فهرست مطالب رضا حیدری

The aim of this study was to investigate the effect of indigenous and local sports games on the quality of life and interpersonal relationships of bad supervised and homeless women. Research in terms of analysis is a kind of semi-experimental research with pre-test and post-test, and in terms of the nature and purpose of the research is considered as applied research. The statistical population of this study consists of all unmarried and unmarried women in the city of Mashhad. According to the information obtained from the Mashhad Welfare Organization, there are 1,500 people. According to the semi-experimental research, 15 people are for each group. This research was used. Data were gathered using a questionnaire of quality of life questionnaire and interpersonal communication skills questionnaire. Data analysis in two sections of descriptive and inferential statistics using SPSS software version 21 showed that there is a significant difference in all components of quality of life except for mental health problems. Significant changes have been made in all aspects of quality of life. Also, the quality of life score is 0.25 units, and this is significant.

Pahlevani and zurkhaneh sports as an intangible Iranian cultural heritage can create a special capacity for development of creative sports tourism in Iran. Therefore, the purpose of this study was to identify the factors affecting the development of creative sports tourism based on intangible cultural heritage.

The present research is applied research in terms of purpose and descriptive-analytical research in terms of nature. The data of this research were also collected in the field. Also, this research is one of the qualitative researches that used the method of data theory and Glaser approach. Library studies along with interviews were the main method of data collection in the present study.

Based on the results of the factors affecting the development of creative tourism based on intangible cultural heritage, 108 concepts (codes), 13 categories and 2 dimensions were classified. Accordingly, political, economic, social and cultural factors, technology and technology, legal and governance and environmental subset of external factors and management factors, human resources, infrastructure, education and research, international interactions, marketing and tourist attractions were identified as internal factors in this study.

It can be concluded that the thirteen factors identified in this study each play an important role in development of this sector of tourism and officials and decision makers in this area can use benefit from the results of this research in order to develop tourism in general and creative tourism and sports tourism in particular.

Cardiovascular and sports experts recommend the use of oxygen consumption cost indicators, heart rate and fatigue index to control work pressure in sports and rehabilitation. Meanwhile, it is very common to control the intensity of exercise by heart rate index. However, the maximum heart rate (HRmax) must first be measured or estimated as the basis for the calculations. The purpose of the present study is to evaluate the validity of HRmax estimation equations in healthy children.

The HRmax was measured and estimated in 205 healthy boys between 8 and 12 years old by two methods, Bruce's incremental exercise test and special equations for children. In order to compare the estimated and measured HRmax, Pearson's correlation, t-correlated and root mean square error (RMSE) were used.

A weak correlation between estimated and measured HRmax was observed (R=0.09-0.18). A significant difference was observed between the estimated and measured HRmax (beats per minute = 1.4-4.8) (P<0.05). So, the Nikolaidis equation led to a significant overestimation and the Shargal and Gelbert equations led to a significant underestimation of the HRmax. Also, the root mean square error values for HRmax estimation equations were high (RMSE = 6.9 - 7.4 beats per minute).

Considering the ineffectiveness of foreign equations for estimating the HRmax in Iranian children, it is recommended to design native equations for estimating the HRmax for Iranian children.

The maximum aerobic capacity (VO2max) is the most important indicator of physical fitness, which is closely related to the general health of the body. Considering the problems of laboratory measurement of VO2max, estimating VO2max by non-exercise equations will be one of the alternative solutions. This study aims to evaluate the validity of the native non-exercise equation in estimating VO2max of healthy adolescent boys.

The variables of age, height, weight, and body mass index were measured in 248 healthy boys aged 15 to 18. The subjects' VO2max was measured and predicted by exercise test and non-exercise native equation respectively. To evaluate the validity of the native non-exercise equation, Pearson's correlation, paired t-test, and Bland-Altman's agreement were used.

There is a significant correlation between measured and predicted VO2max (R = 0.76, p<0.05). Also, no significant difference was observed between predicted and measured VO2max (43.10 ml/kg/min and 43.38 ml/kg/min respectively) (mean difference=0.28 ml/kg/min, P>0.05). Also, the Bland-Altman graphic diagram indicated agreement between the methods of measuring and estimating VO2max.

In the present study, the native non-sports equation had a high validity in estimating VO2max of adolescent boys. It seems that the use of non-sports VO2max equations can be used as a simple, safe, and low-cost tool to evaluate the maximum aerobic capacity of students at a wide level.

The role of Toon in Qohestan, as a part of transportation route between southeast and central Iran with Greater Khorasan, and its industrial and commercial role in Greater Khorasan’s sense with all of its challenges, in Historical and Archaeological studies, is very important. This paper tries to understand the cultural Landscape of Toon on the base of Historical documents and Stratigraphic Archaeological Evidences. Therefore, this paper follows answering two main Questions: in the historical aspect, how the landscape of Toon is understandable in historical and Geographical documents, considering the historical context of these documents. On the other side, in the Archaeological aspect, the main question is how stratigraphic evidence can help us to understand the landscape of Toon in the Islamic era. Historical documents and Archaeological Evidence have been reviewed and interpreted based on their contexts. Results of historical and Archeological interpretations, all to gather about Cultural landscape of Toon can help us to achieve to the goal of this paper. This research’s nature fundamental and its approach is descriptive-interpretive to the historical documents and archaeological evidence. Cultural landscape of Toon along Islamic era in historical documents described as a city with Arg, which has used Qanats as the main water source and has its own importance in production of crops and Carpet. Study of Archaeological Stratigraphic Evidence shows wide interactions of Toon in different periods of Islamic era and its industrial importance in production of some artifacts such as Pottery and Metal works.

Productivity is one of the main concerns of leaders and managers in organizations. The purpose of this research was to test the implementation model of professional ethics and organizational efficiency of sports federations with the mediating role of organizational health and sustainable sports relations with society.

This research was applied in terms of purpose and descriptive-correlation in terms of data analysis method. The statistical population included officials, employees, referees and coaches of 14 selected Olympic federations of the country with a total number of 266 people. A stratified random sampling method was used to select the investigated sample, and finally 190 Questionnaire without problems were returned and the analysis was performed on them. The data collection tool included a professional ethics questionnaire, a Standard Organizational Productivity Questionnaire by Karimi Shahabi et al. (2016), Standard Organizational Health Questionnaire by Hoy et al. (2003), and a researcher-made questionnaire for sustainable sports communication with society. In order to analyze the data, descriptive statistics and inferential statistics were used in SPSS software, as well as structural equation modeling in Smart PLS software.

The findings showed that the model of implementing professional ethics and organizational productivity of sports federations has a favorable fit with the mediating role of organizational health and sustainable sports relations with society. 

It can be concluded that sports federations can move towards their goals with greater strength by observing professional ethics, organizational health and improving relations with society.

Regulation of flowering time requires the activity of various genes that are controlled by environmental and internal cues. The FLC gene plays a key role in transition to flowering and suppresses flowering as target gene of vernalization pathway. Vernalization is a natural adaptation to ensure flowering after winter so that flowers and seeds can develop in favorable environmental conditions. In this study, identification and expression of FLC gene was investigated in rocket mustard (Sisymbrium irio). Total RNA was extracted from cotyledon leaves and cDNA was made. Specific primers were designed based on tsequence alignment of FLC homogenous genes and used for RT-PCR reaction. The identified 449 nucleotids fragment was recorded as SiFLC with accession number KT156751 in the NCBI database. Sequencing of the coding region and comparison of its putative protein (with 150 amino acids) and FLC orthologues in other plants showed their high similarity. Expression relative rate using the semi-quantitative method, in vegetative stage in young leaves, mature leaves, stems and roots was between 56-86%, which the highest (about 86%) and the lowest (about 56%) levels were observed in young leaves and stems respectively. In the reproductive stage, a decrease in expression was observed in these organs especially in leaves (about one percent). However, expression level was higher in flower buds and reached about 31%. Decreasing leaf expression in the reproductive phase indicates flowering induction. FLC higher levels in flowers, after transition to flowering, allows the next generation to reregulate its rate and respond to winter during vegetative phase

The rural economy, especially its agricultural sector, is facing various risks and policies. Support tools, in such conditions, play an important role in maintaining its economic growth. Although the rural areas of Iran are the environment of industrial, service and agricultural activities, according to the share of employment and income sources of the villagers, the dominant economic activity in these areas is agriculture. For this reason, in comparison with industry and services, any changes that occur in the indicators related to the agricultural sector will have a greater impact on the level of development of the villagers. Risk and uncertainty are considered to be prominent features of economic activities. Risks can be of natural origin (natural risk) or economic origin (market risk). The agricultural sector is more susceptible to both natural and market risks than industrial and service activities. Examining the relationship between insurance and economic growth has shown that the insurance industry affects economic growth through 5 different channels (facilitating trade and commerce, effective risk management, strengthening financial stability, equipping domestic savings and support) in direct and indirect ways.

In this study, the Fully Modified Ordinary Least Squares (FMOLS) estimation method was used to investigate the effect of agricultural insurance on the value added of Iranian agriculture. The time period is the annual data of 1989-2019, and the required statistics are from the systems and reports available on the website of the Central Bank of Iran (www.cbi.ir), the website of the Iranian Statistics Center (www.amar.org.ir) and the website of the National Productivity Organization of Iran (www.npo.gov.ir). The penetration rate of agricultural insurance has been calculated using the available statistics. E-views software was used to estimate the desired regression model.

The results of the study have shown that statistically, the variables have positive and significant effects according to theoretical expectations on the economy of the agricultural sector of Iran:The agricultural net capital stock has a positive and significant effect on the value added of the agricultural sector (1.02); so that with an increase of one percent in the agricultural net capital stock, the amount of value added of the agricultural sector also increases by 1.02 percent. In fact, more investment and increasing the amount of capital stock provides the necessary platform for the expansion of production activities in the agricultural sector.The estimated coefficient for the agricultural labor force (0.04) indicates that the role of the agricultural labor force in the economic added value of this sector is positive, significant, but small. With an increase of one percent in the agricultural labor force, the amount of the value added of the agricultural sector also increases by 0.04 percent. Increasing the use of human resources, on the one hand, will lead to the timely performance of operations related to the production of products and its increase, and on the other hand, the use of skilled labor will increase the amount of production in this sector by increasing human capital.The positive and significant coefficient of the productivity variable of the net agricultural capital stock (1.03) shows that with a one percent increase in the productivity of the net capital stock in the agricultural sector, the amount of economic value added of this sector also increases by 1.03 percent. The effects of productivity of production factors, including capital, on economic growth and agricultural value added from two channels show an increase in production and a decrease in their respective costs, which is an expected issue in the evolving agricultural production method of Iran.The effect of agricultural labor productivity on the value added of the agricultural sector has become positive and significant (0.01). So that with a one-percent increase in the productivity of the agricultural labor force, the amount of value added of the agricultural sector will increase also insignificantly (0.01 percent).The variable of agricultural insurance penetration has a positive and significant effect on the value added of the agricultural sector (0.01). So that with an increase of one percent in the insurance penetration variable, the amount of value added of the agriculture sector also increases slightly (0.01 percent). The effect of agricultural insurance on its economic growth by compensating a part of production costs (in case of damages) and giving assurance to agriculture in the face of possible risks, preserves farmers' production incentives.

Considering the high share of agricultural activities in the total economic activities of rural areas of Iran, adopting any policy and implementing any type of action that will lead to the strengthening of value added and, as a result, the economic growth of the agricultural sector, will also strengthen the rural economy.The results of the estimation of the regression model of the value added of Iran's agricultural sector indicate a positive and significant effect of the desired variables (agricultural net capital stock, agricultural labor force, agricultural net capital stock productivity, agricultural labor productivity and agricultural insurance penetration coefficient) in the period of time. This is about the value added of agriculture and as a result, the economic growth of this sector.Although the positive effect of the penetration coefficient of agricultural insurance on the amount of value added and, as a result, the economic growth of Iran's agricultural sector during about three decades of the implementation of the agricultural insurance program, has revealed the importance of insurance in the process of economic growth of this sector, the low coefficient of its effectiveness (0.01 percent), can express several issues:Agricultural insurance in Iran has not yet adequately covered all agricultural products and assets.The amount of insurance support does not answer all the risks in Iran's agricultural sector (market and non-market).The implementation of the country's agricultural insurance programs and policies, despite the high share and extensive cost burden on the government's annual budget (for example, more than 3200 billion tomans in the 2021 budget to pay the government's share to the agricultural products insurance fund), is not targeted and it has encountered deviations in implementation.It should be noted that Iran's agricultural sector (as in many other countries), unlike the country's industry and service sectors, does not have the required capacity and infrastructure for the development of private insurance related to production, and the policy of agricultural insurance development until meeting the necessary conditions, should be carried out through the state sector and increasing its participation.It will be very necessary to adopt a set of policies and measures that prevent capital outflow from the agricultural sector in particular and rural areas in general (through improving the exchange relationship between the agricultural sector and rural areas with other economic sectors and urban areas).

This study seeks to investigate the effect of variables and data related to people's social network on their credit score. In this study, two main goals are pursued: reducing information asymmetry and increasing financial inclusion. Achieving the above goals is done by finding meaningful information about people's social data to measure how such data affects their credit score. The basic hypothesis of this study is that people with a high credit score have social relationships with people who are similar and of the same age. A data set of more than 300,000 loans that have been paid by an Iranian bank to real people has been used to confirm and explain the effect of social network variables on credit score. In order to determine the variables of the study, an in-depth interview was conducted with a number of banking experts and people actively involved in the field of credit scoring, and at the end, the variables were determined and classified into three categories: financial, behavioral, and social. The study continued with the logistic regression method and finally with various regression models based on deep machine learning, including gradient. The results of the analyses conducted using the logistic regression method show that, statistically, people's social variables can predict the probability of their loan default. The results of machine learning algorithms also indicate that social network information can significantly improve the performance of loan default prediction.

With the dramatic development of sport in recent decades and its transformation into a large and popular industry, the legal issues surrounding it have also become more ambiguous and complex. Records show that most of Iran's legal cases have failed in international courts, Therefore, the purpose of this study was to investigate the reasons for the failure of Iranian legal cases in international sports lawsuits. The present study was applied from the perspective of purpose and was of mixed type with the framework of interpretive-positivist paradigm. Data collection was performed using in-depth interviews with experts familiar with sports law issues. The sampling method was snowball that 28 interviews were conducted virtually. Based on the results of Q methodological analysis, the findings showed that six factors of lack of specialization, educational and information problems, legal and contractual gaps, structural problems of the country's sports, the system of brokers and behind-the-scenes groups, and finally monitoring and punishment problems They were instrumental in the failure of Iranian legal cases in international sports lawsuits. According to the findings, it can be concluded that managers, officials and those involved in sports organizations and clubs, for well-known reasons, can be used as a bridge to success in legal cases in international sports lawsuits.

The agricultural sector is one of the most basic and vital component in the social and economic structures of any country. Today, with increasing in the world's population and needing to provide food on the other hand, and increasing in the price fluctuations of agricultural products on the other hand, traditional agriculture is no longer responsible for the sustainable food security of the world population. In recent years, the occurrence of two incidents of the spread of the corona virus and the outbreak of war in Ukraine, have made the price of agricultural products extremely unstable. Today, even many farmers and agricultural associations in developing countries are not aware of the changes in market prices and the latest technological developments in the field of agricultural product prices, and they do not have the ability to discover the optimal price for selling their products. In such a situation, the use of intelligent models in order to accurately forecast the price of agricultural goods is vitally important for farmers and agricultural sector activists.Smart agriculture is an emerging concept that involves the integration of advanced technologies to collect and analyze data in order to solve the challenges and problems of the agricultural sector. In the meantime, forecasting the price of agricultural products involves with some basic challenges; including: 1) Data of agricultural product price is mostly non-linear, unstable, non-normal, and noisy and follows chaotic behavior, 2) There is uncertainty in the forecasted data obtained from different models, 3) In the studies related to price forecasting, the "publishable base model" is not provided in order to provide the forecasted price values. Therefore, the aim of this study is to provide a non-linear hybrid intelligent model for accurate forecasting of the future price of pistachios in the field of smart agriculture through managing the multidimensional nature of data, considering uncertainty in the forecasting data and finally building a publishable base model in the field of product price prediction.The hybrid model proposed in this study has the following innovations; 1) the deep learning neural network model and the Auto-Encoder network have been used to forecast the agricultural product price and determine the optimal lag of price as an input variable simultaneously, 2) The Monte Carlo method has been used as a non-parametric method to provide a confidence interval and calculate the most likely price that can happen, 3) The practical application of price forecasting models, i.e., "publishable base model" is presented in order to provide forecasted price values.

The implementation of the proposed hybrid model in this study includes the steps of "data preparation", "data feature engineering", "training and testing the final deep learning neural network model", "building the optimal base model", "creating the most likely price scenarios" using the Monte Carlo method and "inferring new prices or making out-of-sample forecasting" with new data sets” by feeding new price data into the deep learning neural network model. In the proposed hybrid model, data mining techniques are used, including Wavelet Transform (WT), Long-Short Term Memory (LSTM), Auto-Encoder network (AE), Monte Carlo-Markov chain (MCMC) simulation method and the concept of "inferring new prices".In the data preparation stage, using methods such as data smoothing, data rebuilding, correction of duplicate data in several consecutive days, and correction of missing data, the continuous set of pistachio future price time series is prepared to enter the primary model. Also, the wavelet transform function has been used for de-noising the data, the Auto-Encoder network has been used to determine the optimal lag, the Monte Carlo-Markov chain simulation has been used to create the most probable price scenarios, and the deployment concept has been used for out-of-sample forecasting with new data sets. The data used in this study is the time series of the daily price of pistachio futures on the Iran Commodity Exchange in the period from 10/13/2019 to 12/14/2021 in Rials per kilogram.

The results of this study showed that 1) by using the wavelet theory to de-noise the data, the error rate of the price data was reduced and the data had a stable trend, 2) the results of the implementation of the Auto-Encoder network showed that the optimal lag of one can be used as an input variable to forecast the future price of pistachios, 3) The outcomes derived from employing Monte Carlo-Markov chain simulation, coupled with out-of-sample forecasting using the new dataset, reveal compelling insights into the future pricing of pistachios on the Iranian Commodity Exchange. According to the analysis, the most probable and sanguine projection places the future price at the price ceiling of 213 thousand Tomans. Impressively, the forecasted price exhibits a minimal variance of merely 0.7% from the actual observed price, attesting to the precision of the proposed model. The overall accuracy of the model stands commendably high at approximately 93%.

Based on the results, firstly, the forecasted price has a small error with the actual price and this small error shows the power of the built model in forecasting the future price trend of pistachios. Secondly, the alignment of the price resulting from the Monte Carlo simulation with the new price can also be used as a confidence index in risk management for traders and market participants. Thirdly, the process set is the most complete value chain in the production of price forecasting models. Therefore, the use of the proposed hybrid model and the use of the components used in it, i.e. wavelet transform function, Auto-Encoder network, deep learning neural network, Monte Carlo simulation and the concept of inferring new prices; are suggested.

The real exchange rate is considered a key variable due to its interrelationship with other macroeconomic variables and plays an effective role in the export and import of agricultural goods and inputs. Iran's agricultural sector is one of the most dependent sectors on the exchange rate, and as a result, fluctuations in the real exchange rate can have significant effects on the export and import of agricultural inputs such as chemical fertilizers. Considering the effect of the Covid-19 crises and the Ukraine war on real exchange rate fluctuations and the importance of chemical fertilizers trade in current conditions, as well as observing the non-linear behavior of variables, the purpose of this study is to investigate the non-linear causality of real exchange rates and the value of import and export of chemical fertilizers Using monthly data from April 2015 to September 2022, using the nonlinear hybrid wavelet-Markov switching approach. The results of data denoising using wavelet transformation showed that the error rate flowed in a relatively turbulent domain and the data contained some kind of noise. The results of the Markov switching model also showed that the relationship between the real exchange rate and the value of export and import of chemical fertilizers is non-linear. Also, there is a one-way causality relationship from the real exchange rate to the export value of chemical fertilizers, and only when the import value increases, there is a one-way causality relationship from the real exchange rate to the import value of chemical fertilizers. Therefore, in order to reduce real exchange rate fluctuations, it is suggested to diversify the import sources and export destinations of chemical fertilizers, as well as create bilateral and constructive interactive relations with the target countries in order to reach a win-win agreement.

In recent years, the fluctuation in agricultural commodity prices in Iran is increased and thus, accurate forecasting of price change is necessary. In this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from April 2001 to March 2021, was proposed. In this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (MLPANN-GA, MLPANN-PSO and MLPANN-ICA) together. In implementation stage of hybrid methods, based on test and error method, the optimal MLPANN structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. The results obtained from three hybrid methods indicate that, based on the RMSE statistical index, the MLPANN-ICA method performs the best when forecasting prices for beef, lamb, and chicken. The outputs of three combination approaches show that the shrinkage method, with a parameter value of K=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. In summary, the proposed method outperforms the other three hybrid methods overall.

The area bounded in 46º-48º E and 34º-36º N is selected for probabilistic seismic hazard analysis of Kermanshah region utilizing the Monte Carlo method. Potential seismic sources model includes 7 clusters obtained from the combination of weighted K-means clustering and particle swarm optimization (PSO) method. The peak ground horizontal acceleration (PGA) and spectral acceleration (SA) for 5% damping ratio at 0.2 and 2 seconds corresponding to 10% and 63% probability of exceedances within 50 years are calculated for the region.

The price forecasting and its changes trend is one of the most important factors in decision making and formulating strategies related to agricultural products. This study aimed at presenting a hybrid data mining model for accurate price forecasting of agricultural products, including nonlinear models of wavelet transform, genetic algorithm, deep neural network and Monte Carlo technique. This proposed model involved a two-stage hybrid model and the base model of nonlinear-nonlinear. In this proposed model, the genetic algorithm for determining the optimal lag of price time series, the wavelet function for the de-noising of price data, the deep neural network for price forecasting, the Monte Carlo method for simulating the most probable price probability and finally, the complex soft calculations for "out-of-sample forecasting with new data set" were used. Results of comparison of the proposed model including "Genetic Algorithm-Wavelet Transform-Deep Neural Network-Monte Carlo", through evaluation criteria, with three competing models of "Genetic Algorithm-Deep Neural Network-Monte Carlo", "Genetic Algorithm-Wavelet Transform-Neural Network-Monte Carlo" and "Genetic Algorithm-Neural Network-Monte Carlo" showed that the proposed model had the better performance in forecasting of future price of saffron compared to the three competing models. Also, the use of deep neural network compared to neural network, the application of wavelet theory for de-noising and also the use of Monte Carlo technique to simulate the predicted prices, increase the forecasting accuracy of future price of saffron. In addition, the use of soft calculations showed that the proposed model had the necessary efficiency and high accuracy for short-term forecasting of the future price of saffron. Therefore, the present study has a good position in achieving the index of maximum accuracy, scenario making of future price trends, sensitivity analysis of components affecting the price and finally, forecasting the future price. Accordingly, the use of the proposed model to forecast the price of agricultural products is recommended.

The purpose of this study was to model entrepreneurship development in sports tourism in Iran based on a structural-interpretive approach. The present research is a qualitative research with analytical nature and in terms of nature and purpose is an applied research that was performed by interpretive structural method. The data collection tools required for the research were library studies, interviews and structural-interpretive questionnaires. According to the research findings, 68 indicators were subset of 9 institutional factors including political and legal factors, managerial factors, economic factors, cultural and social factors, education and research, human resources, technology and technology, infrastructure and tourist attractions. The purpose of the study was at 6 levels and political and legal factors were identified at the sixth level as the cornerstone of the model. According to the research findings, it can be concluded that in the first step, government support for entrepreneurs in this field is the biggest success factor for the development of entrepreneurship in sports tourism so that the private sector can encourage and support the government by investing and entering this field. Be an important arm for the development of sports tourism in Iran.

Gastric Cancer (GC) is a common gastrointestinal tumor, and its incidence is increasing. The pathogenesis of GC is very complex and remains unclear. Recent basic studies of GC have focused on gene expression dysregulation. Accumulating evidence has demonstrated that thioredoxin-interacting protein (TXNIP) is abnormally expressed in a variety of malignant tumors but it is obscure in GC. This study aimed to compare the expression of the TXNIP gene in cancerous and adjacent tissue of gastric cancer and evaluate its clinical significance in the prognosis and survival analysis of gastric cancer patients.

A total of 50 tumor tissues and marginal non-tumor control tissues were obtained from GC patients. Also, TXNIP gene expression levels were evaluated by real-time PCR. Meanwhile, bioinformatic approaches were used to evaluate TXNIP expression in two different cohorts of GC patients. A data mining study was also performed to determine the prognostic role of TXNIP expression in the overall survival of GC. Furthermore, a pan-cancer analysis of TXNIP expression was performed using TCGA data.

The TXNIP gene was down-regulated in tumor samples with a fold change of 0.33, and the same results were repeated in bioinformatics analysis. Decreased expression of TXNIP was significantly associated with metastasis, poor differentiation, and drug abuse. Our results provided evidence that TXNIP gene expression level is positively correlated with the overall survival of GC patients. Pan-cancer analysis of TCGA data revealed down-regulated TXNIP in a variety of malignant tumors.

This study established low TXNIP expression as a prognostic biomarker in GC. It also revealed that the decrease in TXNIP expression likely favors metastatic GC.

Evaluation of TXNIP expression is informative in the prognosis of GC patients.

Climate Literature is a type of literature that is an outcome of climatic and geographical conditions. Climate Literature, in Iran, contains a number of branches including the Climate Literature of the South (The School of Khouzestan), the Climate Literature of the North (The School of Gilan), Isfahan's Climate Literature (The School of Isfahan), Khorasan's Climate Literature (The School of Khorasan) and Rural Literature. It is essential to remind that these schools usually find their meanings through Fiction. An important part of Climate Literature is Rural Literature. The cultural and social developments of the 40s (Land Reform, arguments on Westernism, and the need to return to the simple and traditional way of rural life) made the authors take a new look at rural problems and led to a new approach which was entitled as Rural Literature. Rural Literature started seriously with the monographs of Jalal Aleahmad and Gholamhossein Saedi, but Gholamhossein Saedi was the actual leader of Rural Literature in Iran. Most of his stories have a rural theme. Azadaran-e-Bayal, Toop and Tars-o-Larz gain their themes from his journeys and research throughout different parts of Iran. Another one of such active authors is Mahmoud Dolatabadi. Although paying attention to rural issues especially the ones of the rural parts of Azerbaijan is seen in the works of Samad Behrangi and Amin Faqiri, the extensive and focal aspect of this type of literature has revealed its echos in numerous works of Mahmoud Dolatabadi.

Student sports are at the base of the country's sports pyramid and play a fundamental role in developing public, championship, and professional sports. Therefore, the present study aims to identify the factors affecting the development of physical education and student sports in the country's provinces (a case study in this study in Khorasan Razavi province). This study is applied and exploratory research in terms of purpose and nature, respectively, so the systematic method of data theory and Glaser approach were used. From the study's statistical population, 12 people were purposefully considered as a sample in the interview section. Researchers also exerted library studies as a complementary source of interview tools. Human resources, implementation of public and championship sports in schools, economic-financial, sports facilities and infrastructure, management, education-research, socio-cultural, laws and regulations, advertising, media, technology, and information technology were identified as effective factors in the sport's development of Khorasan Razavi province’s schools. Based on the results for the development and promotion of physical education and sports in schools, it is necessary to educate and train physical education and sports teachers in universities, hold educational courses for sports teachers, and provide school sports infrastructure. With the cooperation of donors and related institutions, continuous holding of camps and sports events, creation of talent identification bases in schools, holding scientific conferences and seminars with a focus on student sports, providing up-to-date and practical sports content for students, educational software usage, sports talent identification in schools, and constant monitoring of the activities of sports teachers.

Recent Taekwondo honors in the international arena have made Taekwondo popular in Iran, and with a population of about two million athletes, Taekwondo is one of the most important sports in Iran. However, the achievement of some poor results, especially in the Tokyo Olympics, reveals the importance of examining the injuries in the country's taekwondo body. Therefore, the present study was conducted with the aim of identifying and analyzing the mental patterns of experts regarding the pathology of taekwondo sports problems in Khorasan Razavi province. This research is based on purpose, of applied type, in terms of descriptive-exploratory nature and due to the application of Q methodology, it is of mixed research type.The statistical population of this study consisted of all the elites of the sport of Taekwondo in Khorasan Razavi. Data collection tools in the present study included library studies and interviews. In the interview section, open and axial coding, summarizing and filtering techniques were used to extract the raw data.Also, statistical method of Q factor analysis and principal component analysis were used. Based on the research results, 13 mental models including the mental model of empiricists, scholarly, juvenile, professionalist, competitive, supportive, strategic-oriented, materialistic, communicative, infrastructuralist , Structuralists, globalists, and sports educators were identified; In a general conclusion, it can be stated that in order to eliminate the injuries in the field of Taekwondo, one should first refer to the structure of this sport in Khorasan Razavi province, and then to the surrounding environment and its affiliated organizations. Analyzed.

In the current research, the buried causative body of a magnetic field anomaly was modeled using Encom ModelVision software and its depth was estimated using deconvolution Euler and energy power spectrum methods. The magnetic data of Iileh region of Taybad city was used for modeling. In addition, an optimization algorithm was explained as the Improved Global Particle Swarm Optimization (IGPSO). The obtained results showed IGPSO is acceptably resistant to the corrupted synthetic gravity data by noise. Using this optimization algorithm, the pseudo-gravity data of Iileh region was analyzed to determine the structural parameters of the causative body of the anomaly such as depth, radius, density contrast, origin point coordinate and shape factor. The determined shape of the causative body of the anomaly was a sphere with a radius of 56.2 m and a density contrast of 1.93 gr/cm3. The estimated depths of the center of the sphere by Encom ModelVision software and IGPSO method were 120 m and 111.8 m, respectively. While the drilling results in this area shows an average depth of 103.2 m for the center of the iron mineral body. This indicates the introduced IGPSO method obtained better results.

One of the predominant statistical methods in archaeological predictive modelling is logistic regression. The probabilistic model created by this method is appropriate for the purposes of this paper. When the dependent variable can be studied as a binary value, the logistic regression method is applicable. In this statistical approach, the binary value is referred to as the dependent variable with the value for the presence/absence of the archaeological site. The results in the probability value between 1 and 0. Environmental factors are defined as independent variables, and logistic regression calculates the relationship between the independent variables and the dependent variable, as well as the probable value of the dependent variable at all points on the map. With such an approach to statistical concepts and the use of logistic regression models with data from archeological field studies, this article analyzed and interpreted the results in the area of Bijar city in Kurdistan province. A total of 71 Iron Age sites as input for predictive modeling led to the presentation of an optimal proposal to prepare the model for such approaches in archeology. The results not only help save time and money and increase the accuracy of archaeological investigations for future projects, but also show a predictive accuracy of 90.4% by indicating areas of high probability and reducing the scope of investigation. It has also been shown that the independent variables of rivers and settlements had the greatest impact on the model's output and on the formation of areas in the landscape.

In recent years, the Iran Commodity Market has always been associated with price destabilizing fluctuations. Thus, it is need to use machine Learnings in the form of forecasting methods to recognize early events and prevent damage caused by these fluctuations. Considering the important position of the pistachio trading ring in the Iran Commodity and also the need to use appropriate tools to correctly diagnosis the future price, The purpose of this study is to design and build a suitable hybrid model based on XGBoost and compare its performance with other machine learning models in order to accurately forecast the future price of pistachio. In this study, software’s of Matlab, Rapid Miner and Scikit learn were used to build the proposed hybrid model. The results of applying the wavelet theory showed that the error value of price data was reduced and the data had a stable trend (white noise). Also, the results of the Performance of Auto-Encoder network and the Genetic algorithm showed that the optimal lag of one is the best input variable for forecasting the future price of pistachios in the period under review. Based on goodness of fit indices, the proposed model of this study, Wavelet-XGBoost in comparing to other data mining models, had a better performance in forecasting the future price of pistachios. Also, out-of-sample forecasting with the selected model showed that the forecasted new prices have little difference with the real data, which indicates the efficiency and accuracy of the selected hybrid model. According to the obtained results, it is strongly recommended to use the proposed model based on XGBoost algorithm to forecast the price of other agricultural products.