Innovative Projects Realized

Reading Leigh Hunt Anew

James Henry Leigh Hunt (1784-1859) was one of the most prolific and influential writers on British culture and politics in the first half of the nineteenth century. “Reading Leigh Hunt Anew” will investigate in great depth Hunt’s interactions and networks. “Reading Leigh Hunt Anew” consists of two tightly interrelated projects on Hunt, one of the key figures of the Romantic period in England, known for his work as editor, journalist, poet, and facilitator. The first is an online database devoted to Hunt’s critical writings called “The Leigh Hunt Archive”, which will enhance accessibility and usability of Hunt’s works (along with an experimental exhibition component). The second part of this research program is a new book entitled “Reading Leigh Hunt’s Critical Essays Anew through Digital Humanities Tools”.

Automatic Generation of Personalized Feedback in Intelligent Tutoring Systems

As Intelligent tutoring systems such as Coursera, Korbit become popular, a great way for students to learn is by receiving feedback for incorrect answers. Since manual feedback generation on large educational texts seems impracticable, an important goal is to create hint generation systems from student-teacher conversations. These systems can be deployed as chatbots giving hints on wrong answers, and annotated datasets for NLP research in dialogue-based question answering.
Initial solutions focused on designing rule-based approaches, typically based on deep linguistic knowledge. Although producing useful hints, these methods rely heavily on manual labour.
In this project, we leverage neural NLP models (like BERT, BART etc.) for automatic generation of pedagogical interventions. We are equally interested in less explored domains – active learning by asking the student to select correct hints, conversational QA (to develop intelligent agents driving QA style conversations). Another important direction is to use hint generation for improving tasks like question-answering, question generation etc.

Combining experiments, genomic tools, and an eco-evolutionary framework to understand the biotic basis of harmful algal blooms

Although evolution is considered a gradual process recent evidence suggests that adaptation can be rapid, particularly when there is genetic variation and selection is strong. Determining whether rapid evolution happens predictably in nature, both at the phenotypic and genomic levels, is crucial to understanding the origins and maintenance of biodiversity. Harmful algal blooms (HABs) pose a challenge to freshwater biodiversity conservation that may force organisms to adapt rapidly. Interestingly, in some cases the keystone zooplankton species Daphnia has been shown to feed extensively on enough cyanobacteria to remediate HABs. Rapid evolution is the most likely mechanism but this has not been investigated. My Mitacs internship centres on developing a new genomic tool for monitoring clonal variation in Daphnia that is reliable, rapid, and low cost. This method will be used to analyze Daphnia samples from a multi-generational HABs exposure experiment. The results generated will address the importance of genetic variation in driving ecological outcomes, resulting in new management strategies for mitigating HABs.

Hardware-in-the-loop software for modeling autonomous sailboat performance

The objective of this project is to develop a hardware in-the-loop sailboat model software that can represent the sailboat’s performance during its mission. At its core, the controller will be programmed on an embedded processor, and will communicate with all of the sailboat’s sub-systems that can be modelled in software. First, to represent the sensor inputs, a model of the position and orientation of the sailboat will be provided in real-time, and the wind speed and direction, as well as ocean currents will be provided. Second, a model of the steering system will be developed to represent the actuator and rudder on the sailboat. Third, a model of the propulsion system that is typically impacted by the sails will be programmed. Each of the software modules will provide a realistic model of the actual sub-component. At the end of the project, it is expected that a complete software model will be able to predict the behavior of a sailboat as it navigates between two waypoints on the ocean. To test the validity of the software, it will be used to explain the performance of the SeaLeon and the Breizh Tigresse during their past missions.

The Unaccompanied Violin Works of F.W. Rust

Johann Sebastian Bach’s works for unaccompanied violin, collectively titled “Sei Solo,” are cornerstones of present-day classical violin performance and pedagogy. However, fundamental questions remain about how violinists performed these works during Bach’s own time. In tandem with dissertation research at the University of Toronto, Arlan Vriens’ Mitacs Globalink Research Award internship project will address this knowledge gap by studying the unaccompanied violin sonatas of Friedrich Wilhelm Rust (1739–1796), a direct student of the Bach family.

As a Visiting Research Student at the University of Cambridge, Vriens will consult manuscripts and estate documents in the UK and Europe and collaborate with historically-informed performance experts in Cambridge and London. Through these activities, he will resolve questions about Rust’s violin performance practices, furthering our understanding of how the violin music of Bach and Rust sounded in the 18th century. In turn, this process will offer new tools for present-day violinists to create fresh, innovative performances of well-known repertoire.

Determining retinal biomarkers for the early diagnosis of Parkinson’s disease using diffuse reflectance spectroscopy

Parkinson’s disease (PD) is currently diagnosed by motor symptoms. These symptoms are only present following around 50% degeneration of the brain cells affected by PD. If earlier diagnosis could be made, interventions could be implemented that may slow brain degeneration and delay the progression of the disease. Non-motor symptoms of PD, such as vision problems, appear much earlier on in the disease progression, but are not specific to PD so cannot be used for diagnosis. The vision problems are thought to be due to altered functioning of the retina caused by brain impairment in PD. Developments in retinal diagnostic techniques and recording technology have opened the possibility of determining retinal changes that, together, may be deemed specific enough to give an accurate diagnosis of PD. We hope to determine recordable retinal changes that can
be used for early diagnosis of PD using these techniques and technologies.

MPC control for HCCI Optimization

The growing demand for mobility worldwide and the mass use of internal combustion engine drive systems poses major challenges for today’s society. Currently, the CO2 content in the atmosphere is continually increasing and concentrations of pollutants in many areas exceed the maximum permissible limits in many urban centers. Additionally, these engine emissions contribute significantly to global warming. The proposed research looks to reduce these engine out emissions with the implementation of optimal control, in this case model predictive control (MPC). The research will focus on designing and implementing an MPC controller at RWTH university on a single cylinder research engine. The controller will be tested at various operating conditions. The second phase of the proposed project is the transfer learning of the engine controller that will allow the controller developed in Germany to be used on a different engine in Canada.

Development of a sustainable business model for microplastics monitoring in the environment

After emerging as one of the top 5 finalists in the Acquahacking Lake Winnipeg Challenge 2020, https://www.iisd.org/system/files/2021-01/aquahacking-lake-winnipeg-2020.pdf, the current co-founders of the AbbaTek Group Inc. have been working assiduously to get the company off the ground. AbbaTek is a for-profit social enterprise in the sustainability and clean tech sector that is committed to contribute meaningfully to the achievement of global sustainability and a healthy business environment where technological development, institutional change and social impact live in harmony. Our flagship product, CompoundConnect is a full-suite Software as a Service (SaaS) that helps mircoplastics researchers and research labs/institutes to quickly and accurate identify, characterise and analyze microplastics in various environments, to reduce microplastic pollution and inform policy.
We see opportunity for growth based on market research and customer interviews conducted as part of our engagement in the Memorial Centre for Entrepreneuership (MCE) Entrepreneurial work term (Fall 2021) and the Genesis Evolution Program (Fall 2021). One of the main challenges we have encountered so far is pinning down accurate figures for the microplastics market size and a sustainable business model.

Development of Sustainable Energy Plan for a School Site

District energy systems are specialized and sophisticated energy solutions that require detailed design and analysis. Integration with utility infrastructure strengthens the resiliency and reliability of the energy services as it enables cities to achieve their energy savings targets and greenhouse gas emission targets. This research will investigate and model the use of distributed energy resources like combined cooling heat and power generators, heat pumps, thermal storage units, thermal and PV solar and waste heat to determine optimal system operation. Seven district energy systems will be developed to model business as usual, cooling only, heating only, as well as combined heating and cooling scenarios. Systems will be comprehensively analyzed used primary thermodynamic principles of mass, energy, entropy and exergy analyses. Environmental impact assessment along with techno economic evaluation will also be conducted. Overall, the primary objective of this research is to model and analyze distinct district energy systems for various applications.

Understanding family and community vulnerabilities in transition to net zero

This project proposes to understand the vulnerabilities of families and communities in the transition to a net zero carbon society; and to build a framework that enables policy makers to strategically plan for and manage transition in such a way that it mitigates and reduces inequalities. There are significant risks to family livelihoods in terms of poverty and exacerbated inequalities, and concerns that certain demographic groups and geographic communities hold disproportionate vulnerabilities in transition. A robust consideration is needed of the risks to families and communities which goes beyond the fuller picture of “job losses” or “workforce demographics” (Bird and Lawton, 2009; Brecher, 2020; NESTA, 2020) to human, social and justice concerns. This project responds to the recognised need for new research and policy mechanisms that systematically draw together dimensions of social policy and education policy with energy and climate policy; in order to address and monitor the vulnerabilities of disadvantaged groups to the effects of a low carbon transition (Silveira & Pritchard, 2016).

Improving sheep production efficiency by genomics and metabolomics blood biomarkers of feed efficiency and subclinical parasite infection

This proposal is devoted to two areas of sheep production: animal health (parasitic infection) and feed efficiency, which both areas are economically important and make a substantial impact on the profitability and sustainability of the enterprise. We will investigate to find potential biomarkers that could be indicator of parasite resistance and/or feed efficiency and could be used potentially to differentiate animals for these traits as early as possible in their life time. We will also develop standard operating procedure for feed efficiency test. Two MSc and 1 post doctorate fellowship will involve through this project to accomplish independent tasks and collectively accomplish the project objectives. This project provide great opportunity for Interns who are eager be work on the field, interact with industry partners and implementing advanced technology such as metabolomics, genomics, and machine learning.

Building Social Community Integration

This work facilitates point-of-impact field research and iterative prototyping toward the continued development of a production-quality social media environment – as part of a larger, ongoing program of research and development – with Iter, an electronic resource facilitating the work of a community focused on medieval and renaissance culture and its artefacts via the ‘knowledge management’ of that community, comprised of academic researchers, those in the cultural and heritage sectors, and beyond.