Hassan Ali

Smoking regarded an important predisposing factor for cardiovascular and pulmonary disease and known risk factor for severe outcomes and death result from respiratory infection. This study aims to quantify the correlation between smoking and COVID-19 disease progression. This cross-sectional study done on 200 COVID-19 patients have been recruited from Al-Diwaniyah teaching hospital, Iraq in period of 1st of November, 2020 to the end of February, 2021, to assess the effect of smoking on susceptibility and/or clinical symptoms of SARS-COV2 infection. The study reveals no significant correlation between smoking and susceptibility nor with severity of disease. During the time of this study, no strong evidence was available suggest that smoking have an effect on severity of SARS-COV2 infection. Smoking is not a risk factor for having bad COVID-19progression. even if one might have anticipated the opposite. Thus, the epidemiological data seem to question the role of coexisting active smoking as a risk factor for COVID-19 pneumonia.

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained.