فهرست مطالب محسن رحمانی

Today, one of the most important challenges of organizations, especially defense organizations, is to create capable employees in order to create organizational success and use them efficiently. Therefore, this study aimed to investigate the impact of knowledge management on organizational competency in AJA cadet universities. The research method is applied in terms of purpose, descriptive and correlational method. The statistical population of the study consisted of academic elites, commanders and executive directors of the Islamic Republic of Iran Army which were classified using random sampling and were studied using Cochran's formula of 97 tons. The data collection method was field and the data collection tool was a researcher-made questionnaire. Cronbach's alpha coefficient was used to measure the reliability of the questionnaires, which was obtained for the variables of knowledge management and empowerment processes (0.92 and 0.84), respectively, and its validity was confirmed by content and convergent validity (AVE). Data were analyzed by structural equations using Smart PLS software. Findings indicate that structural equation estimation, i.e. factor load and t-statistics, knowledge management on organizational empowerment in university.

The inquiry is seeking the effect of the innovation by the organizational function of an organization that is represented in the military industry, and the statistical society includes the basis of this equipment, from where the statistical community is research infinite, a statistical sample based on a total of only three hundred and ninety nine. It is also based on an unapproachable paradigm system, and in an accidental statement from the authoritative manner in which the teachers, the connoisseurs, and the connoisseurs have been circulated through the International Statute of Secrecy. Then based on the use of the ELM 'S MAP to grasp the meaning of the' 95 percent of the appraisal paradigm ' and the Atbash treatment of the new operating system that has contained new and vital results of guesswork, that is to say, a certain level of innovation, and a certain level of corporate commitment to the activity of a military industrial enterprise.It's also the impact of organizational efficiency with an organizational institute in view of interacting our promotional role.

The purpose of this research is to analyze the effect of Islamic ethics in the work environment on organizational commitment through the mediating variable of job motivation. This research is considered an applied research in terms of its applied nature, the type of survey research based on correlation and research method.

The research method, from the point of view of the goal, is an application that has been collected using the field method of data using the standard questionnaire of Mohammad and Rana (2020). The statistical community of this research is made up by the employees of Payor headquarters in Nazaja. The analysis methods were carried out using descriptive and inferential statistics based on structural equation method with Lisrel software.

In order to check the significance of the proposed hypotheses, the T statistic is used, considering that the T statistic is not in the range of (1/96; -1/96) in all hypotheses, so it can be concluded that this hypothesis in The error level of 0.05 is significant.

The results of the structural equation test showed that at the 95% confidence level, Islamic ethics in the work environment has a significant effect on job motivation. Also, Islamic ethics in the work environment has a significant effect on organizational commitment and job motivation has a significant effect on organizational commitment. In general, the results of the structural equation test showed that Islamic ethics in the work environment has a significant effect on organizational commitment with the role of the mediating variable of job motivation.

The aim of the current research is to identify, stratify and analyze the main dimensions and indicators affecting the dynamic capabilities of information technology in small and medium-sized companies using the structural-interpretive modeling (ISM) technique. The dimensions and indicators of the dynamic capabilities of information technology have been identified based on the literature study and content analysis and comparisons of related researches and interviews with experts and specialists, and then, using structural-interpretive modeling, the relationships between the dimensions and indicators of the dynamic capabilities of information technology have been determined as Integrated analysis. Finally, by using MICMAC analysis, the types of variables have been determined according to their influence and effectiveness on other variables. The results show that the variable of digitalization of business processes is among the linked variables and has a high power of influence and dependence. This variable with the greatest influence is considered as the driver and motivator of the dynamic capabilities of information technology in small and medium-sized companies, and any action on this variable causes the change of other variables. Also, measurement variables, resources and competencies and coordination are among the key variables for the development of dynamic capabilities of information technology in small and medium companies. These variables have a great impact on the realization and promotion of dynamic capabilities of information technology in small and medium-sized companies and have high influence and low dependence.

Coral reefs are an important part of the tropical shallow water ecosystem and their protection is very important. Classification of coral reef images includes three stages of image enhancement, feature extraction and classification. In this research, by focusing on the feature extraction a method for features extraction for classification of coral corals images is proposed. This method consists of two methods of local binary pattern variants. In addition, instead of using a large neighborhoods, a multi-scale neighborhood with different sizes is used. This method employed a fixed number of points with different size of neighborhoods. This increases the classification accuracy without exponentially increasing the features. By combining the CS_LBP symmetric binary method and the MRELBP median enhanced binary method, some features of the image are merged together, and the local features extracted by the CS_LBP method are reduced by half in each step. In this research, the accuracy of the proposed model has been evaluated on EILAT, EILAT2, RSMAS, and MLC-2008 coral reef image sets. Also a general textures such as CUReT, UIUC, and KTH_TIPS texture are used. The classification accuracy of the proposed method has increased in all recent data, while the number of features extracted is decreased.

Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR feature selection approach. In mRMR, feature selection is performed based on maximum relevance to class and minimum redundancy among the features. The suggested method include six stages: in the first stage, after determining training and test data, training data are distributed horizontally. All subsets have same number of features. In the second stage, each subset of features is scored using mRMR feature selection. Features with higher ranks are selected and others are eliminated. In the fourth stage, features which were omitted are voted. In the fifth stage, the selected features are merged to determine the final set. In the final stage, classification accuracy is evaluated using final training data and test data. Our method quality has been evaluated by six datasets. The results prove that the suggested method can improve classification accuracy compared to methods just based on maximum relevance to class label in addition to runtime reduction.

In non-usury banking, the need for innovation is felt more than before, and its development depends on the creation of Islamic knowledge among employees.

The present research has examined the impact of different kinds of innovation on non-usury banking performance in Melli Bank of Qazvin province and the mediating role of Islamic knowledge in this regard. The purpose of this research is to determine the impact of various innovation types on Islamic knowledge and provide the relevant scientific solutions to improve the performance of non-usury banking.

The research method is a quantitative survey. The data was collected from a sample of 225 employees of Melli banks in Qazvin province using standard questionnaires. The hypotheses were tested using structural equation modeling by partial least squares method and using Smart PLS software.

Findings for the hypotheses with direct effect showed that all types of innovations have a direct, positive and significant effect on Islamic knowledge and non-usurious banking performance (first and second hypothesis), and Islamic knowledge also has a direct, positive and significant effect on non-usurious banking performance (the third hypothesis). The results of the structural model test for the mediation hypothesis showed the confirmation of this hypothesis which emphasizes the vital role of Islamic knowledge among the types of innovation and the improvement of non-usurious banking performance (fourth hypothesis).

In general, the creation of Islamic knowledge can enrich the impact of various innovations on non-usurious banking performance, and considering that Islamic knowledge is an important source for the performance of non-usurious banking, there is an urgent need for methods of production, sharing and application of this knowledge in banking.

The aim of collection will be to share knowledge, which is to promote the value of more knowledge in the organization.

it most of the organizations and companies that move in the fast line of corporate science can't get the top without doing the basic work of directing knowledge in the EFQM model. Substantiative: It was used for use of partial and practical inquiry, and analysed analysis had been taken on two levels of statistics, using the soft instruments of the spss and the pls. The research findings show that strategy, leadership, and least of all to the highest and lowest effect on the welfare of the organization, and that of acquiring knowledge does not affect the most and the least possible effect on the development of knowledge.

Exploring also shows that the key to the development of organized science has a significant effect on the management of knowledge.

Human activity recognition can be employed in systems that provide support to people who need special care in their daily activities. Various machine learning methods, including Hidden Markov Models and their extensions, are widely used to deal with this problem. In the previous works, HMM-based methods use the conditional independent assumption to compute the probability of a segment of observations. In this research, instead of conditional independent assumption, a new probabilistic model for string space, based on Dynamic Time Warping and Weighted Levenstein Distance is proposed. The model, combined with the Hidden semi Markov Model, is applied to a publicly available dataset. The results show considerable improvements in comparison with using the Hidden semi Markov Model independently. The proposed models are flexible and can work together with other probabilistic models.

The The current research was carried out with the aim of identifying the components and model of innovation and value creation in defense policies to promote participation. In terms of design and in terms of data collection, it is descriptive (survey) and in terms of purpose, it is applied and developmental, and in terms of method, it is quantitative and qualitative and has been done using fuzzy Delphi method.The statistical population of this research is the commanders, managers and experts of the Islamic Republic of Iran Army, and by using the snowball sampling method, 16 people (6 academic experts and 10 commanders) were selected from people with experience and opinions in the field of defense policies. The tools of data collection in the qualitative part were library studies and semi-structured interviews, and in the quantitative part, a researcher-made questionnaire.Further, by categorizing and categorizing the final criteria, the number of 46 components and 5 dimensions was obtained. In the final step, by implementing the fuzzy Delphi technique in 2 rounds, the criteria were screened and validated,Substantiative: so that in the first round, 6 criteria were removed, and in the second round, no criteria were removed, which was a sign of the end of the Delphi rounds.Therefore, the findings of the research showed that the model of innovation and value creation in defense policies based on the promotion of employee participation has 40 components and 5 dimensions.

The purpose of this research is to determine the impact of the IT-enabled dynamic capability on innovative capabilities and to provide scientific solutions in this regard to improve the performance of small and medium-sized enterprises. The data were collected from a sample of 225 employees of small and medium-sized enterprises active in Qazvin province by means of standard questionnaires. Hypotheses were tested using structural equation modeling with the partial least squares method and using Smart PLS and SPSS software. The results of the structural model test for hypotheses with a direct effect showed that the IT-enabled dynamic capability has a direct, positive, and significant effect on the innovative capability and performance of small and medium-sized enterprises, and innovative capability also has a significant effect on the performance of small and medium-sized enterprises. Transformational leadership moderates the relationship between innovative capability and the performance of small and medium-sized enterprises. The results of the structural model test for the mediation hypothesis indicated the confirmation of this hypothesis. In general, the creation and development of innovative capabilities combined with transformational leadership can strengthen the impact of the IT-enabled dynamic capability on the performance of small and medium-sized enterprises.

One of the ways to detect spam is classifying emails into two categories: spam and non-spam. The high efficiency of machine learning methods in various fields has developed them in text clasification problems. The mechanism of machine learning-based classifiers that classify emails according to their content is based on a set of features, where due to the high volume of emails, using an efficient feature reduction algorithm plays an important role. Unlike the previous methods which select only the superior features and ignore the rest of the unselected features, in the proposed method of this article we try to use unselected features as well. The method is that after applying an initial feature selection, the unselected features are clustered and then each cluster is mapped to a new feature and the final feature vector forms from the selected ones and those mapped from the clusters. In this study, by applying two methods of selecting the initial feature and also two mapping functions, four methods were presented and analyzed using two datasets PU2 and PU3. The results of the analysis showed that the method based on feature selection DF and the advanced mapping function has the highest efficiency among all the proposed methods. Also, the proposed methods are more efficient than base feature selection methods (without clustering).

COVID-19 is caused by the SARS-COV-2 virus and mainly affects the lung tissue. In addition, other tissues are attacked by the virus. Reports have shown that clinical manifestations in Covid-19 patients vary from asymptomatic to acute respiratory symptoms in the lung and septic shock affecting the immune system in other organs. This review article aimed to investigate the molecular mechanisms associated with SARS-COV-2 pathogenicity and the relationship between these mechanisms and the severity of various clinical manifestations in patients.

Published articles by researchers in PubMed and Google scholar databases from 2019 to 2021 were collected and reviewed based on the keywords SARS-COV-2, COVID-19 and disease severity.

After entering the SARS-COV-2 virus into the body and the activation of the immune system for reasons such as increased NET production, oxidative stress, activation of cell death pathways including ferroptosis and finally the explosion of cytokine storm and pyroptosis, exacerbate the severity of the disease and increase mortality. In addition, the molecular expression of NLRP3 can play a major role in the development of cytokine storms in patients, some depend on their immune system to fight the virus due to the proliferation of NLRP3 and other destructive molecular mechanisms, thus showing the different severity of the disease.

Individuals in the community show varying degrees of Covid-19 disease depending on the different reactions of their immune systems. Various cellular and molecular mechanisms appear to be activated in sufferers that NLRP3 expression plays a large role.

Local binary pattern is one of the most popular descriptor that widely used in feature extraction of texture images. Deep convolutional neural network is also one of the best classification methodthat provides very high accuracy. In this research, by combining the features that produced by these two methods, a structure for noisy texture classification is proposed, which provides a very high classification rate. This method is based on two extracted features. The first part uses completed local binary pattern features and in the second part the features of texture images are extracted by using the DenseNet-121 convolution deep neural network. Another motivation of this research related to feature reduction, which significantly reduces the dimensions of extracted features. It employs a shallow convolution neural network to convert the extracted features into lower number of new features. The accuracy of the proposed method has been evaluated on noisy Outex, CUReT and UIUC datasets. The classification accuracy of the proposed method for different level of noise has increased significantly compared to many advanced methods and has improved between 3 and 25%.

Futurists believe that "the maritime world in 2030 will be almost unrecognizable due to the growing trend of globalization and the emergence of emerging countries, the demand for new resources and increasing consumers"; So the oceans will remain in political and national and international economic considerations and ultimately of great strategic importance. The future of the Navy will continue to be of strategic importance, with ocean space, economic challenges, legal disputes, climate change due to new geopolitical considerations, shifting power in the Indo-Pacific region, and the opening of the Arctic. Increased exploitation of marine resources and reliance on maritime territory to preserve population and support economic and social development leads to environmental tensions and political frictions. In view of the above, the authors argue in this article that for the reasons listed above, the naval position will become increasingly prominent in the future.

Battle scenes become more complicated as humans acquire new technologies. Information is important as one of the most influential elements of victory in battle and it causes the development of information and command and control systems. The emergence of artificial intelligence technology has caused tremendous changes in the dimensions of human life, wars also benefit from this technology as one of the undeniable dimensions of human life. The purpose of this research is to investigate the application of artificial intelligence dimensions in intelligent command and control systems in the battle scene. The current research is of an applied type and with a mixed method, while studying the library and documentary sources, the collected data has been quantitatively analyzed and the results have been used in a qualitative method. Based on the findings of the research, the use of artificial intelligence technology in innovative command and control systems is effective and has an acceptable operational efficiency and gives this possibility to the commanders of the battlefield, which firstly maximizes the speed of correct decision-making to respond to threats and needs, and secondly Significantly reduce the level of human decision making error. At the end, the researchers have provided practical suggestions.

Shi'ism's Geopoliticalization After the Islamic Revolution, and the subsequent expansion of Iran's role and function in international relations in the Middle East, Shi'ism became one of the most important and influential factors in the geopolitical world. The formation of a Shiite government led by religious scholars in a large, Shiite-majority country has never happened in the past. Thus, the Iranian revolution created a focus on reviving and popularizing the Shiite religious identity, and even more generally, the Muslim religious identity, and led to the emergence of Shiite governments and movements in the Middle East that changed or changed the balance of power in the region. The phrase has evolved to increase the power and influence of Shiites in the region. Therefore, explaining these developments in the context of Shiite geopolitics is the subject of this article.

Increasing the use of Internet and some phenomena such as sensor networks has led to an unnecessary increasing the volume of information. Though it has many benefits, it causes problems such as storage space requirements and better processors, as well as data refinement to remove unnecessary data. Data reduction methods provide ways to select useful data from a large amount of duplicate, incomplete and redundant data. These methods are often applied in the pre-processing phase of machine learning algorithms. Three types of data reduction methods can be applied to data: 1. Feature reduction.2. Instance reduction: 3. Discretizing feature values. In this paper, a new algorithm, based on ReliefF, is introduced to decrease both instances and features. The proposed algorithm can run on nominal and numeric features and on data sets with missing values. In addition, in this algorithm, the selection of instances from each class is proportional to the prior probability of classes. The proposed algorithm can run parallel on a multi-core CPU, which decreases the runtime significantly and has the ability to run on big data sets. One type of instance reduction is instance selection. There are many issues in designing instance selection algorithms such as representing the reduced set, how to make a subset of instances, choosing distance function, evaluating designed reduction algorithm, the size of reduced data set and determining the critical and border instances. There are three ways of creating a subset of instances. 1) Incremental. 2) Decremental. 3) Batch. In this paper, we use the batch way for selecting instances. Another important issue is measuring the similarity of instances by a distance function. We use Jaccard index and Manhattan distance for measuring. Also, the decision on how many and what kind of instances should be removed and which must remain is another important issue. The goal of this paper is reducing the size of the stored set of instances while maintaining the quality of dataset. So, we remove very similar and non-border instances in terms of the specified reduction rate. The other type of data reduction that is performed in our algorithm is feature selection. Feature selection methods divide into three categories: wrapper methods, filter methods, and hybrid methods. Many feature selection algorithms are introduced. According to many parameters, these algorithms are divided into different categories; For example, based on the search type for the optimal subset of the features, they can be categorized into three categories: Exponential Search, Sequential Search, and Random Search. Also, an assessment of a feature or a subset of features is done to measure its usefulness and relevance by the evaluation measures that are categorized into various metrics such as distance, accuracy, consistency, information, etc. ReliefF is a feature selection algorithm used for calculating a weight for each feature and ranking features. But this paper is used ReliefF for ranking instances and features. This algorithm works as follows: First, the nearest neighbors of each instances are found. Then, based on the evaluation function, for each instance and feature, a weight is calculated, and eventually, the features and instances that are more weighed are retained and the rest are eliminated. IFSB-ReliefF (Instance and Feature Selection Based on ReliefF) algorithm is tested on two datasets and then C4.5 algorithm classifies the reduced data. Finally, the obtained results from the classification of reduced data sets are compared with the results of some instance and feature selection algorithms that are run separately.

The northern provinces are among the main areas involved in World War II in Iran. During the years of occupation of Iran in World War II, the northern cities and villages of the country were not immune from the direct and indirect consequences of attacks.The type of consequences that the researchers want in this research are security-police consequences.Observing the oppression of the occupiers, the inability and inefficiency of the government to realize the rights of all our compatriots against the aggression of the occupiers, etc. are among the hardships happened to the people of the north of our country.This article seeks to answer the question that what were the security and police consequences of the World War II in the northern cities of Iran through descriptive and historical research method and the use of relevant documents as well as conversations with the northern settlers? In fact, this article takes a new look at World War II in Iran and finally in northern Iran. Findings show that the failure of cultural security and censorship, damage to economic security and inflation, disruption of government exports from the north, the suspension of special government projects, weakening supervision and enforcement of government laws, extortion and looting of people's property, health insecurity, outbreaks of disease, escalating civil strife, blatant violations by the occupiers, temporary evacuation of police and security facilities in the north, social dissatisfaction due to the impacts of the occupation, security problems caused by the presence of war refugees, increased espionage activities and the fifth pillar, were some of the security and police consequences of the World War II in the northern cities of the country. Keywords: World War II, northern Iran, consequences, police-security.

The role of email in communication is seriously threatened by a phenomenon called spam. So far, many methods have been proposed to deal with this phenomenon, one of the most important of which is to classify emails based on their content into two categories: spam and non-spam. Content-based classification mechanisms use the words as features, where applying an efficient feature selection mechanism is critical due to the large number of features. Therefore, the main focus of this paper is to select useful features via proposing a wrapper feature selection approach based on a powerful genetic algorithm. We then apply a Bayesian classifier, which has demonstrated a high efficiency in text classification. The main steps of the proposed method is as follows: first, an initial feature vector is chosen, then it is optimized by multiplying the vector in a matrix called the transformation matrix made by the genetic algorithm, and finally, a set of k feature vectors is generated. An ensemble classification approach composed of k Bayesian classifiers is applied to the feature vectors, and the ultimate class label is determined by voting among ensemble members. The proposed method is implemented on two datasets PU1 and PU2. The results show that the classification accuracy of the proposed method with k=7 reaches 87.86 and 87.91 in PU1 and PU2, rspectively. The results also indicate the efficiency of the proposed method compared to naïve Bayes and two well-known classifiers SVM and KNN.

Due to wide application of conical sandwich shells in the modern industries, it is nessesary to investigate the mechanical behavior of theses structures. In this paper, for the first time, by considering the flexibility of the core in the high order theory of sandwich shells, the the buckling behavior of the truncated conical sandwich shells which include a temperature dependent porous FG core and two temperature dependent homogeneous face sheets are investigated in various thermal conditions. The power law rule which modified by considering the two types of porosity volume fractions are applied to model the gradually variation of functionally graded materials. By applying the principle of minimum potential energy, considering the in-plane stresses in the core and faces, and nonlinear von-karman strains for both mechanical and thermal stresses, the governing equations are obtained under the axial in-plane compressive loads. A Galerkin procedure are used to solve the equations in a simply supported boundary condition. Uniform, linear and nonlinear temperature distributions are used to model the effect of the temperature changing in the sandwich shell. To verify the results of these work, they are compared with FEM results obtained by Abaqus software and for special cases with the results in literature. Critical load variations are surveyed versus the temperature changing, geometrical effects, porosities, and some others in the numerical examples.

Due to wide application of conical sandwich shells in the advanced industries, it is nessesary to investigate the mechanical behavior of theses structures. In this paper, for the first time, by considering the flexibility of the core in the high order sandwich shells theory, the the vibration behavior of the truncated conical sandwich shells which include a porous FG core and two homogeneous face sheets are investigated in various thermal conditions. Temperature dependent materials are used in the core and the facesheets. The power law rule which modified by considering the two types of porosity volume fractions are applied to model the gradually variation of FGMs. By applying Hamilton's energy principle, considering the in-plane stresses in the core and the faces, and nonlinear von-karman strains for both mechanical and thermal stresses, the governing equations of motion are obtained. A Galerkin procedure are used to solve the equations in a simply supported boundary condition. Uniform, linear and nonlinear temperature distributions are used to model the effect of the temperature changing in the sandwich shell. To verify the results of these work, they are compared with FEM results obtained by Abaqus software and for special cases with the results in literature. Eigen frequencies variations are surveyed versus the temperature changing, geometrical effects, porosities, and some others in the numerical examples.

The aim of this study is to investigate the impact of organizational and functional factors on the development of ready officers and naval commissioners of the Strategic Naval Forces of the Islamic Republic of Iran. Accordingly, the present study is applied in terms of purpose and descriptive in terms of data collection. The statistical population of the qualitative part of the research included experts, university professors and faculty members, commanders and senior managers of the Armed Forces and other maritime organizations. He had a record of service and a minimum of a bachelor's degree or higher and 1535 people. The executive method of this research is qualitative and quantitative mixing. First, the qualitative data method of the foundation was used using coding method, indicators and variables were determined and then the questionnaire was quantitatively used to test the research hypotheses. It has been confirmed by convergent and divergent narrative method and its reliability through Cronbach's alpha. Data analysis was performed using structural equation method using Smart PLS software. According to the analysis, the results of this study have shown that the factors of organizational and functional factors affect the development of ready officers and naval commissioners of the Strategic Naval Forces of the Islamic Republic of Iran. The development of ready-made officers and commissioners of the Navy's Strategic Naval Commission has been presented.

It is usual for a production environment to encounter uncertainty and variable data that causes generating random parameters. Failure to pay attention to these changes will make the scheduling not adequately match the reality and cause many losses in production environments. Considering the importance of the issue, in this article we use the Robust Optimization Approach to deal with uncertainty in the aggregate production planning parameters. In this paper, in a robust model, it is assumed that the uncertainty of non-deterministic parameters is continuous and a completely new and innovative approach is proposed for Robust Optimization for risk-averse managers and then, an optimization strategy is used to examine the uncertainty. In order to investigate the model results, examples have been made in small and large sizes and the problem has been solved and analyzed using the GAMS software and Lagrange relaxation method. The results of the implementation of the proposed robust models in this paper, compared to the basic model, show that the results have more stability against uncertainty and this causes a significant reduction in risk.

Understanding movies content and their genre, is always a complex and open issue to researchers. Several activities have been carried out by researchers to find out movies content. Most of the activities conducted in this area have been using audio processing or video processing. Recently a group of researchers have proposed the idea of using movies subtitle texts to understand movies content and considered text processing faster and easier than audio and image processing. In this paper a semantic orientation based method is presented for genre classification in multi-label data of movies subtitles. To do this, a feature extraction method is presented to extract unique features of each genre. Then a method is presented, in which with calculation of a subtitles semantic orientation to each genre, subtitles genres are predicted. Finally, using association rule mining methods, the relationship between genres in raw data is discovered and using these rules, predicted genres have been modified. Obtained results indicate significant improvement of proposed method compared to previous methods.