Search Results (6,105)

Afghanistan is highly vulnerable to climate-driven extremes because of its combination of rugged geography and socio-political instability. Frequent events of extreme precipitation, floods, and droughts pose severe socio-economic and environmental challenges. Floods are particularly destructive, yet national-scale potential flood risk in Afghanistan has not been systematically assessed, largely due to limited data and field access. This study addresses this gap by mapping flood susceptibility, vulnerability, and risk using remote sensing (RS) and geographic information systems (GIS) at both subbasin and provincial scales. We apply a hybrid approach that combines Principal Component Analysis (PCA) to identify key environmental, climatic, and socio-economic indicators with the Analytic Hierarchy Process (AHP) to derive consistent weights and reduce subjectivity in decision-making. The results show that the eastern and northeastern ssubbasins especially within the Panj-Amu and Kabul River basins, have the highest flood susceptibility due to intense precipitation, steep terrain, and efficient drainage. Vulnerability increases in the densely populated northern and northeastern provinces, where land-use change and socio-economic constraints elevate flood-related impacts. Overall, 31% and 20% of study areas are classified as Very High and High vulnerability zones, respectively. The composite potential flood-risk index identifies that approximately 24% and 22% of Afghanistan fall within Very High and High flood risk zones, concentrated in the northern and eastern provinces. Model performance, evaluated using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC), indicates strong agreement between mapped Very High/High risk zones and frequently flooded provinces, with the upper-threshold scenario yielding an AUC of 0.913. These findings support targeted resource allocation, mitigation planning, and disaster-risk reduction in data-scarce and conflict-affected mountain regions. Full article

This study develops a multi-criteria and AI-assisted optimization framework that integrates the Analytic Hierarchy Process (AHP), K-means clustering, and Genetic Algorithm (GA) optimization within a Geographic Information System (GIS) environment to optimize electric vehicle (EV) charging station deployment across Abu Dhabi’s urban–rural gradient. The model generates a community-level Spatial Suitability Index (mean = 0.47) based on residential, commercial, and accessibility factors, which inform clustering into five deployment typologies reflecting distinct socio-spatial characteristics. GA-based spatial optimization under two policy pathways, Progressive and Thriving, balances accessibility, grid proximity, and utilization efficiency. Results show that the Thriving scenario achieves approximately 15–20% higher network coverage and equity compared to the Progressive case, demonstrating the value of adaptive, data-driven optimization for mixed urban–rural contexts. The integrated AHP–Clustering–GA approach provides a transferable and scalable blueprint for equitable, low-carbon mobility infrastructure planning in rapidly developing regions. Full article

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article

Background: Transgender and gender expansive (TGE) people experience high rates of interpersonal victimization, which has been linked to high rates of post-traumatic stress disorder (PTSD, a highly disabling and under-studied mental illness among TGE people). This systematic review identifies, classifies, critically appraises, and synthesizes the peer-reviewed literature describing the association between interpersonal victimization and post-traumatic stress among TGE people. This review collates what is known about the associations between victimization and PTSD among TGE people and makes recommendations to guide future research and intervention development. Methods: Searches were conducted across five databases (PubMed, Embase, Web of Science, PsycInfo, and CINAHL) following PRISMA guidelines. Inclusion criteria were: English language; peer-reviewed original research; articles describing the association between victimization and PTSD among TGE youth or adults; reporting TGE-specific data. Exclusion criteria were: reviews, commentaries without original data, dissertations or theses, conference abstracts, animal studies, studies without TGE-specific findings, and case studies. Quality appraisal was completed for all studies, which included a discussion of bias. Data extraction was completed by two independent authors, and conflicts were resolved by a third. Data were stratified by gender identity, race or ethnicity, and type of violence for further synthesis. Results: 25 studies were evaluated for design, measure quality, and key findings. Findings were highly consistent across studies: multiple forms of interpersonal violence (e.g., childhood maltreatment, sexual violence, intimate partner violence, and transgender-specific victimization) were significantly associated with PTSD symptom severity or diagnosis across diverse identities and geographic contexts. All studies examining childhood sexual abuse reported significant associations with PTSD outcomes, highlighting early life as a critical period of vulnerability. Samples were disproportionately White and adult, with limited examination of intersectional experiences shaped by race, ethnicity, and socioeconomic status. Discussion: Interpersonal violence-related PTSD among TGE populations reflects a pervasive and systemic pattern of trauma rooted in structural discrimination rather than isolated individual risk. Addressing this inequity requires multilevel prevention and intervention strategies. Future research should prioritize longitudinal designs, culturally responsive measurement tools, and intersectional analyses to inform prevention, clinical care, and policy responses. The majority of studies were cross-sectional designs, so causality cannot be inferred. Additionally, the samples were disproportionately White and adult, which may bias the magnitude of associations reported and limit generalizability to racially and ethnically diverse TGE populations. Although many studies reported race and ethnicity descriptively, none disaggregated violence-related PTSD outcomes by racial or ethnic group within TGE samples, representing a critical limitation for intersectional analysis. Full article

Hematologic malignancies arise and progress within a systemic ecosystem in which the gut microbiota is an increasingly recognized, partially modifiable component. Across acute leukemias, chronic lymphocytic leukemia, plasma cell disorders, lymphomas, and clonal myeloid neoplasms, human studies consistently report reduced microbial diversity, depletion of barrier-supportive, short-chain fatty acid-producing commensals, and enrichment of Gram-negative, pro-inflammatory, or hospital-adapted taxa. These alterations are associated with pre-leukemic clonal expansion, adverse genetic and immunological features, progression from precursor conditions, and inferior outcomes after chemotherapy, immunochemotherapy, chimeric antigen receptor T-cell therapy, and allogeneic hematopoietic stem cell transplantation. Mechanistic work in animal models and ex vivo systems demonstrates that microbiota-derived signals and metabolites—including Th17/IL-17-skewing consortia and the lipopolysaccharide intermediate ADP heptose sensed by the cytosolic receptor ALPK1—can actively modulate hematopoietic stem and progenitor cell fitness, inflammatory circuits, and malignant cell survival, supporting a causal role in disease biology. At the same time, major knowledge gaps remain because most human cohorts are small, single-center, and cross-sectional, frequently rely on 16S rRNA profiling, and are vulnerable to dietary, geographic, and treatment-related confounding. Within this context, three translational domains appear particularly promising: pharmaco-microbiomics, microbiome-informed risk stratification, and rational microbiota-targeted interventions, particularly diet-based strategies and antimicrobial stewardship. Here, we provide an integrated, disease-spanning synthesis of these data, emphasizing clonal hematopoiesis and myeloid neoplasms as emerging examples of microbiota–marrow crosstalk and outlining practical priorities for embedding microbiome science into future hematologic trials. Routine microbiome profiling or empiric microbiota-directed therapies cannot yet be recommended in everyday hematology practice, but integrating microbiome science into prospective therapeutic and transplant trials offers a realistic path to improved disease modeling, biomarker development, and rational adjunctive strategies to enhance outcomes for patients with hematologic malignancies. Full article

The China Medium- and Long-term Earthquake Hazard Assessment Database (CMLEHAD) has been established by sorting and summarizing various datasets related to the assessment of earthquake hazard areas in the Chinese mainland. It integrates five key datasets: Seismic Geology, Geodesy, Seismology, Numerical Simulation, and Fundamental Model, providing comprehensive data for long-term and medium-term earthquake hazard analysis and other relevant studies. The data supports the analysis of the fault segments with seismic gap, motion strongly locked, sparse small–moderate earthquakes, and apparent Coulomb stress increase. In system architecture, the CMLEHAD is divided into three layers: data layer, service layer and application layer. It is designed for interactive display through Web and Geographic Information System (GIS) platforms. The data acquisition and method in the database have been widely recognized and used in the corresponding field. The database is continuously updated and maintained through collaboration between provincial earthquake agencies, research institutes, and technical experts to ensure its accuracy and relevance. Currently, the database provides tailored data sharing services to multiple provincial earthquake agencies and immediate centers, granting them access permissions and specific datasets according to their needs. The future work will focus on improving the database’s functionality by incorporating advanced models and automated data services, thereby consolidating the data foundation and serving the purpose of earthquake disaster mitigation. Full article

Environmental noise is increasingly recognized as a major environmental and public health challenge, with road traffic identified as the dominant source of acoustic pollution across Europe. In this context, noise mitigation is directly linked to sustainable development goals related to human health and urban sustainability. Noise barriers are among the most widely implemented mitigation strategies; however, their spatial distribution and adequacy remain poorly documented, limiting their effectiveness for sustainable territorial planning. This study develops the first georeferenced database of highway noise barriers in Andalusia (Spain) and applies a reproducible, transdisciplinary geospatial workflow integrating field surveys, remote-sensing tools, and Geographic Information Systems (GIS). A total of 110 barriers were mapped, classified by material, geometry, and surrounding land use, and analyzed in relation to sensitive receptors, including dwellings, schools, and hospitals. Results show that only 1.6% of the Andalusian highway network is currently protected by noise barriers, with strong territorial disparities: over 50% of all structures are concentrated along coastal metropolitan corridors, while extensive inland areas remain unprotected. Misalignments were also detected between barrier placement and officially reported high-exposure segments, indicating limited correspondence between infrastructural deployment and planning-designated priority areas. Beyond generating a comprehensive regional dataset, the proposed methodology provides a scalable basis for national and European initiatives seeking to harmonize the mapping and assessment of noise-mitigation infrastructures. By offering an open-access, transferable framework, this work contributes to a more equitable distribution of environmental protection measures and supports policy professionals, environmental managers, and planners in advancing healthier and more sustainable urban and transport systems. Full article

The digital preservation and dissemination of historical and cultural heritage is a pivotal area at the intersection of digital humanities and geographic information science. To address the challenges of multi-source heterogeneity, limited dimensionality, and inadequate public engagement, this study designed and implemented an interactive visualization platform using modern Web technologies. Taking the Leshan Confucian Temple (religious heritage) and the former site of Wuhan University (educational heritage) as case studies, the platform integrates four types of heterogeneous data (geospatial coordinates, architectural attributes, visitor behavioral records, and multimedia imagery) into a unified spatiotemporal information model. Core technical implementations are built upon a lightweight front-end stack including the Gaode Map JavaScript API for geographic visualization, ECharts for dynamic statistical charting, and the Tailwind CSS framework for a fully responsive front-end interface. Key interactive features encompass linked map markers with contextual information windows, user-driven chart filtering, and paginated loading of cultural relic cards. Evaluation results demonstrate that the platform achieves cross-device response delay ≤3 s, supports spatially grounded, dynamic, and presentation of cultural heritage information, and attains a System Usability Scale (SUS) score of 82.5. This work offers a lightweight, scalable technical solution for advancing digital recording and public communication of historical and cultural heritage, while contributing to the theoretical discourse on spatial narrative and multi-source data integration in digital humanities. Full article

Population growth and unplanned land use significantly contribute to transforming natural hazards into disasters. Earthquake-induced losses of life and property are often linked to inadequate planning decisions. The city center of Şanlıurfa provides a recent example, where the 6 February 2023 earthquake resulted in 340 fatalities and substantial material damage. Variations in urban planning over different periods have caused disaster risk to fluctuate even across short distances. This study examines Şanlıurfa’s urban development in terms of earthquake vulnerability. Using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP), the earthquake risk map reveals elevated risk in areas near fault lines and regions with high groundwater levels. Approximately 7% of the area is classified as very low risk, 54% as low risk, 37% as moderate risk, and 2% as high risk. Limited consideration of disaster-focused planning has led to both planned and unplanned developments in hazardous zones. Consequently, construction should prioritize low-risk areas, with necessary precautions applied in high-risk zones when unavoidable. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Despite extensive research on sustainable urban mobility, non-designated crossings remain underexplored, particularly in low- and middle-income countries where they are highly prevalent. This study applies agent-based simulation to analyze pedestrian crossing behavior in commercial streets. We adopted a mixed-methods approach, combining video recordings, field observations, and structured questionnaires to capture physical conditions and user perceptions in a case in Cairo. The collected data were spatially analyzed using a Geographic Information System (GIS) to identify key spatial and behavioral variables influencing crossing decisions. These variables were then incorporated into an Agent-Based Model developed using the GAMA platform to simulate pedestrian–vehicle interactions. The simulation assessed pedestrian flow, non-designated crossing rates, average vehicle speed, and traffic volume. Results indicate strong relationships between pedestrian flow and non-designated crossings, and moderate associations between increased pedestrian activity and reduced vehicle speeds, while traffic volume shows weak correlations with pedestrian-related indicators. The model reveals distinct patterns of pedestrian crossing behavior, shaped by street configuration and traffic dynamics, and highlights critical risk points in commercial streets. Based on these findings, the study proposes seven actionable strategies to enhance pedestrian safety while supporting a more sustainable urban mobility. Full article

Understanding the spatiotemporal formation mechanisms of built cultural heritage is essential to interpreting regional cultural landscapes and informing differentiated conservation strategies. Using Fujian Province, China, as a representative mountain–sea transitional region, this study constructs a province-scale, multi-category, and dynamically oriented analytical framework to investigate the temporal evolution, spatial structure, and driving mechanisms of immovable cultural relics. Based on a georeferenced dataset of 940 immovable cultural relics, textual historical records were standardized into continuous temporal variables and integrated with GIS-based kernel density estimation, spatial autocorrelation analysis, distance-to-coast modeling, and category co-occurrence analysis. The results reveal a pronounced temporal concentration in the Ming–Qing and modern periods, with a primary formation peak during the Qing Dynasty and a secondary peak in the early 20th century driven by modern heritage. Spatially, relics exhibit significant positive spatial autocorrelation (Global Moran’s I = 0.375, p < 0.001) and form a structured dual-core pattern, consisting of a persistent coastal heritage belt and a distinct inland modern core centered in western Fujian. More than 75% of relics are located within 110 km of the coastline, confirming strong maritime orientation, while regression analysis reveals that this inland shift is primarily driven by the Modern Era rather than representing a continuous long-term trend. Category-level correlation analysis further demonstrates a clear spatial decoupling between traditional heritage and modern sites, indicating fundamentally different locational logics. Synthesizing these findings, this study proposes a dual-core driven model under a mountain–sea geographical framework, in which a path-dependent, economically reinforced coastal core coexists with a historically contingent, politically driven inland core. The results advance quantitative understanding of how multiple cultural logics, operating across different temporal scales, jointly shape complex regional heritage systems and provide a transferable framework for heritage analysis and spatially differentiated conservation planning. Full article

Ports are increasingly recognized as actors that influence the sustainability of urban environments due to their spatial footprint, operational intensity, and close interaction with surrounding cities. As digital technologies become more embedded in infrastructure management, Digital Twins (DTs) are emerging in port systems as tools that can support more integrated and sustainable port–city development. This paper investigates how DT technologies applied in ports can contribute to broader urban sustainability objectives within port–city systems. The analysis is based on a synthesis of documented DT practices from selected European ports. Geographic Information System (GIS) visualization is used to illustrate the spatial relationship between port infrastructure and the surrounding urban environment, as well as to map the connections between DT application fields and relevant Sustainable Development Goals (SDGs). A comparative interpretation of the extent to which DT applications align with urban sustainability goals across the examined ports is achieved through the development of an SDG contribution scale. Insights derived from the European cases are subsequently contextualized for the Port of Piraeus, exploring how similar DT approaches could support both operational efficiency and the long-term climate resilience of the port–city environment. Overall, the findings provide practical insights for port authorities, urban planners, and policymakers seeking to align digital transformation strategies with sustainable and climate-responsive infrastructure development in port–city systems. Full article

Landscape visual impact assessment is a key component of environmental impact studies, as it enables the identification and management of negative effects on the territory. Traditional methods are often subjective, rely on expert judgement, and consider limited criteria. To address these limitations, this study proposes a quantitative index based on the integration of grey clustering and Shannon entropy complemented with Geographic Information System (GIS). This approach allows classification under uncertainty and the objective weighting of indicators related to physiographic, biotic, and anthropic factors of visual quality, fragility, and accessibility. The methodology was applied to an open-pit mine in Peru. Results show that terrain modifications, presence of artificial elements, and the alteration of water bodies significantly affect visual quality, while the absence of restoration measures, observer exposure, and vegetation type increase fragility and reduce landscape resilience. The proposed method provides a robust, transparent, and reproducible framework that overcomes subjectivity in traditional approaches, supporting more reliable environmental planning and management. Full article