Background: Internet addiction is one of the most paramount problems of societies. Therefore, the knowledge of its influencing factors is of special importance. Methods: The present study was correlational in terms of goals and descriptive in terms of the data gathering procedure. The statistical population consisted of 2,000 male students and included all fourth-grade students of state high schools of Tabriz City in 2017. The sample included 246 students, selected through random cluster sampling. For the collection of data, the Cognitive Emotion Regulation questionnaire (CERQ), Young’s Diagnostic questionnaire (YDQ), and Beck Anxiety Inventory (BAI) were used. Then, the collected data were analyzed by structural equation modeling. Results: Negative cognitive emotion regulation strategies could explain internet addiction symptoms. Self-blame, other-blame, rumination, catastrophizing, and anxiety could explain internet addiction symptoms directly. The indirect effect of self-blame and rumination on internet addiction mediated by anxiety was positive and significant; however, the indirect effect of other-blame and catastrophizing on internet addiction mediated by anxiety was not significant. Conclusions: The findings have practical implications for clinical psychologists and can be used for diagnosis and therapy of internet addiction disorder.

ObjectivesThe aim of this study is to determine the role of impulsivity, sensitivity to reward and also anhedonia in distinction People with symptoms of borderline personality disorder from normal people. Methods This study uses both the descriptive and correlative method. The sampling method was sampling and targeting type. The samples comprised of students from Tabriz University (2016-2017 academic year), initial 200 candidates for screening and subsequent 80 candidates for target sampling. In the first step of this study, the Millon Clinical Multiaxial Inventory (MCMI-III) was used for screening followed by Balloon Analogue Risk Task for measuring the impulsivity and resistance variable as sensitivity to reward and the Snaith-Hamilton scale of pleasure is used to measure anhedonia variable. Results It was illustrated broadly that the impulsivity variable, with 0.61 ratio, has the most important role in the Detection function. The resistance variable towards earning rewards with a 0.47 ratio occupies second place in the Detection function; the anhedonia variable occupied the third place in the Detection function with a ratio of 0.42. Conclusion Reward deficiency syndrome can be considered as a fundamental pathological symptom of impulsivity and anhedonia in borderline personality disorder BPD. The poor functioning of the brain's rewards system is a deprivation of sensory mechanisms in people affected with BPD, that led to anhedonia and low arousal and finally impulsivity.

Background. Internet addiction is one of the serious consequences of recent advances in the use of social media. Early detection of Internet addiction is essential because of its harms and is necessary for timely and effective treatment. Aim. The aim of this study was to use data mining and an artificial intelligence algorithm to estimate the differential power of each question in the Young Internet Addiction Test and build a decision stump model to predict which item in the questionnaire can be representative of the whole questionnaire. Methods. This is a descriptive study conducted at the University of Tabriz, in which 256 undergraduate students were selected in randomized cluster sampling, and they completed Young’s IAT (Internet Addiction Test) questionnaire and some demographic questions. The data were statistically analyzed with SPSS and were divided into two groups, normal and addicted, by using a cut-off point. Also, the data of the subjects was used to model the decision stump tree in WEKA. The clustering item was the normal and addicted specifier. Results. The study shows that Cronbach’s alpha of the IAT is 0.88, which shows good internal integration of subjects that are used to develop the model in WEKA (the Waikato Environment for Knowledge Analysis). Data analysis showed that by using the second question of this questionnaire as the root of the decision stump tree model, it is possible to distinguish between Internet addicts and healthy users with 82% accuracy using this model. Conclusion. The study shows innovative ways in which decision stump trees and data mining can help to improve methods used in Clinical Psychotherapy and Human Science. Regarding this, the study showed that early detection of Internet addiction would be possible by using the 2nd question of the IAT. Also, early detection can result in cost-effectiveness for the whole healthcare system.

Introduction: Good sleep quality has positive effects on happiness, and being unhappy is significantly affect adverse cardiac outcomes. This study aimed to study the relationships between sleep quality and satisfaction in male coronary patients. Methods: One hundred male coronary patients that having been referred to Madani Heart Hospital, Tabriz, Iran, completed the Pittsburgh Sleep Quality Index (PSQI) and Oxford Happiness Questionnaire (OHQ). All participants were selected by purposive sampling (aged 37 to 67 years). Results: There was a significant negative association between happiness with sleep disturbances and the use of sleeping medication in coronary patients. Conclusion:This study showed that sleep quality in coronary patients has an association with their happiness. Therefore, the quality of sleep in these patients can be given more consideration by community health care providers.

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