University of Saskatchewan

About: University of Saskatchewan is a education organization based out in Saskatoon, Saskatchewan, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 25021 authors who have published 52579 publications receiving 1483049 citations. The organization is also known as: USask.

The Drive for Muscularity and Masculinity: Testing the Associations Among Gender-Role Traits, Behaviors, Attitudes, and Conflict.

Abstract: Prior qualitative research has suggested that people assume muscular men are more masculine. This assumption was tested quantitatively in 2 studies. In Study 1, men and women completed measures of gender-role traits and behaviors, whereas in Study 2, men completed measures of gender-role conflict and traditional attitudes about men. Study 1 revealed a correlation between self-rated male-typed traits and behaviors, with a need to be more muscular for both men and women. In Study 2, men with more traditional attitudes about men also wanted to be more muscular; men who wanted to be more muscular were experiencing conflict with regard to society’s expectations that they be successful, powerful, and competitive, and they reported that finding a balance between workand leisure is difficult.

Kalman filter-based identification for systems with randomly missing measurements in a network environment

Abstract: We consider the problem of parameter estimation and output estimation for systems in a transmission control protocol (TCP) based network environment. As a result of networked-induced time delays and packet loss, the input and output data are inevitably subject to randomly missing data. Based on the available incomplete data, we first model the input and output missing data as two separate Bernoulli processes characterised by probabilities of missing data, then a missing output estimator is designed, and finally we develop a recursive algorithm for parameter estimation by modifying the Kalman filter-based algorithm. Under the stochastic framework, convergence properties of both the parameter estimation and output estimation are established. Simulation results illustrate the effectiveness of the proposed algorithms.