Jatropha L. is an exotic genus to Iraq and it has been cultivated in gardens for ornamental purposes because of the shiny red color of the flowers. The plant adapted to environmental conditions and succeeded in growing and blooming, which is why the species was interested to study. The species Jatropha integerrima Jacq. was examined and diagnosed for the first time in Iraq. Morphological and anatomical characteristics for leaves (considering that the variations are the most reliable and taxonomically important) were provided. The Phytochemical screening showed the presence of alkaloids, flavonoids, terpenes, tannins and saponins. The qualitative analysis by TLC indicated the presence of alkaloids and flavonoid that was detected by specia

This research is studying technique sculptures super - realism, search through, how the method of work, and the search for the materials used in their manufacture, and this is the first study in the field of art and the field of academic study in the country.Research consists of an introduction, And four sections, The introduction containing information on: research problem, Importance of research, Goals of the research, Limits of research, research approach, and research tools.The first section contains a technical study sculptures super -realism in contemporary sculpture, while the second section includes a search for alternative materials available in the local markets, for making sculptures super - realism, the third section dedicate

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

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

A selective and sensitive spectrophotometric extraction method was established and used to estimate antihypertensive drug, losartan potassium. The method is based on the formation of blue ion pair of the anionic drug, losartan, and the cationic dye, methylene blue, at adjusted pH 6.5 in aqueous solutions, followed by quantitative extraction to dichloromethane;.The observed maximum absorbance was at Ï€ 654.9 nm. With 4.53321 x 10⁵ M^-1 cm^-1 molar absorptivity, Beer's law was obeyed within a concentration range of 0.03-1.5 μg / ml. The limit of detection and the limit of quantification were 0.01μg / ml and 0.03μg / ml, respectively. The method's precision was estimated by a relative standard devi

Seven leafhoppers (Cicadeilidae). and one plantboppei (Delpbacidae), Homoptera were identified from a one year operated light trap at the College of Agiculture farm in Abu¬Ghraib. The leafhoppers were: Balclutha hortensis Lind.; B. rufaofasciata Merine.; psammctettix alien us Dahlbem.; P. striatus L.; Extianus capicola.; Neoaliturus haematoceps H. R.; and Orozius albicnctus Dist. The planthopper was Sogatella vibix Haupt. one year records of their populations, indicated that B. rufofasciata occured during the fall from October 10 until December 18; E. capicola from October 24 until November 21 and again in the summer from March to October. The others occured only during the summer, from the end of March and early April until Mid-Septemb

One hundred twelve urine samples were collected from Baghdad hospitals and examined by different identification techniques. Seventy isolates (62.5%) were diagnosed as Escherichia coli after microscopic and cultural identifications. The result of PCR product electrophoresis on the isolates showed that thirteen isolates (18.57%) have Pap E gene which are uropathogenic E. coli. Antibiotic susceptibility test was done, and four high resistant strains were mixed with aqueous extract of Quercus infectoria plant in 96 well ELISA plate and incubated for different times. After 0, 6, and 12 hr. of incubation, the effect of the plant extract on the bacterial growth was determined by ELISA reader, and the effect on the expression of P

The aims of this study are to measure the defect rate and analyze the problems of production of ready concrete mixture plant by using Six Sigma methodology which is a business strategy for operations improvement depending basically on the application of its sub-methodology DMAIC improvement cycle and the basic statistical tools where the process sigma level of concrete production in the case study was 2.41 σ.

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has