Bioethanol is an attractive fuel with higher potential for energy security and environmental safety. Olive solid residues were used as a raw material for the production of bioethanol through the use of different preliminary treatments . Separate treatments with cellulose, hydrochloric acid (HCl 5%), sulfuric acid (H₂SO₄ 2%), and liquid ammonia NH₄OH (20%) were used to convert cellulose and hemicellulose into monosaccharaides. The production of ethanol was observed during the fermentation process using R. minuta under anaerobic conditions.  After 3 days of fermentation, lowest concentrations of ethanol of  0.233, 0.249, 0.261, and 0.275 g/ l were produced from ol

Enzyme activity were studied in the sera of children with leukemia than healthy children, where 31 cases were studied, including 21 cases of patients with acute lymphatic leukemia

The present work is to investigate the feasibility of removal vanadium (V) and nickel (Ni) from Iraqi heavy gas oil using activated bentonite. Different operating parameters such as the degree of bentonite activation, activated bentonite loading, and operating time was investigated on the effect of heavy metal removal efficiency. Experimental results of adsorption test show that Langmuir isotherm predicts well the experimental data and the maximum bentonite uptake of vanadium was 30 mg/g. The bentonite activated with 50 wt% H₂SO₄ shows a (75%) removal for both Ni and V. Results indicated that within approximately 5 hrs, the vanadium removal efficiencies were 33, 45, and 60% at vanadium loadings of 1

Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras

Background: The COVID-19 infection is a more recent pandemic disease all over the world and studying the pulmonary findings on survivors of this disease has lately commenced.

Objective: We aimed to estimate the cumulative percentage of whole radiological resolution after 3 months from recovery and to define the residual chest CT findings and exploring the relevant affecting factors.

Subjects and Methods: Patients who had been previously diagnosed with COVID-19 pneumonia confirmed by RT-PCR test and had radiological evidence of pulmonary involvement by Chest CT during the acute illness were included in the present study. The radiol

     Lately, a growing interest has been emerging in age estimation from face images because of the wide range of potential implementations in law enforcement, security control, and human computer interactions. Nevertheless, in spite of the advances in age estimation, it is still a challenging issue. This is due to the fact that face aging process is not only set by distinct elements, such as genetic factors, but by extrinsic factors, such as lifestyle, expressions, and environment as well. This paper applied machine learning technique to intelligent age estimation from facial images using J48 classifier on FG_NET dataset. The proposed work consists of three phases; the first phase is image preprocessing which include

     In this research, we obtained nanoparticles (NPs) using a type of microwave (cold plasma) and water extract from Origanum vulgare with the addition of silver nitrate (AgNO3) solution. Silver nanoparticles are characterized by high production techniques. Experimental factors such as morphology, structure, optical properties and active compounds of the nanoparticle extract were studied through specific tests such as morphology, UV spectroscopy, FE-SEM, FT-IR, and Atomic Force Microscopy (AFM). Plants were used in manufacturing the nanoparticles, as plants are well known for their accessibility and applicability in different pathological areas. The current research provides information on the Green Union System of N

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.