We introduce a new class of affine connections on Hessian manifolds, called hybrid connections, characterized by the compatibility between their projective geometry with the underlying affine structure on the one hand and their infinitesimal holonomy with the Hessian metric on the other hand. In this paper, we investigate the properties of hybrid connections and prove that, on a given Hessian manifold, they are completely determined by the choice of a Hessian potential for the metric. In the special case of pseudo-Euclidean manifolds, we identify canonical models and construct, in particular, a natural connection on the open unit ball that combines features of the Cayley–Klein and Poincaré models of hyperbolic geometry. We also prove the existence and uniqueness (up to scaling) of a pseudo-Riemannian metric h such that the geodesics of ∇ admit parameterizations of constant speed with respect to h, which we call the isochrone metric.
In this paper, we study n-dimensional (n≥ 3) conformally flat minimal Lagrangian submanifolds in complex space forms. Our main result is a classification of such submanifolds under the assumption that the Ricci tensor is semi-parallel. This generalizes the recent classification result of Song–Wang–Xing [1] on conformally flat minimal Lagrangian submanifolds in complex space forms with parallel Ricci tensor.
This study develops a spatial analytical framework to investigate border trade liberalization under the Belt and Road Initiative (BRI). By integrating geographic analysis with an extended gravity model, the paper decomposes trade barriers into transportation, policy, and institutional dimensions using panel data from 2014 to 2023 for China’s neighboring countries. The results reveal significant spatial heterogeneity, with South Asia identified as the primary high-cost cluster. Institutional distance emerges as the most binding constraint, with a standardized coefficient of − 0.088—outperforming both customs clearance efficiency and non-tariff barriers in economic magnitude. Spatial comparison further indicates that regions with lower governance levels benefit more from infrastructure improvements, while regional cooperation mechanisms mitigate institutional frictions. The geographic perspective enhances the interpretation of spatial disparities and cross-border connectivity patterns. These findings provide spatially targeted policy implications for differentiated trade facilitation and regional integration under the BRI.
Rural green infrastructure supports agricultural ecological stability, landscape connectivity, and coastal rural land conservation. This study evaluated 3286.4 km2 of rural agricultural space in Dalian City using a multidimensional remote-sensing and geographic information system (GIS) framework. Sentinel-2 Multispectral Instrument (MSI) and Landsat 8/9 Operational Land Imager (OLI) imagery from the 2023–2025 growing seasons were preprocessed and classified with a class-balanced Random Forest to identify farmland shelterbelts, riparian buffers, pond wetlands, village green spaces, grassland patches, ecological ditches, and field-boundary vegetation. Evaluation against 690 independent validation samples yielded an overall accuracy of 90.72
At the global level, sustainable agro-territorial management depends on cadastral systems that ensure proper land use and protect land rights. Faced with persistent challenges, such as the delimitation and optimisation of agricultural plots, Artificial Intelligence (AI) is emerging as a disruptive tool. Its ability to analyse satellite images and geospatial data enables the automation of plot delimitation, identification of land-use patterns, prediction of crop yields, and monitoring of territorial changes, redefining the future of the agricultural cadastre. This study explores AI applications in agricultural cadastre through a systematic review and bibliometric mapping of publications indexed in Scopus and Web of Science (WoS) for territorial sustainability. A keyword search was conducted to find scientific documents related to the study topic. In addition, AI contributions to agricultural planning activities that integrate the agricultural cadastre are analysed. The primary scientific contributors to agricultural cadastral management are Germany, China, the United States, India, and the Czech Republic. Germany and India lead the way in the use of Machine Learning (ML). At the same time, the USA, Slovenia, and Rwanda showed the highest representation of Deep Learning (DL), applying technologies such as satellite imagery, Unmanned Aerial Vehicles (UAV), Synthetic Aperture Radar (SAR), and Light Detection and Ranging (LiDAR) in urban and rural environments. AI has revolutionised key processes, including land management (29.03
As an important carrying area for China’s high-end equipment manufacturing and export, the Northeast old industrial base’s export efficiency improvement and potential release are of critical significance to regional revitalization and high-quality development of the manufacturing industry. This study builds a stochastic frontier gravity model embedding technological heterogeneity based on panel data of 32 trading partner countries from the three northeastern provinces (Liaoning, Jilin, and Heilongjiang) from 2014 to 2023. It systematically calculates and comparatively analyzes the differences in export efficiency and potential of high-end equipment in the Northeast old industrial base. The results revealed that Liaoning Province had the highest average equipment export efficiency, with all industries maintaining a high range of 0.68–0.75. However, the growth rate was close to zero and the growth momentum was insufficient. The average efficiency of various industries in Jilin Province was generally low, and the overall growth trend was negative, and development reached a bottleneck. Although the initial efficiency of Heilongjiang Province was not as good as that of Liaoning, all industries achieved positive growth, with a growth rate of 0.013–0.014. Looking at the three provinces as a whole, the power equipment industry had the largest theoretical potential release space (171.46
Urban housing markets are critical sites of inclusion and exclusion for forced migrants; however, the “gatekeeper” role of formal intermediaries has not been sufficiently examined in literature. This study investigates how real estate agents, acting as “urban managers”, shape the housing pathways and spatial segregation of Syrian refugees in Bursa, one of Türkiye’s major industrial metropolises. Drawing on semi-structured interviews with 27 real estate agents operating in three socioeconomically diverse central districts, the research identifies a three-layered exclusion mechanism. First, real estate agents rationalize the discriminatory demands of property owners through the myth of the “ideal tenant” and the discourse of physical risk management, thus reflecting customer prejudice in practice. Second, this exclusion from the formal market pushes refugees into an informal “shadow economy” driven by ethnic networks and unregistered brokers. The resulting overcrowded living conditions are then used by formal actors to further legitimize exclusion, creating a persistent vicious cycle. Finally, the state’s administrative quotas, intended to prevent ethnic agglomeration, intersect with market discrimination. Trapped between administrative restrictions and market barriers, refugees are forced into deeper segregation within urban space. This study contends that, in the absence of inclusive housing governance, urban integration policies are structurally undermined by these invisible boundaries inherent in local housing markets.
Rapid urban growth in megacities is transforming hydrological systems and sharply increasing the threat of waterlogging. In Dhaka, one of South Asia’s fastest-growing megacities, accelerating land-use transformation is intensifying stress on natural drainage systems. Despite this, most studies in Bangladesh rarely explore how continuous urban growth may influence waterlogging patterns, leaving a critical gap in understanding. This study examines the spatial relationship between urban expansion and waterlogging risk in Dhaka city for 2025 and projects future scenario of urban expansion for 2030 using a coupled Cellular Automata–Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery (2010, 2015, 2020, and 2025) and auxiliary geospatial datasets were analyzed to quantify urban growth dynamics using the urban expansion intensity index (UEII) and annual urban expansion rate (AUER). Urban waterlogging risk was assessed using a GIS-based analytical hierarchy process (AHP) integrating nine hazard indicators related to topography, hydrology, rainfall, and spectral characteristics, along with five exposure indicators representing land use, road access, settlement density, and population density. Results reveal 25.39 km2 of very high waterlogging risk and 40 km2 of high-risk zones concentrated in dense urban cores, including Mirpur, Kafrul, Lalbagh, Bangshal, Motijheel, Gendaria, and Shahjahanpur. UEII and AUER analyses indicate rapid peri-urban expansion, particularly in Turag, where UEII increased from 0.058
With the acceleration of global urbanization, urban road landscape planning and design face increasingly complex requirements for spatial organization, functional adaptability, and design efficiency. This article proposes a programmatic generation model for urban road landscapes that integrates three-dimensional convolutional neural networks (3D-CNNs) and attention mechanisms to improve the efficiency and accuracy of road landscape generation. The model extracts road spatial features through 3D convolution and uses attention mechanisms to weight and fuse multi-scale and multi-channel feature maps, thereby generating road landscape design schemes that are consistent with urban planning requirements. In the experimental section, this study used a three-dimensional urban road dataset containing roads, buildings, green belts, and transportation facilities. The fusion model achieved an accuracy of 92.3
This study investigates ecotourism as a sustainable livelihood option in Jhargram district, West Bengal, focusing on tribal participation and inclusive rural development. Using an integrated SWOT-AHP framework, it evaluates and ranks internal and external factors influencing ecotourism employment, tourist flow, expenditure, and job patterns. Primary data were gathered from 415 respondents across 16 sites in five blocks. Of these, 200 local stakeholders, either directly or indirectly involved in tourism, contributed to the SWOT analysis. Stratified and purposive sampling, semi-structured interviews, and Likert-scale questionnaires ensured comprehensive coverage, supplemented by secondary data from official reports and spatial datasets. Based on field survey estimates, Jhargram attracts approximately 1,19,250 tourists annually, with peak arrivals in winter (824 per day) and a marked decline during the monsoon season. Tourists spend an average of ₹1173.5 daily, generating an estimated y ₹10 crores annually. However, economic benefits remain unevenly distributed. The SWOT-AHP analysis reveals Opportunities (score: 3.83) and Strengths (3.81), such as unexplored sites, adventure tourism, and cultural heritage, which have significant potential to enhance rural livelihoods. Conversely, Weaknesses (3.25), including overreliance on primary sectors and limited tribal involvement (approximately 9
This article explains the persistence of lethal violence along the India–Bangladesh border (1972–2024) by advancing the concept of a killing–migration nexus. Drawing on a theory-driven qualitative analysis of secondary sources, human rights and NGO reports, ethnographic scholarship, media archives, and policy documents, the study integrates securitization theory, Mbembe’s necropolitics, and structuration approaches to show how diverse forms of mobility are reframed as security threats and routinely met with lethal enforcement. Findings demonstrate that killings are systematic, spatially concentrated in identifiable hotspots, and disproportionately affect marginalized civilians engaged in subsistence mobility such as cattle trade, farming, fishing, and circulatory labor. The analysis identifies three interacting mechanisms: securitizing discourse, necropolitical valuation of certain lives, and routinized institutional practices that produce a self-reinforcing cycle of violence and impunity. The paper concludes that technical reforms including hotlines and non-lethal equipment are insufficient without addressing the discursive, institutional, and socio-economic drivers of the nexus. It offers policy recommendations for accountability, bilateral institutional reform, and development-focused measures to reduce lethal enforcement and protect border communities.
Let S⊂ℝ^3 be a smooth embedded sphere whose normal curvatures have absolute value at most 1. We prove that if S is contained in an open ball of radius 2, then the body bounded by S contains a unit ball.
This study examines the spatial relationship between the built environment and loneliness across Tehran’s 22 districts using an integrated framework combining systematic review, geospatial analysis, and statistical modeling. The research data consist of survey responses from 4,749 residents, along with geospatial indicators derived from official datasets. Built environment indicators were initially identified through a systematic review using the PRISMA framework and subsequently reduced to three principal dimensions—public service access, housing and cultural amenities, and transportation and traffic—using Principal Component Analysis (PCA). These dimensions were incorporated as independent variables in an Ordinary Least Squares (OLS) regression model. The results indicate a statistically significant relationship between built environment characteristics and loneliness (R2 = 0.578; Adjusted R2 = 0.507; p < 0.01). Among the dimensions, housing and cultural amenities exhibit the strongest negative association with loneliness, followed by public service access, transportation and traffic. Spatial patterns reveal substantial intra-urban inequalities, with northern districts characterized by more favorable environmental conditions and lower levels of loneliness, whereas southern and southwestern areas show the opposite pattern. The results of spatial autocorrelation analysis confirm the absence of significant spatial dependence in the model residuals. The findings of this study provide a valuable evidence base for urban planners, policymakers, and urban designers to identify areas more vulnerable to loneliness and to prioritize evidence-informed urban interventions. They also underscore the importance of considering loneliness when planning and evaluating built environment policies and urban development projects.
Long-standing structural inequities have shaped distinct social contexts across Toronto’s neighbourhoods. This study aims to capture how these localized contexts may be associated with diverse COVID-19 risk across the city. A spatio-temporal Localized Conditional Autoregressive Model (ST-LCAR) was used to assess associations between population factors (age, sex, income, visible minority status, and education) and COVID-19 relative risk, accounting for spatial and temporal autocorrelation and local context through spatio-temporal random effects and piecewise intercepts. This study focuses on the first four complete waves of the COVID-19 pandemic across Forward Sortation Areas (FSAs) in the City of Toronto. A 10-percentage-point increase in the proportion of residents who identify as visible minorities in an FSA was associated with a 3
India has improved water, sanitation and hygiene (WASH) access but there are still considerable hidden subnational differences behind national averages. Using NFHS-5 (2019–21) household data and the WHO/UNICEF Joint Monitoring Programme service ladder, this study maps WASH poverty across all 543 of India’s Parliamentary Constituencies. A household is considered WASH poor when it has a service level in the ‘No-Service' category for at least one of three domains: water, sanitation and hygiene. At the cluster-level, data were geocoded and aggregated to constituencies, with analyses conducted using Global Moran’s I and Getis-Ord Gi* statistics. The results exhibit significant spatial clustering, with high deprivation concentrated in Bihar and Uttar Pradesh, disrupting the eastern belt but also extending into Jharkhand, Odisha and West Bengal whilst certain areas of southern and north-western constituencies are far less deprived. This analysis confirms significant spatial autocorrelation (Global Moran’s I = 0.640, p < 0.001), identifying a critical ‘deprivation corridor’ across Bihar, Uttar Pradesh, Jharkhand, and Odisha, where WASH poverty prevalence significantly exceeds the national average. The results show that WASH inequalities are regionally based and politically patterned, emphasizing the potential benefits of constituency-based analysis for planning, resource allocation, and accountability. The study goes beyond traditional approaches to SDG 6. Clean Water and Sanitation in India, by spatially nested deprivation within electoral boundaries.
Agricultural sustainability in the Western Himalayan region was seriously threatened by climate change. This study examined how climate variability affected the spatio-temporal variation in horticultural efficiency in Himachal Pradesh, India, from 2012–13 to 2021–22. Using long-term climatic datasets (1981–2022) and district-level horticultural data, climatic trends were analysed through the Mann–Kendall test, while horticulture fruit crop concentration and efficiency were evaluated using the Location Quotient method, as proposed by Bhatia (1965), and the Agricultural Efficiency method, as proposed by Bhatia (1967a, 1967b). Results revealed a highly significant increase in mean annual temperature and in annual precipitation, specific humidity, and relative humidity. Seasonal analysis showed notable monsoon warming (minimum temperature) and cooling (maximum temperature), reflecting a shifting thermal regime. The horticulture area and production showed mixed growth patterns, with apples and mangoes expanding, while almonds and pears declined. Spatial assessment revealed clear regional disparities in horticulture efficiency, with some districts consistently outperforming others. The findings highlighted climate variability as a major driver of horticultural performance, underscoring the urgent need for region-specific interventions to support climate-resilient, sustainable horticulture planning in Himachal Pradesh.
Rapid urbanization in Global South cities poses significant threats to ecosystem services and long-term environmental sustainability. This study quantifies the spatiotemporal dynamics of land use/land cover (LULC) changes and their impact on ecosystem service values (ESV) in Lahore, Pakistan, over 30 years (1994–2024). Using Landsat satellite imagery integrated with benefit transfer valuation methods, the study analyzed LULC transformations and their ecological consequences. Results reveal substantial urban expansion, with built-up areas increasing from 31,700 ha to 67,900 ha (114 β =-0.028,p<0.01 ), rising land surface temperature ( β =-0.115,p<0.05 ), and deteriorating air quality ( β =-0.009,p<0.05 ) are significant determinants of ESV decline. These findings highlight the urgent need for integrating ecosystem service valuation into urban planning frameworks. The study proposes nature-based solutions, green infrastructure development, and ecological zoning as actionable pathways to enhance urban resilience. So, the study provides empirical evidence and policy-relevant insights for sustainable urban governance in rapidly urbanizing regions of the Global South.
Conflicts in controlling or using natural resources, arising from geopolitical risks (GPR), which have become a new normal today, can damage natural ecosystems and threaten sustainable development. In this context, GPR can turn the positive impact of supporting independent variables on sustainable development (SD) into a negative one. This research investigates the moderating effects of GPR on SD through renewable energy consumption (REN) and economic complexity (EC) in 25 oil-producing countries with relatively higher oil-reserve-related risks between 2000 and 2021. The panel quantile regression analysis reveals. (i) While using REN individually improves SD in countries with high SD (in the 80th and 90th quantiles), this improving effect turns to a worsening effect due to its interaction with GPR, which shows the moderating impact of GPR. (ii): While EC individually improves SD across all quantiles, these improving effects persist in countries with low SD (in the 10th, 20th, 30th, and 40th quantiles) even when complexity interacts with GPR, showing that GPR has no moderating impact on SD. Therefore, policymakers should investigate why the interaction of REN with GPR worsens SD in highly sustainable countries and redevelop regulatory renewable energy policies accordingly.
While the digital economy significantly impacts the ecological environment (EE), their multidimensional spatial interactions remain underexplored. This study investigates the nonlinear and spatial effects of digital economy on carbon emissions (CE) to uncover these complex regional interactions. Utilizing panel data from Chinese prefecture-level cities (2014–2023), this research constructs a digital economy index via the entropy-weighted TOPSIS method and measures CE using the IPCC approach. The analysis strictly employs GIS-supported spatial visualization, mediation models, and the Spatial Durbin Model. Empirical results reveal a significant inverted U-shaped relationship between digital economy and CE, with a turning point around 0.40. By 2023, the average digital economy level exceeded this threshold, indicating an overall transition to a carbon reduction effect. Technological innovation plays a partial mediating role, accounting for 14.5
Understanding long-term land-use and land-cover (LULC) change processes is important for achieving sustainability in urban development and environmental management. This research assessed the spatio-temporal patterns of LULC change within the coastal cities of Jiangsu Province, China; specifically, Yancheng, Taizhou, and Nantong during the time periods of 2004, 2014, and 2024, through the application of a multi-temporal Landsat remote sensing data set. A supervised Random Forest-based classification was employed to generate LULC maps, followed by change detection and transition analysis. Furthermore, the Cellular Automata-Markov (CA–Markov) model was applied to simulate and predict future LULC changes. The classification accuracy assessments indicated high overall accuracies of 89.60
