Search Results (1,692)

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

Background: Multiple Myeloma (MM) is the most common type of blood cancer among black individuals. CAR-T therapy is crucial, but often inaccessible to many black patients and those from underserved communities. The University of Kansas Health System administers over 100 CAR-T treatments annually and aims to evaluate barriers to CAR-T therapy access related to the social determinants of health in the Midwest area. Methods: This study examined patients with MM referred for CAR-T therapy from January 2021 to December 2023, assessing how race, socioeconomic status, and insurance influenced eligibility for leukapheresis. Data on income and travel were gathered from the 2022 US Census and analyzed using R software. Results: The study included 271 referrals for MM CAR-T therapy involving 179 patients, with a median age of 66 years (51% male). Demographics: 80% white, 16% black, 2.2% other races, 1.8% Asian, with a median income of $70,644. Nearly half lived more than 30 min from the center (Mainly from Kansas, Missouri and Nebraska). Apheresis rates were similar across racial groups: 54% for whites, 54% for blacks, and 50% for others, while none of the three Asian patients proceeded. Nine patients (5%) could not proceed because of caregiver or insurance barriers, and cell collection rates were comparable regardless of distance (34% vs. 35%). Conclusion: This study showed that black representation in CAR-T access matches local demographics, indicating less disparity among minorities. Unlike national reports, distance, income, and insurance do not significantly affect access, suggesting the need for a national study on the social determinants impacting CAR-T access for multiple myeloma. Full article

For the first time, this study presents a cyber–physical framework that reconciles the long-standing conflict between transparent queue analytics and adaptive machine learning in flexible manufacturing systems. Deterministic indicators, utilization, expected queue length, waiting time, and idle probability, are fused with topological embeddings of the routing graph and ingested by a graph convolutional network that predicts station congestion with calibrated confidence intervals. Shapley additive explanations decompose every forecast into causal contributions, and these vectors, together with a percentile-based risk metric, steer a mixed-integer genetic optimizer toward schedules that lift throughput without breaching statistical congestion limits. A cloud dashboard streams forecasts, risk bands, and color-coded explanations, allowing supervisors to accept or modify suggestions; each manual correction is logged and injected into nightly retraining, closing a socio-technical feedback loop. Experiments on an 8704-cycle production census demonstrate a 38 percent reduction in average queue length and a 12 percent rise in throughput while preserving full audit traceability, enabling one-minute rescheduling on volatile shop floors. The results confirm that transparency and adaptivity can coexist when analytical priors, explainable learning, and risk-aware search are unified in a single containerized control stack. Full article

Urban forests provide many ecosystem services in urbanized landscapes, including biodiversity conservation. The sustainable management of urban forests requires a thorough understanding of biodiversity changes in the context of rapid urbanization. As biodiversity in rapidly changing urban areas is very dynamic, we need a better understanding of long-term biodiversity changes in urban forests. Birds are very good bioindicators of urban forest biodiversity because they are strongly habitat-sensitive. However, a major knowledge gap exists in long-term trends in bird diversity in temperate urban forests. This study analyzed a comparison of bird-species richness in a temperate Central European urban forest over a time span of 37 years. Bird-counts using the standard line-transect method conducted in 2023–2024 were compared with older field data from 1987 gained using the same method in a lowland hardwood floodplain forest in the Czech Republic. The results revealed significant faunistic similarities in the bird-species diversity of an urban forest during the 1987–2024 period. The high local alpha diversity of the bird community (42 nesting bird species) as well as the relatively high long-term stability in bird richness indicated the importance of the studied urban forest as a stable biodiversity refugium in a dynamic urbanized landscape. Therefore, urban forests can be considered very stable biodiversity refugia in dynamically changing urban areas. Full article

Background: Namibia is experiencing a generalized HIV epidemic, with 7.5% of the population living with HIV. Adolescent girls and young women (AGYW) aged 15–24 account for 28.6% of new infections annually. Various factors increase AGYW’s vulnerability to HIV. To address this, Project HOPE Namibia (PHN)-led consortium implemented the PEPFAR/USAID-funded DREAMS project in Khomas, Oshikoto, and Zambezi regions from 2018 to 2023. This study estimated the AGYW population most in need of HIV prevention and assessed geographic and age-specific gaps to improve program effectiveness and efficiency. Methods: This secondary data analysis utilized the Namibia Population-Based HIV Impact Assessment (NamPHIA) 2017, the Namibia census, and service data from the DREAMS project, which includes entry points for recruitment, screening, and enrolment. We used Python to conduct unadjusted and adjusted Poisson regression and UpSet plots for data visualization. Results: Analysis of NamPHIA data revealed low HIV prevalence in 10–14-year-olds, with only Oshikoto showing a detectable rate of 2.76%, mostly attributed to perinatal HIV transmission. Of the 12 DREAMS eligibility criteria, three could be mapped to 10–14-year-olds, while all except sexually transmitted infections could be mapped for 15–19 and 20–24-year-olds. Nationally, 17.3% of 10–14-year-old AGYW, 48.0% of 15–19-year-olds, and 50% of 20–24-year-olds met at least one DREAMS eligibility criterion. Among 15–19-year-olds, a history of pregnancy, no/irregular condom use, and out-of-school status were positively associated with HIV status. For 20–24-year-olds, transactional sex was positively associated with HIV status. Overall, 62% of screened individuals were eligible, and 62% of eligible individuals enrolled. PHN screened 134% of the estimated 37,965 10–14-year-olds, 95% of the estimated 35,585 15–19-year-olds, and 57% of the 24,011 20–24-year-olds residing in the five districts where DREAMS was implemented. Conclusions: We recommend the refinement of the DREAMS eligibility criteria, particularly for AGYW 10–14, to better identify and engage those at risk of HIV acquisition through sexual transmission. For 15–19-year-olds, PHN efforts should interrogate geographic variability in entry points for recruitment and screening practices. PHN should enhance the recruitment and engagement of AGYW 20–24, with a particular focus on those engaged in transactional sex. Full article

Territorial transformations in Indigenous regions are shaped by intersecting ecological, political, and cultural dynamics. In San Juan Bautista Valle Nacional, Oaxaca, the construction of the Cerro de Oro dam disrupted river flows, displaced livelihoods, and triggered the decline of irrigated agriculture. This study examines the long-term impacts of these changes on land use, demographics, and cultural practices, emphasizing women’s contributions to community resilience. Using a mixed-methods approach, the study integrates geospatial analysis (1992–2021), census data (2000–2020), documentary review, and ethnographic fieldwork, including participatory mapping. Results show a shift toward seasonal rainfed agriculture, fluctuating forest cover, and a rise in female-headed households. Women have emerged as central actors in adapting to change through practices such as seed saving, agroforestry, and backstrap-loom weaving. These spatially grounded practices, enacted across varied socio-ecological zones, sustain food systems, preserve biodiversity, and reinforce biocultural memory. Although often overlooked in formal governance, women’s territorial agency plays a vital role in shaping land use and community adaptation. This research highlights the need to recognize Indigenous women’s roles in managing change and sustaining territorial heritage. Acknowledging these contributions is essential for building inclusive, culturally grounded, and sustainable development pathways in regions facing structural and environmental pressures. Full article

Background: Dubai’s healthcare system is designed to meet the growing needs of its population while maintaining high standards of accessibility, quality, equity, and responsiveness. The Dubai Health Authority (DHA) uses planning tools to assess residents’ health requirements and implement effective regulatory strategies. This study compares trend data from the 2018 and 2022 Dubai Clinical Services Capacity Plan (DCSCP) reports to understand how population changes have impacted healthcare demand and to identify service gaps addressed over four years with particular focus on key medical specialties in high demand. Methodology: This study retained the methodologies used in the 2018 and 2022 Dubai Clinical Services Capacity Plan (DCSCP) reports, capturing healthcare supply through a census of licensed facilities in Dubai and estimating demand using a need-based approach aligned with diagnosis-related groups (IR-DGRs). The data is categorized into eight key health-planning units (KPUs) to highlight gaps across major service categories and assess whether these gaps have been resolved in a rapidly evolving healthcare system. Results: Between 2018 and 2022, there were clear improvements across several key planning units. The shortage of acute overnight beds was resolved, moving from a deficit of 239 beds to a surplus of 1728 beds, an overall gain of 1967 beds. Outpatient consultation rooms also saw major growth, shifting from a gap of 1769 rooms in 2018 to a surplus of 4707 rooms in 2022, a net increase to 6476 rooms. In addition, emergency department capacity increased, and the number of ICU beds also rose from 484 to 691, an overall growth of 43%. These changes represent measurable improvements in acute care and outpatient service capacity. However, despite the addition of 76 beds, long-term care continues to show a shortfall of 138 beds, indicating that this remains a significant gap in Dubai’s healthcare system. Conclusions: Dubai has made significant progress in expanding its healthcare infrastructure between 2018 and 2022, addressing many capacity shortfalls, particularly in critical care and outpatient services. However, challenges in non-acute and long-term care remain, requiring ongoing strategic planning to meet future healthcare needs. Full article

The application of artificial intelligence (AI) techniques has improved the accuracy of forest species identification, particularly in timber inventories conducted under Sustainable Forest Management (SFM). This study developed and evaluated machine learning models to recognize 16 Amazonian timber species using digital images of tree bark. Data were collected from three SFM units located in Nova Maringá, Feliz Natal, and Cotriguaçu, in the state of Mato Grosso, Brazil. High-resolution images were processed into sub-images (256 × 256 pixels), and two feature extraction methods were tested: Local Binary Patterns (LBP) and pre-trained Convolutional Neural Networks (ResNet50, VGG16, InceptionV3, MobileNetV2). Four classifiers—Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Linear Discriminant Analysis (LDA)—were used. The best result (95% accuracy) was achieved using ResNet50 with SVM, confirming the effectiveness of transfer learning for species recognition based on bark texture. These findings highlight the potential of AI-based tools to enhance accuracy in forest inventories and support decision-making in tropical forest management. Full article

Background/Objectives: Later-stage melanoma at diagnosis is associated with increased mortality. Health care access, socioeconomic status, and neighborhood-level factors likely influence stage at presentation. This study aimed to examine whether neighborhood disadvantage, as measured by the Area Deprivation Index (ADI), is associated with later-stage melanoma diagnosis. Methods: We conducted a cross-sectional analysis of a retrospective cohort of 941 patients diagnosed with melanoma at a large academic medical center between 2010 and 2019. Residential addresses were geocoded and linked to ADI and rurality data. Covariates included race, ethnicity, age, gender, and insurance status. Multivariable logistic regression models with robust standard errors clustered at the census tract level were used to assess associations with melanoma stage at diagnosis. Results: Of 941 patients (63% male, 92.8% non-Hispanic White, mean age 64 years), 432 (46%) were diagnosed with late-stage melanoma. Mean ADI was higher among late-stage cases (5.4) compared to early-stage cases (3.3) (p < 0.001), even after adjustment for covariates. Non-Hispanic White race, private insurance, older age, and urban residences were associated with earlier stage at diagnosis. Racial disparities were attenuated after adjusting for ADI, with no significant interaction between race and ADI. Conclusions: Neighborhood disadvantage is significantly associated with later-stage melanoma diagnosis and contributes to observed racial and socioeconomic disparities. These findings highlight the need for targeted educational interventions and health policy initiatives to reduce late-stage melanoma diagnoses in vulnerable populations. Full article

The size of a touch icon is a critical factor affecting smartphone use performance. The existing literature recommends a 10-mm icon side/diameter for a successful touch. Though this can be applicable in routine use, there are circumstances where smaller icons are required. Two experiments were conducted to systematically investigate the effects of icon size and spacing on touch performance: one experiment for square icons and the other for circular icons. The icon size ranged from 2 to 11 mm, while spacing ranged from 0 to 8 mm, depending on icon size. Seventy-five combinations of icon size and spacing were randomly presented on a smartphone. The subjects’ task was to touch an icon as soon as it occurred. Performance was measured with a hit rate for icons and with icon-touch time. A change-point detection algorithm was developed to characterize the icon-touch performance. The results show that icon hit rate increased and icon-touch time decreased with an increase in icon size; a change point in the icon hit rate was identified around the icon size of 6 mm; icon spacing had no significant effect on icon hit rate or icon-touch time. To conclude, smartphone icon-touching performance can be comprehensively characterized using the systematic approach developed in the present study. Full article

Urban green spaces are important nature-based solutions to mitigate climate change. While the distribution of green spaces within cities is well documented, few studies assess whether inequities in green space quantity (i.e., percent cover) are mirrored by inequities in green space quality (i.e., vegetation health or sensitivity to stressors). Green space quality is important to measure alongside green space quantity because vegetation that is healthier and less sensitive to stressors such as climatic fluctuations sustain critical ecosystem services through stressful environmental conditions, especially as the climate changes. We use a 40-year remote sensing dataset to examine the spatial patterns and underlying drivers of vegetation sensitivity to short-term (monthly) climate fluctuations in Chicago. Our results show that although vegetation cover was not equitably distributed between racially and ethnically segregated census tracts, socio-demographic composition was not a key driver of spatial variation in short-term vegetation sensitivity to climate. Instead, we found that vegetation growth form was a strong predictor of differences in vegetation sensitivity among communities. At the census tract level, higher herbaceous/shrub cover was associated with increased sensitivity to climate, while higher tree cover was associated with decreased sensitivity. These results suggest that urban green spaces comprising trees will be less sensitive (i.e., more resistant) to short-term climate fluctuations than those comprising predominately herbaceous or shrub cover. Our findings highlight that urban green space quality can vary spatially within cities; however, more work is needed to understand how the drivers of vegetation sensitivity vary among cities, especially those experiencing different climatic regimes. This work is key to planning and planting high-quality, climate change-resilient and equitable urban green spaces. Full article

The existing literature suggests that exposure to Per- and Polyfluoroalkyl Substances (PFAS) can increase Papillary Thyroid Cancer (PTC) risk by interfering with thyroid hormone signaling, leading to hormonal imbalances that promote carcinogenesis. In addition, significant disparities exist in environmental exposure. However, ecological evidence of these associations has not been established within a statewide database of cancer outcomes. Therefore, this study investigated the relationship between socioeconomic conditions, environmental PFAS exposure, and PTC incidence in Florida using the state’s cancer registry. Data on facilities potentially releasing PFAS and ZIP codes with known PFAS drinking water contamination were retrieved from the EPA’s PFAS Analytic Tool. Proximity to PFAS sites and age-adjusted incidence by patient race/ethnicity were calculated by census tract. Lower socioeconomic status was associated with greater exposure to environmental PFAS. Census tracts with closer proximity to PFAS sites were more likely to have public water systems with PFAS contamination. Lastly, residential proximity to PFAS sites was positively associated with age-adjusted PTC incidence in Non-Hispanic Whites and Hispanics. These results demonstrate disparities in environmental exposure and suggest that exposure to PFAS may be an important factor for PTC risk at the population level and should be considered in the development of public health policies. Full article

In the context of the deep transformation of population structure and the coordinated advancement of high-quality development, exploring the mechanism of the impact of aging on economic growth has become a major issue related to the sustainable development of China. This study takes the 41 cities of the Yangtze River Delta urban agglomeration as a sample, using the population and economic census data from 2000 to 2020. It comprehensively applies an improved Solow model, GIS spatial analysis, spatial econometric models, and mediation effect tests to arrive at the following findings: (1) There is a significant asynchrony between economic growth and population aging in the Yangtze River Delta urban agglomeration. Economic growth has shifted from high-speed to high-quality development, while the aging process is accelerating and becoming more aged. (2) Population aging in the Yangtze River Delta has a nonlinear positive impact on economic growth. The intensity of this impact shows a characteristic of “strong-weak-strong,” with the first aging rate threshold being 11.63% and the second being 17.53%. (3) There is significant spatial autocorrelation between population aging and economic growth in the Yangtze River Delta urban agglomeration. The overall direction of the effect shows a spatial distribution pattern of “positive in the south and negative in the north.” The deepening of population aging in neighboring areas promotes local economic growth. (4) Labor productivity and optimization of the living environment constitute the core transmission pathways. Together, they account for more than 80% of the contribution and serve as the key mechanism for transforming aging pressures into growth momentum. This research provides practical guidance for solving the “rich” and “aging” contradictions in the Yangtze River Delta. It also offers a universal theoretical framework and a Chinese solution for aging economies worldwide to address the risk of growth stagnation. Full article

This study investigates the potential of fine-tuned large language models (LLMs) to enhance geospatial intelligence by translating natural language queries into executable Python code. Traditional GIS workflows, while effective, often lack usability and scalability for non-technical users. LLMs offer a new approach by enabling conversational interaction with spatial data. We evaluate OpenAI’s GPT-4o-mini model in two forms: an “As-Is” baseline and a fine-tuned version trained on 600+ prompt–response pairs related to geospatial Python scripting in Virginia. Using U.S. Census shapefiles and hospital data, we tested both models across six types of spatial queries. The fine-tuned model achieved 89.7%, a 49.2 percentage point improvement over the baseline’s 40.5%. It also demonstrated substantial reductions in execution errors and token usage. Key innovations include the integration of spatial reasoning, modular external function calls, and fuzzy geographic input correction. These findings suggest that fine-tuned LLMs can improve the accuracy, efficiency, and usability of geospatial dashboards when they are powered by LLMs. Our results further imply a scalable and replicable approach for future domain-specific AI applications in geospatial science and smart cities studies. Full article