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These days biosensors have worthy applications in different fields such as biomedicine, disease diagnosis, treatment monitoring, various aspects of the environment, food control, drug production, and assorted sides of medical science. Recently, different types of biosensors such as enzyme biosensors, immune, tissue, DNA, and thermal biosensors have been studied precisely by some research groups. These biosensors have many advantages such as simplicity in implementation, very high sensitivity, automatic performance, intrinsic and natural small size. Another valuable benefit of biosensors is that their high-affinity paring with biomolecules allows sensitive (high-sensitivity) and selective detection from a wide range of analytes. Artificial intelligence (AI) due to its high potency, if combined with biotechnology, like biosensors, can be effective in accurate prediction, diagnosis and treatment of some diseases, including cancer. Today, Machine learning (ML) as one of the branches of AI has become a beneficial tool in analyzing and categorizing obtained data from biosensors for bioanalysis. Using ML algorithms automates the complicated processes of extraction, processing, and assaying data achieved from biosensors. This article is a review for introducing and survey of various biosensors, their applications, and ways to apply them, focusing on cancer and Covid19 which are important diseases in the world obtained from previous studies, as a summary and providing information for researchers which working in this field.
 

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Aims: The project's primary aim is to better understand the skills and knowledge necessary for success in the architecture field, as well as to evaluate the intelligence and aptitude of those intending to pursue a career in architecture, which will ultimately benefit job counseling.

methods: An investigation into Gardner's multiple intelligences theory was conducted by employing the Delphi method and a qualitative research questionnaire, with ten experts being asked questions. By using a methodical quantitative questionnaire, this research approach provides insight into the ratio of intelligence and its sections that are either natural or acquired and determines the influence of nature and nurture on architecture tasks by mathematics calculations.

Findings: The achievement of architectural skills is influenced by the nature and nurture components in approximately equal amounts, which in the contract principles are at least 5/1, consisting of 25/49 for nurture and 75/50 for nature. Likewise, a maximum of 7/16 percent goes to management plans and interaction with clients, 75/41 to nurture, and 25/58 to nature.

Conclusion: Concerning the effects of nature and nurture, the pursuit of architecture at the academic level should be done with one's genes and characteristics inherent in a person. On the other hand, the importance of nurturing in training highly qualified individuals is greater than its counterpart and therefore should be adequately maintained.
 

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Positive emotions are regarded as vital constructs in L2 learning. With the advent of positive psychology in SLA, the link between L2 grit and Foreign Language Enjoyment (FLE), as an achievement emotion, has drawn the attention of numerous scholars. However, despite these investigations, what remains is to see to what extent this link can be accounted for by the control and value appraisals of classroom activities and tasks. In other words, what is the matter of debate is how L2 learners’ perseverance and consistency of interest during the performance of language tasks and activities can predict their enjoyment when they are in control of, or out of control of these activities and tasks. Given this gap, this conceptual study attempts to argue the need for the investigation of L2 grit and FLE in light of control-value theory (CVT) and its methodological orientation. These arguments can pave the way for future research on this link via the CVT framework and provide pedagogical and methodological implications for investigators, learners, teachers, teacher educators, educational policy-makers, and advisors to raise their awareness of how the proximal determinants of enjoyment in learning a foreign language can be realized in terms of their perseverance of effort and consistency of interest in achievement activities.

 

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One of the important abilities to understand emotions of others and oneself is emotional intelligence (EI). In addition, studies in the realm of psycholinguistics have indicated that EI is a highly relevant variable for managing negative emotions such as foreign language classroom anxiety (FLCA). In this study, we investigated the longitudinal association between adult English as a foreign language (EFL) learners’ trait emotional intelligence (TEI) and foreign language anxiety (FLCA). To this end, we conducted Latent Growth Curve Modeling (LGCM) to analyze data collected from 309 Iranian L2 learners in three measurement occasions during a year of learning English in private institutes. The results showed that while L2 learners' TEI increased, their level of FLCA decreased during the year. Also, at the beginning of the study, the significant negative correlation between L2 learners' TEI and FLCA was low but, during the semester, the negative correlation between the two variables turned out to be high.
1. Introduction
One of the important abilities to understand the emotions of others and oneself is emotional intelligence (EI). EI can be conceptualized based on different theoretical approaches (see  Petrides, 2010; Hughes & Evans, 2018). Among these, the trait approach (Petrides et al., 2016) defines EI as an individual’s self-rating of his/her emotional ability. Trait emotional intelligence (TEI) has been categorized into four main subdomains: well-being, emotionality, self-control, and sociability (Petrides& Furnham, 2000, 2001). Despite the fact that research has indicated that TEI is a pivotal antecedent of learning a new language, the basic processes that underpin its effects are yet to be explored (Pekaar et al., 2020). Regarding this, Pekaar et al. (2020) have inspired researchers to take advantage of multilevel designs to capture individual differences in TEI and its fluctuations across time. To do so, the following research questions were raised:

1.  To what extent and in what direction do TEI's subdomains change over 12 months?
2.  To what extent are changes in well-being, emotionality, self-control, and sociability related over 12 months?
3.  How does global factor growth curve of TEI influence language learners’ emotion perception?
4.  To what extend do the growth parameters the subdomains of TEI predict language learners’ emotion perception?

2. Literature Review
A number EI models have been introduced in the field of psychology. Two more dominant models are ability EI model (Salovey & Mayer, 1990) and the TEI model (Petrides, 2017). Using a deductive approach, Salovey and Mayer (1990) introduced the model of ability EI with four branches: (1) the ability to detect emotions precisely, (2) the ability to apply emotions to further thought, (3) the ability to comprehend emotions, and (4) the ability to manage emotions. On the other hand, Petrides and Furnham (2000) proposed TEI model, which captures individuals' self-perceived or subjective emotional abilities, which  is estimated with self-rated questionnaire. TEI entails fifteen facets categorized into four main subdomains or dimensions: well-being, emotionality, self-control, and sociability (Petrides & Furnham, 2003).
With this in mind, considering the advantages of parallel process model (PPM) and factor of curve model (FCM) in this study, we aimed to investigate both the primary growth factors of TEI and their covariations in order to explore the co-development of the different subdomains of TEI and the contribution of each subdomain to the global factor of TEI. Our model was extended by including emotion perception (EP) as a distal outcome.

3. Methodology
In the current study, a convenience sampling approach was applied according to our access to language learners in the private institutes of four cities in Iran. The sampling setting included learners who were acquiring English as a foreign language in these institutes. The data were collected from 28 classes with a range of 8 to 14 students per class. We gathered data from 309 (217 females, 92 males). The data collection occurred from February 2020 to February 2021. The proficiency level of this sample ranged between lower-intermediate to upper-intermediate.
This study aimed to investigate both the primary growth factors of TEI and their covariations in an effort to clarify the co-development of the subdomains of TEI.  Given these, the following four hypotheses were developed for the present study.
The following instruments were used to collect the data in this study:
The Trait Emotional Intelligence Questionnaire–Short Form (TEIQue-SF) (Petrides, 2009). Firstly, the participants filled out the Persian translated short version of the TEI Questionnaire (Petrides, 2009), with 30 items. The TEI questionnaire also allowed us to estimate scores on the four TEI subdomains: well-being, emotionality, self-control, and sociability. Emotion Perception Task which (EPT) consists of 6 short audiovisual clips representing examples of four negative emotions (anger, fear, sadness, and disgust) and two positive emotions (surprise and happiness). We asked the participants to complete several sets of questions. The first set included questions on the participants’ semibiographical background, their age, their gender, and their language learning history, such as the languages known, and their self-perceived proficiency of these languages Additionally, the Persian version of TEIQue-SF (Petrides, 2009) was given to the participants in three measurement occasions with six-month intervals. The software program used to analyze the data in this study was Mplus 8.4 with a robust maximum likelihood estimator (MLR). The analysis followed the incremental steps for FCM recommended by Wickrama, et al. (2016).

4. Results
Corresponding to the incremental steps of conducting an FCM procedure, based on which each research question was developed, the results of the analysis are presented here in four steps. With respect to the longitudinal correlation patterns among repeated measures of each subdomain, correlation matrix revealed that the correlation coefficients between the two adjacent occasions for each subdomain were higher than the correlations between non-adjacent occasions. For Parallel process growth curve model (PPM), the intercept and slope variance of each model is also correlated. The model results showed that all between-subdomain auto-correlated errors were statistically significant and were within the acceptable bounds. In Estimating an unconditional Factor-of-Curves Model (FCM) and achieving empirical proof of the successful estimation of second-order growth factors for an FCM of TEI subdomains, the covariances (or correlations) among primary growth factors of these subdomains, as the indicators of the second-order growth curve, were supposed to be checked first. Concerning the association of the initial level of each subdomain with its slope, the PPM results showed moderately high correlations between intercept and slope growth factors within these subdomains.

5. Discussion
The current study investigated the trajectories of global factor of TEI as well as parallel development of the TEI's subdomains (e.g., well-being, emotionality, self-control, and sociability) over one year in the context of a foreign language classroom using PPM and FCM. With regard to the first research question addressing the direction and amount of change in four subdomains of TEI, the PPM results showed a statically significant increase over one year in these subdomains. We can conjecture that both the situational cues and the contextual factors during a foreign language course might have contributed to the increase in the students’ subdomains of TEI. As for the second research question, the association of the initial level of each subdomain of TEI with its slope, the PPM results revealed moderately high and positive associations between intercept and slope growth factors within the subdomains of TEI. The FCM results, with regard to the third research question, showed that the factor loadings for four primary growth factors on the global factors were high and statistically significant, which indicates that each of the primary growth factors contributed significantly to defining the global factor of TEI. To answer the fourth research question, regarding the direct effects of the global growth factors on the distal outcome after controlling for the effects of the primary growth factors, the results indicated that intercept and slope of the global TEI were associated with EP.
The overall findings of this research showed that the FCM procedure was a privileged and comprehensive analytical approach for the exploration of the co-development of L2 learners’ well-being, self-control, emotionality, and sociability subdomains of TEI in the dynamic context of a language class.

6. Conclusion
There is a shift in SLA from traditional one-time survey methods to more dynamic process-based approaches that allow for a distinction in causes, mechanisms, and consequences. Our model, as an inspiring and comprehensive model, intended to clarify the dynamics of TEI. It goes beyond them, however, in four important respects. First, the multilevel format of our model allows examining individual differences in TEI (which is characteristic of the TEI literature) and within-person emotion processes (which is characteristic of the dynamic perspective literature (Pekaar, et. al., 2020)), in tandem. Second, TEI- FCM also includes combinations of TEI dimensions. Research has suggested that not all individuals use all their TEI dimensions to the same extent, but that a unique mixture of TEI dimensions better resembles reality (Dave, et al., 2021; Pekaar, et al., 2020). Third, our model focuses on the role of time, which is an often-disregarded factor in psycholinguistics research (Hiver & Al-Hoorie, 2019). Incorporating time allows the investigation of the interplay of different subdomains of TEI and their amount of contribution to the global construct of TEI. Finally, our model also incorporated EI as a distal outcome of TEI. The inclusion of EP in investigating the developmental process of TEI over time could shed light on the interplay between different TEI dimensions when individuals are processing the emotions of others. This framework can serve as a starting point for the empirical investigation of more detailed processes that may play a role in the enactment of TEI.

 

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After adventing of computers in the Middle of the 20th century and extensive production of miraculous literature and artistic works by it without any direct intervention of human being, the ways of legal protection of these kinds of works have been challenged. Questions like authorship, duration of protection and moral right in the computer-generated-works (CGW) should be decided in copyright laws. Although some statute laws such as England Copyright, Designs and Patents Act 1988 have specified obviously some legal aspects of these kinds of works, but it's position has yet to be determined in many countries like USA, France and Germany. Current Iran’s copyright law which is under the influence of France intellectual property law, also has not yet been proceeding to this issue. However, considering the expeditious increasing of CGW productions and its economic value, it seems that nowadays the suitable reaction of legislatures and protection of them at least under »sui generis right« are necessary.

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Aims: AI an emerging phenomenon revolutionized the the interior architecture design process, especially in the post-corona era, when the concept of "healthy building" has become more important. The research aims to show the privileged role of AI in creating interaction between "interior architecture" and the concept of healthy building.

Methods: The methodology of the research is based on meta-analysis based on the theory of master architecture. Meta-analysis or meta-analysis, emphasizing the statistical combination of the results of several studies, covers a large part of the analytical literature in the field of the role of artificial intelligence in interior architecture. Based on the selected research approach, in data extraction, combined methods of machine calculations such as hybrid meta-simulation, clustering, prospective interpretation of variables and extraction of effect size, variance and regression have been used.

Findings: Numerical results and quantitative findings in the review of tools developed in the field of interior architecture show that the most developed tools are related to the initial stages of design, followed by the tools related to the operation stage, and then the related tools. to the final stages of architectural design.

Conclusion: The qualitative results of the research show that the set of tools developed in the field of interior architecture do not have high analytical accuracy, for this reason, it is more logical to use them in the idea generation stage. Also, the tools developed in the second part are related to the field of building chemistry, residents' health, biocomputing, etc.

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"English Language and Literature" courses are essential components of university education. They provide a significant avenue for understanding the politics, economics, and customs of English-speaking countries. These courses facilitate a mastery of English grammar, which in turn enhances students' comprehension of spoken and written English content. However, traditional modes of instruction in English Language and Literature often lack engagement and interactivity, thereby limiting the effectiveness of learning in this field. In order to boost learners' interest and efficiency in studying English, it is imperative to shift away from conventional teaching approaches. With the rapid advancement of artificial intelligence in various domains, its integration with English Language and Literature education can yield intelligent learning experiences. This study employs a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to reform the teaching model in English Language and Literature. The results indicate that CNN and GRU methodologies offer substantial support in realizing intelligent approaches to teaching this field. These methods exhibit a high degree of similarity and accuracy in predicting linguistic features in English Language and Literature. They excel in terms of predictive and scatter error distribution, showcasing superior performance.


 

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Many studies show that the long-standing perception of listening as a passive skill in language learning has led to traditional auditory teaching methods leading learners to passive input receivers, while learners should be taught in a way that is passive. They should be able to control and strengthen their auditory function. Metacognitive intelligence strategies enable learners to plan, monitor, problem-solve, and evaluate, making them more active listeners. For this purpose, a class (26 students) of auditory secondary learners of the Shahed and Taallom Educational Collection in an educational center was selected as a sample and after removing outliers, using a quasi-experimental pre-test method - Intervention-post-test, the performance of the learners in the auditory parallel tests, before and after the educational intervention, was examined by SPSS software and paired t-test. The results indicate that the learners have increased their learning skills in the post-exam stage with the awareness of metacognitive intelligence, and self-regulation has increased their motivation and also their learning success. In the light of these findings, concepts and suggestions for further studies were presented.

1. Introduction
Metacognition has been accepted as the main factor in educational progress, especially in language learning. Considering the positive relationship between metacognitive intelligence and improving listening skills, so far no research has been done on the effect of metacognitive intelligence on the listening skills of Iranian learners of Arabic as a second language. Therefore, an independent research based on the effect of metacognitive listening strategy training on improving listening comprehension and metacognitive awareness of Arabic language learners seems necessary. The purpose of the current research is to investigate the effect of metacognitive intelligence awareness-raising on listening skills among secondary level students of the "Shahed and Taallom" educational collection.

Research Questions
By considering the problems stated above, the following questions are raised:
1. What is the relationship between intrinsic cognitive intelligence and Arabic listening skills?
2. Does metacognitive intelligence awareness have an impact on improving listening skills of Arabic language learners?

2. Literature Review
Listening is a complex skill and since it plays an important role in people's daily life, it needs attention and precision. Bervan (2001) believes that learners in the classroom should focus more on listening than speaking. Emphasizing listening skills, he admits that his listening ability is usually more than his verbal ability, and this is the reason why in recent years of language education, he has paid more attention to listening comprehension(Sotudeh & Taqipoor, 2010)
By metacognition theory, we mean the theory of metacognition, the theory of cognition. Metacognitive theories are a subset of theories of mind. Theories of mind include, but are not limited to, theories of cognition. Theories of mind deal with mental phenomena such as emotions, personality, etc. (Astington, 1993). Seif (2012) believes that metacognition is the awareness of a person in relation to how he learns; Therefore, metacognitive theories are those theories of the mind that focus on the cognitive aspects of the mind. In fact, metacognitive theories harmonize the beliefs or hypotheses that allow people to predict, control and explain their own knowledge, the knowledge of others, or knowledge in general (Montgomery, 1992). Bailer and Snowman (1993) believe that metacognition is a person's knowledge about his own cognitive processes and the quality of their useful and constructive use to achieve learning goals. 

3. Methodology
 The research method used in this article is quantitative. In the first step, the research is conducted in a quasi-experimental manner, In order to find out the correlation between subjective intelligence questionnaire and listening skills, the students were asked to answer the questionnaire and participated in a listening test which was designed from the content of the intermediate level book "Shahed and Taallom". In the self-intelligence questionnaire, we considered a score between 0-60 , which were tested in two indicators (do: 2 points and do not: 0). The results of the questionnaire and the listening test were analyzed in the paired t-test, which proved the positive correlation between subjective intelligence and listening skill.

4. Results
The present study first discovered the degree of correlation between inherent cognitive intelligence and listening skills and subjected them to educational intervention. The second step also measured the effectiveness of metacognitive intelligence educational interventions in strengthening learners' listening skills compared to before the intervention. The results showed that targeted training of metacognitive intelligence had a significant effect on increasing learning and improving learners' performance in listening tests. The results of the present research showed that the strategy of comprehensive interaction with peers as one of the strategies to raise awareness of metacognitive intelligence can greatly help improve the listening skills of learners. 
The present research showed that teachers make different aspects of learning visible to learners by raising awareness of metacognitive intelligence, thus turning them from passive recipients into active listeners. The results of Hughes and Schumacher's (1991) study are also consistent with this finding, as they found that students who used the "self-test" strategy increased their scores on class tests from of 57 to 71 percent.

 

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Blood pressure monitoring is a vital component of maintaining overall health. High blood pressure values, as a risk factor, can lead to heart attacks, strokes, and heart and kidney failures. Similarly, low blood pressure values can also be dangerous, causing dizziness, weakness, fainting, and impaired oxygen delivery to organs, resulting in brain and heart damage. Consequently, continuous monitoring of blood pressure levels in high-risk individuals is very important. A Holter blood pressure monitoring device is prescribed for many patients due to its ability to provide long-term and valuable blood pressure data. The pursuit of software techniques and the development of cuffless blood pressure measurement devices, while ensuring patient comfort and convenience, are among the significant challenges that researchers are focusing on. In this study, a deep learning framework based on the UNet network is proposed for continuous blood pressure estimation from photoplethysmography signals. The proposed model was evaluated on the UCI database, involving 942 patients under intensive care, and achieved mean absolute errors of 8.88, 4.43, and 3.32, with standard deviations of 11.01, 6.18, and 4.15, respectively, for systolic, diastolic, and mean arterial blood pressure values. According to the international BHS standard, the proposed method meets grade A criteria for diastolic and mean blood pressure estimations and grade C for systolic blood pressure estimation. The results of this study demonstrate that the suggested deep learning framework has the necessary potential for blood pressure estimation from PPG signals in real-world applications.
 

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In turbulent environment, power and success of organizations depend on the intellectual capita; abilities and the main challenges of these organizations are on that how to establishe new generation of intelligent organizations for knowledge edge. The problem this research wants to solve is that how an organization that its main asset is intellectual capital can obtain organizational intelligence. The conceptual model of this research has been designed based on open system mode,l and the knowledge strategies, knowledge communities, adhocracy and intellectual capital have the enabler role for intelligence processes. Statistical population of this research includes the faculty members of the research centers of the Ministry of Science, Research and Technology. Totally, 278 persons have been selected based on classified sampling. The results showed that adhocracy with %86 of coefficient has the highest effect on structural intelligence processes. Also knowledge strategies and intellectual capitals correspondingly with %67 and %64 of coefficient are in the second and third rankings. Also the results showed that adhocracy with %64, intellectual capital and knowledge strategies with %59 of coefficient correspondingly have the highest effect on human intelligence processes.      

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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.