Projets novateurs réalisés

Smart Work Zone Management – Year two

Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates. Using such information, it is then possible to develop novel prediction models determining the impact zone for future construction zones and selecting optimal work zone size and staging of vehicles and equipment. In addition to the prediction model as part of this collaboration, an innovative anticipatory vehicle routing algorithm will be developed that not only help motorist to avoid construction zones but also guides them to their destination while minimizing travel time and utilizing road network more efficiently.

Smart Work Zone Management

Construction zones are one of the leading contributors to Toronto’s ever-growing congestion. The aim of this study is to develop an integrated construction zone traffic management framework to minimize disruption of the traffic and reduce the effect in terms of congestion. This study leverages historical and real data collected from on-board construction trucks provided by the partner organization to find an insight as to how far upstream and downstream of the work zone congestion propagates. Using such information, it is then possible to develop novel prediction models determining the impact zone for future construction zones and selecting optimal work zone size and staging of vehicles and equipment. In addition to the prediction model as part of this collaboration, an innovative anticipatory vehicle routing algorithm will be developed that not only help motorist to avoid construction zones but also guides them to their destination while minimizing travel time and utilizing road network more efficiently.

Advanced Characterization Techniques for Doped Iridium-based Oxygen Evolution Electrocatalysts

Of late, the leaching of doped cations present in oxygen evolution electrocatalysts is reported to enhance oxygen evolution reaction performances. It is suggested that the enhancement is attributed to the increased roughness and modified composition of electrocatalyst surfaces during the dissolution of the cations. The detailed understanding of the enhancement has yet to be disclosed. In this project, iridium oxide-based electrocatalyst partially doped with monovalent cations will be prepared using aqua chemistry, and its electrochemical and physicochemical behavior during the oxygen evolution reaction will be analyzed with the aid of advanced in situ and ex situ characterization technqiues. This combinatorial research is expected to shed light on understanding and elucidation of the dissolution and reduction-oxidation phenomena of the doped cations present in the iridium oxide based electrocatalyst. Thereby, the influence towards oxygen evolution reaction caused by the aforementioned phenomena will be demonstrated. In addition, the findings will eventually help in designing more efficient electrocatalysts in favor of oxygen evolution reaction.

Physiologically based pharmacokinetic modeling to predict drug metabolism in the rat brain

Drug metabolism is a fundamental step of drugs to act and move in the body. Cytochorome P450 (CYP) enzymes, the most important metabolizing enzymes, are superfamily that metabolize a vast array of compounds, especially drugs. While drug metabolism occurs predominantly in the liver so that CYP enzymes in other organs has been understudied, brain CYP-mediated metabolism of drugs can also impact their local brain concentration. As reaching at effective concentration is a direct parameter to success or failure of drug treatment, brain metabolism could be significantly influence on therapeutic effects of drugs. The objective of this study is to describe brain metabolism via CYP enzymes of drugs in rats in vivo by using physiologically-based pharmacokinetic (PBPK) modeling which is a valuable mathematical approach to predict the temporal profiles of drugs and their metabolite in consideration of physiology of the species. This study is expected to suggest more effective approaches to treat and prevent brain diseases.

Development of next generation vision sensor with coded multi-exposure pixel and compressive sensing based readout

Vision sensor is essential components in sensor applications of industry 4.0 such as autonomous vehicle, Internet of Things (IoT), Neural Network. The main goal of this research is to study, design, and develop a new concept of computational vision sensors called “transport-aware”. Unlike conventional vision sensors which record all incident light, transport-aware vision sensor can be programmed to block some of that light, based on the actual 3D paths it followed through a scene. By using coded exposure pixel (CEP) image sensor, we can control the exposure of the vision sensor at the individual pixel level. And the compressive sensing(CS) based readout will enable very high-speed imaging. In this research, I will do research on designing the novel vision sensor which can see high-speed imaging by utilizing CEP and CS-based readout circuit. This novel image sensor can be used to applications such as self-driving cars, biomedical imaging, drones, robots, and machine vision.

Development of electrochemical carbon dioxide reduction technology using the metal / metal oxide low-dimensional nanostructures

Using my oxide nanostructure-metal nanoparticle structures synthetic skill and ultraviolet-light assist metal nanoparticle loading technology, will synthesize metal oxide nanostructure/metal nanoparticle heterojunction for achieving reducing of the overpotential and effective reduction of carbon dioxide. The host supervisor is excellent in techniques for increasing the selectivity of byproducts of carbon dioxide reduction, including combinations of copper and silver nanomaterials, and analyzing mechanisms [8]. Combining the research of my nanostructure synthesis and host supervisor’s selectivity control, high-priced alcohol and liquid carbonyl can be made with high selective. The material selection will be made based on my previous researches which show high catalytic efficiency and, TiO2 nanorod, ZnO nanorod, Cu2O nanowire, and WO3 nanoplate will be first considered. In addition, the combination of copper and silver, as well as copper will be considered as the metal nanoparticles to be used at the same time which showed high selectivity and actively studied by host supervisor.

Baromètre canadien des achats durables

La façon dont les gestionnaires intègrent les principes du

développement durable dans la fonction achat est un sujet encore peu abordé dans le

domaine de la gestion. Ce stage s’intéresse tout particulièrement à l’approvisionnement

responsable des organisations en réalisant le premier Baromètre canadien des achats

durables. Ce projet a pour objectif de définir les tendances et les perspectives des

directions achats en matière de développement durable auprès d’un échantillon de

grandes entreprises canadiennes. Cette initiative est notamment le fruit d’une collaboration

franco-québécoise entre HEC Paris, ECO VADIS et Saulnier Conseil. Ce dernier organisme agit

notamment à titre d’agent de changement dans les modes de production et de consommation

au Québec en vue d’exercer un effet d’entraînement auprès des partenaires des organisations

et entreprises et ce tout au long des chaines d’approvisionnement à travers le monde.

Harm reduction-based programming and services for people living with HIV/AIDS (PLHIV) in a novel clinical care setting: the opportunities and challenges for clinicians, clients, donors and fundraisers – Year two

Substance use significantly impacts the health and health care of many people living with HIV/AIDS (PLHIV), especially those dealing with additional medical, psychosocial, and economic complications. The need for comprehensive care for this population is particularly important given the current opioid overdose crisis in Canada. In response, harm reduction (HR) services (e.g., supervised injection, naloxone training, etc.) have been implemented to reduce drug-related deaths and harms. However, such services are typically not provided within hospitals/outpatient programs. Little is known about how HR services in these contexts may affect clinical care providers, complex service users, or broader organizational operations. This project provides a unique opportunity to examine the impacts of introducing HR services in a clinical care setting. The Casey House Foundation, supports a small community-based hospital which provides in/outpatient care to PLHIV with complex needs. This research will investigate the opportunities and challenges of implementing HR services from various unique perspectives (i.e., physicians, clinical and foundation staff, clients, donors) and collaboratively create a framework for evaluating these services. The foundation supports HR interventions to optimize safety and retain clients in care, and wishes to introduce these services in ways that also increase collaboration, expand clinical expertise, and engage donor support.

Harm reduction-based programming and services for people living with HIV/AIDS (PLHIV) in a novel clinical care setting: the opportunities and challenges for clinicians, clients, donors and fundraisers

Substance use significantly impacts the health and health care of many people living with HIV/AIDS (PLHIV), especially those dealing with additional medical, psychosocial, and economic complications. The need for comprehensive care for this population is particularly important given the current opioid overdose crisis in Canada. In response, harm reduction (HR) services (e.g., supervised injection, naloxone training, etc.) have been implemented to reduce drug-related deaths and harms. However, such services are typically not provided within hospitals/outpatient programs. Little is known about how HR services in these contexts may affect clinical care providers, complex service users, or broader organizational operations. This project provides a unique opportunity to examine the impacts of introducing HR services in a clinical care setting. The Casey House Foundation, supports a small community-based hospital which provides in/outpatient care to PLHIV with complex needs. This research will investigate the opportunities and challenges of implementing HR services from various unique perspectives (i.e., physicians, clinical and foundation staff, clients, donors) and collaboratively create a framework for evaluating these services. The foundation supports HR interventions to optimize safety and retain clients in care, and wishes to introduce these services in ways that also increase collaboration, expand clinical expertise, and engage donor support.

Developing seismic reliability analysis methods for complex infrastructure systems

Evaluate the seismic performance of complex infrastructure systems considering various uncertainties of structural system based on understanding of structural failure mechanism by the earthquake. To do this, it is necessary to estimate the seismic capacity and seismic demand of the structure in a stochastic manner. Therefore, accurately estimation of the seismic capacity and the capacity correlation between related structures as well as develop of the quantification method of the correlation coefficient will be carried out in this research project. By quantifying the seismic capacity correlation that has been excluded in previous research efforts due to the lack of novel methodology and difficulty of nonlinear structural analysis, it is possible to reduce the analysis time of many researchers around the world and make it easier for researchers to incorporate significant effects of the seismic capacity correlation on reliability analysis of complex systems.

Multi-agent reinforcement learning for decentralized UAV/UGV cooperative exploration – Year two

Over the last decade, artificial intelligence has flourished. From a research niche, it has been developed into a versatile tool, seemingly on route to bring automation into every aspect of human life. At the same time, robotics technology has also advanced significantly, and inexpensive multi-robot systems promise to accomplish all those tasks that require both physical parallelism and inherent fault tolerance—such as surveillance and extreme-environment exploration. Decentralized control laws are key to achieve reliability of these systems (as they eliminate the risks posed by single-points-of-failure). Yet, the effective synthesis of (i) machine learning, (ii) multi-robot approaches, and (iii) field robotics is no small task. Previous machine learning and distributed control research rarely ventures beyond computer simulations. GDLS-C and the University of Toronto will investigate how to effectively use multi-agent reinforcement learning in field robotics. GDLS-C’s goal is to improve situational awareness of ground vehicles by using swarms of Unmanned Aerial Vehicles (UAV). Learning decentralized cooperation strategies will improve the resilience of these multi-robot systems—potentially faced with adversarial environments—and, ultimately, the safety of their human operators. Answering our research questions will also enable large collections of robots to learn how to interact with one another—beyond the point human designers can attain.

Multi-agent reinforcement learning for decentralized UAV/UGV cooperative exploration

Over the last decade, artificial intelligence has flourished. From a research niche, it has been developed into a versatile tool, seemingly on route to bring automation into every aspect of human life. At the same time, robotics technology has also advanced significantly, and inexpensive multi-robot systems promise to accomplish all those tasks that require both physical parallelism and inherent fault tolerance—such as surveillance and extreme-environment exploration. Decentralized control laws are key to achieve reliability of these systems (as they eliminate the risks posed by single-points-of-failure). Yet, the effective synthesis of (i) machine learning, (ii) multi-robot approaches, and (iii) field robotics is no small task. Previous machine learning and distributed control research rarely ventures beyond computer simulations. GDLS-C and the University of Toronto will investigate how to effectively use multi-agent reinforcement learning in field robotics. GDLS-C’s goal is to improve situational awareness of ground vehicles by using swarms of Unmanned Aerial Vehicles (UAV). Learning decentralized cooperation strategies will improve the resilience of these multi-robot systems—potentially faced with adversarial environments—and, ultimately, the safety of their human operators. Answering our research questions will also enable large collections of robots to learn how to interact with one another—beyond the point human designers can attain.