Search Results (2,196)

The rapid urban growth in Saudi Arabia has intensified challenges in sustainable solid waste management, particularly in selecting suitable landfill sites that minimize environmental risks and protect public health. Tabuk Province, located in the northwest of the Kingdom, represents a region where arid climatic conditions, fragile ecosystems, and increasing urbanization make landfill sitting highly complex. Traditional decision-making approaches often struggle to capture uncertainties in expert opinions and spatial data, leading to less reliable outcomes. While Geographic Information Systems and Multicriteria Decision-Making have been applied to this field, the explicit integration of fuzzy logic remains limited, especially in arid regions. This study addresses this gap by combining the Fuzzy Analytic Hierarchy Process with Geographic Information Systems to establish a more robust framework for landfill site selection in Tabuk. Seven critical criteria were considered, including distance from major roads, airports, urban centers, coastlines, wetlands, and protected areas, with expert assessments analyzed through fuzzy reasoning to improve decision reliability. The results generated a spatial suitability map highlighting priority zones for landfill development, particularly in the western and southwestern areas of the province, where environmental sensitivity is lower and accessibility to infrastructure is greater. The findings emphasize that proximity to urban areas and road networks are dominant factors influencing suitability. The novelty of this study lies in its methodological integration, which enhances transparency, adaptability, and objectivity in landfill sitting. By promoting environmentally responsible waste management, the framework directly supports the Sustainable Development Goal of Good Health and Well-Being and the Sustainable Development Goal of Sustainable Cities and Communities, ensuring safer urban development and healthier living conditions. Moreover, the approach is transferable to other arid and semi-arid regions, offering valuable insights for countries facing similar challenges in sustainable urban planning. Full article

Objectives: Research on kernel oil content and secondary metabolites in Chionanthus retusus was conducted to evaluate its potential as an oil crop. Methods: Fruits from 30 individual trees were collected to analyze morphological traits, oil content, and the composition of fatty acids, phytosterols, and tocopherols. Correlation, cluster, and principal component analyses were performed on the resulting data. Results: The mean fresh fruit weight, dry fruit weight, dry kernel weight, and kernel percentage were 77.02 g, 24.33 g, 12.22 g, and 51.14%, respectively. Kernel oil content averaged 35.83%, comprising seven fatty acids with oleic acid as the predominant component. Total phytosterol content reached 279.58 mg/100 g oil, with β-sitosterol being the major constituent among seven detected sterols. Total tocopherols were 571.13 μg/g oil, dominated by γ-tocopherol, indicating a potential antioxidant capacity. These components may reduce the demand for synthetic antioxidant food additives. A significant positive correlation was observed between kernel dry weight and oil content (r = 0.760, p < 0.01), supporting kernel dry weight as a key phenotypic indicator for high-oil breeding. Fruit quality traits did not cluster by geographic origin, whereas secondary metabolite profiles showed origin-based clustering. For breeding oil-producing C. retusus, select seeds with superior provenances based on secondary metabolites and cultivate them under optimal conditions to develop varieties with plump fruit, thereby boosting yield. Accessions WS-4 and WS-3 were identified as promising germplasm resources for oil production. Conclusions: The abundant oleic acid, β-sitosterol, and γ-tocopherol in C. retusus kernels highlight its potential as a woody oilseed crop. Full article

The settlements of the Great Wall are the product of the overlap of ancient Chinese agricultural civilization and non-agricultural civilization. The structure of the settlement system is of great value for understanding the law of defense engineering and social spatial organization. The Great Wall, built by a non-agricultural civilization, is an important part of the development history of the Chinese civilization. Its uniqueness reflects the relationship between institution and space. However, the archaeological remains and related research methods for non-agricultural Great Wall settlements are not perfect. This paper takes the typical case of the Great Wall built by a non-agricultural civilization (Linhuang Lu settlements of the Jin Great Wall) as the object and integrates spatial analysis and social network analysis. It aims to explore the structure of the settlement system. The settlements of Linhuang Lu show non-random distribution characteristics. They can be divided into four levels. The number ratio from high-level to low-level settlements is 70:30:10:1. Through the weighted Voronoi and social network analysis of human connection and geographical connection, this paper clarifies the structural characteristics of spatial association and social association of settlements. Combined with accessibility and geographical environment, the Linhuang Lu settlements were finally divided into 10 Meng’an defense units and 12 Mouke defense units. Quantitative analysis of the settlement system structure shows the hierarchical management of nature and military by non-agricultural civilization. This provides an empirical basis for the reconstruction of the military defense system of the Great Wall of the Jin Dynasty and further explores the applicability of the research paradigm. This paper has methodological innovation value for solving the problem of spatial cognition of settlement heritage. Full article

Background: Return to work (RTW) is a fundamental aspect of recovery after stroke, importantly, for workers of working age. Evidence indicates there is little known about the clinical and systematic predictors of RTW in Hungary. We aimed to determine the independent predictors of RTW for stroke survivors using aggregate hospital data and expert opinion. Methods: A mixed-method study using aggregated national level administrative data from the Pulvita platform (the National Health Insurance Fund of Hungary) and expert interpretation from the physicians who treat stroke survivors. The data analyzed 13,572 inpatient records for stroke hospitalizations from 2015–2024 across Hungarian counties. Results: Stroke severity, cognitive and psychological recovery, and presence of comorbidities were important clinical predictors of RTW. Rehabilitation intensity was greater for people aged 51–65 years, and work-age men appeared to have slightly better access to rehabilitation compared to work-aged women. Patients accessed more medical rehabilitation services than they did occupational or psychosocial services. Access to rehabilitation services may have varied geographically, with patients in counties such as Budapest and Pest having better access due to higher provider availability and cross-county patient movement. In addition, economic extrapolations from the literature on post-stroke care costs may have introduced bias in estimating annual social productivity losses, reported as EUR 19,953 per patient. Conclusions: Clinical and economic factors both impact RTW potential among stroke survivors in Hungary. Although rehabilitation intensity can indicate likelihood of RTW, the lack of a national RTW program acts as a significant barrier to RTW for stroke survivors. This study suggests a need for integrated rehabilitation and RTW systems, with associated future research linking clinical, economic, and labor market status data to develop effective and efficient policy for stroke survivors. Full article

Artificial intelligence (AI) has emerged as a transformative technology in radiology, offering enhanced diagnostic accuracy, improved workflow efficiency and potential risk mitigation. However, its effectiveness in reducing clinical and occupational risks in radiology departments remains underexplored. This systematic review aimed to evaluate the current literature on AI applications in computed tomography (CT) radiology and their contributions to risk reduction. Following the PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus and Web of Science for studies published between 2021 and 2025 (the databases were last accessed on 15 April 2025). Thirty-four studies were included based on their relevance to AI in radiology and reported outcomes. Extracted data included study type, geographic region, AI application and type, role in clinical workflow, use cases, sensitivity and specificity. The majority of studies addressed triage (61.8%) and computer-aided detection (32.4%). AI was most frequently applied in chest imaging (47.1%) and brain haemorrhage detection (29.4%). The mean reported sensitivity was 89.0% and specificity was 93.3%. AI tools demonstrated advantages in image interpretation, automated patient positioning, prioritisation and measurement standardisation. Reported benefits included reduced cognitive workload, improved triage efficiency, decreased manual annotation and shorter exposure times. AI systems in CT radiology show strong potential to enhance diagnostic consistency and reduce occupational risks. The evidence supports the integration of AI-based tools to assist diagnosis, lower human workload and improve overall safety in radiology departments. Full article

Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying on full map-based infrastructure. Methods: A dataset of over 5000 Hungarian postal locations was used to evaluate five models: Haversine-based scaling with circuity, linear regression, second- and third-degree polynomial regressions, and a trained artificial neural network. Models were tested on the full dataset, and three example routes representing short, medium, and long distances. Both statistical accuracy and route-level performance were assessed, including a practical optimization task. Results: Statistical models maintained internal consistency, but systematically overestimated longer distances. The ANN model provided significantly better accuracy across all scales and produced routes more consistent with map-based paths. A new evaluation method was introduced to directly compare routing outputs. Conclusions: Practical route planning can be achieved without GIS services. ML-based estimators offer a cost-effective alternative, with potential for further improvement using larger datasets, additional input features, and the integration of travel time prediction. This approach bridges the gap between simplified approximations and commercial routing systems. Full article

Background: Mpox is an emerging zoonotic disease, caused by the monkeypox virus (MPXV). Since its discovery, it has been considered endemic in Central and West Africa. Mpox is of global significance as of May 2022, due to the report of simultaneous outbreaks in more than 70 countries where the disease was not endemic. The global spread of mpox has shown the importance of maintaining active surveillance for emerging zoonotic diseases, many of which can cross borders. Objective: The aim of this study was to analyse mpox cases and national incidence in Mexico related to the global outbreak. Methods: Epidemiological data (confirmed cases and incidence of MPXV infection) were obtained from the morbidity yearbook of the General Directorate of Epidemiology of the Mexican Ministry of Health. The information was analysed for the construction of epidemic curves, distribution of cases by age and sex and quartiles of geographical incidence. Results: A total of 4081 cumulative confirmed cases were recorded with a peak and national incidence of 1191 and 1.87, respectively, in September 2022. The distribution of cases by age and sex showed that males were more prevalent (above 95%) in the 25–44 years age group compared to females. Finally, geographical analysis showed that cosmopolitan and population-concentrated states had the highest incidence, clustered in the top quartile. The 2022 mpox outbreak in Mexico was consistent with other countries as reported in the international literature, with most cases occurring among exposed individuals in cosmopolitan cities. Conclusions: The need for active surveillance of emerging diseases, access to specific diagnostics and implementation of vaccination strategies is analysed and discussed. Full article

Urban parks play a crucial role in promoting physical and mental health by providing green spaces for recreation, relaxation, and social interaction. However, access to these spaces is often constrained by the structure and performance of public transportation networks—particularly in megacities marked by spatial and social inequalities. This study evaluates equitable access to urban parks in Mexico City through the public transit system, using centrality-based metrics within a Geographic Information Systems (GIS) network analysis framework. Parks are categorized by size (small: 0.3–1 ha; medium: 1–4.5 ha; large: >4.5 ha), and three centrality measures—reach, gravity, and closeness—are applied to assess their accessibility via different transport modes: Metro, bus rapid transit (BRT), trolleybuses, public buses, and concessioned services. Results show that Metro stations are more connected to large parks, while BRT and trolleybus lines improve access to small and medium parks. Concessioned services, however, present fragmented and uneven coverage, reinforcing socio-spatial disparities in access to green infrastructure. The findings underscore the importance of integrated, multimodal transportation planning to enhance equitable access to parks—an essential component of urban health and well-being. By highlighting the spatial patterns of accessibility, this study contributes to designing healthier and more inclusive public spaces in the city, supporting policy frameworks that advance health equity and urban sustainability. Full article

Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to address SDOH-driven disparities through predictive analytics, data integration, and precision medicine. Methods: This review synthesizes findings from systematic reviews and original research on AI applications in cancer-focused SDOH research. Key methodologies include machine learning (ML), natural language processing (NLP), deep learning-based medical imaging, and explainable AI (XAI). Special emphasis is placed on AI’s ability to analyze large-scale oncology datasets, including electronic health records (EHRs), geographic information systems (GIS), and real-world clinical trial data, to enhance cancer risk stratification, optimize screening programs, and improve resource allocation. Results: AI has demonstrated significant advancements in cancer diagnostics, treatment planning, and survival prediction by integrating SDOH data. AI-driven radiomics and histopathology have enhanced early detection, particularly in underserved populations. Predictive modeling has improved personalized oncology care, enabling stratification based on socioeconomic and environmental factors. However, challenges remain, including AI bias in screening, trial underrepresentation, and treatment recommendation disparities. Conclusions: AI holds substantial potential to reduce cancer disparities by integrating SDOH into risk prediction, screening, and treatment personalization. Ethical deployment, bias mitigation, and robust regulatory frameworks are essential in ensuring fairness in AI-driven oncology. Integrating AI into precision oncology and public health strategies can bridge cancer care gaps, enhance early detection, and improve treatment outcomes for vulnerable populations. Full article

Islands and island communities are often perceived as homogenous in mainstream discourse. While many islands share characteristics, such as smallness or isolation, these are experienced differently across and within island contexts and intersect with spatial, socio-cultural, political, and economic landscapes. The concept of islandness is developed to both understand shared island characteristics and their differences across places, communities, and situations. This makes islandness highly relevant to discussions of green transitions as it highlights the need to examine the diverse, intersecting, and local realities that might interfere with green citizenship. However, analytical approaches to islandness are limited, with few spatial, scalable, and transferable frameworks available. This paper argues that spatial microsimulation offers a productive way to engage with islandness using the case of climate change and environmental attitudes across Iceland. We present the SimIceland model, developed within the EU-funded project PHOENIX: The Rise of Citizens’ Voices for a Greener Europe. The model is developed to better understand how Iceland’s citizens’ feel about climate change by taking socio-cultural, environmental, and different geographical administrative regions into account. Through a simple example of an analytical demonstration, we show how this model can support a deeper understanding of islandness in the specific context of climate attitudes in Iceland. Furthermore, we discuss how the model can contribute to public participation initiatives. The model and data are open access, and we conclude by inviting further developments and the use of spatial microsimulation to explore islandness, green citizenship, and participatory approaches to sustainability in island contexts. Full article

The genus Sclerodermus Latreille (Hymenoptera: Bethylidae) comprises over 80 species of ectoparasitoids of insect pests in forests, agricultural environments, and stored products with a cosmopolitan distribution. Despite its growing significance in biological control, behavioral ecology, and public health, the taxonomy of the genus remains poorly resolved. This is largely due to morphological reduction and simplification among species, outdated or incomplete original descriptions, and limited access to type material. A particularly problematic case is Sclerodermus cereicollis Kieffer, originally described from two geographically disjunct populations: Giglio Island (Italy, Palaearctic) and Annobón Island (Equatorial Guinea, Afrotropical). The syntype series includes morphologically divergent specimens, casting doubt on their conspecificity. In this study, we redescribe S. cereicollis based on both the original syntypes and newly collected material from Italy. A lectotype is designated to stabilize the nomenclature, and we provide the first molecular data for the species to assess genetic cohesion among populations. Comparative morphological and molecular analyses reveal that the Afrotropical syntypes represent a distinct, previously undescribed species. Accordingly, we describe Sclerodermus annobonensis Masini, Colombo & Azevedo sp. nov., designating a holotype. This study refines species boundaries within Sclerodermus and highlights the value of integrative taxonomy, combining historical and contemporary data, in resolving persistent systematic ambiguities in morphologically conservative taxa. Full article

Historically, traditional transportation planning has promoted public policies focused on building and maintaining infrastructure for private cars to improve travel efficiency. This approach presents a significant challenge for cities in the Global South due to their unique socioeconomic conditions and urban development patterns. Dedicated public transport infrastructure can make better use of the road network by moving more people and reducing congestion. Beyond its environmental benefits, it also provides the population with greater accessibility, creating new development opportunities. This study uses Mexicali, Mexico, a medium-sized city with dispersed urban growth and a high dependence on cars, as a case study. The goal is to identify the relationship between the supply of public bus routes and actual work-related commuting patterns. The methodology considers that, given the scarcity of economic resources and prior studies in the Global South, using Geographic Information Systems (GIS) for the spatial analysis of travel is a key tool for redesigning more inclusive and sustainable public transport systems. Specifically, this study utilized origin–destination survey data from 14 urban areas to assess modal coverage, work-related commuting patterns, and the spatial distribution of employment centres. The findings reveal a marked misalignment between the existing public transport network and the population’s travel needs, particularly in marginalized areas. Users face long travel times, multiple transfers, low service frequency, and limited connectivity to key employment areas. This configuration reinforces an exclusionary urban structure, with negative impacts on equity, modal efficiency, and sustainability. The study concludes that GIS-based spatial analysis generates sufficient evidence to redesign the public transport system and reorient urban mobility policy toward sustainability and social inclusion. Full article

Background: In Italy, voluntary termination of pregnancy (VTP) is a legally protected healthcare service. However, in Apulia, a southern region, access remains uneven due to ongoing healthcare rationalization, which has reduced service availability, particularly in decentralized areas. Conscientious objection among providers may also contribute, although the number of VTPs per provider has decreased over time. This study examines whether women access VTP services outside their healthcare catchment area (CA) and how socioeconomic deprivation and individual factors may influence mobility. Methods: We applied a ranking method, based on spatial and temporal distance between hospitals and municipalities to define the catchment area (CA) around hospitals of the Apulia region that offers VTP service. A Poisson multivariable clustered model was applied to evaluate the association among demographic and socioeconomic factors and the choice of the VTP service. Results: The analysis revealed that 54.7% of VTPs were performed outside the women’s catchment area. This mobility was significantly more frequent among women from medium and low socioeconomically deprived areas compared to very low deprived areas (RR = 1.20; 95%CI: [1.02–1.42]) and (RR = 1.28; 95%CI: [1.03–1.57]). Higher education level (RR = 1.09; 95% CI: [1.04–1.14]) and employment (RR = 1.09; 95%CI: [1.03–1.14]) were also associated with higher rates of undergoing a VTP outside of CA, with variations observed across local health authorities. Conclusions: These findings have shown the influence of socioeconomic conditions and educational level on women’s access to VTP services, suggesting that structural inequalities continue to shape healthcare choices and mobility. Full article

Cutaneous leishmaniasis (CL) is a vector-borne parasitic disease caused by protozoa of the Leishmania genus. Once confined to endemic regions such as the Middle East, Americas, North Africa, and Central Asia, CL is increasingly emerging in non-endemic areas due to a multitude of drivers, including population displacement, environmental disruption, and political instability. These overlapping drivers contribute to expanding sand fly habitats, degrading living conditions, and weakening health systems, increasing transmission. Rising global temperatures further facilitate vector expansion into new regions, where clinical unfamiliarity often leads to misdiagnosis, delayed treatment, increased morbidity, and greater financial burden. Despite its rising incidence and global spread, CL remains a neglected tropical disease since it is seldom fatal, with scant interest by public health authorities and financial donors, limiting activities that further research and prevent spread of the disease. This review synthesizes current evidence on how geopolitical instability, forced migration, and climate-driven ecological changes collectively reshape CL epidemiology and complicate diagnosis, treatment, and surveillance. As CL extends beyond traditional geographic boundaries, it requires integrated strategies that address its multifaceted drivers through strengthened cross-border surveillance, provider education, and international coordination—focusing on prevention, diagnosis, and equitable access to diagnostics and therapeutics, especially among displaced and underserved populations. Full article

Background: The COVID-19 pandemic significantly disrupted patterns of physical activity and participation in mass sporting events, with recreational runners in Latin America among the most affected. In Colombia, pre-existing inequalities in access to sport further exacerbated these impacts. Nevertheless, evidence on post-COVID-19 impact and recovery experiences among regional runners remains limited. Objective: We examined the sociodemographic profiles, athletic experience, and perceptions of COVID-19-related impact and recovery among participants in the 2023 Medellín Marathon, and to assess differences by educational attainment, employment status, age group, and geographic origin. Methods: A cross-sectional descriptive study was undertaken involving 2486 registered marathon runners. An ad hoc questionnaire assessed COVID-19 symptoms and sequelae, perceived respiratory and physical limitations, fears associated with group exercise, and self-reported recovery. Analyses included descriptive statistics, bivariate comparisons and one-way ANOVA tests. Results: Older participants, retirees and those with lower educational levels reported significantly greater COVID-19 impact, longer recovery periods and higher perceived physical and respiratory limitations. In contrast, younger runners and those with a college education showed more complete physical recovery and attributed protective benefits, such as improved cardiorespiratory function and a lower incidence of respiratory symptoms, to their training. Additionally, runners originating from smaller municipalities and other Latin American countries reported higher levels of impact and lower perceptions of recovery. Conclusions: Post-COVID-19 effects among marathon runners are not uniform but vary according to sociodemographic and contextual factors. These findings underscore the importance of tailored support and readaptation strategies—particularly for vulnerable subgroups—to ensure their safe and equitable return to mass endurance events. Full article