible and optimized geographic site selection for large-scale solar energy farms. Using Microsoft Bing (predecessor to Copilot), Google Bard (predecessor to Gemini), and ChatGPT, we evaluated their capability to address complex technical and social considerations fundamental to solar farm development. Employing a series of guided queries, we explored the LLMs’ “understanding” of social impact, geographic suitability, and other critical factors. We tested varied prompts, incorporating context from existing research, to assess the models’ ability to use external knowledge sources. Our findings demonstrate that LLMs, when meticulously guided through increasingly detailed and contextualized inquiries, can yield valuable insights. We discovered that (1) structured questioning is key; (2) characterization outperforms suggestion; and (3) harnessing expert knowledge requires specific effort. However, limitations remain. We encountered dead ends due to prompt restrictions and limited access to research for some models. Additionally, none could independently suggest the “best” site. Overall, this study reveals the potential of LLMs for geographic solar farm site selection, and our results can inform future adaptation of geospatial AI queries for similarly complex geographic problems. Full article

Selenium (Se) is an essential trace element for human health, which is crucial for antioxidant defense, immune function, and disease prevention. Se deficiency affects around 40 countries worldwide, with China being one of the most severely impacted. While previous research has explored factors influencing soil Se content, such as the parent material, climate, and soil properties, the dominant controlling mechanisms across different spatial scales remain a subject of debate, especially in the Se-rich coastal regions of southeastern China. This study focuses on Fujian Province, using hotspot analysis and geographically weighted regression (GWR) to systematically examine the spatial distribution of soil Se and its key influencing factors. Hotspot analysis reveals multi-scale patterns in Se distribution: at the 1 km scale, Se hotspots are closely linked to metal minerals like sulfide and coal deposits; at the 2 km scale, Se-rich carbonate rocks and carbonaceous mudstones dominate; and, at the 10 km scale, Se accumulation is mainly controlled by organic matter and low-temperature conditions in high-altitude areas (≥1200 m). GWR analysis further clarifies the nonlinear relationships between soil Se and key environmental factors: organic matter strongly correlates with Se in coastal regions but weakly in land, indicating that this relationship is modulated by factors such as weathering intensity and clay content. The mobility of Se increases in alkaline soils (pH > 8.5), thus reducing its content; meanwhile, in acidic soils (pH < 4.5), its fixation is more complex. In acidic, low-aluminum settings, iron oxides adsorb Se effectively, whereas organic matter becomes the main carrier under alkaline conditions. Precipitation affects Se via atmospheric deposition and leaching, temperature promotes sulfide substitution through deposition but also accelerates the breakdown of organic matter, and altitude influences Se through hydrothermal variations. This study provides the first comprehensive analysis of the multi-factor mechanisms controlling soil Se in the Se-rich coastal areas of southeastern China at a regional scale, offering a scientific basis for the sustainable use of Se-enriched land resources. Full article

Traditional power system planning methods are often based on deterministic assumptions, which cannot effectively address the uncertainties brought by high proportions of renewable energy sources. This may result in insufficient power supply or wasted resources. This paper proposes a novel optimization planning method for power systems, combining a hierarchical Copula model with a comprehensive risk assessment approach. The aim is to optimize the balance between investment costs and operational risks in large-scale power systems. The hierarchical Copula model is employed to handle the spatial correlation and temporal dependence between wind power, photovoltaic power, and load. Multiple joint scenarios are generated using the Monte Carlo method to reflect the complex interactions between different geographic locations, providing more comprehensive data support for risk assessment. Additionally, a CVaR-based comprehensive risk assessment method is used to quantify the risks of power loss and resource wastage, which are then integrated into a comprehensive risk indicator through weighted aggregation. An optimization framework considering supply–demand probability balance constraints is proposed, allowing for supply–demand balance at a certain probability level. Benders decomposition is used to improve computational efficiency. Simulation results show that, compared to traditional methods, the proposed model significantly reduces the curtailment rate and supply–demand imbalance frequency, improving the system’s adaptability to uncertainties and extreme scenarios. Full article

Accurately understanding the impact of Significant Wave Height (SWH) on mariculture productivity is crucial for developing a sustainable blue economy and mitigating the effects of increasing marine extreme events under climate change. However, a significant research gap exists in macroscale empirical tools capable of quantifying the complex, non-linear, and spatially non-stationary relationships between SWH and mariculture yield. Addressing this, our study focused on the Bohai and Yellow Seas, a critical mariculture region in China. We developed five novel SWH indices (LSDI, MSDI, HSDI, RSI, NDSI) to statistically link SWH with the Unit Area Yield (UAY) using buoy-calibrated ERA5 reanalysis data and regional fishery statistics. Geographically Weighted Regression (GWR) was further employed to uncover the spatial heterogeneity of this relationship. Results demonstrated that the Normalized Difference SWH Index (NDSI) most effectively captured the SWH-UAY relationship (r = 0.61, R² = 0.37), as its non-linear form integrates the positive effects of low SWH conditions and the negative effects of high SWH conditions. GWR analysis revealed significant spatial non-stationarity, with the SWH impact on yield being stronger in the eastern and southern open waters of the Yellow Sea and weaker in the northern semi-enclosed Bohai Sea. The index framework and spatial analysis method developed in this study provide a transferable tool for quantifying the impact of physical oceanographic processes on mariculture productivity at a macro scale, which can offer a scientific basis for climate-resilient mariculture zoning and adaptive management. Full article

The Lactobacillaceae family encompasses microorganisms of exceptional ecological and biotechnological importance, serving as central agents in food fermentations, health applications, and nutrient cycling across diverse environments. Despite their broad functional and phylogenetic diversity, the global distribution and ecological specialization of Lactobacillaceae are not yet fully understood. In this study, we performed a comprehensive analysis of over 2 million records from the NCBI database to survey and trace the ecological landscape of Lactobacillaceae across thousands of distinct habitats. Our results reveal that food products and animal hosts represent the primary ecological niches for members of this family. The examined taxa exhibit a broad spectrum of ecological strategies, ranging from generalists with wide environmental adaptability to specialists with strict niche preferences. Notably, our findings highlight a profound geographical and ecological sampling bias, with unclassified taxids frequent in animal gastrointestinal tracts, soils, and especially in living plant tissues—habitats identified as promising frontiers for discovering novel biodiversity. The obtained results emphasize the urgent need for expanded sampling efforts in underexplored geographic regions such as Africa, Antarctica, the Arctic, South America, and Central Asia to capture a more complete picture of Lactobacillaceae diversity. The study underscores the necessity of implementing standardized, metadata-rich data deposition practices to enable unbiased, large-scale ecological and evolutionary analyses. Ultimately, these insights not only deepen our fundamental knowledge of Lactobacillaceae diversity but also provide a strategic framework for future bioprospecting, fostering the discovery of novel strains and expanding the biotechnological potential of this influential bacterial family. Full article