Atomic interaction between mediator protein of human prostate cancer (PHPC) and Fe/C720 Buckyballs-Statin is important for medical science. For the first time, we use molecular dynamics (MD) approach based on Newton’s formalism to describe the destruction of PHPC via Fe/C720 Buckyballs-Statin with atomic accuracy. In this work, the atomic interaction of PHPC and Fe/C720 Buckyballs-Statin introduced via equilibrium molecular dynamics approach. In this method, each PHPC and Fe/C720 Buckyballs-Statin is defined by C, H, Cl, N, O, P, S, and Fe elements and contrived by universal force field (UFF) and DREIDING force-field to introduce their time evolution. The results of our studies regarding the dynamical behavior of these atom-base compounds have been reported by calculating the Potential energy, center of mass (COM) position, diffusion ratio and volume of defined systems. The estimated values for these quantities show the attraction force between Buckyball-based structure and protein sample, which COM distance of these samples changes from 10.27 Å to 2.96 Å after 10 ns. Physically, these interactions causing the destruction of the PHPC. Numerically, the volume of this biostructure enlarged from 665,276 Å3 to 737,143 Å3 by MD time passing. This finding reported for the first time which can be considered by the pharmaceutical industry. Simulations indicated the volume of the PHPC increases by Fe/C720 Buckyballs-Statin diffusion into this compound. By enlarging this quantity (diffusion coefficient), the atomic stability of PHPC decreases and protein destruction procedure fulfilled.

This paper presents a numerical method for solving a nonlinear age-structured population model based on a set of piecewise constant orthogonal functions. The block-pulse functions (BPFs) method is applied to determine the numerical solution of a non-classic type of partial differential equation with an integral boundary condition. BPFs duo to the simple structure can efficiently approximate the solution of systems with local or non-local boundary conditions. Numerical results reveal the accuracy of the proposed method even for the long term simulations.

There are diverse effects in consequence of exposure to radiofrequency electromagnetic fields (RF-EMF). The interactions of fields and the exposed body tissues are related to the nature of exposure, tissue comportment, field strength and signal frequency. These interactions can crop different effects.

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.