The shortage in surface water quantities led to a shift in dependence on the groundwater as an alternative water source in southern parts of Iraq. The groundwater is decreasing in quantity and water quality is degrading due to different factors. Therefore, it is important to assess the groundwater quality of the Missan Governorate of the country by analyzing the physicochemical parameters and distinguishing the probable sources of contaminants in the area. The present study used water quality diagrams and statistical methods such as factor analysis and agglomerative cluster analysis to determine the sources of chemical ions in the forty-four groundwater samples collected from wells in the study area. In addition, the Water Quality Index (WQ

Citrus diseases are the main issue for agricultural productivity, which cause a reduction in yield and quality, thus causing economic loss; also, we see that traditional methods of diagnosis, which are very subjective and prone to error, and thus we need to suggest accurate automated detection systems. In this study, we present a hybrid model that integrates conventional machine learning with deep learning models. We used the Gray Level Co-occurrence Matrix (GLCM) for feature extraction, which is classified via machine learning algorithms like k-Nearest Neighbors (KNN), Decision Tree (DT), Linear Discriminant Analysis (LDA), and Random Forest (RF). At the same time, deep convolutional neural networks (DCNNs), GoogLeNet, and ResNet18

This study was conducted at the College of Education for Pure Sciences (Ibn Al-Haitham), University of Baghdad. The aim of this study was to isolate and diagnose fungi from fish feedstuff samples, and also detection of aflatoxin B1 and ochratoxin A in fish muscles and feedstuffs. Randomly, the samples were collected from some fish farms from Baghdad, Babil, Wasit, Anbar, and Salah al-Din provinces. This study included the collection of 35 feedstuff samples and 70 fish muscle samples, and each of the two fish samples fed on one sample of the feedstuff. The results showed the presence of several genera of different fungi including Aspergillus spp, Mucor spp., Penicillium spp., Yeast spp., Fusarium spp., Rhizopus spp., Scopiolariopsis spp., Ep

The high bounce activity according to the fosbery way is regarded as of the difficult sports concerning its way of training and perfection due to hard technique of its performance on one hand and because it depends on the player’s ability to overcome body weight resistance against the gravity. In addition to the strong ability to control the body posture when leaving the land and flying over the barrier. This activity needs to high plosion power at the moment of bouncing and this plosion depends on the period of bouncing, so the two researchers aimed to use a mechanical bouncing platform and an electronic one through several training by one foot and both feet in different directions and positions in order to reduce the time of bouncing an

Wheat straw was modified with malonic acid in order to get low cost adsorbent have a good ability to remove copper and ferric ions from aqueous solutions, chemical modification temperature was 120°C and the time was 12 h. Parameters that affect the adsorption experiments were studied and found the optimum pH were 6 and 5 for copper and iron respectively and the time interval was 120 min and the adsorbent mass was 0.1 g. The values for adsorption isotherms parameters were determined according to Langmuir [qmax were 54.64 and 61.7 mg/g while b values were 0.234 and 0.22 mg/l] , Freundlich [Kf were 16.07 and 18.89 mg/g and n were 2.77 and 3.16], Temkin [B were 0.063 and 0.074 j/mol and At were 0.143 and 1.658 l/g] and for Dubinin-Radushkev

Abstract
This study was conducted by using soil map of LD7 project to interpret the
distribution and shapes of map units by using the index of compaction as an
index of map unit shape explanation. Where there were wide and varied
ranges of compaction index of map units, where the maximum value was
0.892 for MF9 map unit and the lower value was 0.010 for same map unit.
MF9 has wide range appearance of index of compaction after those indices
were statistically analyzed by using cluster analysis to group the similar
ranges together to ease using their values, so the unit MF9 was considered as
key map unit that appears in the soils of LD7 project which may be used to
expect another map units existence in area of