The purpose of this paper is to build a simulation model by using HEC-RAS software to simulate the reality of water movement in the main river of Basra City (South of Iraq) which is known as Siraji-Khoura River. The main objective of the simulation is to detect areas where the water cycle is interrupted in some stations of the river stream, as this river has become an outlet for the disposal of sewage, leading to pollution and causing weakness in some sections of the river & obstructing the water cycle that takes place between this river and Shatt al – Arab river. A field survey data of the river and its banks were adopted to derive the grades, longitudinal and cross sections of the river, these data included three-dimensional coordinates observed by precise GPS device. Depending on the river's derived sections and elevation of water, a one dimensional unsteady flow model was constructed by HEC-RAS software to simulate the behavior of water flow during the tide periods. The results of simulation illustrated the weak areas that cause obstruction in the water flow within the river stream. It also determines the solid waste accumulation areas and stations where bed level rises from the water levels, causing the water cycle to break down in some parts of the river. Thus, an integrated database for the rivers of the study area was obtained, in order to the correct design decisions that are in the interest of these rivers.
After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings
... Show MoreMachine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
... Show MoreThis paper deals with constructing a model of fuzzy linear programming with application on fuels product of Dura- refinery , which consist of seven products that have direct effect ondaily consumption . After Building the model which consist of objective function represents the selling prices ofthe products and fuzzy productions constraints and fuzzy demand constraints addition to production requirements constraints , we used program of ( WIN QSB ) to find the optimal solution
In unpredicted industrial environment, being able to adapt quickly and effectively to the changing is key in gaining a competitive advantage in the global market. Agile manufacturing evolves new ways of running factories to react quickly and effectively to changing markets, driven by customized requirement. Agility in manufacturing can be successfully achieved via integration of information system, people, technologies, and business processes. This article presents the conceptual model of agility in three dimensions named: driving factor, enabling technologies and evaluation of agility in manufacturing system. The conceptual model was developed based on a review of the literature. Then, the paper demonstrates the agility
... Show MoreIn this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho
... Show MoreIn this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending
The present work included study of the effects of weather conditions such as solar radiation and ambient temperature on solar panels (monocrystalline 30 Watts) via proposed mathematical model, MATLAB_Simulation was used by scripts file to create a special code to solve the mathematical model , The latter is single –diode model (Five parameter) ,Where the effect of ambient temperature and solar radiation on the output of the solar panel was studied, the Newton Raphson method was used to find the output current of the solar panel and plot P-V ,I-V curves, the performance of the PV was determined at Standard Test Condition (STC) (1000W/m2)and a comparison between theoretical and experimental results were done .The best efficiency
... Show MoreColorectal cancer is a malignant condition that can arise from multiple causative factors. It ranks second, behind lung cancer, as a leading cause of cancer-related deaths worldwide. Extensive research has been conducted to unravel the genetic underpinnings and molecular mechanisms underlying the development of colorectal cancer (CRC). However, epigenetic modifications of histones at the DNA level have become significantly involved in several malignant diseases such as CRC. Hence, this research sought to assess, for the first time locally, the immunoexpression of HDAC-1 and 3 in a group of colorectal patients. Additionally, we explored potential correlations between the expression of HDAC-1, 3 and VEGF. This retrospective study enco
... Show MoreBackground: Saliva is one of the most important etiological host factors in relation to dental caries. It affects the carious process by its organic and inorganic constituents; in addition to its physiological functions as (flow rate, pH and buffer capacity). The aims of this study were to determine the concentrations of major elements (calcium and phosphorus) and trace elements (ferrous iron, nickel, chromium and aluminum) in saliva among a group of adolescent girls, and to explore the relation of these elements, flow rate and pH with dental caries. Material & Methods: The study group consisted of 25 girls with an age of 13-15 years old. Dental caries was diagnosed by both clinical and radiographical examinations following the criteria of
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