Journal of

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Background: Employees’ empowerment is the best and efficient organizational strategy for improvement of human resources productivity. Therefore, the present study was aimed to investigate the relationship between empowerment training courses with human resources productivity among Tehran University of Medical Sciences employees.
Materials and Methods: This was a descriptive-analytical and cross-sectional study. The study population included 1452 employees of the university deputies. Research sample was calculated 304 according to Krejcie and Morgan table. The research tool consisted of Hersey's empowerment training courses effectiveness translated by Jafari “et al.” and Hersey-Goldsmith's human resources productivity questionnaires. The content and structural validity including convergent and divergent validity of questionnaires were determined, and their reliability was confirmed by Alpha Cronbach with 0.86 and 0.89 coefficient, respectively. Data were analyzed by SPSS version 23, and descriptive results were presented by absolute and relative frequency and analytical results by inferential statistical techniques and Structural Equation Modeling.
Results: The mean of empowerment training courses effectiveness of employees and human resources productivity was relatively desirable. Also, the relationship between empowerment courses effectiveness and human resources productively of employees was confirmed.
Conclusion: Using different procedures of employees’ empowerment in early period of employment including organizational socialization, on-the-job training courses by workshops, distance learning and in-person training for empowerment of self-esteem, compatibility, and innovation of employees’ for promotion of organizational and individual productivity is recommended.

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Background and Objectives
In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction.
Materials and Methods
In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem.
Results
Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R2  are 0.335, 0.260, and 0.851, respectively.
Conclusion
A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach.