Mathematical Models of Microwave and Photonic Devices Engaging Strong Nonlinearities Using Decomposition on Nonlinear Autonomous Blocks

Mathematical modeling technique based on solving the nonlinear Maxwell’s equations (Eqs.) rigorously using the decomposition approach on nonlinear autonomous blocks partially filled by the nonlinear media with a “strong” nonlinearity (NABs) and reliable engineering method for numerical computation of microwave and photonic nonlinear 3D devices engaging strong nonlinearities, applicable in CAD, were developed. To determine the NAB descriptors the iterative computational process for solving the nonlinear 3D diffraction boundary problems with the non-asymptotic radiation boundary conditions on the NAB bounds was performed using the projection method. The iteration method of recomposition of NABs is developed using the linearization of its descriptors. Using the computational algorithm for solving nonlinear diffraction boundary problems performed as NABs and improved computation algorithm of determination of bifurcation points the nonlinearity thresholds in the magnetic nanoarrays at microwaves were numerically simulated.

A Graph-Based Dynamic Modeling for Palm Oil Refining Process

Graph theory is a well-established mathematical concept that is widely used in numerous applications such as in biology, chemistry and network analysis. The advancement in the theory of graph has led to the development of a concept called autocatalytic set. In this paper, a mathematical modeling technique namely graph-based dynamic modeling of palm oil refining process is introduced. The system parameters are identified in detail in the beginning of the paper. The parameters involved are the chemical compounds used or produced during the refining process. These identified parameters are then modeled as the vertices and edges of the graph. The dynamicity of the system is then simulated and analyzed. The system is simulated using MATLAB software programing. The two final products produced by the refining process agreed with results obtained from other published methods. Hence, the effectiveness and simplicity of the model are established.

Physiologically based pharmacokinetic (PBPK) modeling and simulation in drug discovery and development

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic or physiology based mathematical modeling technique which integrates the knowledge from both drug-based properties including physiochemical and biopharmaceutical properties and system based or physiological properties to generate a model for predicting the absorption, distribution, metabolism and excretion (ADME) properties of a drug as well as pharmacokinetic behavior of a drug in preclinical species and humans.

Formulation of Mathematical Modeling to Characterize The Aluminium Metals Using Ultrasonic Non-Destructive Techniques

Predicting the type of aluminium metals and composition of elements present in the aluminium samples through Nondestructive testing (NDT) is a matter of very importance for aluminium Industry. The unique method to determine grade of the aluminium sample is required to characterize the aluminium metals. The Nondestructive Technique (NDT) and determination of characteristics and mechanical properties of aluminium metals are used to identify the grade of aluminium metals so that accordingly it can be used for the specific applications. Therefore a technique is required to predict the percentage of aluminium, Iron, Copper, Manganese of aluminium metals so as to categorize into different grades and applications. In Aluminium samples percentage of Aluminium plays very important role which may help to decide the grade of the aluminium metals hence its applications. The present work is focused on how the percentage of aluminium in aluminium samples can be calculated by adopting the mathematical modeling technique. Â Â There are various parameters which generally affect the percentage of aluminium in aluminium samples, and play a very major role. Therefore through this investigation an attempt is being made to formulate an approximate mathematical model which will certainly predict the percentage of aluminium in aluminium samples. In advent of this a dimensionless pie terms of various prominent parameters or variables have been taken to form a mathematical model. Some of these variables used to formulate this model are given as follows (i) physical properties of the aluminium samples like hardness, density, modulus of elasticity etc (ii) Signal analysis properties like Peak amplitude of Time signal, FFT, PSD and (iii) both the properties. The data of such types of variables have been recorded and calculated and thus the formulation of model is being done by multiple regression analysis. The model is then optimized and the reliability of the model has also been estimated. In fact this type of model will be helpful to estimate the aluminium percentage.