Innovative Projects Realized

Development of climate sensitive growth functions for western North America’s boreal tree species – Year two

The Mixedwood Growth Model (MGM) is used by forest managers in estimating growth and yield outcomes for common boreal tree species in North America. MGM has been shown to effectively model both managed and unmanaged stands in Alberta and surrounding regions. Currently, climate effects are not accounted for in growth functions used in MGM. Recent work for black spruce has shown that there is need to understand and model the effect of climate for other boreal tree species including white spruce, aspen, balsam poplar, lodgepole pine and jack pine. This study is designed to examine the effect of climate, competition, site quality, and their interactions with climate on the growth of the aforementioned tree species. Long term measurement data with at least 11,673 Permanent Sample Plots (PSP) established and measured between 1931 and 2015 across western Canada and Alaska will be analysed for this project. This will include evaluating a wide variety of climate variables, competition indices and tree and site variables as potential predictors of growth . The addition of climate to growth functions in MGM would improve its ability to represent effects of climatic variation in the western boreal and support modeling of climate change impacts.

Development of climate sensitive growth functions for western North America’s boreal tree species

The Mixedwood Growth Model (MGM) is used by forest managers in estimating growth and yield outcomes for common boreal tree species in North America. MGM has been shown to effectively model both managed and unmanaged stands in Alberta and surrounding regions. Currently, climate effects are not accounted for in growth functions used in MGM. Recent work for black spruce has shown that there is need to understand and model the effect of climate for other boreal tree species including white spruce, aspen, balsam poplar, lodgepole pine and jack pine. This study is designed to examine the effect of climate, competition, site quality, and their interactions with climate on the growth of the aforementioned tree species. Long term measurement data with at least 11,673 Permanent Sample Plots (PSP) established and measured between 1931 and 2015 across western Canada and Alaska will be analysed for this project. This will include evaluating a wide variety of climate variables, competition indices and tree and site variables as potential predictors of growth . The addition of climate to growth functions in MGM would improve its ability to represent effects of climatic variation in the western boreal and support modeling of climate change impacts.

Improving Resource Estimation and Reconciliation with Machine Learning

Models quantifying the grade and tonnage of mineral deposits form the basis of important and costly decisions for planning, optimization and extraction of a natural resource. Models are initially generated from sparse exploration sampling; however, information is continuously collected until resource extraction. Predicted values that reconcile well with true values following extraction instill confidence in the production forecasts. Failure to meet production forecasts can have crippling effects on cash flow and ultimately result in failure of the project.
In this research a neural-network-based prediction framework is proposed that incorporates production information to the predictive algorithm to improve forecasts of future production, thereby improving reconciliation at a mining project. The proposed method could be used to continually update resource models to improve decisions being made at all scales. This research will benefit the partner company since the incorporation of a wide array of data in manual reconciliation is complex. The proposed research will simultaneously simplify the workflow for the practitioner and improve reconciliation by improving predicted values in unmined areas. This will generate value through increased operational efficiency.

Developing Optimally Discriminative Subnetwork Markers for Predicting Response to Chemotherapy

Molecular profiles of tumour samples have been widely and successfully used for classification problems. Many algorithms have been proposed to predict classes of tumor samples based on expression profiles. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction data with gene expression profiles for the development of subnetwork markers in classification problems. We hope to design a novel network-based classification algorithm using color coding technique to identify optimally discriminative subnetwork markers. We hope to provide better and more stable performance compared with other subnetwork and single gene methods. Another issue of designing our subnetwork method is to make it being capable of producing predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy.

Dérisquage technologique des applications d’un microscope à grandchamp de vue pour l’imagerie volumétrique avec la technique HiLo

Il est possible avec des techniques spécialisées de rendre des cerveaux de souris transparents permettant ainsi de les analyser pour mieux comprendre leur fonctionnement. Toutefois, ces méthodes demeurent en développement et il est très difficile de perfectionner ces techniques rapidement. Le projet consiste à développer un microscope ayant une vision très large permettant ainsi de voir un cerveau de souris au complet en une image. Ce microscope utilise des technologies très spécifiques pour voir le plus de détail possible pour ainsi aider les biologistes dans le perfectionnement de leurs protocoles. Pour ce faire, on utilise des lasers, des mathématiques et de l’électronique pour rendre le microscope plus performant.

Fire retardant fabrics for active workwear applications

Besides sport activities, active wear has found its way into people’s routine life because of its fashionable appearance and comfort. Active workwear is also preferred for some jobs with strenuous activities, like operators working in factories or construction fields. Each of these environments has its own list of hazards, such as fire, chemical spills, or falling objects. Accordingly, specific protective clothing has been designed for such hazardous environment. However, they are not necessarily comfortable. Therefore, the demand for work clothing with active wear appearance and characteristics is increasing. This project is thus aimed at producing a fabric suitable for comfortable active wear while offering resistance to fire and other functionalities such as liquid barrier, UV-blocking and insect repellency.

Mean flow stress modeling towards development of high strength steel

Hot rolling is one of the most significant processes during modern steel production. Complex metallurgical phenomena could take place during and after the application of high pressure and temperature, which largely affect the product properties. A simulation or model that could predict these microscopic events real time is extremely beneficial in the production. Therefore, the aim of this project is to firstly understand an existing model adopted by the partner organization and secondly, to use recorded production data to improve on an semi-empirical model with well-established philosophy developed by scholars, which eventually will be used to improve on the existing model. With the improved model, partner organization could better predict the quality of the product and could largely reduce cost in developing new production schedules.

A Behavioural Risk Model for Deposit Only Customers

In this joint collaboration with BNS, we will develop a behavioural risk model to predict the likelihood of future risk of breaking the promise to pay debt for customers who only hold deposit products with BNS. The model will be utilized to support business operations such as credit card and loan pre-approvals. That is to say, if you are a customer who only have chequing, saving and/or investment accounts with BNS and plan to buy a car, you will be scored in this model for the car loan pre-approval. This model will also contribute to building a centralized retail models system to model all retail customers of BNS.

Up-scalable production of high efficiency perylene diimide (PDI)-based organic light emitting devices using slot die coating methods

With respect to large-area display applications, it is desirable to have not only the active layers but also the electrodes in the OLEDs that can be formed by solution fabrication process. To address the manufacturing challenges of high-performance OLEDs, several scalable techniques such as doctor blading, ink-jet printing, and ultrasonic spray coating have been developed or employed. Hence, OLEDs provide a wider scope for researchers to either develop or demonstrate a variety of new methods that are cost-effective, large-area, and roll-to-roll (R2R) compatible, and more importantly, have excellent efficiency.
As OLEDs technology becomes more established, further improvement in device performance can be expected. However, successful and timely commercialization of this technology to replace already-existing but expensive LCD technologies depends on how some of the critical issues, such as (1) providing strategies for optimization of OLEDs, (2) combining facile synthetic methods with greener processing for efficient polymer-perylene diimide based OLEDs and (3) employing efficient, high performance slot die coating technology for OLED applications, are addressed.

Investigation of the potential of static liquefaction of tailings by taking into account the evolution of the hydro-geotechnical properties during and after their deposition – Year two

Mines generate large quantity of tailings. In most cases, they are transported by pipes and deposited in tailings ponds and confined by dams. To limit the footprint and land area of tailings pond, the dams have to be uplifted progressively with the increase in the tailings level. Several methods exist to uplift the tailings dams. Our partner is particularly interested by the upstream dam construction and a critical concern is how to evaluate the maximum height of the uplift to avoid any static liquefaction. Several numerical models exist to this end. Most of them use constant hydro-geotechnical properties obtained with tailings samples taken at a specific time and at a specific position. The variation (in space) and evolution (with time) of the tailings’ hydro-geotechnical properties during and after their deposition were not taken into account. The objective of this project is to provide an analytical or a numerical model that can be used to evaluate the tailings’ potential of static liquefaction by taking into account the variation and evolution of the hydro-geotechnical properties of the different tailings layers subjected to the cycle of deposition, self-weight consolidations and loading by the tailings depositions of subsequent overlying layers.

Investigation of the potential of static liquefaction of tailings by taking into account the evolution of the hydro-geotechnical properties during and after their deposition

Mines generate large quantity of tailings. In most cases, they are transported by pipes and deposited in tailings ponds and confined by dams. To limit the footprint and land area of tailings pond, the dams have to be uplifted progressively with the increase in the tailings level. Several methods exist to uplift the tailings dams. Our partner is particularly interested by the upstream dam construction and a critical concern is how to evaluate the maximum height of the uplift to avoid any static liquefaction. Several numerical models exist to this end. Most of them use constant hydro-geotechnical properties obtained with tailings samples taken at a specific time and at a specific position. The variation (in space) and evolution (with time) of the tailings’ hydro-geotechnical properties during and after their deposition were not taken into account. The objective of this project is to provide an analytical or a numerical model that can be used to evaluate the tailings’ potential of static liquefaction by taking into account the variation and evolution of the hydro-geotechnical properties of the different tailings layers subjected to the cycle of deposition, self-weight consolidations and loading by the tailings depositions of subsequent overlying layers.

Action Recommendation Engine (ARE)

Project NOVA will build on the University of Ottawa and Ciena’s advanced analytics capabilities to allow networks around the world to understand where video flows run over their network. This will allow the network operators to improve video Qualify of Experience for their end customers, more quickly and cost effectively fix video impacting network problems, plan their networks to better support video, and provide greater customer service awareness of end customer over the top video quality.

Ciena anticipates this capability will propel it into be the world leader in network video analytics with a growing employee base to support this significant business and associated research initiatives to evolve and expand its capability within this market and in to adjacent markets.