Innovative Projects Realized

Evaluation of Durable Hydrocarbon-Based Proton Exchange Membrane for Fuel Cell Applications

This MITACS project aims to investigate the durability of IONOMR’s PEMIONTM membranes in Proton Exchange Membrane (PEM)-based fuel cells for automotive applications and explore their usage as the PEM in PEM-based fuel cells. Specific test conditions and protocols for use at IONOMR based on industry standards will be developed and the materials will be benchmarked against current state-of the art materials in order to prioritize development efforts and aid in customer adoption efforts. IONOMR will benefit from access to specialized equipment available at UBC in addition to expertise in regards to the testing and optimization of PEM fuel cell solutions provided through the Wilkinson lab.

International Trade Diversification Opportunities for Canada’s Agri-Food Sector: Lessons from New Zealand

The Canadian economy is built on decades-worth of industrial activity and its links to the natural endowment of the country. For an extended period of time, Canadians have looked to our natural resources for wealth creation, value-added processing, and international market opportunities. On the other end, recent studies and experts have demonstrated concern and warnings against the environmental concerns linked to over-harvesting and over-exploiting Canadian natural resources.

As the Canadian economy continues to rely on natural resource extraction and value-added processes, new markets are emerging outside of the United States of America. For Canada, markets like China are becoming more important as demand continues to soar from Chinese middle-class consumers and an appetite for better and more sustainable foods from Canada.

The research internship, which will take place in collaboration with Dalhousie University’s School for Resource and Environmental Studies and Shanghai Jiao Tong University’s School of Environmental Science and Engineering, will look at the current state of international trading from Canada to China for the agri-food sector, in addition to the future outlook of the Canadian single-use plastic packaging dilemma derived from food products.

Reinforcement Learning for Predictive Sports Analytics

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios in sports analytics. The collaboration with our industrial partner SPORTLOGiQ will tackle open problems in deep reinforcement learning to build novel capabilities in sports analytics for ice hockey. Deep reinforcement learning is a breakthrough technology with prominent successes in games such as Go (AlphaGo) and Chess (AlphaZero). We will develop fundamental algorithmic advances and apply them to tasks including:
– player evaluation
– event predictions (match outcomes, next action, expected scores)
– recognizing types of players, teams, play sequences, and tactics
– identifying characteristic strengths and weaknesses of players and teams
Montreal-based SPORTLOGiQ uses advanced computer vision to extract information about events from video of sports matches. Their information is more detailed than that provided by any other company or organization. This project will build significant Canadian capacity in sports analytics, support academic research, advance commercialization in the sports industry, and train highly qualified personnel.

Comparative assessment of Machine Learning methods for fraud detection and improving the interpretability of the best model

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used to decide whether the transaction is fraud or not. After the system makes its prediction, it is important for users to understand the reason behind the prediction in such cases. This research project presents a machine learning classifier for fraud detection that will predict if the transaction is fraudulent or not, and also the interpretation of the predictions made by the model for them to be understood by humans.

A Photovoice Study of Settlement Experiences and Needs of Recent Immigrant Men in Central Alberta

Red Deer College, together with the Red Deer Local Immigration Partnership (RDLIP), received Mitacs Accelerate funding to research the settlement experiences of recent immigrant men to Central Alberta. There have been concerted efforts from the federal, provincial, and municipal governments to attract and retain newcomers in mid-size cities and rural communities in Canada; nevertheless, the overall settlement experiences and needs of newcomers in these communities have not received much empirical investigation; this is especially the case among immigrant men. This research will examine settlement experiences of recent immigrant men in Central Alberta to address the knowledge gap. The research team will share research findings with community members, settlement services provider organizations, and policymakers through Photovoice exhibition, conference presentation, and publication in an academic journal.

Imaging the Shallow Shear Wave Velocity Structure of the Northern Cascadia Subduction Zone via Compliance Signals Recorded by Ocean-Bottom Seismometers

The Cascadia subduction zone, which extends along a significant portion of western North America from northern California to southern British Columbia, has the potential to produce large devastating earthquakes and tsunamis. Therefore, better understanding the complex structure of the Cascadia subduction zone is paramount to accurately assessing the seismic and tsunamigenic hazard it poses to the western hemisphere. However, this region is difficult to study because it lies entirely offshore. Therefore, this study will image the structure of the subduction zone by using data recorded by instruments deployed on the seafloor, specifically, those belonging to the Canadian NEPTUNE (North-East Pacific Time-series Undersea Networked Experiments) array. One outcome of this research is that it will pave the way for further seismological study utilizing NEPTUNE data. For example, a firm understanding of how seismic energy interacts with the structure beneath NEPTUNE stations is critical to further studies using other imaging techniques. Furthermore this work will showcase the utility of the NEPTUNE arrat for subduction zone research.

Development of Optical Fiber Sensing Devices for Strain Measurementin Rotational Components

Rotational components (e.g., shafts) are key parts of machinery equipment in automotive, aerospace,
energy production, etc industries. These components are continuously monitored for structural
sensing and failure diagnostics by attaching sensing devices on them. This project aims to implement
optical fiber sensors for strain sensing in rotational components. The available technologies for strain
measurement in are prone to electromagnetic noise and disturbance and the methods available to
overcome it have caused high-cost products. Considering the unique features of optical fiber sensors
(e.g., small size, robustness to electromagnetic noise, long-range linearity, etc), they can effectively
replace the electromagnetic devices. This requires addressing the challenges associated with their
implementation which is a goal this internship project. The development of this technology at ATA, will
allow the company to enter into high-tech manufacturing and earn revenue. This will result in the
involvement of Managers, Sales staff, Engineers and production associates for both innovation and
production at ATA.

An Integrated Multi-Case Study Approach to Advancing Business-to-Business (B2B) Online Marketing Strategies In an Expanded Multi-sector Study with 15 Companies

Business-to-business (B2B) marketing is considerably different from the business-to-consumer (B2C) marketing. Marketing strategies do not necessarily translate from one segment to another. This is evident in the social media/online marketing arena where many of the strategies and tools have been developed primarily for B2C marketing which results in many B2B companies dismissing social media/online marketing tools as irrelevant for their business. While social and online marketing may differ for a B2B context, it should not be dismissed. A strategic approach for the B2B context can drive brand recognition, acquire new customers, and create significant financial returns for a company.
This proposal seeks to better understand how online and social media marketing are best applied within a B2B context. We propose that utilizing a multi-case study approach will provide project participants with the opportunity to better understand social media/online marketing tools and develop more relevant and applicable best practices for a B2B business environment. The project’s research will focus on earned and paid social media coverage/content and which will be analyzed using the tools determined by the project partners (SEM, Hubspot, CRM, and/or Salesforce) in order to identify broad themes and patterns applicable to the partner’s B2B marketing needs.

Generation of correlation hypotheses between Adverse Events (AEs) and NamedEntity Recognition (NER) of drugs in social media and scientific journals usinga machine learning approach

Pharmacovigilance (PV) has evolved and grown more complex over the past 5 to 10 years due to increasing data volumes, evolving regulations, influence of emerging markets and the emerging social media and innovative technological advances.
Fast detection of Adverse Drug Reactions (ADRs) could allow the pharmaceutical industry to anticipate and then to control more efficiently eventual risks associated to taking some medications. There is a clear that the social medias data base is important for continuous and automated ADRs surveillance and it may reduce the number of ADR related potential deaths [1].
The scientific literature review is requires for tracking and identification of risk/benefit ratio of drugs and safety issues.
Adverse Drug Reactions (ADRs)

Development of a commercial protocol to differentiate human pluripotent stem cells into skeletal muscle progenitor cells (hPSC-SMPCs) with transplantation potential

Recent scientific breakthroughs have led to the development of methods to differentiate human PSCs (hPSCs) into skeletal muscle cells. This has allowed, for the first time, the development of cellular models to study muscle diseases such as Duchenne Muscular Dystrophy and the possibility to utilize these cells for cell therapy applications. However, the reliability, efficiency and prober characterization of cells produced from these differentiation protocols remains a roadblock for their routine utilization by the research community. In this proposal we aim to develop a commercial kit to differentiate hPSCs into skeletal muscle progenitor cells (hPSC-SMPCs) with reproducibility between cell lines with various genetic backgrounds. Building on an existing partnership between STEMCELL Technologies and the Rossi Lab (UBC) this project will perform extensive phenotypic and functional characterisation of hPSC-SMPCs required to validate if these cells represent a therapeutically relevant cell type. We anticipate advancement of this technology will enable researchers to overcome previous roadblocks and facilitate the robust and consistent generation of large numbers of skeletal muscle cells. This new tool will allow researchers to study human skeletal muscle development, model muscle diseases, screen cells for drug discover and investigate the regenerative potential of hPSC-SMPCs in preclinical cell therapy applications.

Healing the body, healing the brain: Nutrition and exercise treatments for childhood neurodevelopmental disorders

In the absence of a clear set of recommendations from doctors for the treatment of

neurodevelopmental disorders (NDD) such as fetal alcohol, cerebral palsy, and autism

spectrum disorders, many parents and children affected by NDDs turn to unproven diet- and

exercise-based treatments. While some research suggests that such interventions can boost

brain functioning, very few studies of these programs look at their effectiveness for NDDs.

This project will produce a long-form piece of investigative journalism seeking to explore the

promises and dangers of relying on diet and exercise to remedy NDD. Sidebar articles with

multimedia components will further examine and provide alternative angles to the use of such

non-drug treatments. Additionally, monthly articles about NDD science and related social and

legal issues will be produced. Work will appear in The Vancouver Sun, in print and online

versions. Ultimately, this work aims to benefit The Sun’s audience by explaining complex

science and ethical concerns in plain language that accurately reflects the facts…..TOBECONTINUED

Curiosity driven reinforcement learning for molecular design

Organic molecules are used in many areas, for example in drugs or solar cells. However, the development of new organic molecules with desired properties is difficult, time-intensive, and requires a lot of trial and error. A new line of research uses artificial intelligence to partially automate this process and make it more reliable. This project aims to further advance the approach. Specifically, the use of curiosity-driven reinforcement learning will be explored. Reinforcement learning is a kind of algorithm, that teaches an artificially intelligent agent to achieve a defined goal, like predicting molecules with desired properties. Curiosity in reinforcement learning generally helps the agent to explore its environment. Here, it has the potential to help agents better explore the chemical space, which can lead to faster development of better molecules that were previously overlooked by human chemists.