The hydatid materials were collected and studied, so they were contained 50 fertile human hydatid cases {33 (66%) females and 17 (34%) males}. They were collected from Al-Ramadi General Hospital during the period from December, 2003 to July, 2004 .Cysts were observed in 40 (80%) from the liver, 5 (10%) from the lungs, 3 (6%) from the kidney and 2 (4%) cysts from urinary bladder. The specimens were taken from patients of different ages. The in vitro viability of protoscoleces was assessed on the basis of flame cell activity and eosein exclusion, which were considered as criteria to determine the death or viability of protoscoleces. In addition to this movement (flame cell activity), another motility like constriction – relaxatio

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

Surge pressure is supplemental pressure because of the movement of the pipes downward and the swab pressure is the pressure reduction as a result of the drill string's upward movement. Bottom hole pressure is reduced because of swabbing influence. An Investigation showed that the surge pressure has great importance for the circulation loss problem produced by unstable processes in the management pressure drilling (MPD) actions. Through Trip Margin there is an increase in the hydrostatic pressure of mud that compensates for the reduction of bottom pressure due to stop pumping and/or swabbing effect while pulling the pipe out of the hole. This overview shows suggested mathematical/numerical models for simulating surge pressure problems ins

The field of climatic geography focuses on the study of the Earth's surrounding atmosphere, particularly the lower (surface) part close to the Earth's surface. This field examines the interactions within the atmosphere resulting from the solar radiation that reaches the Earth and the subsequent distribution of atmospheric pressure. Consequently, certain surface pressure systems, both high and low, emerge and vary in their temporal and spatial impacts on the climate of the Arabian Peninsula. Therefore, it is essential to conduct a comprehensive and detailed study to understand the causes, development, movement, geographical distribution, and monthly and seasonal recurrence of these pressure systems. For this purpose, a minor climatic

This study was designed to monitor the ambient air pollution in several sites within Baghdad City of Iraq. The readings started from May 2016 to April 2017. The highest concentration of sulfur dioxide (SO2) was 2.28 ppmm-3 while nitrogen dioxide (NO2) was 3.68 ppmm-3 and suspended particulate matter was 585.1 ?gm-3. This study also included estimating the value of the air pollution tolerance index (APTI) for four plant's species Olea europaea L., Ziziphus spina-Christi (L.) Desf, Albizia lebbeck(L.) Benth. and Eucalyptus camaldulensis Dehnh. Were cultivated on the road sides. The study includes four biochemical parameters, total chlorophyll content, ascorbic acid content, pH and relative water content of plant leaves. The results show that

This paper reports a.c., d.c. conductivity and dielectric behavior of Ep-hybrid composite with12 Vol.% Kevlar-Carbon hybrid . D.C. conductivity measurements are conducted on the graded composites by using an electrometer over the temperature range from (293-413) K. It was shown then that conductivity increases by increasing number of Kevlar –Carbon fiber layers (Ep1, Ep2, Ep3), due to the high electrical conductivity of Carbon fiber. To identify the mechanism governing the conduction, the activation energies at low temperature region (LTR) and at high temperature region (HTR) have been calculated. The activation energy values for hybrid composite decrease with increasing number of fiber layers. The a.c. conductivity was measured over fr

Spin coating technique used to prepare ZnPc, CdS and ZnPc/CdS blend thin films, these films annealed at 423K for 1h, 2h and 3h. Optical behavior of these films were examined using UV-Vis. and PL. The absorption spectrum of ZnPc shows a decreasing in absorption with the increase of annealing time while CdS spectrum give a clearly absorption peak at~510 nm. Energy gap of ZnPc increases from 1.41 to 1.52 eV by increasing the annealing time. E_g of CdS decrease by increasing annealing time, from 2.3 eV to 2.2 eV. The intensities of the peaks obtained from PL spectra were strongly dependent on annealing time and confirmed the results obtained from UV-Vis. D.C. conductivity measurement showed that all the thin films have two differen

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number