Search Results (1,543)

Understanding the spatiotemporal dynamics of croplands is crucial for guiding agricultural transformation, food security, and sustainable land use in Africa. This study employs 30 m resolution land cover data and multi-source datasets to examine the spatiotemporal changes in rainfed and irrigated cropland and their driving factors in Tanzania from 1990 to 2020 through multiple GIS spatial analysis methods. The results indicate a net increase in Tanzania’s total cropland area, primarily driven by the expansion of irrigated cropland that has offset the volatile decline of rainfed cropland. From 1990 to 2000, rainfed cropland showed intense bidirectional conversion with shrubland and forest; thereafter, the scale of this conversion continued to decrease. In contrast, irrigated cropland expansion exhibited phased fluctuations. Spatially, rainfed cropland dominates the central, lake, and western zones, while irrigated cropland is predominantly concentrated in the western and southern highland. Hotspots of rainfed cropland shifted from extensive expansion in the central and western zones during the 1990s to localized growth in mountainous areas by the 2010s. Concurrently, irrigated cropland hotspots evolved from a lakeside-concentrated pattern to contiguous development in the central and western zones. Both cropland types exhibit a northwest–southeast spatial orientation. The center of rainfed cropland shifted northwest before moving southeast, while that of irrigated cropland migrated southeastward and then stabilized. Rainfall is a key determinant of rainfed cropland distribution, whereas river network and road network density exert a growing influence on irrigated cropland. Full article

This study presents the first application of a deep-towed transmitter–receiver marine controlled-source electromagnetic (TTR-MCSEM) system for gas hydrate exploration in the Shenhu area of the South China Sea. High-resolution electromagnetic data were acquired along a 13 km transect using dynamic source–receiver offsets and a 500 A transmitter. The results reveal the following: (1) unprecedented near-seafloor resolution (20~100 m) for the precise delineation of hydrate-bearing caprock, surpassing conventional ocean-bottom electromagnetic systems; (2) laterally continuous high-resistivity anomalies (~10 Ω·m) extending from the base of the gas hydrate stability zone to the seafloor, which correlate with seismic bottom-simulating reflector (BSR) distributions and suggest heterogeneous hydrate saturation; and (3) fault-controlled fluid migration pathways that supply hydrate reservoirs and lead to seabed methane seepage at structural highs. Through 2D inversion, we show that the inverted resistivity values (~10 Ω·m) are slightly higher than those obtained from resistivity logs (~5 Ω·m). Saturation values derived from inverted resistivity exhibit remarkable consistency with well-log-based measurements. The high efficiency of the system confirms its potential for the transformative quantitative assessment of hydrate systems, seafloor massive sulfides, and marine geohazards. Full article

An in-depth analysis of land use dynamics during the evolution of regional urban systems is crucial for understanding developmental trajectories and promoting coordinated urban growth. This study adopts a land-use perspective, examining the expansion of urban construction land while identifying its source areas. By integrating Zipf’s law and using urban construction land area as an indicator of urban scale, this research analyzes transformations within the urban system. The findings reveal the following: (1) The total area of urban construction land in the Yangtze River Delta has continued to expand over time, exhibiting an inverted U-shaped curve, with high concentration observed in riverine and coastal zones. (2) Cultivated land serves as the primary source for construction land, contributing on average 77.70% over the past 25 years, amounting to a conversion of 5664.51 square kilometers. Rural residential areas rank second, contributing an average of 11.90%. (3) The rank-size distribution of cities based on urban land area largely aligns with Zipf’s law, albeit with deviations at both ends. The Pareto index increased from 0.803 to 0.897, indicating a trend toward weaker dispersion and greater concentration in urban size distribution. In conclusion, future urban development should emphasize rational expansion grounded in sustainable practices, strengthen farmland protection to ensure food security, and effectively manage rural land transformation to promote efficient land use and ecological balance. These measures will support the balanced and coordinated development of large, medium, and small cities within the urban system. Full article

Power flow analysis of low-voltage network (LVN) is one of the most crucial methods for achieving refined management of such networks. To accurately calculate the three-phase (TP) probabilistic power flow (PPF) distribution in LVN, this paper first draws on the injection-type Newton method; by leveraging TP power measurements relative to the neutral point obtained from smart meters, the injected power is expressed in terms of injected current equations, thereby establishing TP power flow models for various components within the low-voltage distribution transformer area grid. Subsequently, addressing the stochastic fluctuation models of load power and photovoltaic output, this paper employs conventional numerical methods and an improved Latin hypercube sampling technique. Utilizing linearized power flow equations and based on the improved semi-invariant method (SIM) and Gram–Charlier (GC) series fitting, a calculation method for three-phase PPF in low-voltage distribution transformer area grids using the improved semi-invariant is proposed. Finally, simulations of the proposed three-phase PPF method are conducted using the IEEE-13 node distribution system. The simulation results demonstrate that the proposed method can effectively perform three-phase PPF calculations for the distribution transformer area grid and accurately obtain probabilistic distribution information of the TP power flow within the grid. Full article

In recent years, arresters in 10 kV distribution transformer areas of the Guangdong power grid have exhibited a rising trend of premature failures, posing a serious threat to distribution network reliability. This paper studied the failure causes of arresters through performance tests on failed arresters and through deterioration tests on new arresters and their prorated sections under typical operating stresses. The failed arresters and their internal varistors displayed varying degrees of physical damage and pronounced degradation in electrical performance, characterized by a strong polarity effect on the DC reference voltage (U_1mA), elevated DC leakage current (I_L) and resistive current (i_R), and excessive residual voltage (U_5kV). In the lightning impulse test, varistors primarily showed pinhole-type damage and significant polarity effects on ΔU_1mA. In the AC aging test, ΔU_5kV increased markedly. In the water immersion test, varistors exhibited salt deposits and aluminum electrode blackening, with ΔU_1mA decreasing, while I_L and Δi_R increased significantly. Overall, internal moisture superimposed on other operating stresses was identified as a major internal cause of arrester failure, while pollution flashover of the housing was considered the primary external factor. Corresponding improvement measures in material optimization, testing and inspection, and operation and maintenance are proposed to enhance arrester reliability. Full article

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems. Full article

Context: It has been claimed that recently, in England, the places with the greatest amount of housing built were the places that least needed them. This is an accusation that has echoes in a number of countries around the globe. The lack of construction leads to greater unaffordability and a lower level of economic activity than could have been achieved if labour, particularly those with high human capital, was not so constrained as to where they could afford to live. The recent National Planning Policy Framework for England imposes mandatory targets on housing planning authorities. As such, the following question is raised: will the targets result in additional residential homes being located in places of greater need than the prevailing pattern? Research Questions: The paper sets out to consider the spatial mismatch between housing additions and national benefit in terms of unaffordability and productivity. Specifically, do the concentrations of high and/or low rates of the prevailing rates of additional dwellings and the target rates of adding dwellings correspond with the clusters of high and/or low unaffordability and productivity? A further question considered is: does the spatial distribution of additional dwellings match the clusters of population growth? Method: The values of the variables are transformed at the first stage into Anselin’s LISA categories. LISA maps can reveal unusually high spatial concentrations of values, or clusters. The second stage entails comparing sets of the transformed data for agreement of the classifications. An agreement coefficient is provided by Fleiss’s kappa. Data: The data used is of additional dwellings, the total number of dwellings, population estimates, gross value added per hour worked (productivity data), and house price–earnings ratios. The period of study covers the eight years prior to 2020 and the two years after, omitting 2020 itself due to the unusual impact on economic activity. All the data is at local authority district level. Findings: The hot and cold spots of additional dwellings do not correspond those of house price–earnings ratios or productivity. However, population growth hot spots show moderate agreement with those of where additional dwellings are concentrated. This is in line with findings from elsewhere, suggesting that population follows housing supply. Concentrations of districts with relatively high targets per unit of existing stocks are found correspond (agree strongly) with clusters of house price–earnings ratios. Links between productivity and housing are much weaker. Conclusions: The strong link between targets and affordability suggests that if the targets are met, the claim that the places that build the most housing are the places that least need them can be challenged. That said, house-price–earnings ratios present a view of unaffordability that will favour greater building in the countryside rather than cities outside of London, which runs against concentrating new housing in urban areas consistent with fostering clusters/agglomerations implicit in the new modern industrial strategy. Full article

Low-voltage distribution area is the “last kilometer” connecting the distribution network and users, and the traditional distribution system is difficult to digitally manage in the low-voltage area, resulting in untimely and imprecise handling of voltage overruns, short-circuit outages, and other abnormal problems. With the deployment of smart meters, new sensors, smart gateways, and other devices in distribution areas, digital intelligent monitoring and management based on digital twins in LV distribution areas has gradually become the focus of distribution network research. In view of the profound changes that are taking place in the low-voltage distribution area, this paper first summarizes the characteristics and shortcomings of the existing digital twin research in the low-voltage distribution area, then puts forward the ‘6C’ development goals for the digital transformation of the low-voltage distribution area, introduces the practice work of Guangxi Power Grid Corporation around the ‘6C’ development goals in the low-voltage distribution area. Finally, the future research work of the ‘6C’ development goals for the digital transformation of the low-voltage distribution area is promising. Full article

The ABI3 transcription factor is a key regulator in plant growth and development. Through transcriptome analysis of the resistant soybean cultivar ‘Dongnong L10′ and the susceptible cultivar ‘Heinong 37′ exposed to soybean cyst nematode race 3 (SCN 3) stress, the differentially expressed gene GmABI3VP1 was identified. The GmABI3VP1 gene was then cloned and analyzed through bioinformatics, subcellular localization, and qRT-PCR analysis of resistant and susceptible soybean germplasms, as well as overexpression and gene editing of soybean hairy roots followed by SCN 3 identification analysis. It was found that the protein encoded by GmABI3VP1 is an acidic and hydrophilic protein with transmembrane domains. It has a collinear relationship with Arabidopsis and is widely distributed in plants. Through the analysis of promoter elements, it was shown that this gene contains multiple hormone-responsive promoter elements like ABRE/ABRE3a/ABRE/4a/as-1 and stress-responsive elements such as Myb/MYC/MYc. Transient expression in tobacco indicated that the GmABI3VP1 gene is located in the nucleus. The transcription of GmABI3VP1 responds to the stress of SCN, and its transcriptional level is relatively high in the roots of resistant materials. Genetic transformation mediated by Agrobacterium rhizogenes was used to obtain GmABI3VP1 gene overexpressed and CRISPR-Cas9 gene-edited soybean hairy roots. In comparison to the wild type (WT), the density of nematodes per area was notably lower in hairy roots overexpressing (OX) the gene, whereas the density of SCN per unit area (per cm of lateral root length) significantly increased in gene-edited (KO) soybean hairy roots. Through SCN phenotyping, GmABI3VP1 was identified as a contributor to SCN 3 resistance. This study provides initial insights into the role of the GmABI3VP1 gene in SCN resistance, establishing a robust basis for future research on the mechanisms underlying SCN disease resistance and offering valuable genetic reservoirs for SCN 3 resistance. Full article

Renewable Energy Communities (RECs) are central to Europe’s strategy for reducing greenhouse gas emissions and advancing a sustainable, decentralized energy system. RECs aim to transform consumers into prosumers—individuals who both produce and consume energy—thereby enhancing energy efficiency, local autonomy, and citizen engagement. This study introduces a novel Geographic Information System (GIS)-based methodology that integrates socio-economic and spatial data to support the design of optimal REC configurations. QGIS 3.40.9 “Batislava” tool is used to simulate site-specific energy distribution scenarios, enabling data-driven planning. By combining a Composite Energy Vulnerability Index (CEVI), Rooftop Solar Potential (RSP), and the distribution of urban gardens (UGs), the approach identifies priority urban zones for intervention. Urban gardens offer multifunctional public spaces that can support renewable infrastructures while fostering local resilience and energy equity. Applied to the city of Rome, the methodology provides a replicable framework to guide REC deployment in vulnerable urban contexts. The results demonstrate that 11 of the 18 highest-priority areas already host urban gardens, highlighting their potential as catalysts for collective PV systems and social engagement. The proposed model advances sustainability objectives by integrating environmental, social, and spatial dimensions—positioning RECs and urban agriculture as synergistic tools for inclusive energy transition and climate change mitigation. Full article

Amid the accelerating global transition toward a low-carbon and intelligent economy, the issues of resource misallocation and mounting environmental pressures in agriculture have become increasingly prominent, posing significant bottlenecks to the modernization of the sector. As a novel factor of production, agricultural and rural big data theoretically offer new avenues for facilitating a green transformation in agriculture. However, institutional constraints have hindered its full potential. Drawing on provincial panel data from 2011 to 2022, this study treats the big data policy pilot as a quasi-natural experiment and employs a difference-in-differences (DID) approach to comprehensively analyze its mechanisms and actual effects on high-quality agricultural development. An indicator system encompassing five dimensions—innovation, coordination, greenness, openness, and sharing—is constructed, and the entropy method is used to measure the level of high-quality agricultural development. Multiple empirical strategies, including parallel trend tests, are utilized to ensure the robustness of the findings. The results indicate that high-quality agricultural development exhibits significant regional gradients and periodic leaps. The implementation of the big data policy in 2016 marked a crucial turning point, yielding a significant positive effect on agricultural development. Notably, pronounced heterogeneity exists regarding regional distribution, major grain-producing areas, and development stages. The policy’s impact primarily operates through pathways of openness and sharing, although some mechanisms remain to be improved. Accordingly, this paper recommends differentiated regional policies and enhanced targeted support, thereby providing theoretical and practical policy guidance for optimizing big data policy design, promoting high-quality agricultural development, and advancing rural revitalization. For policymakers, these findings clarify the priorities for differentiated interventions and offer empirical evidence for optimizing the spatial allocation of big data policy pilots and strengthening open and shared development mechanisms. This, in turn, can improve the precision of agricultural policy and accelerate the green transformation and revitalization of rural areas. Compared to existing literature, the distinct contribution of this study lies in its pioneering use of big data policy pilots as a quasi-natural experiment. The research systematically constructs a multidimensional indicator system to measure high-quality agricultural development, elucidates the heterogeneous effects and specific pathways of policy intervention, and addresses gaps in the empirical assessment and mechanism analysis of agricultural big data policies. Full article

Artificial intelligence (AI) is transforming organizational functions across sectors; however, its application to human resource management (HRM) within healthcare remains underexplored. This study aims to unpack the black-box nature of AI capability’s impact on HR functions within China’s healthcare sector, a domain undergoing rapid digital transformation, driven by national innovation policies. Grounded in resource-based theory, the study conceptualizes AI capability as a multidimensional construct encompassing tangible resources, human resources, and organizational intangibles. Using a structural equation modeling approach (PLS-SEM), the analysis draws on survey data from 331 professionals across five hospitals in three Chinese cities. The results demonstrate a strong, positive, and statistically significant relationship between AI capability and HR functions, accounting for 75.2% of the explained variance. These findings indicate that AI capability enhances HR performance through smarter recruitment, personalized training, and data-driven talent management. By empirically illuminating the mechanisms linking AI capability to HR outcomes, the study contributes to theoretical development and offers actionable insights for healthcare administrators and policymakers. It positions AI not merely as a technological tool but as a strategic resource to address talent shortages and improve equity in workforce distribution. This work helps to clarify a previously opaque area of AI application in healthcare HRM. Full article

In the context of the deep transformation of population structure and the coordinated advancement of high-quality development, exploring the mechanism of the impact of aging on economic growth has become a major issue related to the sustainable development of China. This study takes the 41 cities of the Yangtze River Delta urban agglomeration as a sample, using the population and economic census data from 2000 to 2020. It comprehensively applies an improved Solow model, GIS spatial analysis, spatial econometric models, and mediation effect tests to arrive at the following findings: (1) There is a significant asynchrony between economic growth and population aging in the Yangtze River Delta urban agglomeration. Economic growth has shifted from high-speed to high-quality development, while the aging process is accelerating and becoming more aged. (2) Population aging in the Yangtze River Delta has a nonlinear positive impact on economic growth. The intensity of this impact shows a characteristic of “strong-weak-strong,” with the first aging rate threshold being 11.63% and the second being 17.53%. (3) There is significant spatial autocorrelation between population aging and economic growth in the Yangtze River Delta urban agglomeration. The overall direction of the effect shows a spatial distribution pattern of “positive in the south and negative in the north.” The deepening of population aging in neighboring areas promotes local economic growth. (4) Labor productivity and optimization of the living environment constitute the core transmission pathways. Together, they account for more than 80% of the contribution and serve as the key mechanism for transforming aging pressures into growth momentum. This research provides practical guidance for solving the “rich” and “aging” contradictions in the Yangtze River Delta. It also offers a universal theoretical framework and a Chinese solution for aging economies worldwide to address the risk of growth stagnation. Full article

The digital creative industries have emerged as a critical driver of regional economic transformation, upgrading, and sustainable development. While previous research has primarily focused on creative industry layout and agglomeration in urban areas, with the integration of digital technology and the creative industry, existing research has an insufficient explanation for the digital creative industry. Specifically, few people have studied the spatial distribution and diffusion of digital creative industries in emerging economies from the macro-regional level. To address this gap, this study analyzes the spatial diffusion mode and regional spatial response law of digital creative industries in the Yangtze River Delta during three critical time windows (2016, 2019, and 2022) in the context of national strategy implementation. A range of spatial analysis technologies is utilized to process the full sample of big data from digital creative industries. This study utilizes OLS and a quantile regression model to determine the dominant factors that affect spatial diffusion and response in the digital creative industries. The results demonstrate that, against the backdrop of regional development strategies, digital creative industries exhibit a variety of diffusion modes, including contagious, hierarchical, corridor, and jump diffusion. The response of industries to regional strategies has different rules in terms of regional space, urban development, and sub-industries. Furthermore, the comprehensive influence of institutional environment, urban economy, development and innovation significantly impacts industrial spatial diffusion and regional response. Among them, government investment in science and technology and the number of universities have consistently been important influencing factors, and policy exhibits nonlinear effects and asymmetric characteristics on industry agglomeration and diffusion. This study enhances the understanding of digital creative industry development in the YRD and offers a theoretical basis for optimizing regional industrial spatial structure and promoting the sustainable development of digital industries. Full article

Temperate forest ecosystems in Korea are currently undergoing a successional transition from Pinus densiflora Siebold & Zucc. (evergreen conifer) communities to Quercus mongolica Fisch. ex Ledeb. (deciduous broadleaf) communities. This study aimed to assess interspecific differences in ecological responses to climate change [Representative Concentration Pathway (RCP) 4.5] by evaluating changes in ecological niche characteristics and species distribution. Controlled-environment experiments, principal component analysis (PCA), and MaxEnt species distribution modeling were employed to quantify and predict ecological shifts in the two dominant species under climate change scenarios. Both species exhibited increases in niche breadth and interspecific overlap under climate change conditions. However, Q. mongolica showed a more pronounced increase in niche breadth compared to P. densiflora, indicating greater ecological flexibility and adaptive potential to warming conditions. According to the MaxEnt model projections, climate change is expected to result in an approximate 30% reduction in suitable habitat for P. densiflora in lowland areas. In contrast, Q. mongolica is projected to expand its suitable habitat by over 80%, notably in both low-elevation (below 800 m) and high-elevation (above 1400 m) zones, without being restricted to any specific altitudinal range. Our findings suggest that climate change may increase ecological similarity between P. densiflora and Q. mongolica, thereby raising the potential for interspecific competition. This convergence in niche traits could contribute to an accelerated successional transition, although actual competitive interactions in natural ecosystems require further empirical validation. Consequently, Korean forests are likely to transform into predominantly deciduous forest ecosystems under future climate conditions. Full article