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For decades, the concept of enjoyment has been used to measure the psychological benefits of activities and has been shown to determine future behavior toward activities and objects of interest. However, there has been little consensus on the definition and dimensionality of enjoyment. This study introduced a new measure of enjoyment with scale development and validation reported. CFA and EFA findings from 1466 participants across 739 different activities were reported. The instrument developed measured enjoyment across activities, with demonstrated content validity, internal consistency, discriminant and convergent validity. The final 25-item version of the ENJOY scale is composed of 5 factors: pleasure, relatedness, competence, challenge/improvement, and engagement. Discussion of the ENJOY Scale places it within the conceptual framework of Self-Determination Theory., Author(s): Shayn S. Davidson [sup.1] , Joseph R. Keebler [sup.2] , Tianxin Zhang [sup.2] , Barbara Chaparro [sup.2] , James Szalma [sup.3] , Christina M. Frederick [sup.2] Author Affiliations: (1) [...]

Online social technologies, such as email, social networking applications, texting and others, are now used by a majority of individuals in the U.S. (Pew, 2018a). As a result, it is not uncommon to develop friendships with others that are conducted primarily in an online environment. However, we know little about the qualities of online friendships and how they may, or may not, differ from traditional face to face friendships. The present study focused on exploring friendship quality in online and offline domains using two groups: a gamer group and a non-gamer group that used non-gaming applications to connect with others online. Participants completed a demographic questionnaire to gather information about their online and face to face friendships, the McGill Friendship Questionnaire (Mendelsohn & Aboud, 2014) for their closest online and offline friends and measures of happiness, anxiety, and depression. In Study 1, within group comparison found that gamers' online friendships were of significantly higher quality than their offline friendships. For non-gamers, the opposite results were found. A second study was done using a larger, non-college-based sample. Results of Study 2 found that for gamers and non-gamers offline friendships were of higher quality than online friendships, although both types of friendships existed in both groups. There were no differences between groups in general life happiness, anxiety or depression. Suggestions for follow-up research are presented., Author(s): Christina M. Frederick [sup.1] , Tianxin Zhang [sup.1] Author Affiliations: (1) grid.255501.6, 0000 0001 0561 4552, Department of Human Factors, Embry-Riddle Aeronautical University, , 600 S. Clyde Morris Blvd [...]

In previous studies, narcissism has been found to relate to social media behaviors, such as how often one posts and how many friends or followers one has (McCain & Campbell, 2016; Singh, Farley, and Donahue, 2018). The present study attempted to create a model of social media use predicted by narcissism and self-consciousness. A sample of 397 adults with a mean age of 29 years was collected using MTurk. Participants completed a social media use survey, the Narcissistic Personality Inventory (Raskin & Hall, 1979) and the Self-Consciousness Scale (Scheier & Carver, 1985). Results showed that while narcissism and self-consciousness were correlated, narcissism was not significantly related to social media use. Several explanations for the present study's findings are discussed. General Area of Psychology: Social Psychology Specific Area: Personality Psychology Keywords: narcissism, social media, self-consciousness, In recent years, the use of social media (SM) has become an increasingly popular daily activity worldwide (Kuss & Griffiths, 2011). Pew Research Center (2018) reported that 69% of people [...]

Reynolds-averaged Navier–Stokes (RANS) models have been the mainstay of engineering applications in recent years, and this trend will likely persist in the coming decades. However, the ability of RANS methods to predict complex flows, such as shock-wave/boundary-layer interaction (SWBLI) flows, is inadequate. In the absence of a breakthrough in traditional turbulence modeling, data-driven modeling has emerged as a new paradigm in turbulence research. In this study, a field-inversion and machine-learning framework based on the regularized ensemble Kalman filter (REnKF) was implemented to enhance the predictive ability of the Menter shear-stress transport (SST) model for SWBLI flows. This approach directly modifies the norm and eigenvalues of the Reynolds stress tensor obtained by RANS methods to overcome the limitations of the Boussinesq hypothesis. The spatial distributions of Reynolds stress discrepancies are initially obtained from experimental data using the REnKF method and a parameterization method based on geometric transformation for improved prediction of the training case. Then, a mapping function from local flow variables to discrepancy fields is constructed by selecting appropriate input features and combining them with a random forest algorithm. Our results verify the effectiveness of the framework for SWBLI flows of varying strengths and types. The results also demonstrate that the SST model's simulation ability for SWBLI flows has been significantly improved, providing more accurate predictions of separation and reattachment, considerably lowering the prediction errors for relevant flow variables, and verifying the generalization ability of the proposed framework. [ABSTRACT FROM AUTHOR]