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Covid-19 is a novel pandemic disease with no potential vaccine treatment or medicine, the world is facing currently as of now. The death toll has increased to several lakhs and recovery rate is comparatively very less, was initially spotted in Wuhan (China). This spreads through close contact with people and socializing. The number of infected people varies with different parts of the world In our particular country India we are going through the lock down period which is the only vaccine to promote “social distancing” The hurdle arose due to the widespread of corona is major economy loss in combo with innocent lives. In this manuscript, we are visualizing the dataset which is publicly available to map, differentiate and separate the data in order to segregate the places that are most prone and perform basic regression to identify and predict the increasability of the counts from the dataset.

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks; and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution; while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets. [ABSTRACT FROM AUTHOR]

Background: The nose is a complex organ that shapes and has many roles as an integral part of the face. The nose’s primary purpose is to humidify, warm the inspired air, and also helps to remove the destructive particles from entering the lower respiratory tract. In general, an average adult inspires about 10,000 L of air each day. Nasal mucosa is a highly vascular structure and has a large surface area of 150 cm square. Physiologically, the nose has 50% resistance in the entire airway, which when affected plays a significant role in total respiratory function. that prolonged use of facemasks induces difficulty in breathing on exertion and excessive sweating around the mouth to the Ambulance driver which results in poorer adherence and increased risk of susceptibility to infection Objectives: The aim of this study is to determine the effects of prolonged usage of N95 respirators and surgical facemasks on Ambulance driver Material and Methods: A self constructed questionnaire containing 10 questions regarding the prolonged use of N95 respirators and surgical mask on Ambulance driver between the age group of 35 – 50 years were handed to them Results: This study suggests that prolonged use of facemasks induces difficulty in breathing on exertion and excessive sweating around the mouth to the Ambulance driver which results in poorer adherence and increased risk of susceptibility to infection. [ABSTRACT FROM AUTHOR]