The main objective of the present work is to find a method increases the efficiency of the airfoil that is used for blade in wind turbine, wing in aircraft, propeller and helicopter (like NACA 4412). By overcoming the separation of flow at high angle of attacks, a slotted airfoil had been used and solved numerically through connecting the pressure side in the bottom surface with the suction side in the top surface of the airfoil to energize the separated flow. Slot exit, width and slope were considered as a parameters of slot configuration to determine the effective design of consideration. Reynolds number was taken as [1.6 x106 ] and the angle of attacks were ranged from (0o - 20o ). The numerical solution with Ansys Fluent commercial program had been used to solve a fully turbulent N.S. equations with κ-ω SST turbulence model. The method of changing variables with best one among them was adopted to find the design of the slot. The results of flow field and airfoil characteristics for solid and slotted airfoil were described and illustrated in the present work which showed that the airfoil produces higher lift coefficient value and lower drag coefficient as compared with solid airfoil. Moreover, it delays stalling angle of attack to 19o . The slotted airfoil shows an increasing in maximum lift to drag ratio up to 110% at angle of attack 18o . The most effective slot is found at 60% chord, slope 65o and width 1% chord.
This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,
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This encapsulates the general relationship between plant and bacteria in the natural and agricultural ecosystem. It is based on the activities of useful bacteria, such as plant growth-promoting bacteria (PGPRs) and nitrogen-fixing bacteria, in promoting plant growth and plant tolerance to stressful situations regarding pollution, salinity, and drought. The article also mentions that the bacteria maintain plant health by secretion of phytohormones, nitrogen fixation, solubilization of phosphate, and production of antibiotics against pathogenic bacteria. The article also mentions the existing applications of the interaction in sustainable agriculture and bioremediation of contaminated soils.
Abstract
In this paper, fatigue damage accumulation were studied using many methods i.e.Corton-Dalon (CD),Corton-Dalon-Marsh(CDM), new non-linear model and experimental method. The prediction of fatigue lifetimes based on the two classical methods, Corton-Dalon (CD)andCorton-Dalon-Marsh (CDM), are uneconomic and non-conservative respectively. However satisfactory predictions were obtained by applying the proposed non-linear model (present model) for medium carbon steel compared with experimental work. Many shortcomings of the two classical methods are related to their inability to take into account the surface treatment effect as shot peening. It is clear that the new model shows that a much better and cons
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Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B
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