A Parameter-based Statistical Algorithm for Math Items in Multimedia Education

Castle Rock Research provides quality curriculum-based educational resources to students, parents and educators, both online and in print. This project involves the design of a parameter-based statistical algorithm to automatically generate math questions for multimedia education applications. Rather than relying on a curriculum designer to create questions one by one, multiple questions can be generated by the algorithm. By varying the parameter values, the model is able to control the difficulty level of the questions.

Residential Construction Material Waste Minimization

The objective of this research is to develop a comprehensive material waste minimization program in order to enhance sustainable development and promote innovation in the residential construction field. The scope of the research will include the identification and detailed examination of the factors that contribute to residential construction material wastes, the investigation of opportunities for automation of existing methods and processes as alternatives to current practices and the implementation of best practice concepts to minimize construction waste in the industry.

Optimization of Learning Performance on Multimedia Items using Machine-learning Methods

Castle Rock Research provides quality curriculum-based educational resources to students, parents and educators, both online and in print. Reinforcement has been well-studied in psychology and proven to be an efficient learning technique. However, learning behavior varies from one individual to another. It is important to understand one’s learning capabilities and response pattern so as to customize the reinforcement process and to optimize the performance of individual learners.

Numerical Weather Modelling for Predicting Hazardous Situations in Power Transmission Networks

The goal of this internship project is to develop a system for numerical modelling of the weather conditions contributing to the heightened risk of transmission system outages. A regular weather forecasting will be enhanced by specific models of the risk increasing factors, such as strong winds, freezing rain or lightening. The enhanced model will help to identify the time and location where hazardous situations are likely to occur, allowing users to make informed decisions regarding potential risk sectors and planning future expansions of existing transmission networks.

Modelling Body Composition with Special Attention to Visceral and Subcutaneous Adiposity

The internship research focuses on the regulation of human body composition as expressed by the ratio of lean body mass to total fat mass, a quantitative description of which is relevant to the management of obesity. Special attention will be paid to the distinction between visceral fat, which is associated with increased risk of heart disease and type 2 Diabetes, and subcutaneous fat, which imparts less risk for these conditions.

Calculation of the True Anisotrophic Distance Between Points

Geostatistics uses statistical modelling to assess the uncertainty inherent in natural resource problems. There is always a sparsity of data because of the cost of getting samples. Statistical models have emerged as the preferred method of quantifying the uncertainly in this situation. These models allow mining, petroleum and environmental companies to make better decisions when faced with sparse data. Thus, the intern’s research will develop a methodology to calculate the true distance between samples.

Kinematics Modelling and Trajectory Design for Arm and Shoulder Physical Therapies Performed by Rehabilitation Robots

This project involves studying the kinematics of the human body during physical therapies on the arm and shoulder. With guidance and assistance from Glenrose Hospital, the intern will collect a library of typically prescribed motions of the shoulder and arm during physical therapy. He will then develop a mathematical model to represent the kinematics of the arm and shoulder as well as a parameter identification routine to identify the model parameters using simple moves and coordinate measurement techniques.

Boreal Forest Mortality Modelling

The intern will collect data on tree mortality in the company's woodlands and develop equations to predict mortality rates from tree growth or forest age and composition. Mortality rates for white spruce have been particularly difficult to obtain due to overall low levels and sporadic occurrence of tree death. This project provides an interesting alternative broad survey approach to mortality compared to the present permanent sample plot (tag and re-measure) program. It will also aid the company with modelling the yield of mixed-wood forests.

Virtual Organ Modelling with Advanced Transport and Visualization Tools

Pharmacokinetics, the study of a drug’s course through the body, is an essential quantitative tool used in all stages of drug development and administration. The liver is the primary site of drug metabolism and elimination from the body, but it is difficult to model due to its complex structure. A virtual organ will be developed for the liver using modern mathematical techniques such as fractals in conjunction with flow reservoir modelling software developed by the Computer Modelling Group Inc.

Geostatistical Modelling of Variability and Uncertainty for Natural Attenuation at Upstream Oil & Gas Contaminated Sites

The intern will work on quantifying inherent uncertainties associated with natural attenuation of organic contaminants at upstream oil and gas contaminated sites. Uncertainty and variability in parameters such as hydraulic conductivity, biodegradation rate constant and spatial distribution of the source of contaminants may lead to highly uncertain results to be obtained from routine fate and transport models. Thus, there is a need to quantify these uncertainties and study their impact on the predicted plume size and clean up time.