Communicable diseases have been an important part of human history. Epidemics afflicted populations, causing many deaths before gradually fading away and emerging again years after. Epidemics of infectious diseases are occurring more often, and spreading faster and further than ever, in many different regions of the world. The scientific community, in addition to its accelerated efforts to develop an effective treatment and vaccination, is also playing an important role in advising policymakers on possible non-pharmacological approaches to limit the catastrophic impact of epidemics using mathematical and machine learning models. Controlling Epidemics With Mathematical and Machine Learning Models provides mathematical and machine learning models for epidemical diseases, with special attention given to the COVID-19 pandemic. It gives mathematical proof of the stability and size of diseases. Covering topics such as compartmental models, reproduction number, and SIR model simulation, this premier reference source is an essential resource for statisticians, government officials, health professionals, epidemiologists, sociologists, students and educators of higher education, librarians, researchers, and academicians.

The book examines, contemporary globalisation that signifies a growing interconnectedness between people and societies across the world through increasing flows of people, goods, services, finance and ideas across the borders. The concept of globalisation and its meaning is discussed through insights from scholars such as David Held, Anthony Giddens, David Harvey, Arjun Appadurai, Ulrich Beck, Manuel Castells, Saskia Sassen and many other scholars to explain divergent perspectives of globalisation. The book also studies threats like nuclear weapons proliferation, global terrorism, environmental security issues, global justice, poverty, migration, and global shifts. It aims to generate readers’ interest in understanding globalisation and analysing the changing dynamics of world politics by studying it from diverse viewpoints. The emergence of Covid-19 and resurgence of great power politics has given rise to the debate of de-globalisation and return of a new kind of ‘cold war’. In this context, this volume will also help readers to understand globalisation in the present changing world order. The book will be useful to the departments of Political Science, International Relations, and other interdisciplinary Social Sciences subjects like Political Economy and Global/International Politics.

Communicable diseases have been an important part of human history. Epidemics afflicted populations, causing many deaths before gradually fading away and emerging again years after. Epidemics of infectious diseases are occurring more often, and spreading faster and further than ever, in many different regions of the world. The scientific community, in addition to its accelerated efforts to develop an effective treatment and vaccination, is also playing an important role in advising policymakers on possible non-pharmacological approaches to limit the catastrophic impact of epidemics using mathematical and machine learning models. Controlling Epidemics With Mathematical and Machine Learning Models provides mathematical and machine learning models for epidemical diseases, with special attention given to the COVID-19 pandemic. It gives mathematical proof of the stability and size of diseases. Covering topics such as compartmental models, reproduction number, and SIR model simulation, this premier reference source is an essential resource for statisticians, government officials, health professionals, epidemiologists, sociologists, students and educators of higher education, librarians, researchers, and academicians.

Padmaka is an important drug used as one of the ingredients in many Ayurvedic preparations. The accepted source of this drug is the heart-wood or some times the stem of Prunus cerasoides D. Don of family Rosaceae. The south Indian market sample of Padmaka is identified as the flowers of Careya arborea Roxb of family Lecythidaceae. The present study was undertaken to evaluate pharmacognostical characters, phytochemical analysis and pharmacological studies (antiulcer activity) on stem bark of C. arborea Roxb. To carry out this study, the drug material was collected from surroundings of Papanasam forests vicinity of Tirunelveli district, Tamil Nadu. For antiulcer activity, three models such as ethanol induced, cold stress induced and pyloric ligation model were adopted. To the animals having ulcer, the alcohol and aqueous extract was given orally and observed for reduction in ulcer index, volume of gastric juice and total acidity. Both extracts offered protection against ulcer and the effects were found to be dose dependent. The antiulcer activity was found to be statistically significant when compared with the control group.

Correct identification is often problematic since these plants are known by a variety of vernacular names. One such plant-derived drug product is Brahmadandi. Brahmadandi is an important medicinal plant used in the Indian system of medicine (Ayurveda) for the treatment of numerous diseases. A literature search revealed that different plants are available on the market under the trade name Brahmadandi viz., roots of Echinops echinatus Roxb, and the aerial parts of Tricholepis glaberrima DC which are sold either in their crude or in powdered form. T. glaberrima, E.echinatus and A.mexicana are traditionally claimed to treatment in urinary disorder. Such claims have been well documented to improve the functioning of organs like liver and kidney. Further, this drug has been found to be safe and has been use for considerably longer period in humans. The various herbal formulations of these plants are available in market and are prescribed by ayurvedic physicians for treatment of various kidney ailments.