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Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach.

Short-term mobility is often associated with increased sexual risk behavior. Mobile individuals often have higher rates of sexual risk behavior compared to non-mobile individuals, but the reasons why are not clear. Using monthly retrospective panel data from 202 men and 282 women in Agbogbloshie, Ghana, we tested whether short-term mobility was associated with changes in coital frequency, and whether the association was due to the act of travel in the given month (e.g., enabling higher risk behavior), the reason for travel, or an individual's travel propensity at other times in the year. Overnight travel specifically to visit family or friends, or for education, health, or other reasons, was associated with increased coital frequency for men. However, men with higher travel propensities had lower overall coital frequency and the act of traveling enabled more sex only for the most frequent male travelers. Men who seldom traveled had much higher coital frequency, but the act of traveling was not associated with additional sex acts. For women, travel for education, health, or other reasons increased coital frequency. Occasional female travelers had slightly more sex acts compared to non-mobile women, and the act of traveling for these women was associated with slight increases in coital frequency, supporting the enabling hypothesis. Highly mobile women had fewer sex acts per month on average. Our findings suggest that mobility characteristics measured on a broad temporal scale, as well as the reason for mobility, are important to understand the relationship between short-term mobility and sexual behavior.

Abstract Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships.

Despite sustained global efforts to avoid, reduce, and reverse land degradation, estimates of land degradation nationally and regionally vary considerably. Land degradation reduces agricultural productivity, impacts the provision of vital ecosystem services, and disproportionately affects vulnerable populations. The 2030 Agenda for Sustainable Development, through Sustainable Development Goal 15.3, sets out to achieve land degradation neutrality (LDN) by improving the livelihoods of those most affected and building resilience in areas affected by or at risk from degradation. The United Nations Convention to Combat Desertification (UNCCD) leads the charge in creating a spatially explicit framework for monitoring and reporting on LDN goals that countries can integrate into their land planning policies. However, it remains difficult to operationalize the integration of biophysical indicators of land degradation with climatic and socio‐economic indicators to assess the impact of land degradation on vulnerable populations. We present an integrative framework that demonstrates how freely available global geospatial data sets can be leveraged through an open‐source platform (Trends.Earth) to simplify and operationalize monitoring and reporting on progress towards achieving LDN. Then, we summarize a suite of data sets and approaches that can be used to understand and quantify the socio‐ecological interactions between drought, land degradation and population exposed to desertification, land degradation and drought. We discuss how improvements in Earth observation data sets and algorithms will allow UNCCD land‐based progress sub‐indicators (changes in primary productivity, land cover, soil organic carbon, drought, and population exposure) to be computed at enhanced spatial resolutions. [ABSTRACT FROM AUTHOR]

Coal resources have always been an important driving force for macroeconomic growth and GDP growth in resource‐rich regions. However, this growth has also caused inequality of household income in developing regions. Using survey data on rural household livelihoods in Shenmu county, Shaanxi Province, in northwestern China, this paper analyzes the relationship between coal exploitation and income inequality. Four multivariate econometric analyses are developed. We find evidence for the resource curse theory of natural resource exploitation leading to inequality: the Gini coefficient for household revenue is discovered to be markedly related with the location of and participation in coal exploitation. Nevertheless, this correlation is not linear. Rather, household income inequality primitively rises with environmental and resource subsidies. We analyze the impacts of household income inequality and contrast the characteristics of household livelihood and location characteristics in the coal exploitation site of northern Shenmu with those in the whole survey site. Our statistical analysis shows that out‐migration, the level of total household income, and convenience of household transportation are the important elements devoting to household income in northern Shenmu, the coal resources endowment region. However, the environmental damage and pollution caused by coal exploitation has substantial negative impacts upon household income, especially for households not participating in coal activities. Overall, we found that coal exploitation did not develop local industries. Adversely, the ecological environment problem and the high subsidy for coal mining caused by the exploitation of coal resources have aggravated the inequality of household income in the sample areas. Additionally, the development of coal resources attracted youth labor from agriculture to the coal industry, which exacerbated the lack of labor force and weakened village agricultural productivity. [ABSTRACT FROM AUTHOR]

The emergence of the SARS-CoV-2 virus in 2019 lead to a global pandemic that altered the activity behavior of most people on our planet. While government regulations and public concern modified visitation patterns to places of interest, little research has examined the nuanced changes in the length of time someone spends at a place, nor the regional variability of these changes. In this work, we examine place visit duration in four major U.S. cities, identify which place types saw the largest and smallest changes, and quantify variation between cities. Furthermore, we identify socio-economic and demographic factors that contribute to changes in visit duration and demonstrate the varying influence of these factors by region. The results of our analysis indicate that the pandemic's impact on visiting behavior varies between cities, though there are commonalities found in certain types of places. Our findings suggest that places of interest within lower income communities experienced less change in visit duration than others. An increase in the percentage of younger, Black or Hispanic populations within a community also resulted in a smaller decrease in visit duration than in other communities. These findings offer insight into the factors that contribute to changes in visiting behavior and the resilience of communities to a global pandemic. [ABSTRACT FROM AUTHOR]

Background: The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings: Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions: Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. [ABSTRACT FROM AUTHOR]