Background Over the past decade there has been a growing awareness of, and interest in, the trace element concentration differences between normal and diseased tissues. Significant changes in tissue concentrations of Zinc (Zn) and Copper (Cu) have been previously reported in inflammation and cancer of certain human tissues.
Aim:(1)To correlate between Zn and Cu concentrations and the histological picture of normal and certain inflamed human tissues, namely the gall bladder (GB) the vermiform appendix (VA), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). (2) to detect whether there is a difference in the above-mentioned parameters between VAT and SAT. (3) to obtain recordings for trace element levels in human tissu

Abstract

The research aims to identify the role played by the strategic mind of human resources managers in reducing the levels of strategic drift that can face the organization, as the problem of research to overlook the importance of this role in monitoring and follow-up all the changes that may face the organization during the implementation of its strategy, therefore highlights the importance Research in providing a set of solutions to the problems faced by the researched organization and as far as the research variables, the researchers have adopted the descriptive analytical approach in the applied side, as adopted the questionnaire as a main tool for data collection, which was prepared based on a number of ready me

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

In recent years, the Global Navigation Satellite Services (GNSS) technology has been frequently employed for monitoring the Earth crust deformation and movement. Such applications necessitate high positional accuracy that can be achieved through processing GPS/GNSS data with scientific software such as BERENSE, GAMIT, and GIPSY-OSIS. Nevertheless, these scientific softwares are sophisticated and have not been published as free open source software. Therefore, this study has been conducted to evaluate an alternative solution, GNSS online processing services, which may obtain this privilege freely. In this study, eight years of GNSS raw data for TEHN station, which located in Iran, have been downloaded from UNAVCO website

In the present study, five derivatives have been designed to be synthesized as possible mutual prodrugs for 5-Fluorouracil (5-FU) and non steroidal anti-inflammatory drugs (NSAIDs) to selectively deliver the drugs into the cancer cells. The synthesis of the target compounds were accomplished following multistep reaction procedures, the chemical reaction followed up and the purity of the products were checked by TLC. The structure of the final compounds and their intermediates were confirmed by their melting points, infrared spectroscopy and elemental microanalysis, the hydrolysis of compound III was studied using HPLC technique. According to the results mentioned above, compounds (I−V) can be good candidates as possible mutual prod

       Iraq is one of the most important countries in the world that has received its share of terrorist acts by the terrorist organization the Islamic State of Iraq and al-Sham (ISIS), which has caused instability, especially during the period of ISIS's control of seven Iraqi provinces (2014-2017). This stage has caused a decline in the levels of human and economic development and its inconsistency with the capabilities and needs of the Iraqi population. Therefore, this study aims to investigate the hypothesis that there is a close relationship between the decline in development in Iraq and the brutal practices of ISIS that it committed during his period of control over many Iraqi cities and regions. This study used several method

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback