PrEP uptake in the Netherlands is growing but remains at suboptimal levels. Hence, the analysis of hurdles is paramount. Given the initial focus of PrEP provision among men-who-have-sex-with-men (MSM) via a demonstration project that was launched in June 2015, AmPrEP in Amsterdam, and pharmacies in the main urban areas (so called “Randstad”, entailing Amsterdam, Utrecht, Leiden, The Hague and Rotterdam), investigating regional differences is necessary. This study seeks to unravel regional differences jointly with psycho-social determinants of PrEP uptake. This cross-sectional study included 3,232 HIV-negative Dutch MSM recruited via the EMIS survey in late 2017. Prevalence and standardized prevalence ratio (SPR) of PrEP awareness, intention and uptake were measured on a regional level (Randstad vs. the rest of the country). Multilevel logistic modelling was conducted to identify the association of PrEP uptake with PrEP awareness and intention, sociodemographic, psycho-social determinants, and random effects from regional differences. MSM from the Randstad used more PrEP (SPR=1.4 vs. 0.7) compared to the rest of the country, but there were minor differences for awareness and intention. The regional distinction was estimated to explain 4.6% of the PrEP use variance. We observed a greater influence from PrEP intention (OR=4.5, 95%CI 2.0-10.1), while there was limited influence from the awareness of PrEP (OR=0.4, 95%CI 0.04-4.4). Lower education (OR=0.4, 95%CI 0.2-0.9) was negatively associated with PrEP uptake, however, no significant difference was found between middle and high education (OR=1.2, 95%CI 0.7-2.0). We showed that regional differences – MSM in non-urban regions – and other psycho-social determinants account for lower PrEP uptake. Based on these findings, more fine-tuned PrEP access with a focus on non-urban regions can be implemented, and tailored campaigns increasing intention/use can be conducted among target populations.

The city of Barcelona, ​​like other cities in the world, suffers strong internal socio-economic inequalities. Numerous works have sought to detect, quantify, characterize and / or map existing intra-urban differences, almost always based on quantitative methodologies. With this contribution, we intend to illuminate the complementary role that qualitative methodologies can play in studies on urban socio-environmental vulnerability. We consider aspects that are not quantifiable but that may be inherent to many such vulnerable spaces, both in the constructed environment and in the social ambit. These questions are considered through selected neighborhoods of Barcelona which have been shown (in prior works, mainly studies of quantitative manufacturing) to possess elements of vulnerability including a high presence of immigrants from less-developed countries, low per capita income, aging populations, or low educational levels. The results reveal the multidimensionality of vulnerability in the neighborhoods analyzed, as well as the essential complementarity among methodologies that detect and support possible public actions aimed at reducing or eliminating intra-urban inequalities.

Urban beggars are social problems that are related to the poverty of citizen. In Denpasar City Bali Province, particularly in West Denpasar District, the beggars have specificity in activities of begging. Studies in this article demonstrate their specificity begging activity which can be evaluated from the spatial movement of beggars based on the place, time and the distance. Through the method of grounded research with qualitative analysis showed that: (1) the beggars have permanent objects to visit, which consist of shops, settlement and places of worship of the Muslim community, (2) begging activities performed every Friday for 6 hours from 08.00 to at 14.00, and (3) beggars not only walking, but also using public transportation to visiting the object that is relatively far from beggars lane. The findings of this study reinforces that begging has become a profession that is conducted in a structured and well-organized.

The pandemic outbreak of COVID-19 had begun in March of 2020 on a global scale. This outbreak has originated numerous questions for society and many challenges for public managers. The disease is worrying because it has a high propagation velocity, high lethality levels, and there is no cure. Some groups are considered more vulnerable due to pre-existing disease conditions, age-range, and living conditions. In Latin American countries, people live in different conditions than those who live in countries located in the North hemisphere, such as climatic conditions, less favorable socioeconomic conditions, different educational levels, inequality, precarious urban infrastructure, etc. These factors generate even more concern and uncertainty about the pandemic than in developed countries. This study aimed, although preliminarily, to identify areas of great socio-spatial vulnerability and susceptibility of infection of people over 60 years old of COVID-19 in Brasília, Brazil. In this research, publicly available data and information about the population characteristics and social aspects were used, all connected directly to the census sectors. With the support of Geographic Information System (GIS), a matrix was used to cross-check the data and, thereby, achieve the objective of identifying the most vulnerable sectors for people aged over 60 years old. The results point out to more than 400 census sectors classified as Extremely Vulnerable in Brasília and it should be the object of special attention for public managers to do specific health care actions.

The COVID-19 has caused gigantic negative effects on populace wellbeing, society, education, and the economy in Bangladesh. The aim is to deliver a comprehensive overview of the observed and the possible impacts that could appear in the coming days. The study is based on secondary information. During the early period, due to a lack of accurate facts about the case affected and death tension up-and-down among the nations. The total number of confirmed cases is increasing following geometric patterns in Bangladesh. Dairy farmers, vegetable producers, pharmaceuticals, poultry farmers are in deep crisis due to lower prices. Also, the pandemic has seriously affected educational systems, banking, FDI, ready-made garments, remittances, etc Finally, it is not possible to mitigate the effects of pandemic individually but the integrated effort from the state authority as well as concern people of all sectors need to come forward.

Growth benefits of high speed rail (HSR) for enterprises are not automatic, but require a buoyant local economy and a robust strategy. Against above background, main objectives of ex-ante case study have been to investigate local socio-economic and spatial impacts of proposed HSR project around Ahmedabad station area on Mumbai-Ahmedabad HSR corridor, analyze the opportunities and challenges of HSR development and draw policy implications. Qualitative research methodology has been used to elicit the perceptions and perspectives of randomly selected 43 small, medium and large enterprises. Data has been analyzed using descriptive statistics. The study reveals that HSR induced greater human interaction will activate local economy, improve business efficiency and generate employment. Frequent travels on business trips can influence enterprises decision to settle near HSR station for easy access and likely to promote realty development. The proposed HSR project should be developed in synergy with urban dynamics of cities in transition and metropolitan service cities. Poor public transport connectivity is likely to be a major challenge in improving HSR accessibility and enhancing overall impacts of HSR, which can be improved by developing an integrated urban transport system, for which cooperation of all stakeholders and coherence with firms’ strategies is essential.

Socio-demography of statuses reflects the demographic and social roles and achievements of an individual(s) in a population. In the theoretical frameworks, little was known about the theory, analytical measures, and evolutionary mechanism of the socio-demographics of statuses of an individual(s). In this paper, three (3) methodological issues regarding the concept of socio-demographics were proposed, these are: (a) identified the basic features that clarify our understanding about the conception of the socio-demographic statuses of an individual(s) as common, universal, and desirable positions, achievements, and advantages every individual strives and competes to attain, achieve, maintain, update, and upgrade which are determined by biological and social environments, driven and regulated by a continuum of competitions and natural selections; (b) presented a new paradigm of analytical measures of an individual’s socio-demographic statuses and the established hierarchical transitions; (c) proposed theoretical assumptions for the evolution of a socio-demographically unique population. Seven (7) factors (the driving forces) and eight (8) conditions responsible for the evolution of socio-demographically unique populations were identified and explained. The evolutionary transitions were organized into six (6) socio-demographic ranks.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

Pekalongan is one of several cities that lies in the northern coast of Java island which is often flooded due to sea level rise. This condition impacted its urban development characteristic and increase in the future. In this research both Geographical Information System based and Spatial Metric approach are used. The spatial pattern is analyzed by using spatial metric based on the exploration of land use change that occurred. In this research, the spatial pattern is focused on aggregation pattern and diversity in coastal area. The result shows that the land use of coastal area are dominated with swamp, then followed by settlement and fishpond. It is also shown that the greatest land use change occurred on paddy field and swamp areas. Based on the spatial metric calculation, the aggregation level of land use decrease periodically and has a small growth level. It is indicated from its metric value aggregation and diversity from two periods: 2003-2009 and 2009-2016. Overall the land use of Pekalongan experienced large dynamics, especially in its coastal area. The spatial pattern trend in those area tend to be more sprawl as defined by the decrease of aggregation pattern and low level of land use growth pattern.

Understanding aspects of maritime/underwater cultural heritage (MUCH) and the associated cultural values and integrating them into maritime spatial planning (MSP) endeavors, is a new global challenge, alongside the rapid increase of human activities at sea and climate change impacts on the seas and the oceans. The article highlights the significance of cultural values in shaping human interactions with the marine environments, and how MSP can address the cultural dimensions of marine resources management. To this end, a systematic literature review was carried out to address the research question on how maritime/underwater cultural heritage is incorporated into MSP and map relevant academic literature. Following, a methodology that entailed an academic database search and the application of exclusion criteria, 346 articles resulted from Scopus. These articles were classified using science mapping techniques (i.e the VOSviewer Software ) and several categories (clusters) were created. The research showed a diversity of literature addressing the relationship between MUCH and MSP with a strong focus on sustainability, the significance of cultural ecosystem services (CES) and cultural values, the role of indigenous and local communities, but also of local stakeholders, the transfer of traditional knowledge to MSP and the participatory approaches and tools. The article concludes that for MSPlans to be innovative and acceptable by local communities, the “missing layer”of socio-cultural values and data is indispensable. In the MSP process, MUCH should be understood within this broader framework of socio-cultural considerations and values.

This critical study examines the social consequences of neoliberal urbanism, with a focus on housing policies by implementing qualitative approach. The main goal of the study is to identify downsides of the neoliberalism in residential areas and shed light on the potential social challenges caused by this. After reviewing the existing literature in this field and introducing some cases, the findings highlight that the prioritization of market mechanisms in urban development, which is the key characteristic of neoliberalism, has led to unaffordable housing, gentrification, and displacement of marginalized communities, exacerbating socio-economic inequalities. The commodification of housing under neoliberal urbanism has transformed homes into financial assets, and instead of addressing the structural roots of housing insecurity, strategies such as implementing public-private partnerships (PPPs), urban mega projects and deregulation, have perpetuated housing crises in urban areas. The results suggest that alternative methods such as inclusionary zones for having a mix of market-rate and affordable housing in new developments, as well as strengthening tenant protections through rent control, and tenant rights advocacy programs, can remarkably help address systemic inequalities. By prioritizing the needs of vulnerable populations, policymakers can work towards mitigating the detrimental social effects of neoliberal urbanism and fostering more just and inclusive urban environments. Finally, critical engagement with social justice issues is essential to creating diverse and inclusive urban communities.

Accurately glacier mapping is crucial for understanding climate change impacts, but existing efforts may be biased due to overlooking spatial autocorrelation during map validation. To address this, we compared several widely used machine learning algorithms as gradient boosting machines (GBM), k-nearest neighbor (KNN) and random forest (RF) with parametric logistic regression (GLM) and an unsupervised remote sensing-based method (NDSI) for mapping Peru's glacier regions in a thoughtful experimental setup. Spatial and non-spatial cross-validation methods were used to evaluate model’s performance and compared with a fully independent test set. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results when compared to independent test set, emphasizing the need to consider spatial autocorrelation when using machine learning for glacier mapping. K-nearest neighbors (KNN) was the overall best model across regions consistently demonstrating the highest performance followed by logistic regression (LR) and gradient boosting machines (GBM). Our novel validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

This paper reviews recent literature on Spatial Data Infrastructure (SDI) for remote sensing, aiming to provide a comprehensive analysis of the state-of-the-art practices, challenges, and future directions. It also tries to find out if current projects and initiatives involving the exploitation of satellite remote sensing are still framed in the SDI concepts.

This study used secondary data from the 2017 Korean Community Health Survey to compare the associations of socio-physical environment, social support networks, and social activities with happiness among older adults with three different family types. As per the results, there was a significant difference in happiness index between the three groups (F = 86.688, p < .001). Older adults living alone (odds ratio (OR) = 0.75, 95% confidence interval (CI) = 0.57–0.99) and those living with family (OR = 0.80, 95% CI = 0.65–0.99) showed greater happiness as the frequency of contact with family increased. Older adults living with only their spouse showed an increase in happiness when their contact with friends was higher (OR = 0.69, 95% CI = 0.56–0.84). It was confirmed that there were differences in factors influencing happiness according to the family type of the older adults in this study. Therefore, the happiness of older adults needs to be adequately ensured, while considering their various circumstances, including family type.

Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.

The rapid development of China's economy has led to a rapid increase in energy production and use. Among them, the excessive consumption of coal in fossil energy consumption is the leading cause of air pollution in China. This paper incorporates renewable energy innovation, fossil energy consumption and air pollution into a unified analysis framework, and uses spatial measurement models to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China, and decomposes the total impact into direct and indirect effects. influences. The empirical results show that China's air pollution, renewable energy green innovation and fossil energy consumption are extremely uneven in geographical space, generally showing the characteristics of high in the east and low in the west, and showing a strong spatial aggregation phenomenon. Fossil energy consumption will lead to increased air pollution, and the replacement of fossil fuels with clean and renewable energy is an important means of controlling pollution emissions. The direct and indirect effects of renewable energy green innovation on air pollution are significantly negative, indicating that renewable energy green innovation not only suppresses local air pollution, but also suppresses air pollution in neighboring areas. The consumption of fossil energy will significantly increase the local air pollution, and the impact on the SO2 and Dust&Smoke pollution in the adjacent area is not very obvious. It is recommended to strengthen investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and spillover of renewable energy green innovation.

This study aimed to use spatiotemporal tensor data to measure the level of employment quality in China's provinces and analyzed the magnitude and direction of its influencing factors in the spatiotemporal dimension. Taked 30 provinces, autonomous regions and municipalities directly under the central government in China from 2011 to 2020 as the research object, the employment quality evaluation system was constructed from six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations. The employment quality index data was expressed as a three-order high-dimensional tensor spatio-temporal data form, and the employment quality of China's provinces was measured from the spatio-temporal perspective by using principal tensor analysis, and the visual analysis of the development and change process of employment quality was carried out. The spatial autocorrelation analysis of employment quality was carried out, and the time-space dual fixed effect model of the spatial Dubin model was selected to analyze the direction and magnitude of the influence factors of employment quality on itself and its neighboring provinces. The research showed that: (1) The overall level of employment quality in China was not high and the employment quality varies greatly among provinces, and the employment quality development gap among provinces showed a trend of widening. (2) The development of employment quality in western China was relatively fast, while the development of employment quality in central China showed insufficient stamina. (3) Sichuan Province had a strong radiation effect on the development of employment quality in neighboring provinces, and Beijing and Tianjin had a strong siphon effect on the development of employment quality in neighboring provinces. (4) The level of industrialization and informatization promoted the development of employment quality in China's provinces, while the industrial structure had a significant negative effect on the development of employment quality.

Georgia is ranked fifth highest among states for rates of HIV diagnosis. About 4% of persons living with HIV infection in the United States reside in Georgia, and almost 19% of these people do not know their HIV status. The present study examined the trends and associated factors of HIV testing among adults in Georgia between 2010 and 2014 by analyzing data of the Behavioral Risk Factor Surveillance System (BRFSS). A total of 30,791 persons aged ≥18 years were identified who responded to the question “Have you ever been tested for HIV?” Overall, there were 11,543 respondents who had been tested for HIV, with a decrease in percentage from 49.4% in 2010 to 43.7% in 2014 (p<0.001). Factors associated with HIV testing were being black (p<0.001), being younger than 55 years (p<0.001), single (p=0.02), attaining education level above high school (P<0.001), engaging in HIV high-risk behaviors (p<0.001), and not having healthcare coverage (p=0.03). Overall in Georgia, there has been a decline in the temporal trend of HIV testing, and more than half of adults have never been tested for HIV. For reducing HIV transmission in Georgia, enhancing access and utilization of HIV testing should be a public health priority.

Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allows researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are also at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust framework to assess the capabilities of geospatial analysis on big data platforms. This research begins to address this issue by establishing a geospatial benchmark that employs freely accessible datasets to provide a comprehensive comparison across big data platforms. The benchmark is a critical for evaluating the performance of spatial operations on big data platforms. It provides a common framework to compare existing platforms as well as evaluate new platforms. The benchmark is applied to three big data platforms and reports computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three different datasets.

Following the development and dissemination of new climate-smart agricultural technologies to farmers globally, there has been an increase in the number of socio-economic studies on the adoption of climate-smart integrated pests’ management (CS-IPM) technologies over the years. In this study, we review empirical evidence on adoption determinants of CS-IPM technologies and identify possible science-policy interfaces. Generally, our review shows that socioeconomic and institutional factors are influential in shaping CS-IPM adoption decisions of farmers. More specifically, income was found to positively influence the adoption of CS-IPM technologies while land size owned influences CS-IPM adoption negatively. Registered land tenure (registered secure rights) positively influences CS-IPM technologies’ adoption, implying that efficient land markets that enable competitive and fair distribution and access to land, more so by the vulnerable but efficient smallholder producers do indeed increase the adoption of CS-IPMs technologies. Social capital, achieved via farmers’ organizations is also central in fostering CS-IPM technologies’ adoption, just as markets reforms that minimize market failures regarding access to credit, labor, and agricultural information, which could indirectly hinder farmers’ use of CS-IPM practices. Functional extension systems that improve farmers’ awareness of CS-IPM do also improve CS-IPM technologies’ adoption. However, the adoption of CS-IPM technologies in Ghana and Benin is slow-paced because of factors like lack of access to farm inputs that facilitate uptake of these technologies, lack of credit facilities, and limited extension services among others. Interestingly, our review confirms that CS-IPM technologies do indeed reduce and minimize the intensity of pesticide usage and foster ecosystem (environmental and human) health. Therefore, this review unearths strategic determinants of CS-IPM adoption and makes fundamental guidance around climate-smart innovations transfer and environmental policies that should be prioritized to curb environmental pollution and ensure agricultural ecosystems’ sustainability.

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

At the beginning of 2019, the petroleum crisis impacted many economies dependent on this industry. The Mexican government started programs to identify points and government officials involved in the gasoline stealing from PEMEX (Petróleos Mexicanos), the country’s leading government petroleum company. The programs consisted of supervising and monitoring the Mexican country network of gasoline ducts to detect points where gasoline was being stolen. Consequently, large urban regions faced a lack and shortage of gasoline. This situation generated several reactions in social media and many open data in news media. Although the government provided open data about stealing gasoline locations related to crimes, it did not analyze the collected data to identify patterns, insights, and the spatio-temporal characterization of this phenomenon. This paper presents a study to deal with the regional semantics described in the social media locations of gasoline stealing. Thus, a framework to discover the trends that emerge from social media and how it is correlated with the government’s open data is also presented—the proposed methodology used machine learning techniques based on linguistic and semantic technologies. The analysis was applied to a dataset of 24,317 geo-referenced tweets. The obtained results reflected the Mexican thinking opinion regarding discovered social topics, polarization maps, and regional insights. According to discovered trends, there were long fuel lines between 1.5 and 5 kilometers (on average) at fuel stations in different Mexican states.

To reduce solar power production invariance, it is critical to study varying patterns of power production in the concerned region. Analyzing the patterns of past power production trends can help simulate power production scenarios for future. The current study area is around Amsterdam, located in Netherlands. PVoutput.org website is used to mine 6 months of solar power production data for 120 stations around Amsterdam city. FME Workbench software is used to actively fetch the data from the mentioned website and manage in a MySQL database. Solar attenuation maps created using ArcGIS, helped to graphically visualize the variations in solar power production at different times and locations. Further, spatial autocorrelation is checked between the stations using semi-variograms in geostatistical tool of ArcMap. This feature allows to check whether the stations located close to each other are more correlated to each other rather than stations which are far apart. The statistical data analysis of power production can aid solar power production companies to better interpolate and predict solar power in advance for the concerned study region.

This research undertakes a quantitative exploration of the impact of procurement mo-dalities on economic efficiency and inclusive procurement within the Dominican Repub-lic's public sector. Applying data science methodologies and machine learning techniques, this study analyzes a rich dataset of public procurement contracts spanning from 2018 to 2023. A focus is placed on understanding how different procurement modalities affect contract amounts and processing times—two key indicators of economic efficiency. Machine learning models, including Random Forest and Gradient Boosting, are em-ployed to predict these essential metrics across different procurement modalities. The findings indicate a significant positive correlation between certain procurement modali-ties and economic efficiency. The machine learning models reveal that procurement strategies focused on local sourcing and transparent bidding processes tend to yield high-er economic returns and more equitable outcomes. This research underscores the poten-tial of quantitative analysis to inform procurement policies, fostering a more economi-cally efficient and inclusive procurement ecosystem in the Dominican Republic's public sector. The insights garnered from this study could have significant implications for the design of more inclusive procurement strategies, contributing to socio-economic growth and corruption reduction in the Dominican Republic.

Bayesian Maximum Entropy (BME) is increasingly used in predicting and mapping spatio-temporal data. However, studies that have fully evaluated its robustness empirically are rare. Therefore, this research examined empirically the effect of skewness, sample size and spatial dependency level using simulated data. We considered symmetric data, data positively skewed by 1, 3, 6 and 9, data with weak, moderate, and strong spatial dependency levels, and sample sizes from 100 to 500 at the interval length of 50. The results showed that the variation of sample sizes and spatial dependency levels do not affect the Mean Square Error (MSE) and bias of BME prediction. However, skewness affects the MSE of prediction but does not affect the bias. This result indicates that BME is robust to sample size and is unbiased. Despite the significant difference due to skewness, a graphical plot showed values of MSE close to zero, suggesting that BME can be considered robust to skewness.

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes Stress and Strain of the alternatives that are designed to be employed in the new quantitative ROAM approach.

Understanding the spatial distribution pattern and evolution characteristics of the elderly population in urban areas is of great significance for the development of urban planning and the implementation of public management policies in the context of rapid aging. Accurately identifying the spatial distribution and evolution characteristics of the elderly population in the city requires a comprehensive analysis of multiple indicators and large-scale data. Taking Wuhan city as an example, this article measures the spatial distribution characteristics and evolution trend of the elderly population from 2000 to 2020 at the street/township level, based on the fifth, sixth, and seventh census data, using methods such as kernel density hotspot detection, spatial clustering analysis, and standard deviation ellipse analysis. The results show that: ① there are significant differences in the aging spatial pattern between the central area and the suburban areas of Wuhan; ② overall, Wuhan's aging rate shows a typical "core-periphery" growth mode in space, while the density of the elderly population has significant spatial aggregation characteristics and shows an evolution trend of "centralized concentration, peripheral outliers, axial development, and near-field growth"; ③the center of gravity of the elderly population remains relatively stable over time.

With a worldwide growing concern for greenhouse gas (GHG) emissions and their impact on human health and the environment, transportation has become a central theme in mitigation, responsible for 14% of human GHG emissions. To build endurance to climate change, transportation services must adapt to the current scenario and act quickly to avert future changes. Deeply rooted changes in socio-technical systems will be necessary to achieve significant CO2 reduction and secure the well-being of future generations. This study's objective is to comprehensively review the current state of carbon mitigation in the transportation sector. This is done through a systematic literature review engrained in the socio-technical transition theory and the structural theory of contingency. Twenty-six review papers covering 2,983 original articles are selected for full-text examination concerning carbon emissions in transportation. Enablers, barriers, benefits, disadvantages and metrics in carbon emissions reduction are identified, and a comprehensive framework is built. Results provide a view of the current sustainability scenario in transportation and allow a better understanding of the factors influencing carbon emission initiatives in transportation and its outcomes.

Public Transport has been seen since ages as the most environmentally sustainable mode of transport. The users of public transport are diverse and have different socio-economic character. Hence the public transport which is not only supposed to be environmentally sustainable but also envisaged to ensure equity amongst various stakeholders of society like the females, elderly and other vulnerable groups through its service. Gender in binary terms of being differentiated based on men and women is a social construct. Public transport systems in developing countries like India witness socio-cultural factors that shape the experience of women in public spaces by social norms extant in society. Along with this, gender-based issues related to public transport are social and behavioural shortcomings as a consequence of a lack of gender sensitivity. On similar lines, elderly have a negative experience involving safety threats, physical and psychological discomfort while accessing public transport systems. The literature published regarding such issues on the gender and elderly question in public transport systems have been studied and has been brought forth under a stand- alone narrative literature review. A literature review is a prerequisite to conducting either stand-alone reviews or as a preliminary study to be supported with quantitative or qualitative analysis. Here, a stand-alone literature review concerning issues in the public transport system in India has been performed. A narrative type of review is conducted to provide an overview of pre- existing published literature. Narrative overviews are useful educational articles as they help present a broad perspective on a topic and often define the development of a problem and/or ways to manage it. The semi-systematic or narrative-review approach is designed for topics that have been theorized differently and studied within diverse disciplines making it unfavourable to study under a full systematic-review process which majorly caters to reviewing quantitative researches. As narrative-styled literature reviews prefer a semi- systematic data collection method, utmost care has been taken to include perspectives from diverse disciplines. The scope of this review is restricted to summarizing the Indian policies, schemes of public transport in light of socio-equity consideration while narrowing the inherent discrepancies within the socio-cultural ethos of the Indian society which influences socio-equity consideration in public spaces in general and the modes of public transport in particular. Research articles from electronic databases were selected based on relevance to understand the issues this viewpoint, their essential findings and possible recommendations are formulated to provide a comprehensive summary of previous researches.

Ecosystemic services are those that provide material or immaterial benefits to the inhabitants of a landscape from its water, food, medicines and raw materials. They are classified into four types: provisioning, regulating, supporting and cultural. The latter can be defined as those non-material benefits that humans obtain through ecosystems. They include educational value, cultural diversity, sources of inspiration, spirituality and religious values, aesthetic value, social relations, rootedness or belonging, cultural heritage, recreational and ecotourism services, and scientific knowledge, among others. Characterising such diverse factors requires a systematisation in study facets for their management. We propose three: memory, image and socio-system, as differentiated spheres of approach to the urban landscape. The memory facet would include the ecosystemic services of belonging, cultural heritage and, in certain themes, cultural diversity, sources of inspiration and religious values. The image facet includes the aesthetic value and perceptual aspects of ecotourism. In the socio-system we would study the educational value, scientific knowledge and recreational or spirituality services, reflected in the endowments; but also social relations, rootedness and another vision of ecotourism.

Dengue infection is one of the most important vector-borne diseases worldwide and is a significant public health problem in the tropics. Mosquito control continues to be the primary approach to reducing the disease burden and the dengue virus (DENV) spread. Aside from the traditional larviciding and adulticiding interventions, autodissemination using pyriproxyfen-treated (AD-PPF) ovitraps is one of the promising methods to complement existing vector control strategies. Our paper assessed the efficacy of AD-PPF in reducing DENV infection in two barangays in Parañaque City. Using saliva samples from the participants from both the control and intervention sites, we collected the seroprevalence data for three months in each of the two years. Spatial analysis was conducted to determine hotspot areas and identify DENV infection distributions across the trial periods. Results showed that the intervention site was identified as having clustering of DENV infection in Month 0 of Year 1 and shifted to random dispersion of dengue cases at the end of Month 3 in Year 2. The disappearance of the clustering of the intervention site translates to a decrease in the cases of DENV infection relative to the control site. Furthermore, we also identified that DENV infection transmission occurs at a small-scale level that did not go beyond 86 meters. In conclusion, AD-PPF is suggested to be an effective strategy and may be used as an additional vector control approach, albeit in its short-term implementation.

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics and spatial transcriptomics now allow recording the patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multi-scale dynamics of its evolution. Here, we review latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed a burgeoning growth in recent past in terms of mapping heterogeneity within tumor cell types as well as stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor, and a more systematic investigation of implications of heterogeneity for the patient outcomes.

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

The worldwide competition to vaccinate against COVID-19 has highlighted the importance of immunization measures in containing the virus. Saudi Arabia, like the rest of the globe, must assess socio-demographic subtleties, chronic illnesses, vaccination effects, and public health policy implications as it navigates this complicated environment. This cross-sectional study examined Saudi COVID-19 vaccine in many ways. It examined the socio-demographic makeup of vaccine recipients, the prevalence of chronic conditions in this population, vaccine administration patterns, post-vaccination side effects, and vaccine-infection rates. A questionnaire-based online survey of 401 participants ensured gender, nationality, age, weight, and educational diversity. We analyzed vaccine manufacturer choice for each dosage, side effects after the first and second doses, and chronic diseases in the sample. Pfizer/BioNTech vaccines are preferred for all three doses, reflecting worldwide vaccination acceptance patterns. Side effects were not significantly different between vaccination kinds. Interestingly, immunization reduced infection rates, demonstrating the efficacy of COVID-19 vaccines. The data fills important information gaps and illuminates Saudi COVID-19 vaccination kinetics. These data support the global preference for mRNA vaccines, demonstrating the faith in this breakthrough technology. They also sponsor COVID-19 vaccine safety and effectiveness studies. The dramatic drop in infection rates among vaccinated people shows how vaccinations suppress the pandemic. These findings inform Saudi Arabia's immunization efforts. As booster doses become more common, long-term vaccination research is crucial. Vaccine reluctance, especially in certain demographics, must be addressed. Due to vaccines' protective potential, equitable vaccine distribution and large immunization programs are essential for herd immunity and pandemic management. This thorough cross-sectional study helps policymakers and healthcare practitioners build successful and inclusive COVID-19 immunization regimens in Saudi Arabia.

The rapid development of the regional economy in China has led to the rise of local ecological risks. It is very important to provide enjoyable ecosystem services to residents while reducing ecological risks. In order to understand spatial relationships between ecosystem services and ecological risks, we took Chongqing as an example in this study to assess the spatial relationship between ecosystem services and ecological risks at the county scale based on the ES-DPSIR system. The main findings include: (1) significant variation in the spatial distribution of the comprehensive ecosystem service index, where the lowest ecosystem service index (0.013) was found in the main urban area of Chongqing and the scores gradually increased outward from this center, reaching 0.689 in the outermost areas, (2) the increase of the comprehensive ecological risk index from east to west, ranging from -0.134 to 0.333, (3) the spatial relationship between ecosystem services and ecological risks was prominent, with 52.63% of the districts and counties being imbalanced or mild imbalanced, and (4) the significant differences between development trends of ecosystem services-ecological risks, including 60.53% being imbalanced districts and 30.47% mildly balanced. Overall, it was necessary to improve the relationship between ecosystem services and ecological risks in Chongqing by reducing ecological risks., and these research results could provide effective approaches and technical support for improving regional ecological security and enhancing ecosystem service capabilities.

(1) Background: Most of the existing studies focus on the evaluation of technology finance; the relationship between technology finance and technology innovation. But there are few studies on the development of technology finance and the quality of economic development in our country; (2) Methods: Based on the panel data of 30 provinces in China, this paper constructs an index system to measure the development of technology finance through the improved entropy method, and tests the spatial correlation of the development of technology finance in China by Moran'I index. According to the test results, this paper constructs a spatial econometric model to empirically analyze the promoting effect of scientific, technological and financial development on high-quality economic development, and analyzes its promoting effect in different regions and different time periods; (3) Results: The results show that the quality of China's economic growth is spatially dependent, and the development of science, technology and finance can significantly promote the quality economic development in China. And the promotion coefficient of the central region is the largest, as well as the coefficient of the eastern region is the smallest. The promotion coefficient was small and not significant before 2015, and was significantly positive after 2015; (4) Conclusions: this paper puts forward the corresponding policy recommendations according to the research results.

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue they offer the most promising prospects for the future of spatial machine learning.

Introduction: SARS-CoV-2 infection was first reported in 2019 and has since spread throughout the world. This is a cross-sectional study in cooperation with 118 Department of Pre-Hospital and Territorial Emergency (118 SET) of Taranto City and the University of Bari, Aldo Moro, and the School of Medicine from Italy. We conducted a study on how the COVID-19 epidemics evolved and how it was contained by different countermeasures by taking into account data showing socio-demographic and that older persons, as well as individuals with comorbidities and poor metabolic health, and people coming from economically depressed areas with lower quality of life in general, are more likely to develop severe COVID-19 infection. Objective: Examine the association between county-level socio-demographic risk factors and COVID-19 incidence and mortality, determining the possible emo-biological markers, ferritin, and lymphocytes, that could be indicative of SARS-CoV-2 infection. Methods: Descriptive cross-sectional study on 600 patients examined and treated at general hospital Ninh Thuan from January to September 2022. Results: 33/600 patients were confirmed to be infected with SARS-CoV-2 (5.5%), males 4.8% and females 5.8%, the median age of infected patients is 36 years. Most infections were mild (75.8%). Our results revealed that the structure and the spatial arrangement of socio-demographic arrangements are important either as epidemiological determinants or as disease markers. Conclusion: Approximately 5.5% of patients infected with SARS-CoV-2 come to examination and treatment at the hospital, these findings suggested that possible infection rate in the burden of the COVID-19 pandemic, the sociodemographic risk factors, and their root causes must be addressed. In addition, lab results obtained from affected patients showed that lymphocytes and ferritin could be considered traits of mild COVID-19 infection.

Tourism is increasingly becoming one of the most important components of the economy. Coastal tourism is of particular importance in our country, this is because Albania has a very favorable geographical position. The tourist destination chosen for study in this paper is Durrës. It is analyzed as a complex dynamic system that evolves with a series of techniques and methods drawn from the area of network theory. The main objective of this paper is the comparison of technological and socio-economic networks, through different methods and techniques. The methodology of this research includes different data collection methods for both networks and their analysis. 504 nodes were taken into consideration. A significant part of the research is based on network theory, using algorithms and network analysis techniques to reveal their static and dynamic characteristics. Through this paper we show that the internet network and the socio-economic network of Durrës are similar in some aspects and have a great impact on the development of the tourist destination. The analysis of these two networks provides a deeper picture of the relations between the actors of the destination and helps to improve the tourism strategies and the economic development of the area. Thus, the research is an attempt to better understand the impact of technological and socio-economic networks in the development of the tourist destination of Durrës and uses different ways of network analysis to discover the similarities and differences between them.

In the present research, chronic viral hepatitis in children is approached from a multidisciplinary point of view, considering social status, economic and medical aspects. We conducted a 4-year observational prospective study. A questionnaire regarding the socio-economic status of pediatric patients diagnosed with chronic viral hepatitis B or C was applied. In total, 159 patients were included, 52 % from urban areas, 2.5 % coming from centres for abandoned children. Among 119 school-aged children, 66% were attending classes. All patients are registered with a general practitioner. Regarding the monthly income per family, 49% had less than 1000 RON (5 RON = 1$), of which 17% had no income, 28% had an income ranging between 1000 and 2000 RON and in only 23% of cases the income exceeded 2000 RON (5% had more than 4000 RON). There were between 3 and 12 members per family. Concerning parents` educational level, the average years of study for mothers was 7.8, while for fathers, it was 8.2. For 17 % of children, at least one of the parents was illiterate, and for 5.6 %, both parents were illiterate. For a third of patients, both parents were unemployed. Regarding social living conditions, 38.4% did not have water facilities or sewerage, and 32 % used personal objects (scissors, nail clippers) in common. The socio-economic level can have a significant impact on disease epidemiology (infectiousness) and access to treatment, and it is tightly related to educational level and access to information, which are critical factors in disease prevention through general and specific measures and in disease management (treating infected patients and limiting the transmission).

We are experiencing an unprecedented urbanization process that alongside with physical, social and economic developments is having a significant impact on population’s health. Due to higher apprehensions of pollution, violence and poverty, our modern cities no longer ensure a good quality of life so they become unhealthy environments. This study aims to measure the socio-environmental and hematologic profile of residents of Santo André’s landfill – “Bairro Espírito Santo” by using the contextualization of the studied area. The research method is Observational type and from Retrospective cohort and by convenience sampling in Santo André in the Greater ABC region. The study determined a socio environmental profile and the hematologic diseases screening related to a close location to the landfill. The disease manifests itself within a broad spectrum of symptoms that causes changes in blood count parameters. The full blood counts analysis was performed, indicating that the blood counts of residents living near the landfill led to positive hematological changes and diseases like Leukopenia, Anemia, Neutropenia and lymphocytosis were the most common changes. However it is considered that the proof of the relation of cause- effect to environmental exposures that may trigger chronic manifestations in humans requires specific studies that are often complicated.

The kinetics and modeling of dual-wavelength controlled photopolymerization confinement (PC) are presented and measured data are analyzed by analytic formulas and numerical data. The UV-light initiated inhibition effect is strongly monomer-dependent and different monomers have different C=C bond rate constants and conversion efficacy. Without the UV-light, for a given blue-light intensity, higher initiator concentration (C₁₀) and rate constant (k’) lead to higher conversion, as also predicted by analytic formulas, in which the total conversion rate (R_T) is an increasing function of k’R, which is proportional to k[gB₁C₁]^0.5. However, the coupling factor b₁ plays a different role that higher b₁ leads to higher conversion only in the transient regime; whereas higher b₁ leads lower steady-state conversion. For a fixed initiator concentration C₁₀, higher inhibitor concentration (C₂₀) leads to lower conversion due to stronger inhibition effect. However, same conversion reduction was found for the same H-factor of H₀ = [b₁C₁₀ - b₂C₂₀]. Conversion of blue-only are much higher than that of UV-only and UV-blue combined, in which high C₂₀ results a strong reduction of blue-only-conversion, such that the UV-light serves as the turn-off (trigger) mechanism for the purpose of spatial confirmation within the overlap area of UV and blue light. For example, UV-light controlled methacrylate conversion of a glycidyl dimethacrylate resin formulated with a tertiary amine co-initiator, and butyl nitrite, subject to a continuous exposure of a blue light, but an on-off exposure of a UV-light. Finally, we developed a theoretical new finding for the criterion of a good material/candidate governed by a double ratio of light-intensity and concentration, [I₂₀C₂₀.]/[I₁₀C₁₀].

This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in a freely accessible repository in the BE2SHARE-EUDAT platform so that they can be easily reproduced.

The formula of the Generalized Ambiguity Function (GAF) of passive bistatic SAR system using the non-cooperative illuminators which transmit PSK modulating signals is derived to analyze the spatial resolution of the system. The average GAF is introduced to remove the effect of particular sequence of symbols on resolution because the particular sequence of symbols is usually unpredictable before being received. The influence of the waveform parameters of the PSK modulating signals, such as length of the symbol sequence and roll-off factor, on spatial resolution is investigated by numerical simulation. It is confirmed that the influence of the length of the symbol sequence and roll-off factor is very slight but still exists.

The economy is a complex system, and the interactions between different agents are still not easy to quickly see-through. This complexity should reflect in a spatial dimension; in this way, tracking the tradeoffs opens a new window to the nexus of place and flow. Due to the fact, the economic systems often go through transitions and end up in another state, and this evolution is embedded in cities as the new motor of paradigm shift. To adequately represent and study these dynamics, we aim to develop an integrated method based on network analysis science and geographic economy synthesis to detect a multiscale navigator to track the transition from regional to the local level. This paper seeks to explore the specialization of regional clusters and their innovative behaviour in a particular lagging region, hence unfolding the innovation ecosystem to the smallest granularity then simulating the emergence phase of this complex system. First, our findings reveal that the local scale is relevant to start a bottom-up planning approach on policy implementation. Second, the global challenges could be addressed on a regional scale if we investigate the local complexity to unfold the innovation flow over its complex ecosystem and lead the knowledge as a critical element for inclusive transition, most probably into cities. Finally, the innovation network is an existing fact which can translate as a host for prosperity; In this line of reasoning, we intend to spatialize the track of the innovation flow to achieve transition hotspots and respond adequately to upcoming world concerns.

Land suitability analysis is a basic premise for allocating specific land for specific purpose. The objective of this study was to predict the suitable sites for cultivating Moringa oleifera tree in Ethiopia using Spatial Analytic Hierarchy Process. Findings of this study will have paramount significance in supporting decision making in the agroforestry development sector. This study employs Spatial Analytic Hierarchy Process and Geographic Information System to generate valuable information in land allocation for moringa oleifera tree production. Climate, topography, soil type and land use parameters were evaluated for suitability analysis. The results of the study revealed that most of the central part of the country are categorized as moderately suitable for the production of moringa oleifera tree. Areas classified as highly suitable are distributed along the borders of southern and western part of the country. However, some of the central part was classified as not suitable for Moringa oleifera tree production. This paper tried to investigate analysis of spatial data to predict suitable site for moringa tree production at national level. At national level, highly suitable, moderately suitable, and not suitable class covers an area of 308,508.2, 1,628,930.8 and 59891.3 Square Kilometer respectively.

In the last decades, climate and environmental changes have highlighted the fragility and vulnerability of the landscape, especially in mountain areas where the effects are most severe. The strategy for hazard mitigation must deploy synergistic actions based on a thorough knowledge of the territory and its phenomena to enable integrated and effective land planning and management through suitable communication tools. This study promotes the methodological setup of a Landscape Digital Twin to establish a multi-disciplinary and multi-scalar hazards overview according to a matrix framework implementable over time and space. The original contribution to the research addresses a holistic vision that combines meaningfully qualitative with quantitative approaches to generate risk maps by integrating various indicators within a multi-hazard framework from the socio-economic perspective. This contribution presents road network risk analysis by exploiting flooding and landslide scenarios. The critical road segments or nodes most vulnerable or impacted by network performance and accessibility can be identified with minimal preprocessing from credible open-source sources. The method's applicability is tested in a Piedmont Region, northern Italy case study. The integration proposed will help generate comprehensive risk analysis maps that effectively portray the interconnectedness among natural hazards, infrastructure, and socio-economic factors fostering more resilient decision-making processes.

Safety of source water streams is an urgent environmental issue, while protections in monsoon controlled subtropical regions face difficulties because of the lack of small scaled observation and analysis in small source water catchments. Basing on continuous weekly water quality data (2014-2017) in Pingqiao River Catchment, the annual average values of TN, NO₃, NH₄ and TP are 3.36, 1.64, 0.28 and 0.02 (mg/L) respectively. During dry, normal and wet seasons, the variability of parameters is over 35%, which indicates an obvious seasonality. Multiple methods are combined in order to assess the water quality and find the driving factors during dry, normal and wet seasons. This study suggests precipitation and fertilization are the mainly seasonal factors, which can make water quality better in wet season than dry season due to the dilution effect. The mechanism between seasonality and compound of nutrients can also be traced by log(TN:TP), and log(NO₃:NH₄). Among six main land use types (forest, tea plantation, cropland (paddy), urban, bare soil and water), the former three ones are influential mostly during dry and wet season. Tea plantation has the largest nutrients discharge amount per area, which is similar to cropland in dry season. By contrary, forest has the double power in reducing nitrogen release in wet and normal seasons. When transformed into paddies, croplands can lower the phosphorus concentration. Conclusions of this study can be used in southeastern China and similar regions on source water protection and agricultural plans.

Forest enterprise has been identified as a means of generating income among people; plays a vital role in enhancing the quality of life of forest-dependent people. Despite the opportunities timber marketing offers the people, the disparities in the income generation of the marketers in the Bodija sawn wood Market and the effect of socio-economic factors on income generation of the marketers is not well understood. This study was conducted to assess the socio-economic determinants of contributions of timber marketing to the income of timber merchants in Bodija sawn-wood Market. One hundred structured questionnaires were administered randomly in five zones of the sawn wood Market to obtain information on the socio-economic background of the sawn wood marketers and the contribution of timber trade to their incomes. The result indicated that 99.0% of the respondents were male while females constituted 1.00%. Seventy-five percent of the marketers had post-primary education and 25% had primary education. Two percent of the marketers had below 10 years of marketing experience, twenty-six percent had between 11 and 20 years, 57.00% had between 21 and 30 years, and 15.00% had more than 30years experience. Fifty-eight percent of the respondents earned between ₦10000-₦60000 (1US$ = 360.00) from timber marketing, thirty-one percent earned between ₦60001 and ₦110000, 7% earned between ₦110001 and ₦160000, while 4% earned above ₦160000 per month. Chi-square analysis of the socio-economic characteristics of the respondents and income generation at α level of 0.05 indicated that ethnicity (0.001) and years of experience (0.009) significantly influenced income while the level of education (0.101), age (0.122), and religion (0.745) had no significant influence on the incomes of marketers. Experience is an important factor in sawn wood marketing and a major determinant of the contribution of timber marketing to the income of timber marketers in Bodija sawn wood Market.

Theoretic and empirical studies show that social surroundings experienced by male insects during their larval or adult stage can influence their testicular investment in diverse ways. Although insect pupae do not feed and crawl, they can communicate using sex-specific and/or non-sex specific cues. Yet, it is unknown, in any insect, whether and how male pupae can fine-tune their resource allocation to sperm production and testis size in response to socio-sexual environment. We investigated this question using a moth, Ephestia kuehniella, which produces fertile eupyrene sperm and unfertile apyrene sperm. We held male pupae individually or in groups with different sex ratios, and dissected adults upon eclosion, measured their testis size, and counted both types of sperm. We demonstrated that after exposure to conspecific pupal cues regardless of sex, male pupae increased production of eupyrenes and apyrenes at the same rate but kept testis size unchanged. We suggest that testis size is fixed after pupation because most morphological traits are formed during the larval stage, allowing little room for pupae to adjust testis size. Like adults, male pupae with fully grown testes have sufficient resources to produce more sperm of both types according to the perceived increase of sperm competition risk.

Community is a veritable ingredient for social change and development in a society. The potentials of individuals and groups in the community are a great source or resource for promoting unity, development and patriotism. The general objective of this study is to examine the influence of diversity in community composition, on the operation of community policing style in Nigeria. The study adopted qualitative research approach to collect and analyze data. In-depth interview is the instrument of data collection while content analysis is the method of data analysis. The study took place in Kwara State, North central Nigeria. Twenty community leaders and youth groups heads were purposefully selected through snowball sampling method. Where this great resource or human capital is adequately galvanized by the leaders and community heads, the community becomes formidable and capable of solving her social problems together. However, these benefits of community are hampered by other socio-cultural and economic variables in its members. Community on its own cannot achieve much until members are mobilized to support and partner with government in any developmental projects.

In Bangladesh, microcredit program has been in function for almost half a century. Though popularly termed as a tool for poverty alleviation and women empowerment, criticism about microcredit program’s actual effectiveness isn’t new. This study attempts to evaluate the impact of such programs by measuring borrowers’ social mobility. Using a multidimensional approach, indicators like household income, economic susceptibility, living conditions, consumption pattern, children’s education, healthcare facilities, women’s participation in decision-making were taken into account for measurement. Quantitative method was used. Based on a non-probability sampling, 107 microcredit borrowers were selected for conducting interview schedules who live in a rural, a semi-urban and an urban area. Statistical analysis of data reveals that those who have been taking loans for several number of years have all managed to increase their income level to a variety of extent, but only those have gained some sort of mobility who have been taking loans for more than 4-5 years and have taken 5 times or more. Few of them have actually moved from microcredit to become a microfinance client by taking bigger amount of loans and having savings. Positive responses about the indicators being used in the study were found among the handful of those who have mobilized significantly. In true sense, most of the borrowers are stuck at the bottom end of socio-economic ladder and are struggling to manage a better living standard.

Free Floating Car Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns -- a problem of great importance to an adequate provisioning of the service. We tackle both the prediction of the demand i) over time and ii) over space. We rely on months of real FFCS rides in Vancouver, which constitute our ground truth. We enrich this data with detailed socio-demographic information obtained from large open-data repositories to predict usage patterns. Our aim is to offer a thorough comparison of several machine learning algorithms in terms of accuracy and easiness of training, and to assess the effectiveness of current state-of-art approaches to address the prediction problem. Our results show that it is possible to predict the future usage with relative errors down to 10%, and the spatial prediction can be estimated with relative errors of about 40%. Our study also uncovered the socio-demographic features that most strongly correlate with FFCS usage, providing interesting insights for providers opening service in new regions.

This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policy makers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and regional disparities. The study uncovers intraregional variations, illustrating how different municipalities within Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors, such as unemployment, significantly impact this pattern. The dynamics of the housing market, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. The findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and lack of social capital.

The spread of charging infrastructure (CIS) for battery electric vehicles is crucial for coping with the increasing number of electric vehicles. Therefore, the selection of ideal (fast-) charging locations determines acceptance, utilization and, thus, the economic viability of a single site or the whole charging network. The methodology of the Integrated Model Approach STELLA[1] for site identification of CIS uses proven methods of traffic modeling such as the classic four-step traffic modeling in a new context to enable statements regarding the positioning of CIS. Based on different spatial analyzes and characterizations of urban quarters, traffic generated by individuals is calculated using the FGSV approach of 2010. Because only (electric) motorized individual traffic is of importance for CIS, the share of trips is calculated by differentiating the modal split between various transport groups. One approach is to concretize the modal split share of public transport based on analyzes of different criteria and data sets, e.g. the accessibility of stops. The model approach STELLA, which also combines various extensive data (e.g. transport networks and traffic volumes, settlement structures, vehicle characteristics, power supply data and user requirements), is currently developed for a planning area covering the entire territory of the Federal Republic of Germany. [1] STELLA is the acronym for the German term "STandortfindungsmodell für ELektrische LAdeinfrastruktur”.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

Understanding the vulnerability of areas and the likelihood of specific disasters, particularly earthquakes, is one of the most important issues in Iran. One of the major concerns in Iran is the resilience of rural communities. The present study is devoted to spatial analysis of risk in rural areas and the evaluation of preparedness in the rural districts of the city of Abhar. In particular, this study evaluates the resilience to earthquakes. The research was conducted in two parts in which the first part has used the VIKOR Multiple criteria decision making model as well as the employment of this model in the ArcGis. The second part of the study used field studies, in the form of questionnaires, to evaluate the readiness of the local population against the risks of earthquakes. Four indicators, individual, physical, economic abilities and access, were assessed. The population included rural districts, where statistical samples were villagers. Results of the spatial analysis indicated that 15 villages are in the high-risk areas, 24 villages were in the medium-risk areas and all other villages were in low-risk areas. In terms of readiness of the locals, the results indicated a lack of planning with regards to the four mentioned indicators.

As an important research content in cultural geography, the exploration and analysis of the laws of regional cultural differences has great significance for the discovery of distinctive cultures, protection of regional cultures and in-depth understanding of cultural differences. In recent years, with the "spatial turn" of sociology, scholars are paying more and more attention to the implicit spatial information in social media data and the various social phenomena and laws they reflect. One important aspect is to grasp the social cultural phenomena and its spatial distribution characteristics through the text. Using machine learning methods such as the popular natural language processing (NLP), this paper can not only extract hotspot cultural elements from text data but also accurately detect the spatial interaction pattern of some specific cultures and the characteristics of emotions towards non-native cultures. Taking the 6,128 answers to the question “what are the differences between South and North China that you never know” on the Zhihu Q&A Platform as an example, with the help of NLP, this paper has explored the cultural differences between South and North China in people’s mind. This paper probes into people’s feeling and cognition of the cultural differences between South and North China from three aspects, including spatial interaction patterns of hotspot cultural elements, components of hotspot culture and emotional characteristics under the influence of cultural differences between North and South. The study reveals that 1) people from North and South China have great differences in recognizing each other’s culture. 2) Food culture is the most popular among many cultural differences. 3) People tend to show negative attitude towards the food cultures different from their own. All these findings shed light upon the understanding of regional cultural differences and addressing cultural conflicts. In addition, this paper also provides an effective solution to the study from a macro perspective, which have been difficult for new cultural geography.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

It is found in this paper that ILR-RPCA-back CLR has two problems when dealing with the closure effect of geochemical data. (1) After ilr transformation, RPCA is used for processing. It can be seen from the double-plot diagram that the first principal component and the second principal component transform with the element sequence transformation, and the unique principal component cannot be calculated; (2) the score and the load are transformed into the CLR space through the U matrix. According to the formula CLR=ALR·U, the score result can correspond to the original order of elements. However, the load result obtained by the use of this formula cannot correspond to the original order of elements, and the result can only be obtained through the formula” CLR=UT·ALR”. In order to obtain the best element assemblage of porphyry copper deposit, this paper adopted the mineral assemblage of discovered deposits in Gangdese metallogenic belt for statistical analysis, and obtained that the element assemblage of porphyry copper deposit was Cu, Mo, Au, Ag, W, Bi. Then, by analyzing the singularities of the composite elements, the spatial overlay of the combined element is carried out, and C-A fractal filtering is applied to identify the anomaly and background. In order to facilitate comparison, the different minerals and ore deposit types have been analyzed, the results show that (1) in view of the porphyry copper deposits, recognition effect of combination elements are better than which single element, (2) skarn type copper deposit that it has nothing to do with the porphyry had a high degree of difference, but skarn type copper deposit related to the porphyry had a low degree of difference, (3) This method has a certain advantage over the single element method in porphyry gold deposits, and it can reduce the level of anomalies. Such an advantage is that in the aspect of anomaly evaluation of porphyry copper deposits, the level is reduced at the initial stage of evaluation to reduce the investment in such anomalies, (4) This method has limited ability to distinguish porphyry copper deposit from porphyry molybdenum deposit.

The adapted Posner spatial cue-target paradigm was used to explore the relationship between exogenous temporal attention caused by rhythm and endogenous and exogenous spatial attention. The study was divided into two experiments. In Experiment 1, 33 participants used arrow cues to orient endogenous spatial attention and rhythm to orient exogenous temporal attention. In Experiment 2, with 29 participants, exogenous spatial attention was directed by highlighting the cue box. Exogenous temporal attention was directed by rhythm. Experiment 1 indicated that RTs were faster for attended targets than the unattended. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. RTs were faster for late compared to synchrony. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was not significant. Experiment 2 demonstrated that exogenous spatial attention only exhibited IOR under early four stimulus repetitions. The main effect of temporal attention was significant, with RTs being faster for synchrony and late compared to early. The main effect of stimulus repetition was significant, with faster RTs for targets when the rhythmic cue consisted of five compared to four. The interaction between spatial attention and temporal attention was significant. The results indicated that rhythmic temporal attention supported foreperiod effects. Exogenous temporal attention and endogenous spatial attention independently affect behavior, while exogenous temporal attention and exogenous spatial attention mutually influenced behavior.

This paper presents the results of quantitative research examining the impacts of demographic and socioeconomic factors on community resilience to disasters. The survey was carried out utilising a questionnaire distributed and subsequently collected online from 321 participants during January 2024. The study employed an adapted version of the '5S' social resilience framework, encompassing five sub-dimensions – social structure (10 variables), social capital (9 variables), social mechanisms (17 variables), social equity and diversity (13 variables), and social belief (13 variables). This customized framework includes 62 indicators, providing a thorough assessment of community (social) disaster resilience in the research context. To explore the relationship between predictors and community (social) disaster resilience in Serbia, their attitudes towards the mentioned five sub-dimensions, as well as their engagement in preventive measures and their perception of resilience to various disasters with a particular focus on demographic and socio-economic impacts, statistical methods including t-tests, one-way ANOVA, Pearson’s correlation, and multivariate linear regression were employed. The results of the multivariate regressions across various community disaster resilience subscales indicate that age emerged as the most significant predictor for the social structure subscale. At the same time, education stood out as the primary predictor for the social capital subscale. Additionally, employment status proved to be the most influential predictor for both social mechanisms and social equity-diversity subscales, with property ownership being the key predictor for the social beliefs sub-scale. Despite being considered in the analyses, gender, marital status, income level, and number of household members did not emerge as the most significant predictors across the investigated community (social) disaster resilience subscales. The determined mean value of the community (social) disaster resilience index is M = 2.62, and the findings reveal that participants assigned the highest ratings to the social beliefs subscale (M = 2.76), while the lowest values were observed in the social structure subscale (M = 2.46). Findings can be used to create strategies and interventions aimed at enhancing the resilience of communities in Serbia by addressing the intricate interplay between demographic characteristics, socio-economic factors, and their ability to withstand, adapt to, and recover from different disasters.

This study sought to spatially characterise and explain the differences of peoples’ perceptions on the impact of COVID-19 based on the socio-economic disparities in Gauteng, using choropleth mapping and Geographically Weighted Regression (GWR). Results indicate that respondents from relatively vulnerable municipalities like Merafong, Mogale City and Lesedi reported life being worse, information scant, and overall despondency being high since COVID-19. These are areas that fall under the High to Very High categories in terms of the Socio-economic Risk Index. GWR results, however, did not show a high explanatory power on the COVID-19 variables se-lected for the research, based on R2 values. For instance, the residual in overall satisfaction with life after COVID-19 was the lowest (-0.5 to 0.5) in the less affluent districts of the Rand West City and Sedibeng. Residual variables on life changes after COVID-19 were also the lowest in the southern parts of the same districts, with other low values almost evenly distributed throughout the province for the variable ‘Government information on COVID-19 was scant’. Although overestimation and underestimation existed, most were relatively low falling between -1.5 to -0.5 and distributed evenly across the province. In sum, however, the findings point to the complexity of socio-economic factors in spatially characterising social risk and vulnerability. Additionally, the negative sentiments expressed by people from the more vulnerable locations in the province emphasise the need for more targeted interventions by government to cushion the residents of the province to the continued negative impacts of COVID-19.

The study is devoted to analyzing the economic sustainability of the town of Amherst, MA. The city's top employer and core enterprise is the University of Massachusetts Amherst, with over 32,000 students and almost 2,000 staff members. Based on a literature review, a hypothesis was put forward that a university city should have a high level of economic sustainability. To assess economic sustainability, the USCESI Index was developed. It evaluates sustainability in three groups of parameters: society, economy, and ecology. The first group includes the level of racial diversity, the level of education of the population, and the access to medical services. The second group consists of the Gini coefficient by income level, the median cost of housing, and the unemployment rate. The environmental situation is assessed according to the Air Quality Index developed by the US Environmental Protection Agency. For comparison, the town of Braintree, MA, was taken. The USCESI was calculated for both locations. The analysis showed that both Amherst and Braintree have a high degree of economic sustainability. However, it was revealed that proximity to a significant economic center has a more powerful positive impact on economic sustainability than the location of a large university.

Nowadays, determining the socioeconomic factors' influence on environmental quality is a crucial issue for policymakers. We aim to explore the impact of socioeconomic factors i.e., ethnic conflicts inform ethnic fragmentation, institutions quality effectiveness, and energy consumption on environmental quality by testing the various hypotheses (Pollution Halo Hypothesis, IPAT, and EKC) in 40 selected Asian countries throughout 1993-2019. We also use a set of control variables which are gross domestic product per capita, foreign direct investment inflows, and population growth to determine their impact on environmental quality. We use the Panel Quintile Regression Method of 0.25, 0.5, and 0.75 to analyze the results. We find ethnic conflict negatively affects the environmental quality at all quantiles. The institution's variables regulatory quality and rule of law negatively influence the environmental quality. Our result supports Porter's hypothesis because the effect of direct foreign investment on the amount of CO2 emissions is negative and significant at 0.25, 0.5, and 0.75 quantiles which states that foreign direct investment in the host country supports environmental quality. Furthermore, our results support the IPAT hypothesis in selected Asian countries.

This paper presents the results of quantitative research regarding the examination of the levels of efficiency of the fire protection system and the training of employees in Electrical power distribution of Serbia for fire protection, ie the analysis of the manner of implementation of fire protection measures, and determining whether these measures are adequately applied. Following the subject set in this way, the aim of the research was set, and that is the scientific analysis of the manner of application of fire protection measures. The results of this research showed that the employees of Electro distribution Belgrade perceive the preparedness of their company highly positively and certain socio-demographic characteristics also affect certain attitudes, especially those related to the experience with fire, the knowledge of employees to react in case of fire, and the choice of the appropriate response, assessment of the readiness of the company and the employees themselves. It was found that knowledge of how to react in case of fire is influenced by gender and level of education; the choice of one of the methods of action in case of fire is influenced by gender; the assessment of the readiness of the employees themselves is influenced by the marital status; the desire of employees to participate in training to increase their preparedness for fires is influenced by gender. The results of the research can be used to improve the preparedness of employees in various organizations to respond to disasters caused by fires.

Onchocerciasis volvulus is the second highest infectious cause of blindness in the world, and is estimated to affect 37 million people, of whom 99% reside in sub-Saharan Africa. As a public health problem the disease is most closely associated with Africa, where it constitutes a serious obstacle to socio-economic development. Using the human ecology triad, this paper evaluates the dynamic interplay of population, habitat and behavioural factors in predicting perceived exposure to onchocerciasis among coastal inhabitants in Tanzania. Generalized linear models with log-log link function were fitted to cross-sectional survey data on 1253 individuals in three contiguous coastal regions. A significant proportion of respondents (28%) perceived that they were exposed to onchocerciasis. Residents in urban locations irrespective of wealth status were less likely to report living in onchocerciasis endemic environment compared with their rural counterparts. This is understandable given that urban areas of Tanga and Dar es Salaam are definitely non-endemic and perceived risk of onchocerciasis is related to the fact of living in an endemic area with active onchocercasis transmission. Individuals who had attained secondary (OR=0.51, p<0.01) or tertiary education (OR=0.37, p<0.001), and reported easy access to health facility (OR=0.53, p<0.001) were all less likely to report perceived exposure to onchocerciasis. This is not surprising because higher level of education and easy access to health facilities are characteristics of urban compared with rural areas. Policy implications suggest the need for the Tanzanian national neglected tropical disease control programme (TZNTDCP) to intensify health and educational campaigns at the community level and address susceptibility of vulnerable populations to the disease especially, for rural dwellers.

1) Background: The environmental, financial and social questions in Africa remain unanswered up-to-date, with the rapid increase in human population and the demand for fuel energy, trigger the need to generate data on the socio-economic factors influencing the knowledge of use and adoption of family-sized bio-digesters. The increasing prices of fossil fuels and taxes on energy sources require finding the alternative, clean and economical sources of energy for households in developing countries. Moreover, in Africa, the consumption of firewood and charcoal continues to increase, with wood fuel consumption predicted to increase by 2030 to over 140%. The study objectives were 1) to determine the socio-economic characteristics of the people in Ngoma district, 2) to assess socio-economic factors influencing people to use and adopt family-sized bio-digesters. 2) Methods: Quantitative data collected with semi-structured questionnaires and interviews were analyzed using descriptive statistics. 3) Results: The results show that many households had not realized the potential benefits of biogas use and adoption in Rwanda. The study further found that a number of factors such as household income levels, socio-economic, technological, and institutional influence the household use and adoption of biogas energy. 4) Conclusions: At the end, the study suggests the need for all players such as Government, Non-Governmental Organizations (NGO's) and local communities to work together to provide incentives and favorable environment that can attract individual households to invest in biogas energy production and utilization.

In order to analyze the spatial distribution characteristics of the cultural landscape of the Yangtze River (Nanjing section) and show the role of the Yangtze River culture in promoting regional economic development and cultural radiation, Based on the Knowledge graph technology and 3S technology, this paper conducts geographical positioning and spatial analysis of the Yangtze River cultural landscape in Nanjing, and analyzes the surrounding environment by combining remote sensing images and topographic data. Researches show that: The Yangtze River cultural landscape in Nanjing includes 74 landscape points of seven landscape types, and the distribution of landscape points shows a clustering trend, but the spatial distribution and aggregation degree of different types of cultural landscapes are different. Due to the influence of climate environment and water resources distribution, the surrounding cultural landscape has a low altitude and high vegetation coverage, showing obvious distribution characteristics along the river. This study is helpful to improve the coordination degree between cultural landscape protection and planning management, and can provide useful reference for the protection of cultural landscape of the Yangtze River.

Objectives: The present study was conducted to evaluate the indicators of the aging-friendly using GIS software in the eight districts of Ahwaz, southwest Iran in 2022. Materials and methods: this descriptive-analytical study was conducted on 317 older adults by stratified random sampling method. Data collection tools were demographic characteristics and aging-friendly city indicators. GIS methods were used for the spatial analysis and SPSS version 28 software was used to analyze descriptive statistics. Results: The mean age of the participants was 66.21±6.99 years. Most of the participants in this study were male (53.9%), illiterate (34.7%), married (84.9%) and residents of district 7 (22.1%). The findings of the present study showed that all 8 districts follow spatial autocorrelation and cluster pattern in all components of the aging-friendly city (urban open spaces, intra-urban transportation system, public and religious places and buildings, safety and ease of traffic, social participation and communication, social respect, culture-recreation, health and treatment). Conclusion: According to the results, the city of Ahwaz is far from the ideal level among the components of an aging-friendly city; therefore, provincial and city officials should pay more attention to these indicators and take more effective steps to increase their quality.

Hypertrophic Cardiomyopathy (HCM) is a common inherited disorder that can lead to heart failure and sudden cardiac death, characterized at the histological level by focal areas of myocyte disarray, hypertrophy and fibrosis, and few disease-targeted therapies exist. To identify, focal, spatially restricted alterations in transcriptional pathways and reveal novel therapeutic targets, we performed a spatial transcriptomic analysis of areas of focal myocyte disarray compared to areas of normal tissue, using a commercially available platform (GeoMx, nanoString). We analyzed surgical myectomy tissue from four patients with HCM and control interventricular septum tissue from two unused organ donor hearts that were free of cardiovascular disease. Histological sections were reviewed by an expert pathologist and 72 focal areas with varying degrees of myocyte disarray (normal, mild, moderate, severe) were chosen for analysis. Areas of interest were interrogated with the Human Cancer Transcriptome Atlas designed to profile 1800 transcripts. Differential expression analysis revealed significant changes in gene expression between HCM and Control tissue, and functional enrichment analysis indicated these genes were primarily involved in interferon production and mitochondrial energetics. Within HCM tissue, differentially expressed genes between areas of mild and moderate disarray were enriched for genes related to mitochondrial energetics (moderate disarray) and response to oxygen/cytokine levels (mild disarray). The comparison between areas of moderate and severe disarray were enriched for genes related to the c-Jun N-terminal kinase (JNK) cascade in severe disarray. Analysis of ligand-receptor pair gene expression revealed that HCM tissue exhibited downregulation of platelet-derived growth factor (PDGF), NOTCH, junctional adhesion molecule, and CD46 signaling, while showing upregulation of fibronectin, CD99, cadherin, and amyloid precursor protein signaling. A deconvolution analysis utilizing the matched single nuclei RNA-sequencing (snRNA-seq) data to determine cell type composition in areas of interest revealed significant differences in fibroblast and vascular cell composition in areas of severe disarray when compared to normal areas in HCM samples. Cell composition in normal areas from control tissue was also divergent from normal areas in HCM samples, which was consistent with the differential expression results. Overall, our data identify novel and potential disease-modifying targets for therapy in HCM.

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Also, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.

Target 9.c of the 2015 United Nations (UN) sustainable development goals (SDGs) specifically addresses increasing access to information and communication technology (ICT) resources, and striving for universal access to the internet by 2020. The present study seeks to evaluate the effectiveness of the youth related national strategies implemented in this regard by a select number of countries in the Middle East region. The study does so, by relying on a spatial bivariate copula regression analysis of data on youth respondents from five countries, extracted from the 2018 Program for international students’ assessment (PISA). Focusing specifically on evaluating the availability of ICT resources to the youth population, and also identifying the impact of ICT resources on youth subjective well-being in the region, we find that except for the UAE and Qatar that have above OECD average youth performance on the ICT resource index, youth from the remaining countries reported below OECD level average access to ICT resources. The within region cross-country comparative analysis of ICT resources availability to the youth population at home, also highlighted significant heterogeneity across the five countries, post 2015 SDG adoption by UN country members. Furthermore, looking at the impact of ICT resources on youth well-being, controlling for not only cross-country spatial correlations, and factors such as home educational resources, cultural possessions at home, parental occupation status, youth expected occupation status, economic and socio-cultural status, age, gender, and grade level in school; we found that every standard deviation increase in ICT resources to the youth population in the region raises their self-expressed sense of belonging in school by 1.88% standard deviations. Given the empowering nature of ICT resources to youth, and the potential of both to support national as well as regional economic development initiatives, a concerted effort to ease ICT resources diffusion by member countries in the middle east region could assist not only each country in its own development path, but also the region as a whole to live up to its growth potential by the 2030.

Recently intraductal carcinoma of the prostate (IDCP) has attracted increasing interest owing to its unfavorable prognoses. To effectively identify the IDCP-specific gene expression profile, we took a novel approach of characterizing a typical IDCP case using spatial gene expression analysis. A formalin-fixed, paraffin-embedded sample was subjected to Visium CytAssist Spatial Gene Expression analysis. IDCP within invasive prostate cancer sites was recognized as a distinct cluster separate from other invasive cancer clusters. Highly expressed genes defining the IDCP cluster, such as MUC6, MYO16, NPY, and KLK12, reflected the aggressive nature of high-grade prostate cancer. IDCP sites also showed increased hypoxia markers HIF1A, BNIP3L, PDK1, and POGLUT1; decreased fibroblast markers COL1A2, DCN, and LUM; and decreased immune cell markers CCR5 and FCGR3A. Overall, these findings indicate that the hypoxic tumor microenvironment and reduced accessibility to immune cells, which reflect the pathological characteristics of IDCP, may influence the aggressiveness of high-grade prostate cancer.

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from the weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make FSO links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

This study aims to develop a hybrid approach based on backpropagation Artificial Neural Network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using Aerosol Optical Depth (AOD) and several climatic indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using Motor Vehicle Emission Simulator (MOVES), the measured climatic variables and AOD were obtained from the California Irrigation Management Information System (CIMIS), and NASA’s Moderate Resolution Spectroradiometer (MODIS). The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150, and precipitation was determined to be the most important independent variable. Coefficient of determination ( ), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can accurately predict the PM2.5 concentration and can be used to forecast future trends.

The study provide insights into the technological characteristics of green practices adoption and the potential benefits they offer in terms of socio-economic development, environmental and public health. The study adopted a cross-sectional research design. A structured questionnaire was developed to collect quantitative data from the participants. The questionnaire include closed-ended questions, Likert scale items, and demographic questions. Both stratified and simple random sampling techniques were employed to select the study participants (government officials, employees, managers, and community members). A total of 321 copies of questionnaires were duly filled and returned. The collected data were analyze using descriptive statistics, such as frequencies, percentages, mean, and standard deviation, were used to summarize the data and provide an overview of the key variables related to green practices adoption. The findings indicate that the manufacturing company in Ogun State, Nigeria, has shown moderate commitment to adopting green practices. While there is room for improvement in certain areas, such as waste-to-energy technologies and green building practices. Additionally, green practices have positively influenced employee well-being and community health, while aligning with the company's commitment to safeguarding the environment and public health.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. Controlling and curing diabetes in its early stages is the most significant technique to avoid its effects of diabetes. However, lack of awareness and expensive clinical tests are the primary reasons to skip clinical diagnosis and take preventive methods in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, the study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes. Because clinical features are not always known to all people from lower-income countries. To find the best fit supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. The study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for growing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

The historic western edge of bison (Bison bison) range and the ecological processes that caused its formation are frequently debated with important implications for bison restoration across North America. We test the hypothesis that a combination of bottom-up habitat suitability and top-down harvest pressure from humans were important processes in forming the western edge of bison distribution. Using 9,384 historical journal observations from 1691 – 1928, we employ MaxEnt ecological niche modelling to identify suitable bison habitat across the Western Cordillera from bottom-up climatic, land cover, and topographic factors. We then use mixed-effect logistic regression to test if bison occurrence in journal records can be in part explained by the abundance of Indigenous humans, wolves, or grizzly bears, in addition to MaxEnt-derived habitat suitability. We find support for our hypothesis because of the limited suitable habitat in the Rocky Mountains that likely prevented westward bison dispersal from core habitat, and there was a negative relationship between bison occurrence and human harvest pressure. On this basis, we propose that intensive human harvest from large populations in the Western Cordillera, subsidized by other wildlife, salmon, and vegetation resources, is an underappreciated socioecological process that needs to be restored alongside bison populations. Co-managing bison with Indigenous people will also mitigate the adverse effects of overabundant bison and maximize the ecological and cultural benefits of bison restoration.

China's cotton production has shown a concentration to the advantageous regions in recent years. Based on the relevant data of cotton production in China, the study measures the cotton production agglomeration levels of 16 major cotton-producing provinces from 1978 to 2020, summarizes the spatiotemporal evolution characteristics of cotton production agglomeration, and analyzes the factors influencing cotton production agglomeration using spatial econometric models. The results indicate that: Firstly, since the opening-up and reform, China's cotton production agglomeration levels have shown an overall upward trend, exhibiting periodic dynamic changes over time, and the spatial pattern reveals a dynamic regional gradient. Secondly, there is spatial correlation among the production scales of cotton in different provinces, with a trend resembling a "U" shape, and the agglomeration form shows a low-level convergence. Thirdly, technological level, policies, substitute industrial products and foreign trade are identified as important factors. The cotton production in each province is spatially correlated through these factors, and there is also a presence of spillover effects. In conclusion, the government should prioritize considerations of various influencing factors to formulate tailored support policies, promote large-scale production to enhance technological levels, and focus on policies aimed at improving the quality of cotton.

Dawn of the omics revolution in biological sciences meant that we seek to understand more and in greater detail the molecular constituents of cells and biological systems. While we have gained significant insights from conventional omics tools, we now seek to understand the spatial dimensions of the data where subcellular localisation may impact on cellular physiology and phenotype. This review paper seeks to address current questions in the new field of image-based spatial proteomics as well as outline future challenges of the field. At first glance, spatial proteomics offers enormous potential to expand our understanding of different cell types in different disease and cell states. But limitations in types of fluorophores and issues with spectral overlap significantly hampers the practical implementation of the technique. On the other hand, while we have super-resolution microscopy techniques such as STED, PALM and STORM able to achieve 10 to 20 nm spatial resolution in single molecule localisation, problems with slow image acquisition limits high temporal resolution tracking of multiple protein targets in live cell imaging. Hence, the field of spatial proteomics is a mix of promises and challenges where we could image, in multi-colour, upwards of 10 well-chosen proteins that could inform on the molecular mechanisms of selected biological processes, but, at present, the method could not tackle larger scale questions. In essence, current implementation of image-based spatial proteomics is useful, but it is unable to fulfil the mission of large-scale projects such as the Human Protein Atlas or Human Cell Atlas. Future challenges in the field includes the development of more fluorophores (especially photoswitchable and photoactivable ones) for single molecule localisation microscopy, as well as seeking to improve temporal resolutions to the sub-millisecond range.

The review of previous works shows this study is the first attempt to analyse the lockdown effect using Natural Language Processing Techniques, particularly sentiment analysis methods applied at large scale. On the other hand, it is also the first of its kind to analyse the impact of COVID 19 on the university community jointly on staff and students and with a multi-country perspective. The main overall findings of this work show that the most often related words were family, anxiety, house and life. On another front, it has also been shown that staff have a slightly less negative perception of the consequences of COVID in their daily life. We have used artificial intelligence models like swivel embedding and the Multilayer Perceptron, as classification algorithms. The performance reached in terms of accuracy metric are 88.8% and 88.5%, for student and staff respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

This paper an approach for analyzing the impact of sea level changes on prehistoric human settlement patterns in the Southwestern Iberian Peninsula. The approach is based on highly qualified and fully georeferenced information sources managed within a spatial database. This allows for a more precise analysis of the distance to the coast and its relation to marine resources from a specific location, areas that may have lost their archaeological potential due to being currently submerged, and the actual distribution of sites as a starting point for territorial analysis. Coastal changes, such as sea level fluctuations over the past 120,000 years, have affected the position of the coastline and influenced human settlement patterns. Through an analysis of the archaeological site locations relative to their paleo-coastlines based on available dating data, this study emphasizes the necessity of adopting a comprehensive approach to comprehend human settlement patterns and their correlation with the dynamic coastal changes. This approach provides valuable insights for formulating strategies for exploiting coastal resources and structuring socio-economic systems in the region.

Despite its apparent lack of physicality, the virtual environment produces real experiences to its users. In its intangible context of digital operation, the virtual setting can be a meeting point for individuals to interact and to gain experiences that have an impact upon their lives. After more than three decades of broad accessibility and use of the internet and various digital platforms, the virtual experience has also prompted to rethink many of the assumptions commonly attributed to physical space. From a practical point, the virtual world has been an extension of the real one as a new site that can host people’s activities without many of the limitations associated with the material world. Given its absence of physical restrictions, the virtual space appears as a boundless one, whose potential of evolution is still unclear. This sense of limitedness has caused to shift our common sense about physical space as well, including architectural perception and the methods and practices applied to design it. In response, this present study focuses on the ways in which elements and concepts of the virtual world may be transferred to physical space and enrich architectural aims and the broader design discourse.

This study contributes to the debate on accessibility of higher education in Chile, focusing on both socioeconomic and geospatial dimensions of access to university study. The central question we address in this paper is the following: Does geography (physical distance and neighborhood effects) play a significant role in determining accessibility of higher education in Chile? We use Heckman probit-type (Heckit) models to adjust for selection in the process of completing the trajectory towards higher education – that is, pre-selection, application to study at university, and ultimately admission (or denial) to a higher education institution. The results shows that the geospatial elements have a significant local effect on the student’s application and access to Chilean universities.

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

In order to better realize rural revitalization, this paper analyzes the spatial and temporal char-acteristics and influencing factors of agricultural surface source pollution in the Yangtze River Economic Belt from the three perspectives of government, enterprise and agriculture by using the spatial Durbin model and the dynamic GMM method in the period of 2006-2021, and further re-searches the threshold characteristics of the distortion of the factor market on the agricultural surface source pollution under the different strengths of environmental regulation. The results show that there is a positive spatial correlation between agricultural surface pollution in the Yangtze River Economic Belt, and government environmental regulation, input factor market distortion and labor force transfer all have a significant impact on agricultural surface pollution. Among them, factor market distortion has a significant spatial spillover effect on agricultural surface pollution in the Yangtze River Economic Zone, and has a significant single-threshold ef-fect on environmental regulation. Accordingly, the government should strengthen environmental regulation, continuously improve the agricultural factor market mechanism, and pay attention to the construction of talents to provide support for rural revitalization.

Our study examines how dengue incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales. We used the incidence of dengue in Ibagué, Colombia, from 2013 to 2018, to examine the associations with climate, socioeconomic and demographic factors from the census and satellite imagery at various levels of local spatial aggregation, including Manzanas, Secciones, Sectores, and Comunas. Our findings show a significant effect of spatial variables at finer levels of aggregation, showing varying degrees of correlation with dengue incidence. Temporal variables such as temperature and precipitation displayed consistent patterns across all spatial levels, with notable variations in Relative Risk (RR). Our study employs Geographically Weighted Regression (GWR) to identify relevant socioeconomic and demographic predictors. Then, these predictors were integrated into hierarchical models implemented in Integrated Nested Laplace Approximation (INLA) at each spatial level to assess spatiotemporal interactions. We comprehensively analyzed the three distinct models developed for each level: spatial, temporal, and spatiotemporal. A comparative evaluation of the models reveals that while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling and highlight the potential for targeted public health interventions based on localized risk factor analysis. Notably, the intermediate level Secciones emerged as the most informative, balancing spatial heterogeneity and case distribution density, thereby providing a robust framework for understanding the spatial determinants of dengue.

Green innovation has become an important combination of high-quality economic growth and sustainable development of ecological environment. In this paper, the super-efficiency network SBM model is used to measure the two-stage green innovation efficiency of industrial science and technology R&amp;D and achievement transformation in 30 provinces and cities from 2009 to 2019, and exploratory Data Analysis (ESDA) and spatial econometric model are used to investigate the spatial-temporal evolution characteristics and influencing factors of green innovation efficiency. The results show that: firstly, the overall efficiency of industrial green innovation is low, and the efficiency of scientific research and development and achievement transformation has experienced three stages of "upward-declining-revitalized period". The low efficiency of achievement transformation is an important factor hiding the improvement of the efficiency of industrial green innovation. Secondly, The industrial green innovation efficiency gradually increases from northwest to southeast, forming a centralized "line" and "block" distribution. The high efficiency area is still concentrated in the eastern coastal region, and the balanced development trend is obvious in the central and western regions. Finally, openness has a positive impact on the two-stage green innovation efficiency; Industrial structure and government investment in science and technology have a positive impact on the efficiency of science and technology research and development, but have no significant effect on the efficiency of achievement transformation. Enterprise size has a positive effect on achievement transformation efficiency, but has no significant effect on R&amp;D efficiency. Environmental regulation has a positive impact on R&amp;D efficiency and a negative impact on achievement transformation efficiency.

This research investigates the socio-economic impact of rattan cane processing on the livelihoods of practitioners in Ibadan Metropolis. It comprehensively assesses raw material availability, examines factors influencing demand and supply, and evaluates sales to determine the profitability of rattan processors. With the snowball and total enumeration sampling methods, the study engages with a diverse group of rattan processors. The study reveals a concerning 36% acknowledgement of a decline in the availability of canes attributed to deforestation and over-exploitation. Notably, it emphasizes that rattan product manufacturing in Ibadan Metropolis operates at a craft level, employing tools like saws, hammers, knives, gas cylinders, pinches, and tape rules, impacting production efficiency and product longevity. Additionally, the research underscores the absence of government support and credit facilities reported by all respondents. The Market Margin analysis highlights varying profitability levels across rattan products, with chairs exhibiting notably higher profitability (32.22%) than others. Individual profit margins for specific products, such as chairs (145.00%), underscore the significance of processing costs, production time, and market demand. The study offers strategic recommendations, advocating for district-level service centres for training, technology transfer, and support to enhance skills and technical efficiency. It calls for government intervention through incentives, facilities, tax breaks, and policies to fortify the rattan trade, improving production standards and unlocking export market potential. Furthermore, financial support, technical assistance, and mechanization should be employed to reduce production costs and time, ultimately elevating income generation in the rattan industry. This research contributes valuable insights to the discourse on sustainable livelihoods and economic viability within the rattan sector.

Background Frequent presenters (FPs) define a group of individual who visit hospital emergency depart-ment (ED) frequently for urgent care. Many among the group present with main diagnosis of mental health conditions. This group of individual tend to use ED resources disproportionally and significantly affect overall healthcare outcome. No previous reviews have examined the profiles of FPs with mental health conditions. Aims This study aims to identify the key socio-demographic and clinical characteristics of patients who frequently present to ED with mental health primary diagnosis by performing systemic review from existing literature. Method PRISMA guideline was used. PubMed, PsycINFO, Scopus and Web of Science (WOS) were searched in May 2023. A manual search on reference list of included articles were conducted at same time. Covidence was used to perform extraction and screening, completed by two authors independently. Inclusion and exclusion criteria were defined. Results The abstracts of 3341 non-duplicate articles were screened with 40 full texts assessed for eligi-bility. 20 studies were included from 2004-2022 conducted in 6 countries with total patient number of 25688 (52% male, 48% female, mean age 40.7 years old). 27% were unemployed, 20% married, 41% homeless, and 17% had tertiary or above education. 44% had history of substance abuse or alcohol dependence. Top 3 diagnosis are found to be anxiety disorders (44%), depressive disorders (39%) and schizophrenia spectrum and other psychotic disorders (33%). Conclusion On average, FPs are middle aged and equally prevalent in both gender. Current data lacks rep-resentation for gender diverse group. They are significantly associated with high rate of unem-ployment, homelessness, lower than average education level, and being single. Anxiety disor-der, depressive disorder, and schizophrenia spectrum disorders are the most common clinical diagnosis associated with the group.

The current study is to examine the disparities in physical activity (PA), sedentary behavior (SB), and stress levels in Korean adolescents concerning changes in their perception of family socio-economic status (SES) during COVID-19. Among a total of 6144 Korean adolescents aged 12 to 18, the participants were categorized into two groups based on their responses regarding changes in their family SES during COVID-19: Lower SES (n = 3072) and Non-changed SES (n = 3072), with matching in terms of age, gender, and BMI. All variables were assessed using the 16th year (2020) of the Korean Youth Risk Behavior Survey. Statistical analyses were conducted using SPSS 26.0 version, employing independent t-tests to examine anthropometrics’ differences and multinominal logistic regression to predict the impact of perception of family SES on PA, SB, and stress while comparing the two groups. The significance level was set at α = 0.05. Adolescents in the Lower SES group were less likely to engage in vigorous-intensity PA (VPA) and muscular strength activities compared to those in the Non-changed SES group (p < 0.001) and were 2.3 times more likely to experience a very-severe stress level than the Non-changed SES group (p < 0.001). These results shed light on the importance of promoting VPA and muscular strength activities in adolescents for their physical and mental well-being, particularly during potential future pandemics. Understanding the impact of perceived SES changes on health behaviors can inform targeted interventions and support strategies to improve the health outcomes of adolescents during challenging times.

The size of the home is considered the most critical issue affecting the quality of life. This can be measured by the quality of the house and its environment. This paper highlights the housing quality index at the urban axis level in Bistrita-Nasaud county and the impact of socioeconomic activities on the index. The Household Quality of Life Index (HQLI) was calculated based on 11 variables. Household quality of life comprises two distinctive indices: the Housing Conditions Index (HCI) and the Asset Possession Index (API). The study found a variation in the housing condition variables across the urban focus areas. The impact of the socioeconomic environments is disproportionately dependent on the social-economic profile of the urban environments that make up the Urban Axis and the related economic activities that take place in the area of influence in the administrative-territorial units and the variability of the Housing Conditions Index This can be measured by the quality of the house and its environment. This paper highlights the housing quality index at the urban axis level in Bistrita-Nasaud county and the impact of socioeconomic activities on this index. The study area comprises territories in the municipality of Bistrita. The socio-economic context of the area has been the basis for understanding the patterns of evolution identified in the urban landscape, and being a driver in ongoing urban transformations and processes. The impact of the socio-economic is disproportionate to the socioeconomic profile of the urban environments that make up the Urban Axis and the economic activities related to the economic activities taking place in the catchment area in the administrative-territorial units, hence the variability of the Housing Conditions Index which is a system Housing Quality Indicator (HQI) is a tool for measuring and evaluating housing systems based on quality and not just cost.

This article investigates the dynamics of socio-ecological systems (SESs) unsustainability. By adopting a theoretical standpoint grounded in systems’ theory, the analysis shows how SESs’ teleology (or final cause) is of the utmost relevance for understanding unsustainability and how it is pivotal for envisioning possible evolutionary trajectories towards sustainability. Building on the contributions of both system and social scientists, the study argues that SES’s teleology is determined by dominant social ontologies that require a dialectical lens to be properly dealt with. The article therefore proposes the adoption of the adaptive cycle heuristic complemented by an historical-geographical approach based on world-ecology theory as a means to dynamically model of SESs’ behaviour. Such a perspective allows for the direct comparison between the four stages of the panarchy cycle (reorganization, exploitation, conservation, and release) and the four stages theorized by the world-ecology dialectics (expansion, appropriation, capitalization, crisis). In conclusion, the article claims that both system and social scientists would benefit from including in their analysis concepts and definitions from the other field, since both provide valuable insights about SESs' processes of change and both are necessary to envision transition pathways towards sustainability.

Concurrent waves of Coronavirus disease, Ebola virus disease, avian influenza A and black fungus are jeopardizing the lives in some parts of Africa and Asia. From this point of view, this review aims to summarize both socio-economic and public health implications of these parallel outbreaks along with their best possible management approaches. Various online databases were used to collect the necessary information regarding these outbreaks. Based on the reports published and analyses done so far, the long-lasting damages caused by these simultaneous outbreaks on global socio-economical and public health status can be conceived from the past experiences of outbreaks, especially the COVID-19 pandemic. Moreover, prolonged restrictions by the local government may lead to food insecurity, global recession, and an enormous impact on the mental health of people of all ages, specifically in developing countries. Such overwhelming effects already have been reported to be declining national growth of the economy as well as increasing political insecurity and shortage of basic needs. Although various actions have already been taken including vaccination, clinical management, and further research, social distancing, and lockdown, etc. to improve the situation, the emerging variants and associated genetic mutations may make the containment difficult worsening the situation again. Considering the current mutational dynamics of the pathogens and the past experiences, perpetual preparedness along with updated clinical management backed by epidemiological studies and innovating scientific effort are inevitable to combat the simultaneous waves of multiple infectious diseases.

Social interaction is essential for life and is impaired in many psychiatric disorders like schizophrenia, au-tism, depression and major anxiety disorder. Monoamine transmission plays a key role in social behavior and both genetic and epigenetic modifications of dopamine and noradrenaline neurotransmission-related genes can affect the levels of social interaction. Since heterozygous individuals for a specific genetic trait possess only one mutant allele of that trait, in order to better evaluate the role of the interaction between genetics and epigenetics in unmasking latent genetically-determined predispositions, our interest has focused on studying the interplay between genetics and epigenetics influences on social behavior in male rats obtained by two different breeding schemes: a first group by breeding of knock-out (KO) male rats with wild-type (WT) female dams (homogeneous heterozygous offspring, termed MAT-HET), and a second group of heterozygous DAT male offspring by breeding of KO male and DAT-heterozygous female subjects (to obtain comparable control pups, termed MIX-HET). Their social behavior was then assessed by partner preference, social preference and elicited preference tests. In the first test MIX-HET and MAT-HET male mice had choice between two WT females one in estrous and the other not in estrous. In the second test they met either a MIX-HET or a WT male rodent. Also, the expression of the noradrenaline transporter (NET) was assessed in the prefrontal cortex, hippocampus and hypothalamus of MAT, MIX and WTs by immunofluorescence in order to estimate its involvement in the expression of social behavior. Our results show that MIX-HET focal rodents tend to have an asocial behavior when in contact with a female in estrous, and their behavior is similar to when the stimulus is a MIX-HET male. MAT-HET male rodents, instead, tend to be very attracted by the female in estrous, but they ignore the MIX-HET stimulus. MIX-HET progeny showed a lower expression of noradrenaline transporter in both hypothalamus and hippocampus with respect to MAT-HET rats, whereas MAT-HET rats displayed increased noradrenaline transporter immunofluorescence in the hypothalamus and in the hippocampus with respect to WT rats, while no difference was observed in the prefrontal cortex. Therefore we can hypothesize that the differences observed between the two heterozygous groups may be attributable to an epigenetic factor: the different maternal care received. These data can open new perspectives towards increased the preclinical knowledge about autism and bipolar disorder.

Global environmental and societal changes threaten the cultures of Indigenous Peoples and Local Communities (IPLC). Despite the importance of IPLC worldviews and knowledge systems to human well-being and biodiversity, risks to these cultural resources are commonly simplified or neglected in environmental impact assessments, in part because cultural impacts are often indirect and therefore difficult to demonstrate. Here, we show that dependency of a culture on the environment can be mapped through human connections with biophysical elements in their environment. We illustrate a rich variety of cultural values that connect an indigenous Māori tribe in New Zealand with their local environment, then evaluate the resilience of this socio-environmental value system to environmental changes. Our results detail how loss of access to key environmental elements can have extensive direct and cascading impacts on multiple facets of indigenous cultural heritage. Consequently, considering only direct effects of environmental change on cultural heritage, or treating the richness of IPLC environmental relations simplistically, can severely underestimate the seriousness of environmental impacts on IPLC culture. Thus, protecting Earth’s cultural and biological diversity requires inclusion of human-environment relationships in environmental impact assessments.