Kiffil Shinafiya project was selected as it’s been conducted and covering the areas of grain crops agriculture in Najaf and Qadissiya provinces. Lands of this project are considered as a part of the Mesopotamian plain located in between N32⁰ 15′, N31⁰ 30′ and E44⁰ 44’, E44⁰ 20′. Three hydrosequences were disclosed with three transects (T1, T2, T3) in the area of study. The first hydrosequence was perpendicular on the other two sequences. Fifteen pedons were outcropped, five pedons in each transect in addition to eighteen more surface samples to be a total samples of 33 locations. Pedons were morphologically described due to soil survey manual (Soil Survey Staff, 2017). Spatial distribution of soil salinity showed that the

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

One of the most severe problems with flexible asphalt pavements is permanent deformation in the form of rutting. Accordingly, the practice of adding fiber elements to asphalt mix to improve performance under dynamic loading has grown significantly in order to prevent rutting distress and ensure a safe and long-lasting road surface. This paper explores the effects of a combination of ceramic fiber (CF), a low-cost, easily available mineral fiber, and thermal insulator fiber reinforced to enhance the Marshall properties and increase the rutting resistance of asphalt mixes at high temperatures. Asphalt mixtures with 0%, 0.75%, 1.5%, and 2.25% CF content were prepared, and Marshall stability and wheel tracking tests were employed to stu

Sheep are considered as an important part of livestock in the worldwide, particularly in Iraq, as they provide meat, milk, leather, wool, and manure. The present study aim is isolation and identification of staphylococci, enteric bacteria and Pseudomonas spp. Totally, 115 samples were collected from sheep (100 samples were collected from the nasal cavity of local sheep suffering from respiratory infections, and 15 samples were collected from apparently healthy local sheep). All the samples were collected from seven flocks located in Abu Ghraib and Al-Radwaniyah, Baghdad governorate, Iraq. The samples were taken during the period from October 2020 to February 2021. Staphylococcus spp., Pseudomonas spp., and enteric bacteria were detected fi