The avoidance strategy of prey to predation and the predation strategy for predators are important topics in evolutionary biology. Both prey and predators adjust their behaviors in order to obtain the maximal benefits and to raise their biomass for each. Therefore, this paper is aimed at studying the impact of prey’s fear and group defense against predation on the dynamics of the food-web model. Consequently, in this paper, a mathematical model that describes a tritrophic Leslie-Gower food-web system is formulated. Sokol-Howell type of function response is adapted to describe the predation process due to the prey’s group defensive capability. The effects of fear due to the predation process are considered in the first two levels. It is assumed that the generalist predator grows logistically using the Leslie-Gower type of growth function. All the solution properties of the model are studied. Local dynamics behaviors are investigated. The basin of attraction for each equilibrium is determined using the Lyapunov function. The conditions of persistence of the model are specified. The study of local bifurcation in the model is done. Numerical simulations are implemented to show the obtained results. It is watched that the system is wealthy in its dynamics including chaos. The fear factor works as a stabilizing factor in the system up to a specific level; otherwise, it leads to the extinction of the predator. However, increasing the prey’s group defense leads to extinction in predator species.
BACKGROUND: Diffuse astrocytomas constitute the largest group of primary malignant human intracranial tumours. They are classified by the World Health Organization (WHO) into three histological malignancy grades: diffuse astrocytomas (grade II), anaplastic astrocytomas (grade III) and glioblastoma (grade IV) based on histopathological features such as cellular atypia, mitotic activity, necrosis and microvascular proliferation. Epidermal growth factor receptor (EGFR) is a 170-kDa transmembrane tyrosine kinase receptor expressed in a variety of normal and malignant cells regulating critical cellular processes. When activated, epidermal growth factor receptor (EGFR) triggers several signalling cascades leading to increased proliferatio
... Show MoreIn this research, cyclic compounds derived from 2- furfural mercaptan (oxazole, triazoles) were synthesized, and their biological efficacy was measured and compared with standard drugs. Also, their effectiveness as anti-oxidant was measured and compared with ascorbic acid as a standard substance. Some of the synthesized compounds were deduced with good efficacy. © 2021 Sami Publishing Company. All rights reserved
This paper deals with calculate stresses in Knee-Ankle-Foot-Orthosis as a result of the effect vibration during gait cycle for patient wearing KAFO .Experimental part included measurement interface pressure between KAFO and leg due to action muscles and body weigh on Orthosis. also measurement acceleration result from motion of defected leg by accelerometer .Results of Experimental part used input in theoretical part so as to calculate stresses result from applying pressure and acceleration on KAFO by engineering analysis program ANSYS 14.Resultes show stresses values in upper KAFO greater than lower KAFO that is back to muscles more effective in thigh part lead to recoding pressure higher than pressure in shank part.
Introduction: The current study investigated the use of acid-treated rice husks to remove heavy metals and organic pollutants from water containing heavy metals (R2C and Cd2) and organic pollutants (phenol and atrazine). Methods: The adsorption effect of acid-treated rice husks was compared with other adsorbents such as activated carbon, chitosan, and bentonite clay. Result: both acid-treated rice husks and activated carbon were highly efficient materials, and thus, rice husks were established as a cost-effective alternative. It was revealed that acid treatment of rice husks enhanced adsorption capacity by half, and lead removal was nearly doubled. The most effective pH value for optimizing organic pollutants and heavy metals while
... Show MoreBig data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a
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