University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

The beneficial effects of volunteering for older volunteers and the people they serve: a meta-analysis.

Abstract: The current political-economic climate, which is generally supportive of both private and public sector down-sizing, increasingly demands that human service workers assess, engage, and creatively use consumer strengths and resources. This meta-analysis of thirty-seven independent studies provided the means of inferring not only that elder volunteers' sense of well-being seemed to be significantly bolstered through volunteering, but also that such relatively healthy older people represent a significant adjunct resource for meeting some of the service needs of more vulnerable elders, as well as those of other similarly vulnerable groups such as disabled children. Averaging across studies, 85 percent of the "clients" who received service from an older volunteer (e.g., peer-counseling of nursing home residents) scored better on dependent measures (e.g., diminished depression) than the average person in comparison conditions did (U3 = .847 [Cohen, 1988], combined p < .001). The policy implications of such beneficial effects among both older volunteers and the people they serve are discussed.

Handling missing data in self‐report measures

Abstract: Self-report measures are extensively used in nursing research. Data derived from such reports can be compromised by the problem of missing data. To help ensure accurate parameter estimates and valid research results, the problem of missing data needs to be appropriately addressed. However, a review of nursing research literature revealed that issues such as the extent and pattern of missingness, and the approach used to handle missing data are seldom reported. The purpose of this article is to provide researchers with a conceptual overview of the issues associated with missing data, procedures used in determining the pattern of missingness, and techniques for handling missing data. The article also highlights the advantages and disadvantages of these techniques, and makes distinctions between data that are missing at the item versus variable levels. Missing data handling techniques addressed in this article include deletion approaches, mean substitution, regression-based imputation, hot-deck imputation, multiple imputation, and maximum likelihood imputation.

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.

The Trade-off Between the Selling Price of Residential Properties and Time-on-the-Market: The Impact of Price Setting

Abstract: When a house is placed on the market, the seller must choose the initial offer price. Setting the price too high or too low affects the marketability of the property. While there is near universal agreement that the seller faces a trade-off between selling at a higher price and selling in less time, there is less agreement about how to measure this trade-off. This paper offers a framework for analysis and shows that an increase in the list price increases expected time-on-the-market (TOM). Because house buyers must solve a type of signal extraction problem, the effect of a higher list price is magnified for houses in a market segment having a low predicted variance of the list price. This paper also shows that the list price of houses which are withdrawn before sale has a higher mean and variance, and that the possibility of withdrawal censors information about the time-on-the-market.

Advanced Micro- and Nano-Gas Sensor Technology: A Review

Abstract: Micro- and nano-sensors lie at the heart of critical innovation in fields ranging from medical to environmental sciences. In recent years, there has been a significant improvement in sensor design along with the advances in micro- and nano-fabrication technology and the use of newly designed materials, leading to the development of high-performance gas sensors. Advanced micro- and nano-fabrication technology enables miniaturization of these sensors into micro-sized gas sensor arrays while maintaining the sensing performance. These capabilities facilitate the development of miniaturized integrated gas sensor arrays that enhance both sensor sensitivity and selectivity towards various analytes. In the past, several micro- and nano-gas sensors have been proposed and investigated where each type of sensor exhibits various advantages and limitations in sensing resolution, operating power, response, and recovery time. This paper presents an overview of the recent progress made in a wide range of gas-sensing technology. The sensing functionalizing materials, the advanced micro-machining fabrication methods, as well as their constraints on the sensor design, are discussed. The sensors’ working mechanisms and their structures and configurations are reviewed. Finally, the future development outlook and the potential applications made feasible by each category of the sensors are discussed.