فهرست مطالب amir khalili

In the organization of university units, the core of activities is the responsibility of faculty members and educational groups that are at the forefront of educational and research activities. Research performance impacts the sustainability and growth of the higher education system. The current study pursing the goal to represent the dimensions, components and indicators of creativity and initiative in order to evaluate the research performance of the faculty members of Tehran Islamic Azad University. The quantitative stage community consists of the experts of Tehran Islamic Azad University faculty members and 14 members of Higher Education, getting saturated with 10 ones. The qualitative stage data analysis was done using open coding, axial coding and selective coding with interviewing and Delphi technique. The qualitative stage induced results indicated the above dimensions and components consisting of nine dimensions, namely, research literacy, scientific factors, technical and information skills, methodological literacy, environmental factors, organizational factors, management factors, technological factors and personality traits. It is done this manner, in order to identify the dimensions, components and indicators of creativity and initiative, and the innovation of the research performance of the faculty members of the Islamic Azad University of Tehran, 9 dimensions, 25 components and 80 indicators were finally verified on the whole. The fuzzy Delphi analysis revealed that the experts had consensus on the detected dimensions, components and indicators. And the results displayed that the indicators of creativity and initiative are of personality traits components and included in the individual characteristics.

The autonomy of faculty members is vital for making the right decision in their work and professional development. Given the need and lack of appropriate tools to measure the autonomous work behavior of faculty members, the current study was conducted with the collaboration of the faculty members from Shahid Beheshti University of Medical Sciences, aiming at assessing the psychometrics of the Persian version of a scale for assessing autonomous work behavior of faculty members in Iran.

The Persian version of a scale developed by Evers et alwas validated in this psychometric study. A total of 480 faculty members were selected using convenience sampling. After translation and cultural adaptation, face and content validity and reliability were checked, and construct validity was calculated using confirmatory factor analysis with Lisrel factor analysis software.

Of the 480 study participants, 225 (46.9%) were males, and 255 (53.1%) were females. The mean age of the subjects was 37.39±7.58 years. The majority (360; 75%) had the rank of assistant professor, and 360 (75%) had less than 15 years of work experience. Content validity among 20 experts was 0.88. Confirmatory factor analysis for all 25 items loaded across four factors, and this four-factor scale showed a good fit in the Iranian community. Reliability using Cronbach’s alpha was calculated at 0.85, and 0.9 using the test-retest method.

The Persian version of this scale has good validity and reliability in Iran and is a useful tool for assessing the autonomous work behavior of faculty members that educational administrators can use.

The aim of this study was to identify, explain the dimensions, components and indicators of research performance of faculty members of the Islamic Azad University of Tehran.

The research was conducted with a qualitative approach, using content analysis method. Purposeful sampling, and interviews with experts continued until theoretical saturation was achieved. To determine the logical framework of the data, the steps of identification, note-taking and classification of concepts were performed. Categories were reviewed and duplicates were deleted, similar items were merged, and subject dimensions were identified and leveled. Data analysis method was performed by thematic analysis technique of open, axial and selective coding. Then, in order to assess the validity of the model, an agreement was reached with ten faculty experts in the form of a focus group on the dimensions, components and indicators of the model.

The results showed that the extracted model has 9 dimensions, 25 components and 80 indicators to evaluate the research performance of faculty members. The results showed that the indicators of creativity, initiative and innovation are among the components and characteristics of personality and are in the individual dimension.

The proposed model can be desirable in evaluating the research performance of faculty members of the Islamic Azad University and eliminating possible problems and obstacles and promoting its creativity and initiative

Background and

Chest X Ray (CXR) is one of the most common radiography uses in the emergency department. 24-hour access to trained radiologists is not possible for the emergency residents so this ability is the necessary skills for the students. The precent article evaluates this ability in thr emergency and internal medicine residents.

In the cross-sectional study 11 CXRs with specified interpretation were selected from radiologhic text books and given to emergency medicine residents of three educational hospitals for interpretation. The responses of these individuals to access the research questions were analyzed by statistical software SPSS 18.

In total، 114 residents were studied. Average score of emergency residents was 7. 2 ± 2. 5 (only 65. 4% true answer). The mean score of Emergency Medicine in Imam Khomeini Hospital، Rasool Akram and Imam Hossein was 6. 4 ± 1. 7، 8. 5 ± 3. 2 and 6. 8 ± 3. 2 respectively (P = 0. 001). There was not significant correlation between level of residency and mean score of students (P>0. 05).

It seems that development in the quality and quantity of radiologic courses is necessary to increase ability of emergency medicine students in CXR interpretation

Knowledge embedded within artificial neural networks (ANNs) is distributed over the connections and weights of neurons. So, the user considers ANN as a black box system. There are many researches investigating the area of rule extraction by ANNs. In this paper, a dynamic cell structure (DCS) neural network and a modified version of LERX algorithm are used for rule extraction. On the other hand, intrusion detection system (IDS) is known as a critical technology to secure computer networks. So, the proposed algorithm is used to develop an IDS and classify the patterns of intrusion. To compare the performance of the proposed system with other machine learning algorithms, a multi layer perceptron (MLP) and an Elman neural network are employed with selected inputs based on the results of a feature relevance analysis. Empirical results show the superior performance of the IDS based on rule extraction from DCS in recognizing hard-detectable attack categories, e.g. user-to-root (U2R). Although, MLP with 15 selected input features, instead of 41 standard features introduced by knowledge discovery and data mining group (KDD), has better classification rates for other attack categories. This network performs better in terms of detection rate (DR), false alarm rate (FAR), and cost per example (CPE) when compared with some other machine learning methods, as well.