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Stroke commonly results in gait deficits which impacts functional ambulation and quality of life. Robotic exoskeletons (RE) for overground walking are devices that are programmable to provide high dose and movement-impairment specific assistance thus offering new rehabilitation possibilities for recovery progression in individuals post stroke. The purpose of this investigation is to present preliminary utilization data in individuals with acute and chronic stroke after walking overground with an RE. Secondary analysis on a subset of individuals is presented to understand the mechanistic changes due to RE overground walking. Thirty-eight participants with hemiplegia secondary to stroke were enrolled in a clinical trial conducted at eight rehabilitation centers. Data is presented for four sessions of overground walking in the RE over the course of 2 weeks. Participants continued their standard of care if they had any ongoing therapy at the time of study enrollment. Gait speed during the 10 Meter Walk Test, Gait deviations and the Functional Ambulation Category (FAC) data were collected before (baseline) and after (follow-up) the RE walking sessions. Walking speed significantly increased between baseline and follow-up for participants in the chronic (p

The authors explore their use of learner-centred teaching strategies and Universal Instructional Design (UID) on course websites. UID is based on universal design, the design of products and environments intended to be usable by all people to the greatest extent possible (Burgstahler & Cory, 2008). UID applies universal design to instructional products and environments, including course websites. Seeking to assess and improve course website accessibility, faculty aimed to consider to what extent they employ UID as a means to promote inclusive practices and greater accessibility. Through the University of Windsor’s university-wide learning management system (CLEW) Learning Community, faculty administered teaching evaluations to students in eight undergraduate courses. Although the evaluations indicated students considered website course delivery involved a fairly high level of accessibility, future studies are required to determine to what extent course websites reflect UID-based teaching strategies that promote accessibility.

First-year university students are a diverse group of individuals with various abilities and needs. Failure of the university and its teaching faculty to meet the needs of first-year students may result in abandonment of the pursuit of a degree. This project informs instructors about the practices that strengthen a learning-centred approach and maximize inclusion of first-year students. The principles and teaching methods of Universal Instructional Design are learner-centred and have shown to improve academic achievement and benefit the campus community. Thus, it is suggested that the principles of this method be applied uniformly across university-level instruction.

This paper recommends that faculties of Social Work incorporate Disability Studies in their curriculum by embracing its interdisciplinary deconstructionist perspective. Disability Studies encourages Social Work to move beyond person-in–the-environment and anti-oppressive approaches to find more effective ways of removing barriers for persons with disabilities, and new opportunities to include them in the larger culture. Discussing the critical nature of Disability Studies, the authors point out how Social Work is learning to embrace the interdisciplinary nature of Disability Studies.

In 1829, the perceptive landscape gardener John Claudius Loudon published an essay in which he introduced the design of a “systematic plan” for the layout of an ideal London that became a theoretical model. Little known today, yet the influence of the Scot’s “beau ideal” is measurable on the course of town planning theory and practice for the remainder of the nineteenth century. This includes colonial Adelaide’s renowned park lands that were conceived in 1835 and executed in 1837. [ABSTRACT FROM PUBLISHER]

It is commonly believed that the planning concept of a Neighborhood Unit was the brain-child of Clarence A. Perry as published in 1929. However, the idea was formulated during Chicago's reformist and Progressive milieu in the decade before Europe's new war by the architect William E. Drummond. His theory and terminology were widely exhibited and published during the years 1913–22. [ABSTRACT FROM AUTHOR]

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χMcNemar′s statistic2=163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.