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https://www.selleckchem.com/products/oltipraz.html With the growing awareness of the linkage among open defecation (OD), environment, and health, it is important to understand the factors responsible for OD. It is a necessary step toward developing a strategy to end open defecation for ensuring a better environment and human health. There is no such study available for Pakistan. The study, therefore, aims to bridge this gap. Using household data of Pakistan Demographic and Health Survey (PDHS) 2017-2018, an association of OD with potential predictors, analysis of variance, and a logistic regression model are employed to develop the evidence. The results suggest that place of residence, education, poverty status, social norms, geopolitical regions, and living space significantly predict the OD behavior in Pakistan. This study recommends two things first is to facilitate the households and communities to own latrines, second is to change the behavior through intervention. However, political commitment and effective administration will be key to ascertain ending OD.Globally, urban has been the major contributor to greenhouse gas (GHG) emissions and thus plays an increasingly important role in its efforts to reduce CO2 emissions. However, quantifying city-level CO2 emissions is generally a difficult task due to lacking or lower quality of energy-related statistics data, especially for some underdeveloped areas. To address this issue, this study used a set of open access data and machine learning methods to estimate and predict city-level CO2 emissions across China. Two feature selection technologies including Recursive Feature Elimination and Boruta were used to extract the important critical variables and input parameters for modeling CO2 emissions. Finally, 18 out of 31 predictor variables were selected to establish prediction models of CO2 emissions. We found that the statistical indicators of urban environment pollution (such as industrial SO2 and dust emissions per

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