Hot Clusters Research Articles

Tumor immune microenvironment dynamics and outcomes of prognosis in non-muscle-invasive bladder cancer.

Agents that target PD-1 and PD-L1 have been developed in the treatment of bladder cancer (BC). However, the diversity of immune cell infiltration in non-muscle-invasive BC (NMIBC) and the dynamics of the microenvironment as it progresses to muscle-invasive/metastatic disease remains unknown. To assess tumor immune activity, hierarchical clustering was applied to 159 BC samples based on cellular positivity for the defined immune cellular markers (CD3/CD4/CD8/FOXP3/CD20/PD-1/PD-L1/LAG3/TIGIT), divided into two clusters. There was a "hot cluster" (25%) consisting of patients with a high expression of these markers and a "cold cluster" (75%) comprising those without. The expression of CD39, CD44, CD68, CD163, IDO1, and Ki67 was significantly higher in tumors in the hot cluster. Immunologically, high-grade T1 tumors were significantly hotter, whereas tumors that had progressed to muscle invasion turned cold. However, a certain number of high-grade NMIBC patients were in the cold cluster, and these patients had a significantly higher risk of disease progression. Using an externally available TCGA dataset, RB1 and TP53 alterations were more frequently observed in TCGA hot cluster; rather FGFR3, KDM6A, and KMT2A alterations were common in TCGA cold/intermediate cluster. Analyses of recurrent tumors after BCG therapy revealed that tumor immune activity was widely maintained before and after treatment, and high FGFR3 expression was detected after recurrence in tumors initially classified into the cold cluster. Collectively, we revealed the dynamics of the tumor microenvironment in BC as a whole and identified candidate molecules as therapeutic targets for recurrent NMIBC, e.g., after BCG therapy.

Spatiotemporal evolution of ecosystem health and its driving factors in the southwestern karst regions of China

As one of the most typical karst landscapes globally, the karst regions in southwestern China are characterized by prominent rocky desertification and fragile ecological conditions. Consequently, exploring the spatiotemporal evolution and driving influences on ecosystem health (EH) in this region is of great significance for the improvement of ecosystems and green development. This study focuses on assessing EH in these regions from 2000 to 2020 using the “vitality-organization-recovery-service” (VORS) framework. Spatiotemporal changes in EH are analyzed through hotspot analysis, and the functional relationship between driving factors and EH is quantified using XGBoost and SHAP models. Key findings include: (1) Over the past two decades, the proportion of cities experiencing enhanced EH has generally improved in 73% of cities compared to 27% experiencing deterioration. (2) Spatial analysis reveals EH clustering in three regions. One cold spot cluster in the central north and two hot spot clusters in the southwest and southeast. (3) Urbanization level exhibits an inverse logarithmic relationship with EH. Precipitation affects EH in a cubic polynomial pattern—initial decrease, subsequent increase, and final decrease. Temperature impacts EH through a quartic polynomial function with fluctuating increases and decreases. PM2.5 shows a monotonically decreasing relationship with EH, while the normalized difference vegetation index demonstrates a monotonically increasing association. This research contributes to understanding EH dynamics in southwestern China’s karst landscapes, crucial for advancing ecosystem management and sustainable development efforts.

Flood Exposure, Vulnerability, and Risk Distribution in Urban Areas: Application of Geospatial Data Analytics and Index Development

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

Heterogeneity analysis of food supply-demand patterns and land carbon loss under different dietary structures in China

China’s diets are undergoing a profound transformation, and carbon storage loss generated by dietary structure change is increasingly serious, accompanied by concerns about sustainable diets and food supply–demand conflicts. This paper designed a dual ‘material–environmental’ framework based on the dietary structure classification, around which we conducted the heterogeneity analysis of food supply–demand patterns and land carbon loss, and then tracked hotspot diets of trade-generated carbon loss through the multi-regional input–output (MRIO) model. The research found that 31 provinces could be classified into five clusters with obvious spatial agglomeration patterns, most of which still had a high proportion of staches, but also showed an evolutionary trend towards diversified compositions. Driven by dietary structure, the national food supply–demand match under the material accessibility dimension showed barely balanced overall, while in terms of environmental effects, ecosystems lost significant amounts of carbon stocks, with losses of 331 and 950 Tg11Tg refers to teragram, a weight unit, 1 Tg = 106 t = 1012 g of vegetation carbon and soil organic carbon, respectively, which together accounted for 14 percent of the total carbon stocks in the vegetation and topsoil carbon pools. The clusters to which Henan, Jiangsu, Zhejiang and Guangdong belong were tracked as hot dietary clusters exhibiting hotspots in inter-regional trade, contributing to carbon loss in external provinces through trade linkages. Heilongjiang and Inner Mongolia were at a low level of supply–demand balance but played the role of a carbon storage loser, while Guangdong, Zhejiang, and Jiangsu not only possessed higher balance levels but also pulled more external carbon loss. China's food system should optimise internal cropping structures and monitor external land use conversion. It is equally important to track food consumption side rights and responsibilities.

Investigating the impact of urban growth on land use using spatial autocorrelation methods in Konya/Türkiye.

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.

Bibliometric analysis of the acetabular labrum.

The acetabular labrum (AL) plays a crucial role in the normal physiological functioning of the hip joint. This study aims to present an overview of the current status and research hotspots concerning the AL and to explore the field from a bibliometric perspective. A total of 1918 AL-related records published between January 1, 2000 and November 8, 2023 were gathered from the Web of Science Core Collection database. By utilizing tools such as HisCite, CiteSpace, VOSviewer, and the R package "bibliometrix," the regions, institutions, journals, authors, and keywords were analyzed to predict the latest trends in AL research. Global research interest and publication output related to this topic continues to escalate. The United States leads in international collaborations, number of publications, and citation frequency, underscoring its preeminent position in this field. The American Hip Institute emerged as the most prolific institution, making the greatest contribution to publications. Notably, Arthroscopy and the American Journal of Sports Medicine are the 2 most popular journals in this domain, accounting for 13.29% and 10.1% of publications, respectively, and were also found to be the most co-cited journals. Amongst authors, Benjamin G. Domb leads with 160 articles (8.35%), while Marc J. Philippon is the most frequently cited author. The keyword co-occurrence network showed 3 hot clusters, including "AL," "femoral acetabular impingement (FAI)," and "osteoarthritis." In addition, "survivorship," "FAI," and "patient-reported outcomes" were identified as trending topics for future exploration. This study represents the first comprehensive bibliometric analysis, summarizing the present state and future trends in AL research. The findings serve as a valuable resource for scholars, offering practical insights into key information within the field and identifying potential research frontiers and emerging directions in the near future.

Unravelling the paradox of spatial homogeneity: An analysis of land use planning and monofunctional development in Wroclaw, Poland

Processes of urban development progressing across Europe, with built-up areas expanding at a faster rate than the population, especially in the Central and Eastern Europe region. The importance of ensuring a high quality of urban life and effective environmental planning is widely acknowledged for the sustainable development of functional urban areas. While the literature extensively covers the implementation stage of monofunctional development in Polish suburban zones, the origins of spatial homogeneity in planning have received limited attention in research. This paper aims to assess local zoning plans and examine the diversity of urban planning in the subregion of Wroclaw, Poland. Employing exploratory experts interviews and landscape metrics such as Patch Richness Density and Simpson Diversity Index, along with spatial statistics as the Hot Spot tool and Cluster and Outlier analysis, the findings reveal that monofunctional residential development in Polish functional urban areas may result from a complex interplay of various economic, institutional, educational, professional responsibility, and political, social, as well as historical factors on the urban planning stage. These results contribute to a deeper understanding of the extent and spatial distribution of planning diversity, opening paths for further discussions on the professional requirements of urban planners.

Spatial and temporal distribution characteristics and geographic contexts of civilized villages in China.

Rural civilization is the soul of rural revitalization strategies. As a booster of rural civilization, the civilized village is an indispensable force to promote the modernization of rural areas, in the meanwhile, the study of its spatial distribution has important theoretical significance and practical value in deepening the theory of rural geography, promoting the development of rural civilization, and helping rural revitalization. Considering civilized villages as the study topic, the spatial and temporal distribution characteristics and geographic context were discussed using spatial analysis methods. The number of civilized villages in the country has shown a clear upward trend since 2005. The spatial distribution pattern of civilized villages in China shows unbalanced patterns with a higher concentration in the east and south. Civilized villages in China present significant patterns of clustering with an increasing degree of spatial clustering, showing a multi-core spatial distribution pattern. Civilized villages in China demonstrate clear spatiotemporal clustering characteristics. A spatiotemporal hot spot cluster formed in the northwestern region from 2005 to 2011, and a spatiotemporal cold spot cluster formed in the southeastern region from 2005 to 2008. The spatial distribution of civilized villages in China has obvious geographical differentiation laws, and factors such as topography, climate, economy, transportation, and policy significantly affect their spatial distribution.

Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study.

The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study.

The Joint United Nations Program on HIV/AIDS (UNAIDS) has set the "95-95-95" targets to ensure that 95% of all people living with HIV will know their HIV status, 95% of all people living with HIV will receive sustained antiretroviral therapy (ART), and 95% of all people receiving ART will achieve viral suppression (<1000 copies/mL). However, few countries have currently achieved these targets, posing challenges to the realization of the UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. The Chinese government has implemented corresponding policies for HIV/AIDS prevention and control; however, it still faces the challenge of a large number of HIV/AIDS cases. Existing research predominantly focuses on the study of a particular region or population in China, and there is relatively limited research on the macro-level analysis of the spatiotemporal distribution of HIV/AIDS across China and its association with socioeconomic factors. This study seeks to identify the impact of these factors on the spatiotemporal distribution of HIV/AIDS incidence in China, aiming to provide scientific recommendations for future policy development. This study employed ArcGIS 10.2 (Esri) for spatial analysis, encompassing measures such as the imbalance index, geographical concentration index, spatial autocorrelation analysis (Moran I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence in 31 provinces of China from 2009 to 2019. Geographical Detector was used for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on 9 selected socioeconomic indicators to further investigate the influence of socioeconomic factors on HIV/AIDS incidence in China. The spatiotemporal distribution analysis of HIV/AIDS incidence in China from 2009 to 2019 revealed distinct patterns. The spatial distribution type of HIV/AIDS incidence in China was random in 2009-2010. However, from 2011 to 2019, the distribution pattern evolved toward a clustered arrangement, with the degree of clustering increasing each year. Notably, from 2012 onwards, there was a significant and rapid growth in the aggregation of cold and hot spot clusters of HIV/AIDS incidence in China, stabilizing only by the year 2016. An analysis of the impact of socioeconomic factors on HIV/AIDS incidence in China highlighted the "urbanization rate" and "urban basic medical insurance fund expenditure" as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, among social factors, indicators related to medical resources exerted a crucial influence on HIV/AIDS incidence. From 2009 to 2019, HIV/AIDS incidence in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic.

The role of permeability in lid-driven cavity flow containing a cluster of hot solids

The flow behavior of particle clusters with thermal convection in confined fluid flow has garnered considerable attention. One of the remaining challenges is to understand the role of a porous structure of particle clusters in the confined thermal fluid flow. In present work, a numerical study based on a double distribution function lattice Boltzmann model has been conducted to unveil the two-sided lid-driven cavity flow containing a hot cluster formed by an array of center-placed solids. First, we investigated the heat transfer by varying the Richardson number (Ri, from 0.01 to 1.0) and the solid fraction of the cluster (ϕ, from 0.0143 to 0.6734) with fixed cavity Reynolds number (Re=1000) and Prandtl number (Pr=0.7). The increase in Ri would lead to the formation of non-centrosymmetric streamlines and isotherms and an increase in the total averaged Nusselt number Nu¯a, in which several vortex structures being identified at high Ri and ϕ. Then, we explored the influence of permeability, quantified by the Darcy number (Da), by changing the porous structure of cluster via rearranging the array of solids. It is found that a higher permeability could enhance heat transfer of particle cluster due to the increase in convection, but the smaller surface area of solid particles would limit heat transfer. A correlation of Nu¯a with respect to Ri and Da could be developed based on our two-dimensional simulation results, allowing the evaluation of involved interplay between the porous structure of particle cluster and thermal convection in confined flow via a simple yet effective way.

Individual and Geospatial Determinants of Health Associated With School-Based Human Papillomavirus Immunization in Alberta: Population-Based Cohort Study.

Human papillomavirus (HPV) infection causes nearly all cervical cancer cases and is a cause of anogenital and oropharyngeal cancers. The incidence of HPV-associated cancers is inequitable, with an increased burden on marginalized groups in high-income countries. Understanding how immunization status varies by material and social deprivation, health system, and geospatial factors is valuable for prioritizing and planning HPV immunization interventions. The objective of this study was to describe school-based HPV immunization rates by individual and geospatial determinants of health in Alberta, Canada. Health administrative data for male and female individuals born in 2004 in Alberta were used to determine HPV immunization status based on age and the number of doses administered in schools during the 2014/2015-2018/2019 school years. Immunization status and its relationship with material and social deprivation and health system factors were assessed by a logistic regression model. Geospatial clustering was assessed using Getis-Ord Gi* hot spot analysis. Mean scores of material and social deprivation and health system factors were compared between hot and cold spots without full HPV immunization using independent samples t tests. A multidisciplinary team comprising researchers and knowledge users formed a co-design team to design the study protocol and review the study results. The cohort consisted of 45,207 youths. In the adjusted model, the odds of those who did not see their general practitioner (GP) within 3 years before turning 10 years old and not being fully immunized were 1.965 times higher (95% CI 1.855-2.080) than those who did see their GP. The odds of health system users with health conditions and health system nonusers not being fully immunized were 1.092 (95% CI 1.006-1.185) and 1.831 (95% CI 1.678-1.998) times higher, respectively, than health system users without health conditions. The odds of those who lived in areas with the most material and social deprivation not being fully immunized were 1.287 (95% CI 1.200-1.381) and 1.099 (95% CI 1.029-1.174) times higher, respectively, than those who lived in areas with the least deprivation. The odds of those who lived in rural areas not being fully immunized were 1.428 times higher (95% CI 1.359-1.501) than those who lived in urban areas. Significant hot spot clusters of individuals without full HPV immunization exist in rural locations on the northern and eastern regions of Alberta. Hot spots had significantly worse mean material deprivation scores (P=.008) and fewer GP visits (P=.001) than cold spots. Findings suggest that material and social deprivation, health system access, and rural residency impact HPV immunization. Such factors should be considered by public health professionals in other jurisdictions and will be used by the Alberta co-design team when tailoring programs to increase HPV vaccine uptake in priority populations and regions.

Territorial patterns of COVID-19 in Iran

Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

Assessing the impact of booster vaccination on diphtheria transmission: Mathematical modeling and risk zone mapping

The COVID-19 pandemic caused significant disruptions in the healthcare system, affecting vaccinations and the management of diphtheria cases. As a consequence of these disruptions, numerous countries have experienced a resurgence or an increase in diphtheria cases. West Java province in Indonesia is identified as one of the high-risk areas for diphtheria, experiencing an upward trend in cases from 2021 to 2023. To analyze the situation, we developed an SIR model, which integrated DPT and booster vaccinations to determine the basic reproduction number, an essential parameter for infectious diseases. Through spatial analysis of geo-referenced data, we identified hotspots and explained diffusion in diphtheria case clusters. The calculation of R0 resulted in an R0 = 1.17, indicating the potential for a diphtheria outbreak in West Java. To control the increasing cases, one possible approach is to raise the booster vaccination coverage from the current 64.84% to 75.15%, as suggested by simulation results. Furthermore, the spatial analysis revealed that hot spot clusters were present in the western, central, and southern regions, posing a high risk not only in densely populated areas but also in rural regions. The diffusion pattern of diphtheria clusters displayed an expansion-contagious pattern. Understanding the rising trend of diphtheria cases and their geographic distribution can offer crucial insights for government and health authorities to manage the number of diphtheria cases and make informed decisions regarding the best prevention and intervention strategies.

Application of new nanopolymer materials in intelligent thermal insulation system for building external walls

In response to the current problem of poor energy consumption control effect and overall high energy consumption of new nanopolymer materials for building exterior walls, the author proposes a thermal insulation energy consumption control technology based on the heat transfer performance model of exterior wall insulation panels. Calculate the current heat transfer performance parameters of exterior wall insulation panels using the reaction coefficient method, determine the proportional relationship between the effective heat transfer coefficients, and establish a heat transfer performance model for exterior wall insulation panels, based on the thermal conductivity index of the new nanopolymer material for the wall, the current control parameters are optimized. The optimal data algorithm is used to obtain the energy consumption decision variable value of the new nanopolymer material for the exterior wall of the building under the condition of clarifying the current heat transfer coefficient, and establish constraint conditions, based on the specific energy consumption data of new nanopolymer materials outside the current wall, establish the current thermal energy information transmission ratio relationship, analyze the current wall energy consumption hotspots, propose the K-means clustering analysis strategy, and apply it to the hot spot clustering control. Energy consumption control is achieved by locating the main cluster head parameters of the hot spot. Simulation results show that the hot spot fit of the aforementioned control methods has been improved by 29%, and the accuracy of the design method in cooling load control statistics is significantly higher than the two traditional methods used for comparison. Due to different weights, the final improvement ratio is determined to be 27%, further verifying the hypothesis. It has been proven that it can effectively improve the energy consumption control effect.

Hotspot areas of risky sexual behaviour and associated factors in Ethiopia: Further spatial and mixed effect analysis of Ethiopian demographic health survey.

Sexual behaviour needs to take a central position in the heart of public health policy makers and researchers. This is important in view of its association with Sexually Transmitted Infections (STIs), including HIV. Though the prevalence of HIV/AIDS is declining in Ethiopia, the country is still one of the hardest hit in the continent of Africa. Hence, this study was aimed at identifying hot spot areas and associated factors of risky sexual behavior (RSB). This would be vital for more targeted interventions which can produce a sexually healthy community in Ethiopia. In this study, a cross-sectional survey study design was employed. A further analysis of the 2016 Ethiopia Demographic and Health Survey data was done on a total weighted sample of 10,518 women and men age 15-49 years. ArcGIS version 10.7 and Kuldorff's SaTScan version 9.6 software were used for spatial analysis. Global Moran's I statistic was employed to test the spatial autocorrelation, and Getis-Ord Gi* as well as Bernoulli-based purely spatial scan statistics were used to detect significant spatial clusters of RSB. Mixed effect multivariable logistic regression model was fitted to identify predictors and variables with a p-value ≤0.05 were considered as statistically significant. The study subjects who had RSB were found to account about 10.2% (95% CI: 9.64%, 10.81%) of the population, and spatial clustering of RSB was observed (Moran's I = 0.82, p-value = 0.001). Significant hot spot areas of RSB were observed in Gambela, Addis Ababa and Dire Dawa. The primary and secondary SaTScan clusters were detected in Addis Ababa (RR = 3.26, LLR = 111.59, P<0.01), and almost the entire Gambela (RR = 2.95, LLR = 56.45, P<0.01) respectively. Age, literacy level, smoking status, ever heard of HIV/AIDS, residence and region were found to be significant predictors of RSB. In this study, spatial clustering of risky sexual behaviour was observed in Ethiopia, and hot spot clusters were detected in Addis Ababa, Dire Dawa and Gambela regions. Therefore, interventions which can mitigate RSB should be designed and implemented in the identified hot spot areas of Ethiopia. Interventions targeting the identified factors could be helpful in controlling the problem.

Trend, spatial distribution, and factors associated with HIV testing uptake among pregnant women in Ethiopia, based on 2005-2016 Ethiopia demographic and health survey: A multivariate decomposition analysis and geographically weighted regression.

HIV testing during pregnancy is an integral component and first step of prevention for mother to child transmission, initiation of antiretroviral treatment and diagnosis of HIV/AIDS. However, Ethiopia and other sub-Saharan African countries face challenges in meeting the first target of the 95-95-95 global initiatives. This study examines trends, spatial distribution, and factors influencing HIV testing among pregnant women in Ethiopia from 2005 to 2016, using data from the Ethiopia Demographic and Health Surveys. The study was based on three consecutive demographic and health survey in Ethiopia. A total weighted sample of 13,020 women who gave birth within 2 year proceeding each survey year was included in each survey. Logit based decomposition analysis technique was employed to identify factors contributing to the change in HIV testing uptake among pregnant women overtime. ArcGIS version 10.7.1 and SaT Scan version 10.1software were used for the spatial analysis and geographically weighted regression. HIV testing uptake among pregnant women has significantly increased from 0.51% in 2005 to 32.4% in 2016 with 2.9% annual rate of increment in Ethiopia. About 75.9% of the overall increase in HIV testing uptake among pregnant from 2005-2016 was due to increases in women's composition with knowledge of Mother to child transmission of HIV (3.2%), HIV counseling (10.3%), 4 or more antenatal care visits (31.4%), health facility delivery (6.3%), not perceiving distance from the health facility as a big problem (1.1%), and urban residence (0.6%). Spatial variation of low proportion of HIV testing was non-random in all three surveys (Moran index, p-value<0.05). Hot spot clusters exhibited in all the three waves includes Tigray and SNNPRs in 2005 and consistent hotspot areas in Benishangul-Gumuz, Somali, SNNPR, and Gambella in 2011 and 2016 EDHS. Lack of knowledge of Mother to child transmission of HIV, lack of antenatal care visit, lack of media exposure, and health facility delivery were significant predictors for the spatial variation of low proportion of HIV testing uptake across regions in Ethiopia in 2016. Over all, there has been a substantial increase in HIV testing uptake among pregnant women overtime in Ethiopia, but it still far away from achieving the 2025 HIV testing targets. Knowledge of Mother to child transmission of HIV, HIV counseling, Number of Antenatal care visit, previous place of delivery, residence and distance to health facility were significant contributing factors for the change in HIV testing uptake. There was geographical disparity in HIV testing uptake across regions in all three EDHS. Lack of knowledge of Mother to child transmission of HIV, lack of ANC visit, lack media exposure, and health facility delivery were significant predictors. Geographic-based interventions, together with broader public health strategies, are essential for advancing HIV testing uptake.

Mapping cultural ecosystem services in mountain forests using mobile phone data

The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex “Sudety Zachodnie” using mobile phone data. The study identified the sites with the highest (hot spot) and lowest (cold spot) use. Habitat, stand, demographic, topographic and spatial factors affecting the distribution of activity were also analyzed. Two approaches were applied in our research: global and local Moran’s coefficients, and a machine learning technique, Boosted Regression Trees. The results show that 11,503,320 visits to forest areas were recorded in the “Sudety Zachodnie” in 2019. The most popular season for activities was winter, and the least popular was spring. Using global and local Moran’s I coefficients, three small hot clusters of activity and one large cold cluster were identified. Locations with high values with similar neighbours (hot-spots) were most often visited forest areas, averaging almost 200,000 visits over 2019. Significantly fewer visits were recorded in cold-spots, the average number of visits to these areas was about 4,500. The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation. Results of Boosted Regression Trees modeling of visits in forest, using tree stand habitat and spatial factors accurately explained 76% of randomly selected input data. The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography. The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data. It also allows the identification of factors that may influence the distribution of visits in forests. Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.

Spatial variation and determinants of solid fuel use in Ethiopia; Mixed effect and spatial analysis using 2019 Ethiopia Mini Demographic and Health Survey dataset.

According to UNSD, World Bank, and WHO, a 2020 joint report, 3 billion people who used solid fuel were found in low and middle-income countries. The burning of such type of fuel emits a variety of pollutants such as PM2.5, PM10, CO, NO2, organic compounds, and other substances which a had wide range of public health problems The evidence from the WHO report, indoor air pollution was attributed to around 3.2 million deaths each year in 2020, and more than 237 000 deaths of children under the age of five. This study aims to investigate solid fuel prevalence, predictors, and spatial variation in Ethiopia. This study was conducted in Ethiopia based on the fifth Ethiopian Demographic and Health Surveys 2019 dataset. 8,663 households were successfully interviewed at a response rate of 99%. Weighted by sampling weight was done to do a reliable statistical analysis. Fuel type was used as the outcome variable whereas sex of household head (male or female), wealth index (poor, middle, and rich), educational status (no education, primary, secondary, higher), having television and radio, a separate room used as a kitchen, were individual predictor variables and community level education(lower/higher), residence(urban/rural), community level media exposure (exposed/unexposed), region (pastoralist, semi-pastoralist, Agrarian, and City administration) were community level variables. All the above analyses were conducted using Excel Microsoft 2016, STATA 14, ArcGIS 10.7, and SaTScan 10.1 software. The majority (72.62%) of household heads were males. The overall national level of solid fuel use was 87.13% (95% CI (86.4%-87.82%)). From this 87.13% of solid fuel use, 18.60% accounts for urban and 68.53% for rural parts of the country. Educational status, having television, accessing electricity, and wealth index were individual-level variables community-level education, type of residence, and region were community-level variables significantly associated factors towards solid fuel use in our study. Global (z-score = 33.109144, P-value <0.0001), local (hot spot, cluster, and outlier), and Spatial Scan statistical analyses revealed that there was a major geographical variation in solid fuel use across Ethiopia. Based on this finding, the prevalence of solid fuel use was higher in Ethiopia. Educational status, having television, accessing electricity, and wealth index were individual-level variables and community-level education, type of residence, and region were community-level variables statistically significant factors in determining fuel choice for cooking. There was significant spatial variation in the prevalence of solid-fuel use across the country. In order to addressing such heavily dependent on solid fuel use, responsible bodies like health policy makers, national and international organizations, and public health researchers should work on showing health problems of solid fuel use and the means of increasing clean fuel use. Substantial policy modifications are desirable to reach access to clean fuels and technologies (SDG 7.1.2) by 2030 to address health inequities.

Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on land surface temperature (LST) distribution over West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST and other predictands while preserving the geographical variability of WA. Our results showed that the random forest model performs best in downscaling predictands. This is important for the sub-region since it has limited access to mainframes to power multiplex machine-learning algorithms. In contrast to the northern regions, the southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy vegetation coincide with hot LST clusters. The positive Normalized Difference Vegetation Index (NDVI) trends in the Sahel underscore rainfall recovery and subsequent Sahelian greening. The southwesterly winds cause the upwelling of cold waters, lowering LST in southern WA and highlighting the cooling influence of water bodies on LST. Identifying regions with elevated LST is paramount for prioritizing greening initiatives, and our study underscores the importance of considering LVCET factors in urban planning. Topographic slope-facing angles, heat loads, and diurnal anisotropic heat all contribute to variations in LST, emphasizing the need for a holistic approach when designing resilient and sustainable landscapes.