Background: The purpose of the present study was to assess the validity and reliability of Form A of Multidimensional Health Locus of Control scales in Iran. Health locus of control is one of the most widely measured parameters of health belief for the planning of health education programs. Methods: 496 university students participated in this study. The reliability coefficients were calculated in three different methods: test-retest, parallel forms and Cronbach alpha. In order to survey validity of the scale we used three methods including content validity, concurrent validity and construct validity. Results: We established the content validity of the Persian translation by translating (and then back-translating) each item from the English version into the Persian version. The concurrent validity of the questionnaire, as measured by Levenson's IPC scale was .57 (P < .001), .49 (P < .01) and .53 (P < .001) for IPC, respectively. Exploratory principal components analysis supported a three-factor structure that items loading adequately on each factor. Moreover, the approximate orthogonal of the dimensions were obtained through correlation analyses. In addition, the reliability results were acceptable, too. Conclusion: The results showed that the reliability and validity of Persian Form A of MHLC was acceptable and respectable and is suggested as an applicable criterion for similar studies in Iran.

The accurate detection of emotions has significant implications in healthcare, psychology, and human–computer interaction. Integrating personality information into emotion recognition can enhance its utility in various applications. The present study introduces a novel deep learning approach to emotion recognition, which utilizes electroencephalography (EEG) signals and the Big Five personality traits. The study recruited 60 participants and recorded their EEG data while they viewed unique sequence stimuli designed to effectively capture the dynamic nature of human emotions and personality traits. A pre-trained convolutional neural network (CNN) was used to extract emotion-related features from the raw EEG data. Additionally, a long short-term memory (LSTM) network was used to extract features related to the Big Five personality traits. The network was able to accurately predict personality traits from EEG data. The extracted features were subsequently used in a novel network to predict emotional states within the arousal and valence dimensions. The experimental results showed that the proposed classifier outperformed common classifiers, with a high accuracy of 93.97%. The findings suggest that incorporating personality traits as features in the designed network, for emotion recognition, leads to higher accuracy, highlighting the significance of examining these traits in the analysis of emotions.

There is evidence that Iranian preschool children are increasingly spending their time in front of screens (screen time: ST; time spent with any screen such as TVs, computers, tablets, smartphones, game consoles, or video games), but few studies have explored the possible causes of such an increase. Given this, the present study aimed to qualitatively explore determinants of excessive ST in Iranian children. To this end, parents of preschool children were interviewed, and their answers were qualitatively clustered to identify additional important factors. Key informant interviews were conducted with parents of preschool children in Tehran (Iran). A semi-structured interview was developed to assess child and family life, daily routine, family rules, family interactions, and home climate as possible contributing factors to ST. Parents’ audiotaped statements were transcripted verbatim, coded, and clustered into main themes using thematic analysis with the MaxQda^® software. A total of 20 parents of children aged 2 to 7 were interviewed, and a total of 6 key themes and 28 subthemes were extracted from their interviews. The results of the analysis identified a broad range of both independent and interrelated factors leading to the development and maintenance of ST behaviors among preschool children. Our findings indicate that the central concept is the family. Considering screen-related behaviors, family life encompasses parental health literacy (e.g., parenting pattern, monitoring standards, thoughtful parenting), family psychological atmosphere (e.g., presence of parents, family norms, parent–parent and parent–child interaction, congruency/incongruency of parents with each other) and the digital structure of the home. The child’s and parents’ actions and characteristics can influence family interactions. A child’s and parent’s behavior is also influenced by social/cultural factors. Parents’ behaviors and attitudes, family communications, and interactions contribute to healthy ST habits in children. It is not possible to examine the child’s behavior without considering the family and the dominant environment, since the behavior of family members as a whole affects each family member. Given this, interventions should make parents aware of their role and responsibilities in reducing children’s ST and consider the family system as a whole, and interventions also can benefit from considering the parental perceptions of children’s behaviors.

Emotion recognition based on brain signals has increasingly become attractive to evaluate human’s internal emotional states. Conventional emotion recognition studies focus on developing machine learning and classifiers. However, most of these methods do not provide information on the involvement of different areas of the brain in emotions. Brain mapping is considered as one of the most distinguishing methods of showing the involvement of different areas of the brain in performing an activity. Most mapping techniques rely on projection and visualization of only one of the electroencephalogram (EEG) subband features onto brain regions. The present study aims to develop a new EEG‐based brain mapping, which combines several features to provide more complete and useful information on a single map instead of common maps. In this study, the optimal combination of EEG features for each channel was extracted using a stacked autoencoder (SAE) network and visualizing a topographic map. Based on the research hypothesis, autoencoders can extract optimal features for quantitative EEG (QEEG) brain mapping. The DEAP EEG database was employed to extract topographic maps. The accuracy of image classifiers using the convolutional neural network (CNN) was used as a criterion for evaluating the distinction of the obtained maps from a stacked autoencoder topographic map (SAETM) method for different emotions. The average classification accuracy was obtained 0.8173 and 0.8037 in the valence and arousal dimensions, respectively. The extracted maps were also ranked by a team of experts compared to common maps. The results of quantitative and qualitative evaluation showed that the obtained map by SAETM has more information than conventional maps.

هدف: هدف از پژوهش حاضر وارسی ویژگی‌های روان‌سنجی مقیاس کشمکش‌های مذهبی و معنوی[1] (RSSS) اکسلاین و همکاران (2014) در جامعه‌ ی ایرانی بود. روش: پژوهش حاضر از نوع ارزشیابی است. نمونه‌ی پژوهش حاضر متشکل از 486 نفر از افراد بزرگسال ایرانی در محدوده‌ی سنی 14 تا 75 سال بود که با استفاده از روش نمونه‌گیری در دسترس انتخاب شدند. نسخه‌ی فارسی مقیاس همراه با پرسشنامه‌های به زیستی معنوی الیسون (1983) و تنیدگی ادراک‌شده کوهن و همکاران (1994) اجرا گردید. برای بررسی روایی مقیاس از روش تحلیل عامل تأییدی و روایی همگرا و واگرا از طریق محاسبه‌ی همبستگی پیرسون استفاده شد و برای بررسی اعتبار آن نیز از روش محاسبه‌ی آلفای کرونباخ بهره گرفته شد. جهت بررسی پایایی مقیاس نیز از روش ؛آزمون باز-آزمون استفاده شد. یافته­ها: نتایج تحلیل عامل تأییدی حاکی از برازش و مطلوب بودن مدل شش عاملی بود. همبستگی‌های معنادار میان این مقیاس و مقیاس‌های به زیستی معنوی و تنیدگی ادراک‌شده نیز روایی واگرا و همگرای این مقیاس را تائید می‌کرد. اعتبار مقیاس با استفاده از محاسبه‌ی ضریب آلفای کرونباخ برای کل مقیاس ۸/۰ و برای عامل‌های آن در دامنه‌ی ۷۳/۰ تا ۹۱/۰ و اعتبار مرکب عوامل در دامنه‌ی ۸1/۰ تا ۹۴/۰ و...