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.

An overview of dielectric properties measuring techniques

Abstract: Venkatesh, M.S. and Raghavan, G.S.V. 2005. An overview of dielectric properties measuring techniques. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 47: 7.15 7.30. With a need for the development of improved sensing devices for the control and automation of several agricultural, environmental, and food processes, there is an absolute need for better understanding of the dielectric properties of materials and techniques for measuring these properties. Microwave measurements and the dielectric properties of materials are finding increasing application as new electro-technology is adapted for use in the agriculture and food processing industries. The interest in dielectric properties of materials has historically been associated with the design of electrical equipment, where various dielectrics are used for insulating conductors and other components of electric equipment. Measurement of the bulk dielectric properties (dielectric constant, dielectric loss factor) is not an end unto itself. Rather, these properties are an intermediary vehicle for understanding, explaining, and empirically relating certain physico-chemical properties of the test material. Therefore, in this paper, an attempt is made to fully explore the existing knowledge of dielectric properties (complex permittivity), their role, and importance in the agri-food sector, and the concept of various measurement methodologies and their development. We have summarized the current status of research in this area with some notes on recent developments. An extensive review of the literature on measuring techniques and the comparison and potential application of dielectric properties is reported. Readers are advised to follow the appropriate literature cited for detailed and complete reference.

Evaluation of forest snow processes models (SnowMIP2)

Abstract: Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up t ...

Psychosocial factors in fibromyalgia compared with rheumatoid arthritis : ii. sexual, physical, and emotional abuse and neglect

Abstract: Two recent reports have found associations between fibromyalgia and sexual victimization but had methodologic characteristics that limited their interpretation. The authors compared 36 patients with fibromyalgia and 33 patients with rheumatoid arthritis by using structured interviews for sexual physical and emotional victimization histories as well as dimensional self-report measures of victimization severity. Compared with the patients with rheumatoid arthritis those with fibromyalgia had significantly higher lifetime prevalence rates of all forms of victimization both adult and adulthood as well as combinations of adult and childhood trauma. Although childhood maltreatment was found to be a general risk factor for fibromyalgia particular forms of maltreatment (e.g. sexual abuse per se) did not have specific effects. Experiences of physical assault in adulthood however showed a strong and specific relationship with unexplained pain. Trauma severity was correlated significantly with measures of physical disability psychiatric distress illness adjustment personality and quality of sleep in-patients with fibromyalgia but not in those with rheumatoid arthritis. Fibromyalgia seems to be associated with increased risk of victimization particularly adult physical abuse. Sexual physical and emotional trauma may be important factors in the development and maintenance of this disorder and its associated disability in many patients. (authors)

A review on machine learning principles for multi-view biological data integration.

Abstract: Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.