High-throughput transcriptomic and eQTL analyses of silicon-induced resistance against Fusarium head blight on wheat
Wheat is an essential part of the agriculture and agri-food industry in Ontario with an average annual production of 2.5 Million tons. High yield and quality of wheat is seriously threatened by biotic and abiotic factors, among which Fusarium head blight (FHB, caused by the fungus Fusarium graminearum) has historically been most damaging. The mycotoxin produced by this fungus is harmful for human health and livestock feed and productivity. Conventional chemical fungicides are commonly used as an important mean to control FHB; however, they also pose a serious risk to the health of humankind and the environment. One of the most promising non-hazardous, eco-friendly methods to control different plant pathogens is the use of silicon, which induces resistance.
Backed by SOSCIP high-performance computing resources, and using novel bioinformatic approaches, this study aims at conducting high-throughput genome-wide transcriptomic analysis of silicon-induced resistance against FHB, and possibly other important diseases, in wheat. Such study will lead us to:
1. understanding the complex underlying induced defense mechanisms
2. identifying gene modules and regulatory elements that control such mechanisms, and finally and more importantly
3. providing valuable building blocks and framework for future breeding programs which will be focused on the development of novel disease-resistant wheat cultivars in Ontario.
HPC cloud analytics / machine learning support for Watson Pepper clinical study
Skin cancer is the most common type of cancer. 80,000 cases of cancer are diagnosed in Canada every year. 5000 of these cases are melanoma, the deadliest form of cancer (Canadian Skin Cancer Foundation). Current prevention efforts to reduce skin cancer focus on educating individuals on preventative actions that they can take to reduce the risk of this cancer. However, research has shown that both communication failure and information overload are significant problems affecting the quality of patient centered care. Social robotics and artificial intelligence have been used effectively to communicate and positively influence behavior, thus this research proposes to develop and test these combined technologies as an intervention for skin cancer prevention education.
The research team and collaborating research partner IBM will integrate IBM Watson cognitive computing applications with Softbank Robotics advanced robotics platform, the Pepper robot. The Watson Pepper prototype will be used as a controlled variable in a randomized controlled clinical trial (N = 200) to assess the efficacy of socially assistive robotics intervention for behavioural change in skin cancer prevention knowledge and practices among medical patients, the first clinically tested implementation of a Watson Pepper robot for healthcare communication. The research proposes commercialization and business implementation of the integrated IBM Watson robot in an expanded scale and scope of healthcare communication applications. To support the achievement of this innovative technology milestone, SOSCIP will provide the critical cloud data analytics and memory capacity to support the analysis and modeling of the large multivariate data sets associated with this project.
Improved numerical combustion models for understanding and predicting nvPM/Soot formation and emissions in aviation gas turbine engines
Aviation gas turbine engines that burn hydrocarbon based fuels emit nanometer-sized carbonaceous non-volatile (not readily vaporized) particulate matter (nvPM) in addition to the usual gaseous emissions, such as green-house gases (GHG, largely CO2, actually a combustion product), nitric oxide (NOx) and carbon monoxide (CO). Also known as soot, smoke, or black carbon, these very small size nvPM has been shown to impact global warming and climate change by altering the radiation balance in the atmosphere through induced cloud cover and deposition of PM on arctic ice.
For these reasons, the manufacturers of gas turbine engines are today facing more and more stringent governmental and/or environmental regulations pertaining to PM emissions and there is a pressing need for reduced emission strategies. Unfortunately, the physical processes governing how nvPM and its precursors are formed in the high pressure flames and combustion systems of gas turbines is currently a matter of intense debate and a complete fundamental understanding of soot formation and emission processes is not firmly established.
The proposed two-year research project will consider the development of new and improved mathematical theory and computational models for understanding and predicting nvPM formation and emissions in aviation gas turbine engines. Through collaboration with the industrial partner, Pratt & Whitney Canada Corp. (P&WC), this new knowledge and understanding will be subsequently transferred to an industrial setting where it will be put to use in the design of next generation gas turbine engines having reduced PM emissions.
Improvement of Precipitation Gauge Collection in Remote Locations
To properly understand the global water cycle, improve analysis of climate variability, verify climate models and assist in local decision-making of surface or air transport, it is necessary to have better field tools for measurement of snow. Previous work has developed models of the Geonor precipitation guage and has included k-epsilon based numerical models of the flow around shielded gauges. To improve these results, it is essential to pursue advanced turbulence models as well as to develop benchmark experimental results. Large-Eddy Simulation, or LES, has become the method of choice for computationally-intensive simulations resolved to the necessary scales. Traditional Reynolds-averaged methods (RANS), although useful, require significant assumptions that compromise the fidelity of the flow physics obtained.
Direct Numerical Simulation (DNS) which resolves all the scales remains a prohibitive method due to its computational requirements. LES bridges between RANS and DNS, where the energetic large scales are resolved and computed directly whereas the smaller more universal scales are modeled. By modeling the subgrid scales within the inertial subrange, it is possible to extract high-fidelity flow information that can be used to improve local conditions. However, LES is computationally intensive and micro-climate modeling is beyond the capability of most desktop computers. As well, until recently LES models were either lab-developed codes or commercial codes. Lab-developed codes are difficult to transfer to industry partners as they are not necessarily client-friendly. While there exists excellent commercial codes, using these on multi-processor machines is prohibitor expensive. To model this flow, OpenFoam, an open-source turbulence code, will be used. The use of LES, instead of RANS modeling, would allow improved physical modeling of snow precipitate and ensure better comparison to real flow.
Integrated platform for distributed analytics of biometric data
Avertus is a company focused on providing tools for physicians to provide better patient care and treatment and researchers who are seeking new and improved treatments. Its first platform is a high frequency wireless brain activity monitor with a first application for seizure pattern detection. This will help physicians assess types and causes of seizures as well as response to drugs and other treatments and longer term help deliver future treatment technologies. With support from the SOSCIP and University of Toronto researchers, we will also implement the first commercially available high performance distributed computing platform for easy to use biomarker discovery tools and both batch and real time big data analytic capability for clinicians and researchers.
Intelligent emergency response using the internet of things (IoT)
Smart mobile devices and wireless Internet access are allowing emergency responders to access and share valuable emergency-related information coming from data repositories, citizens, surveillance cameras, and many other sources. However, because there is no accurate and energy-efficient indoor positioning solution, emergency responders cannot translate that wealth of information into a much-needed situational awareness. This project addresses this unique challenge by using data analytics and the Internet of Things (IoT). More specifically, this project devises an accurate, scalable, energy efficient, robust, and resilient indoor positioning solution using Bluetooth Low Energy (BLE) beacons.
These battery-operated beacons will be deployed in buildings as part of emergency preparedness strategy. When an emergency happens, the communication between these beacons and the smartphones and tablets of emergency responders will give emergency responders the necessary situational awareness at the right time and the right location. This positioning solution is based on a thorough analysis of signal propagation and coverage measurements using machine-learning tools. This solution will then be used to prototype a Location-Based Service (LBS). This software application gives emergency responders necessary and sufficient situational awareness while responding to building emergencies. This LBS is designed and developed based on a comprehensive analysis of buildings’ information, emergency response plans, and emergency records.
This combined repository of information is processed using text mining to identify points of interest at any building and during any emergency and give a statistical model for the workflow of emergency responders between these points. Deploying BLE beacons at these points will help expedite the preparation and execution of emergency response plans, and hence enhance the situational-awareness, efficiency, and effectiveness of emergency responders. This is the first project to combine data analytics and IoT in the public safety domain. Its findings will have a ripple effect on the design and development of software solutions for emergency responders.
Joint optimization of route design and schedules for fixed route transit systems
The current method of optimizing routes and schedules for fixed route transit systems is sequential. Typically, route planning (involving determining route path, stops and service pattern) occurs initially, followed by schedule optimization (examining factors such as vehicle availability, operating requirements, safety restrictions, union contracts and employee pay).
This sequential optimization process produces a sub-optimal overall solution, inefficiently allocating agency resources, or allocating them in a way that may not be providing transit users with the best route and service. As a result, a method that could handle both simultaneously would be unique in the industry and extremely valuable to all fixed route transit agencies and service providers. Handling the numerous variables and constraints in route and schedule planning requires a method capable of intelligently searching for solutions.
The proposed method to tackle these dual requirements is simulation-based optimization using constraint programming—which is an optimization technique where knowledge of the problem is used to reduce the solution search space based on constraints. Evaluation of the feasible solutions is proposed to be accomplished using a simulation of the transit service that would more accurately represent service performance and passenger experience, where the structure of constraint programming methods lends themselves well to parallelization—ideal for the multi-core setup of the SOSCIP platforms.
Large scale simulation of nanostructured optical surfaces
Plasmonic metasurfaces are engineered human-fabricated surfaces on which there is nanometer scale structure, usually containing metallic features. These nanoscale features can resonantly interact with light through the excitation of electric charge density oscillations, or surface plasmons. As plasmonic metasurfaces have essentially infinite design potential — including designer resonant behaviour and strong nanoscale light field enhancements — there is currently a great deal of interest in using them for a wide range of applications, from flat optical devices to biosensing to enhanced nonlinear optical signals3 to colour production4. A major focus of this project will be to perform large scale computational electrodynamics simulations on the Blue Gene Q to understand and design plasmonic metasurfaces. The work will be connected to projects underway with several Canadian industrial partners, as well as a plan to engage with several others, and will involve multiple trainees at various stages in their education – from undergraduate to postdoctoral fellow.
Machine Learning for Materials Discovery and Design
The objective of this project is to combine machine learning methodologies and electronic structure theory for the purpose of designing new materials through computational modelling. Achieving this goal will be important for the fields of Advanced Manufacturing and Energy (Materials). We will perform electronic structure calculations on a large database of existing materials (transition metal surfaces) and use results of these simulations as input to a machine learning model. The developed model will then be tested against new materials outside of the test set to confirm the model’s validity and transferability. This machine learning model will be used to identify new catalytic materials for use in water splitting and CO2 reforming devices. The project will combine high performance computing and machine learning to enable accelerated material discovery.
Mining microbiome community structure and biomarker identification through data intensive biology, machine learning, and high throughput technologies
Microbial ecosystems such as those associated with the mining industry are complex networks of interacting species and biochemical dependencies. Modeling responses to different or changing environmental conditions is a considerable computational and statistical challenge. Current approaches largely identify important features through differential abundance, ignoring the disparate influence some species or functions exert on the community, greatly simplifying biological complexity. Additionally, these approaches can ignore poorly annotated features (e.g., hypothetical genes, microbial dark matter, ORFans). Excluding these known unknowns and unknown unknowns reduces the resolution and sensitivity of these analyses. Metagenomic feature selection using machine learning has been most widely applied to the human microbiome, which currently has more extensive data than other systems. We will apply a superficially similar but much higher resolution approach to less studied, more dynamic industrial microbiomes, such as mining.
Mobilizing ‘Big energy data’ for building conservation to socialize sustainability
This project will establish the first customer-centric universal tool using mobile/cloud technology to deliver & share energy information electronically with energy customers to stimulate social change, encourage conservation & help manage/reduce GHG emissions.
A global desire to manage climate change is here. There needs to be a joint effort to help the energy user gain access to electronic information, so the data can be used to better manage conservation, sustainability and technology integration. Innovation in the energy sector continues to create data silos, as new technologies used don’t allow for the data to freely flow out of the systems to other solutions. Customers require easy access & a simplified understanding of electric information (not print or PDFs). For this to happen, the end customer requires data to be managed, simplified & shared, so information can be “open” to innovation and electronic data to be standardized.
The market needs Green practices and conservation to flourish in the private sector and public sector. Internationally and in Ontario, there are regulatory and political requirements to provide end-user access to electronic data, but without a simplified roadmap to interconnectivity how will it happen? The market needs linkages to the “Internet of Things” to learn, inform and conserve. In a few short years, there will be more than 25 billion devices generating data on every topic imaginable. The energy customer and building owner needs simplified data from multiple sources. Screaming has an opportunity to deliver this information cost-effectively to the utility, government & customer.
Modeling of quantum dots using improved MD/DFT highly parallel methodology and its application to commercial MD software
Quantum dots are small ligand-protected chunks of semiconductors which have diameters ranging from 2 to 100 nanometers. Because of their small size, their properties are quite different from those of the bulk semiconductor. In particular, due to quantum confinement, their band gap is larger and the band gap increases as the diameter decreases. In solution, the chunks must be protected by legends in order to prevent agglomeration. Modeling the properties of quantum dots is very helpful in understanding and interpreting experimental results. Efficient parallelization of molecular dynamics software forms a cornerstone of simulation modeling of nanoparticles as it allows for simulation of larger space domain over longer periods of time. The software methodology improvements aimed at increasing computational efficiency on large HPC clusters undertakes as part of this research project aim to achieve this goal.
Modeling of urban wind flow and its interaction with buildings and their components
The design challenge is that as city populations rapidly increase, urban densification through vertical design will demand highly efficient, optimized and safe built environments to suit a changing climate. Therefore building and urban designers in the Architecture and Engineering (A&E) industry can benefit greatly from having access to robust, accurate, fast and cost effective wind modelling processes to assist in building and urban design performance simulation. However, urban wind flows are highly complex due to the time and spatial characteristics of the wind, its turbulence characteristics, and its interaction with the urban environment. Computational wind simulations therefore require very advanced modelling processes and demand enormous computational resources to replicate this phenomena. A validated and trusted computational wind simulation process, developed through this partnership, will offer new ways through which design practitioners can improve design, make buildings safer, more efficient and reduce building construction costs and materials through concept optimization. This research collaboration will allow the Ontario business and research community to continue to play its global and leading role in the application and export of its resources and expertise in physical testing and computational wind engineering.
Modelling, Assessment, and Reduction of Unsteady Pressure Fluctuations in Gas Turbine Testing Facilities
In a gas turbine engine testing facility, the engine draws air in from the surrounding environment through a large inlet. This process entrains additional flow beyond that which goes through the engine. Downstream of the engine, the engine exhaust and this entrained flow both enter a long cylindrical flow channel during which the two streams partially mix. Further downstream the flow diffuses and impinges on a conical flow redirection device (cone) before exiting into an exhaust stack via a perforated cylinder. This project aims to develop solutions and new insight related to reducing undesired large amplitude, low frequency pressure fluctuations in the exhaust stream of a gas turbine engine testing facility. The analysis will be carried out using high fidelity numerical simulations of unstead fluid flow such as a large eddy simulation. Solutions will be investigated by identifying how best to model the unsteady pressure fluctuations numberically and then determining how changes to the geometry of existing facility components can best reduce the pressure fluctations. Simultaneously, the phsyical mechanisms driving the pressure fluctuations will be firmly established and solutions based on reducing the ultimate source of the unsteadiness will be proposed. Thse may be more substantial than simply changing existing facility component geometry and thus will serve as backup solutions if the fluctuations cannot be adequately reduced via simple component geometry changes.
Multi-sensor miner data using smart computing platforms
The mining industry is a significant contributor to the Canadian economy, requiring close to 100,000 people to be hired by 2020. However, mining remains the second most dangerous job across the planet with 46.9 fatalities per 100,000 workers. Until the recent MINER act, there is little interest in providing communication services in mines. The MINER act made tracking all coal miners mandatory. In this project, we aim to develop a smart computing platform that combines data from digital wireless radio, thermal and video images in real time. This platform will incorporate tracking algorithms to aid in precisely locating miners, explosives and vehicles in mines continuously. This is very difficult due to the very nature of the mines. Extremely harsh, irregularly confined, and rough mine environments make radio wave propagation unpredictable. Furthermore, low visible light conditions prevent effective surveillance using cameras. Increasing number of sensors helps accurately locate targets, but will also increase the complexity of the algorithms. Using a variety of sensors will require several algorithms to run simultaneously to extract and fuse useful information from noisy data gathered from the mine in real-time. Especially identification of moving object in low-light video and thermal imaging is computationally very intensive. Hence, such a platform can only be implemented on a supercomputer, or a network of very fast computers. For this purpose, our work will rely on SOSCIP’s extensive computational capabilities. The result of this work will lead to significant improvements in safety of the mine personnel and better resource allocation and management in the mine
Multilevel streaming data analytics infrastructure for predictive analytics
Predictive Analytics for digital media processing is facing the challenge of handling an increasing volume, velocity and variety of big data and there has been an enormous drive lately in the area of streaming data analytics. We are rapidly moving towards the Internet of Things (IoT) where predictive analytics will need to analyze and integrate streaming data from many different devices and digital media sources including structured data from the traditional relational databases and unstructured data from the recent big data storage systems. Therefore, we need an infrastructure to enable long-term multilevel knowledge extraction where 1) the 1st level analytics performed by a stream processing engine will identify important data components from multiple data streams and move them into a memory buffer. 2) Then an in-memory data analytics engine will be used to perform the 2nd and the subsequent levels of analytics for knowledge extraction and integration with other big data sources. 3) Finally, only the important data stream components and the extracted knowledge can be stored for future analytics into a big data store. We propose to develop an infrastructure to facilitate complex multilevel predictive analytics and to streamline the process of knowledge extraction and integration for both streaming and non-streaming data. A variety of open source stream processing engines exist today. However, none support such multilevel analytics. We will use open source streaming and in-memory data analytics engines and SOSCIP’s cloud and big memory systems. The infrastructure will be validated using streaming financial and business news and social media data analytics for identifying business growth, stress and risk signals. It will contribute to Canada’s economy by leveraging predictive analytics for decision support in areas such as cybersecurity, health and e-government.
Next generation low-emission combustor technologies for high-efficiency compact aviation gas turbine engines
The primary objective of the proposed research is to develop next-generation combustor technologies for aviation gas turbine engines that produce extremely low emissions and higher fuel efficiency, while reducing development times/costs and maintenance requirements. Impacts will be realized in terms of local air quality/public health, climate change, sustainability, and commercial engine sales. The current design priorities of aviation gas turbine combustors are operability, efficiency, emissions, durability, compatibility with the main engine core, and safety. Today’s combustors for small aviation gas turbine engines provide reliable and dependable service.
However, increasingly stringent emissions regulations are being enacted (e.g. by the International Civil Aviation Organization) that impose tighter criteria for emissions. Moreover, combustor conditions are continuously becoming more extreme in terms of operating pressure and temperature in order to improve engine efficiency. These conditions result in increased maintenance requirements, and hence increased engine operating cost. Future combustors for gas turbine engines are therefore in need of far-reaching design changes to meet these emissions requirements, while simultaneously being more cost effective and providing better operability/durability.
These next generation combustor technologies are essential to help our main industrial partner, Pratt & Whitney Canada (P&WC), to maintain its market competitiveness in the small aviation gas turbine sector. P&WC is the leading manufacturer of small aviation gas turbine engines, and these engines are extensively used around the globe as well as in Ontario (e.g., all commercial aviation at Toronto’s Billy Bishop Airport). Together with the two industrial partners, P&WC and IBM Canada, we will design novel new combustors that have lower emissions per unit fuel consumed, less fuel consumption per unit thrust produced, and reduced maintenance requirements relative to current systems. This will be achieved by combining the state-of-the-art experimental, computational, and analytical capabilities of the university research team with the practical gas turbine design knowledge of P&WC engineers and the high-performance computing (HPC) expertise of IBM.
Next generation radio interferometry for astronomy, geodesy and radar ranging
Determining the properties of a pulsar and the interstellar medium through which its pulses of radio emission travel may appear to be an endeavor that is rather different from using a radar to localize a satellite and determine its properties. But both share the short bursts of radio emission and while in one case the pulses are refracted and in the other reflected, the analysis of the received radio emission turns out to require very similar techniques. Recognizing this synergy, this project launches a new dimension in the industrial academic collaboration between Thoth Technology Inc. and the University of Toronto, to jointly develop technology for using transient radio signals to localize sources and determine their properties, both to better understand pulsars and the interstellar medium and to enable Thoth to provide and commercialize unique radar capabilities. The project plans to first develop a common interface for the Thoth Algonquin Radio Observatory facilities, enabling the production of data compliant with the VDIF digital media standard, even at very high data rates, and to select and combine parts from the streams at will for rapid transmission and quick-look visualization. Next, we use BGQ resources to develop and optimize the data analysis, following new insights on how bursty signals in particular should allow simpler retrieval of properties of sources.
Non-equilibrium green’s function approach to simulations of active photonic nanostructures
The present project is aimed at creating fast and accurate highly-parallel algorithms and the related mathematical models to simulate active photonics devices (e.g., quantum well lasers) using high performance computers. Computational tools for simulation of optoelectronic devices bring to the industry a considerable reduction of the development cost. New models are developed, implemented in software, verified by experiments and tested for new predictions. Such an approach shortens development time of new devices, thus reduces overall price of finished products. The past era of device simulations was based on drift-diffusion approach. However, the nano-era of optoelectronic devices involves new materials and architectures, along with a number of novel phenomena that must be taken into account. Altogether the field is expanding very fast. At the same time modern simulations require extensive computational resources, and there exists significant room for optimization. One of the approaches to optimization is the use of parallel algorithms on the systems like Blue Gene Q. We hope our project will allow Optiwave to increase significantly their global market share in photonic simulations software