In this study, new heterocyclic compounds were synthesized through the cyclization reactions of o-phenylenediamine (1) with various organic reagents. Benzodiazepine derivatives (2-4) were obtained by reaction of (1) with ethylacetoacetate, malonic acid and acetyl acetone.Treatment of compound (1) with chloroacetamide, chloroacetic acid, p-bromophenacyl bromide and oxalic acid dihydrate afforded quinoxaline derivatives (5-8), respectively. Reaction of compound (1) with benzoic acid, piperonal, cyclohexanone and carbon disulfide resulted in the formation of compounds (9-12), respectively. Finally, reaction of compound (12) with chloroacetic acid in the presence of potassium hydroxide produced compound (13).

The magnetic properties of a pure Nickel metal and Nickel-Zinc-Manganese ferrites having the chemical formula Ni_0.1(Zn_0.4Mn_0.6)_0.9Fe₂O₄ were studied. The phase formation and crystal structure was studied by using x-ray diffraction which confirmed the formation of pure single spinel cubic phase with space group (Fd3m) in the ferrite. The samples microstructure was studied with scanning electron microstructure and EDX. The magnetic properties of the ferrite and nickel metal were characterized by using a laboratory setup with a magnetic field in the range from 0-500 G. The ferrite showed perfect soft spinel phase behavior while the nickel sample showed higher magnetic loss an

Thirty six bacteria were isolated from various sourcesc (soil, starch, cooked rice and other foods) and subjected to a series of primary screening tests to obtain the optimal isolation to production of amylase. The volume of producing zone by logal indicator for (Seven) isolates of the secondary screening by measuring the enzymatic activity and specific enzymatic activity. The isolate A4 was found to be the most efficient for production of amylase. Then this isolate was diagnosed through microscopic, vitek 2 system technique. in addition by gentic diagnesis through gene 16s of the genes nitrogen bases by use the polymerase chain reaction (PCR) which reached 1256 bases. In comparison to the available information at the National Center for

Mixed ligand complexes of Cu(II), Ni(II) and Co(II) with metformin(MTF) as primary ligand and cysteine(Cys) as secondary ligand have been prepared and characterized by elemental analysis, atomic absorption, molar conductivity, magnetic susceptibility measurements, FTIR,UV-Vis ,1H-NMR and 13C-NMR spectral studies. The elemental analysis, atomic absorption data reveal the formation of [1:1:1] [M:MTF:Cys] complexes.The electronic spectra and magnetic moment measurements reveal the presence of complexes in an octahedral geometry and the molar conductivity studies of the complexes indicate their non-electrolytic nature. The infrared and NMR spectral were showed that the chelation behaviour of the ligands towards selected transition metal ions

The study  was conducted to evaluate  the anti fungal activity of

water and alcohol ic  extracts (cold and  hot) and the crude alkaloid

extracts  of  leaves, seeds  and  roots  of  Zygophyllum  fabago  plant against a standard isolate of C. albicans and an isolate of A. jlavus which was proved to produce aflatoxins. Investigation of presence of active antimicrobial compounds in this plant parts was carried out, crude al kaloid extract was also separated using TLC technique. The antifungal activity of all these extracts was estimated against the two fungi.  Results showed  variation  in  anti fungal

In this paper, the conditions of persistence of a mathematical model, consists from
a predator interacting with stage structured prey are established. The occurrence of
local bifurcation and Hopf bifurcation are investigated. Finally, in order to confirm
our obtained analytical results, numerical simulations have been done for a
hypothetical set of parameter values .

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,

This research was carried out at University of Baghdad - College of Agricultural Engineering Sciences during the fall season of 2020 and spring season of 2021 in order to evaluate the effect of organic fertilizer and the foliar application of boron on the growth and yield of industrial potatoes (Solanum tuberosum L.). Using factorial experiment (5*4) within Randomized Complete Block Design  with three replicates, the organic fertilizer (palm fronds peat) was applied at four levels (0, 12, 24, and 36 ton ha-1) in addition to the treatment of the recommended of chemical fertilizer. The foliar application of Boron was applied at four concentrations which were 0, 100, 150 and 200 mg (H3Bo3). L-1. The results Revealed a significant incr

This study was aimed to assess the impact of vermicompost, glutathione, and their interaction on beetroot (Beta vulgaris L.) growth, yield, and antioxidant traits. The experiment carried out at vegetable field of the College of Agricultural Engineering Sciences - University of Baghdad during fall season 2019. The experiment was conducted using factorial arrangement within Randomized Complete Block Design with two factors and three replicates (3X3X3). Applying vermicompost before cultivation represented the first factor (0, 15, 30 ton.ha-1), which symbolized (V0, V1, V2). Glutathione (0, 75, 150 mg.L-1) which symbolized (G0, G1, G2) represented the second factor. Results showed the superiority of secondary interaction treatment V2G2

RG Majeed, AS Ahmed, Jornal of Al-Muthanna for Agricultural Sciences, 2023