فهرست مطالب رضا رادفر

Steel Supply Chain (SSC) is a interpretative segment in Steel's value-creation process for ensuring on-time delivery of the right quality of raw materials, Work In Process (WIP), other goods and services to manufacturing sites, and finished products to the ultimate consumers. Graph theory provides a real-time, end-to-end view of the Steel supply chain, from suppliers to customers. By representing the supply chain as a network of interconnected nodes, graph theory provides a visual representation of the relationships between nodes including: suppliers, manufacturers, distributors, and customers. The main purpose of this study is to focus on Iran’s Steel Supply Chain (ISSC) based on complex adaptive network analysis and graph theory. Data collection performed by steel industry monitoring reports produced by relevant organizations. By mapping the Iranian SSC to the network in the Gephi environment (0.9.2), the graph theory applied to analyze node-level and network-level indices. This hypothesis tested that the corresponding network of the target SSC contained a Complex Adaptive System (CAS) which is an inception to combining social science insights to develop systems-level models and insights that allow for phase transitions and emergent behavior.

With the passage of four generations of theoretical and experimental development of the smart city, it was expected that by relying on technologies such as the Internet of Things, artificial intelligence, cloud computing, and the like, this theory would be the way to face the problem and solve the complexities of resilience planning. But countless recent researches show the opposite. The root of the problem is not in the application of technology, but in the theoretical integration of smart city management and urban resilience. In the meantime, the resilience approach is a new point of view that instead of focusing on reducing vulnerability and looking at confrontation, it emphasizes increasing the resilience and flexibility of cities against natural and human hazards. The purpose of this research is to evaluate the feasibility of smart urban management to improve the resilience of Urmia city. To achieve this goal, a descriptive-analytical research method was used based on the analysis of a questionnaire that was completed by 30 urban experts and specialists. The results of the statistical analysis show that Urmia city is relatively favorable in terms of social and infrastructural resilience, but it does not have a favorable situation in terms of institutional-management, economic and physical-environmental resilience. In general, the overall resilience of Urmia city is lower than the optimal level according to the experts, so that the calculated value of the overall resilience of Urmia city is 2.90, which is less than the average level (3).

Today, in order to increase the efficiency of rail transport lines, the use of advanced high-speed line technology is recommended as a superior option compared to conventional railway technology. One of the fundamental challenges in the application and development of rail transport with new and advanced technologies is the lack of a suitable model for assessing technological capability. The present research is aimed at identifying key dimensions affecting the evaluation of technological capabilities in high-tech rail transport industries. The statistical community includes 57 managers and experts from companies active in the field of rail transport. In this study, 39 influential variables were identified and categorized into 7 dimensions by experts. After collecting data from the statistical community, the data was analyzed by modeling structural equations and smart pls software. The results show that 3 variables with a factor of less than 0.7 were removed from the model and the corrective model was formed with 36 indicators. The results of the principle of software output show that the 7 dimensions are significant at the 99% confidence level and affect the evaluation of technological capability in high-tech rail transport industries.

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decisionmaking problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated. In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market. In this research, we have used of firms’ performance between 1396-1400 in civil engineering, construction, investment and construction materials and tools manufacturers in order to determine portfolio selection. The main advantage of proposed approach is helping an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge

In recent years, the world has been engulfed in a crisis caused by a contagious disease called the coronavirus. The impact of this crisis is so significant that some researchers have also paid special attention to the post-coronavirus world. Therefore, the aim of this research is to identify the factors of sustainable value in using information technology for managing acute viral diseases.

The research method is mixed. In the qualitative section, the thematic analysis method has been employed. The reliability assessment of this section is also conducted using the method of comparison by two coders, resulting in a coefficient of 83%. In this section, interviews continued until theoretical saturation was reached, and 23 interviews were collected. In the quantitative section, structural equation modeling with a partial least squares approach using SmartPLS_3 software was employed to analyze the data, and a semi-structured questionnaire with a five-option Likert spectrum was distributed among 241 individuals. The research population includes healthcare professionals in medical sciences, faculty members in management and technology fields, as well as managers of knowledge-based companies active in medical technology in Tehran province. Simple random sampling using the Cochran formula is also employed in this section.

The results showed that in the area of pandemic management and control, organizational factors (with a coefficient of 0.41), control and supervision (with a coefficient of 0.28), and planning (with a coefficient of 0.43) were confirmed. In the sustainable value section, environmental factors (with a coefficient of 0.29), social factors (with a coefficient of 0.37), and economic factors (with a coefficient of 0.47) were confirmed.

Thus, the sustainable use of information technology can not only control pandemics but also prevent their consequences for future generations.

Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.

Today, research and development activities are considered important sources of transformation in the fields of technology and knowledge production, and research and development departments of knowledge-based companies play an important role in innovation and improvement of products and services. The more advanced and fundamental the level of research activities of knowledge-based companies and the higher the level of technological complexity of these activities, the more innovative and competitive products are available to the domestic and global markets. turns In this regard, the present research was conducted with the aim of investigating the levels of technological complexity of research and development activities in knowledge-based companies. This research is an applied-developmental research in terms of its purpose and descriptive-analytical in terms of its nature and method. The statistical population of the research was all CEOs of knowledge-based companies located in Golestan Science and Technology Park, numbering 85 people. The sample size was considered to be 80 people using Morgan's table and more cautiously, and available sampling method was used. The tool used to collect research data is a researcher-made questionnaire, whose validity was confirmed by measuring face and content validity and its reliability using Cronbach's alpha method. The results of the research indicate that 65% of research and development activities in knowledge-based companies based in Golestan Science and Technology Park are experimental development, 31.2% are applied research and only 3.8%. The percentage is the type of basic research.

In recent years, due to the withdrawal of foreign companies from Iran's fashion and apparel market, it has been possible for domestic producers to operate in more attractive segments of the market, leave the position of cost leadership in the fashion market, and move towards segments with higher profit margins in the fashion industry. The attempt to cover the absence of foreign companies has caused many efforts to find the optimal process of introducing new products, reducing the failure rate of new designs and continuity in the new product development process by market leader companies.In this research, the discovering of the new product development pattern in the fashion and apparel industry is done employing the multi -case study methodology. In this research, based on information obtained from observations, notes, and manuscripts of interviews with senior executives of 9 leader companies in the fashion and apparel market and import this information into the Quirkos software, the initial codes were extracted. Then, using Braun and Clarke thematic analysis and open and axial coding, as well as controlling the initial codes based on the Keywords in context (KWIC) method, have been achieved to 46 concepts and 7 categories that, after re-arrangement and examining the story line, the seven stages pattern of new product development in the fashion and apparel industry proposed, including: design, product engineering, procurement, production, marketing, launch and product success monitoring stage. The details of each step of the pattern and compare its characteristics with the findings mentioned in the literature have been approved by selected experts in the sample and the validity of the model has been confirmed.

The present research was conducted with the aim of presenting a sustainable innovation development model in information technology startups based on grounded theory approach. The method used in the research is qualitative and based on grounded theory strategy. An in-depth interview tool was used to collect data.The target population was the managers and senior experts of startups who participated in the sustainable innovation process and decisions of information technology startups, using targeted sampling after 11 in-depth interviews, theoretical saturation was achieved. In the foundation's data approach, in this research, during three stages of open, central and selective coding, 24 general categories have been expressed in a paradigmatic format, including causal, contextual, intervening conditions, central categories, strategies and consequences.The results showed that the sustainable innovation development in information technology startups by causal conditions (risk tolerance, analysis of the business situation of the startup's sustainable innovative capabilities and technology absorption capacity), contextual (organizational structure of startups, market orientation and organizational readiness), intervening (infrastructures legal, managerial factors, behavioral patterns, environmental factors and economic uncertainty), core categories (open sustainable innovation, technological capabilities and optimization of innovation processes), strategies (development of digital technologies, strategic planning for startups, value creation of startups and co-creation) and consequences (competitive advantage, strategic intelligence, business model improvement, digital transformation and organizational effectiveness) are realized.

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.

Today, knowledge-based companies play a very important role in achieving advanced technologies and producing innovative products, and as a result, the growth and development of countries. The success of these companies is due to the development of advanced research and development activities that have a high level of technological complexity.

The current research is of mixed type. In the qualitative part, the grounded theory approach was used and the data was collected using interview tools and literature review. Quantitative part data was collected from the desired statistical population through a researcher-made questionnaire, then data analysis and model fitting with structural equation modeling method and using smart software pls3 done.

91 effective factors on determining the level of technological complexity in the form of six categories were identified and the presented model was confirmed.

The model for determining the level of technological complexity of research and development activities resulting from the present study can be a suitable tool for managers of research and development departments of knowledge-based companies in order to improve the level of research and development activities based on global standards and creating transformation in the fields of technology and knowledge production.

Todays, tax revenues play an important role in financing governments. The issue of increasing tax revenues and possible ways for this increase is an attractive issue for governments. Especially in a single-product economy like Iran, whose main source of income is the volatile and unpredictable oil income. This research aims, while briefly introducing the presented models and classifying them, effective factors in increasing tax revenues and evaluating public trust and participation in the field of taxation have been examined and identified with a meta-integration approach, and the main components of this model include legal factors, social factors, there are cultural factors, managerial factors, political factors and managerial factors. Then, in order to better understand the variables affecting this relationship and understand the existing dynamics, using the approach of system dynamics modeling and drawing causal-loop diagrams (CLD), a model for evaluating trust and public participation in the field of taxation is presented and finally simulated so It was done by Vensim software. The simulation results indicate the positive effect of economic factors on public trust and participation in the tax field, with the point that the effect of economic factors is greater than the simultaneous application of all components.

As oil and gas resources play an essential role in energy, oil and gas industry technologies have also developed rapidly in recent years, such as smart drilling technology, smart oil and gas transmission pipelines, and digital offshore platforms. The oil and gas industry has four aspects: business, management and decision-making, monitoring and cyber security. Finally, the state of application programs, the level of understanding of block chain in the security of the Internet of Things in the oil and gas industry, opportunities, challenges and risks and the development process are examined. Therefore, the main goal of the research is to provide a model of block chain mechanisms for the security of the Internet of Things in the Iranian National Oil Company. The nature of the current research is exploratory and qualitative data is used in it. The statistical population of the research is the experts and professors of doctorate in management and information technology in the oil and gas industry (NISOC). In this context, the researcher used the snowball method to achieve theoretical saturation, and the researcher achieved theoretical saturation after structured interviews with 10 elites and experts. Data analysis was done with the qualitative method of Foundation Data Theory and Maxqda2020 software. The findings indicate that the causal factors affecting the block-chain mechanisms for the cyber security of the Internet of Things in the oil and gas industry in the form of three concepts, including data aggregation and storage (network), validation and identity management and key management and data encryption were identified. Also, influential background factors include nine concepts of financial resources, management, distribution of infrastructure services, data confidentiality, system stability, scalability, accessibility, transparency, expertise. and intervening (environmental) factors include seven concepts of environment. Operational standards, standards and procedures are the nature of data for mechanisms in various industries, social environment, cultural environment, government laws and regulations, political relations and the strategies of block chain mechanisms for the cyber security of the Internet of Things, in the form of six concepts of electronic health, creating a secure layer, smart transportation, smart networks, the use of smart block chain contracts, the design of industrial Internet of Things (IIOT) devices, and the consequences And the results of the implementation of block chain. Mechanisms for the cyber security of the Internet of Things in the oil and gas industry of the National Company of the Southern Oil Rich Regions(Nisoc), including the attitude of reducing production costs, high capacity for storage, intelligent field, organizational agility, environmental dynamics, security, System accessibility, improvement of the user interface, the quality of information (distribution and variety of information) and the speed of data processing.

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decision-making problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated.  In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market.  In this research, we have used of firms’ performance between 1396-1400 in chemical industries in order to determine portfolio selection. The main advantage of proposed approach is help an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge.  

This study aimed to develop IoT-based machine learning algorithms care and improvement while detecting and predicting real-time epidemics.The target disease is COVID-19 due to its importance and epidemic.The research method is based on design science. The research approach is forward-looking, so the mechanism of disease transmission and its effective characteristics enable us to make predictions about the disease and thus design disease control strategies and health care.The research was carried out in a seven-step process. IoT features were extracted in the present study with experts' opinions. The features obtained in the experiment of two different algorithms, "k nearest neighbor" and "decision tree," were created on the data to determine the best model.After selecting the best depth validation of the model were performed by confusion matrix analysis.The results of running k-nearest neighborhood and Decision Tree algorithms for the prediction of COVID-19 indicated an accuracy of > 98%. Higher sensitivity (99%) was obtained in the Decision Tree algorithm, which is very important diagnosing COVID-19 and indicates the minimum number of false negatives in the test results.

According to the studies conducted on New Technology-Based Firms (NTBFs), the biggest problem of these companies is their access to financial resources. Studies show that among the indirect intervention policies, credit guarantee schemes have been considered by governments for facilitating the financing of these enterprises due to the need for less budget and a higher leverage effect. The purpose of this article is to present the appropriate credit guarantee model for new technology-based Firms.

In this study, which used a combination of qualitative and quantitative methods, firstly, by reviewing the literature, credit guarantee models, key components, and main criteria for choosing the appropriate credit guarantee model were identified and validated by the opinion of an expert group. Finally, the most appropriate model was selected using the Multiple Criteria Decision-Making Model and Analytical Network Process (ANP). Also, to form the grounds for policy implications for credit guarantee programs in Iran, an in-depth study of credit guarantee programs in the country and a comparison of the constituent components of credit guarantee programs with the classified models for credit guarantee in the world was carried out.

The findings of this study indicate a lack of effective policy formulation and a deficiency in the precise and targeted selection of a local credit guarantee model for emerging technology-based firms in Iran. The central outcomes of this study revolve around the identification of credit guarantee models, the delineation of key components, and the establishment of criteria for choosing the most suitable credit guarantee model. Furthermore, the research entails the ranking of both these criteria and the various models under consideration. According to the insights gathered from experts in this domain, the research findings highlight the primary criteria for selecting a credit guarantee model. These criteria, ranked in order of significance include: facilitating the provision of credit services, assessing the consequences of the credit guarantee system, effective risk management, and governance of the credit guarantee system. This ranking reflects the experts' assessment of the most pivotal factors when evaluating credit guarantee programs. The criterion of facilitating the provision of credit services has the most weight (importance). Also, policy implications such as: "Government support policies regarding the development of guarantee tools", " creating a centralized and reliable credit risk database" and "Governance and functional changes in the country's credit guarantee institutions" were proposed to create a local credit guarantee system for NTBFs.

In this research, utilizing the ANP methodology, the selection of an apt credit guarantee model for the nation aligns with the specific requirements of emerging technology-based firms, the prevailing economic and institutional landscape, and global best practices and successful models. This approach holds substantial promise for achieving significant success, not only in shaping effective policies but also in their successful implementation within the realm of financing for these companies. Reforming the institutional and functional structure of the country's credit guarantee institutions in the short term, as well as the formation of new credit guarantee institutions according to successful credit guarantee models in the world and their development in the long run can play a significant role in the financing of NTBFs.

The purpose of this research is to compile a model of digital marketing strategies in successful Iranian startups. According to its purpose, the research method is practical, and in terms of its implementation, it is mixed (qualitative-quantitative). The statistical population of the research in the qualitative part includes 13 academic and executive experts in the field of startups in the country, who were selected using the purposeful judgment method and the snowball method, and the statistical population in the quantitative part includes the marketing unit of active successful startups. There are 250 people in the country (5 companies) and marketing professors of 5 top universities in Tehran, and the statistical sample was 148 people, 180 distributed and 163 analytical questionnaires were selected using Morgan's table. A semi-structured interview and a researcher-made questionnaire taken from the qualitative section were used to collect information. In the qualitative part, the data theory method of the foundation was used, and the data obtained from the interviews were coded and analyzed in the three main stages of open coding, central coding, and selective coding, and in the quantitative part, the factor analysis of the indicators was done. The results of the qualitative part indicated that 241 primary codes, 46 central codes and 14 selective codes were identified and extracted. The results of the research showed that the causal conditions with 4 variables of network capability, use of artificial intelligence, marketing capability and customer knowledge have an effect on digital marketing strategies, and the results of confirmatory factor analysis showed that the fit indices of the model were confirmed.

This article presents a new model for optimizing the safety risk of take-off, as the most important and dangerous flight process, using a combination of BI algorithm and recurrent neural network LSTM. The goal is to train an effective neural network with past data records of air accidents to predict safety risk parameters. For this purpose, 17 safety features, such as weather conditions, aircraft configuration and preparation, flight information and air traffic were obtained. The data related to 2019 to 2020 was selected after performing exploration, summarization, cleaning, normalization operations with 28813 data records. Due to the dependence of flight data on their previous inputs and the need for a kind of memory, training was performed by deep learning algorithm (LSTM) in Python environment. After learning, the learning error was about 6 percent and the mean square error was about 116/0. It shows that the error percentage is negligible and the proposed model has high validity. Also, this model solved the problem of exploration and cleaning of bulk flight data by having advanced tools such as ETL, metadata and real-time monitoring and was able to predict the most important safety risk factor (speed V1) with high accuracy. This pattern helps the flight service in controlling the important parameters of safety risk, such as the speed of aircraft taking off from the runway, controlling the safe take-off speed and most importantly controlling the loss of flight with a reliable strategy.

The development of information technology and its use in the health system has taken many measures to protect and promote human health, however, the world still faces long-term threats and recurrence of infectious diseases. Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed. The disease studied in this study, due to its importance and prevalence, is Covid 19. In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost. The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity. The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results. Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity.

The main goal of this research is the fuzzy analysis of urban management success factors of future city indicators. In terms of purpose, the present research is applied research and in terms of method, it is a descriptive-survey research method. Data collection tools included questionnaires and questionnaires. The statistical population of this research are experts in the field of urban management. Purposive sampling method was used to select sample people. Superdesign software was used for data analysis. In the first step, by reviewing the subject literature and research, a large number of indicators of the subject literature have been examined. Following the urban management model, the indicators of future cities were ranked using fuzzy network analysis. In the current research, the factors are placed in six levels. In such a way that laws and regulations were placed at the last level and tourism at the first level. In the continuation of the research, it is taken from the perspective of the future. Examining the results obtained from the above table, rights and laws and social learning and education were reported as the most influential factors with a score of 22. Also, tourism with a total of 17 factors was placed in the first priority of acceptability. The degree of usefulness of the matrix is 100%, which indicates the high validity of the questionnaire and its related answers. Special vector machine of the obtained software: the environment with a weight of 0.358 is the highest priority. The urban body with a normal weight of 0.267 is in the second priority. Transportation with a normal weight of 0.241 is in the third priority. Economy with a normal weight of 0.134 is one of the lowest priorities.

The aim of the present research is to compile digital marketing strategies model in Iranian successful startups. The research method is qualitative. Using the grounded theory approach, the codes were identified and subsequently, the axial and selective codes of the research concept were classified. Thirteen interviews with the university experts and CEOs of the startups were done using Judgmental Sampling and Snowball Sampling methods. The research data were collected and the theoretical saturation process started with interview 11. The research findings identified 241 initial codes, 46 axial codes, and 14 selective codes. The selective codes included network capability, use of artificial intelligence, marketing capability, customer knowledge, field of activity organizational, cultural, and structural, ability, value creation for the customer, and value creation for the company, social, individual, organizational and technological values. Based on the research results, causal conditions with 4 variables of network capability, use of artificial intelligence, marketing capability, and customer knowledge affect digital marketing strategies. The digital marketing models in successful Iranian startups was formulated and presented by determining the contextual and intervening conditions, and determining the outcomes and strategies for improvement of strategies.

In general, information technology has a sustainable nature, and in line with preserving the environment, it can advance programs that protect the interests of future generations. On the other hand, the presence of acute viral diseases in different periods has caused many human and financial losses in the world. Sustainable information technology can have a great impact on the management and control of acute viral diseases, which human power alone seems to be insufficient. Therefore, the purpose of this research is to design a management and control model using sustainable information technology for acute viral diseases. In the current research, it is an applied research that developed a conceptual and process model by using the qualitative method of theme analysis and the formation of a panel of experts. The data collection tool is an unstructured questionnaire for interviewing experts who include the medical community, university faculty members, and knowledge-based managers. Sampling continued using the targeted method until reaching theoretical saturation with the number of 26 interviews. The results presented a model with four sub-themes including: sustainable value (social, economic, environmental), management and control of viral diseases (planning, organization, control and monitoring), information management (data collection, information dissemination, reporting and data analysis). ) and finally led to sustainable information technology factors (sustainable performance, attitude, technology, policy and implementation). In the process model, the main components of the algorithmic model include identifying, designing, organizing and storing information. Therefore, it can be said that by using the management and control model implementation during

In the present era, under the title of digital transformation era, we are facing a tsunami of data and information, and in this era, we can admit that what will cause this great transformation in the country's banking industry, under the title of digital banking, is the use of open banking using the volume There is a lot of data and information in providing quality and special services to customers. In this field, the main key to enter the banking field is to use fintechs. Financial institutions are moving quickly to develop their response to the advancement of open banking by fintechs and other non-traditional financial institutions. The methodological approach of the current research is scientific and based on the literature of the subject and theoretical foundations, the result of which is the identification and presentation of a scientific framework that enhances knowledge. In this article, after examining and understanding open banking in the era of digital transformation, the ecosystem of open banking, approaches and products of this field have been introduced, and then, with a scientific and research review of the background of open banking, the course of its developments in the world and Iran has been outlined. Finally, the current research shows that the main window to enter the era of digital transformation in the banking industry is the use of platforms and APIs with combined models that suit the needs of each customer.

Global crises have turned the development of the national economy into a fundamental problem. One of the ways to achieve economic development is to improve the commercialization mechanisms of scientific research. By looking at the performance of agricultural researches in the past years, it can be seen that, contrary to the existing capabilities in technology development, many of them have failed in the commercialization of technology. The purpose of this research was to design a model for the commercialization of research findings of agricultural research institutes in the form of a strategic model. The current research is a part of descriptive-survey research in terms of practical purpose and in terms of implementation method, and in terms of qualitative data collection. The statistical population of this research was the academic faculty members of the country's agricultural and commercial research institutes in the field of commercialization. Sampling was followed by the snowball method until theoretical saturation, which included a total of 16 experts. In this research, a semi-structured interview was used as a data collection tool and an approach based on database theory and ATLAS.ti 8 software was used. According to the research findings in the qualitative part, 212 codes were identified in the form of 24 categories, which were placed in the form of causal conditions, background conditions, intervening factors, strategies and consequences. The findings of the research showed that management, laws and regulations, foresight and infrastructure development are the most important factors in the commercialization of research projects.