The aim of this research is to determine the most important and main factors that lead to Preeclampsia. It is also about finding suitable solutions to eradicate these factors and avoid them in order to prevent getting Preeclampsia. To achieve this, a case study sample of (40) patients from Medical City - Oncology Teaching Hospital was used to collect data by a questionnaire which contained (17) reasons to be investigated. The statistical package (SPSS) was used to compare the results of the data analysis through two methods (Radial Bases Function Network) and (Factorial Analysis). Important results were obtained, the two methods determined the same factors that could represent the direct reason which causes Preecla

The present study is about tales attributed to certain people and are not traded. Such a study has a great importance for religion scholars and conveyors of hadiths since it is considered as one of the important sources of Islamic legislation that represent the Holy Sunna, and the majority of the recent study is concerned with non-traded tales of religion scholars. The research starts with the definition of such type of tales and then its types, conditions, legality, exceptions, and the benefit from studying such matter. It is mainly concerned with the opinions of the Imami and Hanafi region scholars respectively, and with comparing between the two doctrines i.e. Imami and Hanafi at the end of each

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Flaxseed from the flax plant (Linum usitatissimum), which has been cultivated for domestic use since prehistoric times. This study aims to investigate presence of antibacterial effect of flaxseed extract against selected oral pathogen in-vitro.

This paper discusses a comparative study to relate parametric and non-parametric mode decomposition algorithms for response-only data. Three popular mode decomposition algorithms are included in this study: the Eigensystem Realization Algorithm with the Natural Excitation Technique (NExT-ERA) for the parametric algorithm, as well as the Principal Component Analysis (PCA) and the Independent Component Analysis (ICA) for the non-parametric algorithms. A comprehensive parametric study is provided for (i) different response types, (ii) excitation types, (iii) system damping, and (iv) sensor spatial resolution to compare the mode shapes and modal coordinates of using a 10-DOF building model. The mode decomposition results are also compared using

Background: Irrigation has a central role in endodontic treatment. Several irrigating solutions have the antimicrobial activity and actively kill bacteria and yeasts when introduced in direct contact with the microorganisms. The purpose of this study was to evaluate the antimicrobial effectiveness of Dandelion (Taraxacum officinale) root and leaf extracts as possible irrigant solutions, used during endodontic treatments, and both were compared to Sodium hypochlorite, Propolis and Ethyl alcohol. Materials and Method: Forty seven human extracted single rooted teeth were selected. The teeth were decoronated using a diamond disk to have a length of 15 mm ±1 mm and they were instrumented using the hybrid technique. All roots were sterilized

NeighShrink is an efficient image denoising algorithm based on the discrete wavelet
transform (DWT). Its disadvantage is to use a suboptimal universal threshold and identical
neighbouring window size in all wavelet subbands. Dengwen and Wengang proposed an
improved method, which can determine an optimal threshold and neighbouring window size
for every subband by the Stein’s unbiased risk estimate (SURE). Its denoising performance is
considerably superior to NeighShrink and also outperforms SURE-LET, which is an up-todate
denoising algorithm based on the SURE. In this paper different wavelet transform
families are used with this improved method, the results show that Haar wavelet has the
lowest performance among