A hybrid computational framework for short-term flood prediction in urban watersheds (characterized by overland runoff) will be developed to improve prediction accuracy. The framework aims to accurately predict an event, e.g. flood or no-flood, as opposed to traditional methods which estimate water flow characteristics, e.g. 6 feet above flood stage. Successful early prediction of these events can help authorities to take appropriate mitigation measures and to minimize losses from the flooding.
Back posture and muscle activation are effected by the alignment and behaviour of jointsbelow: the hip, knee, ankle, and foot. Neuromuscular training insoles use disruptive technology to change how the feet respond to interactions with the ground. If such an insole can improve the foot's ability to sense orientation/position and the required stabilization for normal and effective gait, it may be able to improve the performance of joints in the chain above.
This project aims to develop and evaluate new geometric modeling algorithms of power line and building objects, which are required for conducting power-line related asset risk analysis in challenging environment considering wind blowing effects.The project also aims to integrate newly developed algorithms into York University’s in-house power line modeling test bed and evaluate their performance using GDI’s extensive inventory data.
Presently we do not know the extent to which different types of therapies can assist those who are in the chronic phase of stroke recovery. Technologically innovative rehabilitation devices are now becoming available to clinicians, and often employ video game-like scenarios to motivate the patient to move. However, there is a lack of evidence documenting a) the benefits of enhanced therapy using gaming-like devices, and b) the underlying neuroplastic changes promoted by the use of these devices.
The Limits of Solidarity is an original transmedia research/creation project for new screens, creatively and critically investigating various practices and projects of global solidarity that engage with issues of gender and sexuality.
This internship will extend a research partnership between For Youth Initiative (FYI) organization and the Applied Social Welfare Research and Evaluation Group at the School of Social Work, York University. The internship project will build a comprehensive leadership development model that articulates best and promising evidence-based practices for engaging and building the leadership capacity of youth and youth-led organisations in urban communities. This model will be attentive to the structural constraints that youth in marginalized urban communities experience.
After completing his PhD in Robotics at Carnegie Mellon University in Pittsburgh, he worked at IBM before moving to York University to take up a position at the School of Information Technology in 2001 where he is now an Associate Professor.
Dr. Chen has supervised three post-doctoral fellows through Mitacs-Accelerate internships, each lasting for at least 12 months, and is in the process of applying for two more. He credits Mitacs for helping him secure more funding for his diverse research goals.
The intern will be a member of a team that will look at nutrient strategies for the Great Lakes. The intern will be responsible for collecting research dealing with legislation, policies and programs that deal with the reduction of phosphorus (and other nutrients) in the Great Lakes. The intern will help identify documents and experts and agencies that are relevant to this goal. From this the intern will contribute to creating a plan on how to proceed with existing and also proposing legislation, program and policies dealing with phosphorus management in Great Lakes.
CaseBank provides a service called ChronicX™ to the airline industry for the purpose of detecting and managing repeat defects, i.e. faults that have eluded resolution repeatedly. Each night, airlines upload their raw maintenance records to the CaseBank server. ChronicX applies text mining methods to eliminate irrelevant records, and search for defects that are repeat occurrences of defects previously reported. These are assembled into clusters, each of which is called a ‘chronic’. ChronicX performs reasonably well, but it has limitations that we believe can be improved.