Based on evidence of parasympathetic activation, early studies defined meditation as a relaxation response. Later research attempted to categorize meditation as either involving focused or distributed attentional systems. Neither of these hypotheses received strong empirical support, and most of the studies investigated Theravada style meditative practices. In this study, we compared neurophysiological (EEG, EKG) and cognitive correlates of meditative practices that are thought to utilize either focused or distributed attention, from both Theravada and Vajrayana traditions. The results of Study 1 show that both focused (Shamatha) and distributed (Vipassana) attention meditations of the Theravada tradition produced enhanced parasympathetic activation indicative of a relaxation response. In contrast, both focused (Deity) and distributed (Rig-pa) meditations of the Vajrayana tradition produced sympathetic activation, indicative of arousal. Additionally, the results of Study 2 demonstrated an immediate dramatic increase in performance on cognitive tasks following only Vajrayana styles of meditation, indicating enhanced phasic alertness due to arousal. Furthermore, our EEG results showed qualitatively different patterns of activation between Theravada and Vajrayana meditations, albeit highly similar activity between meditations within the same tradition. In conclusion, consistent with Tibetan scriptures that described Shamatha and Vipassana techniques as those that calm and relax the mind, and Vajrayana techniques as those that require ‘an awake quality’ of the mind, we show that Theravada and Vajrayana meditations are based on different neurophysiological mechanisms, which give rise to either a relaxation or arousal response. Hence, it may be more appropriate to categorize meditations in terms of relaxation vs. arousal, whereas classification methods that rely on the focused vs. distributed attention dichotomy may need to be reexamined.

Previous studies suggested that emotions can be correctly interpreted from face expressions in the absence of conscious awareness of the face. Our goal was to explore whether sub-ordinate information about a face's gender and race could also become available without awareness of the face. Participants classified the race or the gender of unfamiliar faces that were ambiguous with regard to these dimensions. The ambiguous faces were preceded by face images that unequivocally represented gender and race, rendered consciously invisible by simultaneous continuous-flash-suppression. The classification of ambiguous faces was biased away from the category of the adaptor only when the it was consciously visible. The duration of subjective visibility correlated with the aftereffect strength. Moreover, face identity was consequential only if consciously perceived. These results suggest that while conscious awareness is not needed for basic level categorization, it is needed for subordinate categorization. Emotional information might be unique in this respect.

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Event-related potentials offer evidence for face distinctive neural activity that peaks at about 170 ms following the onset of face stimuli (the N170 effect). We investigated the role of the perceptual mechanism reflected by the N170 effect by comparing the adaptation of the N170 amplitude when target faces were preceded either by identical face images or by different faces relative to when they were preceded by objects. In two experiments, we demonstrate that the N170 is equally adapted by repetition of the same or different faces. Thus, our findings show that the N170 is sensitive to the category rather than the identity of a face. This outcome supports the hypothesis that the N170 effect reflects the activity of a perceptual mechanism which discriminates faces from objects and streams face stimuli to dedicated circuits, specialized in encoding and decoding information about the face.

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.