Selah Katona

��˾��ֱ�� researchers develop foam filter system to mop up oil spills faster

Christopher.Sorensen — Thu, 01 Aug 2024 18:11:38 +0000

��˾��ֱ�� researchers develop foam filter system to mop up oil spills faster

Christopher.Sorensen Thu, 08/01/2024 - 14:11

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Associate Professor Amy Bilton, left, and fellow team members Calvin Rieder, Puwaner Gou, Nitish Sarker, along with Jordan Bouchard (not pictured), show off a prototype of their polymer foam filter technology (photo courtesy of Monisha Naik)

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Faculty of Applied Science & Engineering

Research & Innovation

Sustainability

Subheadline

The team, which has advanced to the final stage of a national competition, is testing a polymer foam filter system that would allow oil spill response vessels to treat contaminated water on board

A research team from the University of Toronto has advanced to the final stage of a national competition that seeks to develop better ways to protect Canada from the devastating effects of oil spills.

The team, led by Amy Bilton, an associate professor of mechanical and industrial engineering in the Faculty of Applied Science & Engineering, has been awarded $1.3 million via Natural Resources Canada’s (NRCan) Oil Spill Response Challenge to develop and test a system that treats contaminated water onboard oil spill response ships.

Called FRODO, the prototype allows for treatment of contaminated water stored in response vessels through an engineered polymer foam filter – not unlike a kitchen sponge – that allows water to pass while syphoning off the small oil particles. The filter and treatment system would allow the ship to release previously contaminated water back into the environment. That, in turn, means more oil can be collected in less time.

A FRODO prototype unit showing the canisters that hold the foam filter that allows water to pass through (photo courtesy of Amy Bilton)

By contrast, current approaches involve response vessels gathering oil with large, floating barriers before using a skimmer to suck oil and water into their holds. The ships must then offload the entire load – about 25 per cent oil and 75 per cent water with small trace amounts of oil – to a land-based facility for treatment.

“We’re both engineering the foam material and developing the treatment process to fill in a gap in current oil spill operations,” says Bilton. “We aim to increase the amount of oil these vessels can collect by a factor of four.”

More than four million barrels of oil are transported through Canada daily. With more than 240,000 kilometres of coastline and more than 890,000 kilometres of freshwater systems across the country, effective oil spill response is critical in protecting diverse ecosystems and communities. Through the Oil Spill Response Challenge, the Government of Canada is investing $10 million in the development of innovative and rapidly deployable solutions to oil spill detection, response and recovery in Canada’s aquatic environments.

Assessing the environmental impact is another key area of research for the Bilton team. Canada has a zero-discharge policy, meaning no oil can be present in water when discharging it back into our water systems. These regulations vary by country. Norway, for example, requires the amount of oil in the discharge to be less than 15 parts per million. Bilton and her team are running a parallel project funded through NRCan’s Multi-partner Research Initiative to understand the environmental impact and considerations of implementing this in-situ treatment process.

Bilton’s team is one of the five finalists moving on to Stage 3 of the competition, with the winner set to be announced in winter of 2025. At this stage, the team has one year to accelerate, scale and test their prototype in preparation for commercialization.

“We are in the testing phase now and are planning large-scale simulations at our Ohmsett partner facility in New Jersey,” says Bilton.

The team is partnering with Western and Eastern Coast Marine Response Corporations, VPC Group and Urethane Sciences to ensure their system is scalable and can be manufactured. The current prototype is significantly smaller – around one metre in size – than what the final system will be. Ensuring the engineered polymer foam and other materials can be manufactured and meet strict requirements are top of mind at this stage of the challenge.

“Our goal is that this system can be deployed quickly and effectively to improve oil spill response across Canada, and potentially in other parts of the world,” says Bilton.

The research team that wins the final stage will receive an additional $2 million in funding to commercialize their technology.

��˾��ֱ�� expert navigates water, food and energy challenges for large river basins

rahul.kalvapalle — Tue, 02 Apr 2024 16:47:41 +0000

��˾��ֱ�� expert navigates water, food and energy challenges for large river basins

rahul.kalvapalle Tue, 04/02/2024 - 12:47

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Assistant Professor Mohammed Basheer’s research aims to integrate AI, engineering, social sciences and stakeholder engagement into a holistic model of water resource management (photo by Waddah Hago)

Selah Katona

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Breaking Research

Faculty of Applied Science & Engineering

Subheadline

Mohammed Basheer's research uses the Nile as a case study for how interdisciplinary frameworks can inform resource management

The Nile is one of the world’s longest rivers, providing water and hydropower to millions of people.

But with nearly 86 percent of the water that flows into the Nile originating in Ethiopia – and nearly all of it consumed in Egypt and Sudan – there are complex dynamics surrounding resource management and reconciling the needs of countries along the north-flowing river.

The University of Toronto's Mohammed Basheer is looking to address this by integrating AI, engineering, social sciences and stakeholder engagement into a holistic model of water resource management.

An assistant professor in the department of civil and mineral engineering in the Faculty of Applied Science & Engineering, Basheer’s work focuses on the water-energy-food nexus, incorporating political and economic dynamics to improve policy and planning across the region.

This interdisciplinary approach is laid out in a framework, developed with partners at the International Food Policy Research Institute and published in the Journal of Hydrology, that provides a blueprint to navigate the interplay between water, energy and food resources at basin scale.

“It makes sense to manage these resources together; if you aren’t thinking holistically, then interventions may produce trade-offs,” says Basheer.

Concerns over water availability downstream have mounted ever since Ethiopia began construction of the Grand Ethiopian Renaissance Dam (GERD) in 2011. The project could double electricity generation in a country where around 45 per cent of the population didn’t have access to electricity as of 2021 – but it’s expected to affect the inter- and intra-annual variabilities of the Nile flow, potentially affecting water supply and hydroelectric power downstream in Egypt and Sudan.

The four-step framework for planning dam operation and irrigation development on the Blue Nile (image by Mohammed Basheer)

Currently, water supply from the Nile accounts for around 97 per cent of Egypt’s freshwater resources. A significant amount of irrigation and hydro in Sudan is sourced from the river as well.

“It is difficult to develop fixed long-term plans given the uncertainties stemming from the variability and shifts in the Nile flow,” Basheer said, highlighting the imperative for adaptive strategies.

The framework created by Basheer and partners comprises four steps.

The first involves understanding the food-water-energy nexus by consulting with stakeholders to identify key priorities and trade-offs.

The second stage is simulation – Basheer and colleagues developed sophisticated models to encapsulate the complexities of the Blue Nile River basin. These models serve as virtual laboratories that enable policy-makers to explore scenarios and interventions.

The third stage leverages AI algorithms to identify patterns and recommend strategies to balance trade-offs between water, energy and food performance.

Stakeholder engagement and communication is the final stage, with visualizations and online tools used to communicate findings and actionable insights to policy-makers.

The work has resulted in partnerships with the International Food Policy Research Institute and other government and academic institutions, with Basheer – a recent addition to ��˾��ֱ�� Engineering faculty – keen to apply his methodology to projects in Ontario and across Canada.

“This interdisciplinary approach is applicable to various water resource systems internationally, and I hope it results in policy and operational enhancements,” he said.

“I envision a future where water resource systems are planned and managed holistically, incorporating engineering, economic, social and political dimensions.”

Academic collaboration rethinks urban freight logistics

Christopher.Sorensen — Wed, 13 Mar 2024 17:38:58 +0000

Academic collaboration rethinks urban freight logistics

Christopher.Sorensen Wed, 03/13/2024 - 13:38

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The City Logistics for the Urban Economy, or CLUE, research group seeks to address congestion, emissions and safety concerns surrounding urban freight logistics – all while ensuring an equitable distribution of benefits and burdens (photo by Rene Johnston/Toronto Star/Getty Images)

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Cities

Faculty of Applied Science & Engineering

Sustainability

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The City Logistics for the Urban Economy (CLUE) research group - which involves three universities, government and private sector partners - aims to reshape the urban goods transportation landscape

An academic collaboration between 10 faculty researchers and more than 50 graduate students at the University of Toronto, McMaster University and York University is rethinking urban freight logistics.

The rise of e-commerce and home delivery, although convenient for consumers, has exacerbated challenges in urban freight logistics as the transportation and delivery industry continues to expand and freight emissions outpace those of passenger vehicles.

Led by Matthew Roorda, a professor in ��˾��ֱ��’s department of civil and mineral engineering in the Faculty of Applied Science & Engineering, the City Logistics for the Urban Economy (CLUE) research group is focused on reshaping the landscape of urban goods transportation by addressing four main areas: congestion, emissions, safety concerns and the equitable distribution of benefits and burdens.

That includes examining the equity and environmental justice implications of the rise of e-commerce and home delivery.

“Questions around winners and losers in the transition from brick-and-mortar to delivery, as well as the distribution of emissions from delivery vehicles, are being explored,” Roorda says.

Since launching in 2020, CLUE's faculty researchers – along with 12 research sponsors, including federal, regional, non-governmental organization and industry partners – have worked on more than 24 projects.

Through their various research projects, CLUE aims to quantify the influence on the communities most impacted by freight logistics – particularly those living near highways and loading facilities.

Image outlining the impact of commercial vehicles in comparison to passenger vehicles across the Greater Toronto Area (image courtesy of Marianne Hatzopoulou)

Data collection and science also play a crucial role in CLUE’s research. The group is currently developing the Freight Data Warehouse, a repository for large data sets such as GPS traces of commercial vehicles, with the aim of enabling further research using this valuable data.

“The goal is to build visualization dashboards to provide insights into greenhouse gas emissions, traffic patterns and travel speeds, empowering policymakers and the public to make informed decisions,” says Roorda.

Various pilot projects have been successful outcomes of CLUE’s research, such as Purolator’s bike delivery initiative and an off-peak delivery project, which is providing data to guide the creation of a permanent Ontario program that relaxes noise bylaws for deliveries between 6 p.m. and 7 a.m.

“We have seen impact of our off-peak delivery pilot project on government policy changes, such as allowing night-time deliveries across Ontario,” says Roorda.

To collect evidence of the impact of that change in policy, CLUE conducted community surveys after night-time deliveries were permitted to help assess the noise impact on residents and developed a freight transportation model to assess the greenhouse gas emissions impact.

CLUE also introduced a new truck driving simulator, where novice truck drivers can be trained to navigate extremely challenging urban areas safely while considering pedestrians and cyclists. The goal of this initiative is to enhance safety standards within the freight and logistics industry, another key research theme for the group.

“Everyone makes mistakes while driving, but when you’re driving a huge truck in the city, you need to drive perfectly or else you put people at risk,” says Roorda. “It’s a very challenging job.”

By proposing innovative solutions like new delivery models, curbside loading zone technology and lifting restrictions on late-night goods deliveries, CLUE seeks to make the goods movement network more efficient and less impactful on urban environments.

��˾��ֱ�� undergraduate students explore the use of AI to treat speech disfluency

rahul.kalvapalle — Mon, 26 Feb 2024 14:00:00 +0000

��˾��ֱ�� undergraduate students explore the use of AI to treat speech disfluency

rahul.kalvapalle Mon, 02/26/2024 - 09:00

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Assistant Professor Michael Guerzhoy, top left, meets virtually with a team of undergraduate students who are developing a reinforcement learning-based system to help clinicians predict medication outcomes and adjust dosage accordingly (image courtesy of Michael Guerzhoy)

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Mechanical & Industrial Engineering

Mental Health

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Artificial intelligence, including machine learning systems, could help mental health clinicians optimize treatments for speech disfluency, or interruptions in the regular flow of speech.

Michael Guerzhoy, an assistant professor, teaching stream, in the division of engineering science and the department of mechanical and industrial engineering in the University of Toronto’s Faculty of Applied Science & Engineering, and a team of undergraduate students are developing a “reinforcement-learning-based” system that uses machine-learning algorithms to help clinicians predict medication outcomes and adjust dosage accordingly.

By contrast, many clinicians currently adjust medications based on expensive and sparse observations, making it difficult to identify if a specific drug is working optimally. That’s because patients respond to medication differently and its effects can be subtle or only visible over a long period of time. The effects can also be difficult to distinguish from other factors affecting patient behaviours.

Guerzhoy says complex symptoms like speech disfluency – characterized by chronic and repeated problems with continuous speech – can be particularly challenging to treat.

“Studies show that there is a correlation between mental health conditions like anxiety and depression and speech disfluency,” he says. “I believe that patient care can be substantially improved in situations where low-cost frequent observations are possible through making use of reinforcement learning systems to help prescribe and adjust medications.”

The team outlined their research in a recent paper presented at the Machine Learning for Cognitive and Mental Health Workshop at the Conference of the Association for the Advancement of Artificial Intelligence.

The first component of the system features a module that detects and evaluates speech disfluency on a large data set. The second is a reinforcement learning algorithm that automatically sources and recommends medication combinations. To support the two modules, the team built a plausible patient-simulation system.

Guerzhoy compared this system to the idea of a computer playing chess. “We all know that computers are excellent at playing chess,” he says. “Our hope is that these computer-based reinforcement learning models will help clinicians become sort of chess grandmasters in their field.”

By exploring the potential of automating and fine-tuning medication regimes for patients, the team hopes to provide a pathway to improve the way we treat mental health. Harnessing AI to pick up on small changes in behaviour in more frequent increments would give clinicians another tool in their toolkit, says Guerzhoy, especially since the high cost of sessions is a significant factor in a patient’s treatment.

Guerzhoy emphasized the crucial role played by the team of undergraduate students, which included: Michael Akzam, Micol Altomare, Lauren Altomare, Nimit Amikumar Bhanshali, Kaison Cheung, Jiacheng Chen, Andreas Constas, Pavlos Constas, Vhea He, Aditya Khan, Asad Khan, Heraa Murqi, Matthew Honorio Oliveira, Youssef Rachad, Vikram Rawal and Najma Sultani – all undergraduate students at ��˾��ֱ�� – and Carrie Chen from Cornell University.

“Having such a large team of undergraduate students involved that are passionate about the research was essential.”

��˾��ֱ�� researchers partner with Siemens Energy to tackle sustainable energy production

Christopher.Sorensen — Wed, 10 Jan 2024 19:50:56 +0000

��˾��ֱ�� researchers partner with Siemens Energy to tackle sustainable energy production

Christopher.Sorensen Wed, 01/10/2024 - 14:50

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PhD student Yazdan Naderzadeh (left) investigates flames with lasers in the Propulsion and Energy Conversion Lab at UTIAS (photo by Neil Ta)

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Graduate Students

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UTIAS

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'Together, we hope to unravel the complexities of hydrogen combustion, paving the way for cleaner and more efficient engines'

Researchers in the University of Toronto’s Faculty of Applied Science & Engineering have partnered with Siemens Energy to tackle a key challenge in the energy sector: sustainable energy conversion for propulsion and power generation – such as developing gas turbine engines that can run on sustainable energy sources like hydrogen.

Led by Associate Professor Swetaprovo Chaudhuri from the ��˾��ֱ�� Institute of Aerospace Studies (UTIAS), the initiative aims to rethink traditional gas turbine engines to reduce carbon emissions from both aviation and land-based fuel consumption.

Chaudhuri’s team is exploring hydrogen combustion as a viable option since it can be burned without producing carbon dioxide.

However, the transition is not without its challenges. For one, hydrogen is a small, highly reactive molecule, causing flames to move five to ten times faster than those of natural gas. This makes existing combustors and engines that run on natural gas incapable of handling pure hydrogen.

Another key challenge is the lack of infrastructure available to transport hydrogen in the way pipelines are used to move natural gas. Until such infrastructure is developed, Chaudhuri’s team is researching how to build reliable fuel-flex gas turbine engines that can work on both fuels.

“Hydrogen and natural gas are vastly different - it’s like comparing a Bugatti Veyron to a public bus in both speed and size,” says Chaudhuri, who leads the Propulsion & Energy Conversion Laboratory at UTIAS. “The critical question is: ‘how can engines be designed to accommodate both fuels seamlessly?’”

The team is led by Chaudhuri in collaboration with Associate Professor Jeff Bergthorson at McGill University, Professor Étienne Robert and Assistant Professor Bruno Savard at Polytechnique Montréal, Patrizio Vena at National Research Council Canada and engineers from Siemens Energy Canada in Montreal.

The project received an Alliance Mission Grant from the Natural Sciences and Engineering Research Council (NSERC) to build a comprehensive understanding that will guide the creation of fuel-flex gas turbine engines.

PhD candidate Yazdan Naderzadeh (left) and master’s student Scott Watson from the Propulsion and Energy Conversion Lab work with a swirling hydrogen flame (photo Praful Kumar)

The researchers have constructed a model lab-scale combustor at the Propulsion and Energy Conversion Laboratory at UTIAS, to study the behaviour of natural gas and hydrogen flames within engines. These experiments aim to understand the intricacies of hydrogen combustion to establish engineering principles and guidelines for future engine development.

While practical applications are on the horizon, the immediate goal is to establish a robust knowledge base that will be essential for designing engines that can efficiently and safely use hydrogen as a fuel source.

“Currently, long-range aircrafts cannot, even theoretically, fly on batteries. We need to make significant strides towards combustion engines that use hydrogen or other carbon-neutral fuels to substantially reduce carbon emissions in these critical sectors,” says Chaudhuri.

In a different, stand-alone project, Chaudhuri and his research group are developing a self-decarbonizing combustor, which separates hydrogen and carbon from natural gas within the combustor. This process not only allows for hydrogen to be used for fuel but could also allow the carbon byproduct to be used to offset the additional cost associated with decarbonization.

“Our collaboration with Siemens Energy marks an exciting synergy between academia and industry,” says Chaudhuri. “Siemens Energy’s gas turbines for generating power have historically used natural gas, so this partnership represents a significant step towards a greener future.

“Together, we hope to unravel the complexities of hydrogen combustion, paving the way for cleaner and more efficient engines.”

The development and commissioning of the fuel-flex combustor, capable of safely stabilizing both hydrogen and natural gas flames, presents numerous research opportunities for students.

Yazdan Naderzadeh and Scott Watson, a PhD candidate and master’s student respectively in Chaudhuri’s lab, are working on the project. “I am so excited to work on the ongoing fuel-flex combustor project, addressing concerns related to clean emissions and compatibility with conventional gas turbine burners,” says Naderzadeh. “This endeavor allows for a thorough study and understanding of the challenges associated with hydrogen as a prospective fuel in the aviation industry and gas power plants.”

Learn more about industry partnerships at ��˾��ֱ��

��˾��ֱ�� researcher aims to improve laborious process of summarizing source code

Christopher.Sorensen — Wed, 03 Jan 2024 21:03:11 +0000

��˾��ֱ�� researcher aims to improve laborious process of summarizing source code

Christopher.Sorensen Wed, 01/03/2024 - 16:03

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(photo by MTStock Studio/Getty Images)

Selah Katona

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Artificial Intelligence

Faculty of Applied Science & Engineering

Research & Innovation

Subheadline

Eldan Cohen is leading a team that's developing human-centred machine learning algorithms to automatically summarize a snippet of code into clear and concise language

Software may play an integral role in the modern world, but its development, maintenance and management remain expensive and laborious – a challenge the University of Toronto’s Eldan Cohen aims to address.

Cohen, an assistant professor in the department of mechanical and industrial engineering in the Faculty of Applied Science & Engineering, is leading a team of researchers to develop novel, human-centred machine learning algorithms to automatically summarize a snippet of code into clear and concise language, a process known as source code summarization.

Such summaries are meant to capture the purpose of code, helping developers understand, maintain and work with the codebase. They are particularly important in large software development projects and involve both natural language processing techniques and machine learning.

While there has been significant research into using AI to develop automated source code summarization tools that can generate natural language summaries of code, Cohen says there is still much room for improvement.

“Even state-of-the-art deep learning models are prone to mistakes in prediction, yielding summaries that do not match the provided source code,” says Cohen. “In such cases, software developers must reject the proposed summary and resort to manually documenting the code.”

To address this challenge, Cohen recommends developing a human-in-the-loop technique for automated code summarization that considers the developer’s knowledge, preferences and insight to overcome and learn from model mistakes. The approach allows developers to actively participate in the process of generating code summaries through machine learning algorithms and integrates human insights into the automated code summarization workflow.

He is also developing specialized machine learning algorithms to overcome the limitations of existing approaches, including limited diversity and lower-quality summaries.

“We plan on doing this by creating interactive approaches where developers are presented with a small number of diverse and high-quality code summaries to choose from, reducing the risk of generating a single, incorrect summary,” he says.

The long-term goal of Cohen’s work is to significantly improve the effectiveness of automatic source code summarization. By developing these human-in-the-loop approaches, Cohen and his colleagues hope to incorporate developer input into state-of-the-art deep learning models to improve the quality of generated code summaries.

The approach is expected to have significant scholarly impact with the potential to catalyze both research and commercial activity on human-in-the-loop automation in software engineering.

Cohen is one of 49 researchers from across ��˾��ֱ�� – and one of four from ��˾��ֱ�� Engineering – supported in the latest round of the Connaught New Researcher Awards, which helps early-career faculty members establish their research programs.

“Students are involved in all stages of this project and are actively involved in developing and evaluating the novel human-in-the-loop techniques for automatic source code summarization,” says Cohen. “The funds from this award will primarily go to supporting their research.”

The other three projects from ��˾��ֱ�� Engineering supported by the Connaught New Researcher Awards are: 

Margaret Chapman, Edward S. Rogers Sr. department of electrical and computer engineering: Risk-aware, adaptive and scalable algorithms for smart sewer technology in Toronto
Christopher Lawson, department of chemical engineering and applied chemistry: Engineering untapped anaerobic bacteria for sustainable fuel and chemical production
Jay Werber, department of chemical engineering and applied chemistry: Ultra-thin bipolar membranes for carbon dioxide removal applications

How AI and neuromodulation could help with sleep disorders

siddiq22 — Tue, 11 Jul 2023 18:37:12 +0000

How AI and neuromodulation could help with sleep disorders

siddiq22 Tue, 07/11/2023 - 14:37

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Assistant Professor Xilin Liu and his collaborators are developing electronic devices that could help patients suffering from sleep disorders (photo courtesy Xilin Liu)

Selah Katona

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Breaking Research

Artificial Intelligence

Faculty of Applied Science & Engineering

machine learning

Neurology

Research & Innovation

Sleep

Subheadline

'We still don’t fully comprehend what actually occurs in our brains during sleep'

A new partnership between the University of Toronto's Xilin Liu, assistant professor in the Edward S. Rogers Sr. department of electrical and computer engineering in the Faculty of Applied Science and Engineering, and Andrew G. Richardson, research assistant professor of neurosurgery at the University of Pennsylvania, will develop a new generation of electronic devices to investigate sleep modulation.

Their research will potentially develop new interventions that help deal with a wide range of sleep disorders.

On average, we spend a third of our life asleep. During sleep, the brain undergoes important processes that support memory consolidation, neural restoration and the clearance of toxins. Sleep disruptions can interfere with these processes. But while good "sleep hygiene" is increasingly recognized as crucial to both physical and mental health, sleep disorders remain widespread.

“40 per cent of Canadians have sleep disorders, with over 3 million suffering from insomnia,” says Liu, director of the X-Lab and affiliate scientist at the the KITE Research Institute.

“Sleep deficits negatively affect brain functions such as attention and memory, and immune function, metabolism and heart health. Chronic sleep-wake disruptions are connected to neurodegenerative disorders such as Huntington’s, Parkinson’s and Alzheimer’s disease, and cognitive decline with aging.”

This diagram describes the process of closed-loop sleep modulation via miniaturized electronics
(image courtesy of Xilin Liu)

Liu’s research focuses on developing integrated circuits and systems for advancing health care, digital communication and machine learning. In the new collaboration, he will be building fully integrated wireless systems-on-chips that can autonomously modulate sleep behavior in pre-clinical studies.

“Sleep is a complex procedure involving different stages and patterns,” he says. “To address this, we are integrating machine-learning algorithms into our devices. These new algorithms can recognize sleep patterns and identify sleep disorders that may not be distinguishable using traditional algorithms.”

Liu and his collaborators hope that these new approaches will enable them to gain a deeper understanding of how our brains function during sleep and how to modulate sleep circuits.

They also plan to incorporate various neural interfacing capabilities into the system, which will enable more accurate and precise interventions. The results of this research will contribute to the advancement of neuromodulation, a technology that involves placing devices inside a patient’s brain, spinal cord or peripheral nerves. These devices are designed to regulate neural activity and help reduce symptoms related to different disorders.

Liu is a faculty member of the CRANIA Neuromodulation Institute (CNMI) in the Faculty of Applied Science and Engineering, which brings together experts in engineering and neuroscience in a collaborative hub for neuromodulation research.

Liu’s project was recently awarded $2.2 million by the National Institutes of Health (NIH) through their Research Project Grant Program (R01), and is supported by industry partners such as the Canadian Microelectronics Corporation and Open Ephys.

The NIH R01 grant is a highly competitive award and Liu and Richardson’s proposal fell in the top 1 per cent of applications.

“This is a great sign that NIH recognizes the value and impact of this research and the caliber of the team,” Liu says. “We’re excited to receive this funding and over the next four years we hope to get to a stage where the technology can be used in clinical trials.”

Liu’s research team has a long-term goal of creating wearable devices for sleep modulation that people can use at home to enhance the quality of their sleep.

“While there are medications available to treat sleep disorders, the challenge lies in the fact that we still don’t fully comprehend what actually occurs in our brains during sleep,” Liu says.

“It’s possible that there are low-cost, high-efficacy treatments available for sleep disorders that we are currently unaware of.”

Can AI help make our buildings more sustainable?

Christopher.Sorensen — Mon, 29 May 2023 18:00:57 +0000

Can AI help make our buildings more sustainable?

Christopher.Sorensen Mon, 05/29/2023 - 14:00

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Seungjae Lee, a professor in the department of civil and mineral engineering, is using ��˾��ֱ�� buildings as models to design deep learning algorithms that could optimize the operations of building heating and cooling systems – significantly reducing energy use (supplied image)

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Topic

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Climate Positive Campus

Climate Positive Energy

Artificial Intelligence

Faculty of Applied Science & Engineering

Research & Innovation

Sustainability

Subheadline

Assistant Professor Seungjae Lee is working on a pilot project aimed at reducing ��˾��ֱ��’s climate footprint by using machine learning to optimize heating and cooling systems in existing buildings

We know we should switch the lights off when we leave a room – but what about the furnace, or air conditioning? In studying how unnecessary heating and cooling of buildings wastes a lot of energy, Seungjae Lee, an assistant professor in the University of Toronto's department of civil and mineral engineering in the Faculty of Applied Science & Engineering, has found that artificial intelligence (AI) could offer a better way forward.

Lee’s latest research project, Grid-Interactive Smart Campus Buildings, is a three-year pilot project that aims to reduce ��˾��ֱ��’s climate footprint by leveraging AI to optimize the heating and cooling systems in existing buildings. The project is carried out in partnership with Chi-Guhn Lee, a professor in the department of mineral and industrial engineering and is jointly funded by the Climate Positive Energy and Climate Positive Campus initiatives at ��˾��ֱ��.

“Buildings account for around 25 to 30 percent of total energy consumption and energy-sector greenhouse gas emissions in Canada and worldwide,” Lee says.

“Given that people spend an average of 90 percent of their lives indoors, ensuring comfortable and healthy indoor environments is a critical function of building systems. But we could be a lot smarter about using the resources we have.”

This schematic describes how deep learning could be used to create customized algorithms for optimizing energy use in buildings (illustration by Seungjae Lee)

Lee’s research applies AI solutions to building science to tackle this issue. In the first year of the project the team is focused on creating a digital twin – or virtual representation – of the Exam Centre at 255 McCaul St.

In the next stage, the researchers will develop a deep reinforcement learning algorithm for the optimal control of the heating and cooling systems. This algorithm will be pre-trained with the digital twin to avoid putting excessive stress on the actual building.

The algorithm will then be implemented in the real Exam Centre and further fine-tuned through interactions with the building. If successful, Lee hopes to use the same approach to convert more campus buildings to smart buildings, contributing to ��˾��ֱ��’s Low-Carbon Action Plan.

“60 percent of campus energy consumption on the St. George campus comes from heating and cooling buildings,” he says.

Lee’s research group is also investigating how humans interact with their buildings in an NSERC Discovery-funded project on scalable systems for intelligent and interactive buildings, which is an emerging area of study with relatively little published research – something Lee hopes to change.

Where previous methods relied on data such as the correlation between thermostat setpoint temperature and other parameters (such as the time of the day), Lee and his team are instead using causal relations – for example, the factors affecting occupants’ decision-making on thermostat setpoint temperature – to develop reliable human-centric smart solutions.

“Once we understand how humans interact with their buildings in the light of causation, we can realize more intelligent and human-interactive buildings,” says Lee.

While Lee is not the only researcher interested in using machine learning and AI techniques in buildings, the sector has lagged behind other sectors such as the automotive or health-care industries, because of how different the energy consumption profiles and needs of individual buildings can be.

“A solution customized for one building is not necessarily fully transferable to another,” Lee says.

“This is a major roadblock in the path of making our buildings smarter. If we can seamlessly combine existing building-science domain knowledge and AI, we can build scalable and reliable solutions to create sustainable buildings.”

To tackle this issue, the team is partnering with PLC Group, along with funding from the Ontario Centre of Innovation, to develop a scalable digital twinning tool for building energy systems. If the tool is effective, it will equip the building industry with a solution to create intelligent, interactive and more sustainable buildings around the world.

“The use of AI in building management systems not only has the potential to significantly improve the sustainability of our built environment, but also the way in which we interact with it,” Lee says.