University of Jyväskylä

About: University of Jyväskylä is a education organization based out in Jyvaskyla, Finland. It is known for research contribution in the topics: Population & Neutron. The organization has 8066 authors who have published 25168 publications receiving 725033 citations. The organization is also known as: Jyväskylän yliopisto & Kasvatusopillinen korkeakoulu.

Parents' Physical Activity, Socioeconomic Status and Education as Predictors of Physical Activity and Sport among Children and Youths - A 12-Year Follow-Up Study

Abstract: The purpose of this study was to examine parental influences on children's participation in sport and their later physical activity. The population for the study consisted of a random sample of 1881 9- to 15-year-old boys and girls who were exposed to the extensive research program called "Cardiovascular Risk in Young Finns" in 1980. They and their parents have been followed up for twelve years at three-year intervals by means of a short questionnaire concerning physical activity and other factors. The results indicated that the fathers' physical activity in 1980 was related to their children's habitual physical activity in the same year, and gave in boys and girls a significant prediction of PAI values twelve years later when the starting point was the age of 9, and also among boys from 15 years of age to 27. During the three years follow-up period, the extent of participation in sport was higher in families with active parents than in families with passive parents and single parents. The relationship of...

Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales

Abstract: Summary We present a hierarchical latent variable model that partitions variation in species occurrences and co-occurrences simultaneously at multiple spatial scales. We illustrate how the parameterized model can be used to predict the occurrences of a species by using as predictors not only the environmental covariates, but also the occurrences of all other species, at all spatial scales. We leverage recent progress in Bayesian latent variable models to implement a computationally effective algorithm that enables one to consider large communities and extensive sampling schemes. We exemplify the framework with a community of 98 fungal species sampled in c. 22 500 dead wood units in 230 plots in 29 beech forests. The networks identified by correlations and partial correlations were consistent, as were networks for natural and managed forests, but networks at different spatial scales were dissimilar. Accounting for the occurrences of the other species roughly doubled the predictive powers of the models compared to accounting for environmental covariates only .

Segmental contribution to forces in vertical jump

Abstract: Performance of a vertical jump was analyzed with respect to the contribution of the different body segments to the forces acting on the whole body center of gravity. Both cinematograph and force-platform techniques were employed. The data disclosed that the take-off velocity in vertical jumps was caused by the different components as follows: knee extension 56%, plantar flexion 22%, trunk extension 10%, arm swing 10%, and head swing 2%. However, the average take-off velocity of the total performance (3.03 m/s) was only 76% from the theoretical maximum calculated from the segmental analyses. Optimal timing of the segmental performances was calculated to increase this “efficiency” to 84%. Great variance were observed among individuals in the total performance despite the similarities in utilization of the performance of individual segments.

Prediction of multidimensional emotional ratings in music from audio using multivariate regression models

Abstract: Content-based prediction of musical emotions and moods has a large number of exciting applications in Music Information Retrieval. However, what should be predicted, and precisely how, remain a challenge in the field. We provide an empirical comparison of two common paradigms of emotion representation in music, opposing a multidimensional space to a set of basic emotions. New groundtruth data consisting of film soundtracks was used to assess the compatibility of these models. The findings suggest that the two are highly compatible and a quantitative mapping between the two is provided. Next we propose a model predicting perceived emotions based on a set of features extracted from the audio. The feature selection and transformation is given special emphasis and three separate data reduction techniques are compared (stepwise regression, principal component analysis, and partial least squares regression). Best linear models consisting of 25 predictors from the data reduction process were able to account for between 58 and 85% of the variance. In general, partial least squares models performed the best and the data transformation has a significant role in building linear models.

The construction of academic identity in the changes of Finnish higher education

Abstract: This article sets out to explore how academics make sense of the current transformations of higher education and what kinds of academic identities are thereby constructed. Based on a narrative analysis of 42 interviews with Finnish academics, nine narratives are discerned, each providing a different answer as to what it means to be an academic in the present-day university. Narratives of resistance, loss, administrative work overload and job insecurity are embedded in a regressive storyline, describing deterioration of academic work and one's standing. In a sharp contrast, narratives of success, mobility and change agency rely on a progressive storyline which sees the current changes in a positive light. Between these opposites, narratives of work–life balance and bystander follow stable storylines, involving a neutral stance toward university transformations. The paper concludes that academic identities have become increasingly diversified and polarized due to the managerial and structural changes in hig...