The effect of traffic noise on sleep of young adults in their homes

The disturbance of sleep by traffic noise is a major problem area in noise pollution. Extensive laboratory tests using multichannel electroencephalograms (EEG) have been carried out by many workers to determine the general response of people when exposed to noise during sleep. An experimental technique for obtaining results in people's homes has been developed using a simplified one-channel EEG. The responses of six people in the age range 19-24 were measured. A significant correlation was found between sleep disturbance and traffic noise when the noise is expressed in EPNdB. In the home experiments the subjects appeared to be approximately 10 dB less sensitive to noises than laboratory subjects for similar noise exposure. There also appeared to be some adaptation to the noise exposure; however, only one subject was tested for a period of 20 days and was insufficient to give definite results on adaptation.     

Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Working Methods Paper: Critical considerations with respect to the identification of tin species in the environment

Biogeochemical pathways of tin species in the environment are still controversial, e.g. with regard to methylation and transmethylation phenomena, owing to the fact that the identification of methylated tin-compounds is often difficult. The previous tentative identification of a mixed methylbutyltin compound in sediment and biological samples by GC/AAS after hydride generation gave an illustration of this problem. This compound was previously identified in sediments by other authors and also suspected to occur in a contaminated sediment sample from the Boyardville Marina, France. The retention time obtained by GC/AAS corresponded to the actual retention time of a mixed methylbutyltin calibrant. However, additional checks demonstrated that the compound detected was actually monophenyltin. This evidence was produced by a thorough analysis of a selected sediment sample by alternative techniques such as GC/AAS and GC/AES after pentylation, GC/FPD and GC/MS. The results presented highlight the need for a full identification of compounds to avoid mis-interpretation.