Abstract

The aim of the research is to identify the effect of using the self-questioning strategy on the achievement of fourth-grade students in science and the development of their reflective thinking in physics in the city of Baghdad. The research sample was divided into two groups: an experimental group of (20) students and a control group of (20) students. The researchers developed two tools: a test of (40) multiple-choice questions. The second tool is a test to measure the reflective thinking skills of fourth-grade students. It consists of (25) multiple-choice questions distributed on skills as follow: reflection and observation, detection of inaccuracies, reaching conclusions, giving clear explanations, an

: Cervical cancer representsone of the possibly preventable cancer. The study was designed to find the possible correlation of Tafazzin on the progression of cervical carcinoma. Two groups of paraffinized blocks were included. The study group of 30 cervical tumors as well as 15 biopsies of healthy cervical tissues. After sectioning on a positive charge, immunohistochemical application (IHC) was performed to detect Tafazzin expression. Nighnty percentage (27 out of 30) of the studies group showed positive overexpression as shown in with a significant association of the expression with cervical cancer with a significant association. There is a possible role of TAZ in hastening the development of cervical cancer through different mechanisms. F

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

n-Hexane conversion enhancement was studied by adding TCE (Trichloro-ethylene) on feed stream using 0.3%Pt/HY zeolite catalyst. All experiments were achieved at atmospheric pressure and on a continuous laboratory unit with a fixed bed reactor at a temperature range 240-270◦C, LHSV 1-3h-1, H2/nC6 mole ratio 1-4.
By adding 435 ppm of TCE, 49.5 mole% conversion was achieved at LHSV 1h-1, temperature of 270ºC and H2/nC6 mole ratio of 4, while the conversion was 18.3 mol% on the same catalyst without adding TCE at the same conditions. The activation energy decreased from 98.18 for pure Pt/HY zeolite to 82.83 kJ/mole by adding TCE. Beside enhancement the activity, selectivity and product distribution enhanced by providing DMB (Dimethyl b

The CdSe pure films and doping with Cu (0.5, 1.5, 2.5, 4.0wt%) of thickness 0.9μm have been prepared by thermal evaporation technique on glass substrate. Annealing for all the prepared films have been achieved at 523K in vacuum to get good properties of the films. The effect of Cu concentration on some of the electrical properties such as D.C conductivity and Hall effect has been studied.
It has been found that the increase in Cu concentration caused increase in d.c conductivity for pure CdSe 3.75×10-4(Ω.cm)-1 at room temperatures to maximum value of 0.769(Ω.cm)-1 for 4wt%Cu.All films have shown two activation energies, where these value decreases with increasing doping ratio. The maximum value of activation energy was (0.319)eV f

Films of CdSe have been prepared by evaporation technique with thickness 1µm. Doping with Cu was achieved using annealing under argon atmosphere . The Structure properties of these films are investigated by X-ray diffraction analysis. The effect of Cu doping on the orientation , relative intensity, grain size and the lattice constant has been studied. The pure CdSe films have been found consist of amorphous structure with very small peak at (002) plane. The films were polycrystalline for doped CdSe with (1&2wt%) Cu contents and with lattice constant (a=3.741,c=7.096)A°, and it has better crystallinty as the Cu contents increased to (3&5wt%) Cu. The reflections from [(002), (102). (110), (112), and (201)]planes are more prominen

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h

Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti