Innovative Projects Realized

Developing a citizen science application for monitoring biodiversity

The Alberta Biodiversity Monitoring Institute (ABMI) monitors biodiversity across Alberta. ABMI has developed an application called NatureLynx to facilitate the collection of natural history observations by broad stakeholders, including the public. This application will enable people who are not professional scientists to participate in the process of collecting data; this is called citizen science. Initially, NatureLynx will be released to a few groups of citizen scientists (e.g. naturalist organizations) who will provide feedback to improve its design in preparation for wider distribution. Following testing, the application will be released to the public and will be freely available. After its public release, the quality of the data collected by the application will be tested by comparing it with data collected systematically through ABMI’s monitoring activities. Once the NatureLynx data collected by this application has been assessed, it will benefit the organization by increasing the range of biodiversity monitored (via citizen scientists) and facilitating more effective public outreach and engagement.

Fault Modelling Using Extended Fault Trees

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Évaluation en flexion et en tension de tenue de structures de bouleau assemblées avec des connecteurs métalliques

Le présent projet porte sur une étude de praticabilité de nouvelles solutions technologiques dans la construction des assemblages de chevrons et poutrelles en bois. L’attention est portée sur le bouleau comme un candidat potentiel pour ces types d’utilisations résidentielles et commerciales. En effet, malgré ses caractéristiques favorables prouvées par plusieurs études, le bouleau ne figure pas dans la liste des essences normalisées et destinées à une utilisation dans les fermes de toits assemblées avec des plaques métalliques. La stagiaire retenue pour ce projet, doit se pencher sur la réalisation d’un nombre raisonnable d’essais expérimentaux dans l’objectif de confirmer ce potentiel. La prise en considération de type des chargements permanents, la qualité des connecteurs, la teneur en humidité seront privilégiées. Les résultats obtenus seront analysés statistiquement et des recommandations de conformité seront formulées et transmises ensuite aux instances décisionnelles de normalisation.

Study of Cyclic Solvent Injection (CSI) Process with Carbon Dioxide/Methane/Propane Mixture Solvent

This project is to perform systematic studies to better understand key recovery mechanisms of mixture solvent CSI process and provide fundamental parameters for field-scaled prediction. For mass transfer, a methodology of measuring diffusion coefficients for multiple components simultaneously dissolving into heavy oil systems under bulk volume and porous medium conditions will be established. For foamy oil flow, its properties of non-equilibrium will be investigated by PVT measurement and depletion tests, respectively. The sequence of multiple components in the solvent released and the role of each component in mixture solvent with considering the effect of other associated components on foamy oil flow will be examined in depletion tests. Mixture solvent CSI tests will be conducted by five sandpack models. Numerical simulation models will also be built to perform history matching and predicting study. Then scaling criteria from laboratory tests to field applications will be established based on experimental results, numerical simulation models and field data.

Evaluating the impact of an educational arts program on adolescent socio-emotional and academic growth among inner-city, high needs schools

Capturing the impact of program performance on adolescent outcomes is an important way to understand the ways in which a program has best provided its services for optimal outcome success. However, there is limited literature on valid measurement of program success among arts-based educational programs. The project will undertake an outcome evaluation, which focuses on using evidence-based methods that can be validly and reliably used to capture adolescent outcomes that align with the program’s objectives. In other words, the goal is to align program goals and latent concepts, to measurable activities, ultimately informing observable measures that would indicate that a program’s outcomes are being achieved. Additionally, data will be gathered and analyzed throughout program sessions in order to measure program activities, outputs, and outcomes.

Toward an Understanding of Beautiful Feather Cover in Laying Hens – Year Two

Feather pecking (FP) in egg-laying hens, where individuals peck at other birds to pull out and eat their feathers, is a challenge for the sector with large economic and welfare implications. It is especially of concern in systems where birds are housed in large social groups as it is harder to control.
With new policies in Canada leading to the transition from conventional cage to alternative housing systems, it becomes imperative to reduce the risk of large scale FP outbreaks. This project aims to develop a Canadian FP Management Plan (CFMP) by identifying risk factors for FP in alternative housing systems while developing an illustrated guide for farmers to assess plumage condition. This will be translated into the CFMP and provide the Egg Farmers of Canada (EFC) with advice on how to prevent/redu

Improving Metallic Yield in a Steel Rolling Plant through Optimization

The objective of this project is to use optimization to improve metallic yield (the percentage of raw material that ends up as usable product) in an ArcelorMittal Steel Rolling Plant. The metallic yield of the rolling operations depends upon the length of billets from which the final product is manufactured. Ideally, a single customer order would be filled using billets of precisely the length that would yield the minimum achievable amount of scrap. However, ideal yields for each order in a set of customer

orders cannot be aggregated to fulfill them at the ideal yield for the entire set of orders, as this conflicts with the objective of keeping the inventory at a minimum level. In other words, the optimal way of fulfilling a set of customer orders is a tradeoff between yield maximization and inventory minimization. This project will develop efficient and robust models and algorithms for optimal billet ordering…

Toward an Understanding of Beautiful Feather Cover in Laying Hens

Feather pecking (FP) in egg-laying hens, where individuals peck repetitively and excessively at other birds to pull out and eat their feathers, is a challenge for the industry with large economic and welfare implications. High prevalence of FP is reported (60-80%) and this is associated with mortality rates of up to 20-40%, which translates to hundreds of millions of birds dying due to FP every year. It is especially of concern in systems where birds are housed together in large social groups as it is harder to control.

With new policies in Canada leading to the transition from conventional cage to alternative housing systems, it becomes imperative to reduce the risk of large scale FP outbreaks. This proposal aims to develop a Canadian FP Management Plan (CFMP) to ensure safe and successful transition to alternative systems. Therefore, we will identify Canadian-tailored risk factors for FP in alternative housing systems through questionnaires while developing an illustrated guide for farmers/auditors to assess plumage condition. This knowledge will be translated into the CFMP and this tool will provide advice on courses of action to prevent/reduce/stop FP in Canadian hen flocks allowing for transitioning to alternative housing systems while maintaining high animal welfare standards.

Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics – Year Two

As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data. In particular, we focus on three fundamental problems in a typical MS analysis workflow: peptide feature detection and quantification, de novo peptide sequencing, and protein identification and quantification. Once successfully evaluated, the proposed techniques will be implemented and integrated to PEAKS Studio, the current MS analysis platform of the partner. We believe that the project results will contribute major advances to the research field of MS-based proteomics and substantially improve the performance of the partner’s software products.

Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics

Rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning and neural networks for the analysis of MS data. In particular, we focus on three fundamental problems in a typical MS analysis workflow: peptide feature detection and quantification, de novo peptide sequencing, and protein identification and quantification. Once successfully evaluated, the proposed techniques will be implemented and integrated to PEAKS Studio, the current MS analysis platform of the partner.

Using Deep Learning to Auto-tune GPU Application

The fellowship mainly investigates an analysis of the state-of-the-art approaches, design and implementation of cutting-edge deep neural network models to be used on a mobile platform. It explored ways to optimize the deployment of these machine-learning models for prediction tasks on the mobile devices which requires energy efficiency and accuracy.

Using Deep Learning for Auto-tuning of High Performance GPU Applications

Graphics Processing Units (GPUs) are increasingly used to accelerate applications and to reduce their energy use. GPUs are particularly attractive for mobile platforms, where battery life is important. However, GPUs are hard to use, requiring developers to apply optimizations to their code to realize the performance and energy benefits of GPUs–a tedious and error prone process.

This project involves the analysis, implementation and evaluation of a state-of-the-art machine learning based framework to automatically determine what optimizations to apply to GPU programs. Specifically, we plan to explore cutting-edge deep learning methods that have shown great success in recent years in domains such as image processing, computer vision and text processing. Yet, there is little work applying them to performance auto-tuning.

Adapting learning methods to our tuning problem is a hard task, requiring several challenges to be tackled: availability of training data, determining optimal model parameters and dealing with computational complexity. We will build upon our earlier successes in the field to tackle these challenges. The results of this project will benefit our industrial partner, Qualcomm, a key player in the GPU industry. It will also address important problems of optimizations for GPU applications that are outstanding in the community.