المستودع الرقمي العراقي. مركز المعلومات الرقمية التابع لمكتبة العتبة العباسية المقدسة

Metabolic syndrome (MetS) is a combination of health disorders that mainly result from overweight and obesity. It increases the risk of developing cardiovascular disease and diabetes. (MetS) closely related to the existence weight gain or Obesity and laziness. It increases the serum levels of TNF-α and change the levels of a number of other parameters (e.g., adiponectin, resistin, and PAI-1). TNF-α dose not only appear to cause the production of inflammatory cytokines. It can trigger cell signaling by interacting with TNF-α receptors that can lead to insulin resistance. Usually, the digestive system molders the foods you eat and converts them to glucose. Insulin is an anabolic hormone produced by the pancreas that aids glucose g

The nuclear level density parameter  in non Equi-Spacing Model (NON-ESM), Equi-Spacing Model (ESM) and the Backshifted Energy Dependent Fermi Gas model (BSEDFG) was determined for 106 nuclei; the results are tabulated and compared with the experimental works. It was found that there are no recognizable differences between our results and the experimental -values. The calculated level density parameters have been used in computing the state density as a function of the excitation energies for ⁵⁸Fe and ²⁴⁶Cm nuclei. The results are in a good agreement with the experimental results from earlier published work.

Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using

This study aimed to study the inhibition activity of purified bacteriocin produced from the local isolation Lactococcuslactis ssp. lactis against pathogenic bacteria species isolated from clinical samples in some hospitals Baghdad city. Screening of L. lactis ssp. Lactis and isolated from the intestines fish and raw milk was performed in well diffusion method. The results showed that L. lactis ssp. lactis (Lc4) was the most efficient isolate in producing the bacteriocin as well observed inhibitory activity the increased that companied with the concentration, the concentration of the twice filtrate was better in obtaining higher inhibition diameters compared to the one-fold concentration. The concentrate

The Gaussian orthogonal ensemble (GOE) version of the random matrix theory (RMT) has been used to study the level density following up the proton interaction with ⁴⁴Ca, ⁴⁸Ti and ⁵⁶Fe.

A promising analysis method has been implemented based on the available data of the resonance spacing, where widths are associated with Porter Thomas distribution. The calculated level density for the compound nuclei ⁴⁵Sc,⁴⁹Vand ⁵⁷Co shows a parity and spin dependence, where for Sc a discrepancy in level density distinguished from this analysis probably due to the spin  misassignment .The present results show an acceptable agreement with the combinatorial method of level density.

The importance of efficient vehicle detection (VD) is increased with the expansion of road networks and the number of vehicles in the Intelligent Transportation Systems (ITS). This paper proposes a system for detecting vehicles at different weather conditions such as sunny, rainy, cloudy and foggy days. The first step to the proposed system implementation is to determine whether the video’s weather condition is normal or abnormal. The Random Forest (RF) weather condition classification was performed in the video while the features were extracted for the first two frames by using the Gray Level Co-occurrence Matrix (GLCM). In this system, the background subtraction was applied by the mixture of Gaussian 2 (MOG 2) then applying a number

     Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes.  The data augmentation techniques have been used. In addition to dropout and weight reg

Municipal solid waste generation in Babylon Governorate is often affected by changes in lifestyles, population growth, social and cultural habits and improved economic conditions. This effect will make it difficult to plan and draw up future plans for solid waste management.In this study, municipal solid waste was divided into residential and commercial solid wastes. Residential solid wastes were represented by household wastes, while commercial solid wastes included commercial, institutional and municipal services wastes.For residential solid wastes, the relational stratified random sampling was implemented, that is the total population should be divided into clusters (socio-income level), a random sample was taken in e