Search Results (222)

This study explores the interplay of climate vulnerability and social capital in two rural communities: Brajaballavpur, a high-climate-prone village in the Indian Sundarbans characterized by high ecological fragility, recurrent cyclones, and saline water intrusion affecting water access, livelihoods, and infrastructure; and Jemua, a low-climate-prone village in the land-locked district of Paschim Bardhaman, West Bengal, India, with no extreme climate events. A total of 85 participants (44 in Brajaballavpur, 41 in Jemua) were selected through purposive sampling. Using a comparative qualitative research design grounded in ethnographic fieldwork, data were collected through household interviews, Participatory Rural Appraisals (PRAs), Focus Group Discussions (FGDs), and Key Informant Interviews (KIIs), and analyzed manually using inductive thematic analysis. Findings reveal that bonding and bridging social capital were more prominent in Brajaballavpur, where dense horizontal ties supported collective action during extreme weather events. Conversely, linking social capital was more visible in Jemua, where participants more frequently accessed formal institutions such as the Gram Panchayat, local NGOs, and government functionaries that facilitated grievance redressal and information access, but these networks were concentrated among more politically connected individuals. The study concludes that climate vulnerability shapes the type, strength, and strategic use of social capital in village communities. While bonding and bridging ties are crucial in high-risk contexts, linking capital plays a critical role in enabling long-term social structures in lower-risk settings. The study contributes to both academic literature and policy design by offering a relational and place-based understanding of climate vulnerability and social capital. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

Past and current advances in artificial intelligence (AI) have resulted in a significant impact on business and accounting. Over time, AI has slowly transformed from the 1950s to today, from rule-based systems, also known as expert systems, to the deep learning architectures and sophisticated neural networks of modern generative AI. Early AI accounting applications of expert systems included a GAAP-based expert system to assess the appropriate accounting treatment for business combinations and an expert system to determine the proper type of audit report to issue. Recent accounting expert systems have been developed for document analysis, fraud detection, evaluating credit risk, and corporate default forecasting. The purpose of this study is to examine key events in the history of AI, current applications, and potential future effects pertaining to management accounting and society overall. In addition, the relationship of AI with economic and social factors will be evaluated. The study’s findings will be of interest to management accountants, businesspersons, academic researchers, and others who are concerned with artificial intelligence and its impact on management accounting and society overall. Full article

Transportation agencies face increasing challenges in identifying and prioritizing which infrastructure assets are most critical to maintain and protect, particularly amid aging networks, limited budgets, and growing threats from climate change and extreme events. However, existing prioritization approaches often lack consistency and fail to adequately incorporate diverse stakeholder perspectives. This study develops a systematic, stakeholder-informed method for ranking transportation assets based on their criticality to the overall transportation system. As a novel approach, we use the analytical hierarchy process (AHP) and present a case study of the applied approach. Six criteria were identified for ranking assets: annual average daily traffic (AADT), redundancy, freight output, roadway classification, Social Vulnerability Index (SoVI), and tourism. Stakeholder input was collected via an AHP-based survey using pairwise comparisons and translated into weighted rankings. Thirty complete responses (13.2% response rate) from experts (i.e., engineers, analysts, planners, etc.) were analyzed, with the resulting ranks from highest to lowest priority being AADT, redundancy, freight output, roadway classification, SoVI, and tourism. Stability analysis confirmed that rankings were consistent with a minimum of 15 responses. The resulting method provides a practical, replicable tool for agencies to perform statewide vulnerability/resiliency assessments ensuring that decision-making reflects a broad range of expert perspectives. Full article

Green infrastructure (GI) plays a critical role in addressing urban fragmentation and flood vulnerability, especially in rapidly expanding cities where its optimal placement is essential to maximize social, ecological, and economic benefits. This study presents a multiscale methodology integrating spatial configuration and hydrological modeling to guide GI implementation in Ciudad Juárez, Mexico. The approach applies space syntax theory, fuzzy logic, and geospatial analysis across three spatial levels. At the city scale, the method evaluates street network integration and service accessibility to identify urban centers with potential for regeneration through GI. At the local scale, a 214-hectare area is analyzed using fuzzy multi-criteria decision analysis and Multiscale Geographically Weighted Regression (MGWR) to select the optimal locations for different nature-based solutions. At the microscale, spatiotemporal hydrological simulations of a 25-year return period rainfall event quantify the runoff and infiltration dynamics under different GI configurations, achieving infrastructure layouts that infiltrated over 1000 m³ of stormwater. This framework addresses the research gap on how connectivity and morphology can be combined to prioritize interventions based on flood risk data. The results offer a transferable strategy for integrating Sustainable Urban Drainage Systems (SUDSs) into complex data-scarce urban environments, supporting long-term urban resilience and multifunctional land-use planning. Full article

The increasing frequency and intensity of hydrological events driven by climate change, particularly floods, present significant challenges for the design, construction, and maintenance of bridges and culverts. Additionally, the inadequate capacity of existing structures has resulted in substantial financial burdens on governments due to flood-related damages and the costs of their rehabilitation and replacement. A further concern is the oversight of existing hydraulic design standards, which primarily emphasize structural capacity and flood height, often overlooking broader social and environmental implications as two main pillars of sustainability. This oversight becomes even more critical under changing climatic conditions. This paper proposes a flood risk-based framework for the sustainable design, construction, and modification of bridge and culvert infrastructure in response to climate change. The framework integrates flood risk modeling with environmental and socio-economic considerations to systematically identify and assess vulnerabilities in existing infrastructure. A multi-criteria analysis (MCA) approach is employed to rapidly evaluate and integrate climate change, social, and environmental factors, such as population density, industrial activities, and the ecological impacts of floods following construction, alongside conventional hydrologic and hydraulic design criteria. The study utilizes hydrologic and hydraulic analyses, incorporating transportation networks (including roads, railways, and traffic) with socio-economic data through a GIS-based flood risk classification. Two case studies are presented: the first prioritizes the replacement of existing main bridges and culverts in the Ankara River Basin using the proposed MCA framework, while the second focuses on substructure sizing for a planned high-speed railway section in Mersin–Adana–Osmaniye–Gaziantep, Türkiye, accounting for climate change and upstream reservoirs. The findings highlight the critical importance of adopting a comprehensive and sustainable approach that integrates advanced risk assessment with resilient design strategies to ensure the long-term performance of bridge and culvert infrastructure under climate change. Full article

Understanding consumer heterogeneity is crucial for analysing attitude formation and its role in innovation diffusion. Traditional top-down models struggle to reflect the nuanced characteristics and activities of the consumer population, while bottom-up approaches like agent-based modelling (ABM) offer the ability to simulate individual decision-making in social networks. However, current ABM applications often lack a strong theoretical foundation. This study introduces a novel, theory-driven ABM framework to examine the heterogeneity of consumer attitude formation, focusing on electric vehicle (EV) adoption across consumer segments. The model incorporates non-linear decision-making rules grounded in established consumer theories, incorporating Rogers’s Diffusion of Innovations, Social Influence Theory, and Theory of Planned Behaviour. The consumer agents are characterised using UK empirical data, and are segmented into early adopters, early majority, late majority, and laggards. Social interactions and attitude formation are simulated, micro-validated, and optimised using supervised machine learning (SML) approaches. The results reveal that early adopters and early majority are highly responsive to social influences, environmental beliefs, and external events such as the pandemic and the war conflict in performing pro-EV attitudes. In contrast, late majority and laggards show more stable or delayed responses. These findings provide actionable insights for targeting segments to enhance EV adoption strategies. Full article

When power system equipment fails due to disasters, resulting in the isolation of parts of the network, the loads within the isolated system cannot be guaranteed a continuous power supply. However, for critical loads—such as hospitals or data centers—continuous power supply is of utmost importance. While distributed energy resources (DERs) within the network can supply power to some loads, outages may lead to compensation and fairness issues regarding the unsupplied loads. In response, this study proposes a methodology to determine the appropriate power contract price for key customers by estimating the unsupplied power demand for critical loads in isolated networks and incorporating both outage compensation costs and voltage stability penalties. The microgrid under consideration comprises DERs—including electric vehicles (EVs), fuel cell electric vehicles (FCEVs), photovoltaic (PV) plants, and wind turbine (WT) plants—as well as controllable resources such as battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). It serves both residential load clusters and critical loads associated with social infrastructure. The proposed methodology is structured in two stages. In normal operating conditions, optimal scheduling is simulated using second-order conic programming (SOCP). In the event of a fault, mixed-integer SOCP (MISOCP) is employed to determine the optimal load shedding strategy. A case study is conducted using the IEEE 123 bus test node system to simulate the outage compensation cost calculation and voltage penalty assessment processes. Based on this analysis, a contract price for key customers that considers both disaster-induced outages and voltage impacts is presented. Full article

This study posits the need for a conceptual multi-risk management approach for fresh produce, an essential product category for societal resilience and one constantly affected by climate change, policy volatility, and geopolitical disruptions. The research started with a literature-informed typological risk mapping, leading to Gephi ver 0.10.1 visualizations of networks related to this trade. Network analysis using 2024 bilateral trade data revealed a core–periphery topology, with the United States, Spain, and the Netherlands as central hubs. A gravity-based simulation model was, lastly, used to address the following question: what structural vulnerabilities and flow-based sensitivities define the global fresh produce trade, and how do they respond to simulated multi-risk disruptions? The model used the case of the USA as a global trade hub and induced two compounding risks: a protectionist tariff policy shock and a climate-related shock to its main supplier. The conclusion was that the fragility in the fresh produce trade enhances the cascading effects that any risk event may have across the environmental, economic, and social sustainability dimensions. This paper emphasizes the need for anticipatory governance, the diversification of trade partners, and investment in cold chain resilience, offering a means for policymakers to acknowledge the risk and mitigate the threats to the increasingly fragile fresh produce trade. Full article

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies. Full article

“Moral shocks” are unexpected events or pieces of information that so deeply challenge one’s basic values and sense of the world that they profoundly reorient a person’s understanding of life and even self. Yet those who experience significant moral shocks rarely participate in related activism and instead experience grief as highly privatized and apolitical, a reality that serves the status quo and most powerful. This article considers how religious resources can help metabolize private grief into public lament and catalyze political grievance. Analyzing the rise of gun control activism after an elementary school mass shooting in Nashville, Tennessee, I argue religious resources help metabolize moral shocks into social change in five significant ways: (1) cultivating practiced, purposeful pathos, (2) offering collective lament, (3) building networked resiliency materially and theologically, (4) risking new alliances of accompaniment, and (5) storying hope. This case analysis contributes to a broader claim for political theology: Christianity can be understood as a movement based on a moral shock. This framing then animates practices of care to accompany those in moral distress and help disciple grief into a movement of faith that resists death-dealing political and social policy. Full article

Rising global temperatures and increasing extreme weather events pose challenges to social stability and public security. This study examines the relationship between extreme weather and crime in China using fixed-effects quasi-Poisson and negative binomial regression models, along with a generalized additive model to explore nonlinear effects. The results show that extreme heat significantly increases crime, following an “S” shaped pattern. This intense heat heightens emotional instability and impulsivity, leading to a crime surge. While moderate heat reduces crime, extreme cold and heavy rainfall have no significant effects. These findings highlight the need for stratified policy interventions. Based on empirical evidence, this study proposes three key recommendations: (1) developing a weather warning and public security risk coordination system, (2) promoting community-based crime prevention through mutual assistance networks and infrastructure improvements, and (3) enhancing psychological interventions to mitigate mental health challenges linked to extreme weather. Integrating meteorological data, law enforcement, and interventions to help potential perpetrators can strengthen urban resilience and public safety against climate-induced crime risks. Full article

Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood–road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. This methodology was validated through a case study of the Shanmei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shanmei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shanmei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure. Full article

Location-based social networks (LBSNs) leverage geo-location technologies to connect users with places, events, and other users nearby. Using GPS data, platforms like Foursquare enable users to check into locations, share their locations, and receive location-based recommendations. A significant research gap in LBSNs lies in the limited exploration of users’ tendencies to withhold certain location data. While existing studies primarily focus on the locations users choose to disclose and the activities they attend, there is a lack of research on the hidden or intentionally omitted locations. Understanding these concealed patterns and integrating them into predictive models could enhance the accuracy and depth of location prediction, offering a more comprehensive view of user mobility behavior. This paper solves this gap by proposing an Associative Hidden Location Trajectory Prediction model (AHLTP) that leverages user trajectories to infer unchecked locations. The FP-growth mining technique is used in AHLTP to extract frequent patterns of check-in locations, combined with machine-learning methods such as K-nearest-neighbor, gradient-boosted-trees, and deep learning to classify hidden locations. Moreover, AHLTP uses association rule mining to derive the frequency of successive check-in pairs for the purpose of hidden location prediction. The proposed AHLTP integrated with the machine-learning models classifies the data effectively, with the KNN attaining the highest accuracy at 98%, followed by gradient-boosted trees at 96% and deep learning at 92%. Comparative study using a real-world dataset demonstrates the model’s superior accuracy compared to state-of-the-art approaches. Full article

Community detection is increasingly important in social networks with the rapid growth of big data, which provides a deep understanding of the mesoscopic structure of social networks. In this article, we propose a label improvement algorithm, DegreeRank Label Propagation (DRLP), which is based on the degree centrality of nodes and adopts a PageRank optimization strategy. We present a damping factor reflecting the affinity between nodes, which can be adjusted to affect the change of affinity between nodes caused by unexpected events, aiming to simulate interpersonal communication in real networks. Next, a novel importance index is designed for nodes to solve the random problem of existing similar algorithms by globalizing the local characteristics of nodes. We also develop an update algorithm with low time complexity during the label selection process to ensure the sum of influence propagation is maximized within each community. Experimental results verify that the algorithm achieves stable and excellent community partitioning results on real network datasets and artificial synthetic networks. Especially in large and medium-sized networks, our method demonstrates higher accuracy and better performance in terms of normalized mutual information (NMI) and modularity than other methods. Full article