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Mobile services differ from other services because of their temporal and spatial attributes. Mobile services additionally differ from each other in their value-added to the end-user. Some services—such as emailing and voice—are more business oriented. On the other hand, various free-time oriented services are provided in new smartphones, such as imaging and music playback. The present paper studies how mobile services are used in different contexts. For this, the paper develops a specialized algorithm that can be used with handset-based usage data acquired straight from end-users in an established panel study process. Educated guesses can be drawn on the user context based on the developed algorithm. In the present exercise usage contexts were divided into home, office and “on the move”. The algorithm is used with exemplary data from Finland and the UK covering 324 consumers in 2006. More than 70% of contextual use cases are correctly classified based on raw data. According to exemplary results particularly multimedia services are used “on the move”, whereas legacy mobile services experience more evenly distributed usage across all contexts. The algorithm that identifies context based on raw data provides a new angle to mobile end-user research. In the future, the accuracy of the algorithm will be improved with the integration of seamless cell-id logging and GPS data. [ABSTRACT FROM AUTHOR]

Twenty-seven boys and eight girls with ASD and thirty-five controls matched for gender, age and total score intelligence were studied to ascertain whether boys and girls with ASD display stronger reactive aggression than boys and girls without ASD. Participants performed a computerized version of the Pulkkinen aggression machine that examines the intensity of reactive aggression against attackers of varying gender and age. Relative to the control group boys, the boys with ASD reacted with more serious forms of aggression when subjected to mild aggressive attacks and did not consider a child attacker's opposite sex an inhibitory factor. The girls with ASD, on the other hand, reacted less aggressively than the girls without ASD. According to the results boys with ASD may not follow the typical development in cognitive regulation of reactive aggression. [ABSTRACT FROM AUTHOR]

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (ZSD) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and ZSD observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6–25 FNU; absorption coefficient of CDOM at 400 nm, 1.0–12.2 m−1; ZSD, 0.5–5.5 m; and chlorophyll a concentration, 2.4–80 μg L−1. The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for ZSD. If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of ZSD. Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km2), which are often not included in routine monitoring programs. [ABSTRACT FROM AUTHOR]

The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm). [ABSTRACT FROM AUTHOR]