Since joining 91亚色 in 2018, the Freud Lab 鈥� led by Associate Professor Erez Freud and in collaboration with the Department of Psychology and the Centre for Vision Research 鈥� has focused on how the brain supports object recognition and interaction. Drawing on neuroimaging, neuropsychological research, developmental studies and motion鈥憈racking technology, the group explores how people perceive the world and act within it.

In recent years, the lab has focused on autism, using movement and perception to better understand how people with autism engage with their surroundings.

Over the last six years, the Freud Lab has collaborated with the University of Haifa to collect detailed motion data from autistic participants, using motion鈥憈racking cameras and machine鈥憀earning tools. Among the group鈥檚 successes was a 2025 study that drew wide attention for showing that differences in how grasping and moving objects could be used to distinguish participants with and without autism with a high degree of accuracy.

That work now serves as a foundation for the lab鈥檚 next phase as it is expanding how, where and with whom its data is collected. 鈥淭he idea is to try to expand and to reach out to different educational and clinical institutions in order to help us reach more children and young adults with autism,鈥� Freud says.

Through new clinical鈥� and community鈥慴ased collaborations, the goal is to extend the lab鈥檚 autism studies beyond a single context, while also increasing the number and diversity of participants involved. In doing so, it can broaden both the scope of the data and the questions it can help answer.

Among those efforts is a new collaboration with Autism Therapy & Training, a Vaughan鈥慴ased clinic that works directly with children with autism and their families. It has also partnered with the Summit Center for Education, Research and Training based at Montreal鈥檚 Summit School in Ville Saint鈥慙aurent, a multidisciplinary centre serving more than 600 neurodivergent learners between the ages of five and 21.

Working in clinical and educational settings allows the Freud Lab to study autism in ways that more closely reflect everyday life. Places like Autism Therapy & Training and the Summit School are not controlled study environments, but active spaces where children learn, play and receive support as part of their daily routines.

For researchers, that means observing behaviour as it naturally unfolds in classrooms, therapy rooms and shared activities. Freud and his team are pursuing this work through the use of advanced technologies, in service of a central question that runs through the lab鈥檚 efforts: why people with autism often move differently and what those differences reflect at a neural level.

Previously, much of the group鈥檚 work relied on tightly controlled experiments that required participants to perform specific, constrained motions 鈥� often with tracking markers attached to their fingers. Now, the lab is turning to a tool called Athena, a marker鈥慺ree motion鈥憈racking system developed in collaboration with Jonathan Michaels, an assistant professor in the Faculty of Health.

Athena uses a synchronized array of multiple video cameras to capture motor behaviour from multiple angles at once. Those video streams are aligned in time and analyzed using machine鈥憀earning methods that identify and label different parts of the body, allowing researchers to track and quantify motion in three鈥慸imensional space. For the Freud Lab, that makes it possible to measure how participants move 鈥� such as which hand they use, how quickly and efficiently they complete tasks and how consistent their movements are 鈥� without constraining natural behaviour.

The approach makes it well-suited for work with children with autism; it allows them to engage in familiar, low鈥憄ressure activities, like building Lego models, while the system quietly records information about how they move.

For Freud, these everyday interactions offer enhanced insights into behaviour and lead to more meaningful questions about autism. 鈥淭he goal,鈥� Freud says, 鈥渋s to try and understand what is different about the autistic brain and the autistic representations.鈥�

The findings also point toward a more applied objective: identifying reliable motor patterns that could be used to develop more objective tools for earlier identification. 鈥淚n autism and other neurodevelopmental disorders, we know that early treatment and early intervention are crucially important,鈥� he says, noting that earlier identification can help ensure support is provided at a stage when development is more flexible and interventions may have greater impact.

For Freud, that applied focus is central to his research and reflects an ongoing concern with how scientific work might translate beyond the lab 鈥� how insights about perception, movement and the brain can ultimately help people with autism, their families and the professionals who support them.

鈥淚 see my role as a cognitive neuroscientist as fundamentally about understanding the human mind and brain and how that can meaningfully promote the well鈥慴eing of a broader community,鈥� he says. 鈥淲hen it comes to working with individuals with autism and their families, that responsibility feels especially significant.鈥�

The post New technologies, partnerships advance 91亚色 U research in autism appeared first on YFile.

IoT devices are everyday objects that connect to the internet so they can send, receive and act on data. They range from home thermostats and baby monitors to traffic sensors, medical equipment and industrial controls. Many operate quietly in the background and are rarely updated or closely monitored, making them especially attractive targets for cybercriminals.

鈥淎s devices proliferate globally, so do the botnets that exploit them,鈥� says Arash Habibi Lashkari, a professor in the Faculty of Liberal Arts & Professional Studies and Canada Research Chair in Behaviour鈥慍entric Cybersecurity (BCCC). Botnets are networks of compromised devices that have been quietly taken over by attackers and can be coordinated to carry out cyberattacks, often without the device owner鈥檚 knowledge.

While cybersecurity tools already exist to protect IoT systems, Lashkari says many struggle to keep pace with today鈥檚 threat landscape. Designed for specific networks or environments, these tools are often not suited to the scale or complexity of a borderless digital world, where malicious activity moves easily across regions and frequently reuse similar behaviours in different contexts.

As a result, security frameworks often rely on AI to sift through vast volumes of data and spot patterns too complex or fast鈥憁oving for humans to catch. This, however, comes with a shortcoming: AI can flag suspicious activity, but without explaining how or why a particular behaviour is considered malicious.

鈥淭hat鈥檚 the primary gap of the 鈥榖lack box鈥� nature of AI in cybersecurity,鈥� says Lashkari, referring to systems that can produce answers without making their reasoning visible to humans. 鈥淯nderstanding these gaps is critical, because a detection system that cannot explain why it flagged a behaviour is difficult to trust.鈥�

Lashkari set out to resolve that gap. He and his colleagues aimed to find a way to analyze how botnets operate and build an identification approach to act on that knowledge. In doing so, it can produce results that human analysts can interpret, trust and apply across different networks.

In research now published in Supercomputing, Lashkari and his colleagues built and tested a recognition and profiling system using real鈥憌orld IoT network traffic. Working through BCCC, the team examined how compromised devices communicate across sustained activity, focusing on patterns that could be clearly interpreted.

This allowed the researchers to move beyond individual attacks and focus on broader behavioural patterns, including whether botnets operating in different environments might still act in similar ways.

Lashkari says they expected to see some similarities across botnets, but were still surprised by how consistently those patterns appeared. Even when attacks targeted different technologies or deployments, compromised devices tended to follow the same underlying behaviours, including recognizable bursts of activity. That consistency matters, he explains, because knowing how one botnet operates can help identify and defend against others, even in very different settings.

Lashkari says the real importance of that finding lies in what it enables. 鈥淚t suggests that a breakthrough in understanding a specific botnet profile 鈥� the recurring patterns in how compromised devices communicate and behave 鈥� can be generalized to protect critical infrastructure worldwide,鈥� he says.

That potential is not theoretical. To act on it, Lashkari and his colleagues developed a system that identifies IoT botnets based on behavioural patterns observed across repeated interactions. The system flags suspicious activity while also showing which specific behaviours triggered the alert, giving security teams visibility into why a device was identified as malicious.

While the system itself is presented as a research framework rather than a ready鈥憈o鈥慸eploy product, much of the underlying IoT data and profiling resources developed through the BCCC are publicly available, allowing other researchers to study, test and build on the approach.

Lashkari says this approach is especially important because malicious cyber activity is constantly evolving. As security systems improve, attackers adapt their tactics, often reshaping malicious activity to blend in with normal internet traffic. By focusing on patterns that persist across sustained behaviour, rather than relying on fixed indicators that quickly become outdated, the behaviour鈥慴ased system can help security teams recognize emerging threats even as attackers change how they operate.

鈥淭he hope is that this work will serve as a cornerstone for more transparent, collaborative security frameworks,鈥� Lashkari says. By promoting explainable tools and shared datasets, the team aims to shift industry practice away from simply blocking IP addresses, and toward understanding and anticipating how adversaries behave.

Lashkari says that need is unlikely to fade. As attackers continue to adapt, often operating slowly or subtly to avoid detection, focusing on behavioural patterns across time may become increasingly important. In an internet鈥慶onnected world, he says, effective defence will depend not just on smarter identification, but on tools that help security teams know what they are dealing with.

The post How 91亚色 researchers are strengthening cybersecurity appeared first on YFile.

Max Rudakov is a co-founder and business lead of Lapis Research, an AI-powered research management platform built to help research teams keep all their work 鈥� documents, lab notes, datasets, experimental decisions and project timelines 鈥� in one place. It was developed by a five-student team from 91亚色, Queen鈥檚 University, Western University, the University of Guelph and the University of Waterloo.

When notes, datasets and decisions are dispersed across emails, shared drives and personal laptops, there's no centralized place to see what's been done, why it was done or what the current project status is.

The idea sparked about a year ago with a Reddit post from the team asking whether people were struggling with how documents were organized and used. The strong response 鈥� 187,000 views and 350 comments 鈥� prompted the team to dig deeper, and conversations with researchers soon showed the issue was pronounced in academic research.

More than a year of interviews with over 100 professors, lab managers and researchers, along with about 20 design partners, kept surfacing the same issues: poor visibility across projects, fragmented documentation and knowledge departing when team members moved on.

For Rudakov, the path to Lapis was as much personal as practical. At Schulich, he found himself questioning the traditional routes into finance and looking for something that better matched his strengths.

"I realized that my skill set belongs in building something from the ground up," he says. "It feels good to know that I can make a change, especially in such a rigorous industry like research."

91亚色's contribution to the development of Lapis is concrete. Rudakov led the business strategy, growth planning and early outreach from his side, and many of the early interviews were conducted with 91亚色-based researchers 鈥� including people working in kinesiology and oncology research 鈥� whose feedback helped shape core features.

The real-world insights helped inform the design of the tool, tailoring it to the specific needs of the academic community.

Lapis works by structuring research projects into linked workspaces. When a researcher finishes an experiment, they can save their data and notes directly to Lapis. The tool automatically records who added the notes and when, creating a clear record of progress.

This means a professor or lab lead can view the activity of multiple projects without sending a single email.

When a new team member joins, they can ask the Lapis AI system, Neural Core, questions such as "What approach did we try for this and why did we change directions?" and receive a summary drawn from the project's files.

鈥淥nboarding can drop from months to a couple of weeks or even days because everything is preserved 鈥� the data, the decisions and the reasoning behind why things were done a certain way,鈥� says Rudakov. 鈥淎 new researcher can open the project and understand the full picture without having to ask everyone what happened before they got there.鈥�

During development, 91亚色-based researchers found value in helping to shape those features. Duygu Biricik Gulseren, an associate professor in the Faculty of Liberal Arts & Professional Studies, reviewed the platform and provided insight on how academics manage multiple projects, supervise students at different stages and keep track of different versions of documents and files as multiple people work on them.

"A platform like this can improve coordination and also make the work more transparent and traceable across people and projects," she says.

Eric Ginzburg, an undergraduate student completing an independent study in 91亚色's biomechanics lab, also shared feedback, noting he sees the appeal of more centralized system.

"It simplifies the process of handling a team and a larger research project," he says.

Lapis is currently running pilot programs at the University of Guelph and Queen's University.

Rudakov hopes to bring Lapis to 91亚色 research teams in the next stage of its growth 鈥� a natural fit given its development was informed in part through 91亚色 connections and conversations.

"91亚色 has over 50 research teams and the problems we solve are the same ones they deal with every day," he says. "We want the 91亚色 research community to know Lapis exists, and that it was partly built by 91亚色 students and shaped by 91亚色 researchers."

With files from Mzwandile Poncana

The post Schulich student helps develop innovative AI research tool appeared first on YFile.

Data centres 鈥� large industrial facilities that power cloud computing and AI 鈥� have become critical infrastructure supporting the world鈥檚 growing digitization. Everything from streaming video and online banking to scientific research and generative AI depends on their ability to store, process and move enormous volumes of data.

As demand for digital services continues to rise, these centres sit at the root of that growth. And, as they become more pervasive, conversations about broader implications are growing.

鈥淒ata centres are increasingly part of public debate because of concerns about energy use, environmental impact, local economic effects and data sovereignty in Canada,鈥� says Lyndsey Rolheiser, an assistant professor at the .

Despite the growing significance, there remains a notable gap in publicly available information about these facilities.

鈥淭here is very little systematic evidence to inform that discussion,鈥� says Alexander Carlo, a postdoctoral researcher at Schulich. 鈥淎t a basic level, we do not have a clear picture of where data centres are located in Canada or where new ones are being developed.鈥�

Rolheiser and Carlo set out to address that gap by creating the first comprehensive map of Canada鈥檚 data centre landscape. Their work, now and to be included in the forthcoming Schulich School of Business Real Assets Research Paper Series, documents both existing facilities and the growing pipeline of projects that have been announced or are under construction.

The authors built their analysis around a proprietary dataset from Aterio, a data intelligence firm that aggregates information on large鈥憇cale infrastructure projects. Using permitting records, utility filings and company disclosures, they tracked facilities from initial announcement through construction to full operation, then layered in census and provincial electricity data to assess location, scale and energy implications.

Once completed, they mapped out a much clearer picture of how Canada鈥檚 digital infrastructure is changing. The analysis shows that while Canada鈥檚 current data facilities footprint remains relatively modest, the pipeline of planned facilities is nearly 10 times larger 鈥� and those new centres are far bigger than older ones, reflecting a shift toward hyperscale infrastructure designed to support AI.

Future development is also highly concentrated: Alberta alone accounts for more than 90 per cent of planned capacity, despite relying on a comparatively high鈥慹missions electricity grid. At the same time, new facilities are increasingly being built far from major cities, often hundreds of kilometres from urban cores. Meanwhile, provinces with cleaner electricity systems, including Quebec, Ontario and B.C., have begun restricting or carefully managing grid access for large new data centres.

These patterns reflect a set of broader concerns the authors explore in the paper. Data centres consume enormous amounts of electricity 鈥� often equivalent to tens of thousands of households per facility 鈥� while creating relatively few long鈥憈erm jobs compared with the scale of public infrastructure they require. Their expansion can reshape provincial power systems, raise emissions concerns and crowd out other users. The authors also point to questions of data sovereignty, since most large facilities are owned by foreign firms and to the risk that some projects could become stranded assets if AI demand slows or climate policy tightens.

While Rolheiser and Carlo do point to these risks, the aim of the research is to ground future discussions in evidence. 鈥淭his is a necessary first step for any informed policy or public debate,鈥� Rolheiser says.

鈥淎t a minimum,鈥� Carlo adds, 鈥渢he paper should help clarify what the current landscape looks like and where development is taking place.鈥�

Both researchers hope their work contributes to more informed discussions about data centres in Canada, and provides a solid evidence base that helps policymakers and the public better understand these sites and their impacts on grid access, emissions and economic benefits.

The post Researchers at 91亚色 create first map of Canada's data centres appeared first on YFile.

The study, published in , tested a multi-agent system built with large language models (LLMs) to simulate the work of a liver transplant selection committee 鈥� a multidisciplinary group that decides which patients are placed on transplant waitlists.

Using real-world transplant registry data, the AI system demonstrated high accuracy in identifying patients who are likely to benefit from a liver transplant and those for whom transplantation would be unlikely to help.

鈥淟iver transplantation is a rare case in medicine where access to a life-saving treatment is limited by organ availability,鈥� explains co-senior author Divya Sharma, assistant professor in the Faculty of Science. 鈥淒ecisions about who is waitlisted are complex, and committee deliberations can be subject to unconscious bias聽where a clinician's own background or identity may subtly influence their judgement,聽even when national guidelines are in place.鈥�

Researchers set out to test whether AI agents 鈥� each assigned a clinical role 鈥� could support more objective decision-making. To test the approach at scale, researchers evaluated the system against transplant outcomes data.

The study analyzed 20 years of data from more than 8,000 adult liver transplant recipients in the U.S. using the Scientific Registry of Transplant Recipients. A simulated group of patients with known contraindications was also created to test the system鈥檚 accuracy in flagging cases that should be excluded from transplant consideration.

Results show the AI committee predicted one-year post-transplant survival with 92 per cent accuracy and six-month survival with 95 per cent accuracy. Contraindications were identified with an accuracy of more than 98 per cent, thereby identifying transplant candidates efficiently.

The research team also examined where errors occurred to better understand where the AI system works well, and where it needs careful oversight and improvement. The authors caution that continued monitoring is needed because transplant data can reflect broader inequities in access to health care.

鈥淥ur work positions LLM-based multi-agent AI systems as potential clinical decision-support tools, rather than replacements for human judgement,鈥� says Sharma. 鈥淲hile AI shows promise in making liver transplant decisions more objective, it鈥檚 crucial to emphasize that the final responsibility must always remain with transplant teams and human oversight is critical to address ethical considerations.鈥�

Sharma says while more research is needed to test the AI tools in real-world settings across different health systems, AI-supported committees have potential to help standardize complex medical decisions where resources are limited.

The post Can AI reduce bias in liver transplant waitlists? appeared first on YFile.

Several times a month, a small drone rises above the trees at Swan Lake, following a precise path over the water. Parkgoers who enjoy walking, jogging or birdwatching might assume it鈥檚 there to capture scenic footage. Instead, the drone is part of a 91亚色-led effort to understand 鈥� and help restore 鈥� the health of an urban lake under pressure.

Swan Lake, a former gravel pit transformed into a stormwater pond and community green space, faces ongoing water quality challenges. As rainwater flows into the site from surrounding roads and neighbourhoods, it carries excess nutrients, road salt and other pollutants. Over time, this can fuel frequent algae growth, cloud the water and reduce oxygen levels, stressing fish and wildlife, limiting recreation and, in some cases, raising public health concerns.

Since April 2025, 91亚色 researchers, led by CIFAL 91亚色, have been turning concern about the lake鈥檚 health into measurable data and practical action through the Swan Lake Citizen Science Lab (SLCS Lab). The initiative brings together 91亚色 research centres, including ADERSIM and the One WATER Institute, with local partners such as Friends of Swan Lake Park, a community鈥慴ased volunteer organization dedicated to protecting and improving the area鈥檚 ecological health.

鈥淐ommunities often know when something is not right with a local ecosystem, but it鈥檚 hard to act without clear, comprehensive and consistent information, as well as meaningful community engagement鈥� says Ali Asgary, director of CIFAL 91亚色 and professor in the Faculty of Liberal Arts & Professional Studies. 鈥淭he goal of the lab is to support those concerns with reliable data that can guide real decisions.鈥�

"To assess a lake is to assess ourselves," adds Satinder Kaur Brar, director of the One WATER Institute and professor at the . "Its health card is a mirror of our environmental stewardship."

One way the lab is assessing the lake is through advanced technology, such as the use of multispectral and thermal drones operated by 91亚色 research teams.

Equipped with special cameras that capture different types of light 鈥� including some invisible to the human eye 鈥� the drones can detect potential algae growth and subtle changes in water clarity as they scan the lake from above. Flying low and on demand, they provide detailed, up-to-date views of trends across the entire water body, offering a clearer picture than satellite images and a broader perspective than scattered and spot鈥慴y鈥憇pot water sampling.

The drones have already yielded valuable insights, recently shared in a 91亚色鈥憀ed, under-review study that monitored patterns from spring through fall 2025. By flying the drones roughly once a month and analyzing the findings over time, researchers were able to pinpoint where algae forms, how blooms shift across the seasons and how changes in water cloudiness are driven by biological growth rather than stirred鈥憉p sediment.

The findings confirm what many residents and park managers have long suspected: the lake is rich in nutrients and prone to recurring algae growth. The drone data, however, also reveal something new.

Conditions vary significantly from one area to another, suggesting that targeted, location鈥憇pecific interventions may be more effective than broad, one鈥憇ize鈥慺its鈥慳ll treatments applied across the entire lake. Knowing where problems emerge helps guide chemical treatments, shoreline naturalization projects and future restoration efforts 鈥� and provides a better way to measure whether those interventions are working. "Interconnecting drone data with on-ground water quality can turn ecological signals into informed action that is vital for communities," says Brar.

鈥淲hat the data made clear is that this isn鈥檛 a uniform problem,鈥� adds Asgary. 鈥淲hen conditions vary so much from one part of the lake to another, it changes how you think about solutions. This kind of information allows us to be more precise, more proactive and more strategic in environmental management.鈥�

In addition to monitoring Swan Lake, 91亚色鈥憀ed teams are working to make the data easier to interpret and use in planning. Researchers are developing AI tools to identify patterns in the drone imagery, anticipate conditions such as algae outbreaks and translate complex trends into clearer insights.

Other teams are using virtual reality and simulation to help users visualize the lake over time and explore how different interventions might affect conditions. Meanwhile, geographic information system (GIS) specialists are turning the results into interactive maps and dashboards that help the public and those involved in lake management understand what is happening across the site.

A core goal of the Swan Lake Citizen Science Lab is to encourage meaningful community engagement and shared stewardship.

鈥淔rom the start, this was never about researchers working in isolation,鈥� says Asgary. 鈥淭he goal of the Swan Lake Citizen Science Lab is to create a shared process, where community knowledge and scientific tools come together.鈥�

Local partners are not just observers; they are active partners in the research. Residents take part in field checks, help interpret findings, attend workshops and contribute to outreach efforts that share findings. Alongside them, 91亚色 students gain hands鈥憃n experience applying classroom learning to a real environmental challenge, working with researchers and resident members in a local setting.

For CIFAL 91亚色, which is affiliated with the United Nations Institute for Training and Research, the work at Swan Lake is a pilot that could inform other communities facing similar pressures on small urban lakes and wetlands.

鈥淭he impact here is very tangible,鈥� says Asgary. 鈥淭hrough drones, data and collaboration, we鈥檙e building a deeper understanding of how this ecosystem functions and how it can be protected over time. That kind of shared knowledge is what allows stewardship to last.鈥�

Find out more about the SLCS Lab, and see it in action, in the video below.

The post How 91亚色 is helping to restore an urban lake appeared first on YFile.

Emergency management at airports is uniquely demanding because of the complex, diverse and dynamic systems involved, says Ali Asgary, professor of disaster and emergency management in the Faculty of Liberal Arts & Professional Studies.

With dense traffic, multiple vehicles and operations often unfolding during changing or extreme weather, coordinating airside and landside activity remains a major challenge.

鈥淓ven a small emergency at an airport can have significant political consequences and cascading impacts,鈥� Asgary says. 鈥淭hese are the dynamics that shape airport emergencies, runway incidents and large鈥憇cale disruptions to air transportation.鈥�

Asgary's research has gained renewed relevance amid the March 22 Air Canada collision between an aircraft and a fire truck on a runway at LaGuardia Airport. While investigations are ongoing, the fatal incident underscores how seconds matter during runway operations.

While it鈥檚 still too early to determine what led to the tragedy, Asgary says events often involve factors that emergency managers and aviation operators routinely study: real-time hazard assessment, workloads, communication and warning systems.

鈥淩unway incidents often involve overlapping risks, including split鈥憇econd decision鈥憁aking, heavy controller workload and limited redundancy in warning systems,鈥� he says. 鈥淲hen warning systems rely on a single communication channel, missed messages can quickly escalate into serious incidents.鈥�

Asgary is executive director of 鈥� the Advanced Disaster, Emergency and Rapid Response Simulation lab at 91亚色 鈥� where researchers and students simulate disasters and test response plans before they emerge in real鈥憌orld settings.

At ADERSIM, researchers use agent-based models to simulate aviation scenarios and examine how decisions by pilots, passengers, crew and ground emergency responders influence outcomes.

The lab incorporates virtual reality to help emergency managers visualize airport events and uses AI to analyze disruption patterns. It also explores how tools such as drones could support airside emergency response and risk assessment.

ADERSIM has also developed AeroHaz, a web-mapping application that identifies major hazards for airports worldwide to support hazard awareness and planning.

鈥淭hrough a combination of computer modelling, human鈥慽n鈥憈he鈥憀oop simulations, extended reality and AI, we can test how emergency response systems behave when multiple risks converge and conditions change rapidly,鈥� says Asgary. 鈥淭he work of ADERSIM contributes to 91亚色's leadership in disaster and emergency management.鈥�

Major runway incidents can yield lessons for emergency preparedness 鈥� but only if they are researched, documented and incorporated into revised procedures. The incident also highlights the need for more research into the technological and human factors driving airport safety.

鈥淪imulation-driven research allows emergency planners and responders to review how decisions are made, how workflows unfold in crisis situations and how to improve preparedness,鈥� says Asgary.

In addition to leading ADERSIM, Asgary is also director of CIFAL 91亚色, a UNITAR centre that connects academia with leaders and organizations to tackle global challenges through specialized training in disaster management, sustainability, health and entrepreneurship.

Together with Maleknaz Nayebi, associate professor at the and associate director of CIFAL, he is leading a project to develop AI solutions for airports to minimize risks and enhance response operations. Using AI can help predict weather conditions, coordinate workforces and more.

ADERSIM and CIFAL 91亚色 also share this research through training and professional learning for airport and emergency management leaders, and through public events.

Those who are interested in learning more can attend a two-part webinar series titled Airport Operations, Passenger Management, and Technology in the Face of Geopolitical Crises. Presented by CIFAL 91亚色 and ADERSIM, in collaboration with UNITAR, the event runs April 15 and 25.

CIFAL 91亚色 and ADERSIM will also contribute to UNITAR鈥檚 Airports Global Training Programme, when Nayebi will host 鈥淔uture-Ready Airports: Preparedness for Mega Events Through Safety, Sustainability, and Smart Innovation鈥� on April 22 and 23 in Atlanta, Georgia.

The post 91亚色 U simulation research supports airport emergency preparedness appeared first on YFile.

CIFAL 91亚色 is expanding its work in climate innovation with a new focus on how AI can support real鈥憌orld solutions to some of the most pressing environmental challenges.

Since its establishment in 2020, CIFAL 91亚色, part of the United Nations Institute for Training and Research (UNITAR) global network, has been at the forefront of climate change, disaster management and sustainable development. It offers innovative approaches to climate challenges, including training on emergency management, workshops on disaster risk reduction and programs that help local leaders prepare for both climate and health crises.

With the rapid evolution of emerging technologies showing great potential to support efforts in climate solutions, the centre is now expanding its mandate. 鈥淲e want CIFAL 91亚色 to be a leader in exploring the intersection of AI and climate change,鈥� says Ali Asgary, CIFAL director and professor of disaster and emergency management in the Faculty of Liberal Arts & Professional Studies.

Its first step toward that work is the launch of the Climate AI Innovation Hub, an initiative designed to explore how AI can support creative approaches to addressing climate challenges. Its goal, says Asgary, is to create a network for knowledge sharing, innovation and collaboration that can achieve real-world impact.

The hub鈥檚 first initiative 鈥� a monthly speaker series running until November 鈥� sprang from the idea of leading conversations that explore what is possible with AI.

鈥淭hese computational powers can help us understand and analyze changes in climate. Maybe they can even prevent them by allowing for proactive 鈥� more than reactive 鈥� approaches,鈥� says Maleknaz Nayebi, associate director of CIFAL and assistant professor in the . 鈥淚t鈥檚 not that there is one answer that can be given. For us, it鈥檚 about raising those questions. That鈥檚 how we came up with the speaker series.鈥�

The series will showcase, for example, how AI, IoT (the Internet of Things) and satellite technologies are being used to tackle pressing environmental risks 鈥� from predicting and managing wildfires to designing low-waste, circular buildings. It will introduce participants to the broader climate innovation ecosystem and highlight the role of innovators and entrepreneurs creating scalable solutions for sustainability, resilience, circular economies and low-carbon transitions.

The series will raise awareness about climate entrepreneurship, explore sector opportunities and obstacles, and empower students, early-career professionals, founders, researchers and community innovators to take an active role in environmental research leadership.

鈥淥ur goal is to help people understand how these technologies are being developed and used, and to encourage the sharing of innovations,鈥� Asgary explains. 鈥淲e hope to inspire the next generation of climate innovators and show potential users 鈥� particularly government agencies 鈥� what tools and solutions are available to them.鈥�

The speaker events are the hub's first step in engaging the community, and Asgary says past CIFAL series have served as a foundation for building networks of researchers and practitioners through live group discussions. Recorded content available on also becomes a knowledge repository that draws in new audiences.

鈥淢any of our research projects in recent years have been fed by our speaker series,鈥� says Asgary. Other outcomes have included white papers, book chapters, courses, certificate programs, short courses, community events and more.

Feedback from the first session in February suggests the new series is cultivating projects informed by the insights and networks it generates, highlighting the promise of what CIFAL aims to achieve.

鈥淭he hub is about creating connections, sparking new ideas and ultimately applying AI responsibly to make a tangible difference,鈥� says Asgary. 鈥淎t the end of the day, the goal is to contribute to solving climate change.鈥�

The post CIFAL 91亚色 debuts hub to explore AI solutions for climate change appeared first on YFile.

SDG Month feature

As Earth鈥檚 orbits grow increasingly crowded with satellites and space debris, 91亚色 researcher Zheng Hong (George) Zhu is developing technologies to keep space safe and sustainable.

In addition to the more than 11,500 operational satellites orbiting Earth, according to the Satellite Industry Association, tens of thousands of pieces of space junk 鈥� including retired satellites, discarded rocket components and metal fragments 鈥� now occupy Earth鈥檚 orbital environment.

Managing orbital debris has become central to the long鈥憈erm sustainability of space activity, as NASA prepares new crewed missions to the moon, cargo vehicles continue servicing the International Space Station and companies such as SpaceX鈥檚 Starlink deploy thousands of satellites.

鈥淚n the past, we thought of Earth鈥檚 orbit as having infinite space, but it doesn鈥檛,鈥� says Zhu, professor in the Department of Mechanical Engineering at 91亚色鈥檚 and Tier 1 91亚色 Research Chair in Space Technology. 鈥淚f you don鈥檛 clean up, eventually it becomes very risky.鈥�

As waste accumulates, the risk of collisions with satellites and spacecraft rises. This can eventually lead to the so-called Kessler Syndrome, where collisions trigger a chain reaction that produces more and more fragments which, in turn, cause additional collisions.

Even small objects travel at extreme speeds: a fragment no larger than a bolt can disable a satellite that supports services people rely on every day, from global communications and weather monitoring to navigation and emergency response.

Without effective strategies to reduce and remove debris, humanity鈥檚 ability to safely operate in space may be compromised.

Zhu has spent more than a decade developing solutions to that challenge.

His interest in space debris mitigation goes back to 2010, when he says few researchers were focused on the issue. Two high鈥憄rofile events sharpened his attention: China鈥檚 2007 antisatellite missile test 鈥� which destroyed one of its own aging weather satellites and scattered thousands of fragments 鈥� and the 2009 accidental collision between an operational U.S. communications satellite and a defunct Russian satellite, the first known crash between two intact satellites in orbit.

鈥淚t was a wake鈥憉p call that caught my attention,鈥� says Zhu.

He began exploring how a technology he was studying, called electrodynamic tethers 鈥� long, thin conductive wires that interact with Earth鈥檚 magnetic field 鈥� could help address the problem. Originally investigated as a way to generate electricity in orbit, Zhu realized the technology could also act as a brake, slowing satellites and objects so they safely re鈥慹nter Earth鈥檚 atmosphere.

This helps address a major contributor to space clutter. Most satellites are not designed to return to Earth at the end of their missions. Once they run out of fuel or stop working, they can drift in orbit for years or decades before gravity and atmospheric drag eventually bring them down. By slowing these satellites with an electrodynamic tether, Zhu鈥檚 system accelerates their orbital decay, helping them re鈥慹nter the atmosphere far sooner than they would naturally.

Since 2010, he has been pursuing this technology as a way for satellites and spacecraft to be pre鈥慹mptively equipped with disposal systems, allowing them to safely remove themselves at the end of their missions without adding new refuse or relying on costly clean up efforts. This approach could make sustainable orbital management the default, rather than the exception.

One of his projects, called DESCENT, put this concept into practice as Canada鈥檚 first on鈥憃rbit test of space debris removal technology. Launched from the International Space Station, the Canadian Space Agency鈥揻unded mission consists of two CubeSat satellites connected by a 100鈥憁etre electrodynamic tether, which will deploy in orbit to demonstrate how the system can actively lower a satellite鈥檚 orbit.

Complementing this work is Zhu鈥檚 research in autonomous space robotics, which he pursues alongside his efforts in keeping space clean. His lab develops systems capable of tracking, approaching and manipulating free鈥慺loating and tumbling objects, using advanced perception, robotic dexterity, AI鈥慹nabled decision鈥憁aking and control strategies to rendezvous with and grasp challenging targets. While these systems are developed primarily for on鈥憃rbit servicing 鈥� such as repairing, refuelling or upgrading satellites without human spacewalks 鈥� Zhu believes they also have important applications for active debris removal, where autonomous robots can identify and capture defunct or tumbling objects in orbit.

Building on the autonomous robotic work, Zhu is exploring advanced swarm鈥慴ased approaches. He swarms of small satellites that autonomously coordinate to locate and interact with the waste. 鈥淢y concept is very cheap, small satellites that can be mass鈥憄roduced, launched into space and then work as a swarm,鈥� he says. 鈥淚t鈥檚 decentralized control 鈥� more like ants. When one satellite finds a target, it shares the information so others can approach without collision among themselves and coordinate to dock onto or push the debris.鈥�

Each satellite is designed to nudge or influence space litter using tethers or contact鈥慴ased mechanisms, rather than complex robotic arms, and the swarm is intended to deorbit along with the debris after interaction.

Currently, as part of his Tier 1 91亚色 Research Chair in Space Robotics and AI (2024-29) and as director of the NSERC CREATE Program in Smart Autonomous Robotic Technology for Space Exploration (SMARTART), Zhu is actively publishing and presenting on these concepts while nurturing the next generation of engineers and researchers who could bring them to fruition. Through SMARTART, students gain industry鈥憃riented training in AI, autonomous robotics, computer vision and systems engineering, equipping them with the skills needed to tackle challenges like coordinated spacecraft swarms and active debris removal.

Seeing his students embrace these ideas and contribute to the field, Zhu notes the growing global engagement with space debris issues.

As someone who once felt he was among the few raising concerns about space debris in 2010, Zhu is encouraged by the reception and interest his work now receives, as well as the efforts he sees worldwide from researchers and organizations.

鈥淢y reward is seeing more people following my path to do this,鈥� he says. 鈥淚鈥檓 glad to see more people paying attention and recognizing the importance of this issue.鈥�

The post 91亚色 U engineer develops solutions to make space more sustainable appeared first on YFile.

Since joining 91亚色 in 2022, Oyibo noticed that students sometimes struggled to retain content between classes. This was especially true in memory-intensive disciplines like biology, psychology and human-computer interaction, which require knowing and application of key principles and concepts.

Oyibo wanted to find a way to help.

鈥淚 was trying to look for ways I can support students to learn and retain the content beyond the course,鈥� he says.

He thought of mnemonics 鈥� memory aids that leverage how the brain encodes, stores and retrieves information. 鈥淢nemonics can efficiently encapsulate knowledge in a way that makes it easier to recall and frees up cognitive resources for higher-order processes, such as understanding, analysis and synthesis,鈥� he explains.

Oyibo drew on his expertise in machine learning, persuasive technology, human-centred design and creative writing. Persuasive technology involves designing digital tools that encourage users to take positive actions 鈥� such as fitness apps that motivate regular exercise or contact tracing applications that increase public health participation 鈥� without manipulation or deception. Human-centred design emphasizes building solutions around the needs, experiences and behaviours of real people, rather than forcing them to adapt to rigid systems.

Oyibo applied these principles to previous research, creating personalized digital health tools that adapt to different cultures and communities. This technology, he says, helps users stay healthy while addressing barriers that exclude underrepresented groups.

Now, he is focused on the development of the SANKOFA Project, a toolkit named after the Akan word 鈥淪ankofa鈥� meaning 鈥済o back and fetch what is lost鈥� and an acronym for "Save All New Knowledge Optimally and Fetch Accurately."

The toolkit leverages memory-enhancement principles and has two main application components: SAVE (Selection, Association, Visualization, Elaboration); and RADAR (Recollection, Association, Decoding, Artifacts Review). The SAVE tool allows students to create mnemonics using text, images, audio and video to encode complex information, while the RADAR app supports retrieval practice, helping learners recall, reflect on and reinforce learnings through interactive exercises and games.

Oyibo鈥檚 current work uses AI and genetic algorithms to optimize mnemonics for learning, aiming for what he calls 鈥渕nemonic singularity鈥� 鈥� mnemonics that cannot be significantly improved in terms of effectiveness to maximize knowledge retention. He incorporates the SAVE tool and RADAR app in this work to promote engagement, active recall and consistent practice through interactive exercises and gamified learning.

The toolkit also accommodates different learning styles with multimodal mnemonics 鈥� text, images, audio and video 鈥� with plans to support translations to enhance accessibility across languages and cultures.

Early testing at 91亚色 is encouraging and shows the SANKOFA Toolkit can improve learning and memory retention, says Oyibo. Pilot studies in Ghana are exploring how the approach generalizes in other educational contexts. His findings will be published over the next few months.

While the toolkit is currently designed to serve university students, Oyibo envisions scaling it to learners of all ages and deploying it far beyond 91亚色. 鈥淚 want to organize the world鈥檚 knowledge using mnemonics 鈥� not just mnemonics, but effective mnemonics. I鈥檓 thinking of a platform where teachers and students can collaborate with gamification used to reward meaningful and useful contributions. The goal is deployment in classrooms, not just here in Canada, but globally.鈥�

At its core, Oyibo鈥檚 work builds on the inspiration for the SANKOFA Project: helping people overcome barriers and achieve success.

This philosophy connects much of his past research. Before arriving at 91亚色, he worked at the University of Waterloo on contact tracing applications during the COVID19 pandemic, using persuasive design and personalized behavioural insights to improve public health engagement.

At 91亚色, he is focusing on inclusive design in fitness and health technologies, applying AI and machine learning to tailor digital tools for underrepresented populations 鈥� work that earned him a Gold Award at the 2019 Human-Computer Interaction International student design competition and a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada.

Across all these projects, Oyibo鈥檚 guiding principle remains the same: 鈥淚 want to solve real-world problems that have a great impact and make meaningful contributions. I want to be a key player, not a spectator, in the global stage of research and development.鈥�

The post 91亚色 researcher creates AI tool to improve learning retention appeared first on YFile.