Social networks offer a wealth of information for capturing additional information on people's behavior, trends, opinions and emotions during any human-affecting events such as natural disasters. During disaster, social media provides a plethora of information which includes information about the nature of disaster, affected people's emotions and relief efforts. In this paper we propose a natural-disaster analysis interface that solely makes use of tweets generated by the Twitter users during the event of a natural disasters. We collect streaming tweets relating to disasters and build a sentiment classifier in order to categorize the users' emotions during disasters based on their various levels of distress. Various analysis techniques are applied on the collected tweets and the results are presented in the form of detailed graphical analysis which demonstrates users' emotions during a disaster, frequency distribution of various disasters and geographical distribution of disasters. We observe that our analysis of data from social media provides a viable, economical, uncensored and real-time alternative to traditional methods for disaster analysis and the perception of affected population towards a natural disaster.
Propellants contain considerable chemical energy that can be used in rocket propulsion. Bringing together information on both the theoretical and practical aspects of solid rocket propellants for the first time, this book will find a unique place on the readers' shelf providing the overall picture of solid rocket propulsion technology. Aimed at students, engineers and researchers in the area, the authors have applied their wealth of knowledge regarding formulation, processing and evaluation to provide an up to date and clear text on the subject.
The article describe modelling efforts for evaluating the current level of COVID-19 infections in India, using exponential model. The Data from 15 march 2020 to 30 April 2020 are used for validating the model, where intrinsic rise rate is kept constant. It is observed that some states of India, like Maharastra, Gujarat and Delhi have a much higher daily infection cases. This is modelled by assuming an initial higher infections, keeping rise rate same. The sudden outbursts are captured using offset of values for these three states. Data from other states like Madhya Pradesh, Uttar Pradesh and Rajasthan are also analysed and they are found to be following the same constants as India is following. Worldwide, many attempts are made to predict outburst of COVID-19 and in the model, described in this paper, turning point is not predicted, as cases in India are still rising. The developed model is based on daily confirmed infections and not on cumulative infections and rationalization is carried out for the population of various regions, while predicting infections for various states. Assigning a decay constant at this stage will be a premature exercise and keeping that in mind, exponential model predicts that India will attain 1 lakh case by 15 May 2020. The figure of 2 lakh and 3 lakh will be attained on 22 May 2020 and 26 May 2020, respectively.
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