The plants of genus Heliotropium L. (Boraginaceae) are well-known for containing the toxic metabolites called pyrrolizidine alkaloids (PAs) in addition to the other secondary metabolites. Its spread in the Mediterranean area northwards to central and southern Europe, Asia, South Russia, Caucasia, Afghanistan, Iran, Pakistan, and India, Saudi Arabia, Turkey, and over lower Iraq, Western desert. The present study includes the preparation of various extracts from aerial parts of the Iraqi plant. Fractionation, screening the active constituent, and identification by chromatographic techniques were carried out.Heliotropium  europaeum

Liquid-Liquid Extraction of Cu(II) ion in aqueous solution by dicyclohexyl-18-crown-6 as extractant in dichloroethane was studied .The extraction efficiency was investigated by a spectrophometric method. The reagent form a coloured complex which has been a quantitatively extracted at pH 6.3. The method obeys Beer`s law over range from (2.5-22.5) ppm with the correlation coefficient of 0.9989. The molar absorptivity the stoichiometry of extracted complex is found to be 1:2. the proposed method is very sensitive and selective.

Spent hydrodesulfurization (Co-Mo/γ-Al2O3) catalyst generally contains valuable metals like molybdenum (Mo), cobalt (Co), aluminium (Al) on a supporting material, such as γ-Al2O3. In the present study, a two stages alkali/acid leaching process was conducted to study leaching of cobalt, molybdenum and aluminium from Co-Mo/γ-Al2O3 catalyst. The acid leaching of spent catalyst, previously treated by alkali solution to remove molybdenum, yielded a solution rich in cobalt and aluminium.

Forty one isolates of genus Proteus were collected from 140 clinical specimens such as urine, stool, wound, burn, and ear swabs from patients of both sex. These isolates were identified to three Proteus spp. P. mirabilis, P. vulgaris and P. penneri .The ability of these bacteria to produce L-asparaginase II by using semi quantitative and quantitative methods was determined. P. vulgaris Pv.U.92 was distinguished for high level of L-asparaginase II production with specific activity 1.97 U/mg. Optimum conditions for enzyme production were determined; D medium with 0.3% of L-asparagine at pH 7.5 with temperature degree 35°C for incubation. Ultrasonication was used to destroy the P. vulgaris Pv.U.92 cells then ASNase II was extracted and pu

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

This work reports the development of an analytical method for the simultaneous analysis of three fluoroquinolones; ciprofloxacin (CIP), norfloxacin (NOR) and ofloxacin (OFL) in soil matrix. The proposed method was performed by using microwave-assisted extraction (MAE), solid-phase extraction (SPE) for samples purification, and finally the pre-concentrated samples were analyzed by HPLC detector. In this study, various organic solvents were tested to extract the test compounds, and the extraction performance was evaluated by testing various parameters including extraction solvent, solvent volume, extraction time, temperature and number of the extraction cycles. The current method showed a good linearity over the concentration ranging from

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The development of economic and environmentally friendly extractants to recover cobalt metal is required due to the increasing demand for this metal. In this study, solvent extraction of Co(II) from aqueous solution using a mixture of N,N0-carbonyl difatty amides (CDFAs) synthesised from palm oil as the extractant was carried out. The effects of various parameters such as acid, contact time, extractant concentration, metal ion concentration and stripping agent and the separation of Co(II) from other metal ions such as Fe(II), Ni(II), Zn(III) and Cd(II) were investigated. It was found that the extraction of Co(II) into the organic phase involved the formation of 1:1 complexes. Co(II) was successfully separated from commonly associated metal

The vegetable cover plays an important role in the environment and Earth resource sciences. In south Iraq, the region is classified as arid or semiarid area due to the low precipitations and high temperature among the year. In this paper, the Landat-8 satellite imagery will be used to study and estimate the vegetable area in south Iraq. For this purpose many vegetation indices will be examined to estimate and extract the area of vegetation contain in and image. Also, the weathering parameters must be investigated to find the relationship between these parameters and the arability of vegetation cover crowing in the specific area. The remote sensing packages and Matlab written subroutines may be use to evaluate the results.