Diyala River is a tributary of Tigris River, it is one of the important rivers in Iraq. It covers a total distance of 445 km (275 miles). 32600 km²is the area that drains by Diyala River between Iraqi-Iranian borders. This research aims to evaluate the water quality index WQI of Diyala River, where three stations were chosen along the river. These stations are D12 at Jalawlaa City at the beginning of Diyala River, the second station is D15 at Baaquba City at the mid distance of the river, and the third station is D17 which is the last station before the confluence of Diyala River with Tigris River at Baghdad city. Bhargava method was used in order to evaluate the water quality index for both irrigation and drink

Water pollution is an issue that can be exacerbated by drought as increased concentrations of unwanted substances are a consequence of lower water levels. Polluted water that flows into natural marshlands leads to the deposition of pollutants in the interior of the marsh. Here we present evidence that the interior of the Central Marsh (CM) in southern Iraq suffers from higher levels of pollution than areas closer to the source of water entering the marsh (the Euphrates River). A 1.7m embankment that halts the flow of the Euphrates is only infrequently breached and so the CM is effectively the terminal destination of the waters (and their associated pollutants and agricultural waste) flowing from the West of Iraq.

A range of water

In this study, the turbulent buoyancy driven fluid flow and heat transfer in a differentially heated rectangular enclosure filled with water is quantified numerically. The two dimensional governing differential equations are discretized using the finite volume method. SIMPLE algorithm is employed to obtain stabilized solution for high Rayleigh numbers by a computational code written in FORTRAN language. A parametric study is undertaken and the effect of Rayleigh numbers (1010 to 1014), the aspect ratio (30, 40 and 50), and the tilt angle (10o to 170o ) on fluid flow and heat transfer are investigated. The results of the adopted model in the present work is compared with previously published results and a qualitative agreement and a good

The steady 3-D raw water turbulent flow is numerically investigated. This flow is formed of solid silica sand (quartz) carried by water in stainless steel pipe. The flow in a straight pipe and flow in a pipe with a sudden contraction are analyzed using a two-way coupled Eulerian-Lagrangian approach. Erosion rate is estimated by Oka erosion model combined with the constant coefficient of restitution. The effect of solid particles mass flow rate, inlet velocity, particle diameter, internal pipe diameter, orientation, contraction coefficient, and wall pipe contraction angle on erosion rate are examined. The predicted erosion is distributed homogenously for straight pipe, while the step wall area of the contraction is the most eroded part. The

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance