Innovative Projects Realized

CO2-EOR in Devonian Reefs of Alberta – Reservoir Characterization and Screening

Devonian reservoirs have contributed significantly to the development of the petroleum industry in Alberta. This is mainly due to their favourable characteristics of high permeability enhanced by dolomitization in these often reefal structures. Although many have had good recoveries, much oil remains to be recovered. Many of the same characteristics that led to good production performance during the past decades are expected to contribute to good production potential if CO2-based enhanced-oil recovery (CO2-EOR) methods are employed.
In this research, we will characterize a subset of these reservoirs from geological and engineering points of view and develop a methodology for assessing and ranking of their suitability for CO2 -EOR. This will strengthen position of the partner organization in this area. Furthermore, the partner organization is developing a course for reservoir characterization of carbonate reservoirs, particularly for the application of CO2-EOR. The reservoirs examined in this research will serve as case studies in this course.

AI-Based Automated Methodologies for Supply Chains: High Precision Tabular Detection and Semantic Modeling of Electronic Components from Datasheets

This project will develop a hybrid framework by integrating AI and machine learning methods with tabular information extraction and semantic modeling to improve the state-of-the-art precision and recall in tabular detection while maximizing the value of extracted information for industrial applications. Following a properly designed pre-processing stage to improve the outcomes of AI techniques, the research problem is three-fold: The first phase involves in improving the success of tabular detection as there is significant room for improvement in the recognition of e-Component tables under the billion+ components data. Next, semantic search will be applied on tabular information to extract specific data for electronic components in the second phase. In the first two phases, classification and self-organizing maps will be used as the AI techniques to meet the desired level of success. The design of an end-to-end holistic system will be accomplished in the third phase of the proposed project.

Simultaneous Analysis of Endocannabinoids with Mass Spectrometry Methods in Human Serum using Point-of-care Collection Devices

Endocannabinoids are compounds produced by the human body that bind to and activate cannabinoid receptors. There are two major endocannabinoids: anandamide (AEA) and 2-arachidonoylglycerol (2-AG). Both play a role in regulating the firing of brain cells in the nervous system and attenuating immune system responses. As such, the endocannabinoid system is implicated in several clinically relevant processes including appetite regulation, pain management, fertility, and beyond. Routine measurements of AEA and 2-AG can enable further investigation by healthcare professionals into the impact supplementing the endocannabinoid system can have for patients. Method development for AEA and 2-AG will be explored using serum samples analyzed with liquid chromatography-mass spectrometry (LC-MS). A dried serum spot collection card will be evaluated for use as a point-of-care tool for healthcare professionals.

Data Analytics and Natural Language Processing for Courses Curriculum Design

Nowadays, we have gaps between job market demands and competencies that students acquire during their university studies. Course curricula in many cases lack practical content that is relevant for employers. With advances in data analytics algorithms efficiency and with automatic data collection from online resources such as online job postings and surveys, we plan to utilize modelling, natural language processing and machine learning algorithms to extract useful information about relevant skills and qualifications from data, to find patterns and develop insights. We would use these data-driven research results to identify management competencies and technical skills for students to be included in courses curriculum. The aim of this project is to help universities to create or adapt master programs in data analytics that increase likelihood for students to find jobs after their graduation as graduating students would possess all necessary skills that are needed in the job market.

Quantifying the spatial distribution of soil nutrient movement at the field scale

Our proposed research is to measure soil erosion and runoff in the field and use it to calibrate water runoff models. The approach is twofold: a) Use of field data for to determine runoff rates and nutrient loading and b) use high resolution non-destructive methods to monitor water runoff after a precipitation event through unmanned aerial vehicles (UAV). In this way, data will help to adjust current runoff simulation models, and predict real-life scenarios. These models will accurately portray the effect of conventional agricultural practices in soil architecture and runoff water quality.

Investigating the Role of Heterodimeric Nodal/Gdf3 in the Maintenance of hESC Pluripotency

Pluripotency is the property by which immature cells can give rise to all specialized cell types within an adult organism. Human embryonic stem cells, or hESCs, are pluripotent cells which have remarkable potential in regenerative medicine. Clinical translation of stem cell therapy requires better culture conditions which properly maintain hESCs. One important regulator of pluripotency is Nodal signalling, a highly conserved pathway required for early embryonic development. However, limitations of applying Nodal signalling to hESC culture suggests that our understanding of the pathway is incomplete. Recent work in model organisms has shown that Gdf3, a co-factor protein, is required alongside Nodal to exert its effects. Hence, we are investigating how concurrent use of both Nodal and Gdf3 can improve the maintenance of hESC pluripotency in culture. Our research will yield significant implications regarding the molecular mechanisms of pluripotency and stem cell signalling.

Investigation of Microorganisms’ Co-culture System for Biomass/Biofuel Production

The research focuses on the crucial needs for the optimization of biofuel production process. It supports the Canadian energy and environment sectors which are seriously searching for more efficient process, targeting the increasing concern of the society with respect to the fossil fuel energy resources depletion and environmental footprints.

Chemical and biological characterization of the non-tetrahydrocannabinoid profiles of medicinal plants

We propose to perform detailed studies that characterize chemically and biologically and the unaltered ancestral plant species of the Cannabis genus. Chemically, these studies will provide the chemotype profiles for each parental species for a spectrum of non-tetrahydrocannabinoid compounds. Biologically, these studies will provide the pharmacological profiles for each parental species. These data will enable the development of medical cannabis and cannabinoids (the right drug and the right dose) that can then be used for clinical trials (the right person/disease) to ultimately identify the therapeutic role of cannabinoid based pharmaceutics in disease management (the right time).

Backtesting de strategies en finance

Le projet consiste a evaluer I’excedent dee rendements relies a des strategies

d’investissements precises. Ces strategies d’investissement seront basees sur

I’identification d’anomalies de marche non exploitees par les investisseurs. Ces

anomalies seront pour la plupart de nature comptable. Apres I’indentification des

variables com ptables. je devrai choisir les titres pertinents selon nos criteres avec Ie

logiciel de recherche Factset. Ensuite. je devrai creer un portefeuille fictif avec les

titres selectionnes afin de suivre son evolution dans Ie temps. Enfin je conclurai par

les apprentissages et Ie bien-fonde des ces strategies.

Autonomous structure detection and inspection using unmanned aerial systems

In this project, a new method is developed to optimize the performance of an Unmanned Aerial Vehicle (UAV) for autonomous detection and on-the-job view-planning of infrastructure elements with the purpose of their accurate three-dimensional (3D) modeling. The existing view-planning approaches in the literature have mostly modeled non-complex or small-scale objects and have rarely been adapted to flying robots. In addition, the target object is often identified by human operators. This research addresses these problems by training a drone to find the desired object of interest in an unknown environment during an inspection task without human interventions. To this end, first, a technique for object detection will be developed to recognize and locate the target object while the drone is exploring the environment. Second, based on the available information about the desired object, the drone will start next-best-view and motion planning to acquire an adequate photogrammetric network of images in order to reconstruct the inspection target in 3D both accurately and completely. This research will have important impacts on the evolution of infrastructure monitoring and assessment approaches using UAV systems.

Advancing Human Performance in the Canadian Football League

North American professional football players struggle with mental health challenges such as addiction and depression during and following their athletics careers. Despite the fact that these athletes value positive emotional, psychological, and social mental health, little is known about ways organisations can protect and promote these parts of athletes’ mental health. Therefore, the purpose of this research is to explore challenges and opportunities CFL players face to experiencing positive mental health during a professional season. This research will help inform a CFL Human Performance Program designed to improve player well-being and maximise on-field performance.

Identifying Questions for Game-Based Learning through Deep Learning

Game-based learning tools often make use of questions to measure and encourage learning, but generating questions can be challenging, especially at the scale that companies like Axonify are required to do. In this project, the intern will design, implement, and evaluate a system that can apply machine-learning on a corpus of text (e.g., a textbook) to automatically generate questions that can be used in game-based learning tools. This system will allow Axonify to scale their products to larger corpora of source material, larger sets of questions, and ultimately have a much larger market as a result.