عباس طلوعی اشلقی

Human capital is considered as a valuable and competitive capital of organizations, and managers who are aware of the importance of human resources will be able to create a competitive advantage for the future of the organization and the economic development and growth of the country. The purpose of this research is to provide a model for measuring national human capital. The current research is applied in terms of purpose, descriptive-survey approach, and quantitative in terms of research method. The statistical population of the research is two parts. To collect information, in the qualitative section, after extracting a conceptual model from the research literature, the model was confirmed through a survey of experts and professors of human resource management using the Delphi technique. A small part were managers and human resources experts of government organizations of the Ministry of Cooperation, Labor and Social Welfare, and the statistical sample was determined with the help of Morgan's table. The data was collected with the help of a questionnaire from experts and employees, and the validity of the questionnaire was confirmed with the experts' opinions and its reliability was confirmed with Cronbach's alpha, and the data was analyzed using the structural equation approach. Based on the findings of the research, the dimensions of the national human capital measurement model were personal dimension, occupational dimension, organizational dimension, spiritual capital, intellectual capital. The research results indicate the applicability of the national human capital measurement model in the Ministry of Cooperation, Labor and Social Welfare.

The purpose of this research is identifying & prioritizing the effective factors in physical asset management for oil and gas wells. Oil & gas wells are the main physical assets in the upstream of the oil industry. Regarding the, special technologies ,high cost, surface and sub-surface machineries  and facilities ,  continuous  process production, & life cycle of wells, the physical asset management of this package is immensely critical &,important ..Initially by literature review and through interviews with 15 experts in  administration , critical physical asset management indicators ,dimensions and components for oil& gas wells in the list of acts& methods were gathered, which were then presented through a Delphi questionnaire. The output is identifying 6 dimensions & 14 components in 57sub-components. In the qualitative phase, for connection& continuity between main dimensions & their components an ISM has been used. In the quantitative part, the presented framework was verified by Confirmatory Factor Analysis in AMOS. After that, prioritizing the effective factors in 6 dimensions by FAHP  as below, at first level, equipment information management in oil & gas wells second ,performance management and continuous improvement in oil & gas wells ,the third is, decline prevention in oil & gas wells...

Introduction :

Driving behavior refers to a set of actions and reactions that are relatively stable, visible, measured, and predicted by humans in the field of traffic and the use of transportation systems, which are caused by external stimuli, either software or hardware, on how they and other road users move. It affects and can have positive or negative effects on the flow of traffic. In this research, attention has been turned from a linear view of the problem to a non-linear and multifactorial view. This research seeks to investigate driving behavior using simulation based on the dynamic system approach. The main goal of the article is to present a practical model resulting from the design of driving behavior in Tehran city in interaction with road and technology elements.

In order to achieve this goal, while studying the literature and background of the subject and reviewing the available documents and data, the most important driving behavior variables were collected and based on this, the dynamic hypotheses of the model were identified and selected. In the following, cause and effect diagrams and accumulation-flow diagrams were drawn and mathematical functions related to the relationship of model variables were extracted. In this research, the opinions of experts in different executive departments were used in the form of semi-open interviews. The main part of the required data has been collected and analyzed from the existing data banks in the traffic related units of Tehran. At the end, after testing the dynamic model using Vansim software, different scenarios were proposed to apply the model. The results of the selected scenarios show improvement in variables related to driving behavior in the short term and its reduction in the long term.

The results of the implementation of the dynamic model created for two sets of scenarios, one set has two scenarios (including the variables of economic growth and productivity) and the second set has three scenarios (including the variables of investment speed in technology, depreciation speed technology and the speed of technology growth), determined and showed that the greatest impact on the growth rate of automobile technology is affected by the increase in investment in this field; The effectiveness of preventive technology depends more than any variable on the speed of technology development, and in the matter of accidents, although all the changes in the mentioned situations are important and lead to a reduction in accidents, other analyzes showed that this reduction will decrease over time. And its changes will be close to zero (it will not be equal to zero, but it is close to it) and it is necessary to compensate for this by using other variables such as police authority.

The results obtained can be expressed as follows: First, the promotion of order, which can be considered as a function of attention to safety and the promotion of public culture, and can be achieved as a result of the increase in national income per capita, is an effective factor on reducing accidents; Secondly, the promotion of preventive technologies will play a significant role in increasing driving safety and thus reducing accidents, and thirdly, increasing the capacity of roads cannot play an effective role in reducing the rate of accidents and sometimes it will have the opposite result.Based on this and relying on the introduced model and the findings derived from its implementation, it can be suggested that the more the investment in automobile technology increases, the more products will improve, its effectiveness and growth. Also, the speed of growth of automobile technology can further strengthen the preventive role of technology. It seems that even if the country has a significant growth in its income, it will not be possible to solve the traffic problem through the development of urban infrastructure.

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.

This research adopts an approach that conceptualizes security and develops a comprehensive security model using knowledge representation technologies. It aligns with sustainability and organizational resilience within the context of the fifth industrial revolution, aiming to provide a complete overview of the conceptual components of security.

The objectives of this applied research are pursued through a conceptualization approach. To achieve this, the latest version of Protégé software was used to develop the ontology. Additionally, a life cycle process for ontology creation was designed to align with international methods and design science. Furthermore, to create a comprehensive representation, the OntoGraf tool was employed with a focus on organizational resilience.

The design of a conceptual model for organizational security requirements, controls, and assets was achieved through ontology engineering using the Protégé tool. This process was based on standards, international frameworks, and the specific conditions and needs of the country. The approach aimed to establish sustainability and resilience within the organization while also creating a comprehensive representation of the conceptual components of security in organizations.

In today’s complex environment, smart action is essential. In a large organization like a university, acting intelligently is the most important factor in enhancing competitiveness. Security plays a crucial role across various dimensions of organizations that adopt a smart approach. The field of security is highly dynamic, with new threats constantly emerging. To address these rapidly growing threats, conceptual solutions are needed to enhance organizational security, resilience, and continuity. This research presents the conceptualization and organization of knowledge in security, as well as the creation of a platform for the development and use of common concepts

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%.

Oil&gas wells are the main physical assets in the upstream of the industry&they have a critical role in production&economy energy.Regarding the special equipments,high cost, strategic structure&continuos flow in their production, the issue of maintenance is deemed to be very critical.The purpose of this research is realizing the dimensions,components&sub components in oil&gas wells and to express the inter-relationship for presenting an appropriate maintenance framework. In this paper,initially by literature review and through interviews with15experts in adminstration,critical maintenance indicators,dimensions and components for oil&gas wells in the list of acts&methods were gathered,which were then presented through a Delphi questionnaire.The output is identifying6 dimensions&14 components in 57sub-components.In the quantitative part,the presented framework was verified by AMOS. In the second phase, for connection&continuity between main dimensions&their components an ISM has been used.According to experts panel views and analysis of components,an integrated maintenance framework for oil&gas wells has been presented.The results revealed that these6dimensions:1-cultural changing and organizational content for maintenance2-equipment information management3-premature decline prevention4-maintenance resource and plans optimization5-life cycle activities management6-performance management and continuous improvement in maintenance, affected the revision&redesigning the unplanned and insular methods and given the resistive economy they finally lead to coherence,integrity,optimizing plan management and maximizing the use of resources.

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. Also, this study intends to provide an appropriate answer to the main research questions in which there are uncertainties related to the reinforcement learning elements and how to choose the reinforcement learning methodology in a multi-agent system of the socio-technical systems type.

To collect the information needed to know the elements and identify the hospital accreditation processes, agents, environment, and interaction between them, the systematic review of sources, review of scientific documents, and semi-structured interviews, through experts, to The face-to-face method has been used. Summarizing the interviews was done using grounded-theory-based methods, and a sequential and systematic approach. The sources for collecting the characteristics of the machine learning process using the systematic review method were from the document "Iran Hospital Accreditation Guide 2022". Also, the process of selecting the mentioned features was done through the correct selection of the output features of the model, which are the actions of the agent. The list of agent actions was extracted from the conceptual content of the document above in the form of a general non-binary tree based on the classification of the tree structure.

The extracted reinforcement learning model will seek to find the optimal chains of operational actions, in the conditions where the quantitative data of the hospital is available. The most important elements of the mentioned model are:Set of states: set of hospital accreditation factors such as input variables, output variables, indicators, parameters, and fixed numbers related to the metrics of each conceptual agent in the document "Iran Hospital Accreditation Guide 2022".Set of actions: actions of intelligent agents in each reinforcement learning episode, paths from the hierarchically clustered binary tree are operational actions that can be performed in the hospital and 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 and actions."Policy function: Based on the learning process of each agent, it is based on a DQN deep neural network and gradient reduction algorithm.Operational Agents: the operational goal of each of the conceptual agents, is "maximizing the accreditation points of the metrics of the relevant field by recommending the least measures."The general cycle of the model: in this structure, each of the intelligent agents, a subset of the 9 conceptual agents, has a multi-layered neural network within its scope, and the characteristics of related states are entered into this neural network and Output, based on the definition of the special policy function of that agent, a map of optimal actions will be 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" in which the input, hidden, and output layers of the network are specified.

Summarizing the background of related research showed that the approach to designing hospital accreditation models can be divided into two groups: "conceptual models without using intelligent agents" and "conceptual models using Intelligence and operating systems" should be divided. 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.

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.

Recently, the supply chain of perishable goods, have been considered due to their impact on human life. On the other hand, in the packaging industry, considering paper as a primary and perishable material due to the nature of water absorption, severe rotting in front of sunlight, flammability and turning to ash and finally affecting the final quality of the product produced from this issue. It is not excluded, it has attracted more attention. The high level of speed of changes and ambiguity in decisions has made it impossible to predict the future conditions of supply chains. Therefore, the design and use of a mathematical model for the design of the closed-loop supply chain network, taking into account the optimal pricing of products, the return rate and demand, along with taking into account the wastage of materials in the system, is strongly needed. At first, the supply chain of the paper-cardboard industry was designed and modeled with mixed integer programming, then due to the high volume of calculations and data of the problem, we can't receive exact solution approaches, the innovative approach of searching for harmony was used for the solution. . The problem is a single-objective model that minimizes system costs by considering environmental considerations. The present research shows that the price increase has a positive effect on the product return rate and reducing the level of product corruption. Finally, to validate the model, the numerical solution of a closed loop network has been done in this industry.

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".

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.

Recently,social networks have attracted special attention. In various social networks, users are constantly expressing their public as well as private opinions on various topics. Twitter is one of these social networks that has become very popular in the last decade. This social network provides organizations with a fast and effective way to analyze customers' feelings, views, and criticisms of market success. Emotional analysis is a process in which people's opinions, feelings, and attitudes about a particular subject are extracted. There has been a lot of research on emotion analysis based on user comments, documents and articles. Analysis of what is being said is very different from Twitter data, because Twitter tweets are limited to 280 characters and force users to express their feelings concisely. The best results in emotion classification are obtained from machine learning techniques such as simple Bayes and support vector machine. In this research, a method for analyzing emotions in social networks is presented. In this regard, we have tried to improve the classification of text by Bayesian method to some extent by focusing on the stages of data preprocessing and feature selection.users' feelings are analyzed. The classification problem has been formulated and solved using the latest achievements in the field of machine learning. . To evaluate the proposed method in this dissertation is from the Twitter data set scenario. The proposed method is compared with other classification methods. Has shown the best performance.

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.

Among the most commonly diagnosed cancers, melanoma is the second leading cause of cancer-related death. A growing number of people are becoming victims of melanoma. Melanoma is also the most malignant and rare form of skin cancer. Advanced cases of the disease may cause death due to the spread of the disease to internal organs. The National Cancer Institute reported that approximately 99,780 people were diagnosed with melanoma in 2022, and approximately 7,650 died. Therefore, this study aims to develop an optimization algorithm for predicting melanoma patients' survival.

This applied research was a descriptive-analytical and retrospective study. The study population included patients with melanoma cancer identified from the National Cancer Research Center at Shahid Beheshti University between 2008 and 2013, with a follow-up period of five years. An optimization model was selected for melanoma survival prognosis based on the evaluation metrics of data mining algorithms.

A neural network algorithm, a Naïve Bayes network, a Bayesian network, a combination of decision tree and Naïve Bayes network, logistic regression, J48, and ID3 were selected as the models used in the national database. Statistically, the studied neural network outperformed other selected algorithms in all evaluation metrics.

The results of the present study showed that the neural network with a value of 0.97 has optimal performance in terms of reliability. Therefore, the predictive model of melanoma survival showed a better performance both in terms of discrimination power and reliability. Therefore, this algorithm was proposed as a melanoma survival prediction model.

In recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously, the potential for profit arising from product recycling has further emphasized the importance of these systems. This research focuses on developing a four-stage closed-loop network model for the supply chain, taking into account the uncertainty of product recycling rates. The primary objective of this study is to provide an integer linear programming model aimed at minimizing supply chain costs and customer service time under uncertain conditions. The supply chain model is designed by integrating queuing theory and product recycling system optimization. A critical aspect of this research involves modeling the uncertainty in the return rate of consumer products in the closed-loop supply chain, with the aim of developing a robust approach to address this issue. Additionally, the performance of the proposed model in the paper industry is evaluated, and a sensitivity analysis is conducted with respect to the decision variables using two metaheuristic algorithms: the Multiple Objective Harmony Search and the Non-dominated Sorting Genetic Algorithm.

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.

Efficient supply chain as well as the proper management of its components play an important role in supply chain productivity. The steel industry is one of the mother industries and needs to further enhance its chain productivity. The purpose of the present study is to identify key factors affecting the productivity of national steel supply chain. In order to identify the factors affecting steel supply chain productivity, the SCOR model has been used and its indices have been validated by CVR and CVI forms as well as by steel industry experts, based on which the designed questionnaire was distributed among the managers and experts of the steel industry and the data were collected. Finally, the data analysis was performed using confirmatory factor analysis with structural equations and PLS software. The factors affecting supply chain productivity were confirmed by 30 indices and 6 factors.The results revealed that all 6 factors influence steel supply chain efficiency, however, among these factors, the enabler’s factor

Driving behavior refers to a set of relatively stable, observable, evaluation and prediction actions and reactions of humans in the field of traffic and the use of transportation devices, which, due to external stimuli, affect how they and other road users travel and can have positive or destructive effects on have traffic flow. In this research, attention has been turned from a linear view of the problem to a non-linear and multifactorial view. This research seeks to investigate driving behavior using simulation based on system dynamics approach. The main goal of the article is to present the practical model resulting from the design of driving behavior in Tehran. In order to achieve this goal, by reviewing the literature and reviewing the documents, the key variables of driving behavior were identified and based on this, the dynamic hypotheses of the model were formulated. In the following, the cause and effect and state-flow diagrams were drawn and the equations related to the relationships of the model variables were extracted. In this research, the opinions of experts in different executive departments were used in the form of semi-open or closed interviews. The main part of the required data has been collected and analyzed from the existing data banks in the traffic-related units of Tehran. In the end, after testing the dynamic model using software and wind, different scenarios were proposed to apply the model. The results of the selected scenarios show improvement in variables related to driving behavior.

Today, many fields of research and business deal with the thoughts, images and mental patterns of their users and audiences. Knowing and being aware of the way of thinking of people, customers and audiences of a collection has a great impact on making appropriate decisions by the managers of that collection, in order to advance the goals and solve various problems. One of the important requirements of managers in any collection is to have sufficient knowledge of how they think about the collection and its performance. This helps them guide the group properly and make appropriate decisions in the way of advancing its goals. Therefore, collecting data from customers should be done with measured and accurate methods to achieve high quality information and obtain valid results. This research, by raising the question of "how can you use the analysis of people's feelings to find out how they think about a certain issue?", tries to provide a new approach on the platform of the Twitter social network to obtain reliable and high-quality information from people in relation to a certain topic. The purpose of the current research is to provide a new approach to collect data from people, in order to measure their perception about a specific issue. It will also examine the impact of the presented method on the speed, quality and cost of collecting data from people using a case study approach.

In this research, their comments on the social network Twitter were used to check the satisfaction of users of three internet taxi applications in Iran, namely Snap, Tapsi, and Carpino. The data collection approach used in this research was descriptive, with results used by users, managers, and researchers. The research population consisted of all users of the three applications and members of the Twitter social network who had published their thoughts about these applications. The research collected and analyzed a total of 682 relevant tweets cross-sectionally during the summer of 1400 using the Twitter application interface and related hashtags. The analysis steps included data collection, pre-processing of tweets - including linguistic, sentiment, and thematic analysis, followed by sentiment analysis.Discussion and

The comparison of the analysis results from this research to the face-to-face interview method showed that the participation of respondents was challenging, and the traditional field method came with its own disadvantages. However, this method is able to overcome those issues and provide reliable results at a lower cost and time. Moreover, the results obtained from analyzing the data collected were similar to the results obtained through the face-to-face interview method, which speaks to the accuracy and quality of the data. Overall, the proposed method can provide valuable insights into people's thoughts and opinions on a particular topic, at a lower cost and time.

The proposed method can be widely applicable to various fields, including business, research, and even everyday life. Managers, researchers, and business owners can use it to collect data and insights from their target audience, which can be analyzed to make informed decisions. Further development of the method can lead to even more useful and accurate reports, helping individuals and businesses stay ahead of their competitors. All students and researchers dealing with data collection can use this method to conduct their studies more effectively. Additionally, market research companies can use it to gain valuable feedback from consumers, informing their next steps in providing products and services.Overall, the applications of this research are far-reaching, and it provides a valuable resource for individuals and organizations looking to better understand their audience.Keywords: Sentiment analysis, text mining, how people think, Twitter, Internet taxi.

The significant growth in the amount of use of management information systems in different work departments of organizations has caused researchers in the field of information technology management to pay more attention to improving productivity in these systems. The purpose of this research is to identify the cause and effect (causal) mathematical model of the productivity of management information systems in the field of human resources.

This research has used a mixed exploratory approach. First, the conceptual model was calculated using the previous sources, and then the mathematical model of cause and effect was created using the quantitative method of Fuzzy DEMATEL (in six steps). The reliability and validity of the questionnaires of fuzzy DEMATEL pairwise comparisons have been confirmed by using the test-retest method and the content validity method of Lawshe, respectively.

Among the main productivity dimensions of the human resources management information system, the dimension of "intelligence" is the most effective dimension. The "innovation" dimension is the most effective dimension. The dimension of "innovation" has the highest degree of interaction with other dimensions and the dimension of "intelligence" has the highest degree of causality.

Since "effectiveness" is the main problem and "intelligence" is the main solution to the problem, placing the dimension of "intelligence" and the components of "organizational effect" and "individual effect" and "user satisfaction" in the focus of attention; The main priority of the management information system maintenance and development team in Naja's human resources department is to improve productivity.

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.

The petrochemical industry is in the continuation of the oil and gas industry chain. The innovation projects of a company must be consistent with its resources and goals, the purpose of this study was to present a model to improve the capacity of absorbing green technological innovations in the Iranian petrochemical industry. This research was applied in terms of integrated method (qualitative-quantitative) and in terms of purpose and was carried out in 2022. The statistical population of this research was the petrochemical industry companies, which were used to draw causal circles and model the experts in this field using the snowball method. To collect information in the qualitative part of interviews with experts(13 experts) and in the quantitative part, the data obtained from the first part were analyzed in the form of quantitative analysis. The data analysis stage is a multi-stage process during which the data obtained through the use of collection tools in the statistical sample (community) are summarized, coded and categorized and finally processed to establish the basis for various types of analysis. Data and relationships between these data are provided in order to test the hypotheses. In the qualitative part, the coding method was used, and in the quantitative part, Vensim software version 3.7 and and Super decision version 2 were used.was used. The findings showed that structural barriers, legal barriers, attitudinal barriers and infrastructural barriers of the company have an effect on the capacity to absorb technological innovations in the Iranian petrochemical industry.

Among the types of supply chain risks disruptions are risks that resulting from natural disasters, sanctions, transportation problems and equipment failure.
These risks can seriously disrupt the flow of materials, information and cash flow. This study proposes a hybrid model for managing, evaluating and rating disorders. In this research, by presenting a mathematical model with disruption parameter, supply chain disruption risk assessment is investigated. Initially, the relationships between the disturbances are formulated by fuzzy DEMATEL technique, and the DEMATEL output as a weighted parameter, and then the model is solved using meta-heuristic algorithms, genetic and local search methods. Finally, the disruptions are evaluated and ranked based on the costs incurred in the chain, and then the number of appropriate suppliers for each disruption is determined.