This study investigates the influence of fear, refuge, and migration in a predator–prey model, where the interactions between the species follow an asymmetric function response. In contrast to some other findings, we propose that prey develop an anti-predator response in response to a concentration of predators, which in turn increases the fear factor of the predators. The conditions under which all ecologically meaningful equilibrium points exist are discussed in detail. The local and global dynamics of the model are determined at all equilibrium points. The model admits several interesting results by changing the rate of fear of predators and predator aggregate sensitivity. Numerical simulations have been performed to verify our theoret

The evacuated tube solar collector ETC is studied intensively and extensively by experimental and
theoretical works, in order to investigate its performance and enhancement of heat transfer, for Baghdad climate
from April 2011 till the end of March 2012. Experimental work is carried out on a well instrumented collector
consists of 16 evacuated tubes of aspect ratio 38.6 and thermally insulated tank of volume 112L. The relation
between convective heat transfer and natural circulation inside the tube is estimated, collector efficiency, effect of
tube tilt angles, incidence angle modifier, The solar heating system is investigated under different loads pattern (i.e
closed and open flow) to evaluate the heat loss coefficient

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Carbon nanospheres (CNSs) were successfully prepared and synthesized by Catalytic Chemical Vapor Deposition (CCVD) by using camphor as carbon source only, over iron Cobalt (Fe-Co) saturated zeolite at temperature between (700 oC and 900 °C), with different concentrations of camphor, and reaction time. The synthesized CNSs were characterized using Scanning Electron Microscopy (SEM), X-ray diffraction spectroscopy (XRD), and Fourier Transform Infrared (FTIR). The carbon spheres in different sizes between 100 nm and 1000 nm were investigated. This work has done by two parts, first preparation of the metallic catalyst and second part formation CNSs by heat treatment.

Abstract

   The method binery logistic regression and linear discrimint function of the most important statistical methods used in the classification and prediction when the data of the kind of binery (0,1) you can not use the normal regression therefore resort to binary logistic regression and linear discriminant function in the case of two group in the case of a Multicollinearity problem between the data (the data containing high correlation) It became not possible to use binary logistic regression and linear discriminant function, to solve this problem, we resort to Partial least square regression.

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