Association of COVID-19 with obesity Association of COVID-19 with obesity

COVID-19 is a disease that cause respiratory illness due to novel corona virus. It was reported to WHO on December 31,2019 for the first time. The outbreak of this disease started from Wuhan city, China. Now COVID-19 pandemic is spreading worldwide mostly in Europe and North America, these regions have high prevalence of obesity. In the pathogenesis of COVID-19 disease, obesity assumes a significant job. Theinsusceptible framework, which is official in the pathogenesis of COVID19, assumes asignificant job in weight instigated fat tissue aggravation. In the fat tissue the irritationbrings about metabolic brokenness conceivably prompting dyslipidemia, type 2diabetes mellitus, insulin obstruction, hypertension and cardiovascular sickness. Obesity has been expanded the vulnerability to contaminations. In this pandemic, a large number of obese individual with Covid-19 are reported. Infection rate in obese is greater due to poor immunity, comorbidity and inadequate nutritional needs. Statistical analysis showed that about 41.7% patients reported in New York city were obese. Whereas 40% obese have been reported in United State of America with Covid-19. A report from UK indicated that 38% obese were admitted in ICU with Covid-19. According to Chinese researchers, obese individuals are 3-timesmore prone toward the development of Covid-19. So recent analysis indicated that obesity is the major risk factor of Covid-19. In COVID-19, overweight and obese patients have high danger of metabolic difficultiesand eternal infections that stoutness works. More nutrition care is required for such patients. As nutrition is a key factor for keeping up human wellbeing, for example, denseimpervious framework and satisfactory admission of supplements and dietaryenhancements. Tolerant with COVID-19 create contamination from slight to seriousindications bound to the dietary status. Consequently, assessing wholesome status ofindividuals with contamination turns out to be increasingly significant. Through dietaryhelp, we can bring down the danger of oxidative pressure, infection contamination andexpands invulnerability framework among obese people especially.

Meshfree Methods for the Numerical Solutions of Partial Differential Equations

In this dissertation, meshfree (meshless) methods using meshless shape functions are proposed for the numerical solutions of partial differential equations (PDEs). These PDEs have either integer or fractional order time derivatives. Weighted θ-scheme (0≤θ ≤1) is used for time discretization of integer case, whereas, for fractional case, the same discretization scheme is combined with a simple quadrature formula. For space (spatial) discretization we used meshless shape functions owing Kronecker delta function property. These shape functions are obtained viapointinterpolationapproachandradialbasisfunctions(RBFs). Finallywiththehelpofcollocationmethodthe given PDE reduces to system of algebraic equations, which are then solved via LU decomposition in iterations. For the proposed numerical scheme, stability analysis is carried out theoretically and computational examples are provided to support the analysis. The proposed scheme has been tested via application to several concrete and benchmark problems of engineering interest. ApproximationqualityandaccuracyofcomputedsolutionsaremeasuredusingL∞, L2 andLrms discrete errornorms. Efficiencyandorderofapproximationoftheproposedschemeinspaceandtimeareanalyzedthrough variation of number of nodal points N and time step-size δt. The documented results, in the form of tables and figures, reveal very good agreement to true solutions as well high accuracy to earlier proposed technique available in the literature. In RBFs, the presence of shape (support) parameter c∗ plays a crucial role. Accuracy of the RBFs based scheme can be improved via proper selection of this parameter. For this purpose, an automatic optimal shape parameter selection algorithm is proposed. To check effectiveness and automatic (adaptive) nature of this algorithm in RBFs approximation method, time fractional Black-Scholes models have been solved. It has been noted that the proposed algorithm worked well and gives excellent accurate solutions for various fractional order time derivatives. The RBFs approximation (Kansa) method results in dense ill-conditioned matrix. For the treatment of this issue weproposeahybridRBFs(HRBFs)approximationmethod. Byextendingthisidea, anadaptive(automatic)algorithm is proposed for optimal parameters selection in HRBFs. For validation, again time fractional Black-Scholes models are reconsidered. Simulations revealed acceptable accurate solutions in hybrid RBFs method too. Along with that significant reduction in condition number of the resultant matrix is observed up to several manifold. Hence, HRBFs method can be seen as an alternative remedy for curing ill-conditioning in usual RBFs method. Computer simulations have been carried out via MATLAB R2013a on a personal laptop with configuration, Processor: Intel(R) Core(TM) i5-5200U CPU @ 2.20GHz 2.20GHz, RAM: 4.00 GB, System type: 64-bit Operating System, x64-based processor.