This study was aimed to evaluate the effect of spraying nano chitosan loaded with NPK fertilizer and nettle leaf and green tea extracts on the growth and productivity of potato for the spring and fall seasons of 2021.It was conducted at private farm in Wasit Governorate, Iraq, as a factorial experiment (5 × 5) within randomized complete block design using three replicates. The first factor included spraying with four concentrations of chitosan nanoparticles loaded with NPK fertilizer 0, 10. 15 and 20% in addition to chemical fertilization treatment, the second factor was spraying nettle leaf extract 25 and 35 gL-1 and green tea extract with 2 and 4 g.L-1, in addition to the control treatment, spraying with distilled water only. The

Background: Despite the importance of vaccines in preventing COVID-19, the willingness to receive COVID-19 vaccines is lower among RA patients than in the general population. Objective: To determine the extent of COVID-19 knowledge among RA patients and their attitudes and perceptions of COVID-19 vaccines. Methods: A qualitative study with a phenomenology approach was performed through face-to-face, individual-based, semi-structured interviews in the Baghdad Teaching Hospital, Baghdad, Iraq, rheumatology unit. A convenient sample of RA patients using disease-modifying anti-rheumatic drugs was included until the point of saturation. A thematic content analysis approach was used to analyze the obtained data. Results: Twenty-five RA pa

   In this paper, we investigate the basic characteristics of "magnetron sputtering plasma" using the target V₂O₅. The "magnetron sputtering plasma" is produced using "radio frequency (RF)" power supply and Argon gas. The intensity of the light emission from atoms and radicals in the plasma measured by using "optical emission spectrophotometer", and the appeared peaks in all patterns match the standard lines from NIST database and employed are to estimate the plasma parameters, of computes electron temperature and the electrons density. The characteristics of V₂O₅ sputtering plasma at multiple discharge provisos are studied at the "radio frequency" (RF) power ranging from 75 - 150 Wat

This paper presents ABAQUS simulations of fully encased composite columns, aiming to examine the behavior of a composite column system under different load conditions, namely concentric, eccentric with 25 mm eccentricity, and flexural loading. The numerical results are validated with the experimental results obtained for columns subjected to static loads. A new loading condition with a 50 mm eccentricity is simulated to obtain additional data points for constructing the interaction diagram of load-moment curves, in an attempt to investigate the load-moment behavior for a reference column with a steel I-section and a column with a GFRP I-section. The result comparison shows that the experimental data align closely with the simulation

Titanium dioxide TiO2 has been widely utilized in cleaning and sterilizing material for many clinical tools sanitary ware, food tableware and cooking and items for use in hospitals. Titanium dioxide TiO2 non toxicity and long term physical and chemical stability. It has been widely used decomposition of organic compounds and microbial organisms such as cancer cell, viruses and bacteria as well as its potential application in sterilization of medical devices. The aim of the study the effect of titanium dioxide TiO2 on some Gram negative bacteria and study their effects on some virulence factors and chromosomal DNA.In this study, we obtained (E. coli ? Proteus mirabilis, Proteus vulgaris ? Pseudomonas aeruginosa ? Klebsiella pneumonia and Ac

    This paper deals with finding the approximation solution of a nonlinear parabolic boundary value problem (NLPBVP) by using the Galekin finite element method (GFEM) in space and Crank Nicolson (CN) scheme in time, the problem then reduce to solve a Galerkin nonlinear algebraic system(GNLAS). The predictor and the corrector technique (PCT) is applied here to solve the GNLAS, by transforms it to a Galerkin linear algebraic system (GLAS). This GLAS is solved once using the Cholesky method (CHM) as it appear in the matlab package and once again using the Cholesky reduction order technique (CHROT) which we employ it here to save a massive time. The results, for CHROT are given by tables and figures and show

This research deals with the study of top soil electrical conductive regions located within Baghdad City. The research included measuring the dissolved soil material extraction Electrical Conductivity (EC) with an aqueous solution for the top (0-30 cm) soil layer of the study area. As the electrical conductivity values increase by increasing the amount of dissolved salts in principle, we can consider that the aim of this research is to predict the amount and distribution of (soil contamination with salts) which is represented by the (Salt Index), this factor calculated for each soil representative sample taken from the region with a depth of (30 cm). Laboratory (EC) test values measured by the use of solutions (EC) digital meter for the ex

Reliability analysis methods are used to evaluate the safety of reinforced concrete structures by evaluating the limit state function 𝑔(𝑋𝑖). For implicit limit state function and nonlinear analysis , an advanced reliability analysis methods are needed. Monte Carlo simulation (MCS) can be used in this case however, as the number of input variables increases, the time required for MCS also increases, making it a time consuming method especially for complex problems with implicit performance functions. In such cases, MCS-based FORM (First Order Reliability Method) and Artificial Neural Network-based FORM (ANN FORM) have been proposed as alternatives. However, it is important to note that both MCS-FORM and ANN-FORM can also be time-con

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an