Computational Science

Preparing for Climate Change’s Effects with Geophysics

As climate change causes ever increasing fears of flooding, Dr. Peter Lelievre’s work in geophysical imaging becomes ever more important in the province of New Brunswick and beyond.

An assistant professor in Mount Allison University’s department of mathematics and computer science, Dr. Lelievre is an applied geophysicist who images what’s underground. Earlier in his career, that pursuit was tied to mineral exploration or “trying to help people find the materials such as metals that our society uses for basic building materials.” More recently, he’s become interested in imaging and understanding what happens with flood infrastructure.

“With climate change and rising sea levels, we need to understand more about how water moves through these flood barriers,” Lelievre explains. “Geophysical imaging can help us better understand these things, and also to try and find any erosional issues that could cause a breach.”

Lelievre walks on the fields and marshes near Sackville, N.B. — which are the same places his research now takes place. Being at Mount Allison means he’s surrounded by the dike land and he, his wife, and his dog walk on it frequently.

“It’s far more at the forefront of my mind in my daily life,” he says. “It’s been really nice to be able to shift and connect my research to that part of the local community and my daily life.”

Lelievre uses tools that measure electrical and electromagnetic fields and then he uses heavy mathematical methods and computational power to process the data.

“We run the data through these algorithms that we develop and this creates an image of what’s underground,” he says of his use of the Digital Research Alliance of Canada’s high-performance computing tools. “That’s where the heavy computational part fits in. These are tremendously large computational problems.”

For his heavier research, an imaging task could require 600 central processing units (CPUs), consume roughly two terabytes of random access memory (RAM) and take over four-days to finish. He and his research team use the modern Fortran programming language and create their own software to process their electrical and electromagnetic data and generate images of the Earth.

With the help of his students, postdocs and colleagues, he develops data processing methods that could be used in the field, and he collects field data to help test those methods.

“Eventually we’d like to be able to create data processing methods we can use in the field on a laptop, and so you’d just have a small, everyday laptop, where you get a result in effectively real time,” he says, and adds that the history of computing tells us that could well happen in the near future.

Preventing Freezer Burn and Saving Human Tissue

It sounds simple, but Shah Razul’s work involves trying to understand what water molecules do when they get to below zero degrees Celsius because the possibilities that understanding offers could be life-changing.

When water crystallizes, its volume increases and it forms a regular structure that destroys, for example, cells, or, in the case of an organism, its delicate membranes and systems. As most of us will know, when food is frozen and we leave it too long, the ice separates out, leaving the food “freezer burned.” A wasted chicken breast is one thing, but this problem also exists, for example, in cryopreservation of cells, tissues and organs.

“When water’s in a close-to-frozen state, it has some interesting behaviour,” says Razul, an associate professor of chemistry at St. Francis Xavier University. “We’re trying to understand what the water molecules are doing.”

Razul’s overarching goal is to find a way to keep water from forming crystals and then applying that learning to real-world problems in areas such as food and health care.

“If we can, we can solve many problems related to freezing,” he says, including, for example, potentially creating environmentally safer antifreeze products to replace or minimize the use of road salt.

He looks to creatures such as the wood frog, which uses glucose to stop the freezing process from destroying its tissues in winter. Razul, therefore, is running simulations to look at the process of freezing and analyze what water molecules are doing in the fractions of a second just before they form crystals and freeze.

“A lot of my work now is involved with understanding how small biomolecules, such as different kinds of sugars and salts, stop water from freezing or slow down the process,” he says, adding that he wants to figure out a biomolecular combination that would achieve this goal.

Over the past five years, he’s been using computational principles to develop a cryoprotectant to preserve lobster meat.

“We’ve tried it and it works,” he says, adding that some companies from overseas are testing his system currently. “We had a taste test in Atlantic Canada, and we’ve published a couple of studies where the public indicated that it tastes as if it was cooked yesterday, preferring it to frozen lobster meat that is sold currently.”

His next frontier is looking at the ways in which his process can preserve muscle cells and brain cells to see what kind of applications it might have in the healthcare field.

“No one has a definitive answer to how these cryoprotectants work at the molecular level,” he says. “It’s basically all trial and error. We try a little bit of this and a little bit of that and see whether it works.”

To do his work, he’s watching the behaviour of water in minute detail — fractions of seconds — and then he replicates the test multiple times.

“Because of that, I generate a lot of data,” he says, and that’s why he couldn’t do his work without ACENET’s high performance computing.

“It’s almost impossible to do this work without ACENET,” he says. “I would need a year and a half to do it sequentially as opposed to one month with ACENET.”

Making Data Meaningful

Whether it be for a community project to determine which water is safe for drinking, or a hospital-based project to determine the most efficient and effective ways to treat mental illness, Trishla Shah is an expert in taking the data we collect so easily today, and making them meaningful.

As the IT research scientist at the Nova Scotia Community College and a PhD candidate in computer science at Dalhousie University, Shah focuses on designing solutions by analyzing massive amounts of data. For example, Nova Scotia’s water comes from various places, including freshwater lakes, but also possibly from contaminated lakes, she says.

“We want to educate the organizations taking care of our water resources so we can see how we can preserve the well water and how we can make sure the water from the fresh lakes is not contaminated,” Shah says, adding that many of her projects come to her after receiving funding from a national social innovation fund.

Private IT companies that want to design and test a product from scratch also often approach the research scientist.

“We are being approached by startups, by midsize companies and by large-scale companies,” Shah says. “We are being approached by organizations that take care of Indigenous communities. One project we’re working on involves artificial intelligence, Indigenous communities and Halifax’s storied Bluenose ship.”

For another project, Shah is helping small- and medium-sized enterprises identify the right clean technology investments for their business. Her lab is building a data repository to provide solutions to scale access to low-carbon emissions for businesses in the agri-food, construction and manufacturing industries. The repository is integrated from various sources using web-scraping and text-mining techniques. The parameters of the data repository will be set to allow an algorithm to forecast carbon emission reductions and analyze the return-on-investment of various interventions.

Shah and her team get involved from the beginning, first collecting the data, then building a database, preprocessing, storing and managing the data in a way that ensures they’re secure and making sure there’s no breach. From there, they can make models of the data so they don’t have to analyze them manually.

Shah hasn’t used the services of ACENET directly, but she has circulated its many programs among the information technology students at the college.

“We have a lot of academic programs that have IT in them,” she says. “And students might want extra practice, or students might need to have something like a prerequisite. With ACENET, the courses are really applicable and I like to circulate them among students who need additional practice on that topic. So that’s my interest with ACENET. I’m a fan.”

In addition, Shah is in the process of designing her own student training module that has an online portal where students can enrol themselves in training that is focused on real-time projects.

“I have proposed to the committee that’s approving my student training module and funding that ACENET’s courses be prerequisites.”

Artificial Intelligence to Improve Human-System Interaction

While debates rage over the ethics of artificial intelligence’s role in art or helping students cheat on term papers, Sid Ahmed Selouani has quietly worked with AI to develop industrial applications and improve people’s quality of life.

He and his lab at the University of Moncton, Shippagan Campus – the Human-System Interaction Research Laboratory (Laboratoire de Recherche en Interaction Humain-Système, LARIHS) – use AI to advance human-system interaction, working on subjects as diverse as emotion, pathological speech and language recognition, telepathology automation and intelligent automation for industrial robots.

Machine learning in cyber-physical systems, which integrate physical and digital components, relies on complex algorithms to make decisions and commit to actions in the real world.

“For example,” explains Selouani, “to help someone with difficulty pronouncing words, like someone who has had a stroke, and can only say bits of words, we use algorithms to understand how the person expresses themself, then replace broken words or segments to generate understandable text.” This is the objective of a company built on LARIHS’s research: to commercialize software that provides people with speech disorders an interpreter to help them communicate. The algorithm analyzes speech, replaces unclear sections with text, then generates a sound similar to a person’s voice. The software will also help doctors to monitor patients throughout their rehabilitation.

For an algorithm developed by LARIHS to discern what a person is trying to say, however, it first needs experience listening to human speech. That requires lots of data, analyses, data mining, and comparison, often involving millions of parameters. It takes top-of-the-line supercomputer systems, like those provided by ACENET and the Digital Research Alliance of Canada, to give algorithms the processing power and storage capacity to make the necessary calculations and learn how to interact with the real world. “Our computing capacity has increased at least 10-fold with ACENET,” Selouani explains.

Such processing power opens many doors. Selouani and his team of more than two dozen post-docs, grads, undergrads and research personnel have many projects on the burner, each with their own sub-projects.

For instance, LARIHS is working on a project to teach industrial robots to sort oysters, identifying live oysters from dead ones, and grouping them based on size and overall quality. LARIHS is also working on optimizing telepathology – that is, the classification of human tissues and biological samples at a distance. By processing the images extracted from biopsy analyses, for example, we will be able to better detect abnormalities in the scanned images, thus helping doctors in their assessments.

The applications for these algorithms are as varied as their fields of study. How do you fit all that in one lab? ”Our know-how,” Selouani explains, “which we have developed over the last 20 years, is that we can take an algorithm we have developed for one application and adapt it to other ones.”

Supercomputers at the Frontline of Climate-Resilient Forests

When he was introduced to forest genetics as a 23-year-old researcher in India, Dr. Om Rajora became obsessed and devoted to forest genetics research. “Sometimes, even at the cost of my health,” he says, laughing.

Decades later and half a world away, he is still going at it, and busier than ever. He heads the forest genetics and genomics lab and is a professor of Forest Genetics and Genomics at the University of New Brunswick, where he joined as the Tier 1 Canada Research Chair in Forest and Conservation Genomics and Biotechnology. He also coordinates the Population, Ecological and Conservation Genetics research unit at the International Union of Forest Research Organizations, and he developed and published a pioneering book series called Population Genomics.

Through his work, Rajora seeks to reveal the secrets hidden within the genes of our woodlands, offering unprecedented insights into resilience, adaptability, and the preservation of biodiversity (genetic diversity) in the face of a rapidly changing world. His research has encompassed a wide range of forest genetic and genomics topics and fields, which besides contributing to basic science, can be used for conservation of genetic resources and sustainable forest management.

One of those areas involves analyzing the genes, gene expression, and metabolic pathways of trees under ambient and changing climate conditions. This in turn gives hints to how forests respond to environmental changes. By sequencing and analyzing the genes expressed under different scenarios, Rajora aims to identify the genes, biological processes and molecular functions affected by climate change, including those related to photosynthesis and stress response. “For example,” he explains, “this could provide us information about what traits could be affected with elevated levels of CO₂.”

Rajora’s research obsession and a strong work ethic have their limits, however, and today’s rapid gene sequencing techniques have created the need for advanced methods of data analysis and storage.

In a recent project, Rajora and his post-doctoral fellow, Dr. Rajni Parmar, needed to undertake extensive genetic analysis of the red spruce transcriptome and chloroplast genome, and identification and characterization of genes and pathways expressed differentially in response to climate change conditions. This required significant computational resources. In the past, they relied on personal computers for certain analyses, but the advent of next-generation sequencing—rapid DNA/RNA sequencing techniques—and the accumulation of massive amounts of data necessitated the use of supercomputers or computer clusters. ACENET and the Digital Research Alliance of Canada have been instrumental in providing these capabilities.

Dr. Serguei Vassiliev, a research consultant with ACENET, played a pivotal role in the study. He delved into the project, learning the fundamentals of computational bioinformatics, installing software, and creating parallelization schemes to process large problems simultaneously. He also trained the group in essential computational techniques, including genome assembly and annotation, transcriptome construction, statistical analysis of gene expression, and protein interaction network identification.

The outcomes were remarkable: computation time was slashed from weeks to hours, the group acquired advanced computational skills, and they published an article in the International Journal of Molecular Science, with another in progress. Vassiliev’s significant contribution earned him co-authorship on the publication.

“The knowledge provided by this research is beneficial to the global plant genomics community,” explains Rajora. The variations found in gene expression clue scientists into what genes may allow forests to survive adverse conditions. Through international collaboration, scientists can pool their resources, data, and insights to develop innovative solutions that can be applied globally in the face of climate change.

Machine Learning: Connecting the Brain to the Body

Xianta Jiang uses machine learning and wearable-sensor technology to study how the brain works in telling the body what to do and how to move. Currently, he’s using these techniques to solve a health-care problem for those who have prosthetic hands. But he doesn’t work in a hospital setting; rather, he works in a computer lab.

A professor of computer science at Memorial University, Jiang works with a team of students to study all of the different ways humans use their hands. With that information, they’ll use machine learning to help those who wear prosthetics control them.

“Controlling the prosthetic hand is a super difficult problem to solve, because when a human’s hand is amputated, the brain has a harder time communicating to the arm about how to maneuver the prosthetic limb,” Jiang says. “We are trying to help people control them as naturally as possible, and without surgery. To try to solve the problem, we use muscular sensors attached to a part of the arm to infer movement intentions from the area of the brain used to control the hand.”

However, it’s difficult to make the connections that allows the brain to send the right signals to the hand.

“For that, we attach a camera to the prosthetic and when the camera can identity the target, the hand can configure correspondingly — just like self-driving,” he says.

That’s the hands-on part of the research, but, in the end, much of the work is done by his students in a computer lab using the high-performance computing resources of the Digital Research Alliance of Canada and ACENET. They identify the grasp types the prosthesis user will need and use computer modelling to fine-tune the fit.

“It’s quite a basic question, but we need a lot of computing resources for it,” he says. “We are working to cover 95 per cent of daily life grasps with a total of 16 movements and need a lot of data to train this model, so we use high-performance computing.”

He said he couldn’t do his work without ACENET’s services.

“It’s not affordable to purchase the computing power we’d need,” he says. “We need a lot of memory.” In 2022 alone, Jiang’s group used 128 CPU years and 23 GPU years of compute power.

Another of Jiang’s projects involves monitoring human activity using wearable devices such as smartwatches. The goal is to find a way to allow rehabilitation staff or sports coaches to monitor their patients’ progress digitally. This work is in the early collaboration stage and not yet commercialized. “For this one, we’ll collaborate with industry over the next few years,” he says.

Putting the Stars in Perspective

Catherine Lovekin’s main research is dedicated to understanding the heat-transfer process in stars. In particular, she studies the “convective overshoot” where the circular currents that exist in stars go a little beyond their theoretical boundaries.

“We don’t really know how much of this overshoot there should be, so I use asteroseismology, which is a way of measuring the variations in stars, to try to figure that out,” explains Lovekin, associate professor of physics at New Brunswick’s Mount Allison University. “Stars pulsate and they have variations like waves on a head of a drum or something. And I’m trying to use those waves to understand the interior of the stars.”

The overshoot is important, she says, as it changes how big the core of the star is, so it also changes all kinds of things about the subsequent evolution, and what elements are produced — for example, how much fuel it has for nuclear fusion. It can change the lifetime of the star.

The hope is that eventually she and others who are working on the same question will get enough of a sample that they can start to see patterns. For example, maybe the overshoot behaves differently in high mass stars compared to low mass stars, or maybe it changes as stars evolve, meaning that older stars have different values than younger stars.

While Lovekin is working mostly on individual stars, her students are looking at stars that have pulsations and that are part of a binary system — in effect, two stars in orbit around each other. Looking at the stars this way, she says, is a good way to constrain things such as the diameter, temperature and mass of the star.

As her research has expanded to the way in which binary stars behave, her models have become that much more computationally intensive, to the point where she now can’t do her work without the services of high-performance computing (HPC) — in this case, from ACENET.

“I could do some aspects of my work without those resources, but it would take me years longer without something like ACENET.”

Lovekin’s relationship with HPC goes back a long way. She was an early user of the HPC computing from the Digital Research Alliance of Canada, then called Compute Canada, which is the national partner of ACENET. And today, her students are all using ACENET.

“The ACENET training that is offered every May is so useful,” she says. “I just tell my students to sign up. It really helps get them started. The ACENET folks have so much more experience with helping to train people on the system. When my students start working with me, they all know how to run these models because of their ACENET training.”

Timing Travel with Precision

Davod Hosseini is a good guy to know if you’re a commuter. That’s because his “operations research” or OR, tries to solve complex problems in transportation planning and logistical management.

“The core process of OR involves identifying an issue, formulating a mathematical model to represent it, gathering and analyzing relevant data to understand its behaviour, and, finally, providing actionable recommendations by finding optimal or near-optimal solutions,” explains Hosseini, assistant professor of management science at Saint Mary’s University’s Sobey School of Business.

Specifically, Hosseini leverages OR to optimize routing, scheduling and vehicle assignment in two types of transportation systems — regular systems, in which he looks at transportation costs and customer satisfaction, and in riskier transportation systems involving transportation of hazardous materials (hazmat) via railroads, in which he aims to minimize risk.

“The optimization process in both cases is performed by considering factors such as traffic patterns, distance, service and delivery times, demand forecast and vehicle capacity, while ensuring all operational constraints, government regulations and customer requests are fulfilled,” Hosseini says. “However, the inherent variability in these factors adds a layer of complexity to the model, making it more challenging to achieve an optimal solution.”

“Companies such as Amazon or FedEx leverage advanced route optimization to plan daily deliveries for thousands of packages,” Hosseini explains. “The system considers factors such as delivery windows, traffic conditions, package size and driver location to create efficient routes and meet tight delivery deadlines.”

Another application is home health-care, where providers visit multiple patients daily. The system can account for patient location, appointment times, last-minute appointment cancellations, new patient requests, care urgency and unexpected traffic delays, to name a few, and then generate route planning that minimizes travel time, ensures on-time visits and maximizes patient experience.

On the hazmat side, one could imagine a use for chemical manufacturers who want to transport their products by rail. The software considers such factors as population density along the route and prioritizes less populated routes in case of an accident, weather conditions and track maintenance.

Hosseini says he uses ACENET services because he usually has large datasets, numerous constraints and variables in the mathematical model, and complex optimization algorithms.

“Traditional computing can become slow and impractical when dealing with vast amounts of data, such as railroad networks with thousands of tracks and intersections or logistics scenarios involving millions of delivery points. HPC provides the processing power to handle these large datasets efficiently.”

He says he has benefited from ACENET staff’s expertise in supercomputing to migrate his workflows from his university desktop to an HPC cluster.

Without ACENET’s tools, Hosseini says his work would take weeks or months to complete.

Mining Data to Mine Metals, Among Other Things

Geophysics professor has the key to determining how deep down a mineral deposit might be without digging a hole.

Geophysicist Colin Farquharson is a mining executive’s dream come true and maybe even a godsend to a detectorist with deep pockets.

The professor at Memorial University’s Department of Earth Sciences can take the data from geophysical tools that collect information on everything from metallic ore deposits to water sources and determine how deep these prospects are, unlocking great potential for whomever has asked him to do so — or at least information on whether they should pursue a specific site.

“Any scenario where you want to figure out what’s down in the ground without digging a hole — that’s where geophysics comes in,” Farquharson explains.

The equipment used by prospectors is often a helicopter that slings out a loop-shaped transmitter that’s 20 metres in diameter. The transmitter loop has electric current running through it and will offer a response if the helicopter happens to fly over, for example, an ore deposit, even if it’s embedded in rock.

“Metallic ore deposits tend to have a higher electrical conductivity than the rocks they’re sitting in, so they can conduct electricity, which is a bit surprising but true,” he says. “So these are geophysical methods where it’s exactly like the metal detectors people use when they’re trying to find relics and treasure.”

The main application for what Farquharson does is mining, but there are others, too. Often, instead of precious metals, geophysical equipment is used to look for sources of water, particularly in developing countries that might have fresh water aquifers near the coast with ocean water causing problems by creeping in.

Once the equipment determines there is something desirable there, the next thing the prospectors want to know is how hard it’ll be to unearth.

“People want to know how deep those deposits might be to determine whether it’s worthwhile pursuing,” Farquharson says. “That’s where computer modelling comes in. We use the information to calculate synthetic data.”

But making those calculations can be expensive and computationally challenging so Farquharson takes advantage of the high-performance computing power offered by ACENET.

“We can do in hours what would otherwise take weeks and weeks,” says Farquharson, who often gets his graduate students to run the datasets. “Some of the datasets are rich. Even on ACENET some of my students find it’s taking a few days to do these computations.”

He says the support services ACENET provides have been extremely useful to him and his students.

“It’s really valuable to me to have ACENET and the Digital Research Alliance of Canada, which both have big computers that are accessible for free. It doesn’t cost me anything for what I do, so it means I’m not spending thousands of dollars to do that part of my work.”

Using Modelling to Make Energy Use More Efficient

Kush Bubbar came to academia from industry. Leveraging his extensive background in technology development, including semiconductors, biomedical engineering, telecommunications, manufacturing, and renewable energy, Bubbar leads the Sys-MoDEL lab within the Faculty of Engineering at the University of New Brunswick, where he focuses on delivering value to societal projects by applying methods at the forefront of innovation into his academic practice.

The lab’s mission is to understand complex systems and present their clients with feasible, yet optimal solutions to address their challenges.

The lab’s diverse project portfolio includes advanced vehicle dynamics, marine renewable energy, and renewable energy system optimization. As Bubbar explains, “Our work on integrating renewable energy sources into existing power systems and optimizing wave energy conversion processes requires substantial computational power, often at a scale that would be unmanageable without the resources ACENET provides.”

Currently, Sys-MoDEL is engaged in three pivotal projects – power system planning with NB Power, oceanic wave energy conversion with Sapphire Energy, and off-road vehicle design with Potential Motors.

For the NB Power project, “We’re trying to understand how to improve the planning process for power systems in the future with the knowledge that we are integrating more renewables in there,” he says. “We are looking into how we can incorporate energy storage as a means to supplement our transmission network.”

Bubbar says this project is very novel, but also very computationally heavy.

“Under normal operating conditions, it’s easy to understand, but it’s at those times and under the conditions where you have faults, reliability issues, when things break, that you want to ensure the system is as reliable as it can be,” he says, referring, for example, to a wind storm across the province. “We’re looking at these cases where there is huge opportunity to reduce cost, and to be more efficient, through implementing energy storage mechanisms into the transmission system.”

“These are computationally intensive tasks that require robust simulation capabilities. ACENET’s supercomputing systems enable us to perform these large-scale simulations efficiently, reducing both time and cost,” Bubbar states.

ACENET not only provides the necessary computational infrastructure but also offers a supportive ecosystem for research development. This includes training modules, expert support, and a network of resources that are instrumental in troubleshooting and refining research methodologies. “The value of ACENET extends beyond just hardware. Their training and support have been crucial in helping us set up and optimize our computational experiments,” Bubbar says. “If my students have trouble for any reason, they can connect with a local resource on the UNB campus.”

By collaborating with ACENET, Sys-MoDEL gains a strategic advantage, ensuring that Bubbar and his team can continue to push the boundaries of what is possible in researching complex systems. This partnership exemplifies the synergy between advanced computing resources and innovative research, highlighting ACENET’s role in accelerating scientific discovery and technological development across the Atlantic region.