mohammadali keramati

The current research was conducted with the aim of analyzing the indicators of the alignment model of organizational strategies in order to create a suitable platform for digital marketing with digital developments in the petrochemical industry. The participants of this research were managers of the petrochemical industry with a teaching experience of at least 15 years and had a master's degree or higher. The selection of people was done by purposive sampling with the criterion of teaching experience in the primary education period. Sampling was done with the participation of 15 experts. The data collection tool consisted of two parts, 1- examination and exploration of upstream documents, petrochemical industry documents in the library section, 2- semi-structured interview in the field section, where the semi-structured interview with the participants continued until the theoretical saturation stage. To analyze the qualitative data, the theme analysis method based on the Atreide-Sterling model was used. In order to ensure validity, the interview questions were approved by 3 experienced management and planning experts of the petrochemical industry, 1 of whom had a master's degree and 2 had a doctorate. And in order to measure reliability, Krippendorf's alpha coefficient was used, which was confirmed. ATLASTI software is used in the theme analysis. The results of the factor analysis show that among the 136 indicators (items) available, 20 basic themes can be identified and 5 categories of constructive themes have been obtained. In the identified model, 5 constructive themes were identified: the requirements for realizing strategic alignment, industrial and operational obstacles and challenges, the state of digital transformations, focus on strategy implementation and all-round productivity. Aligning organizational strategies with digital transformations in the petrochemical industry requires fulfilling the requirements of achieving strategic alignment. In this model, 20 primary themes were identified. These themes are: infrastructure, market, governance factors, financing, expertise and knowledge, laws and regulations, executive, economic conditions, social and cultural conditions, political conditions, technological and specialized limitations, characteristics and potential of the new generation, public environment. , industry environment, positioning, executive requirements, financial and economic dimensions, business development and developmental role and improving the quality of life.

The aim of this study is to present an optimized model for bank profitability by considering the role of modern banking technologies. This research, being exploratory in nature due to its model development, is also considered applied because its results are utilized by beneficiaries. A quantitative method is used in this study to optimize bank profitability while considering the role of modern banking technologies. In the first step, influential components are identified through library techniques and document review, followed by mathematical programming for modeling. The proposed model is optimized using the epsilon-constraint algorithm in the MAPLE software and further optimized using MATLAB software. The research population, as a case study, includes documents and articles related to the banking industry. Initially, a single-component bank, considered as part of the system, was examined. Assume the bank operates in a seven-hour shift, six days a week. The maintenance activity time and fintech interventions were set to 7 time units, and the service delivery and corrective actions were set to 12 time units. With a 10% and 20% increase in the δ_E and δ_(M/C) data, significant changes in the objective function and decision variables were observed. However, changes in other data did not result in noticeable alterations in the model, indicating the importance of preventing the process from shifting to an out-of-control state and the significance of changes in the mean and standard deviation of the targeted quality characteristic. The proposed model determines the optimal value of each of the four decision variables: sample size (n), sampling interval (h), control limit coefficient (k), and intervals for preventive fintech actions (t_PM), which minimizes the total expected cost from integration per time unit. A numerical example demonstrated the economic impact of cost parameters on the integrated policy of profit control and preventive fintech actions. Additionally, in this study, the integrated model was compared with the independent model through a numerical example, and the significant difference in total cost highlighted the superiority of the integrated model over the independent model.

This research was to provide a business intelligence model forevaluating higher education using intellectual property components. Intellectual propertyplays an important role in the sustainable development of the university.

In this research, using the opinion of experts, indicators, components anddimensions of the business intelligence model for evaluating higher education wereidentified using the components of intellectual property. Regarding the method ofextracting these indicators, it should be mentioned that this was done by examining theexisting theoretical foundations and implementing the text of the interviews using Brownand Clark's analysis method in the ATLAS TI software. In this regard, interviews wereconducted until theoretical saturation with 20 experts of the business intelligence modelto evaluate higher education using intellectual property components, then the text of theinterviews was analyzed using coding.

112 primary themes, 20 first-level constructive themes and 5 second-levelconstructive themes have been identified. Topics include intellectual property metrics,business intelligence metrics in higher education, higher education evaluationindicators, advocacy practice in higher education, higher education perspectives.

Using the business intelligence model, focusing on the components ofintellectual property, increases the recognition and better management of the intangibleassets of the university or educational institution, and this can help the growth,development, and improvement of the quality of higher education and research. Thisapproach not only creates an effective environment for individual and organizationaldevelopment, but is also effective in realizing the strategic and competitive goals of theuniversity or educational institution.

Iranian-Islamic cities have witnessed several developments over time. In the current era, with the advancement of technology, the necessity of providing urban electronic services to improve the quality of life of citizens is felt more than ever. The main purpose of this study is to investigate the impact of a dynamic supply chain on the performance of urban electronic services in the historical context of Yazd city. This research is applied in terms of purpose and descriptive-analytical in terms of research method. To collect data, documentary methods with library and field tools were used, and a sample of 250 people was selected by simple random sampling. The results of the research showed that a dynamic supply chain plays a key role in improving the performance of urban electronic services in the historical context of Yazd city.

Citizens' health, as the most basic human asset, has long been considered one of the basic human concerns, and throughout history, it has attracted the attention of many experts and scientists in various scientific fields, including social experts. The aim of the research was to design a smart city governance model based on urban justice with an electronic participation approach in line with citizens' health.

The research was carried out in an exploratory and survey way. In the qualitative part of the Delphi method and in the quantitative part of the interpretive structural model method and structural equations, it was done. In the qualitative part as well as the interpretive structural model method of the community, managers and experts in urban planning were examined, and 18 people were selected from the available judgmental sampling method. Using the library technique, the components affecting the model were identified. First, using the Delphi technique, screening and evaluation of the identification components have been done. In the following, modeling has been done using the Delphi method and the interpretive structural method. The software used is EXCEL and MICMAC. The small community of citizens of Tehran was an unlimited number. Using Cochran's formula, 365 people were selected as samples. Based on the researcher's questionnaire based on qualitative analysis, data analysis was done using coding and path analysis. Data analysis was done using coding and path analysis. Based on the sampling, in order to fit the proposed model, the structural equation model technique was used in the SMARTPLS software.

Based on the Delphi technique, 9 criteria for supporting smart innovation, smart economy, smart strategies, smart infrastructure, cooperation between the private and public sector, smart interaction, knowledge integration, smart sustainability and smart governance were confirmed and in a model 6 interpretive structural levels were identified. The criteria of smart infrastructure and smart strategies were identified as the most effective components. Finally, smart governance was identified. Meanwhile, other components were placed as intermediary criteria from the least effective to the most effective. In the model of smart city governance based on urban justice in line with the health of citizens, smart infrastructures play a key role in creating the necessary foundations for the development of urban services and facilities. These infrastructures include advanced communication networks, intelligent transportation systems, and information and communication technologies (ICTs) that enable extensive data collection and analysis.

By using these data, cities can more accurately identify the needs and problems of society for the health of citizens and provide solutions based on real data. This contributes to a more equitable distribution of resources and services, reducing urban inequalities, and improving the quality of life of all citizens.

Protecting the privacy of internet customers is crucial in the field of health. In this area, there is sensitive and personal information, and privacy can increase customers’ trust in companies and create a stronger relationship between them.

The target sample was chosen using a criterion-oriented purposeful sampling method. The sampling procedure was continued until the theoretical saturation of data was reached. Accordingly, 12 professors and administrators participated in the study. The data collection tool was a semi-structured interview. Nvivo software was used for theme analysis.

Based on the theme analysis method, two constructive themes of level one and 14 themes of level two were identified. Constructive themes of level one were technological infrastructure and obligations of the seller to the consumer. The themes of technological infrastructure were personalization services, social interaction performance, access control, information technology security, security enforcement measures, safety algorithm and data-based planning, and decision making. The constructive themes of the seller’s obligations to the consumer were awareness, user and seller education, safety, maintenance and support of information, responsibility, framework and principles, and trust.

The proposed model showed that privacy protection was essential. Medical device businesses should implement a robust privacy policy, closely monitoring access, training employees on privacy protection, and upgrading security systems.

Various methods have recently been proposed to predict essential economic and non-economic variables. Each forecasting method has its own advantages and disadvantages based on the nature of the input data. Box-Jenkins and Halt-Winters methods are among the new approaches for increasing the accuracy of forecasting results. Therefore, this research aimed to predict OPEC average oil price data for June 2022 to May 2024 based on the data from 2003 to 2022 using Box-Jenkins and Holt-Winters methodology with a single variable. The errors of Box-Jenkins methodology, among the time series, processes ARIMA 5.1.5, ARIMA 4.1.5, ARIMA 3.1.5, and ARIMA 5.1.3 have the best accuracy with MSE of 61.86, 63.21, 63.29, and 63.62, respectively. The accuracy of the Holt-Winters method was not appropriately compared to the time series method due to the nature of the data.

Improving the financial and operational performance of supply chains requires a strong focus on inventory management. Additionally, implementing financial measures at the operational level is crucial for enhancing overall performance. By establishing effective financial strategies at this level, the efficiency and profitability of the supply chain can be significantly increased. This article aims to apply the Atride-Sterling approach to enhance the financial and operational performance of supply chains.

The participants in this research were supply chain managers with a minimum of 15 years of teaching experience and a master's degree or higher. Participants were selected through purposive sampling, focusing on individuals with teaching experience at the elementary school level. A total of 22 experts participated in the study. Data collection was conducted using semi-structured interviews, which continued until theoretical saturation was reached. Thematic analysis, based on the Atride-Sterling model, was employed to analyze the qualitative data. To ensure validity, Holstein's coefficient, Scott's Pi coefficient, Cohen's kappa index, and Krippendorff's alpha were utilized, all of which were confirmed. Atlas.ti software was used for the thematic analysis.

The results of the thematic analysis indicate that from the 54 available indicators, 13 fundamental themes can be identified, leading to the emergence of two categories of constructive themes: financial performance and supply chain operations. The identified model encompasses these two constructive themes, which include 13 primary themes: inventory management, financial reporting, operational financial measures, coordination, risk management, supply strategy, demand planning, technology management, logistics strategy, customer engagement, flexibility, supplier relationship management, and quality assurance on a global scale. Given the critical importance of the supply chain to organizational success, enhancing the financial and operational performance of this supply chain is essential.

Optimizing coordination between internal and external stakeholders, along with timely logistics management and the sharing of financial information, will enhance productivity, reduce costs, and improve competitiveness. Also, recognizing and managing various risks such as price risk, supplier risk, and market risk, are crucial measures for effectively addressing environmental challenges and seizing opportunities.

This research models and simulates the development of transmission capacity considering electricity supply and demand dynamics. After presenting a causal loop diagram and designing a stock and flow diagram, the model's validity is confirmed using validation methods for system dynamics models. The analysis then proceeds to scenarios for the total electricity demand in Iran. Firstly, the country's electricity demand structure is broken into industrial, household, agricultural, and other sectors. By studying consumption trends in each sector, linear and nonlinear regression are used to predict total electricity demand. Next, three scenarios - optimistic, moderate, and pessimistic - are defined in terms of electricity demand, and the required transmission capacity is calculated and designed for 400, 230, 132, and 63-66 kilovolt substations to cover and meet future electricity demand over twenty years. The research findings over a 20-year horizon indicate that in the moderate scenario, where electricity demand increases by 90 percent, the transmission capacity needs to increase by 106 percent to meet the demand. In the optimistic scenario, where electricity demand increases by 71 percent, the transmission capacity needs to increase by 85 percent. In the pessimistic scenario, where electricity demand increases by 110 percent, the transmission capacity needs to increase by 126 percent.

Today, healthcare has become an essential part of life, and in the meantime, the Internet of Things (IoT) is widely recognized as a potential solution to reduce the pressure on healthcare systems, which, by its very nature, optimizes the ability The performance reliability of diagnostic equipment, wearable sensors and medical equipment in the Internet environment has also been the focus of many recent researches.  Therefore, in this research, using neural networks (LSTM), an algorithm for optimal diagnosis of medical equipment was proposed and its efficiency was evaluated. The results showed that the LSTM architecture together with the Dropout layer and the Tanh activation function showed better performance and had the lowest average absolute value of error (MAPE) as well as the root mean square error (RMSE) in determining the abnormalities of medical equipment. The accuracy of the proposed method shows 96\% and the accuracy, recall and evaluation criteria of the model are 95\% respectively. 94.5 and 97\% have been calculated, which fully shows the suitability of the proposed algorithm in predicting anomalies and, of course, its suitability for improving the assurance of the proper functioning of medical equipment and sensors.

Risk management is one of the solutions of organizations to deal with uncertainty in the business environment, which has an effective role in using resources, as well as maintaining and promoting efficiency and effectiveness, and as a result,improving the organization's performance. The purpose of this research is to analyze the components of resource utilization with a risk management approach using the evolutionary theory (foundation data). In order to collect data in the qualitative part,semi-structured interviews were used, and the interviewees included managers and experts of one of the companies active in the copper industry and were selected purposefully. In order to model, the version of Strauss and Corbin was used in the foundation data approach.In this process, 60 concepts and 21 categories were created and they were grouped in the main 6 axis of Grounded theory. In the quantitative validation part of the model, structural equation modeling method was used.At first, questionnaires were compiled and their validity and reliability were examined.Content validity, divergent and convergent validity were used to measure validity, and Cronbach's alpha method was used for reliability and all were confirmed.Then it was distributed among the statistical sample. The statistical sample of managers and experts of the studied company was 82 people, and based on this, sampling was not done and the total number was counted.Based on the analysis of the questionnaire data, considering the non-normality of the data distribution,the research model was tested and confirmed by the partial least squares method with the help of SmartPLS3 software.

Predicting abnormalities of wearable medical devices plays a very important role in saving the lives and health of patients. This importance has opened new horizons for researchers with the development of newer algorithms. The long-term memory algorithm (LSTM) is one of the most important methods that are a special type of recurrent neural network (RNN) that has a high ability in this field and greatly increases the accuracy of correct and incorrect prediction of these abnormalities. In the current research, by using this algorithm and taking into account different parameters, the anomalies related to the sensors of the research field were determined. The results showed that there are influential parameters in the construction of this architecture, which include 3 very important factors: the number of neurons in the LSTM layer, the batch size, and the activation function. Also, the LSTM architecture together with the Dropout layer, with parameters Batch size = N = 128 and Tanh activation function shows a better performance and the lowest amount of error (MAPE) as well as the amount of the calculated mean square error (RMSE) in determining the anomaly. have sensors in the medical field. Investigations related to the results of 16 repetitions of optimization also showed that the process of reducing errors in the correct and incorrect identification of anomalies in the training phase has reached its lowest level with the increase in the number of tests, which shows the optimality and appropriateness of the work process. Therefore, this algorithm has a very good ability to identify errors in sensors and medical wearables, and it will be of great help in identifying the possible failure of sensors, and critical conditions of the patient, informing and finally helping patients in time.

modern governance in developing countries has a special place in policies related to the granting of aid in order to establish and institutionalize civil society. The purpose of this research is to identify the effective and influential factors of modern governance based on the functions of artificial intelligence in the police command of the Islamic Republic of Iran.

This research is applied in terms of purpose and mixed (qualitative and quantitative) in terms of method. The research community is made up of experts in related fields and managers of high, middle and operational levels in the artificial intelligence and information technology department in the police command. A. A. They form Based on the criterion of theoretical saturation, 17 people were used in a targeted way and 16 managers were used in a small part. The data was collected by semi-structured interview and analyzed using MaxQuda software and DeMetal method.

In the present study, 367 indicators (open code), 121 components (core code) and 27 dimensions (selective code) were obtained, which were presented in the paradigm model (Strauss and Corbin) including 6 selective codes.

In the quantitative part, using the Dimtel method, it was found that among the identified dimensions, social factors, political strategies, institutionalization of religious rituals, cultural factors, time management, motivational factors, qualification and optimization, environmental conditions, ethical behaviors, individual ethics, structural factors. , risks of governance by artificial intelligence, laws and regulations, empowerment, modeling of roles, expanding the central role of managers, infrastructure, creativity and innovation, economic factors, ethical interactions, teamwork, technology, transparency, public and social services, separating management from political issues, environment Kari and globalization were respectively the most influential indicators.

The scarcity of water resources can be considered one of the most important problems that has impacted all aspects of people's lives in Iran today. Given the significance of the issue, in this study, the dynamic hypotheses within the water resource supply system were initially illustrated through a causal loop diagram. This was done using the tool of system dynamics, which has proven its utility in analyzing and simulating various socio-economic systems. Subsequently, a Stock Flow Diagram was developed, and after model simulation and validation, scenarios involving tariff and governance in relation to water resources up to the year 1420 were analyzed. The results showed that changing household water consumption tariffs will not have much impact on water resource stocks. However, as the agricultural and industrial water consumption patterns are expected to be modified in the long term, increasing tariffs for these sectors will have a significantly better effect on water resource management over a 20-year horizon. If government regulations regarding water-intensive industries, cultivated land expansion, and agricultural water consumption become 20% more stringent, assuming stability in other model parameters, underground water resources and reservoir water resources are projected to increase by 17% and 20% respectively by the year 1420.

In the context of providing a model of intelligent management systems for sustainable and resilient production systems in the cement industry in order to improve social development, it is very vital to perform multiple analyzes to select the best algorithms to improve the performance of management systems. In this research, an issue related to production based on the model of intelligent management systems for sustainable and resilient production systems in the cement industry has been investigated. The main goal of this problem is to improve the production system in such a way that stability is maintained uniformly in the production process. In this production process, Poisson distribution and implementation of artificial intelligence are used in production with exponential distribution. Also, two main limitations have been set on this issue; One related to the total number of productions that are created and the other related to the maximum production time limit. This issue has three goals in mind. The first objective is to reduce the average production expectation as the main objective. The second goal is to minimize the amount of environmental damage in the production process. The third goal is to maximize the total functionality of the devices per unit of time. For this purpose, VIS, CNSGA-II, NSGA-II, MISA, NNIA and NRGA algorithms were used in MATLAB software. VIS algorithm showed the best performance in most criteria.

The current research was conducted with the aim of providing a supply chain logistics dynamic model based on the Internet of Things with an emphasis on online marketing. The participants of this research were supply chain managers with at least 15 years of teaching experience and a master's degree or higher. The selection of people was done by purposive sampling with the criterion of teaching experience in the elementary education period. Sampling was done with the participation of 18 experts. The data collection tool included two parts, 1- examination and exploration of upstream documents, documents related to the supply chain, logistics and Internet of Things in the library part, 2- semi-structured interview in the field part, where the semi-structured interview with the participants continued until the theoretical saturation stage. Found. To analyze the qualitative data, the theme analysis method based on the Atreide-Sterling model was used. In order to measure reliability, Holstein's coefficient, P-Scott's coefficient, Cohen's kappa index and Kruppendorf's alpha were used, which were confirmed. ATLASTI software was used in the theme analysis section. According to the results, four quantitative criteria were used to check the validity, transferability, verifiability and reliability: Holstein coefficient, Scott P coefficient, Cohen's kappa index and Krepinderoff's alpha. The degree of correlation of the experts' opinion was obtained by calculating the Holstein coefficient (PAO) or "observed agreement percentage" of 0.810, which is a significant value. According to the flaws in the Holstein method, the P-Scott index was also calculated, and its value was 0.799. The fourth indicator of validity of qualitative research is Cohen's Kappa index. Cohen's kappa index is 0.746 in this study. Finally, Kerpinderoff's alpha was used and its value was estimated at 0.834 in this study.

Chronic, progressive, and acute diseases gradually lead to disability in daily activities. Telemedicine technology enables remote diagnosis and care through diagnostic equipment and devices. This technology allows patients with chronic and severe conditions to receive necessary consultations and care using information technology tools. The present study aimed to identify and analyze the factors affecting the adoption of telemedicine technology for patients with specific conditions in Iran.

Following a review of previous studies, in-depth semi-structured interviews were conducted with 10 experts in health and health information technology to identify the factors affecting the adoption of telemedicine in Iran. After qualitative analysis of the experts' opinions and categorization of the factors, the Dematel method was employed to determine the influence, impact, and direction of the factors on each other. Subsequently, the Interpretive Structural Modeling (ISM) method was employed to classify the factors and identify the relationships between the criteria.

The structural model of this study comprises five levels, with organizational culture, appropriate resource allocation, and the reconfiguration of processes and services identified as the most influential and fundamental factors. Changes in these factors can have widespread effects on other factors and the overall system. In addition, patient acceptance and willingness to use telemedicine, the attitudes and beliefs of healthcare professionals toward telemedicine, and trust in this technology are the outcomes of higher-level factors.

Given the medical needs of patients with chronic conditions who require regular visits to physicians and healthcare centers, strategies should be devised to facilitate access to services and support for these patients. The results of this study indicated that organizational culture, management support, the allocation of necessary resources, and process reengineering could significantly contribute to the expansion of telemedicine technology in Iran and benefit patients with chronic conditions.

Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. In this article, by using data mining algorithms such as J48 and PSO, we were able to significantly improve the accuracy of detecting anomalies and attacks.

 The purpose of this research is to apply the meta-synthesis technique to identify optimization components of FinTech based on artificial intelligence (AI) indicators in the financial market. AI offers the financial industry a unique opportunity to reduce costs, improve customer experience, and increase operational efficiency, among other benefits. Financial companies can provide excellent financial services to their clients. Various features of AI are used by different FinTech companies worldwide to make operations safer and more efficient.

 The researcher employed a systematic review and meta-synthesis approach to analyze the results and findings of previous researchers. By following the seven-step method of Sandelowski and Barroso, the researcher identified influential factors. Out of 198 articles, 35 were selected based on the CASP method. The validity of the analysis was confirmed using Holsti’s coefficient, Scott’s Pi coefficient, Cohen's kappa index, and Krippendorff's alpha. For reliability measurement and quality control, the transcription method was used, which was found to have an excellent agreement level for the identified indicators. The results from the data analysis, conducted using MAXQDA software, led to the identification of 21 initial codes in four categories.

 Based on the meta-synthesis method, 21 codes were identified in four categories. The validity and reliability of the research findings were confirmed. The identified categories include machine learning algorithms, text analysis and information extraction, portfolio optimization, and recommender systems. In FinTech optimization, one of the fundamental components is machine learning algorithms.

 The use of artificial neural networks for predicting market prices, detecting price patterns, and structuring an optimal investment portfolio has significantly improved the performance of capital management systems. Additionally, genetic and evolutionary algorithms have been effective in optimizing parameters and structures of financial models, aiding in achieving optimal investment solutions. However, it is essential to recognize that while AI indicators can enhance financial system performance, one must also be aware of the associated limitations and risks. These include issues such as model complexity, sensitivity to input data, and risks related to machine decision-making.

The effort of this article is to solve one of the main problems in the field of banking, which is closely related to the field of information technology. The combination of the management discussion of this topic with the field of information technology will be one of the important topics in the field of information technology management. The main goal of this article is the clustering of bank customers.At first, all customer characteristics were extracted from the bank's database, which was randomly extracted for 900,000 customers, which will be provided as input to the proposed method of this article. All the characteristics of these customers were extracted and 10 characteristics (except four characteristics of the LRFM method) were listed using the opinions of experts. The proposed method should be able to choose among these 10 features for clustering customers, which results in more resolution in clustering. Due to the high number of cases of this problem, it is not possible to do it manually, and the proposed method tries to provide a separate model for clustering for the customers of each bank by examining different cases. Also, the problem of choosing the right value for the number of clusters in the K-means method is solved by the method proposed in this article. The results show that it is better than the basic RFM and LRFM methods.Keywords: relationship management with bank customers, clustering, RFM model, LRFM model, particle swarm algorithm, K-means method.

Supply chain disruption is an event that disrupts the production of goods and services. Resilience refers to the ability of an organization to manage disruptions or the ability of the supply chain network to quickly return to its previous state, ultimately positively impacting the company's performance. Many companies cannot maintain productivity during disruptions, losing competitiveness, increasing business continuity risk, and incurring financial losses. Sustainability considerations in supply chain operations have become a key issue. A common concept in sustainability is the triple approach: economic, environmental, and social, which must be observed by supply chain members. Sustainable supply chain management development is not a limiting factor but an approach to improve performance.

This applied research study was conducted using a mixed qualitative-quantitative analysis with a cross-sectional survey method. The qualitative sample included academic and industry experts, while the quantitative sample comprised managers, heads, and experts in the studied company's headquarters, operations, and projects. Data collection tools included documentary studies, expert surveys, and a researcher-made questionnaire. Factors were identified using the meta-synthesis technique, screened with the fuzzy Delphi technique, and validated with partial least squares. The SWARA method was used for weighting and ranking factors. Supply chain processes were defined based on the SCOR model and ranked using the WASPAS method. The thinking process tools identified limitations in the third-level bottleneck process, and improvement solutions were presented.

The meta-synthesis method extracted the desired indicators, which were screened and localized using the fuzzy Delphi technique and confirmed by experts in 7 dimensions and 39 indicators. The initial model was validated with partial least squares. Among resilience and sustainability factors, the "Risk Management" dimension with a weight of 0.2241 and the "Considering the risk factor in decision-making" index with a weight of 0.1224 were the top priorities. It was concluded that risk management is crucial for business continuity and dynamism. Supply chain managers should facilitate their participation in identifying and controlling risks and opportunities while continually increasing their subordinates' knowledge and skills. Evaluations identified the "sourcing and supply process," "goods and logistics supply process," and "purchase planning" as the most critical bottleneck processes. The root of disruptions in the "purchase planning" process was found to be in the identification, estimation, and allocation of human, infrastructural, and financial resources. 

Practical suggestions for company managers and decision-makers include employing expert personnel in purchasing planning, drafting executive plans, using advanced tools for measurement, analysis, forecasting, resource allocation, identifying uncertainties, determining prerequisites, and managing main and support suppliers and changes, and reviewing and modifying the existing mechanism.

The current research was conducted with the aim of analyzing the indicators of the smart city governance model based on urban justice with the approach of electronic participation. The participants of this research were municipal managers and urban planners with a teaching experience of at least 15 years and a master's degree or higher. The selection of people was done by purposive sampling with the criterion of teaching experience in the elementary education period. Sampling was done with the participation of 18 experts. The data collection tool consisted of two parts, 1- examination and exploration of upstream documents, urban planning documents in the library section, 2- semi-structured interview in the field section, where the semi-structured interview with the participants continued until the theoretical saturation stage. To analyze the qualitative data, the theme analysis method based on the Atreide-Sterling model was used. In order to ensure validity, the interview questions were approved by 3 experienced urban planning and management experts, 1 of whom had a master's degree and 2 had a doctorate. And in order to measure reliability, Krippendorf's alpha coefficient was used, which was confirmed. ATLASTI software is used in the theme analysis. The results of the factor analysis show that among the 176 indicators (items) available, 33 basic themes can be identified and 9 categories of constructive themes have been obtained. The 9 first-level themes identified are: smart governance, smart interaction, smart sustainability, public and private sector cooperation, knowledge integration, smart innovation support, smart infrastructure, smart

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 new governance is an issue, especially in the development of the issue in the establishment and institution of civil society in the policies related to the granting of aid from a special position, which is raised with the aim of achieving sustainable development and in which it is reduced. Poverty, job creation and sustainability, growth and development of the environment and development of women become life.

The research was carried out in terms of purpose, basis, and method of theory. The research community of experts in the field of management in the field of government management, human resources and information technology (book or internal article) and managers and assistants and senior experts in the field of artificial intelligence and information technology created. Theoretical saturation software 17 people were selected by purposive sampling method. The data were collected and standardized by semi-structured interview using maxqda12 software.

The findings of this research yielded 146 basic concepts(open code), 27 main concepts(personality code), which in the form of a paradigm model includes 6 codes including causal factors(4 components and 20 indicators), background factors(6 components) and 39 indicators), strategic factors(5 components and 35 indicators), intervening factors(3 components and 14 indicators), the main phenomenon(4 components and 18 indicators) and consequences(5 components and 20 indicators) were presented.

The result of this research indicated that the main phenomena (institutionalization of religious rites, expansion of the central role of managers, individual ethics and qualification and optimization) had a greater impact than other categories in the paradigm model.

The purpose of this research is the effect of knowledge management on organizational performance considering the mediating variable of business process management. The current research is applicable in terms of purpose, and descriptive-survey in terms of data collection, and correlative type. The statistical population of the current research includes managers and experts of a company active in the field of export and import as many as 160 people, which was considered to be 113 people using Cochran's formula, and non-random sampling method was used. The collection tools in this research include knowledge management questionnaires by Fong et al., (2009), business process management by Rouhani et al., (2014), and organizational performance by Choi and Rainey (2010). The reliability of the research was checked and confirmed using Cronbach's alpha criterion in SPSS software. PLS software was used to fit the conceptual model of the research. The findings of the research showed that based on the significant values ​​of them, all three hypotheses were confirmed. The impact of knowledge management on business process management was 0.742, the impact of knowledge management on organizational performance was 0.422, and the impact of business process management on organizational performance was 0.652. Therefore, the most important proposal of the research is to pay attention to the preservation, sharing and application of knowledge, which is effective both in managing business processes and in increasing the level of organizational performance.