Search Results (12,996)

Tornadoes pose growing threats to both communities and the built environment, yet few studies have quantified how spatial characteristics of the built environment interact with social and economic factors while influencing tornado impacts. This paper introduces an integrated metric that combines tornado risk and exposure to evaluate localized disaster impact. Focusing on Florida’s Panhandle, we examine how housing density and affordability, network connectivity, and urban form efficiency, together with demographic and socioeconomic attributes, shape tornado impacts at the U.S. census block group (CBG) level. To address spatial autocorrelation and non-stationarity, five statistical models were compared, including both global and local spatial regressions. The findings indicate that multiscale geographically weighted regression (MGWR) most effectively captures the spatial heterogeneity of tornado impacts. Built-environment and affordability factors show clear spatial heterogeneity— smart location indexand housing cost burden (h_ami) are positively associated with tornado impact in CBGs near Tallahassee and parts of Pensacola—suggesting amplified impacts in location-efficient urban areas where exposure is concentrated and affordability stress may limit preparedness and recovery. In contrast, network density is negatively associated with the impact of key clusters, consistent with the idea that denser, more redundant road networks can reduce canopy-weighted disruption by providing alternative routes for emergency access and restoration. Overall, these findings can inform our understanding of how the built environment influences tornado exposure, offering critical insights for planners and policymakers seeking to strengthen communities against tornadoes. Full article

The increasing densification of cell-free massive multiple-input multiple-output (MIMO) networks makes access point switch on/off (ASO) a key mechanism for improving energy efficiency in future wireless systems. While reinforcement learning (RL) has been explored for ASO, differences in modeling assumptions and evaluation scope leave open questions regarding robustness and scalability. In this work, ASO is investigated from an explicit energy-efficiency perspective using a RL framework based on Proximal Policy Optimization (PPO). The policy learns state-dependent AP activation under partial observability using compact per-access point (AP) large-scale fading statistics and power parameters, without requiring instantaneous small-scale channel state information or combinatorial search, enabling practical online implementation. A comprehensive evaluation is conducted under a unified and reproducible simulation framework across three cell-free deployment scenarios of increasing size that preserve AP density while incorporating realistic channel and power consumption models. Performance is assessed through both average and distribution-based metrics. Numerical results show that the PPO-based policy consistently outperforms random activation and the all-on baseline, achieving energy-efficiency improvements of up to 66% and nearly 50%, respectively, while activating a comparable number of APs. Moreover, the learned policy maintains robust performance as the network scales, reducing the likelihood of highly energy-inefficient operating regimes. Full article

Forests are important food granaries. The accessibility of a healthy diet is the key factor in food and health equity. However, there is a lack of research focusing on its influence on locals at different levels of development. China’s population comprises various groups of farmers, allowing for the comparison of influence pathways across different economic levels of farmers. This research explores the topic with an empirical study conducted in Jiangxi Province, China, using data from 1939 valid responses collected across 216 villages. The analysis was performed using a mixed-effects ordered logistic model and a mediation effect model. The results of the baseline and mediation effect analyses reveal that there are four influence pathways. First, farmers’ forest resource endowments play a significant role in improving farmers’ perception of healthy diet accessibility (direct access type). Second, farmers’ forest resource endowments increase the accessibility of healthy diets by reducing the perceived costs of healthy diets (cost-relieving type). Third, farmers’ forest resource endowments increase the accessibility of a healthy diet by enhancing the perceived quality of the natural environment (quality scarcity type). Fourth, farmers’ forest resource endowments increase the perceived environmental quality, decrease the perceived costs of healthy diets, and affect the perception of healthy diets’ accessibility (cost-reducing type). The results of heterogeneity analysis based on the independent variables (health-related information, age, education level, disposable income, household size, communication and transportation convenience) reveal that for disadvantaged groups, the effect type tends to be the “direct access type” and “cost-relieving type”, and for advantaged groups, the effect type tends to be the “quality scarcity type”. Through empirical analysis, this study explains how forest resource endowments of different farmer groups influence their access to healthy diets, which lays a foundation for better understanding the association and formulating relevant policies. Decision makers should recognize the distinct influence of forest resource endowments on different farmer groups and develop policies related to forest resource management and healthy diets for farmers. Full article

Judicial documents have become a significant data source for crime geography research, offering advantages in accessibility and scale compared to highly restricted police-recorded crime data. However, extracting crime addresses from these texts is challenging due to sparse, inconsistent, and incomplete address information. Without proper classification, errors in geocoding and spatial analysis can arise, compromising data quality. To address these limitations, we employed large language models (LLMs) and a structured prompt engineering strategy tailored for this task. Specifically, we propose a fine-tuned LLM, named CAEC_LLM, to extract addresses from judicial documents and classify these crime addresses at various categories with different spatial scales. Experimental results demonstrate that the model achieved an F1-score of 0.79 for address extraction and a classification accuracy of up to 0.74 for the best-performing category, significantly outperforming other LLMs. This study makes two primary contributions: (1) designing an address classification scheme specifically for crime addresses, and (2) developing a fine-tuned LLM for extracting and classifying crime addresses from Chinese judicial documents, enabling LLMs to be used to classify crime addresses into different categories on a spatial scale. These advancements facilitate more accurate crime pattern analysis and data-driven urban planning. Full article

Energy-based cities often develop resource-dependent spatial structures that reinforce carbon-intensive daily routines, yet the mechanisms linking neighborhood form to low-carbon consumption remain unclear. This study investigates the core urban area of Ordos, China, by integrating geographic information system (GIS)-derived 5D built-environment indicators with questionnaire data from 825 residents and estimating a structural equation model (SEM) with bootstrap mediation tests. The results show clear dimension-specific effects. Density, land-use mix, and street connectivity have significant positive total effects on low-carbon consumption behavior and retain significant direct effects after the mediators are introduced, indicating partial mediation. By contrast, distance to transit and shopping accessibility operate mainly through the perceived built environment and psychological factors, with non-significant residual direct effects, indicating full mediation. Psychological factors show the strongest direct association with behavior (β = 0.545, p < 0.001), and the perceived built environment also exerts an indirect effect through psychological factors. Overall, the findings indicate that low-carbon transition in energy-based cities depends not only on spatial upgrading, but also on neighborhood environments that enhance perceived convenience and behavioral readiness. Full article

Low Earth Orbit (LEO) satellite signals of opportunity (SOP) provide a viable positioning alternative in GNSS (Global Navigation Satellite System)-denied environments, yet their accuracy is fundamentally constrained by the low-quality orbital data typically available, such as SGP4 (Simplified General Perturbations model 4) predictions derived from Two-Line Elements (TLEs). To address this limitation, this paper proposes a dynamics-informed non-causal deep learning framework that enhances low-quality orbital data into high-fidelity trajectories for accurate SOP positioning. The proposed Non-Causal Dynamics-Informed Representation Temporal Convolutional Network (Non-Causal DIR-TCN) integrates phase space reconstruction and a Temporal Convolutional Network to explicitly model the chaotic dynamics inherent in LEO orbits, while relaxing the causality constraints of standard temporal convolutions to utilize both past and future context from the available SGP4 stream. Experimental results demonstrate that the framework significantly reduces orbit estimation errors and accelerates model convergence. When applied to LEO-SOP positioning, it achieves approximately 20% improvement in 2D positioning accuracy compared to conventional SGP4-based methods. This work effectively bridges the gap between accessible low-precision orbital data and high-accuracy state estimation, advancing the practical deployment of opportunistic signals for resilient positioning in challenging environments. Full article

Access to quality seed is a critical driver of smallholder productivity and household food security in South Africa, yet rural communities in the Eastern Cape continue to rely heavily on informal seed systems. Limited seed literacy among farmers and vendors is widely recognized as a constraint to the effective selection and use of high-quality seed. The purpose of this study is to assess seed literacy levels among smallholder farmers in KwaMkhiva village and evaluate how knowledge gaps shape farmers’ seed sourcing patterns and access to quality seed. The study hypothesizes that low seed literacy significantly increases reliance on informal seed systems and reduces adoption of certified or improved varieties. A quantitative, cross-sectional survey design was used to collect data from 50 smallholder farmers and 12 informal seedling vendors, complemented by semi-structured interviews with three extension officers. Descriptive statistics, chi-square tests, correlation analysis, and a composite Seed Literacy Index (SLI) were employed to assess literacy dimensions and their association with seed choices. Findings show that 49% of farmers rely on local markets and 40% use farm-saved seed, with 75% assessing quality visually rather than through germination or varietal indicators. Only 10% had received any seed-related training, and awareness of seed adaptability and crop rotation was below 20%. Higher SLI scores were positively associated with adoption of certified seed (r = 0.42, p < 0.01) and crop diversification. The study concludes that seed literacy is a critical yet underserved capability that shapes smallholder seed access within dual seed economies. Strengthening farmer-centred seed literacy programmes, revitalising extension services, and supporting community seed banks could enhance access to quality seed and improve smallholder resilience. Full article

Modern enterprise information systems must simultaneously support complex organizational structures, ensure robust security, and remain scalable and maintainable over time. Traditional Role-Based Access Control (RBAC) models, while effective for permission management, operate primarily as post-design security layers and do not provide a unified methodology for structuring system architecture. This paper introduces the Zoned Role-Based (ZRB) model, a mathematically formalized and comprehensive framework that integrates organizational modeling, system design, implementation, access control, and long-term maintenance. ZRB models an organization as a hierarchy of zones, each containing its own roles, applications, operations, and users, forming a recursive Zone Tree that directly mirrors real organizational semantics. Through formally defined role hierarchies, zone-scoped permission sets, and inter-zone inheritance mappings, ZRB provides a context-aware permission calculus that unifies authentication and authorization across all zones. The paper presents the theoretical foundations of ZRB, a multi-phase engineering methodology for constructing integrated enterprise systems, and a complete implementation architecture with permission inference, navigation design, administrative subsystems, and deployment models. Primary validation and evaluations across several developed systems demonstrate significant improvements in permission accuracy, administrative efficiency, scalability, and maintainability. ZRB thus offers a rigorously defined and practically validated framework for building secure, scalable, and organizationally aligned enterprise information systems. Full article

The genus Polygonatum encompasses numerous species with complex phenotypes, necessitating robust molecular markers for accurate species identification and superior germplasm screening. This study identified and developed SSR markers based on transcriptome analysis of three Polygonatum species to assess the genetic diversity of Polygonatum resources. The results showed that a total of 43,217 SSR loci were detected, and 31,703 primer pairs were successfully designed. Characterization of SSR motifs revealed mono-nucleotide repeats (SNRs) were the most frequent (59.45%). Unigenes containing SSRs were annotated across seven databases. In KEGG, 222 pathways were assigned, with genes annotated to carbohydrate metabolism being the most abundant. To validate and apply these markers, 100 primer pairs covering all eight SSR locus types were tested across 21 Polygonatum accessions. Of these, 49 polymorphic markers were identified, revealing high genetic diversity, with average expected heterozygosity (He) and polymorphism information content (PIC) values of 0.763 and 0.718, respectively, alongside significant population differentiation (Fst = 0.307). Cluster analysis grouped 21 accessions into three groups, which correlated with certain agronomic traits. Nine core markers were selected that effectively distinguished six species and intraspecific groups. Notably, the FB-9 marker, associated with polysaccharide biosynthesis, effectively discriminated among six Polygonatum species and also distinguished distinct germplasm resources within P. kingianum var. grandifolium. Overall, the transcriptome-derived SSR markers validated in this study constitute valuable resources for gene function analysis, population genetics research, and variety identification and genetic improvement of Polygonatum. Full article

Information disorders are a significant global issue but are particularly relevant and underexplored in the Middle East, where political instability contributes to their spread. Despite the critical role fact-checking platforms play in combating information disorders, we need to learn more about how these platforms operate in such a complicated regional context. This study analyzes three fact-checking platforms: Akeed (Jordan), Teyit (Turkey), and Factnameh (Iran) to better understand the differences in how they approach fact-checking, the strategies they use, and the obstacles they face, including social and political conditions but also regarding the impact of AI. Using a multimethod qualitative approach based on document analysis and interviews, the study highlights recurring issues such as censorship, limited access to data, and audience engagement. The findings reveal how these platforms address these challenges and provide valuable insights into effective methodologies for fighting mis-/disinformation. The results offer broader implications for enhancing media literacy, strengthening the role of fact-checking platforms in the Middle East, and providing recommendations for best practices that can be applied regionally. Full article

Regional energy self-sufficiency based on microgeneration from clean, local energy sources is essential and strategic for meeting growing electricity demand. In this context, initiatives driven by local governments are decisive in achieving such progress. This study proposes a methodology for sizing photovoltaic (PV) capacity at the parish level, which is the basic political–administrative unit in Ecuador. Rooftop-based microgeneration and self-supply are considered to entail minimal environmental impact while offering significant potential to meet the basic energy demands of buildings in the Andean equatorial climate. The results demonstrate that, using accessible tools such as drones, computer-aided design software, and Agisoft Metashape, and through low-labour processes, it is feasible to estimate the PV potential of buildings at the parish scale. A total of 1698 rooftops were surveyed, and after discarding those with precarious construction materials, the estimated solar potential was found to be between ten and twenty-three times higher than the electrical demand of the analysed parishes. The estimated annual generation potential reaches 28,101 MWh, compared to an annual demand of 1827 MWh for both parishes combined. The proposed process enables the incorporation of rooftop-based technological capacity, relying on a low-technology, affordable methodological approach and instruments for low-income parish governance offices, with low-density populated areas as the main novelty, providing clear information to both authorities and the local population. Full article

Public debates about psychoactive substances have traditionally been organized around the pharmacology of compounds and the institutional control of supply. In digitally mediated societies, however, the pathways through which people encounter psychoactives are increasingly informational: search engines, recommender systems, social platforms, and—distinctively—conversational AI. These systems do not merely deliver neutral facts. They rank, frame, personalize, and conversationally validate claims in ways that can shape perceived norms, acceptable risk thresholds, and willingness to seek help. This opinion advances the concept of AI-mediated exposure to capture how algorithmic curation and interactive dialogue become upstream determinants of psychoactive-related harms and benefits across the continuum from everyday medicines to non-medical use. From a social-scientific ethics perspective, the central question is not whether AI is “good” or “bad,” but what obligations apply when AI performs interpretive authority in contexts characterized by vulnerability, stigma, and unequal access to trusted expertise. The paper argues for an ethics-centered governance framework grounded in four commitments: epistemic responsibility (how claims are generated, warranted, and communicated), relational responsibility (how users are treated in moments of uncertainty, distress, and stigma), distributive justice (who benefits and who bears risk under unequal conditions), and accountability (how behavior is evaluated, contested, and corrected over time). The aim is to treat conversational AI as a public-facing institution whose design choices must be ethically legible and publicly contestable, oriented toward harm reduction without intensifying surveillance, moralization, or inequity. Full article

Quantifying the vulnerability of buildings is fundamental to seismic risk mitigation, and fragility curves are among the most widely used tools for this purpose. With the growing availability and access to post-earthquake damage data from reconnaissance campaigns and measured ground motion parameters, new opportunities have emerged to derive and refine fragility models, improving the seismic damage prediction models and the quantification of seismic risk. Following the 2010 Haiti earthquake, extensive datasets were compiled that include both structural and geotechnical characteristics, as well as observed damage states for a wide range of building classes. In this study, two such datasets are employed in a complementary manner to develop disaggregated fragility models to probabilistically quantify structural damage conditioned on specific building and site attributes, rather than parameters averaged over an entire building stock. The first dataset comprises approximately 335,000 building assessment tags collected, and it is used to develop a set of baseline fragility curves conditioned on parameters such as number of stories, soil type, wall system, topography, roof type, and building age. The second dataset comprises 170 reinforced concrete buildings with more detailed and reliable information, and it is used to update the baseline fragility models using Bayesian estimation. The Bayesian updating introduces fragilities specific to the presence of captive columns and priority index, a metric representing the ratio of wall and column area to floor area. Bayesian updating is performed within a Markov Chain Monte Carlo (MCMC) framework using the Metropolis–Hastings algorithm. The resulting fragility functions reveal the high vulnerability of the Haitian building stock and demonstrate how multiple site and structural attributes influence seismic fragility. Full article

Rapid access to building intelligence is critical for emergency response, yet paper fire safety instructions (FSi) often provide limited utility under stress. This structured narrative review addresses the “information gap” between unit arrival and decision-making by analyzing the legal admissibility, technological requirements, and security risks of digital FSi across Poland, Germany, France, Belgium, and Hungary. While no explicit prohibition of digital forms was identified, enforcement practices prioritize paper as the evidentiary master. Consequently, we propose a hybrid model: a paper master for compliance and redundancy, supplemented by a digital operational overlay accessed via “zero-install” offline-first progressive web apps (PWA). The review defines a minimum operational dataset (MOD)—prioritizing critical data like utility shut-offs and hazards over full documentation—and addresses cybersecurity threats, specifically QR-phishing (“quishing”). We conclude that the hybrid model minimizes legal and operational risks while significantly reducing time-to-information, provided that strict content identity and change management protocols are maintained. Full article

The revitalization of vitality in historic cultural districts can enhance a city’s cultural attractiveness and promote the upgrading of the urban cultural industry and sustainable development. Revealing the threshold and synergistic effects of different districts’ scene elements on district vitality helps to identify the distribution patterns of district vitality and provides a basis for managerial decision-making. This study first uses a geographic information system (ArcGIS) to overlay Baidu heatmaps with the street-network distribution in order to depict the spatiotemporal heterogeneity of district vitality and to compute vitality values by partitions at the district scale. Subsequently, based on an explanatory framework that integrates the physical space and subjective cognition, multi-source data such as street-view panoramas and points of interest (POIs) are quantified to obtain scene-element values for each unit area. Then, the scene-element values and vitality values are integrated into a consolidated database. Additionally, the LightGBM model and the SHAP method are employed to evaluate each element’s marginal contribution and relative importance to district vitality, thereby screening out the key scene elements. Finally, by means of SHAP dependence plots and interaction-effect analysis, the threshold intervals of the key elements and their synergistic relationships are identified, revealing the nonlinear threshold effects and synergies by which scene elements influence spatial vitality. The results show that during rest days, district vitality exhibits stronger diffusion, and the synergistic effect between Leisure-Facility Attractiveness and Street-Network Accessibility is the most prominent in enhancing vitality. High Exhibition-Facility Attractiveness is difficult to sustain crowds on its own; only when Leisure-Facility Attractiveness is likewise high does its effectiveness increase significantly. When Transport Accessibility is within the 0.20–0.40 interval, the positive effect of Leisure-Facility Attractiveness is significantly amplified. An excessive Traditional–Modern Facility Mix readily leads to homogenization of districts; therefore, when introducing modern business formats, local cultural characteristics must be retained. Overall, the generation of district vitality relies more on the synergy between material factors and subjective cognition than on improvements to any single element. The findings of this study provide suggestions for the planning of scene elements and the enhancement of vitality in historic cultural districts. Full article