The current study aims at investigating the effect of cooperative learning (Jigsaw) on motivation of female students. Department of kindergarten to learn Human biology. This is of be dove through verification of the hypothesis that there is no significant difference at the 0.05 level between the motivation of experiment of group subjects who study according to (Jigsaw) cooperative learning and that of the control group subjects who study traditionally.
The study is limited to female students al the first year-Department of kindergarten college of Education for women university of Baghdad during the academic year 2007-2008.
An experiment of design of partial control and post-test for two groups is used. The experiment groups consist

Micro metal forming has an application potential in different industrial fields. Flexible tool-assisted sheet metal forming at micro scale is among the forming techniques that have increasingly attracted wide attention of researchers. This forming process is a suitable technique for producing micro components because of its inexpensive process, high quality products and relatively high production rate. This study presents a novel micro deep drawing technique through using floating ring as an assistant die with flexible pad as a main die. The floating ring designed with specified geometry is located between the process workpiece and the rubber pad. The function of the floating ring in this work is to produce SS304 micro cups with profile

The study consisted in the development and use of a practical method to detect and
monitor, analyze and produce maps of changes in land use and land cover in the district of
Mahmudiya in Baghdad during the period 1990-2007 using the applications of remote sensing
techniques and with the assisstant of geographic information systems (GIS),as a valuable
contribution to land degradation studies.
This study is based maiuly on the processing on two subsets of landsat5 TM images picked up
in August 1990 and 2007 respectively in order to facilitate comparision and were thengeometrically and radiometrcally calibrated ,to used for digital classification purposes using
maximum liklihoods classification or six spectral bands of

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

study the effect of radiation microwave (MW) in inhibition the growth of some types of bacteria in a minced meat and barker were exposed to MW for different times included (0, 10, 20, 30 and 40) sec.The results showed a high inhibition rate for 40 sec, reached to 100%. It is the other side studied the effect of microwave radiation against four types of bacteria included (Staphylococcus aureus, Escherichia coli, Proteus mirabilis and Klebsiella spp), when were exposed to for (0, 5, 10, 20, 30 and 40) sec the inhibition ratio reached to 100% in each of the Proteus mirabilis and Klebsiella spp at 30 sec and Staphylococcus aureus and Escherichia coli at 40sec. using MW in the sterilization media, such as Nutrient agar, Macconkey agar and Man

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff