clustering

The primary objective of mobile network operators is arguably to maximize their efficiency. Beyond operational and investment costs, maximizing the utilization of available resources can help them achieve this goal. To this end, operators offer discounted data plans during off-peak hours to encourage users to utilize the network during these times. These data plans are typically based on the average traffic load across the entire network at different times of the day. However, they often overlook the fact that traffic patterns can vary significantly across different population areas within a city at various times. In this paper, different population areas are automatically identified using clustering based on traffic patterns. By identifying these areas and considering the traffic patterns specific to each area, the allocation of appropriate data plans for users, based on the regions they frequent, is analyzed and discussed. Additionally, other potential applications of this clustering method for offering various services are presented, followed by a conclusion.

In order to invest properly and identify the right short and long positions, the existence of an efficient strategy is necessary. Pairs trading system is one of the most famous algorithmic trading systems. In this system, a pair of stocks that have same trend in the long-time (reversion to the mean) and have fluctuations (spread) in the short-time is used. The most important point is to identify such a pair of stock. In this research, an approach is used to identify pairs of stocks and also to find the right short and long positions in trading pairs. In the first step, a stock pair that has a long-time statistical relationship is selected. The DTW method is used to calculate the distance between each stock pair and their movement similarity. Then, the hierarchical clustering method is used to cluster stocks, and then pairs of stocks that have a greater degree of similarity are selected. In the second step, the SVM method is used to identify buying and selling positions. In order to check the performance of the method, the S&P 500 index, which includes the top 500 stocks of the New York Stock Exchange, has been used.

The purpose of this study is to cluster the Iranian online shopping consumer market using artificial neural network, so that based on it, customers' needs can be better identified, the characteristics of each cluster can be determined more accurately, and the best clustering method can be chosen. And finally, the development of appropriate strategies for management, communication and better service to customers was achieved. Based on the purpose, the present research is descriptive and of the estimation and evaluation type, and in terms of practical purpose and in terms of time period, the situation of customers has been studied during the years 2021 to 2022. The statistical population included 52,403 online stores, and the present study selected 349 stores based on simple sampling. The method of analysis and classification of data is done by RFM, K-Means and self-organizing fuzzy-neural network. The technique used was the sum of squared error criteria and the Davies-Bouldin index. The findings showed: food items (they had the least delay in purchasing due to frequent needs); cosmetics (the majority of purchases were made by women); Luxury appliances (have the highest monetary value of purchase); industrial supplies and their accessories (the most purchases were made by men) and finally, sanitary supplies, detergents and clothes (have the most frequency of purchases). The results of the research have shown that the use of self-organizing neural networks along with the RFM method is the most suitable method for clustering and separating and valuing customers. Also, Kmeans+ANFIS also achieved good values, but the WRFM+ANFIS method has been more successful in this index.

Clustering is one of the basic techniques in data mining and machine learning, which is used to divide a set of data into homogeneous subsets. There are different methods for clustering, each of which has its own strengths and weaknesses. One of the main challenges in clustering is finding the optimal number of clusters and optimal allocation of data to these clusters. Genetic algorithm, as an optimization method based on natural evolution, has a high ability to solve complex problems and search for large solution spaces and can be used as an effective tool in clustering. The purpose of this article is to investigate the efficiency and accuracy of genetic algorithm in data classification and compare it with traditional clustering methods for classification. In order to evaluate the performance of this algorithm, several insurance data sets are used and the obtained results are analyzed with different criteria such as accuracy. Also, different parameters of the genetic algorithm are examined and their effects on the final performance of the algorithm are studied in order to determine the most optimal settings for data classification.

In this research, to form chromosomes, at first, the number of clusters was determined. Considering that each cluster center had as many features as the number of features in the data set, the length of each chromosome was determined by multiplying the number of clusters by the number of features. New and diverse methods were used for Crossover, Mutation and Survival processes. Also, the evaluation criterion similar to the K-means algorithm was chosen to optimize the clustering performance. This innovative approach led to improving the accuracy and efficiency of the classification process.

By applying the method described in this article to three insurance data sets for fraud detection, we have interesting results with 12% improvement in F1 and 10% increase in accuracy in the first data set, 1% improvement in F1 and 1% improvement in accuracy in the first data set. Second and finally, 1% improvement in F1 and 2% improvement in the accuracy of the third data set compared to the K-means method and other methods have been achieved. Due to the 2-mode data in this data set, the problem is solved for two clusters using the algorithm and the best label for each cluster is selected according to the real labels of the data and the result is presented as the results of classification problems. Additionally, significant improvements in metrics such as ARI and other clustering evaluation criteria have been achieved, and remarkable progress has been made compared to the standard genetic algorithm.

Genetic Algorithm is able to solve complex problems without definite solution and can perform better in data clustering than traditional methods such as K-means. By combining probabilities and randomness, this approach provides the possibility to examine more points as cluster centers and improve clustering performance. The results show that this method works better than the famous methods in some cases and provides a suitable structure for data clustering.

In during Iran's economic, social, and cultural development programs, the government has implemented various policies to support wheat farmers. This study, considering the strategic importance of wheat as a product, investigates the provincial impact of policies related to input subsidies and agricultural insurance. In this context, the budgetary support indicators, including energy subsidies, fertilizers and pesticides, agricultural insurance, and banking facilities for wheat in different provinces, have been calculated across the first to fifth development programs. Using the central limit theorem, the provinces have been clustered. The results show that the government’s uniform support policies have created inequality in different provinces. Specifically, in the first to fifth development programs, the highest budgetary support per kilogram of wheat production was allocated to Mazandaran (812 rials), Tehran (479 rials), southern Kerman (517 and 669 rials), and Ilam (1896 rials). This disparity in provincial support is due to various factors, such as farm management practices, input consumption patterns, technological level, and productivity. Based on these findings, it is recommended that budgetary support be allocated based on provincial clustering so that supportive policies are adjusted in a way that provinces with higher relative advantages can benefit more effectively from these supports.

Food insecurity can overshadow vital values and even challenge the national security of countries. Therefore, in the effort to improve food security, it is necessary for the policy maker to manage this issue with a conscious approach and a systematic method. In this regard, knowing the current state of food consumption and its determinants is not only a necessary condition for preparing future programs, but it is necessary to continue this work with the aim of monitoring and evaluating the results of implemented programs and actions. With this approach, the present study investigated the pattern of food consumption and the factors affecting it in the urban areas of Iran in 2022. To attain these objectives, the cost-income data from the Iranian Statistics Center was utilized. Additionally, the nutritional performance matrix was employed, along with the classification system provided by the Iranian Statistics Center. To better comprehension regarding the food consumption in urban regions across the provinces of the country and to pinpoint provinces exhibiting comparable behavioral patterns, the K-means clustering technique was employed. Subsequently, a map illustrating the food consumption patterns in urban areas of Iran was designed. Finally, by using the matching method, factors affecting food consumption in urban areas of Iran were identified. According to the results, the current food pattern of households in the urban areas of Iran mainly includes all kinds of cereals and this product group provides nearly 58% of the daily energy requirement. According to the clustering results, a grand total of five distinct behavioral patterns were detected. Examining the patterns shows that the pattern of food consumption in the urban areas of different provinces of the country is heterogeneous and while having a lot of diversity, it has little relationship with the geographical location of the provinces. Finally, based on the results of the matching method the factors that explain the difference in food consumption between the province include the size of the household, the age of the head of the household, the literacy level of the head of the household, the employment status of the head of the household, the average daily income of the household, the average daily subsidy received by the household, and the food variety.

One of the main uses of text mining is to help discover hidden knowledge among various texts that business people seek to acquire. The current research aims to discover knowledge from the textual analysis of articles related to the field of health tourism marketing.

Design/methodology/approach: 

In the first step, a total of 456 articles between 2010 and 2024 were selected using the term "health tourism marketing" in databases and valid search engines. Through reviewing the title, abstract, and main text of the articles and in different stages of screening the articles, the researchers finally selected 42 articles related to the topic of health tourism marketing to perform the text mining process. In the second step, after pre-processing the data, using non-hierarchical cluster analysis, the number of 3 optimal clusters was obtained according to the Davies-Bouldin index, and a keyword was drawn for each cluster. Then, using the supporting indicators, the confidence and attractiveness of the associative rules of each cluster were extracted and analyzed. In the third step, using the results of the first and second steps, a fishbone diagram of the research was drawn and a summary of the results was presented in the form of a diagram.

Applying cluster analysis led to the placement of articles in three clusters based on the Davis-Bouldin index, in the zero, first, and second clusters respectively, the words service, patient and tourism occupied the centrality of the cluster and in order to provide a better view of the keywords of each cluster was drawn. The application of associative rules led to the identification of if-then rules separately for each cluster, from among thousands of extracted rules, 15 rules were presented separately for each cluster based on attractiveness, support, confidence, and experts' opinions.

Summarizing the results and designing the fishbone diagram showed that in order to exploit the benefits of health tourism, it is necessary to improve and optimally use natural, hard, soft and marketing infrastructures.

Originality/value:

 Conducting many researches in the field of health tourism shows the importance and micro-macro benefits of this field, so understanding and explaining the researches of this field through text mining can be the beginning of a way to exploit the knowledge accumulated in it.

Monetary and financial institutions play an essential role in the economic development of any country. Financial systems can make an economy more productive by concentrating scarce resources and funds for massive investments. Examining the bank's financial performance and creating a suitable clustering system based on indicators that affect the financial performance of banks is of great importance for bank supervisors, bank depositors and shareholders, and banking sector policymakers. In the current research, the clustering of the member banks of Iran's capital market is done based on credit risk and indicators affecting financial performance using data from the period of 2009 to 2021 related to eleven selected banks of Iran's capital market and the support vector regression (SVR) model. Two measures of return on assets (ROA) and return on equity (ROE) have been implemented and analyzed as indicators of the bank's financial performance. For this purpose, the coefficients of the banks were first extracted using the support vector regression model and then clustered with these coefficients using the average linkage method. The results indicate that the clustering of banks using both financial performance measures is similar. Based on this, in the clustering with three clusters, Saderat, Mellat, Parsian, Postbank, Pasargad, Sina, Saman, Ekhztannovin and Karabhan banks are placed in one cluster and trade and capital banks are placed in other clusters. In the clustering with four clusters, Saderat, Mellat, Parsian, Pasargad, Sina, Saman, Ekhztannovin and Karabhan banks are in one cluster and Postbank, Trade and Capital banks are in other clusters. In the clustering with five clusters according to the data of the examined banks, Saderat, Mellat, Parsian, Sina, Saman and Ekhtaznovin banks are in one cluster (category), Pasargad and Karabehan banks are in the second cluster, and Postbank, Capital and Tejarat banks are in other clusters.

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

Today, mental health is a critical issue worldwide that not only impacts individuals' quality of life but also leads to economic and social consequences for societies when there are deficiencies in this area. Despite the development of mental health infrastructure and services in recent years, significant funding has yet to be allocated to address these needs. To promote and enhance mental health, it is essential to not only identify weaknesses but also allocate an adequate budget for this area. This becomes feasible when there is a comprehensive understanding of the mental health status within local communities. To understand the mental health of a society, it is essential to examine the research conducted in this field. By evaluating scientific outputs, one can gain a comprehensive understanding of the mental health status of the local community. Additionally, by assessing the financial support for scientific articles, it is possible to gauge the efforts of organizations aimed at improving mental health within society. The current research aims to analyze the financial support of institutions concerning research outputs in the field of mental health in Iran, using a scientometric approach. The goal of this study is to conduct a scientometric analysis of research outputs that received financial support in the field of mental health in Iran, as indexed in the Web of Science database.

The current scientometric research consists of applied studies utilizing a quantitative approach. Data collection was conducted through documentary analysis, employing bibliometric techniques and co-occurrence of words. The research community comprises 3,833 sources, representing all research outputs with financial support in the field of mental health in Iran, which are indexed in the Web of Science database. In this research, no time limit was imposed, and all research outputs related to financial support in the field of mental health in Iran, as indexed in the Web of Science database, were considered. To extract data, a search strategy was employed. In exploring strategies within the field of mental health, concepts with the strongest semantic connections to mental health were taken into account. The analysis of the research questions was conducted using three software programs: Excel, BibExcel, and VOSviewer

The leading financial sponsors among university units in the country were Tehran University of Medical Sciences, with 628 cases; Isfahan University of Medical Sciences, with 231 cases; and Shiraz University of Medical Sciences, with 230 cases. Among non-academic organizations, the most financial support has been provided by the Iran National Science Foundation (INSF) with 156 cases, the Ministry of Health and Medical Education (MHME) of Iran with 67 cases, and the National Institute for Medical Research Development (NIMAD) with 59 cases. The British Medical Research Council (MRC) contributed 49 cases, followed by the US National Institutes of Health (NIH) with 40 cases, and the Australian National Health and Medical Research Council (NHMRC) with 38 cases, making them the top three foreign organizations, respectively. The studies identify nine subject clusters, with significant emphasis on issues such as depression, anxiety, stress, mental health, mental disorders, COVID-19, quality of life, and schizophrenia. The top three researchers receiving financial support in the field of mental health are Shahin Akhundzadeh, Mojgan Mirghforvand, and Vafa Rahimi Movaghar.

Most of the financial sponsors for mental health research in Iran are affiliated with organizations under the Ministry of Health, Treatment, and Medical Education, along with other entities that have not traditionally engaged as financial sponsors in this field. This lack of participation may stem from an improper division of responsibilities within the country's organizations; consequently, the Ministry of Health is recognized as the primary center of activity. Additionally, on an international level, financial support is restricted to a few specific institutions. Therefore, there is a pressing need to enhance collaboration and establish more connections with foreign organizations. Finally, it is anticipated that the results of this study will contribute to the development of an evidence-based information system regarding mental health research and its supporting infrastructure in the country. Scientometric analysis can be instrumental in evaluating the growth and application of knowledge, thereby aiding effective planning for scientific policymakers to enhance financial research support.

Social marketing is one of the most widely used branches of marketing knowledge that is used in various social issues. Social marketing seeks to create behavioral changes that benefit individuals and society (Romero-Domínguez et al., 2021, p. 3). Non-profit organizations (NPOs) have faced intense competition for charitable donations due to declining government support and rapid growth (Hsu et al., 2021, p. 52728). A review of previous literature on donor behavioral loyalty indicates that donors may be loyal to more than one nonprofit organization in different ways (O'Reilly et al., 2012, p. 69). Achieving charity marketing goals like business marketing, in addition to choosing an efficient strategy, also requires determining effective techniques and tactics such as market segmentation, which can increase the loyalty of philanthropists even more. But statistics show that organizations lose up to 60% of their first responders. Therefore, an adequate understanding of the determinants of donor loyalty is necessary and essential (Zogaj et al., 2021, p. 379). The closest research to the current topic is the research of Masoodipoor (2018), which used other criteria for classification, so the topic is completely new and original. Therefore, the main question of this research is how people's loyalty is in the three indices of loyalty to the charity brand, loyalty to the distribution channel, and loyalty to the donation subject. The main purpose of this research is to measure the behavior of benefactors in charity affairs based on the components of loyalty to charity marketing and behavioral segmentation in the statistical population of Qom province.

The current research is a descriptive-survey type of quantitative research with an exploratory approach and is included in applied research. The statistical population of this research includes the Qom metropolis. Therefore, by using the available sampling method, two urban areas, three and seven, were selected and sampling was done from the people in those areas. According to Cochran's formula including the confidence factor of 95%, the value of 0.5 for p and q, and the error factor of 0.05, the sample size is at least 384 people. The method of data collection in this research is a questionnaire that evaluates the donation behavior of individuals in terms of loyalty to charity brand, loyalty to distribution channel, and loyalty to the subject of donation in different places and times through 42 questions in two forms of specialized and demographic questions. All questions were designed according to the Five-point Likert scale according to the variable; Therefore, the evaluation of options is variable. In total, 571 questionnaires were distributed to people physically and electronically, and 405 questionnaires were obtained that could be used for statistical analysis, and then the data of the questionnaires were analyzed using SPSS software and clustering technique and two-step clustering analysis.

Surveys show that from the total of 405 people who answered the questionnaire, the highest percentage of frequency is related to men with approximately 51.4 frequency, and the lowest number of responses is related to women with a frequency of 48.6. The youngest declared age is 14 years and the highest age is 75 years. The average age of the people studied in the research is about 32 years, and people with the age of 20 include the largest number of this sample. 59.5% of them were married. In terms of education, 37% of people have a bachelor's degree. The classification of people based on their job shows that the highest frequency of people in the studied sample belongs to freelancers with 24.7%. The most frequent percentage in the income classification, i.e. 25.4%, belongs to the category of people who do not have a fixed and independent income. Based on the answers provided, these people were classified into 5 separate clusters, 15.8% in the first cluster, 10.4% in the second cluster, 22% in the third cluster, 28.4% in the fourth cluster, and 23.5% in the fifth cluster. The highest level of loyalty to the charity brand includes the third cluster, the highest level of loyalty to the distribution channel includes the fifth cluster, and the highest level of loyalty to the donation topic includes the fourth cluster.

Due to the optional nature of charitable donations by people in the community, the competitive conditions between charitable organizations to earn more money reveal the necessity of attracting the attention of the audience. Attracting more attention from the target audience means identifying their behavior in the face of charitable affairs, and this information is used to predict people's future behavior and has a significant contribution to the growth of altruistic donations. The general results of this research indicate the existence of a gap between theory and practice. Based on the classic marketing process, segmentation is necessary to accurately and better identify the giving behavior of people and also to provide them with optimal marketing mixes. Segmentation causes people to be placed in categories with similar behavioral patterns so that advertising and promotion programs suitable for each segment are selected and implemented. The existence of criteria or criteria for segmenting people is one of the strategic challenges in the field of social policies. These criteria can measure the status of benefactors in different dimensions of charitable behavior. Since the loyalty component of philanthropists is very important for all charitable organizations, this research was conducted to investigate the loyalty behavior of philanthropists based on the geographical location of the Qom metropolis. Therefore, using the two-stage clustering technique, this research divided people's loyalty into three indicators: loyalty to the charity brand, loyalty to the distribution channel, and loyalty to the donation issue. The increase in their charitable contributions was expressed.

The occurrence of heavy rains is related to the synoptic patterns of the earth's surface and the upper levels of the atmosphere. The main goal of this research is to find the synoptic-thermodynamic pattern of heavy rainfall in selected stations of Kermanshah province. For this purpose, daily rainfall data and maps of sea level and height geopotential of 500 hPa were obtained for the statistical period (2010-2019) and were processed using principal component analysis (PCA) to identify the components. CAPE, LI, KI, TTI and SI instability indices were used for thermodynamic analysis. The results of the factor analysis showed that three factors in total explain and justify 80.5% of the variance of productive patterns. Further, four patterns were obtained by clustering using Ward's integration method and Euclidean distance. These patterns include the eastern Mediterranean and Iraq descent, the low-altitude Mediterranean cut bandal, the deep Russian descent and the eastern Mediterranean and Iraq quasi-omega bandal. The results showed that in the days of heavy rain, the dominant pattern of low pressure was located over the eastern Mediterranean Sea and Iraq and the location of the southwestern part of the omega-shaped bandal and the eastern Mediterranean trough in the region, which had the highest frequency of heavy rain with 22 days out of 38 days. The supply of moisture these days from the Mediterranean and Red seas, the presence of relative humidity close to saturated humidity and negative omega has led to an increase in humidity in the air, the rise of moist air, strengthening convection, creating an unstable atmosphere and heavy rains in the region. The results of thermodynamic analysis showed that the instability indices related to the second and third patterns were not very strong, but in the first and fourth patterns, the indices revealed severe instability and high potential.

Given that university-industry cooperation is associated with the transfer of knowledge and technology, one of the key indicators of a country's development is the effectiveness of collaboration between universities and industry. Due to the significance of this topic, the present article seeks to analyze the state of cooperation between Iranian universities and industry based on the co-publication of articles indexed in the Web of Science database from 2010 to 2022, particularly in terms of financial support. The findings of this research will inform national policymakers and planners about the current state of university-industry cooperation in Iran, enabling them to devise appropriate strategies to enhance this collaboration.

The research method employed in this study is both descriptive and quantitative, utilizing scientific techniques such as word co-occurrence analysis. The statistical population for this research comprises 2,891 articles. This study encompasses all articles that received financial support, focusing on the collaboration between universities and industry in Iran from 2010 to 2022; therefore, no sampling method was applied. Instead, a census sampling method was utilized. To examine the collaboration between universities and industry through scientific publications (articles), each article must include at least one author affiliated with an industrial organization and one author affiliated with an academic institution, both of whom must have a financial sponsor. Specific labels were used to identify the organizations involved. Data collection was conducted using the Web of Science database, and the data were analyzed and visualized using BibExcel and VOSviewer software.

According to the findings of the current research, the highest number of financial supports for cooperation between industries and universities in Iran was recorded in 2021, with 430 articles published. In contrast, the lowest number was recorded in 2010, with only 89 articles. On the international front, 78 countries have co-published with Iran, with the United States leading in the number of collaborative articles. Other countries that have engaged in significant cooperation with Iran include England, Canada, Australia, France, New Zealand, China, Germany, Italy, and Russia are other countries that have had a large number of cooperation cases in Iran. The data related to financial support institutions showed that the National Iranian Oil Company along its subsidiaries (9.79%); Iran National Petrochemical Company along its subsidiaries (4.74%); Support fund for researchers and technologists (4.08%); Islamic Azad University (3.22%); National Gas Company and its subsidiaries (3.18%); University of Tehran with (2.94%); and Tehran University of Medical Sciences (2.80%) have provided the most financial support. Out of a total of 251 subject areas of Web of Science, 151 areas have received financial support in cooperation between the university and the industry. Based on thematic analysis, chemical engineering fields (10.27%); environmental sciences and materials science (4.28%); energy and fuels (4.21%); and water resources (3.65%), are the most used topics in the articles.

The annual growth rate of articles receiving financial support indicates that the 13-year collaboration between universities and industry has experienced significant fluctuations, with some years witnessing a decline. Furthermore, the leading industries providing financial sponsorship include oil, petrochemicals, and gas. Most of these companies are supported by government organizations and are among the most profitable in the country. Consequently, a substantial portion of the investments in this collaboration is derived from the government budget. In terms of the subjects covered in the articles, the results reveal that, while the fields are diverse and extensive, there is a noticeable absence of certain topics, particularly in the humanities and social sciences.

Many research have been made about of polar vortex (P.V) and its effects on precipitation in the world and in the Iran. Most of them emphasize the direct effect of the polar vortex on winter precipitation. Lashkari (1375) investigated the synoptic patterns of heavy rainfalls in the southwest of the Iran. The intensity, weakness and fluctuations of the subtropical high-pressure system restrain the climate of the mid-latitudes, including the Middle East and Iran. According to the related researches, in the cold season, when subtropical high pressure belt retreats the polar vortex goes forward and causing the transfer of energy and momentum and creates meridian waves. At the beginning of the warm period with the weakening and retreat of the polar vortex, the subtropical high pressure goes forward to north and penetrates to middle East and Iran and arid season is started.

 StatisticsThis paper has focused on the effects of polar vortex on the extreme and widespread precipitation in the Midwest of the Iran for a 30 years period (1986-2015). In the statistical section, after collecting the rainfall data above 1 mm and considering the minimum rainfall of 10 mm , three percentiles of 90, 95 and 98 were selected and based on this, 5 rainfall categories were introduced. By using the Mann-Kendall test, the average of heavy and super heavy rains was obtained, which showed that 50% of the stations had a decreasing and negative trend and 50% of them had an increasing and positive trend. The classification of heavy rains by the Mann-Kendall test also shows that among 18 stations 13 stations (72% of the data) had a positive and upward trend. In addition none of the stations had significant trends in rainfall. In the next stage, the correlation of the polar vortex passing curve from the region (5500 Gpm) with extreme precipitations has been investigated.The geopotential values of this curve with the amounts of rainfall was tested statistically, and the average rainfall with the transition curve was found to be significant at the level of 0.05 (-0.120), which indicates the decrease of rainfall with the north displacement of the polar vortex curve. On the other hand, in the extreme rainfalls condition in the study area, the passing polar vortex curve over the study region at the 500 Hpa level has suffered a southward drift and geopotential height drop in historic period. synopticSynoptic patterns of heavy rainfalls at 850 and 500 Hpa levels are classified by using cluster analysis. In clustering level 850 after selection the representative days of each pattern, the synoptic maps in level of 850 Hpa and vorticity, relative humidity, wind vectors in level of 700 Hpa, and finally the maps of jet stream in 250 Hpa level were drawn. In these maps northern, central and southern representative curves of polar vortex, the location of jet stream, isopleths of vorticity and their relationship were drawn. Then, by classifying the data of representative days at the 500 Hpa level for both heavy and super heavy rainfalls, several patterns were obtained and in each map the wind above 30 m/s and the representative curves of vorticity are marked. It should be noted that the created maps are the result of the integration of manual and visual methods.

The results of this research showed that in most of the stations there is an inverse relationship between the criterion rainfalls and polar vortex curves. In fact, with the northward and southward movement of the central polar vortex curve, there are decrease and an increase in rainfall in the study area. According to other studies, there is a direct relationship between heavy and super heavy rainfalls with the curves of polar vortex. In fact by the southward movement of polar vortex curves the rainfall in the region has increased. Among the three curves that are considered for the polar vortex, the main one and the most impact is related to the central or 5500 Gpm curve. The correlation coefficient of the geopotential height of the transition curve and the amount of precipitation of each category is -0.12, which is significant at the 5% level. The mentioned number indicates the decrease of precipitation in the area that is related to the changes in the curve of the P.V at 500 Hpa level.This precipitation's change can be the effect of climate change which has moved the area of subtropical high pressure to higher latitudes by about 4-5 degrees. These conditions cause the northward displacement of the polar vortex curves and the curve passing through the study region at the level of 500 Hpa and it has caused a decrease in rainfall (non-significant) during the study historical period. In the synoptic section, heavy and super heavy rains occur in relation to the synoptic weather patterns of 850, 700 and 500 Hpa levels were investigated. In the most of the stations and patterns that were obtained, precipitation indicates the presence of low pressure systems from the Arabian, Red and Mediterranean seas as premier cause of rainfall in the study region. By checking the position of the jet stream at the upper level and the moisture advection at the 700 Hpa level, it can be recognized that the most moisture that enters to the west of Iran comes from the Arabian and Red seas. According to these investigations, there is a direct link between temporal relationship of these rainfalls and the activity period of the polar vortex. These systems have entered to the country through displacement of polar vortex and have caused heavy and super heavy rains in the Midwest of Iran.

The main goal of this research is to assess the readiness of countries for the Fourth Industrial Revolution. The study aims to provide a model using artificial intelligence algorithms, such as clustering and classification, to predict the movement of countries towards Industry 4.0.

The research utilizes fast-density clustering (FDC) to categorize countries into distinct clusters. Subsequently, the kNN and SVM algorithms are employed for modeling and predicting countries' movements. Additionally, swarm intelligence algorithms are used to optimize the performance of the algorithms.

One of the achievements of this research is the development of a composite index for the classification of countries and predicting their movement toward Industry 4.0. The countries are categorized into three clusters based on their readiness for the Fourth Industrial Revolution, and the characteristics and trends of each cluster are elucidated. Another finding is the optimization of the kNN and SVM algorithms, demonstrating their superior performance compared to other algorithms.

Originality: 

The proposed composite index outperforms similar algorithms, indicating the effectiveness of the proposed method in predicting countries' movement toward Industry 4.0. The study presented a comprehensive analysis of the Iran cluster, including its overall ranking and rankings within specific sub-indices. The findings offer insights into the strategic movement patterns of Iran and provide recommendations for advancing its technological capabilities. This research not only contributes to enhancing the country's readiness in technology but also aims to elevate the power and standing of the Islamic Republic of Iran, particularly in critical sectors such as national defense and security.

Using network data envelopment analysis (NDEA) models to assess the efficiency of Decision Making Units (DMUs) is a widely accepted method in academic research. An ongoing challenge in this field involves the computation and implementation of enhancement solutions within homogeneous clusters utilizing Machine Learning techniques. The primary aim of this paper is to identify the optimal clustering algorithm for a two-stage sustainable supply chain within the petrochemical industry in Iran. Subsequently, the application of NDEA within each cluster aims to ascertain efficiency levels and devise improvement strategies to facilitate a more targeted development approach for inefficient units. This paper investigates the best clustering algorithms in the area of Machine Learning by using quality measurement indicators and using Network Data Envelopment Analysis (NDEA) for measuring the efficiency of DMUs with sustainability approach. Upon examination, it has been determined that the Deep Embedded Clustering algorithm yields the most favorable results when applied to the data set. Furthermore, the comparison of the clustering result with the standard NDEA model has demonstrated the utility of clustering and comparing units in homogeneous categories for the purpose of efficiency calculation and determining the distance to the efficient frontier. This article, showed that how to find the best algorithm for two-stage supply chain clustering. Also, by comparing the effect of clustering on measuring the distance of inefficient units to the efficiency frontier, it was shown that clustering of units can play a significant role in planning to reach a practical development plan in each cluster.