This experiment was conducted to evaluate the effect of foliar spraying with gibberellic acid concentrations (0,50,100)mg.L­¹ and proline acid concentrations (0, 25, 50)mg.L­¹ and their interactions on some growth parameters of pea plant using clay pots in the botanical garden of Biology Department College of Education for pure science Ibn –Al-Haitham ,Baghdad University, for the growing season 2012-2013 the experiment involved the studing of some growth parameters as plant?s height, dry weight, wt. of pods.plant­¹, biology yield and the concentration of some major elements (nitrogen, phosphorus and calcium) in plant?s seeds. The experiment was designed according to Completely Randomized Desig(CRD) with three replications. R

A factorial experiment was conducted at the College of Education for Pure Sciences Ibn al Haitham/University of Baghdad for the 2023 growing season to study the effect of the biofertilizer represented by the mycorrhizae vaccine and the NPK Nano fertilizer on some indicators of vegetative and root growth, yield, and volatile oil production of the basil plant, Ocimum basilicum L., the experiment included two factors. The rst factor was mycorrhizae inoculum at 2 levels: 0 (no addition) and an addition of 25 g plant–1, as the seeds were inoculated upon planting. The second factor was spraying the plant’s shoots with NPK Nano fertilizer at four levels (0, 0.5, 1, 1.5) g l–1. The results showed a signicant eect of the treatment of inocula

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

The study aimed to prepare a nanocapsules formulation from the acetonic extract of Moringa oleifera leaves, using polymeric capsules, and test its toxicity against the third instar larvae of Culex pipiens mosquitoes. The leaf extract was prepared using acetone as a solvent, and the nano polymeric capsules were prepared using the synthetic polymer polyethylene glycol 4000. The results showed the successful preparation of nano polymeric capsules from the leaf extract, with an average particle size of 259.2 nm, and a nanocapsule diameter of 263.83 nm, as determined by DLS and SEM analysis, respectively. The toxicity results indicated that the nano polymeric capsules of the leaf extract exhibited higher mortality rates, reaching 97.6% a

The extraction process of chlorophyll from dehydrated and pulverized alfalfa plant were studied by percolation method. Two  solvent systems were used for the extraction namely; Ethanol-water and Hexane-Toluene systems . The effect of circulation rate, solvent concentration, and solvent volume to solid weight ratio were studied. In  both ethanol water, and Hexane-Toluene systems it appears that solvent concentration is the most effective variable.

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Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated usin

Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl^-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their perfor