Material Requirements Planning System (MRP) is considered as one of the planning and controlling of production and inventory systems which is used to prepare plan of the final production requirements and its parts of subcomponents raw materials and the time at which it was needed for the purpose of preparing orders of production and purchase.

The problem of the present work is represented in the general company of electrical industrialization adoption of traditional methods and personal experience of the process of the products and\or purchase quantity and inventory quantities and limiting the required time for acquiring the required quantities of the materials and parts used in the finish product of the

     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

The nuclear structure of ³⁸Ar, ⁵⁹Co, ¹²⁴Sn, ¹⁴⁶Nd, ¹⁵³Eu and ²⁰³Tl target nuclei used in technology for nuclear batteries have been investigation, in order that, these nuclei are very interesting for radioisotope thermo-electric generator (RTG) space studies and for betavoltaic battery microelectronic systems. The single particle radial density distribution, the corresponding root mean square radii (rms), neutron skin thicknesses and binding energies have been investigated within the framework of Hartree-Fock Approximation with Skyrme interaction. The bremsstrahlung spectrums produced by absorption of beta particles in betavoltaic process and backscattered p

In this study, biodiesel was prepared from chicken fat via a transesterification reaction using Mussel shells as a catalyst. Pretreatment of chicken fat was carried out using non‐catalytic esterification to reduce the free fatty acid content from 36.28 to 0.96 mg KOH/g oil using an ethanol/ fat mole ratio equal to 115:1. In the transesterification reaction, the studied variables were methanol: oil mole ratio in the range of (6:1 ‐ 30:1), catalyst loading in the range of (9‐15) wt%, reaction temperature (55‐75 °C), and reaction time (1‐7) h. The heterogeneous alkaline catalyst was greenly synthesized from waste mussel shells throughout a calcin

In this work, we have developed a model that describes the relationships between top predators (such as tigers, hyenas, and others), crop raiders (such as baboons, warthogs, and deer), and prey (such as deer) in the coffee forests of southwest Ethiopia. Various potential equilibrium points are identified. Additionally, the model's stability in the vicinity of these equilibrium points is examined. An investigation of the model's Hopf bifurcation is conducted concerning several significant parameters. It is found that prey species may be extinct due to a lower growth rate and consumption by top predators in the absence of human interference in the carrying capacity of prey. It is observed that top predators may be extinct due to human interfe

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi