The corrosion behavior of bare and chemical conversion coated (through anodizing) aluminum ASA 606 I in stagnant chromic acid solutions . Solutions of 2, 6 & J O wt. % Cr03 at 45°C, have been investigated using polarization technique. The anodizing experiments were conducted under fixed conditions of 35 minute exposure time and 30 volt supplied voltage. The most important feature achieved was the great difference in behavior between the anodic polarization curves for bare and anodized aluminum in different concentrations of chromic acid solutions.

Modified asphalt is considered one of the alternatives to address the problems of deficiencies in traditional asphalt concrete, as modified asphalt addresses many of the issues that appear on the pavement layers in asphalt concrete, resulting from heavy traffic and vehicles loaded with loads that exceed the design loads and the large fluctuations in the daily and seasonal temperatures of asphalt concrete. The current study examined the role of polyphosphoric acid (PPA) as a modified material for virgin asphalt when it was added in different proportions (1%, 2%, 3%, 4%) of the asphalt weight. The experimental program includes the volumetric characteristics associated with the Marshall test, the physical properties, and th

Five species of Lactic acid bacteriawere isolated from raw milk, yoghurt, vegetables and pickles, Lactobacillus plantarum, Lactobacillus acidophilus, Lactobacillus brevis, Lactobacillus casei and Lactobacillus bulgaricus isolates were identified by 16S rRNA gene. Evaluate of antimicrobial activity against all the bacterial strains Staphylococcus aureus, Salmonella spp., Pseudomonas fluorescens, Escherichia coli, Bacillus cereus and Bacillus subtilis. It showed that bacteriocin of Lactic acid bacteriamore effective than supernatant of lactic acid bacteria, the results showed that isolatemost efficient isolate belonging to Lactobacillus brevis, the diameter of the inhibition of the bacteriocin of Lactobacillus brevis were 27.7, 26.3 and 25.1

AASAH Enass J Waheed, Shatha MH Obaid, Research Journal of Pharmaceutical, Biological and Chemical Sciences, 2019 - Cited by 5

      In this research, the nonparametric technique has been presented to estimate the time-varying coefficients functions for the longitudinal balanced data that characterized by observations obtained through (n) from the independent subjects, each one of them is measured repeatedly by group of  specific time points (m). Although the measurements are independent among the different subjects; they are mostly connected within each subject and the applied techniques is the Local Linear kernel LLPK technique. To avoid the problems of dimensionality, and thick computation, the two-steps method has been used to estimate the coefficients functions by using the two former technique. Since, the two-

The geochemical study of the Oligocene-Miocene succession Anah, Euphrates, and Fatha formations, western Iraq, was carried out to discriminate their depositional environments. Different major and trace patterns were observed between these formations. The major elements (Ca, Mg, Fe, Mn, K, and Na) and trace elements (Li, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Zr, Cs, Ba, Hf, W, Pb, Th, and U) are a function of the setting of the depositional environments. The reefal facies have lower concentrations of MgO, Li, Cr, Co, Ni, Ga, Rb, Zr, and Ba than marine and lagoonal facies but have higher concentrations of CaO, V, and Sr than it. Whereas dolomitic limestone facies are enriched V, and U while depletion in Li, Cr, Ni, Ga, Rb, Sr, Zr, Ba, an

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i