Doppler assessment may lead to intervention that reduces the risk of fetal brain damage. Aim of thestudy: to assess the relation between ultrasonic hemodynamic Doppler indices of middle cerebral and umbilical arteries (PI, RI), growth indices to immediate neonatal outcomes (weight, head & abdominal circumference, APGAR scores at 1 and 5 minutes and neonatal unit admission) in women with mild, moderate and severe anemia during pregnancy. Present study is a clinical prospective study carried out in Al-Elwiya Maternity Teaching Hospital during (January-Jun) 2019, all anemic pregnant women presented to Obstetrical wards in hospitals for emergency cesarean section were the study population. The final sample selected was 120 pregnant women. Ultra

The present study was undertaken to use individual terrestrial crustacean from Isopoda such as the species Porcellionides pruinosus (Brandt 1833) as environmental cleaner and that through the test of their abilities in decomposition of residues of some cellulosean wastes such as wood pieces which contain high ratio of urban wastes, and residue of Zea mays and particularly leaves as plant waste in agricultural fields and residue of Cynodon dactylon plants which compose the main wastes in most of gardens and parks. Experiments were conducted relatively in stable laboratory conditions to ensure environmental conditions similar to crustaceans' life. The results showed presence of good efficiency of these individuals in treating such wastes as

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

Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective

This paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors. This system is built using a personal computer and  two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor. The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique. Results showed that the motor current signature analysis (MCSA) technique, and measurement of vibration technique have high possibility in the detection and diagnosis of most mechanical faults with high accuracy. Subsequently, diagnosi

High-resolution imaging of celestial bodies, especially the sun, is essential for understanding dynamic phenomena and surface details. However, the Earth's atmospheric turbulence distorts the incoming light wavefront, which poses a challenge for accurate solar imaging. Solar granulation, the formation of granules and intergranular lanes on the sun's surface, is important for studying solar activity. This paper investigates the impact of atmospheric turbulence-induced wavefront distortions on solar granule imaging and evaluates, both visually and statistically, the effectiveness of Zonal Adaptive Optics (AO) systems in correcting these distortions. Utilizing cellular automata for granulation modelling and Zonal AO correction methods,