Interpretive structural modeling of knowledge network in car industry’ R&D centers

The current research has been done with the aim of knowledge network interpretive structural modeling in car industry’s R&D centers. The key factors for implementing a knowledge network in car industry’s R&D centers have been determined and then the final graphical model has been drawn by Interpretive Structural Modeling (ISM) approach.The method of the current applied research includes a survey of experts and then the variables extracted through investigating research background, after that the MATLAB R2013 software is used for making compatible matrix as well as drawing graphical relations of the model by Interpretive Structural Modeling approach.After studying related works & interviewing with under-studied firms’ managers, interpretive structural modeling (ISM) & MICMAC analysis was used to generate a model for knowledge network. Previous studies had not investigated the knowledge network in car industry’s R&D centers; however, the present study implemented the knowledge network model in R&D Centers

The Relationship of Knowledge Management and Organizational Performance in Science and Technology Parks of Iran

Any effective and sustainable changes in an organization refers to three areas related with each other and play the best way in the humans, structure and technology fields. The Knowledge management by emphasizing the three areas with the axis of man and preparing him as a knowledge worker tries to achieve organizational goals.Purpose: The current study aims to investigate the existing relationship between knowledge management infrastructures, knowledge management process capabilities, creative organizational learning, and organizational performance.Originality/value: Previous researches did not appraise the effect of knowledge management and its capabilities on organizational performance, and the specific influence of creative organizational learning was disregarded. The present study demonstrates the mechanism of knowledge management effect on organizational performance and describes the comprehensive dimensions of knowledge management performance.Methodology: Statistical population includes executives of Knowledge based companies in Science and Technology Parks of Iran. The 336 questionnaire was distributed to the census, 248questionnaireswerecompletedcorrectly. The research data were analyzed by PLS software. The unit of analysis is a company that has adopted a KMS. Target population of the research consisted of 700 Top Managers of Knowledge based companies in Science and Technology Parks of Iran (N=700). Random sampling method applied in this study and 248Top Managers were considered as the statistical sample based on "Morgan Table". One standard 5-point Likert questionnaire adopted and distributed between Top managers in the park. 252 questionnaires were returned among which 248 ones were statistically investigated. The structural relations among variables were tested using the partial least squares (PLS) method.Findings: This study shows that the KM processes can mediate between creative organizational learning and factors in the KM infrastructure. The results of the study demonstrate that knowledge management process capabilities has the most crucial role in creative organizational learning. The results indicate that there is a significant influence of the infrastructure capabilities (Collaboration, Trust, Learning Culture, Decentralization, Top Management, Promotion, IT support) on the process capabilities, also the impacts of knowledge management process capabilities on creative organizational learning and the impacts of creative organizational learning on organizational performance was confirmed

THE FUTURE OF IRAN-CHINA RELATIONS: AN ALLIANCE OR PURE COOPERATION?

The future of the Iranian political relations with other countries especially China in the post-nuclear-agreement era is of serious importance in the Iranian foreign policy in the Rouhani administration. Despite turbulences in the relations of the two countries in recent years, theirs have been stronger than the trade relations between Iran and western countries. For this reason, the shift from ordinary to strategic relations between Iran and China is attracting serious attention in the political circles in Iran. The supporters argue for the present and future stance of China in the international society and its constructive role during the western-imposed sanctions against Iran. The opponents refer to the poorer Chines technology as opposed to their Western rivals and to ambivalent and at time anti-Iranian stances Beijing adopted in the Iranian nuclear problem. The question this paper seeks to answer is whether there is a possibility of the promotion of the relations of the two countries in future and the possible obstacles in the way

Developing implementation indicators for public policy, case study: Tehran and Qom Agricultural Organizations

Public policies are problem oriented and solve a public problem. Making decision and policies does not solve problems by itself but they must be executed effectively. As executing policies is a main step of policy making, formulating indicators for implementing policy is necessary. In this article we conducted a content analysis of elites’ opinions to improve implementation of public policies. Therefore, three major factors have been introduced including policy making, environmental policy implementation and organizational structure factors. Sample data were taken from agricultural organizations of Tehran and Qom. For data gathering library research, interview and questionnaire were used. To analyze the data, k-s, Pearson’s correlation coefficient, confirmatory factors analysis and means comparisons were applied using SPSS and LISREL. Results show all of proposed indicators and measures are valid for implementation of public policies and about important of indicators between two participant groups, indicators in Tehran groups is more important

BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets

The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.Comment: 20 pages, 5 figure

Interpretive structural modeling of knowledge network in car industry’ R&D centers

The current research has been done with the aim of knowledge network interpretive structural modeling in car industry’s R&D centers. The key factors for implementing a knowledge network in car industry’s R&D centers have been determined and then the final graphical model has been drawn by Interpretive Structural Modeling (ISM) approach.The method of the current applied research includes a survey of experts and then the variables extracted through investigating research background, after that the MATLAB R2013 software is used for making compatible matrix as well as drawing graphical relations of the model by Interpretive Structural Modeling approach.After studying related works & interviewing with under-studied firms’ managers, interpretive structural modeling (ISM) & MICMAC analysis was used to generate a model for knowledge network. Previous studies had not investigated the knowledge network in car industry’s R&D centers; however, the present study implemented the knowledge network model in R&D Centers

THE FUTURE OF IRAN-CHINA RELATIONS: AN ALLIANCE OR PURE COOPERATION?

The future of the Iranian political relations with other countries especially China in the post-nuclear-agreement era is of serious importance in the Iranian foreign policy in the Rouhani administration. Despite turbulences in the relations of the two countries in recent years, theirs have been stronger than the trade relations between Iran and western countries. For this reason, the shift from ordinary to strategic relations between Iran and China is attracting serious attention in the political circles in Iran. The supporters argue for the present and future stance of China in the international society and its constructive role during the western-imposed sanctions against Iran. The opponents refer to the poorer Chines technology as opposed to their Western rivals and to ambivalent and at time anti-Iranian stances Beijing adopted in the Iranian nuclear problem. The question this paper seeks to answer is whether there is a possibility of the promotion of the relations of the two countries in future and the possible obstacles in the way

Prediction of Mechanical Properties of LDPE-TPS Nanocomposites Using Adaptive Neuro-Fuzzy Inference System

The changes in the behaviour of mechanical properties of low densitypolyethylene-thermoplastic corn starch (LDPE-TPCS) nanocompositeswere studied by an adaptive neuro-fuzzy interference system. LDPE-TPCScomposites containing different quantities of nanoclay (Cloisite®15A, 0.5-3wt. %) were prepared by extrusion process. In practice, it is difficult to carry out several experiments to identify the relationship between the extrusion process parameters and mechanical properties of the nanocomposites. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) was used for non-linear mapping between the processingparameters and the mechanical properties of LDPE-TPCS nanocomposites. ANFIS model due to possessing inference ability of fuzzy systems and also the learning feature of neural networks, could be used as a multiple inputs-multiple outputs to predict mechanical properties (such as ultimate tensile strength, elongation-at-break, Young’s modulus and relative impact strength) of the nanocomposites. The proposed ANFIS model utilizes temperature, torque and Cloisite®15A contents as input parameters to predict the desired mechanical properties. The results obtained in this work indicatedthat ANFIS is an effective and intelligent method for prediction of the mechanical properties of the LDPE-TPCS nanocomposites with a good accuracy. The statistical quality of the ANFIS model was significant due to its acceptable mean square error criterion and good correlation coefficient (values > 0.8) between the experimental and simulated outputs