The effects of the permeation cement grout with fly ash on the sandy soil skeleton were studied in the present work in two phase; first phase the shear strength parameters, and the second phase effect of these grouted materials on volume grouted zone by injection (51) cm³ of slurry in sandy soil placed in steel cylinder model with dimension 15 cm in diameter and 30 cm in height. The soil sample was obtained from Karbala city and it is classified as poorly graded sand (SP) according to USCS. The soil samples were improved by cement grout with three percentages weight of water cement ratio (w:c); (0.1w:0.9c, 0.8w:0.2c, and 0.7w:0.3c), while the soil samples were dehydrated for one day

In this paper solar radiation was studied over a region of Baghdad (Latitude 33.3o and longitude 44.4o). The two parts of global solar radiation: diffuse and direct solar radiation were estimated depending on the clearance index of measured data (Average Monthly mean global solar radiation). Metrological data of measured (average monthly mean diffuse and direct solar radiation) were used to comparison the results and show the agreement between them. Results are determined by applying Liu and Jordan two models (1960). Excel 2007program is used in calculation, graphics and comparison the results.

When dye is present in wastewater, it is considered a hazardous organic pollutant and must be eliminated. The goal of the current study was to evaluate the elimination of Malachite green (MG) and Methyl violet (MV) dyes using Ni foam (NiF) as an anode, along with stainless steel mesh electrodes as cathodes, and alum sludge (AS) as a third particle electrode in a three-dimensional electrocoagulation-flotation system (3DECF). With an electrolysis period of 30 minutes and pH = 7, response surface method was used to estimate the optimum conditions of studied parameters. These parameters were current density within the range of 1–5 mA/cm², concentration of NaCl within the range of 0.4 –1 g/L, and air flow rate within a range of 1–5 L/min.

The present study aimed to determine the genetic divergence of seven maize genotypes (Al-Maha, Sumer, Al-Fajr, Baghdad, 5018, 4 × 1 single hybrid, and 4 × 2 single hybrid) under two varied levels of nitrogen fertilization (92 and 276 kg N ha-1). The experiment occurred in 2022 in a randomized complete block design (RCBD) with a split-plot arrangement and three replications at the College of Agricultural Engineering Sciences, University of Baghdad, Iraq. The nitrogen fertilization levels served as main plots, with the maize genotypes allocated as the subplots. The results revealed that genetic variance was higher than the environmental variance for most traits, and the coefficient of phenotypic variation was close to the genetic va

 Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

In order to understand the effect of the number of piles (N), the history response of dynamic pile load in piled raft system and deflection time history of piled raft under repeated impact load applied on the center of piled raft resting on loose sand, laboratory model tests were conducted on small-scale models. The results of experimental work are found to be dynamic load increase with increase height of drop, the measured repeated dynamic load time history on the center of piled raft was close approximately to three a half sine wave shape with small duration in about (0.015 Sec). The maximum peak of impact loads occurs in pile and deflection time history occur after at the time of the peak repeated impact loads, dynamic pile load

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par