Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Patient-centered care: clarification of its specific elements to facilitate interprofessional care

Abstract: Patient-centered care (PCC) has been described as a vague concept, which yields an inconsistent operationalization and implementation of this approach to care. This integrative review of the literature, guided by the conceptualization of PCC as a complex intervention, aimed to identify the specific elements of PCC. Conceptual, empirical and clinical literature in different health professions (n = 178 articles) was critically analyzed. Comparing and contrasting the definitions and descriptions of PCC revealed three specific elements that were represented in these components: holistic, collaborative and responsive care. Activities that constitute each component were specified. The implementation of PCC components is facilitated by a non-specific element: the therapeutic relationship. The results inform the development of protocols that can be used to promote the fidelity with which PCC is delivered by different professionals in a variety of healthcare settings.

Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval

Abstract: This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis.

PV Isolated Three-Port Converter and Energy-Balancing Control Method for PV-Battery Power Supply Applications

Abstract: A photovoltaic (PV)-based stand-alone power system is usually used to manage the energy supplied from several power sources such as PV solar arrays and battery and deliver a continuous power to the users in an appropriate form. Traditionally, three different dc/dc converters would have been used. To reduce the cost and improve the power density of the power system, an integrated solution of PV isolated dc/dc three-port converter (TPC) is proposed in this paper. Zero current switching can be achieved for all main diodes and MOSFETs to improve the efficiency, and a continuous input current of solar array is maintained by adding a magnetic switch derived from a fourth winding of the half-bridge transformer. Based on the energy-balancing part formed by boost, the control methods for the single module to realize maximum power point tracking (MPPT), battery charge control, and main bus regulation are proposed. The power system control method for multimodules in parallel is also derived, and the operation of the TPC power system can be transited between conductance mode and MPPT mode automatically. Finally, the experimental results verify that the proposed TPC, together with the proposed control method, meets the requirements of a high-power-density stand-alone PV-battery power system.