Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

One of most the important compounds which have active hydrogen (substrate) is the thiols which used in a wide field in preparation of Mannich bases . A large number of Mannich bases have been prepared as a biologically active compound (pharmaceutical, pesticides, bactericidal, fungicidal and tuberculostatic) and in order to correlate their structure and reactivity with their pharmacological activity such as . It has been reported that the reaction is easily proceeded by using primary and secondary amine beside formaldehyde. But when we tried the reaction of thiols as substrate and formaldehyde and succinimide instead of amine, the reaction did not proceed to give Mannich base but product were methylenene – bis – sulfide . Mann

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Background: Coronavirus disease 2019 (COVID-19) is an emerging zoonotic disease caused by the new respiratory virus SARS-CoV2. It has a tropism in the lung tissues where excess target receptors exist. Periostin plays a role in subepithelial fibrosis associated with bronchial asthma. Since the Coronavirus's target is the human respiratory system, Periostin has been recently described as a valuable new biomarker in the diagnosis and evaluation of disease in patients with COVID-19 lung involvement. Objectives: To assess the level of Periostin in the serum of COVID-19 patients and to correlate its role in disease severity and prognosis. Subjects and Methods: Periostin serum levels were measured for 63 patients attending three main COVID

Some parameters for advancement of Leishmania tropica infection were examined in three groups of golden hamsters, Group (1) inoculated with autoclaved killed Leishmania tropica , Group (2) inoculated with BCG vaccine alone while Group (3) Inoculated with mixed vaccine (autoclaved killed Leishmania with BCG). The follow up of experimentally infected animals with virulent isolation of Leishmania tropica was done for 90 days, the animals inoculated with mixed vaccine (autoclaved killed Leishmania with BCG) showed the minimum average in each of foot pad thickness (2.3 ± 0.05) mm after (60) days of infection, spleen enlargement (1.13±0.38) after (45) days of infection, spleen length (23.9±0.08) mm after (30) days of infection, liver weight(3.

Before the unit environmental problems serious the issues of the environment and conservation of contemporary issues important in the developed and developing worlds, it was natural that leads increasing global awareness to alert a group of intellectuals, scientists and politicians to the seriousness of this problem and the call to take steps deeper and more comprehensive with respect to the environment humanitarian based on the study of the various elements of this environment and a greater understanding of the relationships among them, and on this basis, steps have been taken to target the environment and to identify problems and make efforts to achieve the goals I: stop the deterioration of the environment and the second impro

 A simulation study is used to examine the robustness of some estimators on a multiple linear regression model with problems of multicollinearity and non-normal errors, the Ordinary least Squares (LS) ,Ridge Regression, Ridge Least Absolute Value (RLAV), Weighted Ridge (WRID), MM and a robust ridge regression estimator MM estimator, which denoted as RMM this is the modification of the Ridge regression by incorporating robust MM estimator . finialy, we show that RMM is the best among the other estimators

Abstract The results of isolation, morphological and microscopic diagnosis, Chromic Agar, Vitik technology and Bact Alert showed that the diagnosis of fungi isolated from blood samples of end-stage renal patients who did not undergo dialysis and those who underwent dialysis was 60 samples for each type. The total number of fungal isolates isolated from people who did not undergo dialysis was 26 pathogenic fungal isolates, with a percentage frequency of 43.33%. In this study, 4 genera of pathogenic fungi were identified: Candida spp, Rhodotorula spp, Cryptococcus spp. and Aspergillus spp. The number of Candida isolates reached 13 isolates, with a frequency of 50%. The results also showed that the diagnosed species from the genus Rhodotorula