درخت حوزه‌های تخصصی

People capability maturity model (PCMM) is a framework to help develop the level of maturity and improve the field of human resources of organizations. The present study is conducted with the aim of designing and validating an indigenous PCMM for managers among faculty members of the Islamic Azad University of Fars Province. First, the initial indicators are extracted through literature review, then the interview questions are designed and approved by 3 experts. After that, semi-structured interviews are conducted with 17 faculty members who, in addition to having executive records over 5 years, also are assistant professors and above. The sampling method is purposeful snowball. Data analysis is done based on Strauss and Corbin grounded theory model in 6 paradigms of causal conditions, main phenomenon, intervening conditions, context, strategies and consequences. The results of data analysis obtained during 3 open, axial and selective coding processes lead to the extraction of 29 indicators and 98 components. Then, using the obtained variables, a questionnaire is designed and distributed among faculty members, and data are collected and indicators are ranked using confirmatory factor analysis, based on which an indigenous model is presented. Validity and fitting of the model is measured through structural equations using PLS software. The results of validation show that the model has a high validity. According to the discussions and conclusions, one of the research suggestions in increasing the maturity and capability of faculty members can be holding perpetual training courses for managers, which should be implemented by teachers who have a brilliant record in human resource management.

The arrangement of machines and how to move them is one of the most important issues in factories and production units, which always imposes a lot of costs on the collections. Although the arrangement of machines is done once over a long period of time, its effects are very widespread. Accordingly, it is necessary to pay more attention to the matter of arrangement. Today, cellular production is also one of the widespread production methods at the industrial level, which requires this precision. The current research aims to produce new arrangements by using artificial neural networks. The way of working is that by using the data related to the number of production parts, the production time of each part, and the group of parts under investigation, as well as the costs of the devices, this clustering is done in 3 modes of 4, 6, and 9. Performing this type of clustering has higher accuracy and speed than other methods, and the results may be somewhat different in each scenario and with each clustering time, which increases flexibility in selection.

Analysis of investors’ behavior is a new scientific field in financial behavior, which is a good tool for acquiring this knowledge. Uncertain circumstances cause investors to make systematic mistakes and face cognitive biases in their expectations and decision making, including in assessing stock returns. The purpose of this study is to investigate the extent to which investors follow the pattern of belief updating as a cognitive bias in the use of earnings accounting information to evaluate the stock returns of 205 active companies in Tehran Stock Exchange. Multivariate regression analysis was performed using Eviews 9 software. The results show that by controlling year and industry effects, investors use profitability for most of the past years in following the pattern of belief updating based on two indicators of reducing realized earnings per share and equity returns. The research model is also confirmed by the sensitivity analysis and controlling company effects based on the equity return index. However, it is not confirmed by the two indicators of realized earnings per share and return on assets.

Day by day demand for object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving object. The proposed methodology is linear about the valuation of mean and covariance parameters. Finally, the proposed work considered less time as compared to the state of art methods while tacking the moving objects. Its shows less absolute error and less object tracing error while evaluating the proposed work.