A new Azo‐Schiff base ligand L was prepared by reaction of m‐hydroxy benzoic acid with (Schiff base B) of 3‐[2‐(1H–indol‐3‐yl)‐ethylimino]‐1.5‐dimethyl‐2‐phenyl‐2,3‐dihydro‐1H‐pyrazol‐4‐ylamine. This synthesized ligand was used for complexation with different metal ions like Ni(II), Co(II), Pd(II) and Pt(IV) by using a molar ratio of ligand: metal as 1:1. Resulted compounds were characterized by NMR (¹H and ¹³C), UV–vis spectroscopy, TGA, FT‐IR, MS, elemental analysis, magnetic moment and molar conductivity studies. The activation thermodynamic parameters, such as ΔE*, ΔH^*, ΔS^*, ΔG^*and

The synthesis of ligands with N2S2 donor sets that include imine, an amide, thioether, thiolate moieties and their metal complexes were achieved. The new Schiff-base ligands; N-(2-((2,4-diphenyl-3-azabicyclo[3.3.1]nonan-9-ylidene)amino)ethyl)-2-((2-mercaptoethyl)thio)-acetamide (H2L1) and N-(2-((2,4-di-p-tolyl-3-azabicyclo[3.3.1]nonan-9-ylidene)amino)ethyl)-2-((2-mercaptoethyl)thio) acetamide (H2L2) were obtained from the reaction of amine precursors with 1,4-dithian-2-one in the presence of triethylamine as a base in the CHCl3 medium. Complexes of the general formula K2[M(Ln)Cl2], (where: M = Mn (II), Co(II) and Ni(II)) and [M(Ln)], (where: M = Cu(II), Zn(II) and Cd(II); n =1-2, expect [Cu(HL2)Cl]) were isolated. The entity of ligands and

In this study, synthesis of polymer Nanocomposites through the blending of prepared polymers with polyvinyl alcohol (a synthetic polymer) or chitosan (a natural polymer) then mixed with nano oxide silica by many steps. The new compound [I] was obtained via reaction of 3,3’-dimethoxybiphenyl-4,4’-diamine as starting material with malic anhydride in DMF then treatment with ammonium persulfate (NH4 )2 S2 O8 (as the initiator) in order to produce polymer [II]. Also, we prepared new polymers [III-V] by using the same starting material (3,3’-dimethoxybiphenyl-4,4’-diamine) with glutaric acid or adipic acid or isophthalic acid in DMF and pyridine. In this study, new polymer blending [VI-IX] and [X-XIII] were synthesized from a prepared pol

In this work, a series of new Nucleoside analogues (D-galactopyranose linked to oxepanebenzimidazole moiety) was synthesized via multisteps synthesis. The first step involved preparation of two benzimidazoles 2-styrylbenzimidazole and 2-(phenyl ethynyl) benzimidazole via reaction of phenylenediamine with cinnamic acid or ?-phenyl propiolic acid. Electrophilic addition of the prepared benzimidazoles by three anhydrides in the second step afforded (4-6) and (14-16) which in turn were treated with 1,2,3,4-di-O-isopropylidene galactopyranose in the third step to afford a series of the desirable protected nucleoside analogues (7-9) ,(17-19)which after hydrolysis in methanolic sodium methoxidein the fourth step afforded the free nucleoside analog

Eighteen new cyclic imides (maleimides) conncted to benzothiazole moiety through sulfonamide group were synthesized via multistep synthesis.The first step involved preparation of two maleamic acids N-phenylmaleamic acid and N-benzylmaleamic acid via reaction of maleic anhydride with aniline or benzyl amine.Dehydration of the prepared amic acids by treatment with acetic anhydride and anhydrous sodium acetate in the second step afforded N-phenylmaleimide and N- benzyl maleimide which in turn were treated with chlorosulfonic acid in the third step to afford 4-(N-maleimidyl) phenyl sulfonyl chloride and 4-(N-maleimidyl) benzyl sulfonyl chloride respectively.In the Fourth step of this work each one of the two prepared maleimidyl sulfonyl chlorid

Background: Prostatic adenocarcinoma is the most widely recognized malignancy in men and the second cause of cancer-related mortality encountered in male patients after lung cancer.

Aim of the study:  To assess the diagnostic value of diffusion weighted imaging (DWI) and its quantitative measurement, apparent diffusion coefficient (ADC), in the identification and localization of prostatic cancer compared with T2 weighted image sequence (T2WI).

Type of the study: a prospective analytic study

Patients and methods: forty-one male patients with suspected prostatic cancer were examined by pelvic MRI at the MRI department of the Oncology Teaching Hospital/Medical City in Baghdad

For modeling a photovoltaic module, it is necessary to calculate the basic parameters which control the current-voltage characteristic curves, that is not provided by the manufacturer. Generally, for mono crystalline silicon module, the shunt resistance is generally high, and it is neglected in this model. In this study, three methods are presented for four parameters model. Explicit simplified method based on an analytical solution, slope method based on manufacturer data, and iterative method based on a numerical resolution. The results obtained for these methods were compared with experimental measured data. The iterative method was more accurate than the other two methods but more complexity. The average deviation of

A field experiment was carried out during two winter season 2013, 2014 at the field of the Department of Field Crops, College of Agriculture, University of Baghdad, to study the effect of seeds soaking with Gibberellic acid and foliar with Abscisic Acid on the growth, yield, and content of Anise oil seeds using factorial experiment within RCBD design with three replicates. The seeds was treated within GA3 were soaked with two concentrations of  30, and 60 mg. litter-1 in addition to without soaking and the code has been B0 , B1 , B2 overlapped these transactions with two concentrations of Abscisic Acid  3, and 6 mg. litter-1 in addition to without foliar A0 , A1 , A2The seeds to be treated  with GA3 are soaked for 24 hours prior

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,