Education systems in the world are enduring COVID-19 induced perturbations and consequences. Given the growing use of E-learning during COVID-19 epidemic and expansion of Internet-based infrastructure, the need for a resilient approach to e-learning systems is deeply felt. This paper aims to address the issue of how to provide a model for evaluating the resilience of E-learning in Iranian virtual universities during the outbreak of coronavirus employing an Adaptive Neuro-Based Fuzzy Inference System (ANFIS). In the present paper, 5 substantial factors including individual, assessment and support, content, agility, and technology were identified as inputs, and e-learning resilience was considered as single output. Moreover, ANFIS was employed to model the resilience of E-learning systems. Findings revealed almost medium to low degree of resilience for the e-learning system established in Iran’s virtual university. Statistical analysis demonstrated that there was no meaningful difference between experts’ opinions and our proposed procedure for E-learning resilience measurement. The proposed model showed significant sensitivity to changes in agility. Therefore, agility should be considered as the first priority in achieving the desired level of resilience for the e-learning systems of the Iranian virtual university. تفاصيل المقالة

Complexity is seen as a major challenge in supply chain research because of interactions, interdependencies, and uncertainties. The main purpose of this study is to develop agent-based models and simulations that focus on Steel Supply Chain Resilience (SSCR) based on complex adaptive network analysis and graph theory. Before and after the simulation, and by mapping the Iranian SSC to the network in the Gephi environment (0.9.2), we used graph theory to analyze node-level and network-level indices. This hypothesis was tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS). Agent Based Modeling (ABM) has been proposed as a way to track Iran's steel industry supply chain behavior during the crisis using NetLogo (6.2.0). BehaviorSpace, as NetLogo's integrated software tool, was selected for the proposed parameter sweep, design, and experiment execution of agent-based modeling. For sensitivity analysis, the output files were taken from two types of spreadsheets and six scenarios in the table for XLSTAT statistical analysis. تفاصيل المقالة