Innovative Projects Realized

Development of forest legacy-based mine reclamation techniques for recolonizing the soil food web and promoting vegetation establishment

The August 2014 tailings dam breach at the Mount Polley Mine, BC severely disturbed downstream forest ecosystems through erosion and tailings deposition. The impacted area presents an opportunity to research using ecosystem legacies (components that survive a disturbance) to rehabilitate disturbed sites. A field trial at Mount Polley is testing seedling establishment and soil food web recolonization using three soil legacy reclamation methods: transplants of forest soil into seedling planting holes; spatial belowground connection with the undisturbed forest; and bringing forest floor buried by tailings to the surface. Greenhouse experiments are investigating the volume of soil to transplant, the role of the soil biological community in soil transplants, and the potential for seedlings to regenerate soil legacies, promoting growth of the next generation of trees. TO BE CONT’D

Electroencephalogram signal enhancement using Nonnegative Matrix Factorization for BCI applications

The electroencephalogram (EEG) reflects the neural electrical activity and used in brain-computer interface (BCI) systems which provide assistive technologies for people with various disabilities. The proposed research is mainly focused to apply Nonnegative matrix factorization for EEG signal enhancement for BCI applications to mitigate the limitations of existing BCI system such as slow speeds, limitation of hardware requirement and power dissipation and susceptibility to artifacts. The finding of the proposed research will contribute greatly to the benefit of the society considering that brain-computer interface used in various fields such as in advanced healthcare, neuroergonomics and smart environment, educational and self-regulation, security, and authentication fields. The expected outcome of the proposed project would be to deliver a sophisticated algorithm to process and analyze EEG data which can improve the performance of the existing BCI systems.

Environmental Regulations and Firm Performance

Environmental regulations have been widely used in all developed and some developing countries to mitigate pollution. While these regulations potentially reduce pollution, their impact on companies and businesses is unclear. Economists traditionally think that these regulations will increase production costs and deteriorate a firm’s position in market. However, modern economists argue that environmental regulations may foster innovation and help regulated firms boost their productivity. Despite decades of research how environmental regulations affect firm performance, results are subject to debate and we still continue
to find conflicting evidence which suggests that the existing studies are not sufficient and more precise evidence is needed in order to reach a robust conclusion. This project aims to identify the true effect of environmental regulations on the productivity of Indian manufacturing firms. TO BE CONT’D

Parallel implementation of the variant calling process using the UPMEM technology

Organisms have enormous amounts of instructions encoded in their DNA. When DNA sequences are found using sequencing methods, such as New Generation Sequencing, they are stored as very large datasets. Merely knowing the sequence of an organism’s DNA is not enough, however, and there needs to be further processing of the datasets to extract meaningful data about the organism’s cellular structure and function. The way to process DNA sequences in order to understand why certain cancers and other diseases occur is to look at the variance between the DNA sequences of organisms, this is called variant calling. In this project, we are implementing a more rapid and energy-efficient method to perform variant calling by using High Performance Computing techniques. The approach of this method will be the processing-in-memory architectures by using the UPMEM company’s DRAM chips.

Cost-benefit Analysis of Logistic Models Implemented in Operational Processes

ATB Financial is a large Alberta based full service financial institution. The Card Services

department was forrned 6 years ago and is growing rapidly. The Card Strategy team within

Card Services is developing models and processes to effectively manage marketing

campaigns, collections and fraud. The primary objective of this program is a detailed

cost/benefit analysis of a variety of strategy alternatives such as a dynamic interest rate

program, a new rewards structure, collection strategies, and several other risk reduction

strategies. Particular attention will be paid to the fact that managers want the technical

results in plain language.

Magnetic Resonance Imaging of Fluids in Petroleum Reservoir Core Plugs

This MITACS Accelerate project supports the development of new MR/MRI methods of interest and importance to the international petroleum industry. The project joins the UNB MRI Centre, the leading academic research lab in MRI of petroleum reservoir core plugs, with Green Imaging Technologies, the market leader internationally in the provision of MR/MRI methods for laboratory core analysis to the international petroleum industry. The project results will be incorporated into existing and future products and services sold by Green Imaging Technologies.

RESOLUTION: Resilient Systems for Land Use Transportation

The “RESOLUTION: Resilient Systems for Land Use Transportation” is an ongoing project that focuses on the ways in which the impact of transportation infrastructure in terms of operation and provision leads to social and spatial segregation across major metropolitan regions across the World. My job would be to collaborate with Federal University of ABC to investigate the similarities and differences of segregation patterns between Toronto and São Paulo. Using the data from one city to probe another will be an effective strategy to gain insight, develop solutions and forecast issues for both cities. The findings from this comparative study will be relevant to decision makers and policy analysts in municipal government. I believe that this program hold’s incredible potential for experiential learning that will not only benefit my ability to advance as a research practitioner in my field of study but also in becoming a more inter-culturally competent person.

Catalytic effect of nano-NiO particles in Pyrolysing of Biomass materials

Biomass materials have been a source of energy for years to now and their effective conversion into usable energy form is always under research to get improved results. Various techniques and systems have been developed to exploit different biomass materials so far and this research aims to study the effect of nano NiO particles as catalyst in pyrolysing of agro residues such as rice husk, saw dust, and groundnut shell, etc. The catalyst will be prepared and employed in different size to observe the effect of particle size in pyrolysis and the operating conditions of the test will also be varied to know their effect. Different approaches such as TGA, FTIR, and XRD will be used to characterize the precursors and nano-NiO particles. The experimental results will be correlated with theoretical and kinetic modeling to find the activation energy and conclude the results.

Mobile Remote Implicit Communication

What would it mean to have a background, visceral sense of a partner on an ongoing basis, as we moved throughout our day? How would it change our behaviour and how might it assist us? The SenseProxy application at the core of my Ph.D. research seeks to answer these questions. By joining Prof. Jessica Cauchard’s lab at IDC Herzliya, Israel for at least 12 weeks, I would be able to carry out user experiments in a culture distinct from that of Canada, as well as incorporate additional biometrics such as breathing rate, which are currently being explored in her lab. Expected outcomes include publishing the results, incorporating the experiment into my Ph.D. thesis, and establishing an ongoing collaboration between our two labs on this and related projects.

Martial Art Destination Training Camps as Racial, Classed and Gendered Sporting Projects: A Global Comparative Study

In my proposed PhD project I will interrogate privilege at the nexus of sport, tourism and issues surrounding race, class and gender. There is a new trend emerging in sporting tourism: martial art training camps abroad. Martial art training camps are now established in the places of origin of certain martial arts such as Brazilian Jiu Jitsu in Brazil, Judo in Japan and Muay Thai in Thailand. Regardless of the martial art being studied, all these training camps use similar discourses to sell their services such as invoking discourses and rhetorics of ‘authenticity’, commoditizing race and advertising comfort. TO BE CONT’D

Hydrogel resonators integrated with piezoresistors

Various hydrogel membrane resonators will be fabricated with hydrogels exhibiting different molecular weights and structures along with varying concentrations of the photoinitiator and ultraviolet doses. Then, resonance sensitivity of fabricated hydrogel membranes will be investigated with optical and electrical readout techniques. Using the dynamic mask, various hydrogel membrane resonators with different sizes and shapes will be rapidly fabricated and their sensitivities will be investigated. Once an optimum design is found, the design will be efficiently mass-produced. Initially, gold piezoresistors will be introduced into the hydrogel membrane resonators to read out their resonance signals. Then various 2D materials which show piezoresistivity will be explored and implemented into our hydrogel membrane resonators. Using the hydrogel membrane resonators which are mechanically and electrically optimized, various gas/vapour sensing experiments could be performed in the future.

High-throughput phenotyping of plant health using machine learning and computer vision

Phenotyping is used to develop new strains of plants, understand plant-affecting diseases (phytopathology) and evaluate the effects of various substances on plants. A growing variety of sensors and sensor technology is used to gather data used for phenotyping, in a non-destructive manner, and this overall process of data acquisition and analysis is being automated, leading to high-throughput phenotyping. These technological changes pose challenges both in terms of which models to apply to these heterogeneous data, as well as the scalability of the data and analytics pipeline. The proposed project aims to develop a deep learning model that would produce estimates of plant health based on imaging and other sensor data generated by Terramera, with high classification accuracy, according to threshold tolerances defined by Terramera plant physiologists. TO BE CONT’D