درخت حوزه‌های تخصصی

Intelligence is an essential factor in supply chain. It prepares the ground for effective and timely communication and facilitates information sharing among supply chain components in both production and service sectors. Hence, the present study seeks to identify the components of services intelligent supply chain and categorize them as a comprehensive model by using the meta-synthesis approach. In this research, total of 437 articles in the field of services intelligent supply chain was identified and examined at first step. Articles were in the period of 1980 to 2022. Finally, after evaluation of articles, 53 articles remained. Then, based on the process of the meta-synthesis and the use of coding method, four main fields and a number of sub-fields were found for the services intelligent supply chain variable. Initial codes introduced in the software for conceptualization and categorization. The main fields of services intelligent supply chain included intelligence dimensions, intelligence conditions, intelligence strategies and intelligence consequences. Each of these main fields has a number of sub-fields. The result of the study shows that in order to implement the services intelligent supply chain, several dimensions and components are considered, concepts such as real-time online information flow, intelligent fund flow, intelligent services flow, process management and technology infrastructure play an important role. Suggestions are presented in accordance with the comprehensive model identified. Also, the conceptual model designed can be useful as a basis for future research in the direction of practical manual in the effective use of the services intelligent supply chain.

Innovation capability refers to a complete set of characteristics of the organization that facilitates innovation strategies, and the food industry plays a pivotal role in the processing of agricultural raw materials and food supply. The aim of the present study was to Identify and Analyze of the dimensions of innovation capability, clustering and ranking indexes in Urmia food industries. The sample size of the statistical population encompassing 221 companies was equal to 143 according to Cochran's formula. Obtaining required information was based on field study method. In the applied part of the study, exploratory factor analysis, clustering with K-MEANS algorithm and cluster ranking based on AHP technique were used. The results of the exploratory factor analysis showed that the relationship of each item with the factors classified into three categories was properly expressed. Companies with similar features but different from other clusters were put into 8 clusters. Finally, according to the weights of study variables, "human", "operability" and "structural” factors ranked first, second and last respectively.

The incorporation of STEM education into the curriculum has been an aspiring objective for many nations around the world. Most students choose not to pursue STEM-discipline studies because they are losing interest slowly. Moreover, the level of engagement required for STEM education is limited due to inadequate interactive teaching and tools that facilitate effective learning in a classroom setting. The objective of this project is to assess how educational game applications can help incline students’ interest in science, develop an educational game application, and conduct user experience testing. A mobile application on Earth and Space Science has been developed for 10 – 11-year-old school students. The project is based on the Rapid Application Development methodology considering the short development timeframe.  The application was created using the Ionic Framework, Angular 5, C#.NET and SQLExpress. The findings indicated that this experiment motivates students to be more inclined to science.

The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models.

Predicting financial markets has always been one of the most challenging issues, attracting the attention of many investors and researchers. In this regard, deep learning methods have been used a lot recently. Due to the desired results, such networks are always in development and progress. One of the networks that is being implemented in various fields is capsule network. The first time the classification capsule network was introduced, it was able to attract a lot of attention with its success on MNIST data 1 . In such networks, as in the other ones, the parameters are obtained by minimizing a loss function. In this paper, we first change the classification capsule network to a regression capsule network by modifying the last layer of the network. Then we use different information measures such as Kullnack-Leibler, Lin-Wang and Triangular information measures as a loss function, and compare their results with wellknown models including Artificial Neural Network (ANN), Convolutional Network (CNN) and Long Short-Term Memory (LSTM) as well as common used loss functions such as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Using appropriate accuracy metrics, it is shown that the capsule network using triangular information measure is well able to predict the price of bitcoin for the medium and long term period including 10, 90 and 180 days.

The secret to achieving your organization's goals in complex and challenging environments is to make the right managerial and rational decisions. In this regard, the accounting information system as one of the sources of information for the decision of managers is of particular importance. Therefore, in order to achieve these goals, it is necessary to have an accounting information system with dynamic capabilities. The dynamic capability of the accounting information system (hidden variable) was measured by the observed variables of flexibility, continuous evaluation, continuous investment and system variability. Therefore, based on this argument, the aim of the present study is to provide a dynamic capability model of the accounting system based on the financial effectiveness of the management accounting system. Data envelopment analysis is a well-known methodology that is applied to evaluate the selected firms based on the most important features. The results of analysis of the proposed method on 86 companies listed on the Tehran Stock Exchange and analysis and analysis of data by structural equation modeling show that the dynamic capability of the accounting information system consists of (flexibility, continuous evaluation, continuous investment and system variability). The result indicate that the management accounting system is effective.

The earliest movement which is required to be considered during a crisis is grasping the existing circumstances. Crises in business are not exempt from this rule. Principally, precise, accurate grasping and realization of any dilemma will be half of the solution. The alternative significant technique that companies have changed their business models is through enlarging the magnitude of cooperation between competing organizations. Even policy makers are involved in competing activities due to the impact of this pandemic and necessity to stable economic and sanitary objectives. Some businesses have been able to achieve success by setting up an online shops and producing contents and measures which will be discussed in present paper.