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Using network data envelopment analysis (NDEA) models to assess the efficiency of Decision Making Units (DMUs) is a widely accepted method in academic research. An ongoing challenge in this field involves the computation and implementation of enhancement solutions within homogeneous clusters utilizing Machine Learning techniques. The primary aim of this paper is to identify the optimal clustering algorithm for a two-stage sustainable supply chain within the petrochemical industry in Iran. Subsequently, the application of NDEA within each cluster aims to ascertain efficiency levels and devise improvement strategies to facilitate a more targeted development approach for inefficient units. This paper investigates the best clustering algorithms in the area of Machine Learning by using quality measurement indicators and using Network Data Envelopment Analysis (NDEA) for measuring the efficiency of DMUs with sustainability approach. Upon examination, it has been determined that the Deep Embedded Clustering algorithm yields the most favorable results when applied to the data set. Furthermore, the comparison of the clustering result with the standard NDEA model has demonstrated the utility of clustering and comparing units in homogeneous categories for the purpose of efficiency calculation and determining the distance to the efficient frontier. This article, showed that how to find the best algorithm for two-stage supply chain clustering. Also, by comparing the effect of clustering on measuring the distance of inefficient units to the efficiency frontier, it was shown that clustering of units can play a significant role in planning to reach a practical development plan in each cluster.

With the start of the Coronavirus epidemic, remote education systems were widely accepted in all universities around the world. The University of Tehran, the top university in Iran, had an electronic education system since 2010, which provided services in this field to the academic community. With the spread of the pandemic, these services became widespread in all departments of the University of Tehran. Compared to traditional learning, E-Learning brings challenges for Users, the main challenge of which is Users’ Satisfaction. In this paper, by examining the developed model based on the TAM model and the factor analysis of this model, the level of satisfaction of student users with the elearning management system of the University of Tehran during the Corona era is carefully evaluated. The exploitation of this model with the analytical method of partial least squares in the construction of structural equations is based on the information collected from the questionnaire that has been provided to the users of the e-learning system of the University of Tehran. The results obtained from this study show that the Technology Acceptance Model (TAM) describes well the factors affecting the level of user satisfaction with the E-learning management system. The positive effect of the visual beauty and the quality of the information in this system has improved the technical quality of the service; as a result, the level of user satisfaction in using this system has increased.

During the COVID-19 pandemic, universities worldwide favored distance learning systems. Although e-learning provides numerous benefits, it also poses challenges for learners, so learners’ satisfaction is a significant concern to be assessed. The present study aimed to assess learners’ satisfaction with the University of Tehran's LMS during the pandemic. It applied a model based on the Technology Acceptance Model (TAM) and supplemented it with factor analysis. Research validity was assessed by using confirmatory factor analysis, while Cronbach's alpha was employed to measure the reliability of the research instrument. The study used the Partial Least Squares (PLS) analytical method to construct structural equations. In order to do so, data was collected from a comprehensive questionnaire distributed to 334 students at the University of Tehran, in which 143 participated. The findings reaffirmed the TAM's effectiveness in understanding the factors influencing learners’ satisfaction with the LMS. Consequently, these insights empower the technical team to enhance system functionality and infrastructure, aiming at improving service quality in the first semester of the academic year 2021. The Results showed that: 1) The TAM-based proposed scale has successfully explained factors predicting learners’ satisfaction. 2) Technical knowledge contributes to the perceived ease of use and influences perceived usefulness with a coefficient of 0.89, and 3) The significant relationships between technical knowledge and attitude were met. The paper's contribution is finding that learners’ technical knowledge significantly impacts their satisfaction, which helps the ICT team develop a user-friendly environment and increases the flexibility of templates. The study focused on creating training videos to improve learners’ technical knowledge. These results can change and improve the software development strategy for the next semester.