problems with its unobvious effect on scientific creativity and information. Problem solving is one of main goals of researchers because it develops their right logical thinking methods. The present study aims at measuring logical thinking among female it structures in the university mea swing problem solving among them ,identifying statically differences significance in logical thinking among female instructors in the university according to (Specialization Variable), identifying differences significance in problem Solving among female instructions in the university according to ( Specialization Variable) and identifying the Correlation between logical thinking and problem solving among female instructors in the university. The sample consists of the main application sample Consisting of (100) female instructors (50) from Scientific specializations and (50) from human ones that had been chosen randomly. The researcher concludes with aunnber of results as:
1- The individuals of the sample, according to the comparison between arithmetic mean and hypothetical on, have logical thinking and the ability of problem Solving at (o,05) significant level.There are no statistically significant differences on the scale of logical thinking according to specialization variable ( Scientific- human) at (0,05) significant level.
3- There are statistically significant differences on the scale of problem solving according to Specialization variable (Scientific- human ) at (0,05) significant lever.
4- There is a correlation between logical thinking and problem solving which is (0,77) among the female instructors at (0,05) significant level.
The researcher reaches at some of recommendations, suggestions and conclusions.
The rapid and enormous growth of the Internet of Things, as well as its widespread adoption, has resulted in the production of massive quantities of data that must be processed and sent to the cloud, but the delay in processing the data and the time it takes to send it to the cloud has resulted in the emergence of fog, a new generation of cloud in which the fog serves as an extension of cloud services at the edge of the network, reducing latency and traffic. The distribution of computational resources to minimize makespan and running costs is one of the disadvantages of fog computing. This paper provides a new approach for improving the task scheduling problem in a Cloud-Fog environme
In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonic
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