عباس طلوعی اشلقی

Collaborative filtering (CF) is the most widely used recommendation technique because it relies on user interactions rather than specific user or item features. The neighborhood-based (memory-based) CF method predicts user preferences by measuring similarities between users or items and similarity weights have a noticeable effect on recommendation quality. Therefore in this study, the similarity calculation between users is formulated as an optimization problem, and a novel hybrid metaheuristic algorithm is proposed to improve its weights. This algorithm combines Grey Wolf Optimizer (GWO), Lion Optimization Algorithm (LOA), and Differential Evolution (DE) to determine the optimal similarity weights. The performance of the proposed algorithm is evaluated using Mean Absolute Error (MAE), Precision, Recall, and F-measure. The average MAE for the proposed algorithm, GWO and PSO are 0.4919, 0.5544, and 0.5578 respectively, indicating a significant reduction in MAE, and an improvement in prediction quality compared to baseline algorithms. Also, the evaluations using 175 nearest neighbors and varying numbers of recommendations demonstrate improvements in Precision, Recall and F-measure, reflecting enhanced recommendation quality. Finally, the proposed algorithm was assessed on a set of standard benchmark functions and compared with GWO and PSO algorithms, showing superior performance in minimizing.

 Technology development based on strategic alliances in the automotive industry has emerged as a significant strategy in recent decades. This study is descriptive-analytical in terms of data collection method and follows an inductive-deductive approach in terms of research methodology. The qualitative phase involved interviews with 17 experts in the Iranian automotive industry and academic professionals specializing in technology within strategic management. Participants were selected purposefully. The initial interview questions were developed based on a review of books, articles, and related studies. The interviews continued until theoretical saturation was achieved, which occurred at the 15th participant. After transcribing the participants’ responses, an analysis of the explicit and implicit content of the collected information began. The goal of this process was to extract the components required to propose a technology development model based on strategic alliances in the Iranian automotive industry using a grounded theory approach. For analyzing the text of the semi-structured interviews, Strauss and Corbin's (1998) method was employed using MAXQDA software. The findings revealed a total of 203 initial open codes. After removing duplicate codes and merging similar ones, 78 final open codes were identified. Following the analysis of the interview texts, 14 axial codes within six selective codes were extracted. The results indicate that identifying customer needs through data analysis and surveys leads to the design of products aligned with their preferences. Modern technologies such as artificial intelligence and data mining serve as powerful tools for identifying customer behavioral patterns. Aligning product design with customers' cultural and economic needs enhances their loyalty. The experience of successful global companies demonstrates that this approach contributes to increasing market share.

The current research is applied in terms of purpose and descriptive-fundamental in terms of nature and implementation method. The statistical population consists of two parts. 1- Experts with post-graduate education and doctorate and work experience in the field of entrepreneurship. 2- Managers of active knowledge-based institutions in the country. For the sample size of the qualitative community, articles were available, and for the expert community, interviews were conducted using targeted sampling. The interview continued until theoretical saturation, 20 people were interviewed. Using Morgan's formula, 225 people were selected from the managers of knowledge-based institutions by non-probability sampling method. Modeling is used to answer questions and test hypotheses. The results of this research were identified and classified from 43 sources in the form of 12 main categories and 50 subcategories of entrepreneurship of knowledge-based institutions in line with the development of their social responsibility. The results showed that the entrepreneurship of knowledge-based businesses in the direction of promoting social responsibility explains and predicts 51.8 percent of the variance of the social value creation variable.

People's travel behavior is reflected in their choice of transportation modes and is influenced by various factors. Travelling by private car often leads to numerous problems. Therefore, policymaking to shift citizens’ travel behavior from private car use to public bus transportation is important. Hence, the purpose of this research is to examine the travel behavior of Tehran’s residents using a system dynamics simulation model. Accordingly, after identifying the main variables affecting travel behavior through library studies and expert interviews, the hypotheses of the model were formulated. Subsequently, by drawing the cause-and-effect diagram and the stock and flow model, the relevant mathematical equations were derived and validated, and the model was then tested for accuracy and reliability. Subsequently, policies related to the three variables of the number of BRT buses, access to BRT buses and parking capacity were implemented through several scenarios. The results revealed that increasing the rate of parking construction does not lead to favorable results. Moreover, the increase in the number of BRT bus fleet alone cannot have an effective role either under current conditions or when combined with the scenarios involving reducing or increasing parking construction rates. Reducing the rate of parking alone has favorable results. Similarly, increasing the number of BRT stations yields positive results. Moreover, implementing both scenarios simultaneously- reducing the rate of parking development and expanding BRT stations- represents the most effective scenario among those analyzed.

The “Statement on the Second Step of the Islamic Revolution” and recent regulatory developments have underscored the heightened necessity for establishing robust systems for the identification and effective risk management of political risk, particularly with respect to Politically Exposed Persons (PEPs) in Iran. This article, adopting a review method and critical analysis of the literature, elucidates the theoretical concepts of risk, political risk, and the position of (PEPs). It further presents global risk management frameworks (FATF, Basel, OECD), critically examines the mismatch between these frameworks and Iran’s politico-economic structure, and addresses the specific challenge concerning “the divergence in the definition of terrorism and the legitimacy of resistance from the perspectives of Iran and international organizations.” Ultimately, the paper proposes strategic recommendations and a conceptual model for the identification, prevention, and control of (PEPs) risk in Iran’s financial system. The findings indicate that effective management of this risk necessitates advanced identification systems, regulatory transparency, and institutional capacity-building. Full realization of these objectives depends on resolving international legal and political issues and strengthening the independence of financial institutions.

Oil & gas wells are the main physical assets in the upstream of the oil industry& regarding the strategic structure, special equipments ,drilling cost, continuos process production, complexity, various facilities & life cycle of wells,the physical asset management of this complex industry is immensely critical. The purpose of this research is identifying & realizing the dimensions, phases,important components & sub components in oil & gas wells and to express the inter-relationship for presenting an appropriate physical asset management framework. in oil & gas wells through life cycle management.Initially by literature review and through interviews with 15 experts in adminstration, 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.An integrated physical asset management framework for oil & gas wells has been presented.The results revealed that these 6 dimensions : cultural changing and organizational content for maintenance,equipment information management,premature decline prevention ,maintenance resource and plans optimization ,life cycle activities management and performance management and continuous improvement ,heavily , affected the coherence ,integrity &optimizing plans management and activities in physical asset management in asset-intensive companies

The present article introduces an innovative model (BI.M-LSTM), which combines the BI algorithm and the Long Short-Term Memory (LSTM) neural network to predict flight safety risks during the approach phase. The approach phase, accounting for 3% of the total flight process, is considered the most dangerous stage of any flight. The proposed method involves training supervised neural networks to estimate target parameters. For this purpose, a standardized dataset from the years 2019 to 2020 was used. After summarization, cleaning, and normalization, a total of 28,813 records related to safety risk parameters, such as weather conditions, aircraft configuration, flight information, speed, altitude, and air traffic, were selected. Due to the dependency of flight data on previous inputs and the need for some form of memory, training was performed using the LSTM algorithm in the Python environment. After learning, the mean squared error of deviations was approximately 6.38%. The result showed that the error rate is negligible, and the proposed model has high credibility compared to similar models. This model, equipped with advanced tools including ETL (Extract, Transform, Load), metadata, and real-time monitoring, addressed the challenges of exploring and cleaning large-scale flight data. It successfully predicted the most critical safety factor during the approach phase: speed and altitude control during landing. This reliable approach assists flight crews in controlling important safety parameters, including avoiding loss of control, aircraft speed, touch-down position, and preventing runway excursions.

Optimization is crucial in finding the best solutions to complex problems across various scientific fields. This study examines and compares four algorithms: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, and Ant Colony Optimization. The research aims to perform a comparative analysis of these algorithms' performance in various optimization domains and to identify their features, strengths, and weaknesses in industrial and financial test functions, including production optimization, finance, inventory control, and vehicle routing. Key performance metrics such as mean, standard deviation, success rate, convergence time, accuracy, and population diversity were used for evaluation, identifying the strengths and weaknesses of each algorithm. The results indicate that the choice of optimization algorithm depends on the specific characteristics of the problem, and no algorithm is absolutely the best. This study emphasizes the need for developing novel algorithms and can aid in selecting appropriate optimization tools to improve optimization processes in industrial and financial problems.

Nowadays, performance evaluation and target setting for an organization are unavoidable. In addition, Data Envelopment Analysis is a non-parametric mathematical technique, which utilizes multiple inputs and outputs, which have been proved a usable approach for achieving these goals. The purpose of this paper is to determine a sequence of targets, each one within an appropriate, short distance of the preceding for the two-stage unit. Moreover, it is guaranteed that original unit is dominated by each element sequence of targets. To the best of this paper knowledge, few papers have looked at the sequence target setting in network DEA. From this perspective, this paper discusses a situation where a set of two-stage DMU considered and assumed as given the upper bounds on input and output changes. It is based upon the assumption of constant return to scale (CRS). The larger the bounds, the more changes in the input and output of DMUs as well as the number of steps required to efficiency improvement. This approach is illustrated by an empirical application to 24 non-life insurance companies, in Taiwan. Results determine that inputs always decrease (or stay the same) and final outputs always increase (or stay the same) along each step as well as the number of steps is variable. Moreover, Taiwan Fire, Fubon and Union achieve to the final target after one step as well as Kuo-Hou is in the worst case, and reaches its final goal after thirteen step.

Analyzing consumer behavior is an effective way to know the characteristics of customers of an economic enterprise and to promote and improve effective marketing strategies. Most retail industries, including chain stores, are connected with a huge collection of unclassified and heterogeneous information, so it is necessary to identify appropriate marketing strategies in order to improve the level of customer loyalty, profit and sales. In this research, the behavior of retail consumers was modeled with a mixed approach (qualitative and quantitative). The statistical population is consumers in the retail industry. In the qualitative part, a conceptual model was designed by the opinions of 22 experts in marketing using an open questionnaire. In the quantitative stage, through simple random sampling and unlimited population with a sample of 112 customers of the store in question, the model designed with the Structural Equation Modeling approach was tested. Based on the results, consumer behavior is a function of causal conditions (product condition, product price, place of purchase, discounts, economic conditions, technological conditions, and cultural conditions), contextual conditions (motivation, advertising, education, and mental image) and intervening conditions (consumer culture, personal characteristics and social relations). These factors provide the conditions for actions and interactions (searching for information and evaluating options) that lead to consequences (product selection, vendor selection, brand selection, purchase amount, purchase time, and payment method). In the quantitative part, information analysis was done in two parts of the measurement and structural model. according to the significant components, the final retail consumer model was presented.

Forecasting cryptocurrency market trends remains a significant challenge due to its fundamental differences from traditional currencies. This complexity arises from the interplay between conventional financial indicators, advancements in information technology, and government macroeconomic policies influencing market acceptance. This study introduces a novel decision support framework that, rather than analyzing individual cryptocurrencies, focuses on the overall acceptance of the cryptocurrency market. The proposed approach enables a more precise and realistic assessment of market trends, facilitating the generation of buy and sell guidance tables for any specified time interval. To achieve this, maximum likelihood estimation and Bayesian belief networks are employed, allowing for a comparative analysis of these methodologies. Additionally, a high-edge-strength Bayesian belief network is constructed from the generated networks to enhance prudent trading decisions. The method is validated using 1155 weekly and 484 daily time points across 21 cryptocurrencies with the highest market capitalization, covering two periods: the last quarter of 2024 and March–May 2025. The findings demonstrate that the proposed framework, with its high precision, accuracy, recall, and model robustness, supports buy and sell decisions with an average accuracy of at least 78% on a daily basis and 64.5% on a weekly basis. This approach offers a valuable tool for navigating the dynamic and uncertain nature of the cryptocurrency market.

Development of artificial intelligence in broad industrial and commercial fields, and significant increasing of non-linear data which produced by various units of organizations, on the other hand, would be necessary for using artificial intelligence (IA) in inventory management (IM).That's why the strategic model recommended in this article aims to use deep learning methods of long-term memory (RNN) and bidirectional long-term short-term memory (BILSTM) neural network based on regular inventory control methods such as the economic value of the order( EOQ) and the economic production quantity model (EPQ) also use of ABC analysis which facilities would increase effectiveness of model. The achievement of this article is reach a unique strategic model, by integration of traditional inventory control methods and artificial intelligence, which has high efficiency in terms of speed and accuracy of forecasting. However the findings have been showed that the recurrent neural network(RNN) ​​with an accuracy of 85% did not acceptable and combined approach of the (BILSTM) neural network, after building and training by considered parameters in line with the purpose of the article, has explained the accuracy of 93%.

Inventory management as a live subset of whole organization would be organize the expectation of supply chain base on an intelligent platform. This article was created with the aim of facing the challenges of industrial big data on data which are effective in decision-making based on historical records, but are not linear data due to the valuable content they have.As consequence, it is obvious that are not exploited by traditional methods such as time series for forecasting and decision-making. Accordingly, in this article, to present a strategic model based on artificial intelligence techniques, long short-Term Memory (LSTM), while we discussed the use of industrial big data, which enables the ability to use non-linear data to predict orders. Moreover, the methods of economic order quantity (EOQ) and economic production quantity model (EPQ) in order to prepare and training data have been used. In continue for getting better processing in order to identify strengths and weaknesses in inventory management, "ABC" analysis has also been used. The findings show that the integration of the traditional approach with deep learning method improves learning performance for complex big data analysis tasks. Many evaluations of the proposed deep network have reached an acceptable error value. Finally, this model has provided promising results for predicting expectation of fields that related to controlling amount of production based on objective in inventory management sector.

 Today, business processes are the key to success in any organization. If organizations, especially mining and industrial companies, do not have a suitable business process model, they cannot capture a significant part of the market. Business process management is a holistic management approach to promote organizational efficiency and effectiveness by striving for innovation, flexibility and integration with technology. The structure of a company depends on the strategy and the structure changes with the evolution of the strategy. Achieving a suitable competitive position for a company is only possible with the help of a suitable business model. So, the ability to design and implement a strategy model and business process is considered vital for the growth and continuity of an organization's activity. Therefore, considering the importance of the issue on the one hand and the existence of sanctions in the country's industry and mining sector and its position in the twenty-year vision document, it has prompted us to design a dynamic model of strategy and business processes in Golgohar Mining and Industrial Company in this research. let's pay Therefore, this research was carried out with the aim of designing and formulating business process strategy as a missing link for mining and industrial organizations and seeks to answer the question whether it is possible to develop a dynamic model consisting of strategy and business process in Golgohar Mining and Industrial Company If yes, how is this model  In terms of its purpose, this research is analytical and applied, and in terms of its nature and method, it has been written in a causal descriptive manner using library documents and internet resources. And he tries to mention the definition of the dynamic model of business strategies and the necessity of using it in organizations, especially mining and industrial organizations for the comprehensive development of the country, to point out the factors, advantages and obstacles of its use and provide solutions. In terms of the present research approach, it is qualitative-quantitative. In fact, this research started from examining a qualitative approach and strategy map, then the qualitative-quantitative approach of system dynamics was used. The process and implementation steps of this research with the approach of systems dynamics is that at first the problem of expression and different parts of the system were described in the form of dynamic hypothesis. Also, the key variables were determined by the analysis of the dynamic hypothesis and the boundary of the model. Then, by examining the behaviour of the variables over time and the available data and documentation, the relationships of the variables have been identified and the corresponding cause and effect diagram has been compiled. The final model was developed for the proposed model using the data of Golgohar simulation company and structural and behavioural validation and other validation methods such as limit conditions and boundary adequacy. At the end, with the dynamic analysis of the model by Vensim PLE software, suggestions for improving the system were presented.  Variables affecting the business strategy and process were identified and formulated in the form of causal circles according to the diagnosis and opinion of experts and managers who have been active in this industry for many years. which include mining, pelletizing, processing, line modification, transportation, sales and human resource development. We explained some of the most important loops of the model that represent the dynamic hypothesis of the problem. After formulating the cause-effect relationships between the main variables, a comprehensive cause-effect strategy model was drawn in Golgohar Sirjan company. After conceptualizing the system and actually determining the boundaries of the model and its general framework and determining the relationships between the key variables in the form of cause and effect loops, the model formulation was done using accumulation and flow diagrams and the mathematical relationships between the variables were also determined. In the form of the system dynamics model, to ensure the validity of the model's performance, it was subjected to several tests by Vensim software, and the results of some of them were mentioned according to their importance. To validate the model according to Sterman's business dynamics book, the limit condition test method has been used. By increasing and decreasing the parameters of the model, the behaviour of the variables has been investigated. Therefore, the relationships of the model have sufficient accuracy. By comparing the stated statistics and the results of the simulated model, the validity of the model was confirmed. The results of the implementation of the scenarios on the company's annual income showed that the first scenario and the second scenario have almost the same effect on the income, and the third scenario has a lesser effect. The research showed that choosing an incorrect strategy and the absence of a comprehensive model causes problems for planners and consultants in the field of technology, not benefiting from the advantages of the used model, and losing the opportunity to take advantage of the strengths of other models to develop a strategy. Using this dynamic model, it is possible to predict the amount of production profit for several future periods, which provides managers with a powerful tool to control costs. Therefore, the results of this research showed that the proposed dynamic model can be used as a strong and flexible method for simulating the strategy and business process in mines in order to find the root causes of profit reduction and obtain maximum efficiency and productivity and growth of the company. Although the outputs of the model need further investigation, the analysis of complex structures and dynamic behaviour of effective factors can provide valuable information for decision makers. Therefore, the processes of a mining and industrial company can be dramatically transformed, these processes can be integrated with the business strategy and also provide useful insights in emphasizing the link between processes and strategy and the tools needed to create success as a backbone Provide management processes.

Rapid and extensive developments in the field of information and communication technologies (ICT), especially in recent decades, have created a widespread foundation for profound and fundamental transformations in managerial, social, and organizational structures. These changes have not only revolutionized the way work is conducted but have also redefined the work environment, organizational relationships, and interpersonal interactions. In this context, the emergence of the Fifth Industrial Revolution has played a pivotal role as a turning point in the history of technological advancement. This revolution, characterized by the intelligent integration of physical, digital, and biological worlds—particularly through technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data, cyber-physical systems, and advanced automation, while maintaining human-centric values and capabilities—is driving unprecedented changes in production, service delivery, and management patterns. This integration has not only enhanced organizational productivity and efficiency but has also challenged traditional concepts of work and physical presence. Under these circumstances, remote work has gained increasing attention as one of the most significant concepts in contemporary management and a novel approach to organizing job activities. Remote work is defined as performing job duties outside the organization’s central office, typically through digital environments and virtual communication. This work model became particularly prominent during the COVID-19 pandemic, emerging as an unavoidable necessity in many organizations, while simultaneously being recognized as an opportunity to increase flexibility, reduce operational costs, and improve employees’ work-life balance. Today, remote work is no longer an option—it is a necessity. However, the successful implementation of remote work requires careful attention to multiple factors spanning technological, organizational, psychological, and social dimensions.

Identifying the key factors affecting the process of technology transfer is one of the most important prerequisites of this process. The lack of comprehensive research in the area of technology transfer within automotive powertrain sector mptivated the researchers to focus on this study. Hence, the purpose of this research was to identify and analyze the key factors affecting the technology transfer process and determine the existing gaps in the car powertrains in Iran Khodro Industrial Group. The statistical population of the study consisted of ten experts for the Delphi phase and 93 automotive powertrains specialists for the statistical analysis phase.  This research utilized interviews and open-ended questionnaires as the data collection instruments. The research method was Delphi and the statistical analysis was conducted using SPSS software, specifically through exploratory factor analysis. In this research,after identifying the factors and the gap between the current and desired situations, the researchers analyzed and determined the relationship between them.The KMO, Bartlett, r test values as well as the sharing indices showed the correlation between the variables with a sufficient degree of validity.The results indicated that if Iran Khodro wants to reduce the 19 identified gaps,it should pay special attention to three key factors of trans-organizational participation, infrastructure, awareness and knowledge. Additionally, it was found that identified nine gaps can be reduced throiugh planning and focus on the trans-organizational participation factor, five gaps can be addressed by planning and prioritizing the infrastructure factor, and five other identified gaps can be mitigated by planning and emphasizing the awareness and knowledge factor.
 

The purpose of this research is the dynamic analysis of the foreign exchange policies of the Central Bank of Iran. In this regard, the concepts, dimensions, and indicators of the final model were identified using the meta-integration method, which can be used as a suitable tool to evaluate the central bank's currency policies and changes in macroeconomic variables. After simulating the desired model, the results show that with the improvement of economic factors, its sub-components are improved, which increases the efficiency and effectiveness of the central bank's foreign exchange policies, and this will continue as a dynamic cycle over time. Also, the lack of improvement in economic and legal factors is one of the most important obstacles to efficiency, effectiveness, and improvement in the implementation of the Central Bank's foreign exchange policies. Based on the results of simulation and examination of different policies and scenarios, we come to the conclusion that for the central bank's currency policies to be effective and successful, it is necessary to increase internal and external trust and participation in economic investment, improve monetary and budgetary factors, and export growth. Social, managerial and political factors are in the next stage.

With the expansion of technologies and digital transformations, the metaverse has emerged as a novel platform for remote work. However, challenges such as lack of inclusivity, centralized resource control, environmental concerns, and limited access to advanced technologies pose barriers to the widespread adoption of this working model. This research examines metaverse-based remote work models, analyzing the challenges, opportunities, and implementation strategies in this field. In this study, Critical Systems Heuristics (CSH) and Fuzzy AHP methods are employed to systematically analyze the issues related to the development and implementation of metaverse work platforms. CSH aids in identifying and assessing the assumptions, values, and power imbalances in system design, while Fuzzy AHP enables multi-criteria decision-making based on the perspectives of various stakeholders. The results of this study indicate that by designing inclusive models, fostering interdisciplinary collaboration, and adopting sustainable approaches, many of the existing challenges can be addressed, and a successful and efficient metaverse platform for remote work can be established. This research adds to the existing literature on remote work and the metaverse, offering solutions for designing equitable and sustainable work environments.

Efficient and effective customer relationship management increases customer satisfaction and retention. Customer relationship management helps organizations evaluate profitability and customer loyalty using criteria such as repeat purchases, money spent, and durability. The main goal of this research was the dynamic modeling of communication with customers and the dynamic analysis of this model. The current research is applied in terms of purpose and in terms of survey method with model development approach. The time frame of the research is five years (2017-2021). For this purpose, information on the indices of 33 factors affecting competitive advantage in Tejarat Bank were entered into Bayesian averaging models (BMA), dynamic moving average model with parameters that can be changed over time (TVP-DMA) and dynamic selection model with parameters that can be changed over time (TVP). -DMS) Based on the error rate, the BMA model had the highest accuracy. After estimating the model, 8 main variables were identified. which consists of: long-term account balance; the amount of use of mobile bank; the amount of internet bank usage; real customers; legal clients; special or normality of the customer; Type of job and education. These 8 main identified variables were entered into the dynamic model and then the model was validated based on boundary adequacy tests, structure evaluation, integration error and sensitivity analysis.

This paper aims to identify and explain the dimensions and components of a qualitative model for research and development management, based on big data analytics.
The current study is a qualitative research design utilizing an exploratory approach. Data collection was conducted through semi-structured interviews with 12 R&D managers and experts who are knowledgeable in data science. Data analysis was performed using open, axial, and selective coding techniques from grounded theory, facilitated by MAXQDA 18 software. Ultimately, a qualitative model was developed and presented.
The research findings identified eight dimensions (code axes) and twenty-four components (categories), which include: systematic management, the ability to utilize big data analysis, the application of data science, higher education, technical and non-technical infrastructure, government support, resource availability, internal factors, and external organizations. These findings are organized within the framework of a paradigm model based on grounded theory, encompassingsix dimensions: causal factors, the central category, strategies, intervening conditions, background conditions, and consequences.
The qualitative model of research and development management was presented based on a process-oriented approach and integrated with the identified core components. In this model, data sources were incorporated into the input phase of the R&D management process. The model processing phase includes the ability to utilize big data analysis, research and development strategies, data science tools, system management, and internal organizational factors. Additionally, external organizational factors, higher education strategies, government support, technical infrastructure, and organizational culture are identified as influential external components of the model.

Effective knowledge-based processes are essential for companies operating in the information technology industry. These processes rely on the expertise of skilled workers and play a crucial role in the value chain of such organizations. Decision-making is a critical element of knowledge-based processes, highlighting the need to identify decision rules and models accurately. In this paper, we examine the process of identifying and deciding on proposed ideas in the software industry, analyzing decision logs from a leading software company. The Rough sets theory and fast Reduction algorithm are employed to provide a step-by-step approach to data analysis and decision mining. The algorithm identifies vital features used in decision-making and presents the decision model as if-then rules, utilizing existing equivalence rules between data. The results demonstrate that this model can significantly reduce the direct involvement of decision-makers and the duration of the decision-making process.

There are many indicators and criteria in the effectiveness of skill training so that the trained person can enter the labor. These indicators and sub-indices should have an operational aspect and be weighted and ranked based on modern methods. The purpose of this study is to identify the weight and the rank of these criteria so they can be used in codification and evaluation of educational and skill models. The statistical population of the research consists of 7 managers and administrators of industrial companies, school managers of skills training and experts in the field of developing training courses, technical courses. These people reviewed the research works and interviews at the first place. The indicators and criteria of the research have been identified with experts. Then, using Fuzzy Delphi method, 5 main indicators and 12 sub-indicators have been approved, and at the end, with the fuzzy best -worst method, the ranking of indicators and sub-indicators were calculated. The results show that the criterion of education alignment with employment status is the most important and main indicator. Also, the criterion of participation of business owners in curriculum planning is the most important sub-indicator of the effectiveness of skill training.

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...

Granting facilities is an important part of every bank's operations. This part of banking activities is economically important. With its operations, banks can provide the transfer of resources from those that have directly invested to those who need money, this repayment makes another people use these resources. Failure to repay facilities on time will cause the bank's resources to stagnate and in the long will cause the country's economic recession. It is important to monitor the correct allocation of resources because if bank resources are used and unfounded payments are made, the banks will not be able to pay the depositors and will become bankrupt. By examining the researches in the field of banking, it was found that most of the researches have focused on the optimal combination of the investment portfolio in the capital market, and less researchers have paid attention to the discussion of the optimal combination in the money market. Most of the researches that used operational and statistical research methods were related to industrial issues, and less in financial issues and operational research discussions were used. In the optimal portfolio of the combination of facilities or investments, mostly the genetic algorithm has been used and less than other fuzzy methods have been used in the conditions of uncertainty. Therefore, in this research, the improvement of facility payment modeling has been addressed by using convolutional neural networks and CNN-LSTM modeling.