ABSTRACT
Communicable disease pandemic is a severe disease outbreak all over the countries and continents. Swine Flu, HIV/AIDS, corona virus disease-19 (COVID-19), etc., are some of the global pandemics in the world. The major cause of becoming pandemic is community transmission and lack of social distancing. Recently, COVID-19 is such a largest outbreak all over the world. This disease is a communicable disease which is spreading fastly due to community transmission, where the affected people in the community affect the heathy people in the community. Government is taking precautions by imposing social distancing in the countries or state to control the impact of COVID-19. Social distancing can reduce the community transmission of COVID-19 by reducing the number of infected persons in an area. This is performed by staying at home and maintaining social distance with people. It reduces the density of people in an area by which it is difficult for the virus to spread from one person to other. In this work, the community transmission is presented using simulations. It shows how an infected person affects the healthy persons in an area. Simulations also show how social distancing can control the spread of COVID-19. The simulation is performed in GNU Octave programming platform by considering number of infected persons and number of healthy persons as parameters. Results show that using the social distancing the number of infected persons can be reduced and heathy persons can be increased. Therefore, from the analysis it is concluded that social distancing will be a better solution of prevention from community transmission.
ABSTRACT
Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data.
ABSTRACT
The aim of this study was to determine the aerobic contribution to upper body and lower body Wingate Anaerobic tests (WAnT). Eight nonspecifically trained males volunteered to take part in this study. Participants undertook incremental exercise tests for peak oxygen uptake and two 30-s WAnT (habituation and experimental) for both the upper and lower body. The resistive loadings used were 0.040 and 0.075 kg·kg body mass(-1), respectively. Peak power output (PPO) and mean power output (MPO) were calculated for each WAnT. The aerobic contribution of each WAnT was assessed using breath by breath expired gas analysis. Peak oxygen uptake was lower for the upper body when compared with the lower body (P = 0.001). Similarly, PPO and MPO were greater for the lower body (both P < 0.001). Absolute oxygen uptake during the upper body WAnT was lower than for the lower body (P = 0.013), whereas relative oxygen uptake (% peak oxygen uptake) was similar (P = 0.997). The mean aerobic contribution for the upper body WAnT (43.5% ± 29.3%) was greater than for the lower body (29.4% ± 15.8%; P < 0.001). The greater aerobic contribution to the WAnT observed for the upper body in comparison with the lower body is likely due to methodological differences in upper and lower body WAnT protocols and potentially differences in anaerobic power production and exercise efficiency. The results of this study suggest that differences may exist for the aerobic contribution of upper and lower body Wingate anaerobic tests.
