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When groundwater is contaminated, removal of contaminants and the restoration of quality may be slow and sometimes, impractical. It can be harmful for human health, the ecosystem and can result in water shortage. Thus, simulation of contaminant transport can be an important task in hydro-environmental studies and consequently, it is necessary to develop the robust models which can determine the temporal forecast of pollution. For temporal modeling groundwater level and contaminant concentration (GLCC), several computational methods, namely, finite difference method, finite volume method, finite element method and boundary element method have been applied for numerical solution of governing physical-based partial differential equation (PDE). Although the physical-based numerical technique are widely used for temporal and/or spatial modeling of systems, some real-world conditions such as anisotropy and heterogeneity can have meaningful impacts on GLCC and restrict the usefulness of such methods. As a result, these method may be replaced by other techniques. In situation where there is no sufficient field data and output accuracy is preferred over perception of phenomena, a data-driven or black box model can be proper subsided. The uncertainty and complexity of the groundwater process have caused data-driven models such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are widely used by hydrogeologists. Several studies have been performed to examine the susceptibility of artificial intelligence (AI) models for GFCT modeling. Wavelet transform coherence (WTC) is a technique for examination the localized correlation coefficient and their phase lag between non-stationary time series as a function of both time-frequency spaces. Furthermore, the cross-wavelet power is indicated as high common power of two time series and is found in time-frequency space by cross wavelet transform (XWT). Specifically, XWT investigates the regions in time-frequency space with large common power about a consistent phase relationship, and accordingly suggestion for causality between and time series. On the other hand, the WTC explores the regions in time-frequency apace in which and time series co-vary, but not essentially with high power. So, while analyzing two time series for evaluating both causality and local co-variance, the WTC is more suitable. In order to examine the applicability of the proposed AI-meshless model in real world conditions, the contaminant transport problem in Miandoab plain located in the northwest of Iran was considered as the case study. Miandoab plain, is located in a delta region of Zarrineh and Simineh Rivers. Urmia Lake in north of Miandoab plain, the largest salt-water lake in the Middle East, has been experienced climate change in early 2 decades. The wavelet transform coherence used in this study can be considered as a novel method for spatial clustering of piezometers, for detecting the interaction of aquifers in the plain and relationship between water level of the lake and GLs and CCs of piezometers located near the lake shore witch can present helpful information in GL and CC modeling. The results showed that the efficiency of ANFIS model was more than ANN model up to 30%. Reliability of ANFIS model is more than ANN model in both calibration and verification stages duo to the efficiency of fuzzy concept to overcome the uncertainties of the phenomenon.

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Reliability-based design optimization (RBDO) has been used for optimizing engineering systems in presence of uncertainties in design variables, system parameters or both of them. RBDO involves reliability analysis, which requires a large amount of computational effort, especially in real-world application. To moderate this issue, a novel and efficient Surrogate-Assisted RBDO approach is proposed in this article. The computational intelligence and decomposition based RBDO procedures are combined to develop a fast RBDO method. This novel method is based on the artificial neural networks as a surrogate model and Sequential Optimization and Reliability Assessment (SORA) method as RBDO method. In SORA, the problem is decoupled into sequential deterministic optimization and reliability assessment. In order to improve the computational efficiency and extend the application of the original SORA method, an Augmented SORA (ASORA) method is proposed in this article. In developed method, A criterion is used for identification of inactive probabilistic constraints and refrain the satisfied constraints from reliability assessment to decrease computational costs associated with probabilistic constraints. Further, the variations of shifted vectors obtained for satisfied constraints are controlled to be exactly equal to zero for the next RBDO iteration. Several mathematical examples with different levels of complexity and a practical engineering example are solved and results are discussed to demonstrate efficiency and accuracy of the proposed methods.

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All of the financial institutions for gaining the best profit of their investment are always looking for the best investors, consulters, and borrowers. Besides, different sciences attempt to represent accurate methods for the separation of the customers. For that reason, sciences such as psychology, management sciences, mathematics, financial and etc…seek to achieve this aim. The subject that comes into consideration in this paper is the necessity of using the new methods in data mining in mixture with artificial intelligence techniques in order to deal with the sophisticated issue and answer to this question that do the usage of combined approach predict the customer rating well? If we want this process occurs, another dimension must not be forgotten that is the select measurement criteria and in this regard, the researcher has used judging journalist and non-parametric analysis in order to rank criteria thatfinally, select the number of indicatorsin order to implement the hybrid model will lead the researcher to answer this question: do the journalist’s ideas selection criteria result in a good prediction of the credit status of customers? The three indicators “age”, “previous relationship with the bank”, and “credit”to implement a fuzzy neural hybrid model are chosen. The model has been implemented in three layers and results suggest that 89.67% times the system can accurately estimate the proportion of customers provide ratings.To optimize the fuzzy neural network, the ant colony algorithm was used which results in improved performance of the model was 90.5%.

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Knowledge management is an approach enabling the pioneer organizations have benefited from it in an appropriate manner. Knowledge management as a system of two aspects of the organization and structure that success and its intelligence on the basis of its structure coupled with other organizational systems and subsystems are defined. The focus of this paper is to identify coupling and success factors of knowledge management system based on a coupling with other organization’s subsystems. In this research, the first, coupling structure components of knowledge management system identified then with factor analysis the significance of each of the subsystems of the knowledge management system was evaluated, then by using statistical techniques the importance of subsystems in coupling prioritized. In this study, the success of a knowledge management system in organization with coupling structure approach is considered. This approach, causing greater compatibility knowledge management system with other subsystems base of priority of coupling and intelligent knowledge management system in the organization provides.

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Nowadays organizations are involved with different crises which impair their existence and survival through various directions. In such conditions, organs will gain success with enough recognition about current crises, predict and prevent them, provide proper conditions as well as reducing and controlling them. This study was conducted with the purpose of knowing effects of organizational intelligence on managementinstitute crises and the base of description correlation.
Statistical study community includes managers and personnel working in command center and operational part of Esfahan Power Choler Factory with their minimum degree have been expertise in 400 people. Model volume using Kukeran formula in the number of 178 persons has been accounted and method random classificatory sampling has been suitable to statistical community volume. Device for gathering data has proven Albrkht’s standard 49 questionnaires (2010) for testing organizational intelligence and 21 questionnaires for testing crisis management which was justifiable in two contents, structure and final methods of questions with use of Alfay krunbakh method for organizational intelligence (0/942) and crisis management (0/908) has been confirmed. Data analysis with use of Amos 21 software and in modeling method of structural equivalences has been done. The results of revision of study hypothesis showed that organizational intelligence with index of (0/945) has sever direct effect on organizational crisis control which has been created before, during and after crisis. This is when among 7-factors components organizational model “common destiny” component with (0.987) effect has had most role in control of institute crisis in comparison with other components.

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Two-way slabs are one of the common structural systems. The benefits of such systems have led to extensive use of them in building construction. However, these systems are prone to pushing shear problem which causes sudden failure. There are lots of equations to predict punching shear of slabs. The main proportion of the existing equations are based on statistical results from previous experimental studies. However, these equations are approximate and have large errors. Therefore, more exact and reliable equations that can estimate punching shear capacity are desirable. The aim of this study is to propose an applicable method to predict punching shear in thin and thick slabs using artificial intelligence. For this reason Genetic Programming (GP) and Biogeography-Based Programming (BBP) are employed to find a relationship between punching shear and the corresponding effective parameters. GP that is inspired by natural genetic process, searches for an optimum population among the various probable ones. Two main operations of GP are crossover and mutation which make it possible to form new generations with better finesses. Unlike the GP, BBP is a Biogeography-Based Optimization (BBO) technique which is inspired by the geographical distribution in an ecosystem. BBP employs principles of biogeography to create computer programs. First, 267 experimental data is collected from the past studies. Next, using the aforementioned algorithms, a relationship to predict punching shear is proposed. To evaluate the error of prediction, several error functions including RMSE, MAE, MAPE, R, and OBJ are utilized. Matlab software is used to build the models of prediction. 10 different models are built and the one with the minimum error is selected. Based on the results, GP3 and BBP9 models could reach the best fitness. These models contain 3 sub-trees that use operators of plus, minus, multiplication, division, ln, sin, power 2, power 5 power 0.5, power 0.33, power 0.2, and power 0.25. Overall, the final tree includes several variables and integers, the variables are inputs of column dimension, effective depth, rebar ratio, compressive strength of concrete, and yielding strength of the rebars, and the output of punching shear capacity. The results of modeling are compared with recommended values of the ACI318 and EC2 codes. Comparison shows that code equations are scattered and therefore are not very reliable. Maximum error for both model and code equations occurs when the yielding strength of the rebars is low. Minimum estimation is related to GP and ACI codes with the ratio of 0.485 and 0.52, respectively which is due to very low thickness of the slab (41 to 55 mm). The maximum estimated shear belongs to ACI code in which the estimated value is two times the real one. Also, standard deviation of ACI values is about two times the others. Among the code equations, EC2 values yield more accurate results. However, GP and BBP models give much less mean error. Also, standard deviation of these methods is less than code values. In total, results show that the methods based on artificial intelligence are able to estimate pushing shear with around 2% error, compared to existing code equations which give 14-28% error.

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The effective role of managers and leaders’ political intelligence in organization fundamental changes are undeniable. Political intelligence is the intelligence with which the leaders can make influence over appropriate changes in organization. Political intelligence is one of the social skills that a transformational leader must know well and use it for more Effectiveness in organizational change. Each organization Culture has an important role in acceptance of change by employees and staff readiness to change. The leaders make culture through a process of social influence and with impact on employees, guiding them towards the realization of the noble objectives of the organization. In this paper, the impact of political intelligence on organizational change in government agencies of Guilan as well as moderating role of organization a culture, regarding political intelligence and organizational changes is examined. The research data has been collected from the government agencies of Guilan. This research is practical and its method is descriptive and a survey which collect information through questionnaires. For data analysis, Structural equation modeling and algorithms of partial least squares are used. Results show that the political intelligence influence on organizational change and organizational culture moderate the relationship between political intelligence and organizational change. 

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Policy, research, and extension support are among the various drivers that helped India to become self-sufficient in food production. In order to contribute better towards agricultural development, the extension and advisory agents need new capacities to confront the present challenges in agriculture. The present study was conducted in four Indian Council of Agricultural Research (ICAR) zones of Krishi Vigyan Kendra’s (KVK) selected by simple random sampling without replacement to map the present level of competencies of the extensionists. Twenty KVK from each zone were selected randomly and three extensionists from each KVK were selected by using simple random sampling technique. The total sample size was 240. Mapping of the competency was dealt in two parts viz Emotional Intelligence (EI) and Professional Competency (PC). Correspondence Analysis (CA) technique was used to map the professional competencies of the extensionists. The first part of mapping dealing with EI showed that the respondents had average level of EI. The analysis of the competencies showed that most of the competency statements for the extension professionals were clustered around the center of the biplot showing medium level of PC. Hence, this provides an opportunity to the policy makers to devise suitable strategies to develop these competencies of the extensionists so that they become efficient and effective in their job.

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The most important tools to perform tasks effectively in environments with heterogeneous and diverse workforce are cultural intelligence and work adjustment. The cultural intelligent helps to make relationship with people that have different culture in appropriately mood. According to importance of this issue, this study is to investigate the influence of cultural psychological capital, cultural intelligence and organizational trust on work adjustment. Research method is based on practical purpose and descriptive-correlation. The Statistical population is Hajj and Ziarat Organization in Tehran. After simple random sampling, 175 valid questionnaires were collected. The questionnaire are all in Likert range and after assessed and certified by experts have been distributed, collected and analyzed by SPSS22 and LISREL8.8 software. By calculating Cronbach's alpha coefficient of reliability and validity through first-order and second-order confirmatory factor analysis was performed. Research hypotheses were tested using path analysis. The results showed that psychological capital on cultural intelligence, Cultural intelligence on organizational trust have significant positive impact. Also organizational trust and work adjustment is positive and significant effect.

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In this study, we have tried to identify two different approaches for dealing with social problems and issues. The first one is causal explanation, which is retrospective, static and physical. The other one, functional analysis, is prospective, dynamic, and normative. Institutions are the main subject of economics. Human social-institutional reality has a common underlying structure and these structures are matters of status functions. We proposed a method which aims at efficacy of these functions using intelligence rather than assuming these functions as intrinsic and trying to grasp the realty from without by a rationality apparatus. The aim of this article is to show that, Dewey’s Logic provides us with this alternative functionalistic approach in a comprehensive way.
 
 

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This study is intended to examine the relationship between critical thinking disposition and English learning achievement among Iranian high school third-grade EFL learners mediated by emotional intelligence. A sample of 264 students (145 males and 119 females) was assessed for their level of critical thinking disposition and emotional intelligence. Participant's scores on their final English test were also used as the measurement of their English achievement. The results revealed a positive correlation between total critical thinking dispositions (r=.506, p<.01) and its subscales i.e., engagement(r=.33), maturity(r=47), and innovativeness (r=44.6) with English learning achievement. The results also suggested that emotional intelligence acts as a mediator of the relationship between critical thinking disposition and English learning achievement. 
 
 

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Extracting the required information from the design file is one of the main steps in the computer aided process planning. In previous methods of extracting machining features, various methods such as graph-based method, volume analysis method, logic rules method and other methods have been used. In all the previous methods, whether traditional methods or methods based on artificial intelligence, the input data to the machine feature identification system is the output information of a computer-aided design system. Converting the output information of a computer-aided design system to input data of a machining feature identification system is faced with limitations such as the variety of format and type of data arrangement, deleting some data from the design file due to geometric interference of features, slow extraction of features due to extensive information in the design file and the limitation of identifying different types of machining features by a unity feature identification system. In the present study, using artificial intelligence techniques based on deep learning, machining features are extracted directly from the two-dimensional image of a workpiece. The image may be prepared by a computer-aided design file, or it can be taken by a camera.
 

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With the development of Web 2.0 technologies and social media tools in organizations, the way of the organizationchr('39')s information systems operation has improved to gain competitive intelligence. The best sources of information in organizations is user generated content about the company, product and competitors that are shared on social media and companies can take advantage of the hidden knowledge pattern in this information by using analytical technique in big data. To this end, important goals of this study are providing social market intelligence framework based on web 2.0 Using Text-Mining on Social Media and also comparing web 2.0 social market intelligence status between Emersun and Samsung brands through competitive analysis. The method of this study is qualitative and the way of data gathering is done through literature review, interview with experts and also using text mining among 3860 customer textual data. The findings of the literature review and interviews led to the presentation of 4 dimensions of the web 0.2 based social market intelligence framework. The clustering technique was used to extract the indicators and sub-indicatores in each of the dimensions, all of which were confirmed by the experts. In order to compare the web 2.0 based social market intelligence status of brands, Emotional analysis of words was used, and the results show that the Samsung brand has attracted positive reviews from customers in most of the indicators. Finally, due to the existence of hidden relationships in the main indicators, structural-interpretive method was used to determine the relationships between them.

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The present study is aimed at determining the role of teachers' emotional intelligence components (composition class­) in predicting the aspects of creativity (writing­) among students. A descriptive-correlation method is adopted in the course of this study. The sample includes 291 students of the 10th grade as well as 10 composition teachers from the city of Qom in the academic year of 2018-2019. They were selected through multi-stage random cluster sampling and were evaluated by Torrance Test of Creativity and BarOn Emotional Intelligence Questionnaire. The results of correlation analysis and multivariate regression indicated that there is a meaningful relationship between teachers' interpersonal skills and innovation and the elaboration of creativity among students. Also, there is a meaningful relationship between teachers' stress management and flexibility, innovation and elaboration of the students. The results also indicated that teachers' interpersonal skills meaningfully predict innovation and expansion in students; their stress management meaningfully predicts flexibility and innovation in students; and their general mood meaningfully predicts the elaboration of students' creativity. The results of the present study support that the emotional intelligence components of teachers are significant to foster the aspects of students’ creativity. Therefore, holding Teacher Training Emotional Intelligence Course seems to be necessary.
 
 

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Value creation strategies from the management of tangible assets to knowledge-based strategies and intangible asset management are changing direction. In this regard, regulating an appropriate communication strategy and information amassed can affect the process of value creation. The aim of this study was to investigate the reflection of strategic communication in employee value creation with the mediating and moderating role of information capital and organizational intelligence. This study is applied based on the purpose and survey description in terms of how to collect data the statistical population of this study consists of employees of four companies affiliated the Iran Air Industries Organization, which were selected as a sample based on stratified-random sampling and using Cochranchr('39')s formula, 204 people. Strategic communication, both directly and indirectly, with the mediation of information capital, has a positive and significant effect on employee value creation. Organizational intelligence as a moderating variable also modulates the relationship between strategic communication and value creation. The results of the research in the field of value creation will be innovative and will help managers and employees in understanding the importance of strategic communication and managing intangible assets as stimuli for value creation.

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With the shift of research attention from human malfunctioning to human optimal functioning in the workplace, job engagement which is regarded the opposite of job burnout has attracted researchers’ attention in organizational psychology. This exploratory study aimed to investigate the predictive role of emotional intelligence (EI) as a personal resource in determining levels of job engagement among Iranian English language teachers within job demands-resources model (JD-R). To this end, 442 English language teachers who were teaching in both public and private contexts were selected through non-probability convenience sampling and were surveyed regarding their demographic information, the perception of their levels of job engagement and emotional intelligence through a demographic questionnaire, the Utrecht Work Engagement Scale (UWES), and emotional intelligence scale. The results of hierarchical multiple regression analyses revealed that after controlling for the effect of demographic variables, there were significant positive predicting relationships between two emotional intelligence subscales of management of own emotions (MOE) and management of others’ emotions (MTE) and job engagement components of vigor, dedication, and absorption. The results imply that training teachers to improve their emotional intelligence can be a strategy to boost their job engagement. 

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Nowadays, using the artificial intelligence technologies in law-based criminal sciences has gained a significant place. In the field of substantive criminal law, topics such as the determination of criminal liability due to crimes caused by the performance of robots or self-driving cars are among the most interesting and, of course, the most controversial topics in this field. In the field of procedural criminal law, the use of this technology in the five stages of criminal proceedings has faced many discussions. The main question of this study is whether the technologies related to artificial intelligence can be applied in the process of criminal detection and prosecution or not and what are the challenges facing it in the assumption of application? The results of the current research indicate that the technologies related to artificial intelligence are playing a role in many countries today according to the requirements of different stages of criminal proceedings and taking into account the requirements of each crime. In terms of crime detection and prosecution, a variety of police tools for predicting the time and place of crime and facial recognition technologies (FRT) with the aim of facilitating police actions and moving from "reactive" police to "preventive" police in many parts of Europe and the United States United States have been developed and deployed. What causes the steps to be taken more slowly towards the expansion of the use of this technology in the field of criminal law in general and in the stage of crime detection and prosecution in particular is the existence of challenges such as violation of privacy and freedom of citizens, violation of presumption of innocence and the risk of militarization of criminal justice. The authors believe that using the artificial intelligence technology in the field of detecting and prosecuting crimes is useful and necessary in order to deal with the crime phenomenon as much as possible, but we should not be fascinated in this regard. Using this technology in the important stages of detecting and prosecuting crimes should not conflict with the general principles governing criminal proceedings as well as the rights and freedoms of individuals. In this regard, regulating and establishing special laws can reduce the upcoming concerns to some extent. This is the reason why the need to regulate artificial intelligence is widely discussed, especially in the European region. In this regard, reports and strategic guidelines have been predicted and published.

 

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The role of artificial intelligence-related technologies in legal knowledge is significant like most other scientific fields. Legal criminal sciences, and criminal justice system, have not unbenefited from the presence of this technology. In many developed countries, artificial intelligence-related systems are applied in various stages of criminal proceedings. The basic question is, "are the technologies related to artificial intelligence applicable in the process of issuing a criminal sentence? And if yes, what are the challenges in front of it?" The method of research is descriptive and analytical. It indicates that, the influence of personal feelings in judicial proceedings, lack of accuracy and speed in handling cases, application of prejudices and individual tastes, as well as conscious or unconscious biases, are among the most important reasons of applying artificial intelligence technologies at sentencing level. However, using the cloak of judgment in an autonomous is the main barrier of this path and will bring harmful challenges, such as partiality, lack of transparency, dehumanization in the decision-making process, and homogenization in judgment. On this basis, the presence of artificial intelligence as a decision-making tool, facilitator and assistant in the criminal sentencing phase is recommended. However, the use of this technology in a decision-taking and independent manner is contrary to the modern requirements of judgment and punishment doctrine. In fact, it will bring legal breaks and question the human contribution in the administration of justice.

 

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In the framework of Howard Gardner's theory of multiple intelligences, we have studied the possible impact of emphasis on multidimensional intelligence on developing learner autonomy and we asked ourselves if a syllabus designed based on the learners' multiple intelligences help develop learner autonomy more effectively. Autonomy is considered to be a key factor in the promotion of the process of learning a foreign language, through enhancing learner motivation and self-confidence. Nevertheless, it is not always the focus of teaching. We believed it possible to help develop learner autonomy more efficiently, by emphasizing on individual intelligence profiles. Thus, we aim to establish, if and how effectively, an Iranian learner’s autonomy is influenced, when reached out to, through his multiple intelligences. In this research, a descriptive and synthetic approach will be applied. After presenting the main theoretical guidelines on which our research is based, we will share results of a field study conducted in this respect, on 30 adult Iranian learners of French as a foreign language (FFL) of the lower intermediate level (B1 of the CEFRL) and analyze the data quantitatively and qualitatively.