Based on a finite element analysis using Matlab coding, eigenvalue problem has been formulated and solved for the buckling analysis of non-prismatic columns. Different numbers of elements per column length have been used to assess the rate of convergence for the model. Then the proposed model has been used to determine the critical buckling load factor () for the idealized supported columns based on the comparison of their buckling loads with the corresponding hinge supported columns . Finally in this study the critical buckling factor () under end force (P) increases by about 3.71% with the tapered ratio increment of 10% for different end supported columns and the relationship between normalized critical load and slenderness ratio was g

This paper studies a novel technique based on the use of two effective methods like modified Laplace- variational method (MLVIM) and a new Variational method (MVIM)to solve PDEs with variable coefficients. The current modification for the (MLVIM) is based on coupling of the Variational method (VIM) and Laplace- method (LT). In our proposal there is no need to calculate Lagrange multiplier. We applied Laplace method to the problem .Furthermore, the nonlinear terms for this problem is solved using homotopy method (HPM). Some examples are taken to compare results between two methods and to verify the reliability of our present methods.

Non-thermal argon plasma needle at atmospheric pressure was
constructed. The experimental setup was based on a simple and low
cost electric component that generates a sufficiently high electric
field at the electrodes to ionize the argon gas which flow at
atmospheric pressure. A high AC power supply was used with 1.1
kV and 19.57 kHz. Non-thermal Argon plasma used on blood
samples to show the ability of non-thermal plasma to promote blood
coagulation. Three tests have been done to show the ability of plasma
to coagulate both normal and anti-coagulant blood. Each blood
sample has been treated for varying time from 20sec. to 180sec. at
different distances. The results of the current study showed that the
co

In all process industries, the process variables like flow, pressure, level, concentration
and temperature are the main parameters that need to be controlled in both set point
and load changes.
A control system of propylene glycol production in a non isothermal (CSTR) was
developed in this work where the dynamic and control system based on basic mass
and energy balance were carried out.
Inlet concentration and temperature are the two disturbances, while the inlet
volumetric flow rate and the coolant temperature are the two manipulations. The
objective is to maintain constant temperature and concentration within the CSTR.
A dynamic model for non isothermal CSTR is described by a first order plus dead
time (FO

Two hundred fifty mid-stream urine specimens were collected from Baqubah Teaching Hospital and Al-Batool Teaching Hospital from patients with urinary tract infections (UTI). Of these investigated urine specimens, 66 (26.4%) specimens showed positive growth culture of Gram-negative bacteria. From these, Escherichia coli was the most prevalent bacteria of the examined culture (41, 62.12%). Additionally, the cup assay was used to determine colicin producers while the most efficient colicin producers were estimated by the formation of larger inhibition zone. Approximately half of the investigated E. coli isolates (20, 49 %) was colicin producers. Colicins was extracted after induction by mitomycin-C showed a concentration o

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl