Why do you dance? Development of the Dance Motivation Inventory (DMI)

Dancing is a popular form of physical exercise and studies have show that dancing can decrease anxiety, increase self-esteem, and improve psychological wellbeing. The aim of the current study was to explore the motivational basis of recreational social dancing and develop a new psychometric instrument to assess dancing motivation. The sample comprised 447 salsa and/or ballroom dancers (68% female; mean age 32.8 years) who completed an online survey. Eight motivational factors were identified via exploratory factor analysis and comprise a new Dance Motivation Inventory: Fitness, Mood Enhancement, Intimacy, Socialising, Trance, Mastery, Self-confidence and Escapism. Mood Enhancement was the strongest motivational factor for both males and females, although motives differed according to gender. Dancing intensity was predicted by three motivational factors: Mood Enhancement, Socialising, and Escapism. The eight dimensions identified cover possible motives for social recreational dancing, and the DMI proved to be a suitable measurement tool to assess these motives. The explored motives such as Mood Enhancement, Socialising and Escapism appear to be similar to those identified in other forms of behaviour such as drinking alcohol, exercise, gambling, and gaming

Exercise addiction – the emergence of a new disorder

An optimal level of regular physical activity plays an important role in the maintenance of physical and mental health. However, excessive exercise in a minority of individuals can have adverse effects on health and lead to exercise addiction. Exercise addiction can be conceptualised as a behavioural addiction. The aim of this paper is to outline the current knowledge on the concept, epidemiology, aetiology, comorbidity, and possible interventions of exercise addiction

Measuring compulsive buying behaviour: Psychometric validity of three different scales and prevalence in the general population and in shopping centres

Due to the problems of measurement and the lack of nationally representative data, the extent of compulsive buying behaviour (CBB) is relatively unknown. Methods: The validity of three different instruments was tested: Edwards Compulsive Buying Scale (ECBS; Edwards, 1993), Questionnaire About Buying Behavior (QABB; Lejoyeux & Adès, 1994) and Richmond Compulsive Buying Scale (RCBS; Ridgway, et. al., 2008) using two independent samples. One was nationally representative of the Hungarian population (N=2710) while the other comprised shopping mall customers (N=1447). Results: A new, four-factor solution for the ECBS was developed (ECBS-R), and confirmed the other two measures. Additionally, cut-off scores were defined for all measures. Results showed that the prevalence of CBB is 1.85% (with QABB) in the general population but significantly higher in shopping mall customers (8.7% with ECBS-R, 13.3% with QABB and 2.5% with RCBS-R). Conclusions: Due to the diversity of content, each measure identifies a somewhat different CBB group

The fine- and generative spectra of varieties of monounary algebras

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Solving the Pulsar Equation using Physics-Informed Neural Networks

In this study, Physics-Informed Neural Networks (PINNs) are skilfully applied to explore a diverse range of pulsar magneto-spheric models, specifically focusing on axisymmetric cases. The study successfully reproduced various axisymmetric models found in the literature, including those with non-dipolar configurations, while effectively characterizing current sheet features. Energy losses in all studied models were found to exhibit reasonable similarity, differing by no more than a factor of three from the classical dipole case. This research lays the groundwork for a reliable elliptic Partial Differential Equation solver tailored for astrophysical problems. Based on these findings, we foresee that the utilization of PINNs will become the most efficient approach in modelling three-dimensional magnetospheres. This methodology shows significant potential and facilitates an effortless generalization, contributing to the advancement of our understanding of pulsar magnetospheres.Comment: 9 pages, 8 figures, version

A cross-cultural re-evaluation of the Exercise Addiction Inventory (EAI) in five countries

Research into the detrimental effects of excessive exercise has been conceptualized in a number of similar ways, including ‘exercise addiction’ , ‘exercise dependence’ , ‘obligatory exercising’, ‘exercise abuse’, and ‘compulsive exercise’. Among the most currently used (and psychometrically valid and reliable) instruments is the Exercise Addiction Inventory (EAI). The present study aimed to further explore the psychometric properties of the EAI by combining the datasets of a number of surveys carried out in five different countries (Denmark, Hungary, Spain, UK, and US) that have used the EAI with a total sample size of 6,031 participants. A series of multigroup confirmatory factor analyses (CFAs) were carried out examining configural invariance, metric invariance, and scalar invariance. The CFAs using the combined dataset supported the configural invariance and metric invariance but not scalar invariance. Therefore, EAI factor scores from five countries are not comparable because the use or interpretation of the scale was different in the five nations. However, the covariates of exercise addiction can be studied from a cross-cultural perspective because of the metric invariance of the scale. Gender differences among exercisers in the interpretation of the scale also emerged. The implications of the results are discussed, and it is concluded that the study’s findings will facilitate a more robust and reliable use of the EAI in future research

Modelling Force-Free Neutron Star Magnetospheres using Physics-Informed Neural Networks

Using Physics-Informed Neural Networks (PINNs) to solve a specific boundary value problem is becoming more popular as an alternative to traditional methods. However, depending on the specific problem, they could be computationally expensive and potentially less accurate. The functionality of PINNs for real-world physical problems can significantly improve if they become more flexible and adaptable. To address this, our work explores the idea of training a PINN for general boundary conditions and source terms expressed through a limited number of coefficients, introduced as additional inputs in the network. Although this process increases the dimensionality and is computationally costly, using the trained network to evaluate new general solutions is much faster. Our results indicate that PINN solutions are relatively accurate, reliable, and well-behaved. We applied this idea to the astrophysical scenario of the magnetic field evolution in the interior of a neutron star connected to a force-free magnetosphere. Solving this problem through a global simulation in the entire domain is expensive due to the elliptic solver's needs for the exterior solution. The computational cost with a PINN was more than an order of magnitude lower than the similar case solved with classical methods. These results pave the way for the future extension to 3D of this (or a similar) problem, where generalised boundary conditions are very costly to implement.Comment: 11 pages, 10 figures, submitted for publication in MNRA

Do gaming motives mediate between psychiatric symptoms and problematic gaming? An empirical survey study

Previous research has suggested that motives play an important role in several potentially addictive activities including online gaming. The aims of the present study were to (i) examine the mediation effect of different online gaming motives between psychiatric distress and problematic online gaming, and (ii) validate Italian versions of the Problematic Online Gaming Questionnaire, and the Motives for Online Gaming Questionnaire. Data collection took place online and targeted Italian-speaking online gamers active on popular Italian gaming forums, and/or Italian groups related to online games on social networking sites. The final sample size comprised 327 participants (mean age 23.1 years [SD = 7.0], 83.7% male). The two instruments showed good psychometric properties in the Italian sample. General psychiatric distress had both a significant direct effect on problematic online gaming and a significant indirect effect via two motives: escape and fantasy. Psychiatric symptoms are both directly and indirectly associated with problematic online gaming. Playing online games to escape and to avoid everyday problems appears to be a motivation associated with psychiatric distress and in predicting problematic gaming

Performance Analysis of a Consensus Algorithm Combining Stochastic Activity Networks and Measurements

A. Coccoli, P. Urban, A. Bondavalli, and A. Schiper. Performance analysis of a consensus algorithm combining Stochastic Activity Networks and measurements. In Proc. Int'l Conf. on Dependable Systems and Networks (DSN), pages 551-560, Washington, DC, USA, June 2002. Protocols which solve agreement problems are essential building blocks for fault tolerant distributed applications. While many protocols have been published, little has been done to analyze their performance. This paper represents a starting point for such studies, by focusing on the consensus problem, a problem related to most other agreement problems. The paper analyzes the latency of a consensus algorithm designed for the asynchronous model with failure detectors, by combining experiments on a cluster of PCs and simulation using Stochastic Activity Networks. We evaluated the latency in runs (1) with no failures nor failure suspicions, (2) with failures but no wrong suspicions and (3) with no failures but with (wrong) failure suspicions. We validated the adequacy and the usability of the Stochastic Activity Network model by comparing experimental results with those obtained from the model. This has led us to identify limitations of the model and the measurements, and suggests new directions for evaluating the performance of agreement protocols. Keywords: quantitative analysis, distributed consensus, failure detectors, Stochastic Activity Networks, measurement