This study aimed to identify attitudes towards mental illness in pregnant female clients to clinics women in the province of Ramallah and Al Bireh, for this purpose applied to study procedures on a sample of (200) of pregnant mothers were selected a sample available, have reached results no statistically significant differences in the level of attitudes towards mental illness due to the variable age in mothers pregnant female clients to clinics for women. Ther were astatistically significant differences in the level of these trends depending on the variable-level scientific research for the benefit of pregnant class university students and older and then high school and so on all areas except the area of social interaction, The results also showed a statistically significant differences in the level of these attitudes towards mental illness due to a variable number of times the load on the field of treatment for the first time to download, and no statistically significant differences on the rest of the fields, and reached results are also statistically significant differences in the level of attitudes towards mental illness in Pregnant variable depending on the number of abortions and that the areas of social compliance and the total score for the lack of abortion, and there is no statistically significant differences depending on this variable on the rest of the fields.
The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p
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The incorporation of recycled concrete aggregate (RCA) into asphalt concrete supports circular economy goals by reducing reliance on virgin materials and minimizing construction waste. However, RCA’s inherent limitations, such as high porosity, microcracking, and poor interfacial bonding, compromise the structural integrity and durability of asphalt mixtures. This study introduces sugarcane molasses (SCM), a naturally derived, carbohydrate-rich byproduct of sugarcane refining, as a novel and eco-friendly surface treatment for RCA aimed at enhancing its compatibility with asphalt binders. SCM was applied at 5-6% by weight of RCA replacing coarse aggregate at varying levels (0-100%) to assess its effect on asphalt mixture performance. A com
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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In this study, nano TiO2 was prepared with titanium isopropoxide (TTIP) as a resource to titanium oxide. The catalyst was synthesized using phosphotungstic acid (PTA) and, stearyl trimethyl ammonium bromide (STAB) was used as the structure-directing material. Characterization of the product was done by the X-ray diffraction (XRD), X-ray fluorescent spectroscopy (XRF), nitrogen adsorption/desorption measurements, Atomic Force Microscope (AFM) and Fourier transform infrared (FTIR) spectra, were used to characterize the calcined TiO2 nanoparticles by STAB and PWA. The TiO2 nanomaterials were prepared in three crystalline forms (amorphous, anatase, anatase-rutile). The results showed that the nanoparticles of anatase TiO2 have good cata
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Aspartate aminotransferase was purified from urine and serum of patients with type 2 diabetes in a 2 steps procedure involving dialysis bag and sephadex G-25 gel filtration (column chromatography). The enzyme was purified 346.23 fold with 1467% yield and 3.46 fold with 142.85% yield in urine and serum of patients with type 2 diabetes respectively. The purified enzyme showed single peak. The results of this study revealed that AST activity of type 2 diabetes urine and serum increased significantly (p<0.001) compared with control group.