Seven fish species were collected from the drainage network at Al-Madaen region, south of
Baghdad with the aid of a cast net during the period from March to August 1993. These fishes
were infected with 22 parasite species (seven sporozoans, three ciliated protozoans, seven
monogeneans, two nematodes, one acanthocephalan and two crustaceans) and one fungus
species. Among such parasites, Chloromyxum wardi and Cystidicola sp. are reported here for
the first time in Iraq. In addition, 11 new host records are added to the list of parasites of
fishes of Iraq.

This study included the Zakhikhah area in the Al- Anbar desert, which it bounded on the north, east, and west by the Euphrates River and on the south by the Ramadi-Qaim road. Several exploratory field trips were taken to the study area. During this time, a semi-detailed area survey was carried out based on satellite imagery captured by American Land sat-7, topographic maps, and natural vegetation variance. All necessary field tools, including a digital camera and GPS device, were brought to determine the soil type and collect plant samples. All of these visits are planned to cover the entire state of Zakhikhah. All vegetation cover observations, identifying sampling sites and attempting to inventory and collect medicinal plants in t

The Main Outfall drain pumping station in Nassiriyah is an important part in operation  system of the Main Outfall Drain (MOD) where it reduces the water levels in the U/S area of its and converted through Syphon freely under the Euphrates, its consists of several parts: U/S Basin, Station Building which contains 12 pumps, and Head Basin with Syphon, This station suffers from high levels especially in D/S area due to the current situation which is represented by establishing of dyke with pipes on MOD which located at 24 km from D/S of pumping station and Al-Khamisiyah Canal which located at 2.3 km from the U/S of dyke which feeds(Al Hammar Marsh) during drought season, several scenarios were adopted in order to expl

In this work, monitoring of monthly variation (from May 2016 to October 2016) in the concentration of the metals (Co, Zn, Cd, Pb, Ni and Fe) from Al-Diwaniya city of Iraq. Investigation about the pollution with these metals was achieved from five selected sites locate in study area by flame atomic absorption spectroscopy. The results showed a wide variation in the levels of heavy metals from site to site and from month to month. A total of 180 surface soil samples were analyzed to detecting the pollution with selected samples. The resultsshowed that the highest concentration with Ni was 6.290 mg kg-1 while the lowest concentration detected with Ni was 0.080 mg kg-1. The results of pollution index (enrichment factor, contamination factor, po

Besides the role of state institutions that come in the forefront of their priorities and obligations to provide security and development of the economy and reduce the unemployment rate and the reduction of inflation and improving education, health and others, the Community Partnership At frames what has become known as the institutions of society civil-with the state does not eliminate the role, but rather complements its role ; it is the role of civil society partner and an extension of the role of the state in the face of challenges and crises, but it may be sometimes a race role in addressing social, political and economic issues of the role played by the state, not complementary.
The highlighting the specificity of the relationsh

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with