https://www.jmor.ir/ تحقیق در عملیات سیستم‌محور fa daily 1 Sat, 22 Nov 2025 00:00:00 +0330 Sat, 22 Nov 2025 00:00:00 +0330 https://www.jmor.ir/article_717825.html The purpose of the current research was to identify and prioritize the effective factors on measuring the success of the alignment of business and information and communication technology. During this research, the factors were obtained according to internal and external studies and in four dimensions of common understanding between information technology and business. and work, the capability of information technology department, information technology architecture and information technology governance were categorized. In this research, the proposed method called Fuzzy DEMATEL was implemented in order to identify and prioritize factors affecting the success of business alignment and information and communication technology. The findings showed that in the dimension of common understanding between information technology and business, the factor of senior management's knowledge of information technology is the most effective and the factor of communication between information technology managers and managers of other departments is the most impressible factor. In the dimension of the capability of the information technology department, the information technology efficiency factor is the most effective and the information technology responsiveness factor is the most impressible factor. In the dimension of information technology architecture understanding, the presence of integrated macro architecture in the organization is the most effective factor and the information technology infrastructure flexibility factor is the most impressible factor, and finally in In terms of information technology governance, the existence of information technology strategic planning has been the most effective factor and the budget control factor has been the most impressible factor. https://www.jmor.ir/article_717826.html Improving the quality of supply chain performance in production sectors, factories and various businesses is one of the most important goals of managers in every country, and the economic future of every country depends on the quality of the performance of these institutions. By connecting items with information technology through embedded smart devices or through the use of unique identifiers and carrier data that can establish intelligent communication with the support of network infrastructure and information systems, the whole production process can be optimized. The Internet of Things provides the possibility for customers to have complete product information from raw materials to production through the Internet and use them to make purchasing decisions. This information can include ingredient information, production process information, information related to the manufacturing company, distributor information, product warranty, or other information required by the customer. For this reason and considering the importance of this topic, this research, has tried to use How to evaluate the fuzzy nonlinear analysis method of Internet of Things applications in the supply chain. Understanding these priorities can help the effective implementation of these systems. https://www.jmor.ir/article_717828.html In the last decade, fractional calculus has attracted much attention and various fractional models have been developed by researchers. While the numerical simulation of fractional dynamics is very important to study, the parameter recovery of these models has many industrial and scientific applications and there is an overlook to this area in the literature on fractional dynamics. On the other hand, recently, artificial intelligence and deep learning methods have solved most of the challenges of dynamical system parameter recovery and the obtained results in the literature show the outstanding performance of these methods in comparison with other state-of-the-art numerical methods. In this paper, a deep convolutional neural network architecture which is named 'Deep-Inference', is utilized for the problem of parameter recovery of fractional Lotka-Volterra equations. This network is trained on the simulated behavior of the fractional Lotka-Volterra equation that is obtained by fractional Adams-Bashforth-Moulton. The obtained results illustrate the robust and high-precision performance of the proposed algorithm.