mahdi esmaeili

The development of smart devices in many aspects of our daily lives is accompanied by the increasing use of appropriate mechanisms to counter them against various attacks and applications in the Internet of Things environment. In this context, it is emerging as one of the most successful and suitable techniques for use in various aspects of IoT security. The aim of this is to systematically review and analyze research studies on research eyes conducted in different Internet of Things security scenarios. The reviewed researches are classified according to different perspectives in a coherent and structured classification to identify the gap in this research area. This research has been published on articles related to the keywords "concept learning", "security" and "Internet of Things" in the four main databases IEEEXplore, ScienceDirect, SpringerLink, and ACM Digital Library. In the end, 90 articles have been selected and reviewed. These studies are conducted according to three main research questions, i.e. the security aspects involved, the network architectures used, and the datasets used in IoT security. The final discussion explores the research gaps and acknowledges the outstanding flaws and vulnerabilities in the IoT security scenario.

Predicting the results of the current quality improvement activities is one of the concerns of the officers of the armed forces officer universities. Taking advantage of the capabilities of the artificial neural network and taking into account the aforementioned concern, the present research has presented a model to predict the process of the results of the effective factors in the military educational activities of one of the officers' universities.

The statistical population is all the young officers under training in the combat camp in Kohestan University under study (from 2018 to 2022). In the first step, the data related to the factors and parameters of the training-educational activities of the military camp in the mountains were collected for the mentioned five-year time period. In the next step, pre-processing of the data was done and using the proposed IRNN algorithm and its coding in Python, the appropriate model was built and its validation was done.

By using the built model and the available data, it was predicted the quality of the training performance of the combat camp.

Originality: 

The results showed that with the continuation of the current policies, all the factors will have an upward trend except for the factor of "updating the level of knowledge and skills of the teaching staff". Therefore, proper planning should be done in order to update the level of knowledge and skills of the educational staff of specialized educational activities

Selection and allocation in the resilient supply chain, when disruption threatens the supply chain, has become a strategic decision and the focus of many researches; increase in the applications of machine learning in supply chain studies has led to the emergence of faster and reliable decision-making methods, however, in few studies, machine learning has been used to deal with the problem of selecting and assigning suppliers to customers in resilient mode. The purpose of this research is to take a step towards solving this gap by using machine learning algorithms on real world data from the automotive supply chain in Iran. the performance data of 441 suppliers and 7 customers in 1401 was used. In this research, two clustering algorithms have been used to generate labels based on the concept of resilience capacity; Then, since the interpretability of the results was a priority, based on the labeling of the clusters by the experts, the decision tree was used to classify the suppliers based on their performance. The results showed the K-means tree performs better than the DBSCAN tree and criteria such as on-time delivery, capacity, production line stoppage, quality alert, logistics performance and quality performance are effective on suppliers' resilience.

One of the central issues in criminology is the emergence of correlations that can be considered as the causes of the emergence and spread of crime in society; Henceforth, criminologists and sociologists are interested in theoretically and empirically examining the causes of the emergence of various rates of social deviance, including the rate of crime. The purpose of this article is to investigate the relationship between social capital and crime rates with emphasis on robbery in Iranian society.

This study is in line with the basic and practical objectives of tracing the causes of the emergence and occurrence of theft with reference to the processed data by official reports from 1390 to 1395 and the method of this study is secondary analysis; For this purpose, after scientific studies, crime rate reports have been included in the official reports of the Civil Registration Organization and the police on the one hand.

The results of this study indicate that social capital in Iranian society is not in a favorable condition and the average social capital of the country (2. = 2.45) is less than the average. Also, out of 31 provinces of the country, the average of 18 provinces that are ranked one to eighteen is lower than the national average, and Kurdistan and Kermanshah (= = 1.97) have the lowest and Lorestan (3. = 3.15) have the highest average of social capital.

With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches

False or unconfirmed information is published on the web like accurate information, so it can become viral and influence public opinion and decisions. Fake news and gossip show the most popular forms of false and unverified information, respectively, and they should be detected as soon as possible to avoid significant effects Interest in effective identification techniques has been increasing in recent years.The problem of detecting fake news is known as a classification problem in natural language processing and text mining, and its purpose is to distinguish fake news from real and extracted texts, and to improve the accuracy of detecting fake news is the main issue of this research. Convolutional neural networks, as one of the most important models of deep learning, have gained high accuracy on these issues. These networks include problems such as not considering the position of words, which is solved by using the capsule network, and in order to achieve optimal accuracy, two problems of heavy processing of all connected layers and reducing the parametric space using the algorithm XGBOOST and particle swarm optimization (PSO) algorithm are proposed.

This study is an applied research in which about 42,000 Persian news from different cities of Iran were collected from Twitter and using additional methods of cleaning and preprocessing, additional information was removed and after tagging, the news was ready to be used for the proposed approach using Python software and related libraries are equipped with machine learning and deep learning algorithms.

During testing, some machine learning algorithms had more power in classification problems, but with the changes in the structure of the convolutional network and Capsul network algorithm, better results were obtained than machine learning algorithms and other similar algorithms.

The proposed solutions in this research in comparison with the approaches of basic algorithms or solutions to solve the mentioned problems by replacing the optimal classifier and reducing the parametric space, by changing the input has been able to achieve better and more acceptable results than other approaches. And achieve an accuracy of about 96%.

Nowadays, fertilizers are one of the factors affecting the yield of plants, but their high use, especially when combined with inappropriate management practices such as burning of plant residues, drastically reduce soil organic matter. Long-term use of chemical fertilizers also results in the leaching and contamination of groundwater. Using biofertilizers to reduce chemical fertilizer use and increase crop yields is an important strategy to move towards sustainable agriculture. So far, no research has been conducted on the effect of vermicompost and vermiwash on the growth and development of African daisies. Therefore, the present study was conducted to investigate the effects of vermicompost and vermiwash on growth and development of African daisies plant.

The present study was conducted to assess the effect of vermicompost and vermiwash on morphological and physiological properties of African daisies (Gazania rigens L.) in 2018. For this purpose, vermicompost was applied at 4 levels (control, 20, 40, and 60 % V/V), and vermiwash was applied at 4 levels (control, 50, 100, and 200 mg/l) as a factorial based on randomized block design. Plant height, number of flowers, flower diameter, root volume, fresh and dry weight of shoot and root, chlorophyll content, insoluble sugar, K, N and P concentration were studied. Statistical analysis of the data was performed using SAS software version 9.1 and comparison of means by Duncan’s Multiple Range Test at 5% probability level.

Vermicompost and vermiwash treatments had significant effects on morphological and physiological properties of African daisies. Interaction effect of vermicompost and vermiwash was significant for all studied traits except P concentration. The highest number of flowers (10) was observed in 60% vermicompost and vermiwash treatment at 100 mg/l. There were no significant differences between 40 and 60% vermicompost volume and also between 100 and 200 mg/ l vermiwash in flower number and diameter. Therefore, increasing the amount of vermicompost and vermiwash to some extent increase the reproductive properties of the plant and higher than that amount can even reduce related traits. By increasing the volume of vermicompost from 20% to 40%, the root volume of African daisies significantly changed. The fresh and dry weight of shoots also showed a similar response to vermicompost and vermiwash treatments. By increasing vermicompost volume from 20 to 40%, the soluble sugar increased significantly in African daisies. Vermicompost and vermiwash increased chlorophyll content in African daisies. The highest amount of chlorophyll b was observed in vermicompost treatment at 60% volume and 50 mg/ l vermiwash.

According to the results of the experiment, vermicompost and vermiwash, with effective nutrient absorption and high water absorption and storage capacity, increased the reproductive traits of African daisies. The effect of vermicompost was higher than vermiwash on studied traits. Treatment of 40% vermicompost and 200 mg/l vermiwash was the best treatment for improvement of African daisies growth

The development and expansion of computer science and the growing desire to use it, in addition to creating a technological revolution in the world, has also provided favorable conditions and context for the emergence of crime in cyberspace and its expansion has caused various damages in Communities have become. The present study intends to prevent and deal with these crimes and the damages caused by them and how to formulate criminal policies on cybercrime in the context of the legislative criminal policy of Iran and the European Union in order to take the lead of this union in the fight against crime. Cyberspace and the development of an efficient criminal policy system can also benefit from their experiences in eliminating the weaknesses and gaps in the country's criminal policy in dealing with cybercrime.

The present study is written by descriptive-analytical method.

The study indicates that the strength and correlation of the range of criminal policies adopted in the European Union, given the history and antiquity of legislation and the fight against cybercrime, is greater and deeper than the Iranian penal system. Iran's legislative criminal policy based on criminal policy and distancing itself from non-criminal preventive measures and not adopting situational and social preventive measures of crime and lack of proper understanding of the law inability to predict emerging crimes in the future and the culture of misuse of space Cyber ​​is one of the main reasons for the inefficiency of Iran's criminal policy.

The purpose of this study was to investigate the formal and informal costs of crime by employing a descriptive survey method. The statistical population of this study consists of those convicted of robbery, fraud and intentional assault and released from prison in 1989. Using the available sampling method, 150 individuals were selected as the final sample size. The data-gathering tool used was a researcher-made questionnaire, which was validated by the opinions of three experts. In addition, its reliability was calculated using the Cronbach’s alpha coefficient of 0.81. After explaining the demographic data and distributing the frequency and percentage of questions answered, multivariate analysis of variance and t-test were used for data analysis and the Tukey test was used to determine the differences between groups. According to the findings of this study, investigation of official and informal crime costs of fraud is greater in thieves and in thieves is greater than in the perpetrators of deliberate assault. Also, when assessing formal and informal costs, in general, fraudsters pay close attention to both types of costs, thieves focus on informal costs more than formal costs and perpetrators of deliberate assault and crime pay little attention to official and social costs. The reason for the discrepancy in the assessment of crime costs is due to the forensic nature of the crime, simply put, the fraudsters regarded as white collar criminals knowingly commit the crime, whereas the perpetrators of deliberate assault and intentional assault are regarded as blue collar criminals as they commit spontaneous crimes.

With the increase of news on social networks, a way to identify fake news has become an essential matter. Classification is a fundamental task in natural language processing (NLP). Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of fake news classification. In this paper, new baseline models were studied for fake news classification using CNN. In these models, documents are fed to the network as a 3-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the texts. Besides, analyzing adjacent sentences allows extracting additional features. The proposed models were compared with the state-of-the-art models using a collection of real and fake news extracted from Twitter about covid-19, and the fusion layer was used as the decision layer in selecting the best feature. The results showed that the proposed models had better performance, particularly in these documents, and the results were obtained with 97.33% accuracy for classification on Covid-19 after reviewing the evaluation criteria of the proposed decision system model.

Convolutional neural networks, as one of the most important models of deep learning, have gained high accuracy on these issues. In this study, discussion and text analysis at the sentence level and improving the performance of neural networks to detect fake news has been convolution. The network of words for bags of words in the data model so that each word according to the two-dimensional vector space to become matrices. One of the limitations of convolutional networks is that it works at the word level and cannot consider the relationship and distance between sentences. And sentence-level analysis is a major problem in this research. Sentence level analysis is a major problem in this research.In this research, a basic model based on convolutional networks is proposed in which documents are given to the network in the form of 3D tensors to solve the mentioned problem. Considering 3D tensors allows the model to learn the position of words in a sentence and achieve more accurate results in detecting fake news.

This study is applied research in which about 42,000 Persian news from different cities of Iran were collected from Twitter and using preprocessing, additional and useless data is deleted and after tagging the deleted texts, the news text is used for the proposed approach using Python software and related libraries.

During testing, some machine learning algorithms had more power in classification problems, but with the changes in the structure of the convolutional network algorithm, better results were obtained than machine learning algorithms and other similar algorithms.

Considering 3D tensors allows the model to learn the position of words in a sentence, and this proposed model has gained considerable accuracy compared to the proposed approaches in the literature. The proposed model without adding additional overhead in terms of the number of features and network depth, by changing the input has been able to achieve better and more acceptable results than other approaches in the literature and achieve an accuracy of more than 94%.

White-collar crimes are important crimes in the administrative system of the Islamic Republic of Iran, which need to be addressed. Considering the instructive life of Imam Ali (AS) in protecting the treasury, in this article, an attempt has been made to examine the criminal policy from the perspective of Imam Ali (AS) with emphasis on white-collar criminals. The main question that is raised and examined in this article is that in the noble book of Nahj al-Balaghah, which expresses the speech and manners of Imam Ali (AS) in relation to dealing with the crimes of white-collar workers, what are the criminal solutions?

The present article is a descriptive-analytical and uses the library method to investigate the issue.

The results of the research indicate that Imam Ali (AS) believed in two approaches to penal policy, punishment and correction. In the punitive approach, they believed in the certainty of punishments, and in the corrective approach, they believed in pardoning and not blaming criminals and avoiding labeling. Regarding the criminal policy of white-collar crimes, they also emphasized on the public execution of punishments, determination, severity and equality in the execution of punishments. In addition, the results indicate that in order to implement criminal policy, high judicial authorities and judges of the courts should take a strong and decisive approach to this issue by using the ideas of Imam Ali .

Although rational choice theorists have acknowledged that criminals do not always perform best in assessing the situation to commit a crime, their ideas, especially when it comes to preventing crime, are based on the premise that Criminals make rational decisions. The aim of the present study is to study the foundations of the position of rationality from various aspects such as "philosophical" and "positivist" in microeconomic theories from a criminological perspective by referring to the roots and backgrounds of the theory of rational choice.

The method of conducting research according to the nature of the subject, objectives and method of data collection is library and descriptive-analytical.

Rationality is considered as a basic premise in studying the behavior of economic agents in a conventional economy. This rationality or the science of rational choice (Hodgson 1998), formed from the foundations of Western anthropology after the Renaissance, has different meanings. In classical economics, rationality is used as a tool to maximize utility and profit, which is derived from the philosophical ideas of scientists such as Bentham. Adherents of the theory of rational choice in criminology believe that there are many potential criminals in society and say that there are special circumstances that make the perpetrator decide to commit a crime and believe that by identifying these conditions and opportunities and reducing them, The power of theft and crime in general has become irrational for these people.

The pathology of the family institution is one of the important challenges that has an important role and place in the Islamic political system. However, the growing importance of the media, the promotion of individualism, as well as the weakening of the family institution, led some views to raise their own views to reform and play the role and position of those views. Among these, the model of Islamic media literacy has a special place as a framework for representing the mass media to explain the pathology of the family institution. Accordingly, the main purpose of this study is to investigate the position of Islamic media literacy in the representation of mass media to explain the pathology of the family institution. The main question of the present study is: In what ways can Islamic media literacy protect and stabilize the institution of the family in the context of the discourse of the Islamic Revolution? The hypothesis of the article is: Islamic media literacy plays an important role in providing a suitable model for supporting the family institution by adopting a critical approach, instinctualism, truth-seeking, promoting dialogue and good debate, and emphasizing human dignity. The present study uses a descriptive-analytical method and has collected information in a library and documentary manner.

This article deals with the US military threats against the Islamic Republic as declared in the statements of the Supreme Leader Imam Khamenei (ph) as a question. To that end, it seeks to address this issue by enumerating the components and indicators of US military threats against the Islamic Republic of Iran. This research is descriptive which exploits content analysis in reference to the statements, measures and decrees of the Supreme Leader Imam Khamenei (ph). This article categorizes his Majesty's orientations, points of emphasis, and analysis of US military threats against the Islamic Republic of Iran. Studies show that the components of the US military threat are hostile military actions, undermining territorial integrity, soft warfare, and denial of access to military knowledge, technology, and equipment, the indices of which are composed of support of aggressor, creation of insecurity, full-scale war, illegal military passage, military presence in the region, psychological warfare, Iranophobia, military sanctions and military affiliation, respectively.

One of the important features of the heavy-duty internal combustion engine is power density in such a way that the limitations created by the engine's features and accessories are the main challenges in evaluating the performance and power enhancement of advanced diesel engines. In other words, the complexity and limited performance of some of these devices do not allow the use of different power enhancement methods. Among these limitations, temperature constraints are one of the main challenges in the power enhancement process. In this study, the feasibility of increasing the power of the R43L MTU4000 heavy rail diesel engine has been considered. In this regard, the limitations of turbocharger inlet temperature as one of the basic performance challenges of the engine have been investigated using a one-dimensional simulation. For validation, the simulation results from the GT-SUITE software are compared with the experimental results. In the results section, the influence of increasing fuel mass, decreasing the compression ratio (CR), and the start of injection timing (SOI) has been investigated. The results show that by raising the fuel quantity by 5%, the power increases by about 7.6%; however, this increase in power leads to an increase in the turbocharger inlet temperature by 20K. Due to the operating limitations of various engine systems, attempts were made to control the rise of exhaust gas temperature by reducing the CR. On the other hand, reducing the CR from 18 to 15 increases the BSFC by 2.5%, but these changes in the CR do not have a significant effect on the output power. Finally, to examine the SOI timing in the enhanced engine at the maximum speed and power, different SOIs are tested and the optimal point is determined.

Narration is the most basic part of the story and its skeleton. Narration is the manner or technique by which the story is narrated by the author. The narrative structure of the stories determines the artistic value of the story, and in order to study this criterion, it is necessary to examine the narrative style of the stories separately or comparatively. In this paper, based on the narrative style, two popular social novels by two renowned Arab and Iranian writers, The Yacoubian building by Alaa Al Aswany, and Bioten by Reza Amirkhani, are investigated. After reviewing and analyzing the two novels, it became clear that the personal narrative style of these two authors was largely similar. The particular styles of Aswany and Amirkhani in these two novels are "suspension, anachronism and time deletion, descriptive pause, short circuit and focalization." The results of this study show that both authors have used a kind of short jump in the process of story suspension to generate interest in the audience and from the perspective of the plot element processing, we also see a kind of action system. The two novels are very similar in structure and content and have a strong connotation because of reason and the causal relationships. The narrative structure of time in both is circular, nonlinear and lacks proper order and sequence, which leads to anachronism. The use of temporal summaries in both works has given rise to short frequency. Aswany and Amirkhani, with their inner focalization. Stand beside each character and express their goals with his voice and language

Suppliers, as one of the vital partners of the supply chain, contribute to creating a sustainable supply chain. Also, resilient supplier selection can help to reduce the severity of the disruptions' effects. Thus, the simultaneous application of the sustainability and resilience principles in the process of supplier selection and order allocation plays a significant role in achieving sustainability and continuity of business processes in the occurrence of disruptions. This study aims to select a set of sustainable and resilient suppliers and to allocate demand in an automotive supply chain under disruption risks.

In this research, a hybrid approach has been presented for evaluating and ranking suppliers based on sustainability criteria and modeling the problem of selecting a sustainable-resilient supplier and order allocation under disruption risks. VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje which means multi-criteria optimization and compromise solution, in Serbian) as a multi-criteria decision-making technique has been used to determine the sustainability score of suppliers and the results have been considered as the input parameter for the mathematical model. The proposed mathematical model is two-stage stochastic programming to minimize total cost and maximizing sustainability performance in the case of disruptions, which is solved using the Ɛ-constraint method. Multiple sourcing and flexibility in the production capacity of suppliers have been considered as effective strategies to reduce the severity of disruptions. Iran-Khodro supply chain data has been also used to validate the research model. Finally, sensitivity analysis has been performed to investigate the effects of parameters' change on the final results.

The results indicated that by increasing the importance coefficient of sustainability performance in the supply chain, the overall value of the supply process increases during disruptions, and multiple sourcing and the use of suppliers' flexible capacity are effective strategies in reducing the severity of disruptions. Also, examining the effect of reliability on the expected cost indicates that the occurrence probability of disruption risk in suppliers can affect the total cost of the procurement process and can also reduce the sustainability goals.

In this research, the effect of disruption risk has been examined. As a subject of future study, the operational risk is suggested to be recognized in the supplier selection problem. Besides, unique events have been considered in the model of this research. In the future, semi-super and super events can be also studied. Finally, using other resilience strategies in supplier selection problem such as adopting backup suppliers, protecting suppliers, and pre-positioning inventory can be a potential direction for future research.

The company under study can consider the results of the presented model and focus on utilizing suppliers' flexible capacity, multiple sourcing instead of single sourcing of parts, partially. Given the importance of sustainable development, particularly in automotive supply chains, it is essential to consider the sustainability performance of suppliers in sourcing decisions with disruption risk in addition to cost and reliability criteria. Thus, the company can evaluate the suppliers' sustainable performance in the occurrence of disruption to achieve sustainable development and business continuity and to meet the needs of stakeholders.

Due to the subject of this study, i.e. sustainable supplier selection in the resilient supply chain, the performance of each supplier has been evaluated based on social and environmental criteria to consider social responsibility and environmental issues in the sourcing decisions under disruption risk. Originality/value: A few studies have taken into account the dimensions of sustainability and supplier risk simultaneously in the field of supplier selection and demand allocation. Flexibility in the supply capacity of the supplier, as an effective resilience strategy, and economic criteria such as C/100 index, PPM production line, payday resistance, communication system, etc. have not been studied in the literature of sustainable and resilient supplier selection.

The research was conducted at SAPCO, which manages the Iran Khodro Industrial Group (IKCO) supply chain, and uses the DEMATEL-ANP combination to determine the importance of third-party logistics services evaluation, ranking and selection and used for outsourcing. The results of the study, which were obtained by examining 15 logistic criteria and based on the views of experts in the field of logistics, show that the weight of criteria such as experience in the same industry, customer line stops, price, flexibility, defective delivery ratio and on time delivery is important than other criteria, and criteria such as waiting time for return, inventory turnover and environmental sustainability are less important than other indicators. Also, using the ANP-PROMETHEE combination, three logistic services companies were evaluated. The results show the company's No 3 performance is better than other companies. There is also present a way to assess the performance of logistics services companies during the cooperation period based on measurable indicators by the customer.

The imbalance data can be seen in various areas such as text classification, credit card fraud detection, risk management, web page classification, image classification, medical diagnosis/monitoring, and biological data analysis.
The classification algorithms have more tendencies to the large class and might even deal with the minority class data as the outlier data. The text data is one of the areas where the imbalance occurs. The amount of text information is rapidly increasing in the form of books, reports, and papers. The fast and precise processing of this amount of information requires efficient automatic methods. One of the key processing tools is the text classification. Also, one of the problems with text classification is the high dimensional data that lead to the impractical learning algorithms. The problem becomes larger when the text data are also imbalance. The imbalance data distribution reduces the performance of classifiers. The various solutions proposed for this problem are divided into several categories, where the sampling-based methods and algorithm-based methods are among the most important methods. Feature selection is also considered as one of the solutions to the imbalance problem. In this research, a new method of one-way feature selection is presented for the imbalance data classification. The proposed method calculates the indicator rate of the feature using the feature distribution.
In the proposed method, the one-figure documents are divided in different parts, based on whether they contain a feature or not, and also if they belong to the positive-class or not. According to this classification, a new method is suggested for feature selection. In the proposed method, the following items are used.


If a feature is repeated in most positive-class documents, this feature is a good indicator for the positive-class; therefore, this feature should have a high score for this class. This point can be shown as a proportion of positive-class documents that contain this feature. Besides, if most of the documents containing this feature are belonged to the positive-class, a high score should be considered for this feature as the class indicator. This point can be shown by a proportion of documents containing feature that belong to the positive-class.
If most of the documents that do not contain a feature are not in the positive-class, a high score should be considered for this feature as the representative of this class. Moreover, if most of the documents that are not in the positive class do not contain this feature, a high score should be considered for this feature.


Using the proposed method, the score of features is specified. Finally, the features are sorted in descending order based on score, and the necessary number of required features is selected from the beginning of the feature list.
In order to evaluate the performance of the proposed method, different feature selection methods such as the Gini, DFS, MI and FAST were implemented. To assess the proposed method, the decision tree C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per Micro F , Macro F and G-mean criteria show that the proposed method has considerably improved the efficiency of the classifiers than other methods.

Media and citizenship rights are interconnected in many ways. Considering the positive aspect, it can be asserted that in the field of citizenship life, media can spread information and awareness; and regarding the negative side, they can also be considered as making the context for the threat to citizenship by representing crime and provoking insecurity. Therefore, the media has dual impacts on the lives of citizens. However, from the aspect of examining the negative impacts of the media, one can point out their undesirable effects on increasing insecurity, fear of crime, its normalization and even the generalization of crime practices among all citizens. In this regard, the present study seeks to examine the impact of the media on increasing the sense of insecurity and crime from the perspective of citizenship law and provide solutions to counter the adverse effects of the media. From this perspective, security is regarded as one of the fundamental pillars of citizenship. The most important findings of the present study are that the media is also an agent contributing to the spread of insecurity by presenting crime, normalizing crime, fast transmission and educating criminals and thus, is considered as a threat to citizenship. Contrarily, the criminal laws of governments, the monitoring of the performance of media administrators, the need for a review of the content of media that provokes insecurity and the fear of crime, as well as the participation of citizens in monitoring and producing media content are among the solutions that can eliminate the media's undesirable effects while at the same time, defend citizenship rights. The present research is a descriptive-analytical one that utilizes documentary and library resources, papers and studies related to the subject to collect the data.

Quran’s Text differs from any other texts in terms of its exceptional concepts, ideas and subjects. To recognize the valuable implicit patterns through a vast amount of data has lately captured the attention of so many researchers. Text Mining provides the grounds to extract information from texts and it can help us reach our objective in this regard. In recent years, Text Mining on Quran and extracting implicit knowledge from Quranic words have been the object of researchers’ focus. It is common that in Quranic experts’ arguments, different sides of the discussion present different intellectual, logical and some non-integrated minor evidence in order to prove their own theories. More often than not, every side of these arguments disapproves of the other’s hypothesis and in the end it is impossible for them to reach a state of consensus on the matter, the reason is that, they do not have a common basis for their arguments and they do not make use of scientific, logical methods to strongly support their theories. Therefore, using modern technological trends regarding Quranic arguments could lead to resolving so many of current discrepancies, caused by human errors, which exist among Quranic researchers. It can help providing a common ground for their arguments in order to reach a comprehensive understanding.
The method used in this research implements frequent pattern mining algorithms, singular frequent patterns as well as dual and tripe frequent patterns in order to analyze Quranic text, in addition to this, Association rules have also been evaluated in the research.
Out of 54226 extracted association rules for Quranic words which have been evaluated by the use of criteria such as confidence coefficient, support coefficient, lift criteria as well as Co-efficient criteria. Top 10 rules for each criterion have been analyzed and reviewed throughout the project.

In the past few decades, due to negative environmental and social impacts of companies, there has been more pressure from the government and non-governmental organizations on companies to observe these issues in their operations, including their supply chain operations. To achieve an environment-friendly supply chain, the role of suppliers as the initial point of the supply chain is very crucial. So in addition to traditional criteria for evaluating and selecting suppliers such as price, delivery, and quality, it is essential to use new criteria, that while encompassing environmental issues, are fit to the characteristics of the industry under study. In this study, taking into account a set of economic and environmental criteria, a multi-objective integer mathematical programming model is proposed for solving the green supplier selection and order allocation problem under the multi-product, multi-sourcing, and single period assumptions. The case of this study is part of the IRAN KHODRO supply chain and focuses on the supplier selection and order allocation processes for 31 components of four types of the final products provided by 45 suppliers. The proposed mathematical model for the supply chain under study has 507 constraints and 449 decision variables. Then proposed model is solved via the ε–constraint method and Total Value of Green Purchasing (TVGP) is calculated. Results of the solution and sensitive analysis of the proposed model demonstrate that with increasing the importance of environmental issues in the supply chain, TVGP will increases.

The main cause of collapse of bridges is scour around bridge piers. Therefore, knowledge about the effective parameters and estimation of maximum scour depth has an important rule on the safe design of bridges in rivers. The aim of this research is evaluation of optimized ANFIS parameters with GA on prediction of maximum scour depth and comparison with all FIS generator of Matlab Toolbox ANFIS. 48 set of experimental data of scour around incline bridge piers were used. To comparison of performance of results, R2 and RMSE were utilized. Analysis of results showed that GENFIS1 generator of ANFIS toolbox in Matlab had the best performance, which could predict maximum scour with R2 and RMSE, 0.976 and 0.053, respectively. Comparison of predicted scour depths indicated that optimization of ANFIS parameters had better prediction of desired parameters with R2 and RMSE, 0.992 and 0.02537 in comparison of ANFIS toolbox in Matlab software.

Endotracheal intubation is an invasive technique which is associated with severe cardiovascular complications. This study aimed to compare the effects of lidocaine 10% spray on the laryngoscope blade with intravenous lidocaine on cardiovascular responses to laryngoscopy and intubation in elective surgical patients. In this clinical trial that is performed in 2013, 90 candidates of elective surgery in Gonobad City, Iran hospitals were randomly divided to three groups; control (no drugs were used), intravenous lidocaine (three minutes before laryngoscopy) and lidocaine 10% (just before laryngoscopy). Heart rate and systolic blood pressure, diastolic blood pressure and mean arterial blood pressure were recorded immediately before, immediately after, and 1, 3 and 5min after laryngoscopy. Data were analyzed using SPSS 20 by repeated ANOVA and Tukey post hoc tests. There were significant differences in systolic, diastolic and mean arterial blood pressure between control group and each intravenous lidocaine and lidocaine 10% groups after laryngoscopy (p<0.001). There was no significant difference in any of the three variables between intravenous and spray groups. No significant difference was found between the three groups in heart rate (p=0.16). Compared with intravenous lidocaine administration, lidocaine 10% spray on the laryngoscope blade lead to better stability in blood pressure after laryngoscopy, but has no effect on the heart rate.