درخت حوزه‌های تخصصی

The objective of this research is to identify the technology of cloud computing in terms of its concept, its development, its objectives, its components, models, classifications, and the advantages of its use in the teaching process at the University of Samarra, as well as to identify the most important challenges and obstacles that teachers face in using University of Samarra. The researcher used the descriptive approach in his research. This research was carried out on an objective sample of 40 teaching staff members in the Department of Life Sciences and English at the Faculty of Education / Samarra University. A questionnaire consisting of 22 questions were used as a data collection tool, which included two axes: The first axis: the advantages of using cloud computing technology in the process of teaching at the University of Samarra. The second axis: the challenges of using cloud computing technology in the process of teaching at the University of Samarra. The researcher used the statistical program (SPSS 20) in order to examine the differences between the responses of the members of the research sample. The results revealed that the estimates of the teaching of the advantages of the use of cloud computing technology in the teaching process at the University of Samarra, which included the search tool, came very much, and the results revealed the most important challenges which stand in the way of the use of cloud computing technology in university education. The study recommended institutions of higher learning and universities with a set of important recommendations regarding the use of cloud computing technology in educational institutions. 

Opportunistic networks in the Internet of Things (IoT) scenario, also known as OppIoT, espouse IoT devices interactions opportunistically in order to improve connectivity, the lifetime of the network, and network reliability. An increase in opportunistic utilization is fostered by IoT applications to find communication opportunities whenever possible to route and deliver data efficiently. In this opportunistic scenario, devising an efficient path for data delivery is a challenging work due to uncertainty in the connection between the nodes and the selection of intermediate forwarder nodes for data delivery towards the destination. Considering the scenario of uncertainty in device location and exploiting IoT devices opportunistically, this paper propounds a routing algorithm for OppIoT called Hybrid Multi-Copy Routing Algorithm (HMCRA). The proposed algorithm finds potential forwarder nodes by using fuzzy logic wherein residual energy, distance, and speed of the nodes are considered as input values while preparing fuzzy rules. Genetic Algorithm (GA) is considered along with fuzzy logic to select an efficient path for data delivery. In GA, the delay is taken as the fitness function to select a reliable path for data delivery. Simulation results of the proposed algorithm perform well in contrast with relative existing routing algorithms with respect to latency, overhead ratio, delivery probability, and hop count. The work uniqueness lies in the selection of potential nodes and finding path having less hop count in an opportunistic IoT network scenario.

Personality is one of the most important study structures in organizational behavior that can play an important role in predicting human behavior. To describe the differences in personality, emotional, and social behavior, researchers have proposed the theory of five personality factors, known as the Big Five. One of the decisions investors make is to allocate wealth to financial and non-financial assets. The importance of this decision is especially evident in financial assets due to their nature and the need for more knowledge and expertise in this field because the mistakes of investors in this regard can create many risks and challenges for them. The main purpose of this study is to explain the effect of personality traits and demographic features on the risk-taking behavior of investors in the Tehran Stock Exchange. The present research is applied research in terms of goals and descriptive-survey research from the method view. The statistical sample of this research includes 358 investors of the Tehran Stock Exchange during 2019 and 2020. Research data were collected based on Sivarjan's (2018) questionnaire. The results of data analysis using the structural equation modeling method indicate that personality traits including neuroticism, extraversion, openness, agreeableness, and conscientiousness have a positive and significant effect on risk-taking behavior. Still, demographic characteristics including gender, age, marital status, and education do not have a significant effect on risk-taking behavior.