The aim of this study was extraction of jojoba oil using different solvents. A mixture of waterhexane and water-ethanol are used as solvents to extract jojoba oil in a batch extraction process and compared with a pure solvent extraction process. The effects of particle size of crushed seeds, solvent-to-water ratio and time on jojoba oil extraction were investigated. The best recovery of oil was obtained at the boiling temperature of the solvent and four hour of extraction time. When seed particle size was 0.45 mm and a pure ethanol was used (45% yield of oil extraction), whereas, it was 40% yield of oil at 25% water-hexane mixture. It was revealed that the water-ethanol and water-hexane mixtures have an effect on the oil extraction yield. T

In many industries especially oil companies in Iraq consumed large quantities of water which will produce oil-contaminated water which can cause major pollution in agricultural lands and rivers. The aim of the present work is to enhance the efficiency of dispersed air flotation technique by using highly effective and cost-efficient coagulant to treating gas oil emulsion. The experimental work was carried out using bubble column made of Perspex glass (5cm I.D, 120cm height). A liquid was at depth of 60cm. Different dosage of sawdust +bentonite at ratio 2:1 (0.5+ 0.25; 1+ 0.5 and 2+1) gm and alum at concentration (10,20and30mg/l) at different pH ( 4 and 7) were used to determine optimum dosages of coagulant. Jar test exper

       In this paper, the topic of forecasting the changes in the value of Iraqi crude oil exports for the period from 2019 to 2025, using the Markov transitional series based on the data of the time series for the period from January 2011 to November 2018, is real data obtained from the published data of the Central Agency Of the Iraqi statistics and the Iraqi Ministry of Oil that the results reached indicate stability in the value of crude oil exports according to the data analyzed and listed in the annex to the research.

Keywords: Using Markov chains

This research has been prepared to isolate and diagnose one of the most important vegetable oils from the plant medical clove is the famous with Alaeugenol oil and used in many pharmaceuticals were the isolation process using a technique ultrasonic extraction and distillation technology simple

   AlPO₄ solid acid catalyst was prepared in order to use it in transesterification reaction of edible oil after supporting it with tungsten oxide. The maximum conversion of edible oil was obtained 78.78% at catalyst concentration (5gm.), temperature 70°Ϲ, 30/1 methanol/edible oil molar ratio, and time 5hr. The study of kinetics of the transesterification reaction of edible oil indicates that the reaction has an order of 3/2, while the value of activation energy for  transesterification reaction is 51.367 kJ/mole and frequency factor equal 26219.13(L/ mol.minute).

Ultimate oil recovery and displacement efficiency at the pore-scale are controlled by the rock wettability thus there is a growing interest in the wetting behaviour of reservoir rocks as production from fractured oil-wet or mixed-wet limestone formations have remained a key challenge. Conventional waterflooding methods are inefficient in such formation due to poor spontaneous imbibition of water into the oil-wet rock capillaries. However, altering the wettability to water-wet could yield recovery of significant amounts of additional oil thus this study investigates the influence of nanoparticles on wettability alteration. The efficiency of various formulated zirconium-oxide (ZrO2) based nanofluids at different nanoparticle concentrations (0

In the last years, new non-invasively laser methods were used to detect breast tumors for pre- and postmenopausal females. The methods based on using laser radiation are safer than the other daily used methods for breast tumor detection like X-ray mammography, CT-scanner, and nuclear medicine.  

      One of these new methods is called FDPM (Frequency Domain Photon Migration). It is based on the modulation of laser beam by variable frequency sinusoidal waves. The modulated laser radiations illuminate the breast tissue and received from opposite side.

      In this paper the amplitude and the phase shift of the received signal were calculated according to the orig

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences