Service robots can offer personalized service for people with disabilities and empower the staff with efficient support. This study investigates using a collaborative service robot, Aether in a long-term care home. We work collaboratively with people living with disabilities, families, frontline staff, operation leaders, and industry) to Identify user experience, impact and challenges to inform future robot development and adoption. The study will be conducted through three phases: (1) Plan, (2) Adapt, and (3) Evaluate.
Screening with low-dose CT has been shown to significantly reduce lung cancer related mortality in high-risk ever-smokers. Interval cancer (IC) is a rising challenge in lung cancer screening because it usually presents in an advanced stage (stage III/IV non-small cell cancer) or is more biologically aggressive (i.e., small cell histology) and have a poorer prognosis than prevalent cancers. The dilemma is how to catch IC early because the regularly scheduled follow-up CT is often too late.
Chinook salmon may be especially vulnerable with limited food available when they first enter the ocean and during their first winter in the Straight of Georgia. To evaluate this, researchers will use lab studies to produce a new tool, that can be used without causing harm to the fish, to see whether wild Chinook salmon are getting enough food. We will then use this new tool to determine the impact of food limitation on Chinook salmon in the wild and how this may impact the survival of different populations in the winter.
The goal of this research project is to better understand the dilemma faced by displaced persons needing to prove their identities to aid institutions without the assistance of formal documentation. While there are processes in place for addressing these situations, advances in distributed ledger technologies (DLT) may offer ways to improve efficiency and thereby improve the processing experience of these vulnerable persons.
Canada is a multi-cultural nation that welcomes immigrants from around the world, many of whom present a high prevalence of chronic medical conditions and its risk factors. To minimize this burden, it is essential that individuals at risk of such diseases are connected to evidence-based health information. Therefore, there is a need for strategies that align scientific knowledge
with health care practice in terms of chronic disease prevention in minority groups.
The goal of this project is to examine the effect of one-time unconditional direct cash-transfer to recently homeless individuals in Vancouver, BC, Canada. Building on promising results from our research team’s ground-breaking pilot randomized controlled trial (RCT) with 50 cash recipients, the current project seeks to confirm this preliminary evidence using a larger RCT with 200 cash recipients. To evaluate the impact of the cash transfers over time, participants will complete surveys before receiving the cash, and 1, 2, 3, 6, 9, and 12 months after the cash transfer.
There is a strong risk of rejection after a patient receives a heart transplant which leads to death in 1 in 5 people that receive the transplant. Biopsies are taken frequently to determine if the new heart is rejecting by looking at the piece of tissue under a microscope. A specialist doctor will then make an assessment as to the rejection status. However many doctors do not agree on the diagnosis and therefore on a definitive diagnosis.
The primary role of the lubrication system of aero-engines is to remove the excess heat generated by the movement of the bearings, gears and shafts. An aircraft engine has bearing chambers to contain the oil used for lubrication and excess heat removal. Inefficient scavenging of the oil out of the bearing chamber can result in overheating of the bearing chamber parts. A better understanding of the flow phenomena of the air and oil mixture in the bearing chamber can allow better chamber design and bearing controls to avoid overheating by scavenging the oil out efficiently.
In this proposal, one of the two focuses is to leverage recent advances in large language models (e.g., BERT, T5, GPT-2) in Natural Language Processing (NLP) to extract valuable information from unstructured documents, which include clinical documents and user-generated content. The second is on machine learning using medical imaging data. Our aim is to develop robust privacy-preserving diagnostic and prognostic models that are explainable.
Efforts to sequester atmospheric carbon in soil require effective monitoring methods. Soil water content confounds the conventional application of infrared absorption. Raman spectroscopy contends well with water, but suffers from the overwhelming fluorescence typically encountered in the analysis of soil samples. Here we
propose to combine an modulated two-colour illumination scheme with antiphase lock-in detection that will serve to suppress fluorescent backgrounds and uncover Raman signatures of organic substances captured in soils.
