Spirituality as an Essential Determinant for the Good Life, its Importance Relative to Self-Determinant Psychological Needs

This study focuses on the relevance of spirituality as an essential element for the Good Life. Despite spirituality’s prominence in people’s lives and cultures, it has been mostly neglected in psychological needs theories. This paper investigates the value of spirituality compared to that of the three basic psychological needs of self-determination theory: relatedness, competence and autonomy. In a scenario study design, participants in two samples (students and train passengers) were asked to judge a survey on the personal well-being of an imaginary person. The results show that spirituality positively contributes to the qualification of a good life, in terms of desirability and moral goodness. In addition, the crucial role of relatedness was confirmed

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures