This study was carried out to investigate the effects of magnetized water on accumulated infiltration depth. A test rig was designed and constructed for this purpose was installed at the water tests laboratory of the Department of Water Resources Engineering at the University of  aghdad. The investigation was carried out by using two types of soil, different flow velocities throughout magnetizing device and different configuration of magnets over and under the water passage of the magnetizing device. The soils that were used in the experiments are clayey and sandy soils.  Six different flow velocities throughout magnetizing device ranged between 0.29 to 1.19 cm/s and ten configurations of arranging the magnets over and under th

The Indoor Environmental Quality (IEQ) describes an indoor space condition that the wellbeing and comfortability are provided for the users. Many researchers have highlighted the importance of adopting IEQ criteria, although they are not yet well defined in the Kurdistan region. However, environmental quality is not necessary for the contemporary buildings of the Kurdistan Region, and there is no measurement tool in the Region. This research aims to develop an IEQ assessment tool for the Kurdistan region using Mixed method methodology, both qualitative and quantitative. Therefore, a Delphi Technique was used as a method initially developed as systematic, interactive forecasting on a panel of experts. Thirty-five Delphi C

The gamma dose rates and specific activity of 137Cs, 60Co and 40K in
samples of soil taken from places near the landfill radiation at Al-
Tuwaitha site were measured using a portable NaI(Tl) detector. The
results of gamma dose rates in samples were ranged from 52.6
nGy.h-1 to 131nGy.h-1. Then the specific activity of 137Cs, 60Co and
40K in soil were determined using high pure germanium (HPGe)
detector. The specific activities were varied from 1.9 to 115500 Bq.
kg-1 for 137Cs, from 6.37 to 616.5 Bq. kg-1 for 60Co, and from 3 to
839.5 Bq. kg-1 for 40K. The corresponding health risk for the annual
effective dose equivalent varied from 1.85×10-14 to 15.7mSv/y. The
results were compared with various internationa

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

Abstract

Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization al

This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

A study on the treatment and reuse of oily wastewater generated from the process of fuel oil treatment of gas turbine power plant was performed. The feasibility of using hollow fiber ultrafiltration (UF) membrane and reverse osmosis (RO) membrane type polyamide thin-film composite in a pilot plant was investigated. Three different variables: pressure (0.5, 1, 1.5 and 2 bars), oil content (10, 20, 30 and 40 ppm), and temperature (15, 20, 30 and 40 ᵒC) were employed in the UF process while TDS was kept constant at 150 ppm. Four different variables: pressure (5, 6, 7 and 8 bar), oil content (2.5, 5, 7.5 and 10 ppm), total dissolved solids (TDS) (100, 200,300 and 400 ppm), and temperature (15, 20, 30 and 40 ᵒC) were mani

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In