Hotspot Areas Research Articles

The Clustering of the Population at Building Scale in Bursa City (Türkiye)

Research on spatial statistical methods related to population estimation at the building scale and its implications for urban land use has attained little attention. The main target of this study is to propose a new method for population estimation at the building level with minimal data and methodology and a high accuracy rate. In addition to this, it discusses urban population from various perspectives by using spatial statistical methods (Local Moran’s I and Hot–Cold Spot) to examine the population calculated based on the number of residential units in buildings and the household size of the neighborhood along with urban land use types in the case of Bursa. The results showed the following: (1) The suggested method achieves a 76% accuracy rate in population estimation at the building level; (2) 64.6% of the city’s population (2,101,581 individuals) is located in areas classified as Discontinuous High-Density Urban Fabric (50–80%) and Continuous Urban Fabric (>80); (3) 13.2% of the population is located in hot spot areas of these two types, while 14.5% is in cold spot areas. This research provides decision-makers with a framework for addressing urban problems related to housing, transportation, health, and energy in addition to the methods it proposes.

On the Assessment of Wind Microclimate in a Complex Urban Environment Utilising Computational Fluid Dynamics

This paper presents an insight and key considerations for using Computation Fluid Dynamics (CFD) to simulate the Wind Microclimate in a Complex Urban Environment. The current study involved developing a local 10m height “reference” wind rose for the project site and then combining statistical meteorological data with aerodynamic information and wind comfort and wind safety criteria. Where wind speeds were found to be at undesirable wind levels at areas of interest (ground level, podium level, terraces, balconies, etc.), recommendations were made to reduce detrimental wind effects, e.g. using landscaping, porous windbreaks, canopies, etc. The criteria used in the evaluation of pedestrian-level winds surrounding the proposed development were based on the well-established “Lawson” criteria which couple the probability of exceeding winds at given statistical levels with wind speed magnitudes originally related to the Beaufort Land Scale. To take into account the influence of the immediate surrounding environment, all neighboring buildings and local topography within a diameter of almost 1,000 m around the site were included in the developed CFD model. Furthermore, all small canopies, balconies, and semi-open spaces were modeled in detail as per the provided architectural drawings. Based on a mesh sensitivity assessment, polyhedral elements were used for the entire computational domain. CFD analysis offers a comprehensive range of output including the velocity distribution in three directions and turbulence levels, allowing the identification of hot spot areas that have potentially unacceptable wind conditions for further assessment and mitigation treatments to reduce wind speed to acceptable levels. The baseline (no mitigation) scenario simulation results contributed to a better understanding of the environmental wind impact for the project at the site, enabling a targeted approach to the development of effective windbreak options This paper provides a comprehensive approach toward the establishment of a robust CFD assessment of human comfort to ensure that proposed building developments and their streetscapes create a comfortable wind environment to live and visit.

Changing of the coordination of socioeconomic development and the environment as sustainable development progresses

The relationship between socioeconomic development and the environment is intimately linked with the level of regional sustainable development. Clarifying the evolution pattern of this relationship during the sustainable development progress is crucial for achieving all of the Sustainable Development Goals (SDGs), but less research has focused on this problem. Here, we have utilized statistical and remote sensing data from 290 municipal units in China to analyze the evolution of the coupling coordination degree (CCD) between socioeconomic development and the environment along a progression of sustainable development measured by the SDG Index. The results show that the hotspot areas of CCD are concentrated in coastal regions, gradually decreasing as they move inland. Additionally, as sustainable development progresses, both socioeconomic and environment levels exhibit a nonlinear rise. The coupling coordination between the two demonstrates a pattern of initial increasing, then decreasing, and finally increasing again, which may be related to changes in industrial structure. Our study delves deeply into the patterns of evolution in the relationship between socioeconomic and environments, exploring the challenges and opportunities faced by regions at different stages of sustainable development. The findings can deepen our understanding of sustainable development and provide policy suggestions and theoretical support for achieving SDGs.

Spatial heterogeneity and interacting intensity of drivers for trade-offs and synergies between carbon sequestration and biodiversity

Global crises of biodiversity loss and climate change highlighted the urgent need to understand the trade-off and synergy between carbon sequestration and biodiversity and the driving mechanisms behind them. However, existing research on driving mechanisms primarily focused on the influence of independent variables on dependent variables, neglecting the interaction between independent variables. The InVEST and PLUS models were utilized in this study to evaluate the dynamics of carbon sequestration and biodiversity in the Chishui River Basin, Southwest of China. By integrating the factor detection of the Geographic Detector and the Multi-scale Geographically Weighted Regression (MGWR) model, this study identified the key drivers affecting trade-off and synergy and revealed their spatial heterogeneity. Furthermore, utilizing the interaction detection of the Geographic Detector and the Geographic Cross-Convergence Mapping (GCCM) model, this study analyzed the interaction direction and interaction intensity among key drivers. The results showed that: (1) From 2012–2035, the total values of carbon sequestration and biodiversity exhibited an overall increasing trend, with 58.49 % of the area showing trade-off effects, 23.86 % showing positive synergy, and 17.65 % showing negative synergy. (2) Elevation, slope, topographic relief, temperature, and land use intensity were identified as key factors influencing trade-off and synergy, and these factors displayed significant spatial heterogeneity. (3) When multiple drivers interacted, their explanatory power for trade-off and synergy increased significantly. The interaction patterns among drivers presented a “topography➙climate↔human activity↔distance➙soil” structure, where human activity interacted bidirectionally with other factors, with the strongest interaction occurring between climate factors and human activity. This study advanced the understanding of the driving mechanisms behind trade-off and synergy through MWGR and GCCM model. Based on the supply-demand theory, a synergistic management framework was proposed to emphasize the “pattern-process-service-feedback” nexus. The hotspot areas identified through scenario simulation could be used for synergistic enhancement of carbon sequestration and biodiversity.

Extent and drivers of land productivity degradation at the territorial level in Eastern DR Congo

Land degradation is a global environmental problem that affects the livelihoods of communities. Understanding the extent and causative factors of this phenomenon is important to design the cause-targeted strategies to cope with this problem. This study aimed to assess the effects of biophysical and socio-economic factors on the occurrence of land productivity degradation at the territorial level in Eastern DR Congo, using the Kalehe territory as a case study. The land productivity dynamics were determined based on the trend analysis of Landsat-based NDVI annual composites over the 1987-2020 period. Additionally, a binary logistic regression model was developed to identify the determinant factors and to map the susceptibility of this degradation at the territorial level. The results revealed that 31.25% (1303.175 km2) of the territory had experienced a decrease in land productivity, with 10.8% (453.83 km2) characterized by a significant decrease at the confidence level of 90%, representing the hotspots area of land productivity degradation where conservation actions should be prioritized. This decrease in land productivity is location-specific as it depends on the land cover types and health zones. It is likely to occur at high altitudes, in areas close to artisanal mining sites, rivers, and administrative centers. These results imply that policy interventions to cope with the problem of land productivity degradation should be developed following the heterogeneity of the landscape, and sustainable land management practices that take into account both the biophysical and socio-economic drivers of land degradation should be implemented in this region to achieve the land degradation neutrality by 2030.

Spatial Interaction Between Leptospirosis and Enteric Fever in Kelantan, Malaysia: A 2016-2022 Notification Registry Analysis.

Leptospirosis and enteric fever are prevalent tropical acute bacterial febrile illnesses in Kelantan, Malaysia, that exhibit overlapping features and shared transmission dynamics, yet their spatial relationship remains understudied. This study aimed to analyze their spatial distribution, investigating potential interactions and intersecting patterns. A total of 212 laboratory-confirmed cases of enteric fever and 1106 of leptospirosis between 2016 and 2022, were retrieved from the national e-Notifikasi registry. Point pattern analysis revealed clustering of both diseases in the northern region, but leptospirosis was predominant in the south, exhibiting higher spatial risk. Seven co-infection cases were identified in overlapping hotspot areas. Spatial dependence between the diseases was identified within 4 km distance on average, with varying patterns over time and regions. Recognizing spatial dependence has implications for accurate diagnosis, timely intervention, and tailored public health strategies. The findings underscore the need for multi-disease interventions to address shared risk factors and co-infections in similar geographical contexts.

Habitat Suitability of Ixodes ricinus Ticks Carrying Pathogens in North-East Italy.

Ixodes ricinus ticks are ubiquitous in Europe, including in North-East Italy. These ticks are important vectors of several zoonotic pathogens of public health relevance. In this study, the habitat suitability range of I. ricinus ticks infected with zoonotic pathogens was predicted in North-East Italy, and relevant spatial predictors were identified. In 2015-2021, ticks were collected at 26 sampling sites in the study area. The collected ticks were screened for the presence of pathogens using PCR assays. For Borrelia, Rickettsia and Anaplasma/Ehrlichia species, data allowed for ecological niche modelling using Maxent. Environmental determinants potentially related to tick habitat suitability were used as model inputs. Predicted suitable habitat distributions revealed hotspots of the probability of pathogen presence in I. ricinus ticks mainly in the central and upper parts of the study area. Key environmental predictors were temperature, rainfall and altitude, and vegetation index for specific pathogens (Rickettsia and Anaplasma/Ehrlichia species). Increased risk of exposure to tick-borne pathogens upon tick bites in the predicted hotspot areas can, therefore, be expected. This provides useful information for public health risk managers in this and other similar regions.

Spatial distribution of teenage pregnancy and its associated factors in Ethiopia: spatial and multilevel analysis of EDHS 2019

BackgroundOne of the reasons for the high rates of maternal and child morbidity and mortality in Sub-Saharan Africa is the rising proportion of teenage pregnancy. Preventing teenage pregnancy is critical to meeting sustainable development goal number three which aims to reduce the global maternal mortality ratio to less than 70 per 100,000 live births. To support the achievement of this goal, this study aimed to assess the spatial variation and factors associated with teenage pregnancy in Ethiopia using the recent nationally representative data.MethodsA secondary data analysis of the 2019 Ethiopian mini Demographic and Health Survey was conducted with a total weighted sample of 2211 (unweighted 2100) teenagers. The Bernoulli model was fitted using SaTScan version 9.6 to identify hotspot areas and the geospatial pattern and prediction of teenage pregnancy were mapped using ArcGIS version 10.7. A multilevel logistic regression model was fitted to identify factors associated with teenage pregnancy among teenagers. Adjusted OR with 95% CI was calculated and variables having a p-value less than 0.05 were statistically significant factors of teenage pregnancy.ResultThe prevalence of teenage pregnancy among adolescents aged 15–19 years in Ethiopia was 12.89% (95% CI: 11.56%, 14.36%). The SaTScan analysis identified a primary cluster in the Gambella region of Ethiopia (log-likelihood ratio = 14.02, p < 0.001). A high prevalence of teenage pregnancy was observed in Somalia, Afar, Gambella, and the southern part of the Oromia regions of Ethiopia. Age, educational status- primary and secondary, religion- protestant, having television, contraceptive knowledge, household head-female, and region- Small peripheral were significant determinants of teenage pregnancy.ConclusionThe spatial distribution of teenage pregnancy in Ethiopia was nonrandom. Age, educational status, religion, having television, contraceptive knowledge, sex of household head, and region were significant determinants of teenage pregnancy. Therefore, concerned government bodies and other stakeholders should organize periodic educational campaigns and youth-friendly reproductive health services. Collaboration between healthcare professionals, and religious and community leaders could also form a strategic partnership that makes interventions more comprehensive, culturally sensitive, and effective in reducing teenage pregnancy.

Changes in enzyme activity, structure and growth strategies of the rhizosphere microbiome influenced by elevated temperature and CO2

The impacts of global warming and increased CO2 levels on soil processes and crop growth are concerning. Soil enzymes in the rhizosphere, produced mainly by microbes, play a vital role in nutrients mobilization for plants. Nevertheless, a comprehensive understanding of how microbial communities in the rhizosphere respond to increased temperatures and CO2 levels, particularly in relation to nutrient acquisition, is still lacking. Addressing this problem, we grew soybeans under elevated temperature (ET, +2 °C) and CO2 levels (eCO2, +300 ppm), both individually and in combination (eCO2 + eT), in rhizobox mesocosms. Enzyme activity and microbial communities in soybean rhizospheres were investigated using soil zymography. eCO2 increased enzyme activity by 2.5 % to 8.7 %, while eT expanded the hotspot area from 1.8 % to 3.3 %. The combined factors amplified both the hotspot area by 5.3 % to 10.1 % and enzyme activity by 35.4 % to 67.3 %. Compared to ambient conditions, rhizosphere communities under eCO2 were predominantly comprised of r-strategist keystone taxa, including Acidobacteria, Proteobacteria, and Ascomycota. On the contrary, eT induced a shift in the microbial community towards K-selected taxa, characterized by an increased relative abundance of Basidiomycota and Actinobacteria. Furthermore, the combination of eCO2 and eT led to an increase in the relative abundance of key bacterial species (Acidobacteria, Proteobacteria, and Actinobacteria) as well as fungi (Ascomycota and Basidiomycota). These findings indicate the potential significance of enzyme hotspots in modulating responses to climate change. Changes in enzyme activity and hotspot area could indicate the alteration in microbial growth strategies. The treatments exhibited distinct changes in the composition of microbial communities, in network organization, and in the proportion of species designated as r or K-strategists. Overall, these findings highlight the combined effects of global change factors on bacterial and fungal communities, providing insights into their growth strategies and nutrient mobilization under climate change scenarios.

Trade-off between soil enzyme activities and hotspots area depends on long-term fertilization: In situ field zymography

Mineral fertilizers and livestock manure have been found to impact soil enzyme activities and distributions, but their trade-off and subsequent effects on soil functioning related to nutrient cycling are rarely evaluated. Here, we investigated the long-term effects of manure and mineral fertilization on the spatial distribution of enzyme activities related to carbon, nitrogen, and phosphorus cycling under field-grown maize. We found that the legacy of mineral fertilizers increased the rhizosphere extension for β-glucosidase and N-acetylglucosaminidase by 16–170 %, and the hotspots area by 37–151 %, compared to manure. The legacy of manure, especially combined with mineral fertilizers, increased enzyme activities and formed non-rhizosphere hotspots. Furthermore, we found a trade-off between hotspots area and enzyme activities under the legacy effect of long-term fertilization. This suggested that plants and microorganisms regulate nutrient investments by altering spatial distribution of enzyme activities. The positive correlation between hotspots area and nutrient contents highlights the importance of non-rhizosphere hotspots induced by manure in maintaining soil fertility. Compared to mineral fertilization, the legacy effect of manure expanded the soil functions for nutrient cycling in both rhizosphere and non-rhizosphere by >1.7 times. In conclusion, the legacy of manure expands non-rhizosphere hotspots and enhances soil functioning, while mineral fertilization expands rhizosphere extension and intensifies hotspots area for nutrient exploitation.

Spatial distribution and associated factors of cesarean section in Ethiopia using mini EDHS 2019 data: a community based cross-sectional study

Maternal health is a major public health tricky globally. Cesarean section delivery reduces morbidity and mortality when certain complications occur throughout pregnancy and labor. Cesarean section subjected to the availability and use of essential obstetric services in regional factors in Ethiopia. There was a scarcity of studies that assess the spatial distribution and associated factors of cesarean section. Consequently, this study aimed to assess the spatial variation of cesarean section and associated factors using mini EDHS 2019 national representative data. A community based cross-sectional study was conducted in Ethiopia from March to June 2019. A two-stage stratified sampling design was used to select participants. A Global Moran’s I and Getis-Ord Gi* statistic hotspot analysis was used to assess the spatial distribution. Kuldorff’s SaTScan was employed to determine the purely statistically significant spatial clusters. A multilevel binary logistic regression model fitted to identify factors. A total of 5753 mothers were included. More than one-fourth of mothers delivered through cesarean section at private health institutions and 54.74% were not educated. The proportion of cesarean section clustered geographically in Ethiopia and hotspot areas were observed in Addis Ababa, Oromia, Tigray, Derie Dewa, Amhara, and SNNR regions. Mothers’ age (AOR = 1.07, 95% CI 1.02–1.12), mother’s had secondary education (AOR = 2.113, 95% CI 1.414, 3.157), mother’s higher education (2.646, 95% CI 1.724, 4.063), Muslim religion followers (AOR = 0.632, 95% CI 0.469, 0.852), poorer (AOR = 1.719, 95% CI 1.057, 2.795), middle wealth index (AOR = 1.769, 95% CI 1.073, 2.918), richer (AOR = 2.041, 95% CI 1.246, 3.344), richest (AOR = 3.510, 95% CI 2.197, 5.607), parity (AOR = 0.825, 95% CI 0.739, 0.921), and multiple pregnancies (AOR = 4.032, 95% CI 2.418, 6.723) were significant factors. Therefore, geographically targeted interventions are essential to reduce maternal and infant mortality with WHO recommendations for those Muslim, poorest and not educated mothers.

Magnitude and hotspots of soil erosion types during heavy rainstorm events on the Loess Plateau: Implications for watershed management

In the context of climate change, rainstorm events are becoming increasingly frequent. In particular, on the Loess Plateau, heavy rainstorms are the primary cause of soil erosion. This study investigated and analysed different types of soil erosion hotspots and influencing factors in small watersheds under different rainstorm events in different areas of the Loess Plateau. The results indicate that the erosion intensities of rills, gullies, landslides and collapses ranged from 13600-46244, 1982-772201, 1163-172153 t km-2 and 1867-94985 t km-2, respectively. Newly constructed terraces exhibited an erosion intensity 1.6 times greater than that of old terraces, while terraces constructed before the rainy season in the current year exhibited an erosion damage intensity 2.6 times greater than that of terraces constructed after the rainy season in the previous year. In addition, under rainstorm conditions, landslides represented the most severe type of erosion in the watersheds, with the maximum amount of erosion accounting for more than 90 % of the total erosion amount, followed by gully or collapse erosion, with the collapse of terrace risers as the main contributor. Slope cultivation land, unpaved roads, terrace risers, and valley slopes below the gully shoulder line were identified as erosion hotspot areas. Rainstorm erosion was significantly influenced by the land use type and slope, which explained 14.2 %-41.5 % and 9.7 %-15.1 %, respectively, of the total variance in erosion intensity. We suggest that soil erosion prevention and control efforts on the Loess Plateau should focus on landslides on valley slopes below gully shoulder lines, followed by gullies on unpaved roads and the collapse of terraced fields. Drainage ditches and water cellars should be constructed above the gully shoulder line and on the inside of roads and terraces, thereby reducing erosion. Our research is crucial for optimizing and adjusting watershed management measures and preventing rainstorm erosion disasters.

Dust Source Susceptibility in the Lower Mesopotamian Floodplain of Iraq

The identification of susceptible dust sources (SDSs) based on the analysis of effective factors (i.e. dust drivers) is considered to be one of the primary and cost-effective solutions to deal with this phenomenon. Accordingly, this study aimed to identify SDSs and delineate their drivers using remote sensing data and machine learning (ML) algorithms in a hotspot area in the Lower Mesopotamian floodplain in southern Iraq. To model SDSs, a total of 15 environmental features based on remote sensing data such as topographic, climatic, land use/cover, and soil properties were considered as dust drivers and fed into the four well-known ML algorithms, including linear discriminant analysis (LDA), logistic model tree (LMT), extreme gradient boosting (XGB)-Linear, and XGB-Tree-based. Dust emission hotspots were identified by visual interpretation of sub-daily MODIS-Terra/Aqua true color composite imagery (2000-2021) to train (70%) and validate (30%) ML algorithms. Considering the variability of the spatial-temporal patterns of SDSs as a result of changes in dust drivers, the modeling process was carried out in four periods, including 2000-2004, 2005-2007, 2008-2012, and 2013-2021. Our results show that dust events in the study area occur most frequently in April, June, July, and August. Overall, all ML algorithms performed well and provided reliable results for identifying SDSs. However, the XGB-Linear provided the most reliable results with an average area under curve (AUC) of 0.79 for the study periods. Precipitation was determined as the most important dust driver. The SDS maps produced can be used as a basis for the development of rehabilitation plans in the study area to mitigate the adverse effects of dust storms.

Homogenization of fish assemblages in an endemic biodiversity hot spot: Evidence from 70‐year data from the Yun‐Gui Plateau, China

Abstract The combined impacts of anthropogenic activities and global climate changes threaten native fish communities. Such threats are particularly critical in biodiversity hot spot areas with many endemic species, as exemplified by the isolated Yun‐Gui Plateau, China. The risk of biotic homogenization is also expected to be high in such areas, but this risk and its underlying drivers remain poorly studied. Here, we established a database of fish distributions and used environmental filtering for spatial clusters (n = 3759) in river basins located at Yun‐Gui Plateau. This database was based on public data sources and a comprehensive review of relevant literature (n = 481) covering the period from 1950 to 2021. We divided the plateau into nine subbasins for spatial comparison and into two periods for temporal comparison—before and after the start of rapid economic growth development in 1978. We found: (a) a marginal increase in replacement (species exchange in different subbasins) and similarity index but a decrease in richness difference index, indicating homogenization and the loss of natural barriers across the entire plateau; (b) an increase in species richness accompanied by a discernible decline in phylogenetic diversity, also indicating homogenization; (c) the increase in species richness by species loss‐gain analyses was associated with climatic variables (temperature and precipitation), elevation and human activities in all subbasins. Temporal and spatial comparisons indicated that homogenization of fish assemblages in the Yun‐Gui Plateau was exacerbated by climatic and anthropogenic pressures. The observed homogenization of fish species is of great concern, and we call for systematic assessments and precautionary mitigation actions to maintain the uniqueness of biodiversity in the Yun‐Gui Plateau region.

The role of the hepatitis B virus genome and its integration in the hepatocellular carcinoma.

The integration of Hepatitis B Virus (HBV) is now known to be closely associated with the occurrence of liver cancer and can impact the functionality of liver cells through multiple dimensions. However, despite the detailed understanding of the characteristics of HBV integration and the mechanisms involved, the subsequent effects on cellular function are still poorly understood in current research. This study first systematically discusses the relationship between HBV integration and the occurrence of liver cancer, and then analyzes the status of the viral genome produced by HBV replication, highlighting the close relationship and structure between double-stranded linear (DSL)-HBV DNA and the occurrence of viral integration. The integration of DSL-HBV DNA leads to a certain preference for HBV integration itself. Additionally, exploration of HBV integration hotspots reveals obvious hotspot areas of HBV integration on the human genome. Virus integration in these hotspot areas is often associated with the occurrence and development of liver cancer, and it has been determined that HBV integration can promote the occurrence of cancer by inducing genome instability and other aspects. Furthermore, a comprehensive study of viral integration explored the mechanisms of viral integration and the internal integration mode, discovering that HBV integration may form extrachromosomal DNA (ecDNA), which exists outside the chromosome and can integrate into the chromosome under certain conditions. The prospect of HBV integration as a biomarker was also probed, with the expectation that combining HBV integration research with CRISPR technology will vigorously promote the progress of HBV integration research in the future. In summary, exploring the characteristics and mechanisms in HBV integration holds significant importance for an in-depth comprehension of viral integration.

Spatial Analysis of Diabetes Mellitus in Jambi Province: Methods Space-Time Scan Statistic and Spatial Autocorrelation

Diabetes Mellitus (DM) is a chronic disease that threatens human health, the research aims to determine distribution, clusters, and spatial autocorrelation. The type of research is an exploratory study with an ecological design. Diabetes Mellitus using a Geographic Information System (GIS) is analyzed spatially. The research was conducted in the Jambi Province Region. The research sample was participants with a diagnosis of DM sourced from data from visits by the Social Security Administering Agency as name (BPJS) at FKTP in 2020 - 2022. The results of the space-time scan statistical analysis during the 2020-2022 period found 10 DM clusters with details of 3 most likely clusters and 7 secondary clusters. Spatial autocorrelation analysis with Moran's I index found that there was spatial autocorrelation of DM in Jambi Province during the three-year research period. The conclusion obtained is that there are clusters and autocorrelation of DM disease in Jambi Province, the need for intervention in prevention and control programs in cluster and hotspot areas.

Exploring the temporal-spatial characteristics and determinants of high-quality development of city clusters in China

The complexity, severity, and uncertainty of the international situation have prompted the development of city clusters to focus more on resilience and the building of infrastructures and safeguards. Chinese-style modernization proposes a new realization path for the high-quality development (HQD) of city clusters, based on which an evaluation system for HQD indicators of city clusters is constructed. We also measured the HQD levels of 19 city clusters from 2011 to 2021 and analyzed their spatial differentiation characteristics, agglomeration evolution characteristics, and influencing factors by using kernel density, standard deviation ellipse, Moran's index, geographic detector, and geographically weighted regression. The study revealed that (1) the overall level of HQD of China's city clusters shows a trend of continuous growth, and there is obvious polarization in the high quality of city clusters in different regions. (2) The spatial distribution of HQD in city clusters decreased in the "East, Center and West" direction, but the spatial patterns of "Southeast highlighting" and "Northwest rising" became more obvious. (3) The HQD of city clusters shows obvious spatial agglomeration characteristics and overall presents a spatial pattern of "hot in the east and cold in the west", with the scope of the cold spot area gradually shrinking, and the hot spot area tends to spread outward, with mature city clusters at the core. (4) The influencing factors of HQD in Chinese city clusters are diverse, with financial levels, digital economics, human capital and green innovations having decreasing influence on HQD in city clusters but showing an obvious two-factor enhancement trend, with financial levels being able to effectively stimulate the driving potential of other factors. Financial levels can effectively stimulate the driving potential of other factors. (5) The coefficients of the driving factors affecting the HQD of city clusters vary significantly spatially, with human capital, financial levels and green innovations showing a north‒south hierarchical banded distribution of "high in the south and low in the north", and digital economy shows an east-west hierarchical belt distribution of "high west and low east". Based on the above conclusions, the realization path of accelerating the HQD of China's city clusters is proposed by optimizing the functional division of labor of the city clusters, giving full play to the comparative advantages of the hinterland city clusters, and relying on the high level of the city clusters for opening up.

GIS-DRASTIC integrated approach for groundwater vulnerability assessment under soil erosion hot spot areas in Northern Pakistan

GIS-DRASTIC integrated approach for groundwater vulnerability assessment under soil erosion hot spot areas in Northern Pakistan

Evaluation of soil erosion rate using geospatial techniques for enhancing soil conservation efforts

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures.

A novel center-hybrid pin-fin microchannel heat sink with embedded secondary microchannels for hotspot thermal management

With the rapid advancement of microelectronics technology, the issue of excessive localized heat flux in electronic devices has become increasingly prominent. This study introduces a center-hybrid pin-fin microchannel heat sink (CHPFMHS) for hotspot thermal management, comprising a central hexagonal pin-fin region flanked by two lateral straight microchannel zones located at the inlet and outlet. Furthermore, four types of various secondary microchannels are integrated within the pin-fins of CHPFMHS (Case 1: with no secondary microchannel; Case 2–5: with 3-direction, 4-direction, 6-direction, and vertical intersecting secondary microchannels, respectively) for effective heat dissipation with low pressure drop. The performances of CHPFMHSs and non-hybrid straight microchannel heat sink (NHSMHS) are evaluated with Re = 50–400. For all flow rates, the pressure drops in Cases 2–5 are lower than that in Case 1, with Case 4 exhibiting the lowest pressure drop among all CHPFMHSs. The thermal resistance (Rth) and temperature non-uniformity (δT) of Case 5 with hotspot area (Ah) of 2.25 mm2 and pin-fin region length (Lrpf) of 3 mm are 47.3 % and 30.8 % lower than those of NHSMHS, respectively. Among Case 1–5, the configuration of Case 5 is the most optimal. The impacts of hotspot and the pin-fin region size of the CHPFMHS have also been investigated. As Ah rises to 6.25 mm2, in comparation with NHSMHS, Rth and δT of Case 5 decrease by 52.7 % and 40.2 %, respectively. As Lrpf rises, the values of both δT and Rth, for Case 5, approach their minimum when Lrpf is close to the length of the hotspot. However, they exhibit slight changes as Lrpf continues to increase.