Canada’s Private Sponsorship of Refugees (PSR) program has gained a lot of international attention from countries around the world. Since the program’s inception, emerging literature has appropriately captured the increasing shift from the ‘welcoming the stranger’ model to ‘kin-linked’ based sponsorships. Today, linked-sponsorships make up the majority or private sponsorships. The sponsor community is anticipating the rolling out of the new Program Integrity Framework (PIF).

Bird populations in North America have been in decline since mid-1900s due to a multitude of stressors including pesticide use, habitat loss, predation by cats, as well as window and vehicle collisions. Tommy Thompson Park is a constructed wilderness park in Toronto (Ontario) that is along the migratory path for many species that are exhibiting the largest declines.

Although home hemodialysis (HHD) benefits patients including improving their lives’ quality, self-cannulation is currently a limiting and challenging step in their treatment. In this study, we aim to solve this problem by developing an epidermal film/patch with a vein visualization capability. A detachable mobile needle-guide tool can be mounted on the epidermal patch which can help patients to accurately perform self-cannulation through their skin.

This research investigates the experiences and perspectives of skilled newcomer women in Ontario to understand the factors and facilitators which support their transition and integration to the Canadian labour market.

Engineering consulting is a unique occupation that requires professionals to possess both strong technical and non-technical abilities. Engineering consulting organizations have identified the the gap between top performers and their peers to pertain to be social, relational and leadership skills. Our work seeks to identify the leadership skillset that differentiates top performers in engineering consulting organizations by taking a qualitative exploratory approach, collecting data through interviews and focus groups with engineering consultants at various stages of their career.

This project will facilitate the development and validation of a novel AI-based model that can improve the quality of PET images such that diagnostic-quality images can be reconstructed from administering a smaller radiopharmaceutical dose to the patient. The two main advantages of this are decreased radiation dose to the patient which decreases the chance of developing a radiation-induced cancer, and also decreased cost to the healthcare system because less radiopharmaceutical is required to produce a diagnostic-quality study.

Immunotherapies that restore T cell anti-tumor immunity have revolutionized cancer treatment in the last decade. While exciting that many cancer types, including Melanoma and Non-Small Cell Lung Cancer (NSCLC), respond well to immunotherapies, long-term response rates remain poor in some malignancies, notably Ovarian (OC) and Breast Cancer (BC).

This research study proposed a unique platform that can provide information on location of the seniors inside the home, the type of activities they are performing, the intensity level of their activities and their progress in performing exercises that have been assigned by their therapists, remotely. This platform will combine different types of data
to increase the accuracy of the algorithms and to reduce the false alarms. Different data fusion techniques will be evaluated to provide different packages for the users according to their health status and privacy concerns.

The project will significantly advance and integrate three sophisticated contaminated site characterization tools: stable isotope analysis, microbial community profiles, and 3D reactive transport modeling. These three tools will be applied to a series of poorly studied semi volatile compounds, including (di)chloronitrobenzenes and (di)chloroanilines, and incorporated in a conceptual site model along with concentration profiles and hydrogeological data to identify cost-effective remediation strategies.

Most approaches to intelligent, speech-enabled devices today in the research world involve training neural networks on prohibitively large amounts of examples, with little direct control over what is actually learned. On the other hand, this approach often cannot be applied in real-world settings where the required amount of examples does not yet exist, or the expectations of the system's performance are so high that its mistakes must somehow be indirectly corrected. Instead, in real applications, very inflexible, application-specific grammars are often used.