This paper focuses on developing a self-starting numerical approach that can be used for direct integration of higher-order initial value problems of Ordinary Differential Equations. The method is derived from power series approximation with the resulting equations discretized at the selected grid and off-grid points. The method is applied in a block-by-block approach as a numerical integrator of higher-order initial value problems. The basic properties of the block method are investigated to authenticate its performance and then implemented with some tested experiments to validate the accuracy and convergence of the method.

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

BACKGROUND: Tribulus terrestris has been commonly used in folk medicine to energize, vitalize and improve sexual function and physical performance in men and laboratory rats. OBJECTIVE: To study the effect of Tribulus terrestris on the number of Leydig cells. MATERIALS AND METHODS: Tribulus terrestris was given to mature male rats as an oral single herbal suspension in a dose of 2.0mg /1000gbody weight for 14 days to stimulate spermatogenesis. Formalin fixed paraffinembedded tissue sections were performed for histological, immunohistochemical and morphometrical studies. RESULTS: Histological study revealed wider seminiferous tubules and increased spermatocytes population with an increased sperm density inside the lumen of the tubul

Implantation of blastocyst is considered as a sensitive process in the life of growing embryo. Thus this critical period could be under threat from the harmful effect of many drugs ,among them is the carbamazepine which is used by many women for a treatment of epilepsy . The present experiment was designed to study the effect of this drug on the process of implantation ;especially during the days 7 and 9 of pregnancy. For those purpose 169 pregnant albino mice, of 25 gm weight for each one, were used .The mice were divided into control group given orally normal saline, and treated group given orally carbamazepine with concentration of 15 mg\kg. Samples from pregnant uteri containing the embryos were used. They were first fixed in Boun's sol

Background: Aortic valve stenosis results from minor to severe degrees of aortic valve maldevelopment. This stenosis causes mild to severe obstruction of the left ventricular outflow .

Objectives : to study the immediate and intermediate results of percutaneous balloon aortic valvuloplasty in patients with congenital valvular aortic stenosis .

Type of the study: A prospective study.

Methods: The study was done on thirty five patients with congenital valvular aortic stenosis who had percutaneous balloon aortic valvuloplasty  in Ibn Al- Bitar Center for Cardiac Surgery from May 2009 to February 2011.

Results

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Background: Oral squamous cell carcinoma represents the vast majority of oral cancer it is a common malignant tumor with an increasing incidence. Around the world, the 5 year mortality rate of oral cancer is about 50%. Thus novel biomarkers for early detection oral squamous cell carcinoma are needed. The level of three salivary microRNAs namely hsa-miR-200a, hsa-miR-125a and hsa- miR-93 were measured in saliva of patients with oral squamous cell carcinoma and compared their levels in saliva of healthy control subjects to determine their potential as oral cancer biomarker. Materials and methods: The level of these three microRNAs was measured by using revers transcription, preamplification and quantitative PCR. Results: Only miR-200a presen