RESUMO
The objective of the current investigation is to examine the influence of variable viscosity and transverse magnetic field on mixed convection fluid model through stretching sheet based on copper and silver nanoparticles by exploiting the strength of numerical computing via Lobatto IIIA solver. The nonlinear partial differential equations are changed into ordinary differential equations by means of similarity transformations procedure. A renewed finite difference based Lobatto IIIA method is incorporated to solve the fluidic system numerically. Vogel's model is considered to observe the influence of variable viscosity and applied oblique magnetic field with mixed convection along with temperature dependent viscosity. Graphical and numerical illustrations are presented to visualize the behavior of different sundry parameters of interest on velocity and temperature. Outcomes reflect that volumetric fraction of nanoparticles causes to increase the thermal conductivity of the fluid and the temperature enhances due to blade type copper nanoparticles. The convergence analysis on the accuracy to solve the problem is investigated viably though the residual errors with different tolerances to prove the worth of the solver. The temperature of the fluid accelerates due the blade type nanoparticles of copper and skin friction coefficient is reduced due to enhancement of Grashof Number.
RESUMO
The aim of this work is to design an intelligent computing paradigm through Levenberg-Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona virus disease 19 (COVID-19) propagation via human to human interaction. The model is represented with systems of nonlinear ordinary differential equations represented with susceptible, exposed, symptomatic and infectious, super spreaders, infection but asymptomatic, hospitalized, recovery and fatality classes, and reference dataset of the COVID-19 model is generated by exploiting the strength of explicit Runge-Kutta numerical method for metropolitans of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad. The created dataset is arbitrary used for training, validation and testing processes for each cyclic update in Levenberg-Marquardt backpropagation for numerical treatment of the dynamics of COVID-19 model. The effectiveness and reliable performance of the design LMANNs are endorsed on the basis of assessments of achieved accuracy in terms of mean squared error based merit functions, error histograms and regression studies.
RESUMO
A newly isolated fungus Penicillium verruculosum SG was evaluated for the production and characterization of bioactive colored secondary metabolites using solid-state fermentation along with their cytotoxic activities against normal and cancer cell lines. Logical fragmentation pattern following column chromatography, thin layer chromatography and liquid chromatography and mass spectrometry of crude culture filtrate of fungus revealed the presence of different polyketide pigments and other bioactive compounds. Cytotoxicity of the selected colored fractions of fungal filtrate containing different compounds revealed IC50 (µg/ml) values ranging from 5 to 100. It was significantly higher in case of orevactaene (5 + 0.44) and monascorubrine followed by pyripyropene (8 + 0.63) against cancer cell line KA3IT. Overall, these compounds considerably showed less toxicity toward normal cell lines NIH3T3, HSCT6, HEK293 and MDCK. XRD of a yellow crystalline compound (224.21 m/z) confirmed its 3-dimensional structure as phenazine 1 carboxylic acid (C13H8N2O2) (broad spectrum antibiotic), and it is first time reported in fungi.