Narrow Results

The ambient noise system consisting of a vertical linear hydrophone array (VLA) with 12 elements was deployed in the waters of the Arabian Sea at a depth of 16m, off Goa, India, for extracting the ambient noise during the fishing season (March, April and May, 2013 before the onset of the south west monsoon). This study focuses on fishing vessel activity by finding the domination of the vessel and wind noise at two 12 hourly periodic cycles that start at midnight and noon, using the statistical analysis. It is performed using statistical parameters, like the mean, median, and standard deviation, skewness, percentile and spread of data. It is observed that the vessel noise dominates during the 12h period that starts at midnight and is an indication of the activity of fishing vessels while the wind generated noise is more during the 12h period that starts at noon, which is a sign of the domination of the sea breeze effects. This is the first time that a statistical analysis has been carried out to study the ambient noise data collected off Goa, in order to find the fishing vessel activity during the pre-monsoon season. The results are verified with the fishing information from the Directorate of Fisheries, Goa, ship traffic data from the Mormugao Port Trust and wind speed measurements.

The ability to calculate precise likelihood ratios is fundamental to science, from Quantum Information Theory through to Quantum State Estimation. However, there is no assumption-free statistical methodology to achieve this. For instance, in the absence of data relating to covariate overlap, the widely used Bayes’ theorem either defaults to the marginal probability driven “naive Bayes’ classifier”, or requires the use of compensatory expectation-maximization techniques. This paper takes an information-theoretic approach in developing a new statistical formula for the calculation of likelihood ratios based on the principles of quantum entanglement, and demonstrates that Bayes’ theorem is a special case of a more general quantum mechanical expression.

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO${}_2)$ was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted from the process of the CO₂ concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant’s ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO₂ in combination with environmental parameters, as well as the development of advices for ventilation.