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https://www.selleckchem.com/products/gi254023x.html In recent years, examining the determinants of health behaviors on a multi-country level remains limited. Therefore, the purpose of this study is to explore the key factors that may enhance the adoption of health-protective behaviors during the COVID-19 pandemic in Morocco and India. A theoretical framework derived from the health belief model (HBM) was used for this research. Data was collected from a sample of 444 adult individuals split across Morocco (n = 215) and India (n = 229). Data analysis was carried out using two-stage multiple-analytic techniques. First, structural equation modeling (SEM) was employed to test the hypothesized relationships. Second, an artificial neural network (ANN) model was employed to rank the significant independent variables obtained from SEM analysis. The results of SEM showed that perceived benefit is the key predictor of the protective behavior in Morocco, followed by self-efficacy, and then perceived severity. By contrast, ANN analysis showed that perceived severity was the most vital factor for predicting the protective behavior in Morocco, followed by perceived benefits, and then self-efficacy. For the Indian sample, both SEM analysis and the ANN model revealed that the impact of perceived susceptibility on the adoption of the protective measure is stronger than that of cues to action. Theoretical contributions and managerial implications are also discussed toward the end.The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether

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