The healthcare industry is shifting from randomized controlled trials (RCTs) care to real world data (RWD) to understand how well an intervention performs in clinical practice. The best source of RWD is source data â that is, data that are collected at the interface of the patient and the health care system.
Aqueous rechargeable lithium battery has received great attention recently due to the less toxicity, lower cost and higher safety compared to the non-aqueous systems. When using the commercially available lithium manganese oxide as active materials, there are demands in suppressing manganese dissolution and graphite consumption in the cathode. As a potential solution to achieve these goals, in this proposal, two dimensional graphene materials are integrated on the surface of the cathode, forming a hybrid cathode aqueous battery.
The internâs study builds on an international survey previously completed in partnership with ICLEI. The study aims to test key structural features in the partnerships for implementing community sustainability plans and how these features are related to plan outcomes (i.e., sustainability progress). To study structure features, the intern will test key factors, such as partner engagement, decision-making systems, communication process & monitoring/reporting procedures, and their relationships with outcomes. This will be done through statistical analysis. The ways community resources (e.g.
The performance of non-precious metal catalysts (NPMCs) for proton exchange membrane fuel cell (PEMFC) has now reached a stage at which they can be considered as possible alternatives to expensive Pt, especially for low power applications. However, despite significant efforts on catalyst development in the past, only limited studies have been performed on NPMC-based electrode designs. Thus, it is required to develop an effective NPMC-based electrode that can correctly balance the complex parameters to maximize the performance it can bring.
As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data.
In collaboration with University of Waterloo-Mitacs researcher, Yael Zilberman-Simakov, LeNano's portable device tests blood for a specific biomarker, a type of protein, elevated by the onset of heart failure. “If a patient has a higher-than-usual concentration of the biomarker, they are at an increased risk of having heart failure,” says Yael. “Similar to how glucose levels should be monitored among diabetic patients, this type of protein needs to be checked every day.Compared to a lab test, personal test devices like LeNano's are faster, simpler, and more convenient.
The proposed research project targets anomaly detection of event data. The project has a duration of six months and aims to achieve two objectives: (1) to evaluate the effectiveness of a novel approach for real-world data, and (2) compare it to alternative methods. The intern will use existing research resources, and will apply them to real-world data provided by the partner, Acerta Analytics Solutions, Inc. to evaluate the different methods.
Automated physiotherapy motion tracking system may improve clinical outcomes by providing subjective measures and continuous monitoring. A study into different metrics that PTs may find useful for diagnosis and a user interface study assessing the current usability of the Automated Rehabilitation System, a system being developed by Cardon rehabilitation, will be conducted. A method to model the central nervous system using controls will be investigated to see if fatigue can be detected, which is a useful metric to provide both patients and physiotherapists.
Advanced Chemical Technologies has developed a unique combination of existing processes to create a new method of making methanol. This proposed method actually consumes the greenhouse gas carbon dioxide, or CO2, meaning it could help reduce the environmental impact of Ontarioâs manufacturing. This internship would involve the simulation of this proposed process using advanced chemical engineering software.
The proposed postdoctoral research focuses on addressing the challenges associated with energy access in developing countries as well as promoting energy-independent communities in Canada in a bid to promote sustainable development. The study would investigate a consortium-like financing model for small and medium scale renewable energy projects in which energy service companies are financed by a number of investors to in order to reduce the investment risks.