High gain Balun-low-noise-amplifier (LNA) is proposed for tuner of digital televisions (DTVs). The proposed Balun-LNA is based on CS-CG (common-source-common-gate) structure. To improve the isolasion and frequency response, Balun-LNA has cascode transistors before load resistors. Balun-LNA uses current-bleeding circuit to increasie transconductance of CS transistor, so that current-bleeding transistor has transconductance of N-1 times larger than transconductance of cascode transistor. Thereby, transconductance and current of CS transistor are increased N times, as N-1 times of current pass to current-bleeding transistor. Therefore current of CG and CS stages stay identical. Also, Balun-LNA employs a positive feedback to satisfy input impedance matching and CG transistor has higher transconductance. By increasing transconductance of CS and CG transistors, the proposed Balun-LNA achieves to high voltage gain. Accordingly, CG and CS tansistors have symmetrical currents and loads at the differential output of the proposed Balun-LNA. Symmetrical loads cause the balanced differential outputs. This proposed Balun-LNA is designed in 90-nm CMOS technology and covers the frequency range of 40 MHz to 1GHz. This Balun-LNA achieves the voltage gain of 22.6 dB, S11 of less than -10 dB and the Minimum NF of 5 dB. This Balun-LNA operates at the nominal supply voltage of 2.2v.

The aim of this study was to explore the effect of positive intelligence training on emotional intelligence of employees. In a semi-experimental design with a pre-test post-test paradigm about 80 employees filled the emotional intelligence Tests and 30 subjects with lower score in EI were selected after checking the inclusion/exclusion criteria. Subjects were then divided randomly into two equal experimental and control groups (n=15). The subjects of the experimental group received 6 sessions of 120 minutes of group training of positive intelligence, while no intervention was administered to the control group besides simple meetings with the examiner. Participants of both groups completed again the EI Questionnaires at the end of intervention. Results of multivariate analysis of variance showed significant differences between two groups in the EI total scores and its 4 subcomponents. The positive intelligence training program could successfully improve the emotional intelligence of employees of the Securities and Exchange Organization and is then recommended for employees of this organization.

Background & Objectives: Teachers, as one of the most important assets working in the educational organization and as influential role models, play a very important role in the formation of students' personalities. One of the psychological components that plays a significant role in the quality of teacher–, student interactions and relationships is the emotional intelligence of teachers. Flow experience is another psychological component that is related to teachers' perceptions and their level of internal satisfaction with the work environment. Positive intelligence, as one of the constructs of the positive psychology approach, is defined as the maximum power to use mental abilities that help people find new psychological solutions, a positive attitude to daily hassles, and living in the moment. Positive intelligence leads to increased self–, awareness, improvement in interpersonal interactions and relationships, improvement in the thinking and behavior, and rethinking the meaning and concepts of phenomena. This study aimed to investigate the effects of positive intelligence training on the emotional intelligence and flow experience of elementary school teachers. Methods: The method of the present study is quasi–, experimental with a pretest–, posttest design and a one–, month follow–, up. The statistical population included primary school teachers in Tehran in the academic year 2020. Forty–, four elementary school teachers were selected conveniently regarding the inclusion and exclusion criteria and randomly allocated to the positive intelligence training group (n=22) and control group (n=22). The inclusion criteria were as follows: sufficient mastery of working with the Internet and how to participate in the webinar, necessary commitment and cooperation. The exclusion criteria included receiving a diagnosis of any psychiatric disorder based on a clinical interview, having a history of epileptic seizures during the last 2 years, having a medical condition that forces the person to seek immediate treatment, being absent more than 2 times in online training sessions, and being reluctant to continue participating in the study for any reason and at any time during the study. All participants were assessed at the pretest, posttest, and follow–, up using an Emotional Intelligence Questionnaire (Siberia Shirring, 1996) and Flow State Scale (Jackson & Eklund, 2002). Only the training group received eight 1–, hour training sessions online. The content of the training intervention on positive intelligence, which is based on the positive psychology approach, included components of empathy, curiosity, the meaning of life, creativity, and flow. All training sessions were held online from 1 to 2 pm due to the special circumstances of the coronavirus outbreak. Data analysis was done in SPSS version 19 using the analysis of variance with repeated measures and subsequent post hoc methods. Also, in the analysis of the demographic characteristics of the sample members, including their educational status and marital status, the Chi–, square test was used. Regarding the analysis of the age difference between the two groups, the t test of two independent groups was used. The significance level of statistical tests was considered at 0. 05. Results: Analysis of the demographic characteristics of the sample members showed no significant difference between teachers in the two groups of positive intelligence training and the control group in terms of age (p=0. 319), educational status (p=0. 339), and marital status (p=0. 265). The results of repeated measures analysis of variance showed that the main effect of time (p<0. 001), group (p<0. 001), and the interaction of time and group (p<0. 001) were also significant on emotional intelligence and flow experience. In the posttest stage, a significant difference was found between the training and control groups in the sub–, components of self–, motivation (p<0. 001), self–, awareness (p<0. 001), empathy (p<0. 001), social skills (p<0. 001) and self–, control (p=0. 011) of emotional intelligence variable and flow experience variable (p<0. 001). Also in the follow–,up phase, there was a significant difference between the two groups in the sub–, components of the emotional intelligence variable and the flow experience variable (p<0. 001). There was a significant difference between the pretest–, posttest (p<0. 05) and pretest–, follow–, up (p<0. 05) stages in the comparison of the changes in the sub–, components of the emotional intelligence variable and the flow experience variable in the training group. However, no significant difference was observed between the posttest–, follow–, up stages (p>0. 05), which indicated the stability of the changes resulting from the positive intelligence training package in the one–, month follow–, up. Conclusion: Based on the findings of this research, positive intelligence training has successfully improved the level of emotional intelligence and the flow experience in primary school teachers. Therefore, its use is suggested for all teachers and trainers.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Background: Moral virtues are important in the development of culture and respect for human rights in students. Identifying the components of moral promotion can be important, so the purpose of this study was the relationship between perfectionism, emotional intelligence and positive thinking with moral value in students. Method: The research method was descriptive and correlational. The statistical population was all students of Najafabad Azad University in the academic year of 2016-2017 who were selected by stratified random sampling of 390 people. The research instruments consisted of the Shrink emotional intelligence questionnaire, Terry-Short perfectionism, Carver and Scheier positive thinking, and the Haidt and Graham ethical values questionnaire. Research data were analyzed using correlation coefficient and regression tests. Results: The findings showed that positive perfectionism can predict the value of meditation, justice, loyalty, respect for authority and purity. Self-awareness predicts care and empathy predicts justice. Spontaneity can predict loyalty. Predicts emotional intelligence and positive thinking, loyalty. 26% of the variance of moral values ​ ​ can be explained through positive perfectionism, emotional intelligence and positive thinking. Conclusion: Positive perfectionism, emotional intelligence and positive thinking can predict the components of moral values.

Background: Since the essential of positive psychological components, as compliment of deficiency oriented approaches, has begun in recent days, we decided to take into account this new branch of psychology which scientifically considers studying forces of human, as well as because of the importance of this branch of psychology, we also tried to search the contribution of emotional intelligence and parenting styles components to predict positive psychological components.Materials and Methods: In this cross sectional study 200 psychological students of Azad university (Rudehen branch) selected using cluster sampling method. Then they were estimated by Bradbery and Grivers emotional intelligence questionnaire, Bamrind parenting styles and Rajayi et al positive psychological components questionnaire. Research data was analyzed using descriptive statistics (mean and standard deviation), inferential statistics (multiple regression and Pierson correlation coefficient) and SPSS software.Results: Among the components of emotional intelligence, the component of emotional self consciousness (b=0.464) had the greatest predictable, and reaction leadership showed no predictability in this research between parenting styles, authority parenting styles had positive significance relationship with positive psychological components. And no significant relationship was found between despot parenting styles and positive psychological components. Conclusion: Regarding the results of this research and importance of positive psychological components, it is suggested to treat the emotional intelligence from childhood and to learn it to parents and remind them the parenting way to decrease the satisfaction of individuals which leads to promotion of society mental health.

This study was carried out to investigate the relationship between mothers’ emotional intelligence, resilience, and positive thinking and preschoolers’ separation anxiety. The research design was descriptive, correlational. The statistical population of the study comprised 310 preschoolers attending child care centers in Sari. The sample size was determined to be 150 based on Krejcie and Morgan Table and the sample was drawn through random cluster sampling method. The instruments included Conner-Davidson Resilience Scale (2003), Emotional Intelligence Scale (Schutte et al., 1998), Oxford Happiness Inventory (Argyle et al., 1989), and the Preschool Anxiety Scale (PAS). The obtained data were analyzed via pearson correlation and regression analysis (enter method). The results showed that mothers’ emotional intelligence (and its different dimension such as emotion perception, managing emotions, utilizing emotions), resilience, and positive thinking were negatively associated with children’ s separation anxiety. In addition, emotional intelligence, resilience and positive thinking are good predictors of separation anxiety of preschool children. Moreover, a significant share of variation in preschoolers’ separation anxiety has been explained by emotion perception, managing emotions, resilience, and happiness. According to the final model, managing emotions, emotion perception, resilience, and positive thinking were respectively the most effective factors in reducing children’ s separation anxiety.