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Scientific and technological developments, especially in the field of information and communication technology, have caused significant changes in people's daily lives, and remote work has been promoted as a new approach in managing work and performing activities, especially in the conditions created by the Corona pandemic. This study examines the concept of remote work in a special framework that includes the Corona period and the fifth industrial revolution and emphasizes the need for comprehensive scientific attention and innovative methods. To solve these challenges, this research presents a comprehensive model by applying the soft systems methodology (SSM), which takes into account the perspectives and limitations of different stakeholders and aims to increase the implementation of remote work. Using systematic methods and qualitative data analysis, this study develops the model in a flexible and comprehensive way. In addition, it highlights the role of technology, organizational culture and management strategies in reducing social isolation and increasing telework efficiency. The findings emphasize the dynamic aspect of the remote work ecosystem and emphasize the importance of multifaceted solutions for organizational success in the era of the fifth industrial revolution.

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%.

Abstract The purpose of this research is to combine the genetic algorithm in the optimization of the stock portfolio in the financial decision making of investors; in a simulation project, the final use of the input data is to build the simulation model. This process includes collecting input data, analyzing the input data, and using these analyzed input data in the simulation model. The statistical population of the research includes 20 symbols (companies) from among the industries (Vabsadar, Vetjarat, Akhaber, Fakhuz, Fars, Balbar, Tapampi, Khasapa, Khodro, Sasharq, Sosofi, Shobhorn, Shapna, Ghopino, Fould, Ghasabat, Kesra, Vanbank, Vanneft, Veniki) and the information related to the daily stock price and the daily index value from Decembre 22, 2008 to January 16, 2020 was considered as a sample. The tool for collecting information and data is using the Phipiran site, and the amount of beta (risk) of stocks is calculated monthly using Excel software, and the frequency of return and beta (risk) calculated using Spss software, and distribution functions were discussed using Easy fit software; the results showed that if the agents are beginners to earn more profit than normal behavior and accept 40% risk, the amount of profit obtained after optimizing the model with genetic algorithm is more than the initial model. If the agents are professionals to earn more profit than risk-averse behavior and accept 80% risk, the amount of profit obtained after optimizing the model with genetic algorithm is more than the initial model. Extended Abstract Introduction Today, in order to reduce the investment risk, investors in the financial markets prefer to allocate capital to a portfolil consisting of several shares rather than investing in only one share; because this enables them to bear a lower level of risk in order to achieve a certain amount of return in a certain period of time. An issue that has occupied the minds of many financial analysts and investors for many years is how to choose stocks and optimize the investment portfolio over time in a way that meets the investor's expectations in order to maximize the return on investment. When investors are exposed to uncertainty, the investment portfolio selection framework should include a quantitative measure of uncertainty to achieve the expected return or a quantitative measure of risk (Shahraki Sanavi, 2023). Today, with the increasing growth and changes of financial markets in developed and developing countries, or due to the unpredictable performance of investors, pure mathematical solutions are not enough to solve such problems, and therefore robust algorithms are considered to solve them. (SheidaeiNarmigi et al, 2020). Genetic algorithm can solve the problem of stock portfolio optimization by considering different levels of risk or success. By means of the genetic algorithm, you can create an optimal portfolio within a few minutes, which will cause a change in the capital market and will greatly help the efficiency of the capital market. One of the main signs of a country's progress is its economic progress, and if new methods are not used in the field of financial transactions, it will harm the economy (Pakmaram et al, 2017). Therefore, the researcher is trying to answer the question; how is the genetic algorithm in optimizing the stock portfolio in the financial decision of investors? Theoretical Framework Genetic algorithm The principles of genetic algorithm were first presented by Jan Haland, (1975). The genetic algorithm is an optimization algorithm with general application and is modeled on Darwin's evolutionary theory. Genetic algorithms are meta-heuristic algorithms based on biological evolution that try to obtain high-quality answers at the right time by creating an initial population and improving its quality by applying various operators (Farughi et al, 2020). Genetic algorithms are suitable for multi-objective optimization due to examining a set of possible solutions and also less sensitivity to a specific form of optimal points. Objectives are in the form of mathematical functions and represent appropriate use to improve decisions in optimization (Sheydaei Narmighi et al, 2020). The concept of stock portfolio optimization has become like a tool in the direction of developing and understanding financial markets and financial decision making. Consider the spread of risk and return at the same time and choose the amount of capital allocation between various investment opportunities based on the interaction between the two. Therefore, the investment that seeks to maximize the expected return and minimize the risk has these two opposite goals that must be balanced against each other. One of the interesting results of these two opposing goals is that the investor should diversify by buying several types of securities. On the other hand, due to the complexity of management, the system of rules and regulations and the policies of asset; management companies in the framework of financial markets, investment managers impose restrictions on their optimal asset set, which makes the model nonlinear and complicates the problem. (Faridi et al, 2022). Niko & Bazrafshan (2023) investigated the stock portfolio using a combined genetic algorithm and simulated refrigeration. The results show that the hybrid algorithm searches for the optimal solution in a shorter period of time than the genetic algorithm and performs better than the other two algorithms in terms of risk and return. Shahbazi (2022) investigated stock portfolio optimization with beta coefficient clustering approach. The goal of the challenge is to minimize the portfolio variance. The stock clustering method is to group beta coefficients of stocks into four clusters. The investor has an expected return that the portfolio must meet. Expected returns and clustered shares are inputs to the problem. It is not possible to borrow and sell on credit. A non-linear mixed integer mathematical model is presented to introduce the proposed portfolio and in one case it is checked on 50 most active stock companies. Finally, the Sharp index shows a favorable result. Research methodology In a simulation project, the final use of the input data is to build the simulation model. This process includes collecting input data, analyzing the input data, and using these analyzed input data in the simulation model. The statistical population that was used for the implementation of the mentioned research was accepted in the stock exchange company with 52 industries and approximately 1,543 symbols, which were used for daily trading in the stock market. In this research, 20 brands (companies) are considered including (Vabsadar, Vetjarat, Akhaber, Fakhuz, Fars, Balbar, Tapampi, Khasapa, Khodro, Sasharq, Sosofi, Shobhorn, Shapna, Ghopino, Folad, Qathabat, Kesra, Vabank, Veneft, Vaniki). The tool for collecting information and data is using the site https://www.fipiran.com. Research findings The amount of beta (risk) of stocks was calculated monthly using Excel software, the frequency of return and beta (risk) was calculated using Spss software, and the distribution functions were analyzed using Easy fit software. The results showed that by making more transactions, people turn from beginners to professionals, and people turn from risk-averse to normal and then risk-taking. Examining the system in a longer time horizon provides more realistic results and the simulation model can be examined in more realistic conditions. Conclusion The current research has been carried out with the aim of combining the genetic algorithm in the optimization of the stock portfolio in the financial decision making of investors. The results of this research are consistent with the results of Shahbazi (2022), Kalayci et al, (2020), Li & Tam (2020), Mirabi & Zarei (2020). Pakmaram et al, (2017) showed that the genetic algorithm had a lower objective function value, or in other words, it reached the best result with the least error; it performed better than other algorithms and shows the relative superiority of this algorithm in choosing the optimal stock portfolio. According to the present research, it is suggested that investors (risk averse/risk taking) should also examine other models with regard to the expected return, because according to their risk tolerance or risk aversion, their returns will be different. One of the important points for investors is portfolio management and considering future transaction costs. Investors always want to achieve their desired portfolio with the least number of transactions and consequently the lowest future transaction costs.

Purpose: In recent decades, a significant number of information technology-based businesses have risen to the top of the world's leading companies list. Until the early years of the third millennium AD, large companies and owners of oil wells and mineral resources, along with industrial production complexes that are several decades and several hundred years old, have been the flagships of the list of the largest and most successful companies in the world. However, this leadership has been astonishingly challenged by IT-based start-ups in the last two decades. The value of the production of information technology businesses from the total production of the world increased by about 29% in approximately 5 years, between 2015 and 2020, reaching $24.615 trillion. The share of the digital economy in the total world economy reached about 25%. In other words, the digital economy has absorbed two and a half percent of the traditional economy in just 5 years. These instances underscore the importance of considering the position of information technology-based businesses. According to the 2018 report of the Ministry of Information Technology, the share of the digital economy in the total gross domestic product of the country is approximately six and a half percent, while the global average of this index is around fifteen percent. This fact highlights the need to focus more on the development of these businesses in the country, which is the rationale for conducting the present research. This research was carried out with the aim of identifying the various dimensions influencing the formation and growth of these businesses and how these factors are interconnected in the country. Method: The present research was conducted in two distinct stages, qualitative and quantitative. Initially, the problem was identified and defined, followed by a review of previous related studies to identify the main dimensions of the model. Subsequently, a total of 12 in-depth semi-structured interviews were conducted with managers, entrepreneurs, technology policy makers, and information technology experts. Additionally, archival sources were consulted. Through content analysis and the coding method, the research conceptual model was developed, comprising three main categories. In the quantitative phase, the model was refined to form the basis of the research questionnaire. After assessing the content validity, the questionnaire was distributed to three groups within the industry community: 1) managers and executives of relevant businesses, 2) entrepreneurs and business owners, and 3) academic and business experts. Data collected from these questionnaires were analyzed using various statistical tests, and the validity of the model and its structures were confirmed through structural equation modeling. Findings: After conducting the systematic steps of the research and analyzing the data from the experiences of the participants in the qualitative part of the research, three categories were identified as the main ones: internal organizational characteristics, environmental drivers and obstacles, and political factors. Based on the obtained results, policy factors in this model are an exogenous and independent variable that plays a key role in the development of these businesses. This finding confirms the necessity of the government's presence and adoption of policies to support the development of these businesses. According to the results of this research, another crucial dimension directly affected by government policies is environmental factors. The third category of factors indirectly influenced by government policies includes organizational factors. These factors encompass having the right project team and access to expert human resources, the appropriate business model, a focus on innovation and marketing power, and market penetration power. Conclusion: By identifying these factors, the path becomes clearer for the government and other policy-making institutions. The development of the digital economy in the country necessitates simultaneous attention to environmental and internal factors. The government, as a key policy maker, plays a crucial role in fostering digital businesses. Activists and entrepreneurs in this sector view financing as a significant bottleneck in business development. Therefore, it is imperative for relevant institutions to prioritize current policies that support these businesses and facilitate financing for them.

Introduction: This study presents the elements and methodology of the reinforcement learning model according to the agent-based conceptual model of hospital accreditation in Iran. The elements and methodology of the mentioned model will create a favorable study base for creating a smart and multi-agent hospital accreditation system and environment simulation trends to provide efficient guidelines to relevant agents and policymakers. This study aims to address the main research questions concerning the uncertainties related to the reinforcement learning elements and the methodology selection in a multi-agent socio-technical system.Methods: To collect the necessary information to understand the elements and identify hospital accreditation processes, agents, the environment, and their interactions, systematic reviews of sources, scientific document reviews, and semi-structured interviews with experts were conducted. Interviewees were selected from university faculty members, hospital managers, and quality improvement officers through a targeted non-random snowball sampling method. The interviews were summarized using grounded-theory-based methods and a sequential and systematic approach. The characteristics of the machine learning process were collected using a systematic review method from the "Iran Hospital Accreditation Guide 2022". The process of selecting the features was done by correctly choosing the output features of the model, which are the actions of the agent. The list of agent actions was extracted as a general non-binary tree based on the classification of the tree structure from the conceptual content of the document.Findings: The extracted reinforcement learning model seeks to find the optimal chains of operational actions under conditions where quantitative data of the hospital are available. The most important elements of the model are:Set of States: Hospital accreditation factors such as input variables, output variables, indicators, parameters, and fixed numbers related to the metrics of each conceptual agent in the "Iran Hospital Accreditation Guide 2022".Set of Actions: Actions of intelligent agents in each reinforcement learning episode, paths from the hierarchically clustered binary tree, which are operational actions that can be performed in the hospital per set of state features.Reward Function: "Obtaining the highest possible score in the hospital ranking system by performing the least number of necessary actions."Policy Function: Based on the learning process of each agent, it relies on a DQN deep neural network and a gradient reduction algorithm.Operational Agents: The operational goal of each conceptual agent is "maximizing the accreditation points of the metrics of the relevant field by recommending the least measures."General Cycle of the Model: In this structure, each intelligent agent, a subset of the nine conceptual agents, has a multi-layered neural network. The characteristics of related states are entered into this neural network, and in the output, based on the special policy function definition of that agent, a map of optimal actions is created according to the agent's current conditions and states.Neural Network Model: The neural network of the intelligent agent is derived from the conceptual agent "management and leadership," specifying the input, hidden, and output layers of the network. Conclusion: Summarizing the background of related research showed that the approach to designing hospital accreditation models could be divided into two groups: "conceptual models without using intelligent agents" and "conceptual models using intelligence and operating systems". The investigations showed that these studies had the expected results and that the efficiency and effectiveness of the models and processes proposed by them had the necessary validity.

Introduction: Academic transgression (AT), as any behavior, belief, or condition that violates a social and academic norm, is a serious problem threatening academic integrity, especially when committed by faculty members, who are supposed to be educational role models. Societies employ social control means to respond to, prevent or reduce transgressive acts, to maintain social order and morality. The current study aimed to examine the perceived effects of self-control, job satisfaction, and life satisfaction, as social control means, on five types of interpersonal, educational, research, organizational, and sexual ATs among faculty members, and also to measure perceived prevalence of these transgressions in Iranian higher education. Material & Methods: A modified version of “perceived causal relations” methodology was adopted to study the perceived relations between reciprocal pairs of the variables. Network analysis was employed to analyze the data gathered from faculty members via an online questionnaire. Results: The results demonstrated that faculty member participants perceived high effects of their self-control, job satisfaction, and life satisfaction on decreasing ATs, particularly interpersonal, organizational, and educational ATs. Conclusion: As faculty member participants perceived high prevalence of all types of ATs in Iranian academia, there is an urge to consider new policies to employ effective social control means suggested in this research, especially self-control, to reduce, and even restrain the perpetration of these transgressions. We also suggest perceived causal relations as a useful and strong methodology to conduct research on sensitive topics, especially, transgression and crime

Increasing operating profit is a challenge faced by pharmaceutical distribution companies. Most of the researches conducted in this field have a cost reduction approach. The online monitoring system is one of the effective methods that can be used for managers' decision-making and improving the performance of the pharmaceutical distribution chain with the approach of reducing costs and increasing revenues. To create this system, conceptual, mathematical and computer modeling is needed. The aim of this research is to develop a mathematical model of the online monitoring system to improve the pharmaceutical distribution system based on a conceptual model using a reinforcement learning approach. The mathematical model was derived based on the factor-based conceptual model and using the multi-factorial reinforcement learning approach. After extracting the mathematical model, the effectiveness of the model was validated by comparing the results obtained from the output of the mathematical model and the actual results in a pharmaceutical distribution company. The findings of the research showed that the developed mathematical model has the ability to continuously improve the goals, decisions and performance of the pharmaceutical distribution chain processes, according to the interactions and changes in the behavior of agents and the state of the environment.

Considering that the users are the main focus of immersive journalism, any study in this field without understanding and recognizing them is incomplete. The quality of the VR news experience depends on many parameters, the most important of which are related to the cognitive and behavioral science of the users, apart from the technological factors that are prerequisites for making VR. In this regard, through interviews with experts in journalism and cognitive sciences, this research identified the categories that influence the depth of user’s immersion based on the Grounded Theory methodology and finally presented a conceptual model. The phenomenon of the model is “user involvement”. This category is affected by contextual factors such as "user’s demographic characteristics" and "type of news", as well as the intervening factors of "trauma" and "preventing factors of using virtual reality". In addition, the three categories of "cognition", "narrative" and "crafting pieces" provided the causal conditions that are the basis for the immersion in the news narrative. Finally, "focusing on user’s cognitive factors" in creating VR pieces is the interaction strategy that brought two consequences of "increasing immersion" and "changing norms and behaviors".

Background: Melanoma is one of the most commonly diagnosed cancers and the second cause of cancer-related death among people. This disease is the rarest and most malignant type of skin cancer. In advanced conditions, it has the ability to spread to internal organs and can lead to death. In Iran, for several years, significant data about melanoma have been collected either manually or electronically, due to its prevalence and the high costs it leaves on the country's healthcare system, but despite these valuable data, the health system is still unaware of the high potential of data mining in predicting the survival of patients with melanoma. Therefore, the aim of this study was to design an intelligent system to predict the survival of these patients. Methods: This study was practical in terms of nature and descriptive-analytical and retrospective in terms of method. The research population consisted of patients with melanoma cancer from the database of the National Cancer Research Center affiliated to Shahid Beheshti University, located in Tajrish Martyrs Hospital, Tehran, Iran (between 2007 and 2012), who were followed up for 5 years (n = 4118). SPSS and Weka software were used to design the support system for melanoma cancer survival prediction. The final model for predicting melanoma cancer survival was selected based on the evaluation indices of data mining algorithms. Findings: Neural network algorithms, simple Bayes, Bayesian network (BN) and combination of decision tree with simple Bayes, logistic regression, J48, and ID3 were selected as the used models of the country's database. Based on the findings, the neural network performed better with a value of 0.97 in terms of accuracy and 91.03 in terms of features. Conclusion: The performance of the neural network in all evaluation indices was statistically higher than other selected algorithms. Therefore, this algorithm was chosen as a support system for predicting melanoma cancer survival.

Purpose: Collecting data from users using conventional traditional methods, in order to achieve a specific purpose, is often expensive, time-consuming, and accompanied by disadvantages affecting the research results, the aim of the current research is to provide a model based on gamification in order to obtain the way of thinking of people through a case study of internet taxi applications in Iran (Snapp, Tapsi, and Carpino). Method: Research is applied in terms of aim and descriptive in terms of nature. Data collection was done by a researcher-made questionnaire, whose validity and reliability were confirmed based on expert opinion and Cronbach's alpha coefficient. The statistical population was all the users of the mentioned applications and members of Telegram, which was 180 people as a statistical sample. Findings: Comparing the results to determine the popularity of brands in the two methods was almost the same, in addition, the cost and time of using the proposed method was significantly less and the percentage of audience participation was higher. Conclusion: By presenting the proposed framework, the limitations of common data collection methods were removed and more accurate and higher quality data were used to understand people's thinking, it is also possible to use this method for all researchers.

Artificial Intelligence is an emerging technology that simulate human intelligence in machines and systems for their application business. Development of artificial intelligence requires extensive R&D activities. Management of R&D in field of AI needs to deploy novel knowledge to formulate and implementation of strategy, assign resources, organize and use of special tools. This paper aims, to identify and prioritize the components of data driven management of R&D in artificial intelligence technology. A hybrid technique was employed to perform the research. In Qualitative part, the literature of topic is reviewed, and 12 experts are interreviewed. Their opinions are analyzed based on grounded theory and 8 axial components were identified. In Quantitative part, by a questionnaire, the opinions of 85 experts of R&D and artificial intelligence were gathered through a questionaire and analyized based on structural equations model. The relvance and validity of the components were confirmed. The found components were weighted and proiritized through SWARA method as: systematic management, resources supplying, capability of big data analytics, supportive policies, infrastructures, data science development, organizational factors and business advantages.

IntroductionIn recent decades, a large number of information technology-based businesses have risen to the top of the world's top companies list. Until the early years of the third millennium AD, large companies and owners of oil wells and mineral resources, along with industrial production complexes that are several decades and several hundred years old, have been the flagships of the list of the largest and most successful companies in the world, but this dominance has been challenged by IT-based start-ups during the last two decades. According to the report of the Economist magazine, between the years 2006 and 2016, the top of the list of the world's top companies was suddenly empty of companies active in the fields of energy and production, and their places were filled by information technology young companies. These changes are clear examples of the transition of the world economy from resource-oriented to data-oriented.According to international reports, the value of the production of information technology businesses from the total production of the world’s economy has increased by 29% in the period of 5 years, between 2015 and 2020, reaching 24 trillion and 615 billion dollars, and the share of the digital economy has reached 25% of the total world economy. In other words, the digital economy has swallowed two and a half percent of the share of the traditional economy in 5 years. (UNCTAD, 2019)These facts show the need to pay attention to the position of businesses based on information technology.Theoretical LiteratureIT-based businessesBusinesses that convey their value proposition to customers through various information technology tools, and the main components of their business model are based on information technology, are called information technology-based businesses.Digital EconomyThere are three defined levels in the definition of digital economy. Its limited definition as the main core of the digital economy includes specialized products in the field of information and communication technology, including hardware production, software development, and the provision of Internet and telecommunication services. In the second level, in addition to the central core, it also includes the added value resulting from digital platforms and services. The third level of definition, referred to in the literature as the digitized economy, includes the added value resulting from the use of information technology in all industries.MethodologyThe present research is based on a model that is the product of a qualitative study. The mentioned model became the basis for designing the research questionnaire. After evaluating the validity of the content, the designed questionnaire was sent to a community consisting of three different groups of industry activists, and the received data was analyzed using the Structural Equations Method.ResultsAfter conducting the tests and making the corrections, the final research model was confirmed, consisting of 5 main categories and 22 subcategories or indicators. The main categories counted include 1) organizational factors, 2) environmental factors, 3) policy factors, 4) economic results, and 5) non-economic results.Internal characteristics refer to the factors that a business must focus on for success. In other words, if the following factors are present in a business, the chance of being successful in the market are much higher than that in businesses that did not pay attention to these factors. In other words, this group of factors refers to the internal coordinates of businesses prone to growth and development. These factors include the right project team, business model, specialized human resources, access to financial resources, innovation, and effective marketing.The second group of factors affecting the development of businesses under research are environmental factors. These factors include five categories of sanctions, inappropriate laws, weak information technology infrastructure, economic stagnation of the country, and problems of the information technology innovation system.The third category of factors explained in this research is the role of the government in the development of such businesses in the country. These factors have focused on how the government intervenes in this matter in such a way as to facilitate and accelerate the growth of these businesses. Although these factors are also relevant for businesses in different sectors, the examples of these policies in IT businesses and how the government supports them are different.ConclusionBased on the results of this research, policy factors in this model are an exogenous and independent variable that plays a key role in the development of this category of businesses. This issue confirms the necessity of the government's presence and adoption of policies to support the development of these businesses.

The purpose of stance detection is to identify the author's stance toward a particular topic or claim. Stance detection has become a key component in applications such as fake news detection, claim validation, argument searching, and author profiling. Although significant progress has been made in stance detection in languages such as English, little attention has been paid in some other languages, including Persian. One of the main problems of research in Persian stance detection is the shortage of appropriate datasets. In this article, to address this problem, we consider data augmentation, the artificial creation of training data, which is used to conquer the shortage of datasets. In this research, we studied several methods of data augmentation such as EDA, back-translation, and merging source dataset with similar one in English language. The experimental results indicate that combining the primary data set with the translation of another dataset with similar content in another language (for example English) result in a significant improvement in the performance of the model.

Making more profit in organizations requires accurate tools to strengthen the business for proper financial planning. Therefore, the purpose of this study was to demonstrate knowledge of the neural network to provide a framework for achieving the desired profit in the organization's financial planning. This research was of the applied type and was performed by the analytical survey method. The study population was the organizations present in the Exchange and Securities Organization, and the data extracted from the official system of the Exchange and Securities Organization of Iran (Cadal) were used. This research was conducted using the neural network method in a MATLAB program environment. Findings The range of changes of factors affecting the main index in the organization's financial planning (profit) and the most important factors affecting it were determined. The proposed framework was reviewed in three different sets of three various industries and had acceptable results. Therefore, the study results indicated that the proposed framework in this study could be used for other organizations present in the stock exchange and securities.

با توجه به پیشرفت فناوری و ارتباطات، روش‌های مدیریت آموزشی سنتی جوابگویی نیازهای آموزشی نبوده لذا، نیاز است تا با استفاده از فناوری‌های جدید، نظام آموزشی ارتقا داده شود. به همین دلیل، هدف این پژوهش طراحی مدلی است که محتوای دوره‌های آموزش مهارتی را بر اساس نیاز بازار کار و توانمندی هنرآموز با استفاده از قراردادهای هوشمند بلاک چین تطبیق دهد. روش کلی این پژوهش، علم طراحی است که برای طراحی مدل از روش فراترکیب استفاده‌شده است. ارزیابی مدل نیز از طریق یک پرسش‌نامه طراحی‌شده انجام پذیرفت که این پرسش‌نامه توسط یک نمونه آماری 30 نفر، از جامعه آماری که شامل معلمان دروس فنی، مدیران بخش آموزش فنی و حرفه‌ای ادارات آموزش‌وپرورش و افراد مسئول تدوین رشته‌ها مهارتی می‌باشند تکمیل شد که این نمونه‌ای آماری با روش نمونه‌گیری هدفمند انتخاب‌شده است. اطلاعات پرسش‌نامه با روش‌های آماری توصیفی و استنباطی با روش آزمون آماری تی تک نمونه‌ای تحلیل شد. مدل معرفی‌شده که با استفاده از فناوری قرارداد هوشمند طراحی‌شده است، می‌تواند برای اثربخش بودن آموزش مهارتی بسیار مفید باشد و ارتباط بین بازار کار و آموزش را بهبود ببخشدو نتایج تحلیل‌های آماری که برای پردازش مدل به‌دست‌آمده بیانگر این نکته است که مدل پیشنهادی از درجه اعتبار مناسبی برخوردار است.

The beliefs of the stakeholders who provide tourism services in destinations play an important role in the sustainability of destinations. The values ​​of variables affecting these beliefs are always ambiguous, and the causal relationships between them are uncertain. The reality of opinion dynamics reflects even more complexity. Therefore, the present study used fuzzy logic, Bayesian belief networks, and opinion dynamics models to investigate a complex topic. The models were applied to the data obtained from the Iranian stakeholders in a bid to attract Chinese tourists. The data was collected by administering the questionnaire to 540 stakeholders during 2019, 2020 and 2021. Finally, the research provided not only an agent-based simulation to visualize trends and their evolution or co-evolution, but also the possibility of predicting defective and desirable trends with the ultimate aim of preventing or reinforcing them.IntroductionDespite the apparent regularity of the tourism process, ambiguity, uncertainty, and dynamism in the host community complicate the control of the process. It is ambiguous because different tastes are applied to it (Liu et al., 2021). It is uncertain because the stakeholders have different beliefs (Merigó et al., 2012). It is dynamic (Sainaghi & Baggio, 2017) because it is affected by environmental changes such as diseases (Soltan-Alinejad & Soltani, 2021), natural disasters (Rosselló et al., 2020), information technology (Xiang, 2018), and environmental issues (Saviolidis et al., 2021). Accordingly, conventional statistical tools are ineffective in capturing the whole complexity at work (Sun & Müller, 2013). In this respect, the current study used fuzzy logic, conditional probability, and diffusion processes on complex networks in order to model ambiguity, uncertainty, and dynamism, respectively. The fuzzy membership functions were used for the initial processing of the collected data (Serrano-Guerrero, 2021). The study utilized Bayesian belief networks to measure the community’s parameters in the current situation (Rohmer, 2020). Moreover, opinion dynamics was employed to predict future trends (Ureña et al., 2019). Specifically, the structural and parametric training of Bayesian belief networks was used to model the current beliefs of stakeholders in tourism destinations (Rohmer, 2020). The developed Bayesian belief network is able to make predictions, prescriptions, and causal inferences about the stakeholder-related variables of the community (Nadkarni & Shenoy, 2001). Therefore, various parameters of the community can be estimated by Bayesian belief networks (Gallardo, 2022). Stakeholders’ opinions are dynamic because they are influenced by contextual factors and other stakeholders (Steils et al., 2021). The study also intended to simulate the dynamics. There are many variables in stakeholders’ interactions that are influenced over time. Each stakeholder, also called the agent, has an opinion on each variable, so in interactions, stakeholders are influenced by their own opinions and those of others (Cape et al., 2018).Materials and MethodsThe current study aimed to create a structure for the beliefs of tourism stakeholders on the basis of tourists’ wishes and needs. Therefore, Bayesian belief networks, as a kind of causal network based on conditional probability theory, were used in this research. In the next step, the research employed opinion dynamics which is a subset of agent-based methods in the field of simulation. Agent-based methods, though recommended, are less addressed in the field of tourism. The data was collected by administering the questionnaire to 540 stakeholders in Iran during 2019, 2020, and 2021. The population of the stakeholders consisted of five classes including 265 hotels and accommodation centers, 170 tourism companies, 85 bus companies, 15 airlines, and 5 railway companies. The stratified sampling method was used to select the research participants.Results and DiscussionBy generating a joint probability distribution function for the variables governing the beliefs of the Iranian tourism stakeholders, the research integrated the beliefs into a coherent mathematical structure. In practice, this equation represented a kind of causal network structure that can answer different questions with the advantage that it can be retrained to be used to create a more updated distribution function when new data is entered. Relying on the agent-based method of opinion dynamics, the study simulated the future behavior of tourism stakeholders. The simulation showed what behaviors can be useful in attracting tourists, what behaviors are not in line with protecting the environment, and what behaviors can be suitable or unsuitable for attracting female tourists.ConclusionThe research was designed to use the methods separately in order to avoid increasing the complexity of the research process due to the simultaneous use of diverse and powerful tools. Therefore, it is possible for researchers to use each of the methods independently. In addition, the obtained results are useful, diverse, and practical, and suggest the suitability and further use of agent-based methods (e.g., opinion dynamics) in the tourism research

Objectives: A service level agreement is a commitment between the service provider and customer in which certain aspects of services, quality, availability, and responsibilities are agreed upon between the service provider and service user. The purpose of this research is to design the service level agreement model using the database theory.Methods: This research was conducted based on rounded theory and a qualitative-quantitative approach. The method of data collection in the qualitative section was an in-depth interview and the interview texts were analyzed in three stages open, central, and selective coding. Atlas software was used for data analysis. The sampling method was snowball type, which was achieved by conducting a total of 16 interviews with experts in the field of information and communication technology. In the quantitative part, the statistical population of knowledge-based companies active in the field of information and communication technology was determined, and based on confirmatory factor analysis, a sample size of 120 people was selected. For the quantitative validation of the model, the structural equation modeling method was used.Results: In the qualitative part, determining the causal factors, background, strategy, intervention, and consequences of designing the service level agreement model was done.In the quantitative part, the effect of causal conditions on the main phenomenon is equal to 0.662, the effect of the main phenomenon on the strategy is equal to 0.428, the effect of background conditions on the strategy is equal to 0.551, the effect of intervening conditions on the strategy is equal to 0.408 and the impact of the strategy on the outcome was calculated as 0.639.Conclusions: In the qualitative part, the research resulted in the design of a new service level agreement model based on data theory, and in the quantitative part, the significance and standard coefficient of the model components were confirmed. Keywords: service level agreement model, database theory, customer, service.

Background: The global spread of the COVID-19 virus has not only posed a severe threat to public health but has also triggered a profound economic crisis affecting numerous industries. Addressing and mitigating the economic repercussions of the pandemic is imperative to prevent further damage. While prior research has explored the negative impacts of the COVID-19 pandemic on various industries, there has been a notable gap in understanding the specific factors that influence how industrial crises stemming from viral outbreaks are managed. Methods: This article undertakes a comprehensive investigation into these influential factors. Through in-depth interviews with industry experts, we identified a set of 30 pivotal variables in this context, forming the basis of an initial model. Subsequently, a questionnaire was administered to a cohort of one hundred managers and industry experts to assess these variables. Employing exploratory factor analysis, we categorized the 30 variables into six distinct categories: producer-related factors, supplier-related factors, distributor-related factors, retailer-related factors, consumer-related factors, and government-related factors. Results: Our findings revealed several strategic considerations for effectively addressing industrial crises in the face of viral outbreaks. These include the importance of building trust with customers in emerging markets, streamlining the adoption of digital technologies by customers, enhancing the customer relationship management process, prioritizing awareness, concern, and environmental consciousness, and providing support to consumers during times of viral spread. Conclusion: To effectively navigate industrial crises triggered by the widespread dissemination of viruses like COVID-19, businesses and governments should prioritize strategies that align with the identified factors. By focusing on these key areas, industries can bolster their resilience and minimize the adverse effects of such crises, ultimately contributing to economic stability and recovery in the face of global health emergencies.

Despite the advantages of transportation development, its negative consequences, including greenhouse gas (GHG) emissions, air pollution, increasing energy consumption, traffic, and accidents in transportation, have caused serious concern in the world community. Following the global efforts to introduce the framework for sustainable development (SD), the sustainability concept entered the transportation of literature known as sustainable transportation (ST). We didn't find a universally agreed definition and indicators for (ST). To overcome this limitation, we extracted special terms in different definitions of (ST) and widely used indicators in selected articles. Using 60 widely used indicators and the importance-performance analysis (IPA) method, we evaluated the transportation of Tehran. Like some metropolises, Tehran city has high air and noise pollution, congestion, traffic, and accidents. The main advantage of our research is the possibility to simultaneously assess the importance, performance, and prioritize performance of indicators for improvement, with optimal cost and time. Our evaluation showed that 41 indicators (68.34%) have high importance, but their performance is low and should be improved. 80% of these indicators were related to social and environmental dimensions. We prioritized Tehran's transportation indicators and provided recommendations to improve their performance. Prioritizing indicators showed that improving the performance of Tehran's transportation, reducing natural resource consumption, attention to human health, and reducing energy consumption have high priority. Finally, we have provided a comprehensive definition (ST) of widely used terms.

In the era of rapid technological progress, companies which are important and influential pillars in society, cannot continue their existence with the same old slow methods, commensurate with the speed of impact of technology, especially smart technologies on society, and they should think about using the new and more agile ways of directing the corporation. As the working environment of companies has become more complex, which is caused by the need to process large amounts of information and data and make faster decisions, and on the other hand, due to scandals caused by the improper performance of company directors, trust in directors has been weakened. Restoring this trust requires the selection of directors who are far from personal interests and bias and perform their duties with enough loyalty and care. In such a situation, the characteristics of artificial intelligence have attracted the attention of company directors and corporate law experts to the possibility of using it as a director of commercial companies, and in scientific circles, the question of whether AI can be used as a director in the economic and social sectors, especially in commercial companies has become a hot debate. However, the entry of artificial intelligence into the board of directors faces challenges that mostly arise from the autonomy and different characteristics of autonomous artificial intelligence from the technologies before it. The competence to accept the position and how to perform the fiduciary duties and obligations of directors, as well as identifying the regime of responsibility, are the most important challenges in choosing artificial intelligence as a director. The approach and purpose of the article are to explain the competence and the possibility of performing the duties and obligations of directors by artificial intelligence and to identify the efficient responsibility regime, to the extent of proving the possibility of becoming a director. In this regard and in the upcoming article, by studying and reviewing the opinions of artificial intelligence experts and lawyers, it has been compared the performance and characteristics of artificial intelligence and human directors and identified the capabilities of artificial intelligence in the position of a member of the company's board of directors. Regarding the eligibility to accept the position of directorship, it can be said that artificial intelligence, like humans, whose natural characteristics enable them to perform the actions of a representative, has the practical ability to become a director and perform duties as a representative of the company and can adjust its performance to achieve a clear goal that is the company's goal, in a way that we can say it intends. Although artificial intelligence has the practical ability to represent the company, in order to become a director, only practical ability is not enough and there must be a legislative prescription. In this regard, it is necessary for domestic legislators and international institutions, considering the capabilities and characteristics of artificial intelligence, to pass laws in order to identify the legal competence for it. Regarding the performance of duties and obligations of directors, due to the characteristics of artificial intelligence, including autonomy, logic, creativity, and the ability to make decisions very quickly in complex and difficult situations and process a large amount of data and information, without human intervention, artificial intelligence has the possibility to make decisions as a director and perform the tasks of directors. The authors' point of view is that with the legal design and coding of artificial intelligence, based on the established standards of company law regarding the duties of directors, artificial intelligence can fulfill the fiduciary duties of directors, away from conflicts of interest and bias, and with sufficient transparency and care. Regarding responsibility, although this technology is new and rapidly growing, most of the currently accepted principles and laws regarding responsibility are still applicable and there is no need to adopt a new approach to responsibility. However, considering the speed of artificial intelligence development, a responsibility regime must be described and implemented that does not lag behind the growth of technology and can act quickly in order to compensate for the damages. On the other hand, considering the risks that identifying any personality for AI will bring, an approach should be taken in the area of responsibility that always the developer or the company using AI as a director is accountable for the actions of artificial intelligence. It seems that a combination of legal regime and compensatory regime can solve the problem of responsibility. In the legal regime, responsibility instead of artificial intelligence and during judicial proceedings is attributed to the developer or company using AI as director, and in the compensation regime, losses are paid through compulsory insurance or a compensation fund or both. The compensatory liability regime increases the speed of compensation because the problems and slowness of the legal regime do not occur, and ensures the damages without entering into the judicial process. Finally, given the topics discussed in this article, AI has important capabilities that make it a good option to be a director. This approach is especially important for countries facing aging populations, migration of directors to more advanced countries, or cruel sanctions from other countries. Therefore, the legislator should make the necessary regulations to allow companies and the community to benefit from artificial intelligence as a member of the board of directors.

Objective: In this study, we aimed to compare the clinical and radiological results of the extensile lateral (EL) approach and sinus tarsi (ST) approach in calcaneal fracture surgery. Materyal-Metod: A total of 40 patients with Sanders II-III calcaneal fractures, including 22 patients in the EL group and 18 patients in the ST group, were included in this retrospective study. In the radiological evaluation, changes in the calcaneus Bohler and Gissane angle, length, width, and height were measured during the preoperative and postoperative period. Clinically, American Orthopedic Foot and Ankle Society (AOFAS) and the visual analog scale (VAS) were evaluated. In addition, postoperative complications were also evaluated. Results: In terms of demographic data, the results of both groups were similar. Both the length of hospital stay and the time to surgery were shorter in the ST group (p=0.019, p=0.02, respectively). There was no significant difference between the groups regarding radiological, clinical AOFAS and VAS scores. 13.6% superficial infection was seen in the EL group, and no wound problems were encountered in the ST group (p=0.102). The rate of sural nerve injury was 36.4% in the EL group and 5.6% in the ST group (p =0.003). Conclusion: Both approaches clinical and radiological outcomes were similar. However, the advantages of the ST approach were shorter hospital stays, lower rate wound infections, and sural nerve injury problems.

Background and Aims The purpose of this article was to present a local model for ecosystem analysis of the IoT business model in the field of rehabilitation in Iran with an emphasis on improving the quality of life of the disabled community in the country.Methods The current article can be categorized as applied, combined (qualitative and quantitative), and survey-based descriptive. The society, i.e., the statistical sample, included eight experts in the qualitative part and consisted of 100 executives and experts in the quantitative part. A questionnaire was used to analyze the dimensions and indicators. The face validation of the questionnaire was performed by the Delphi technique. Content validity ratio and content validity ratio were used as two content-related validity indicators for qualitative validation. The reliability of all dimensions and indicators was confirmed through Cronbach’s test with a population consisting of 30 experts. To analyze the proposed model of the researcher, we used inferential tests, loading factor analysis, t, structure equation modeling, and fitting techniques on a 100-person sample.Results The fitness of the model was approved by experts with 99% confidence according to the philosophy, goals of the theoretical foundations of dimensions and components, feedback, and output of the model.Conclusion The findings of this paper led to a model with several dimensions: (1) environment, (2) actors, (3) ecosystem cycle, and (4) IoT architecture in business. The corresponding indicators for the identified dimensions were validated. Independence, interaction, and empowerment were considered three principle values for the target community.

Soft operations research (OR) from a pluralist paradigm offers effective approaches to structure and improve problem situations with different stakeholders' worldviews and conflicts of interest. These approaches aim to structure and improve messy situations. In this study, we utilized Soft System Methodology (SSM) as a popular soft approach at two levels. Firstly, we applied Soft System Methodology process (SSMp) to plan the systemic intervention process in the employee promotion system of an oil and gas company. Then, Soft System Methodology content (SSMc) was employed to investigate the content of the company's employee promotion system, which was causing dissatisfaction among employees. By taking into account the worldviews of managers and employees, three root definitions were formulated. During this phase, a fair environment was created to facilitate the expression of different perspectives, allowing employees with varying levels of organizational power to openly voice their opinions. Finally, through stakeholder participation and discussion, an agreed model, root definition, and proposed actions to address the flaws in the employee promotion system were developed.IntroductionDue to the disorder of problematic situations and the environments that contain them, the traditional thinking of operations research, which has been devoted to solving problems for years, faces serious criticism. Today, complex and diverse management problems require different solutions. A significant group of issues is not single, single-purpose, clear, and indisputable. In order to mitigate the inadequacies of the aforementioned paradigm in solving real-world problems, the interpretive (learning) paradigm from 1970 to 1980, using situation improvement methodologies, has been developed under the title of research in non-classical operations. In this study, the issue of the inefficiency of the career promotion system of a company active in the field of oil and gas has led to the lack of promotion and job stagnation for a significant part of the specialists in this field. This has resulted in a decrease in the level of productivity of the organization's human resources. The purpose of this research is to use the soft systems methodology specifically on two levels (process and content) in the problem context to make a systematic intervention in improving the mentioned situation. By understanding the relationships governing the investigated system and with the participation of actors and stakeholders within the boundaries of the system, an effective step can be taken in the direction of employee satisfaction.Materials and MethodsThe underlying theory in the soft systems methodology is action research. It is a cycle where action leads to critical thinking and possibly further action. Therefore, in the soft systems methodology, every part of implementation is part of research, and every use of this method requires learning and new research. In this study, according to the explanations presented, the soft systems methodology is used on two levels to improve the conditions. In level one, process-oriented soft systems methodology (SSMp) is used to plan intervention in the system. After planning and structuring the way to intervene in the system at the second level, the seven-step model is used to intervene in the employee promotion system. At this level, the methodology of content-oriented soft systems (SSMc) is used to enter the system and intervene in the content of the conditions. The mentioned process has intervened in the employee promotion system in the form of an action research cycle.Discussion and ResultsSoft systems methodology, as a practical approach, tries to create improvement by intervening in problematic social situations. In this article, two aspects of this methodology have been used to manage the project and carry out the project to improve employee dissatisfaction with the career promotion system. Firstly, soft systems methodology using the classical model has been used. This approach has been used in the initial application not as a method or technique but as a set of principles to guide the planning of the process of involvement in the employee career promotion system, and in this way, it has been implemented in project management. In the next step, the content-oriented soft systems methodology has been used to investigate employee dissatisfaction with the career promotion system. The researchers investigated the cultural and political characteristics of the problematic situation. This section has been used in response to the critics of soft systems methodology based on the inability in systems where there are serious conflicts between stakeholders. Therefore, by studying the cultural situation and power structure, it is possible to manage conflicts better, and in this way, the implementation of the project in a liberating manner prevented discussions from being drawn to the priorities of powerful people in the system, which is exactly in accordance with Checkland's opinion regarding the significant dependence of the project on the method of its implementation to fix the mentioned weaknesses.ConclusionIn this research, firstly, the process of intervention in the career promotion system of the employees has been structured, and with the initial planning, the possibility of systemic and systematic involvement has been provided. In this way, the researchers and the project team, consisting of managers and employees, structured the study process by using the process-oriented soft systems methodology in coordination and consensus with each other. In the next step, the content of the career promotion system was examined with a content-oriented soft methodology approach. Since both stages have been carried out with the participation and cooperation of a number of senior managers, middle managers, and employees, and with the researchers' facilitation, the research output has a strong possibility of acceptance and implementation. The steps of doing the work are mentioned along with the necessary details to increase the recoverability and, as a result, improve the validity of the research implementation process. In this way, researchers have been provided with the possibility of deeper investigation and similar implementation in other fields.

The aims of this study is to provide a conceptual model of hospital accreditation in Iran through qualitative research. The grounded theory model was compiled using the results of the analysis of the interviews. Then, with the help of the grounded theory model and the results of qualitative analysis, a state diagram was obtained. Using the state diagram, initial sampling, systematic review of sources and results of interviews, 9 conceptual agents "governance organizations, management and leadership, clinical personnel, support personnel, hospital infrastructure, assessor, standards, assessment method and service recipient" were identified. Finally, the conceptual model of agent-based, environment, behavioral rules of agents and their input and output interactions was presented. In future researches, reinforcement learning models can be designed according to the conceptual model of this study, so that by using it, software developers can develop a suitable framework for solving complex problems in the field of hospital accreditation. Because the field of hospital management systems is one of the desirable types of socio-technical systems that have high capacities.

Abstract The purpose of this research, which was conducted with a quantitative approach and using a questionnaire tool, is to validate one of the conceptual models available in the literature and provide a framework of factors affecting the development of the digital economy in Iran. The statistical population of this research includes the founders, managers, employees and experts of businesses based on information technology and digital economy activists in the country, and the minimum sample size for the validity of the results according to the method used for quantitative analysis is 160. In this research, the findings of the reference qualitative research were implemented in the form of a questionnaire using the Google Form tool and sent to a community of industry activists. The data received through 240 completed questionnaires were analyzed using the confirmatory factor analysis method, and the results were presented in the form of a final model. The results of this research show that the development of the digital economy requires simultaneous attention to environmental factors and inter-organizational factors, and the government as a policy maker plays a key role in the development of digital businesses. Extended Abstract Introduction In recent decades, a large number of information technology-based businesses have risen to the top of the world's top companies list. Until the early years of the third millennium AD, large companies and owners of oil wells and mineral resources, along with industrial production complexes that are several decades and several hundred years old, have been the flagships of the list of the largest and most successful companies in the world, but this dominance has been challenged by IT-based start-ups during the last two decades. According to a report by Economist magazine, and from 2006 to 2016, the top of the world's top companies was suddenly evacuated from the active companies in the field of energy and production, and the information technology companies captured the vacant place. These changes is a clear sample of transient of the world economy from source-based to data-based, Challenging the position of super companies such as ExxonMobil and Royal Dutch Shell by emerging companies like Alphabet and Facebook are clear signs of the digitization of industries and the superiority of the digital economy over the traditional world economy. The value of the production of information technology businesses from the total production of the world has increased by 29% in the period of 5 years, between 2015 and 2020, has reached 24 trillion and 615 billion dollars, and the share of the digital economy has reached 25% of the total world economy. In other words, the digital economy has swallowed two and a half percent of the share of the traditional economy in 5 years. (UNCTAD, 2019) These facts show the need to pay attention to the position of businesses based on information technology. According to the importance of the subject and based on the mentioned materials, the main problem of this research is to identify the main factors affecting the development of businesses based on information technology in the country as the most important players of the digital economy era. Theoretical Literature IT-based businesses Businesses that convey their proposed value to customers through various information technology tools, and the main components of their business model are based on information technology, are called information technology-based businesses. In another definition, based on the research done by Witt et al., those businesses whose ways of doing work and creating income change with the changes made in digital technologies can be considered as information technology-based businesses. (Weit et al., 2014) Digital Economy The digital economy is opposed to the classic economy in which economic activities are being done in traditional offline platforms. In the digital economy, all activities are carried out on digital platforms based on information technology. There are three defined levels in the definition of digital economy. Its limited definition as the main core of the digital economy includes specialized products in the field of information and communication technology, including hardware production; software development, and the provision of internet and telecommunication services. In the second level, in addition to the central core, it also includes the added value resulting from digital platforms and services. The third level of definition, referred to in the literature as the digitized economy, includes the added value resulting from the use of information technology in all industries. Kazemian & Mardani (2016) has considered the environmental, organizational, and human factors as the most important factors of the success of the business under study. Khayatian et al, (2014), in order to identify the way of growth of the knowledge-based companies in Iran, have identified environmental features, infrastructure, financial provision, organizing, and human force as the most important factors effective on the growth of these businesses. Methodology The present study is qualitative and applicable, and questionnaire is its data-collecting tool. The questionnaire distribution has been done electronically and in the google form field. The statistical community includes founders, managers, employees, and knowledgeable persons in the information technology businesses, and persons active in the country's field of digital economy, After identifying and defining the problem, previous related studies have been reviewed and the possible dimensions of the model have been identified. Then, the most appropriate and comprehensive conceptual model was selected among the previous researches and with brief modifications, it became the basis for designing the research questionnaire. After evaluating the validity of the content, the designed questionnaire was sent to a community consisting of three different groups of industry activists and the received data was analyzed using Structural Equations Method. Discussion and Results The inferential statistics was used to test hypothesis, and the spss software was used to investigate the hypotheses of the study. Then the PLS software was used for the conceptual model of the study. After conducting the tests and making corrections, the final research model was confirmed, consisting of 5 main categories and 22 subcategories or indicators. The main categories counted include 1) organizational factors, 2) environmental factors, 3) policy factors, 4) economic results and 5) non-economic results. Internal characteristics refer to the factors that a business must focus on for success. In other words, if the following factors are present in a business, the chances of being successful in the market are much higher than businesses that did not pay attention to these factors. In other words, this group of factors refers to the internal coordinates of businesses prone to growth and development. These factors include: the right project team, business model, specialized human resources, access to financial resources, innovation and effective marketing. The second group of factors affecting the development of businesses under research is environmental factors. These factors include five categories of sanctions, inappropriate laws, weak information technology infrastructure, economic stagnation of the country and problems of the information technology innovation system. The third category of factors explained in this research is the role of the government in the development of such businesses in the country. These factors have focused on how the government intervenes in this matter in such a way as to facilitate and accelerate the growth of these businesses. Although these factors are also relevant for businesses in different sectors, the examples of these policies are different in IT businesses and how the government supports them. Conclusion During the past years, many studies have been conducted on the topic of information technology businesses, but few researches have comprehensively and multi-dimensionally addressed the important factors affecting the growth of these businesses and the development of the digital economy in the country. According to this research gap, the current research was defined. The main result of this study is a conceptual model consisting of several categories of factors affecting the development of the digital economy in Iran. The scientific contribution of this study is the quantitative validation of the mentioned model and the explanation of the relationship between the identified factors in the ecosystem of the Islamic Republic of Iran. The present study was conducted to investigate the effective factors of the development of thchnology information-based businesses in Iran. One of the important results of the current research is to emphasize the key role of the government and the policy-making departments of governance as an independent factor in the development of these businesses. This issue confirms the necessity of the government's presence in this area and the need to adopt supportive policies for the growth of these businesses and the development of the digital economy in the country. Mosleh and Fani have also considered the government as the biggest customer of information technology products and services, have emphasized that the policy making and supporting role of the government in the development of information technology is very important and effective (Mosleh and Fani, 2020). The findings of this research show that other factors affecting the development of these businesses are influenced by government policies. One of these categories is external factors. The high speed of changes in the information and communication technology industry has doubled the importance of paying attention to these factors (Hajipourfard et al, 2021). One of the important indicators under this category of factors is information and communication technology infrastructure. According to international statistics, investment in the development of information technology infrastructure is the driver of economic growth and development of countries (Sayeh Miri and Abbas Khani, 2019; Salehi Abar, 2016). Another important environmental factor is sanctions and economic stagnation, which have made it difficult for companies and related institutions to work in different economic sectors. Another group of factors that are indirectly influenced by policy factors are organizational characteristics. These factors include the right project team and access to expert human resources, the right business model, innovation and marketing. Ghazinouri and his colleagues have also pointed out the importance of specialized human resources in the success of the research subject businesses. (Ghazi Nouri et al, 2019) According to the model of this research, the results of the development of these businesses can be classified in two parts: economic results and non-economic results. Productivity growth, economic prosperity and employment are among the most important economic consequences of digital economy development. These findings have been confirmed in other studies such as Amjadi and Shafei and Salehi Abar. Amjadi and Shafei have found a direct relationship between the development of information technology and the growth of productivity. (Amjadi and Shafei, 2017) In Salehi Abar's research, the effect of information technology development on the country's economic growth is also highlighted. (Salehi Abar, 2016) The second category of digital economy development results in the country are long term non-economic results, which include protecting the environment, promoting entrepreneurial culture, and promoting information transparency. The findings of this research can effectively help policymakers in the field of digital economy and clarify the way of the government in the optimal allocation of support resources for these businesses.

The main purpose of this applied research is to describe the methods of "improving the productivity of the professors of the command and staff university of the Islamic Republic of Iran Army on satisfaction with the services received", based on the Kano model in the form of basic needs, functional needs, motivational needs, ineffective needs and reverse needs. Data collection was done through field and library methods. According to the standard steps of the Kano technique, the questionnaire questions were used in two positive (functional) and negative (non-functional) modes and based on the five-choice Likert scale. The statistical population was internal professors of the Command and Staff University of the Islamic Republic of Iran Army with the academic rank of professor, associate professor, assistant professor, and instructor. The findings of the research showed that if the sixteen functional needs are met, the level of satisfaction of the professors will be equal to 80% and if they are not met, the level of their dissatisfaction will be equal to (-52)%; If the specified eight motivational needs are met, the level of satisfaction of the professors will be equal to 73%, and if they are not met, the level of their dissatisfaction will be equal to (-32) percent.

Customer retention is an important issue for any organization, so finding a way to retain the customer is one of the critical needs of any organization. In this regard, the goal in the field of machine learning is focusing on the problem of accurate customer needs with a method based on extracting opinion and sentiment analysis and quantifying customers' emotional orientation.In the other words, the issue is designing a recommender system to provide appropriate services to customers, using their opinions and experiences. The proposed solution, by receiving and reviewing customers' opinions and experiences in the form of extracting variables such as user sentiment score for tweets, relation score, cosine similarity, and confidence factor, and considering groups of relevant features and registration ideas in the process of training and testing, the result is presented in the form of a banking service suitable offer. In order to provide a recommending solution, appropriate classification methods are used along with opinion mining methods and an appropriate validation approach, and the final designed system with a small error, in order to provide personalized services, will step in to help bank managers.Since currently there is no complete provision of banking services tailored to the situation of customers, so in this regard, this mentioned system will be very helpful.

INTRODUCTION: One of the common reasons for admission to the emergency department is hip fractures. Mortality and morbidity rates are high in these fractures. According to many orthopedic surgeons, it is of the opinion that the radiographic image is sufficient for the treatment planning of these fractures. However, computerized tomography (CT) is sometimes needed to beter understand complex fractures. Our aim in this study is to determine whether there is a need for CT as well as radiography in determining the surgery plan in hip intertrochanteric (IT) fractures by evaluating among different observers. METHODS: Between January 2016 and January 2020, 71 patients who were operated for hip IT fractures and had both radiography and CT in automation records were evaluated by nine observers. RESULTS: The meanage of 71 patients (37 females, 34 males) was 80.8 +- 8.35 years. Observers required CT in 19.9% of the patients for treatment planning of these patients and to beter understand the fracturecon figuration. Observers found that 5.7% of the patients made changes in treatment planning after CT imaging of the fracture. DISCUSSION AND CONCLUSION: Considering beter understanding of hip biomechanics, obtaining three-dimension alimages with anatomical CT and developing implant technology, it was determined that surgeons' need for CT imaging in hip IT fractures has increased. Keywords: Hip, intertrochanteric fracture, advanced age, radiography, computed tomography.

Growing global pressures for higher industrial sustainability has persuaded organizations to implement new managerial techniques at all organizational levels to achieve supply chain sustainability. However, such novel management practices should be measured in terms of the extent to which they may promote sustainability performance. The purpose of this study was, thus, to examine the dynamic behavior of LARG supply chain management practices and their impact on sustainability in the auto parts supply chain. Expert views were utilized in the present study to prioritize the LARG practices to achieve sustainability through fuzzy DEMATEL and the fuzzy analysis network process to present an integrated approach to LARG practices. Finally, the system dynamic approach was employed to assess the sustainability performance of LARG supply chain management practices. The finding indicated theAuto parts Supply Chain, Fuzzy Analytic Network Process, Fuzzy DEMATEL, System Dynamics, Sustainability significant role of improvement scenarios in the implementation of comprehensive quality management and punctual production, and thereby, in supply chain sustainability. The proposed model can assist industrial managers and decision makers to identify and adopt effective practices on sustainability in LARG supply chain management.

The purpose of paper is to present a conceptual model of the IOT-based online monitoring system to improve the distribution system‌‌ of pharmaceutical, using the agent-based modeling approach.First, by reviewing the research literature and interviewing industry experts, the basic concepts are extracted and using the grounded-theory method, the conceptual grounded-theory model is compiled; finally, using the obtained model and performing the agent-based modeling steps, the conceptual model of the agent-based is extracted.Based on the findings, data quality, information and communication technology infrastructure, automatic measurement and evaluation, and automatic action and evaluation are among the factors affecting the IoT-based online monitoring system. Also, the agents of the organization, customers, suppliers, governance and information technology infrastructure, interact with each other and with the environment. Based on the results, the IoT-based online monitoring system is an effective way to improve processes, and decision makers can make smarter decisions with this approach.

Nowadays, recommender systems have reshaped the ways of information filtering between websites and the users in order to identify the users’ interests and generate product suggestions for the active users. Recommender systems are generally divided into three groups: Content-based, Knowledge-based, and collaborative-based, and in some cases hybrid. The main idea of collaborative filtering is that they predict a user’s interest in new items based on the recommendations of other people with similar interests. This Approach does not require having knowledge about items. Collaborative filtering has two main types: Memory-based and Model-based. Memory based Collaborative filtering makes use of user rating dataset to compute similarity index between set of users or set of items. The main purpose of this article is to offer a Memory-based Collaborative recommender system in order to optimize the results of Collaborative filtering algorithm. In the proposed method, the combination of fuzzy Grey Wolf Optimizer algorithm and Lion Optimization Algorithm is used to find the most similar users to the target user. The results of the proposed method confirmed a significant increment in Precision, Recall and F-measure in comparison with baseline methods.