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This article presents the status of countries affected by COVID-19 (as of mid-May 2020) and their preparedness to combat the after-effects of the pandemic. The report also provides an analysis of how human behavior may have triggered such a global pandemic and why humans need to consider living sustainably to make our future world livable for all. COVID-19 originated in the city of Wuhan, China in December 2019. As of mid-May, it has spread to 213 countries and territories worldwide. The World Health Organization has declared COVID-19 a global pandemic, with a death toll of over 300,000 to date. The U.S. is currently the most impacted country. Collaborative efforts of scientists and politicians across the world will be needed to better plan and utilize global health resources to combat this global pandemic. Machine learning-based prediction models could also help by identifying potential COVID-19-prone areas and individuals. The cause of the emergence of COVID-19 is still a matter of research; however, one consistent theme is humanity's unsustainable behavior. By sustainably interacting with nature, humans may have avoided this pandemic. If unsustainable human practices are not controlled through education, awareness, behavioral change, as well as sustainable policy creation and enforcement, there could be several such pandemics in our future.

Chapter1: Groundwater arsenic contamination and availability of safe water for drinking in Middle Ganga Plain in India -- Chapter2: Geomorphic controls on spatial arsenic distribution in aquifers of the Brahmaputra River floodplains -- Chapter3: Reductive dissolution of Fe-oxyhydroxides a potential mechanism for arsenic release into groundwater in the alluvial plain of river Brahmaputra -- Chapter4: Sources of arsenic exposure in well-nourished children -- Chapter5: Environmental arsenic exposure and human health risk -- Chapter6: Sustainable arsenic mitigation: problems and prospects in India -- Chapter7: Arsenic Contamination of Soil in Relation to Water in Northeastern South Africa -- Chapter8: Arsenic contamination of India's groundwater: a review and critical analysis -- Chapter9: Biosorption of Arsenic: An Emerging Eco-technology of Arsenic Detoxification in Drinking Water -- Chapter10: Assessing and mapping human health risks due to arsenic and socioeconomic correlates for proactive arsenic mitigation -- Chapter11: Arsenic-contaminated drinking water and the associated health effects in the Shahpur block of Bihar: a case study from five villages -- Chapter12: Arsenic contamination of drinking water and mitigation in Pakistan: a case of Indus river basin

Most of the papers presented at the international seminars on Gulf and Emerging Asia : Defining Regional Architecture, on People, Culture and Goods in Motion : India-Arab Maritime Historical Relations & on Contemporary UAE and the Emerging Indo-UAE Relations and Contemporary Iraq and Indo-Iraq Relations, organized by Gulf Studies Programme, Jawaharlal Nehru University during 24-26 March 2009, 3-4 March 2010 & 15-16 March 2012 respectively

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk. ; Validerad;2020;Nivå 2;2020-04-27 (johcin)