An investigation was conducted for the determination of the effects of the forming conditions in the production of Gamma Alumina catalyst support on the crushing strength property. Eight variables were studied , they are ;binder content which is the sodium silicate , Solvent content which is the water, speed of mixing , time of mixing, drying temperature , drying time , calcinations temperature and the calcinations time
Design of the experiments was made by using the response Surface method in Minitab 15 software which supply us 90 experiments .
The results of this investigation show that the crushing strength for the dried Gamma alumina extrudate was affected by the drying temperature and the drying time only and there is no inter

Abstract 

This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per

Baghdad is considered the largest city in Iraq, with a population of approximately nine million people, according to the 2022 statistics. Due to this population increase, the city of Baghdad has experienced horizontal expansion rather than vertical expansion, resulting in a shortage of basic services, an increase in population migration, and a rise in prices. Therefore, our research focuses on shedding light on the patterns of population concentrations in the city of Baghdad and the nature of population distribution within it. We addressed the topic from various angles, starting with the population distribution among the city's districts and their density. Additionally, we explored the factors influencing population density in the province

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

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

Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-201

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was t

This paper deals with the description of the system of formation and derivation of words in the Russian language. In this work, we will present recent trends in the study of the Russian language that deal with vocabulary formation. The lexical system of the Russian language is associated with a common (or opposite) meaning; similar (or opposite) in stylistic characteristics; united by a common type of word formation; related to a common descent and belonging to a vocabulary of much or little use, etc. The results of the most prominent linguists and specialists who dealt with this topic will be presented, in addition to presenting their different views on word formation. The words of the Russian language consist of mor vimat that participate

In petroleum industry, the early knowledge of “pore pressure gradient” is the basis in well design and the extraction of these information is more direct when the pore pressure gradient is equal to normal gradient; however, this matter will be more complex if it deviate from that limit which is called “abnormal pore pressure”, if this variable does not put in consideration, then many drilling problems will occur might lead to entire hole loss. To estimate the pore pressure gradient there are several methods, in this study; Eaton method’s is selected to extract the underground pressure program using drilling data (normalized rate of penetration) and logs data (sonic and density log). The results shows that an abnormal high press

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration