This study aims at evaluating the performance of MA students in the College of Education for Women in using the digital transformation and identifying the significant difference in performance evaluation according to the variable of academic qualification (Master or PHD). In order to achieve the aim of the research the researcher prepared a questionnaire of 20 items, and this happens after the researcher's getting acquaintance of the literature of previous studies related to the variable of the research. The apparent validity of the items was examined by exposing them to 10 juries specialized in education, psychology and evaluation and measurement. The stability of the items was examined via two methods, the test-repetition and half-divisio

The synthesis, characterization and liquid crystalline properties of N4,N40-bis((1 H-benzo[d]imidazol-2- yl)methyl)-3,30-dimethyl-[1,10-biphenyl]-4,40-diamine and of their corresponding Mn(II), Fe(II), Ni (II), Cu(II), and Zn(II) complexes are described. The ligand and complexes have been characterized by elemental analysis, magnetic susceptibility measurements (meff), conductometric measurements and Fourier Transform Infrared (FTIR), Nuclear Magnetic Resonance (1H NMR), (13C-NMR) and UV–Vis spectroscopy. Spectral investigations suggested octahedral coordination geometrical arrangement for M(II) complexes. The phase transition temperatures were detected by differential scanning calorimetry (DSC) analysis and the phases are confirmed by op

The synthesis, characterization and liquid crystalline properties of N4,N40 -bis((1 H-benzo[d]imidazol-2- yl)methyl)-3,30 -dimethyl-[1,10 -biphenyl]-4,40 -diamine and of their corresponding Mn(II), Fe(II), Ni (II), Cu(II), and Zn(II) complexes are described. The ligand and complexes have been characterized by elemental analysis, magnetic susceptibility measurements (meff), conductometric measurements and Fourier Transform Infrared (FTIR), Nuclear Magnetic Resonance (1 H NMR), (13C-NMR) and UV–Vis spectroscopy. Spectral investigations suggested octahedral coordination geometrical arrangement for M(II) complexes. The phase transition temperatures were detected by differential scanning calorimetry (DSC) analysis and the phases are confirmed

The synthesis, characterization and liquid crystalline properties of N4,N40-bis((1 H-benzo[d]imidazol-2- yl)methyl)-3,30-dimethyl-[1,10-biphenyl]-4,40-diamine and of their corresponding Mn(II), Fe(II), Ni (II), Cu(II), and Zn(II) complexes are described. The ligand and complexes have been characterized by elemental analysis, magnetic susceptibility measurements (meff), conductometric measurements and Fourier Transform Infrared (FTIR), Nuclear Magnetic Resonance (1H NMR), (13C-NMR) and UV–Vis spectroscopy. Spectral investigations suggested octahedral coordination geometrical arrangement for M(II) complexes. The phase transition temperatures were detected by differential scanning calorimetry (DSC) analysis and the phases are confirmed by op

Background :Alkaline phosphatase (ALP) was a widely used marker for skeletal and hepatobiliary disorders, but its activity was also increased in atherosclerosis and peripheral vascular disease. Several study has showed that ALP activity was increased in the sera of diabetic patients. The current study was conducted to evaluate ALP activity in type 2 diabetic patients and optimum conditions for enzyme activity in their sera.Methods: This study was carried out at in AL-Yarmok hospital(diabetic center) between February /2009 and April /2009. Fifty two patients with type 2 diabetes have been enrolled. Besides BMI, WHR, serum fasting blood glucose, ALP, HbA1C,uric acid and lipid profile levels have been performed .The relationship bet

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor