New complexes have been prepared from the new ligand [N1,N5-bis(3-hydroxyphenyl)-2- oxopentanediamide] derived from 2-Oxoglutaric acid and 3-aminophenol. Accordingly its binudear Mn(II),Co(II),Ni(II),Pd(II) and VO(II) complexes were prepared.. These compounds have been characterized by FT-IR, UV-Vis,Mass, 1H-NMR spectra, TGA curve, Chloride containing ,Molar conductance and atomic absorption. The characterization results gave binuclear complexes and pentadentate coordination and tetrahedral geometry for each Cobalt, Nickel, Manganese and Copper complexes otherwise Palladium complex gave a square planar geometry and Vanadium complex gave a square pyramidal geometry and the ligand is tetradentate. The biological activities for the new compoun

In this work, thiadiazole derivatives were prepared by taking advantage of active sites in (2-amino-5-mercapto-1, 3, 4-thiadiazole) as a starting material base. The main heterocyclic compounds (1, 3, 4-thiadiazole, oxazole) etc, 2-amino-5-mercapto-1,3,4-thiadiazole compound (1) was prepared by cyclic closure of thiosemicarbazide compound with anhydrous sodium carbonate and carbon disulfide. Oxidation of (1) via hydrogen peroxide, to have (2) which was treated with chloro acetyl chloride to get (3). Preparation of thiazole ring (4) was from reacting of (3) with thiourea. Synthesis of diazonium salts (5) from compound (4) using sodium nitrite and HCl. Compound (5) reacted with different ester compounds to prepare a new azo compounds (6–8).C

Objectives: This research aims to study the artificial intelligence (AI) skills re-quired by employees in information institutions, specifically university libraries in Iraq, to enhance their services and align with modern technological advancements. It highlights the gap between the current knowledge of employees in Al technologies and their practical applications to improve the services of information institutions. Methodology: The research adopted a descriptive survey method, targeting em- ployees in three prestigious university libraries in Baghdad: the Central Library of the University of Baghdad, the Central Library and House of Books of Al-Mustansiriyah University, and the Central Library of the Iraqi University. A sample of (160)

It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a