Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. 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.

Control of a cascaded H-bridge multilevel converter for grid connection of photovoltaic systems

Abstract: In this paper a control method for cascaded H-bridge multilevel converters for grid connection of photovoltaic systems is analyzed. The use of the multilevel converter introduces a series of advantages: improved power quality (lower current ripple), smaller filters, reduced switching frequency, no need of boost dc-dc stage and possible elimination of step-up transformer, all of which have a positive impact on the system efficiency. However it requires a more sophisticated control method specially due to dc-link voltage drifts produced by circulating power in the converter. The proposed control method is based on traditional voltage oriented control with a cascaded dc-link voltage control and grid current control loop. Two extra stages are added to this traditional control scheme, one to introduce maximum power point tracking for each module or string, and another to control the dc-link voltages drift due to circulating power in the converter. The latter is performed by the addition of a simple feedforward control strategy directly in the modulation stage. The proposed control method is modular and easy to adapt for any number of converters in series. Simulation results are presented to support the theoretical analysis.

Disproportionate use of lethal force in policing is associated with regional racial biases of residents

Abstract: Due to a lack of data, the demographic and psychological factors associated with lethal force by police officers have remained insufficiently explored. We develop the first predictive models of lethal force by integrating crowd-sourced and fact-checked lethal force databases with regional demographics and measures of geolocated implicit and explicit racial biases collected from 2,156,053 residents across the United States. Results indicate that only the implicit racial prejudices and stereotypes of White residents, beyond major demographic covariates, are associated with disproportionally more use of lethal force with Blacks relative to regional base rates of Blacks in the population. Thus, the current work provides the first macropsychological statistical models of lethal force, indicating that the context in which police officers work is significantly associated with disproportionate use of lethal force.

Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data

Abstract: Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low-to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimers brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimers disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimers disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimers subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.

Food Citizenship and Community Food Security: Lessons from Toronto, Canada

Abstract: The Toronto Food Policy Council (TFPC) was created in 1990 as a vehicle for “food citizenship.” Its creators challenged the assumptions that hunger was mainly a problem of income and that the food system was nourishing all Canadians adequately. Working from a vision of food security based on both social justice and environmental sustainability, the TFPC was designed to be multi-sectoral and cross-jurisdictional, and to support project innovation and policy advocacy. The paper develops the concept of “food citizenship,” emphasizing the need to move beyond food as a commodity and people as consumers. Critiques of corporate control and a loss of food skills, or “de-skilling,” within the public, and the limits of anti-hunger advocacy, or charity for achieving food security are offered.