Urban land uses are in a dynamic state that varies over time, the city of Karbala in Iraq has experienced functional changes over the past 100 years, as the city is characterized by the presence of significant tourist and socio-economic activity represented by religious tourism, and it occur due to various reasons such as urbanization. The purpose of this study is to apply a Markov model to analyze and predict the behavior of transforming the use of land in Karbala city over time. This can include the conversion of agricultural land, or other areas into residential, commercial, industrial land uses. The process of urbanization is typically driven by population growth, economic development, based on a set of probabilities and transitions bet

In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

Background BK polyomavirus is one of the common post-transplant viral infections, affecting ∼15% of renal transplantation recipients (RTR), leading to graft loss in more than half of cases. Objectives Study the rate of detection of BK virus (BKV) in RTRs in Pap-stained urine cytology specimens. Methods A single center study, urine samples were collected from 99 RTR patients, with 15 Living Donors (LD) and 15 patients with chronic kidney disease (CKD) were taken as controls. And urine cytology smears were Pap stained for detection of decoy cells (DCs). Results Out of the 99 RTRs, 27 (27.3%) patients were decoy positive, 8 out of these 27 patients had uncommon DCs, and 5 out of these 27 cytology positive patients (18.5%) had biopsy proven B

Drastic threat to the natural system is caused by the uncontrolled release of synthetic pollutants, including azo dyes. This study centered on the decolorization and biodegradation of water soluble azo dye reactive blue (RB) in a batch mode sequential anaerobic-aerobic processes. A local sewage treatment plant was the source where activated sludge was collected to be used as non-adapted mixed culture with both free and the alginate immobilized cells for RB biodegradation. Under anaerobic conditions, the free and immobilized mixed cells were proved to completely decolorize 10 mg/ L of RB within 20 and 30 h, respectively. Alginate- immobilized mixed cells, resulted in 88%, 87%, and 87% maximum COD removals with samples con

Deep Learning Techniques For Skull Stripping of Brain MR Images

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

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

One of the important objectives of the varistor is for a sustainable environment and reduce the pollution resulting from the frequent damage of the electrical devices and power station waste. In present work, the influence of Al2O3 additives on the non –linear electrical features of SnO2 varistors, has been investigated, where SnO2 ceramic powder doped with Al2O3 in three rates (0.005, 0.01, and 0.05), the XRD test improved that SnO2 is the primary phase, while CoCr2O4, and Al2O3 represent the secondary phases. The electrical tests of all prepared samples confirmed that the increasing of Al2O3 rates and sintering temperature improves and increase the electrical features, where the best results obtained at Al2O3 (0.05) and 1000℃, the non