13 research outputs found

    Archaeoacoustic analysis of Cybele’s temple, Imperial Roman Palace of Felix Romuliana, Serbia. An interpretation using a method complementary to archaeology.

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    Archaeoacoustic and physical phenomena research at ancient sites has developed beyond the initial stage. Our research group uses a practical standard (SBSA) complementing the field of archaeology. Studying archaeoacoustics and natural phenomena over the last four years, has enables us to offer an explanation as to some of the enigmas of ancient archaeological sites that were not possible to explain with other methods. Following our experience, we applied the same method to look at an interesting question about the orientation of Cybele’s Temple situated within the Imperial Roman Palace Felix Romuliana, South-East Serbia. This temple and its fixtures are the only place within the palace that is not oriented along the east-west axis of the complex as was the Roman tradition (Decumanus). Historians also made reference to mysterious rituals, so we used archaeoacoustical methods to better understand why this ought be. We found that the temple’s orientation followed the direction of some infrasound and low frequency vibrations most likely originating from an underground flow of water. These frequencies would have increased the effect of rituals by enhancing the psyche of the participants due to the influence of these low vibrations on human brain waves. This suggests the builders of this temple had some sort of knowledge of this effect

    Examples of predicted probabilities of anxiety at baseline.

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    <p>(*)The possible range of the SF-12 is 0–100. High scores indicate good health/well-being. Mean (Standard Deviation) Short Form 12 (SF-12) mental and physical subscale scores for Spain are 47.1 (12.4) and 43.8 (11.4), respectively <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106370#pone.0106370-Belln3" target="_blank">[39]</a>.</p><p>Examples of predicted probabilities of anxiety at baseline.</p

    Model to predict drop-out.

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    <p>(*) Multi-level logistic regression with health center as a random component. We selected variables included in the final model from the 39 measured in this study using a threshold for inclusion of p<0.20 in bivariate regression. From the model thus obtained, those variables with p>0.05 were extracted step by step to obtain a more parsimonious model.</p><p>Model to predict drop-out.</p
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