mohammadreza rostami

The objectives of spinal surgery include the effective decompression of neural structures, ensuring spinal stability, and the proper fixation of an unstable vertebral column. Such surgical aims necessitate unavoidable harm to healthy tissues, such as the spine and adjacent soft tissues, once spinal surgery commences. Significant injury to the normal vertebral structure and adjacent paraspinal tissues during the procedure can result in negative outcomes characterized by ongoing axial discomfort and the need for further operations due to new instances of spinal instability. A variety of strategies, including the use of microscopes, tubular retractor systems, percutaneous tools, and the exploration of innovative surgical techniques, have been employed to minimize damage to healthy tissues and enhance surgical results. Introduced approximately thirty years ago, endoscopic spine surgery (ESS) represents a less invasive approach and has gained widespread acceptance with the advancement of endoscopic surgical tools and the introduction of novel endoscopic techniques over the last two decades. In theory, ESS could be considered the optimal method for spinal operations due to its reduced impact on tissues and superior visualization of the operative area. Nonetheless, surgeons are often reluctant to adopt ESS because of its challenging learning curve and the absence of robust evidence supporting its surgical outcomes. This article examines the logic and benefits of ESS by analyzing relevant literature.

The current research was conducted with the aim of designing a dynamic, responsive and competitive structure model for IRI Railway Company.The research method was mixed exploratory (qualitative-quantitative).The statistical population in the qualitative part is high and middle managers of IRI Railway Company, professors of railway engineering,transportation engineering and public administration in higher education centers and specialists related to the subject,and in the quantitative part,employees of IRI Railway Company in The company was in the northeast of the country in 2023.In the qualitative section,20 experts were surveyed using a semi-structured and structured checklist,and in the quantitative section, a 119-item researcher-made questionnaire was distributed among 327 people using a multi-stage relative cluster sampling method.To determine the validity in the qualitative phase,the content of the checklist was approved by experts,and in the quantitative phase,the validity of the questionnaires was confirmed by three

form,content and structure.The reliability of the quantitative and qualitative part of the instrument was also confirmed.Qualitative data were analyzed with Delphi technique in SPSS software and quantitative data were analyzed with structural equation modeling technique and exploratory and confirmatory factor analysis tests,Friedman,Spearman and single sample T in Smart PLS software.The results of the qualitative part indicated that the final model has 9 dimensions,27 components and 119 indicators as described by "intelligence and agility","human and non-human capital","non-technical factors","creativity and knowledge governance","laws and policies","non-financial performance","business structure","technical and technological factors" and "national and international strategies".The results of the quantitative part also showed that all dimensions and components of the research model were confirmed.

The current research was conducted with the aim of designing the optimal model of equity management in order to increase social welfare with a comparative approach.The research method was mixed exploratory(qualitative-quantitative).The statistical population in the qualitative part of university professors in the field of public administration,managers of the Ministry of Economic Affairs and Finance,senior managers of the privatization organization and provincial equity investment companies at the country level and in the quantitative part,top and middle managers,deputies and senior experts of the privatization organization,the organization Stock Exchange,Ministry of Economic Affairs and Finance and provincial investment companies were equity shares in 2023.In the qualitative section,25 experts were surveyed using a semi-structured and structured checklist,and in the quantitative section,a 108-item researcher-made questionnaire was distributed among 169 people using a stratified sampling method.To determine the validity in the qualitative phase,the content of the checklist was approved by experts,and in the quantitative phase,the validity of the questionnaires was confirmed by three

form,content and structure.The reliability of the quantitative and qualitative part of the instrument was also confirmed.Qualitative data were analyzed with Delphi technique in SPSS software and quantitative data were analyzed with structural equation modeling technique and exploratory and confirmatory factor analysis tests,Friedman,Spearman and single sample T in Smart PLS software.The results of the qualitative part indicated that the final model has 6 dimensions,25 components and 108 indicators as "social management","cultural management","economic management","strategic management","educational management" and "political management".The results of the quantitative part showed that all dimensions and components of the research model were confirmed.

Meeting the needs of shareholders and investors, generating profit and income, and covering current expenses are the primary objectives of any economic entity. Banks and financial institutions are no exception, and calculating and understanding costs is one of their most critical activities. In today's competitive environment, organizations must adopt strategies that enhance the quality of services and products while reducing their costs. To achieve this, having tools for analyzing related costs and formulating cost policies for services and products is essential. Companies that previously used traditional costing systems have had to transition to newer, more dynamic, and flexible costing systems that account for various costs, including products, activities, distribution channels, and customers, reflecting the complexity of modern business and production processes. This article presents a model for calculating the cost of banking services using activity-based costing.

The research described in this article is developmental-applied. Given the various factors affecting costs and their nonlinear and cyclical effects, which render banks as complex systems, the methodology of system dynamics was used for modeling. This research is descriptive in terms of data collection methods, which included library studies reviewing sources and references on activity-based costing and systems dynamics principles, simulations concerning the cost of banking services using systems dynamics, and other sources such as books, magazines, articles, and theses from reputable libraries, information search centers, and credible websites.

 In this study, detailed models for two products, mudarabah and deposits, were developed. Cause-and-effect diagrams and their rates and statuses were drawn, and after testing the models, proposed scenarios were designed and compared. Four scenarios were suggested to improve system behavior: improving productivity, adjusting human resources, re-engineering business processes, and automating processes. To select the best policy—one that achieves the most significant cost reduction over a similar period—all policies were compared. The study's findings indicate that the policy of mechanization and process re-engineering is the most effective. Therefore, the bank's general policy for reducing product costs can be a combination of re-engineering and mechanizing processes.

Considering the dynamic business environment and banks as complex systems where many variables influence each other, the systems dynamics approach was employed to model the cost of banking services. The simulation results show that a combination of process re-engineering and automation can serve as an effective policy for reducing the cost of banking services.

The success and growth of organizations are due to their timely actions against challenges and problems and having dynamic capabilities, responsiveness and competitiveness. The purpose of this research is to design and validate a dynamic, responsive and competitive structure model for the Railways of the Islamic Republic of Iran. The research method was qualitative and the statistical population in the qualitative department included senior and middle managers of the Railways of the Islamic Republic of Iran, professors of railway engineering, transportation engineering and public administration in higher education centers and specialists related to the subject and written documents related to the research. In the validation section, the statistical population included faculty members of public administration in higher education centers and senior intermediate managers of the Railways of the Islamic Republic of Iran. In the qualitative section, 15 experts and 6 documents (texts) were selected using the snowball and purposive sampling methods for experts and documents, respectively.  20 experts were selected in the validation section using the purposive sampling method. For data analysis, in the qualitative section, the grounded method with open, axial and selective coding in One Note software was used while in the validation section, the Delphi method in SPSS software was used. In order to determine the validity and reliability of the resulting data, necessary reviews including acceptability (expert review) and verifiability (expert review) were used. According to the results of the qualitative section, the paradigmatic model has 10 dimensions (categories) and 23 components (concepts) as follows: the causal conditions (upstream policies, control and supervision, laws and regulations, commercialization of the company and changes in the organizational structure), contextual conditions (organizational agility, intelligent marketing, human capital, upstream support and non-human resources), intervening conditions (private sector investment, top and middle managers, organizational values, supply chain and re-engineering and new technologies), strategies (motivation and innovation, being knowledge-oriented, optimality, discarding old strategies and national and international interactions) and consequences (company profitability, improving non-financial performance and service quality). The results of the validation section indicated the confirmation of the components identified in the qualitative section.

Non-linearity feature and high fluctuations in financial time series have made the forecasting of stock prices and financial indicators face many challenges. However, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of financial time series is the decomposition of capital market signals through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the financial markets, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the Tehran Stock Exchange index. In this regard, the daily data of the total index of the Tehran Stock Exchange in the period of 2012/12/01 – 2022/02/20 be used and the results were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the introduced model (CEEMD-DL(LSTM)) has higher efficiency and accuracy in stock exchange index forecasting. Accordingly, the use of this model in financial forecasts is suggested.

In this article, a mixed flow pump was designed using CFturbo software and analyzed during stable and transient process using CFX software. For the accuracy of the results of numerical analysis, a validation was done, and the numerical results were in good agreement with the experimental results. During the transient process in the numerical analysis, an instantaneous maximum pressure was observed. Pump head changes in this case are divided into three steps. In the first step, the head increases linearly, then in the second step, a momentary maximum pressure occurs, in the last step, the head curve tends to be stable. In addition, a parametric study was carried out in steady and transient mode, which includes the investigation of the effect of changing the blade outlet angle in four different angles in steady mode and two angles in transient mode, as well as the effect of changing the pump speed on its performance. By changing the speed of the pump impeller, the head and efficiency also increase or decrease in proportion to the increase or decrease of the speed. By changing the blade outlet angle, it was observed that with the increase of the outlet angle, the efficiency first increases and then decreases after the angle of 26 degrees, also the pump head increased by about 23 percent.

In different societies, supporting vulnerable groups and improving income distribution has always been one of the concerns of the government, economists and sociologists. Income distribution is not only one of the economic issues, but also it is one of the important social and political indicators. One of the financial privatization methods in the world is the privatization of coupon payments (justice shares), whose goals are to prevent the income gap between different strata of society and help to distribute ownership and as a result, income more equitably in the country. The purpose of the research is to determine the factors affecting the optimal management of justice shares in order to increase social well-being. The current study is a fundamental (exploratory) research and a mixed method (qualitative-quantitative) has been used to investigate the research objectives. The statistical population of this research in the qualitative section includes university professors in the field of public administration, managers of privatization organizations and the Ministry of Economic Affairs and Finance, and managers of justice share provincial investment companies. The statistical population of this research is in the quantitative section, including high and middle managers, deputies and senior experts of the Privatization Organization, the Stock Exchange and Securities Organization, the Ministry of Economic Affairs and Finance, and justice share provincial investment companies. The data required for the research has been collected through a questionnaire. In order to analyze the data collected in the qualitative section, the grounded theory method is used. In the quantitative section, the DEMATEL method is used. The results showed that the causal, intervening, axial, contextual, individual and environmental factors had effects on the optimal management of justice shares in order to increase social welfare.

Despite the increasing consumers’ environmental concerns, these concerns are not in line with their actual shopping behaviors. Therefore, to achieve sustainable development, we should focus on sustainable consumption instead of focusing solely on production. Relying on the qualitative research strategy, this study tries to design and validate substantial green consumer behavior using nudge theory. First, in-depth interviews were conducted with Iranian managers and professors who are active in green consumer behavior and green productions issues. The result was coded and categorized using the data theorizing method of the foundation in MAXQDA software. In the next stage, the questionnaire extracted from the paradigm model of research, design and distribution among consumers of green cosmetics in the country and 384 items were collected. Total 1321 basic codes, 46 concepts, 11 sub-categories were finalized. Through axile coding, 6 categories were identified including environmental attitudes, consumer innovativeness, personal norms, behavioral intentions, sustainable consumption behaviors, nudge marketing, contextual conditions, intervening conditions, and consequences. The result confirms the relationships between latent variables, confirming the research hypotheses and correctly identifying the components of the relevant model. The results of the study showed that the use of green nudges along to other factors can have a positive and significant effect on satisfaction, purchase of green products and conservation of natural resources without interfering with the right of consumers to choose products. The results can be effective on the strategies of managers and marketers in motivating customers to buy more green products and turning their attitudes into real purchases.

One of the most vital research programs of financial knowledge today, which is at the forefront of rejecting the theory of efficient markets, is financial behavior theory, which is the cause of joint efforts between financial sciences and social sciences and has deepened our knowledge of financial markets. Today's financial topics, which we are going to explain, describe and compare, are called financial behavior, which in simple terms is a pattern of thinking in which markets use patterns consisting of social sciences, psychology, finance and several other disciplines. They are studied, and in other words, economic agents are not rational in behavioral patterns contrary to neoclassical theories, but are normal either because of their preferences or because of cognitive biases. The purpose of this study is to design and explain a model for identifying behavioral factors affecting stock price fluctuations in the Tehran Stock Exchange. This research is applied in terms of purpose and in terms of working method is a type of survey research. The data of this research were analyzed using SmartPLS software. The results of the study showed that the emotional bias of short-sightedness, the emotional bias of optimism and the emotional bias of adaptation affect the fluctuations of stock prices of companies.

Contrary to the popular belief in modern financial theory that states decision makers behave completely rationally to maximize their profits, studies conducted in the field of behavioral finance show that the human decision-making process is not completely rational and is not based on it. Most of the time, the financial-behavioral factors of the decision-making process examine the perceptions of investors and their reactions to different conditions of the financial market, and emphasize the influence of the personality, culture and judgment of investors based on investment decisions. Recognizing behavioral biases makes investors more aware of their decision-making process, and if faced with biases, they can react well and avoid deviations in decision-making. Therefore, the purpose of this research is to design a model of stock return fluctuations and investors affected by cognitive biases. The statistical population of this research is experts, managers, consultants and experts in financial affairs and the statistical sample includes 160 people.This research is practical in terms of purpose, and in terms of working methods, it is a type of survey research. The study period is in 2022 AD. For sampling, purposeful and snowball sampling methods were used, and with 30 semi-structured interviews with experts, the research reached theoretical saturation. The results obtained from the research showed that cognitive bias, ambiguity avoidance, familiarity, self-documentation, and tardiness have an effect on the volatility of companies' stock returns. The path coefficients (beta) for each bias have been calculated separately, which determines its effect on stock return fluctuations.

Thrombotic thrombocytopenic purpura (TTP) is associated with microangiopathic hemolytic anemia, thrombocytopenia, and microvascular thrombosis. No comprehensive report exists on clinical characteristics and risk factors of relapse and mortality in Iranian TTP patients. In this study, we aimed to report clinical features of Iranian TTP patients, to evaluate disease relapse and mortality rate and their associated risk factors.

This study was a cohort study of patients diagnosed with microangiopathic hemolytic anemia admitted to the Shariati Hospital, Tehran, a referral center for TTP patients, from 2010 to 2017. Demographic, clinical, and laboratory data were recorded and patients were followed for 3 years regarding disease relapse and mortality.

  114 patients (80 female, 34 male) with a mean age of 39.3 ± 14.99 years were included.  Hematologic and neurologic symptoms were the most common manifestations. Abnormal laboratory findings at the presentation included thrombocytopenia, anemia, and elevated LDH. All patients were treated with plasma exchange, and 75.5% of them had a response to treatment, while the 3-year relapse and mortality rate was 23.6 and 26.3%.  Lower platelet count was a predictor of disease relapse. Age, hematological, or neurological initial presentation were associated with TTP mortality.

Based on the largest study of TTP patients ever in Iran, the demographic and clinical characteristics of Iranian TTP patients are similar to other existing reports. Knowledge of the risk factors for TTP relapse and mortality could be useful to alert hematologists for prompt therapeutic actions when necessary.

The stock market has played an undeniable role in economic growth and is a complement to the banking sector in allocating financial resources to manufacturing firms. Hence, the role and importance of the stock market in the economy have always been emphasized. There are several factors that affect stock market returns, one of which is the Rule of the law and the Regulatory Quality of countries. Therefore, in this study, the importance of the Rule of the law and the Regulatory Quality on stock returns has been studied in Iran and selected emerging countries in the period 2000-2021 by the system generalized method of moments. The results of this estimate show that at a significance level of 5%, the rule of law and Regulatory Quality have a positive and significant effect on stock returns. Also, the effects of inflation, exchange rate, and growth of industrial production have been positive and significant. The effect of crude oil prices is negative and significant, and trade openness has a positive effect on stock market returns, but this effect is not statistically significant at the significant level of 5%.

Financial markets play an influential role in economic development and most developed countries have more dynamic and stronger financial markets. Due to this market’s crucial role in the process of economic development, several studies have evaluated the role of financial markets in economy and Its effects on performance. These studies have often confirmed the positive impact of financial markets on the economic growth of countries. One of the most important components of financial markets is the stock market which in most countries, along with the banking sector, plays an effective role in financial resources allocation. Several factors can affect stock market returns. One of these is the government effectiveness, the regulatory quality, and the rule of law. Therefore, the aim of the present study is to study the role of government effectiveness, Regulatory quality and rule of law on stock returns in Iran and selected developing countries. For this purpose, the data of the period 2021-2000 and the quantile regression have been used.Based on the model estimation results, at a significant level of 5%, government effectiveness, regulatory quality and rule of law have a positive and significant effect on stock market returns. Also, the effects of inflation, per capita income, exchange rate and growth of industrial production on stock market returns have been positive and significant. In contrast, the impact of crude oil prices and interest rates on stock market returns is negative and is not statistically significant at the 5% level.

The purpose of the current study was the optimal allocation of assets using the cyclical behavior of commodities. For this purpose, the three commodities of copper, aluminum, and steel, along with gold and foreign exchange rate, were studied and their price data, along with the Tehran Stock Exchange (TSE) index during the period of January 2, 2009, to September 26, 2020, were analyzed. To examine the data, cluster analysis was used based on regime change behavior and Markovian state transition models were fitted to the data. Homogeneous and heterogeneous portfolios were constructed in terms of regime change behavior in both ascending and descending market regimes and the performance evaluation criteria of the portfolios were calculated. The results showed that portfolio diversification through the assets in the homogeneous cluster led to the best portfolio performance in the descending trend, while diversification by using commodity metals in the heterogeneous cluster might become the best performance in the ascending trend. In general, the results demonstrated that the use of commodities to diversify the portfolio led to better results in portfolio performance.

With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of cryptocurrencies time series is the decomposition through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the cryptocurrencies field, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the bitcoin return (as the most popular currency). In this regard, the daily data of the total index of the Tehran Stock Exchange was used in the period of 2013/01/01 – 2022/05/28 and the results obtained were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the use of the introduced model (CEEMD-DL(LSTM)) has increased the efficiency and accuracy of bitcoin return forecasting. Accordingly, the use of this model in this field is suggested.

Herd Behavior is one of the biases behavioral that affects financial markets and decision making. Herd Behavior in financial markets is the tendency of a group of investors to follow the actions of others and to ignore personal beliefs and information and following the market consensus. The purpose of this study is to investigate the relationship between Herd Behavior between crude oil prices and the stock market. In addition, the effect of variables such as upturns and downturns periods, investor tendencies and market liquidity on the relationship between the two variables will also be tested. This paper contributes to the debate on the cross-markets correlation by extending tests of herding behavior between the stock market and the crude oil market. We study herding behavior at the sector level during extreme oil market movements. Furthermore, we investigate the cross herding behavior between the two markets while including the volatility of the oil prices and investors’ sentiment. We use a modified version of the cross section absolute deviation model to infer the impact of the oil market on industry herding.

This research is an applied research in terms of purpose and a correlational research in terms of method. The research sample, after imposing the intended restrictions, included companies enlisted on the Tehran Stock Exchange, in the period from 2015 to 2020. To test the research hypotheses, a panel data model was used, and to measure the Herd Behavior extends the work of Chang & et al (2000). Then we tested the research hypotheses.

The research’s Results confirm the Herd Behavior in the Iranian stock market. The return of the crude oil market has an effect on the Herd Behavior  in the Tehran Stock Exchange. In addition, this behavioral biasess is greater during periods of crude oil market upturns than during periods of downturns. The results also showed that more liquidity in the market is associated with more Herd Behavir.

This paper examines herding behavior within Tehran Stock Exchange industries and explores the impact of oil prices on the intensity of herding. It further investigates the asymmetric effect of oil prices moves (downturns and upturns) on herding within sectors, while including both the volatility of the crude oil market and investors’ sentiment. The reason behind the inclusion of oil prices and volatility is to study the possible integration between the stock market and oil market and to infer the possible cross-herding behavior between them. There is herd behavior in the Tehran Stock Exchange and the crude oil market as one of the most important sectors of the economy and the largest source of government revenue also affects it. Therefore, the capital market and the effects it receives from the crude oil market should be considered more and more by economic decision makers.

Cognitive impairments in patients with multiple sclerosis (MS) are suggested as a prognostic factor for disease development, and consequently higher disability and more deficits in daily and social activities. In this regard, we aimed to investigate the association between quality of life (QOL) and cognitive function in patients with MS.

We conducted a cross-sectional study on patients with relapsing-remitting MS (RRMS). General characteristic variables were carried out, and then all patients underwent assessments such as Multiple Sclerosis Quality of Life-54 (MSQOL-54), Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS), Expanded Disability Status Scale (EDSS), Beck Depression Inventory-II (BDI-II), and North American Adult Reading Test (NAART).

In the present study, a total of 92 patients, including 76 women with a mean disease duration of 6.82 ± 4.80 years were involved. Results of simple Pearson correlation revealed a significant positive relation between California Verbal Learning Test (CVLT) total learning with MSQOL mental health (r = 0.267, P = 0.017) and physical health (r = 0.299, P = 0.007). After adjusting for potential confounders, there was a negative correlation between MSQOL mental health with Delis-Kaplan Executive Function System (D-KEFS) (r = -0.303, P = 0.015) and Judgment of Line Orientation (JLO) (r = -0.310, P = 0.013). Besides, MSQOL physical health was negatively associated with Brief Visuospatial Memory Test-Revised (BVMT-R) in the adjusted model (r = -0.270, P = 0.031).

There is a statistically significant association between specific aspects of cognitive decline and QOL. Therefore, more attention should be paid to cognitive impairment in patients with MS as based on our findings, it is significantly associated with QOL.

This paper presents a reliable numerical technique based on Lucas polynomials for a family of fractional differential equations and multi order fractional differential equations by means of the least square method. The fractional derivative is in the Caputo sense. A relevant feature of this approach is the analyzing of the suggested technique by Gauss quadrature method and using the theory of Lagrange multipliers to solve a constrained optimization problem. An upper error bound, the convergence, and error analysis of the scheme are investigated and the CPU time used, the values of maximum errors, the numerical convergence analysis based on the proposed technique for different values of parameters are discussed. Furthermore the results of present technique are compared with the, operational matrix of hybrid basis functions, the Jacobi orthogonal functions and pseudo-spectral scheme. In order to introduce the numerical behavior of the proposed technique in case of nonsmooth solutions, this issue is discussed. In this case, the obtained results imply an elegant superiority of our proposed technique. The numerical examples illustrate the accuracy and performance of the technique. Finally extending the proposed technique to high dimensions and system of fractional differential equations can be examined as a further works.

In this study, the least square method, the Gauss quadrature method and the theory of Lagrange multipliers are used to solve a constrained optimization problem.

Several numerical examples are examined using the proposed technique. The numerical examples illustrate the accuracy and performance of the technique. Also, the numerical results reported in the tables indicate that the accuracy improve by increasing the degree of the Lucas polynomials.

In this paper, Lucas polynomials have been successfully applied to compute the approximate solution of the fractional differential equations and multi order fractional differential equations. The results show that: • The proposed technique provides the solutions in terms of convergent series with easily computable components in a direct way, without using linearization, perturbation or restrictive assumption. • The proposed technique is very straightforward and the solution procedure can be done easily. • The numerical behavior of the proposed technique in case of non-smooth solutions, demonstrated that the obtained results imply an elegant superiority of our proposed technique.

Success in customer relationship typically depends on increasing knowledge management. As the hiring of a scientist in customer relationship management becomes more prominent because there is so much information about drugs, their properties and their behavior. In such cases, use efficient algorithms and techniques to analyze this data is essential. The purpose of this study is to provide a model for customer relationship management (CRM) in discount chain stores through the use of the Internet of Things and Big Data. The data analysis method is a detailed construction modeling method. In this research, in order to fill in the questionnaires, the opinions of 20 experts in this field have been used. According to the interpretive structural model, the first level of the hierarchy belongs to “Customer Relationship Management Success (3)”, the second level of the hierarchy belongs to “Custom Marketing Advertising (1) and Customer Improvement (2)” and the third level of the hierarchy belongs to “Pricing strategy (3)” can be, which means that these variables are affected by other variables. The most influential variable is “real-time analysis” (4). The results can be a good model for the country's chain stores to better manage customer relationships and gain a competitive advantage.

The increase in the elderly population in the country and the basic need for health products is an issue that is less considered by humanities experts and more in the field of medical sciences. Perhaps because for us, treatment has always been prior to prevention; Not prevention to cure! The purpose of this study is to introduce new methods of marketing health products for the elderly in order to increase the desire to consume health products among this group and reduce their per capita medical costs. Research Methods; Survey and step by step and data collection tools; Interviews and questionnaires are open and closed. The research study community is Army retried (AJA) with more than 260,000 elderly retirees. Direct staffs of various departments (all of whom are senior officers) were identified as research experts and the required information was collected during 5 stages of interviews and questionnaires from experts. Finally, 10 marketing methods for health products related to the elderly community through The Weighted Aggregates Sum Product Assessment (WASPAS) model in Excel software are prioritized and presented as a model for performance. Vertical marketing with 0.825, Word of mouth marketing with 0.787, Information marketing with 0.783, behavioral marketing with 0.770, Newspaper marketing with 0.647, One to one marketing with 0.584, Scientific marketing with 0.566, Catalog Marketing with 0.546, Traditional marketing with 0.533 and finally Analytical marketing with 0.416 Index with λ = 0.5; are the most effective methods of marketing health products in the elderly community of the country.

Demand for the use of polymers in different industries with different properties is increasing. However, pure polymer does not have the ability to create a wide range of desired features. To solve this problem, various additives are used, many of which are minerals. The polar nature of these additives has made them impossible to use in non-polar polymers, such as polyolefins, due to the incompatibility and poor interface adhesion. Various methods are used to increase compatibility, one of which is the use of compatibilizers. Functionalized waxes are among the materials that researchers have studied over the years. The backbone of functional waxes is miscible with a polymer, and the functional groups on the wax surface can interact with the groups present on the additive surface. Therefore, their use improves the dispersion and distribution of additive particles in the polymer. Also, due to their low molecular weight, these waxes can penetrate the pores and capillaries on the additive surface and act as a successful co-compatibilizer. In this study, first, the methods of increasing compatibility and the cases used for this purpose are briefly reviewed. Then, the performance of functionalized waxes as a compatibilizer and co-compatibilizer and their effect on the physical, mechanical, and thermal properties of the polymer composites are investigated in details.

Despite theoretical and empirical validity of capital asset pricing model, empirical evidence shows that investors ask Risk premium due to idiosyncratic volatility. Considering the idiosyncratic volatility in capital asset pricing، this study aims to Determine Firm characteristics which explain idiosyncratic volatility in Tehran Stock Exchange. In this study, we measured the idiosyncratic volatility, employing the 5 and 7 multi factor models and using the conditional and non-conditional methods. more over we investigate the relation between firms characteristics and idiosyncratic volatility by panel Regression. The research population contains the whole firms in Tehran Stock Exchange and OTC during 2011-2017, amongst them 82 firm were selected based on specified sampling conditions. Employing various Ivol measurements, our results show that book to market equity ratio, size، sales growth, asset growth and liquidity, are the Determinants of idiosyncratic volatility and firms with greater size, lower liquidity, lower sales growth, higher book to-market equity ratio, and lower Asset growth have less idiosyncratic volatility

Hematopoietic stem cell transplantation (HSCT) severely undermines the recipients' immune status and makes them prone to complications following viral infection. Here, we report a 3-year-old boy with mucopolysaccharidosis type VI who acquired SARS-CoV-2 infection after HSCT. The boy was diagnosed with SARS-CoV-2 during the post-transplant period (19 days after HSCT) when dealing with acute graft versus host disease (GVHD). He was successfully treated with remdesivir and tocilizumab and recovered. Well-timed treatment with tocilizumab might reduce the risk of invasive mechanical ventilation and death in patients with severe COVID-19 pneumonia in the early post HSCT period.

The present study has identified the effective components and indicators to evaluate the performance of listed companies. The present research is applied research in terms of purpose and descriptive-survey in terms of nature. The data collection tool is a questionnaire. In order to identify the components and indicators for evaluating the performance of insurance companies in the stock market, library and internet studies including books, documents, articles and researches of other domestic and foreign researchers were conducted. The indicators were reviewed after extracting the opinion of experts (17 experts and representatives of the Central Insurance) using the Delphi method. AHP software was used to identify the indicators and t-test and then AHP network analysis technique was used to prioritize the components. The most important indicator for evaluating the performance of insurance companies in the stock market is the insurance loss ratio, and the second priority is the profitability of the third priority, the reliability of investors, the fourth priority, the extent of the sales network and the fifth priority, the volume of the contract. According to the results, among the 17 insurances in the stock market, Parsian Insurance received the highest score and Saman Insurance Company obtained the lowest score.