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

with an organized propaganda campaign. This military campaign was helped to formulate its speech by many institutions, research centers, and knowledge and intelligence circles in order to mobilize public opinion gain supporters and face the opponents by different means depending on a variety of styles to achieve its required effects. 
          After the US occupation of Iraq, US media fighters sought to influence the Iraqi public opinion and making them convinced them of the important presence of US military forces in Iraq which necessitated finding its justification through the use of persuasive techniques in its intensive propaganda campaigns. 
  This research discusses the most important

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

Quantitative real-time Polymerase Chain Reaction (RT-qPCR) has become a valuable molecular technique in biomedical research. The selection of suitable endogenous reference genes is necessary for normalization of target gene expression in RT-qPCR experiments. The aim of this study was to determine the suitability of each 18S rRNA and ACTB as internal control genes for normalization of RT-qPCR data in some human cell lines transfected with small interfering RNA (siRNA). Four cancer cell lines including MCF-7, T47D, MDA-MB-231 and Hela cells along with HEK293 representing an embryonic cell line were depleted of E2F6 using siRNA specific for E2F6 compared to negative control cells, which were transfected with siRNA not specific for any gene. Us

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

Isobaric Vapor-Liquid-Liquid equilibrium data for the binary systems ethyl acetate + water, toluene + water and the ternary system toluene + ethyl acetate + water were determined by a modified equilibrium still, the still consisted of a boiling and a condensation sections supplied with mixers that helped to correct the composition of the recycled condensed liquid and the boiling temperature readings in the condensation and boiling sections respectively. The VLLE data where predicted and correlated using the Peng-Robinson Equation of State in the vapor phase and one of the activity coefficient models Wilson, NRTL, UNIQUAC and the UNIFAC in the liquid phase and also were correlated using the Peng-Robinson Equation of State in both the vapo

A simple setup of random number generator is proposed. The random number generation is based on the shot-noise fluctuations in a p-i-n photodiode. These fluctuations that are defined as shot noise are based on a stationary random process whose statistical properties reflect Poisson statistics associated with photon streams. It has its origin in the quantum nature of light and it is related to vacuum fluctuations. Two photodiodes were used and their shot noise fluctuations were subtracted. The difference was applied to a comparator to obtain the random sequence.

A field experiment was conducted during the autumn of 2021 at the Agricultural Research Department station / Abu Ghraib to evaluate the soil moisture, water potential distribution, and growth factors of maize crops under alternating and constant partial drip irrigation methods. In the experiment, two irrigation systems were used, surface drip irrigation (DI) and subsurface irrigation (SD); under each irrigation system, five irrigation methods were: conventional irrigation (CI), and 75 and 50% of the amount of water of CI of each of the alternating partial irrigation APRI75 and APRI50 and the constant partial irrigation FPRI75 and FPRI50 respectively. The results showed that the water depth for conventional irrigation (C1) was 658.3