Projets novateurs réalisés

Quantifying the financial benefits of technological solutions for improved water and tailings management

Of the many sustainability issues facing the mining sector, water has emerged as one of the most business critical. This project quantifies the benefits of investing in water and tailings technologies in financial terms in order to identify contextual environments wherein water-related savings make the most economic sense. The outputs of this project will be of direct benefit to the mining sector and the industry partner through providing information needed to justify investments in water-saving technologies and thereby enhance more effective organizational decision-making. The project also provides participating interns with an opportunity to gain direct exposure and experience with one of the world’s largest equipment service and process knowledge suppliers for the mining sector. Furthermore, research outputs will be published in blog pieces, conference presentations and journal publications that will be made publicly available to other companies operating in Canada and beyond.

Développement de substituts de terre végétale à partir de matériaux locaux pour la végétalisation de la mine Canadian Malartic

L’exploitation des mines à ciel ouvert entraine souvent un changement du paysage, des horizons et de la structure du sol. L’utilisation de matériaux peu coûteux disponibles sur les sites miniers et dans les environs constitue une alternative pour remédier à la problématique du manque de terre végétale nécessaire végétaliser les parcs à rejets miniers. Les rejets miniers générés sur ces sites sont peu propices à l’installation de la végétation à cause de leur faible teneur en nutriments et de leur structure physique inappropriée. Ce projet a donc pour objectif d’évaluer la capacité de matériaux alternatifs au sol végétal à répondre aux différentes fonctions du sol pour assurer le succès de végétalisation sur diverses zones d’un site minier. Tous matériaux disponibles sur site et dans les environs de la mine susceptibles d’être utilisés pour la végétalisation seront inventoriés et des essais de caractérisation au laboratoire ainsi que des tests de culture seront réalisés. Les résultats attendus de ce projet permettront d’identifier les recettes prometteuses pour végétaliser les parcs à résidus selon différentes zones topographiques.

Concrete Slab-On-Grade Analysis for Slab-Mounted Jib Cranes

Various industries and warehouse operations often need free-standing jib cranes to be installed on the floor. Industrial floors typically consist of unreinforced or very lightly reinforced concrete slabs poured directly on the ground which may not have sufficient strength to support the crane. Improper design may result in severe cracking which raises concrete durability issues or catastrophic collapse which could endanger lives. The project will develop a design software to assist engineers design slab-mounted jib cranes based on an in depth theoretical and numerical study of concrete slab behaviour. This project will help broaden the portfolio and profitability of crane and material handling system manufacturers as well as help a large number of industries that require the use of slab-mounted jib cranes.

Prevention of noise- and cisplatin-inducedhearing loss by oral administration of theflavonoid-enriched fraction AF4 in mice

Flavonoids are polyphenolic compounds which give fruits and vegetables their bright colours. Habitual
consumption of dietary flavonoids reduces the risk for neurodegenerative disorders such as Parkinson’s
disease, stroke and Alzheimer’s disease. Pre-clinical studies performed using animal models for these
disorders have convincingly demonstrated that flavonoids not only halt the pathological processes
which cause neurodegeneration but also promote recovery after brain damage. These findings and the
fact that flavonoids can be consumed safely even in large amounts suggest they be useful treatments
for neurodegenerative disorders. With this in mind, we have isolated a flavonoid-enriched fraction
from the peel of Nova Scotia apple called AF4. Oral administration of AF4 reduces inflammation,
brain damage and motor deficits in mouse models for stroke and multiple sclerosis. The purpose of the
present application is to determine whether AF4 also reduces the inflammatory and degenerative
events responsible for noise- and cisplatin-induced hearing loss in mice. Hearing loss is a frequent
complication of cisplatin…TOBECONT’D…

Genes to affordable medicines – Stream 1-B1

The Structural Genomics Consortium (SGC) is a not-for-profit public-private partnership research organization that aims to accelerate the discovery of new medicines through open science. This Mitacs cluster will bring together SGC’s industry and academic collaborators to work together towards new and affordable medicines for challenging diseases. Sixty-three post-doctoral fellows will spend 2-3 years developing open source tools and knowledge for previously understudied proteins, thereby unlocking new areas of biology and identifying new opportunities for drug discovery. SGC’s spin-offs, the M4 companies, will be the vehicles to translate this knowledge into new medicines for rare and challenging diseases that have been excluded from traditional pharma company programs. The M4 companies are committed to open science and sharing, and to affordable pricing to ensure patients can access the new drugs.

Genes to affordable medicines – Stream 1-A2

The Structural Genomics Consortium (SGC) is a not-for-profit public-private partnership research organization that aims to accelerate the discovery of new medicines through open science. This Mitacs cluster will bring together SGC’s industry and academic collaborators to work together towards new and affordable medicines for challenging diseases. Sixty-three post-doctoral fellows will spend 2-3 years developing open source tools and knowledge for previously understudied proteins, thereby unlocking new areas of biology and identifying new opportunities for drug discovery. SGC’s spin-offs, the M4 companies, will be the vehicles to translate this knowledge into new medicines for rare and challenging diseases that have been excluded from traditional pharma company programs. The M4 companies are committed to open science and sharing, and to affordable pricing to ensure patients can access the new drugs.

Random bin picking with industrial robot

Materials with different sizes, shapes, or materials come into the process every day, they need to be sorted, placed, and usually fed into a machine for processing. Traditionally, this task is very hard to be automated because of the non-fixed position of the part and frequent part changes. For this random bin picking project, we are targeting to build a solution that can pick and place the part from a box with the aid of the latest computer vision technologies. We will be looking into find ways to create a model for any random part, match the part in the clustered point cloud, and retrieve the part’s precise location in the 3D spaces.

Efficacy and feasibility of online cognitive training platforms for older adults with subjective cognitive complaints: A randomized controlled trial

With an aging population on the rise, the prevalence of cognitive decline is expected to increase substantially. Goal Management Training® (GMT) and the Memory and Aging Program® (MAP) are cognitive interventions that have been studied extensively and applied clinically to address these needs. Although previous research has demonstrated efficacy of the in-person versions of both MAP and GMT, significant barriers exist in the utilization of these programs. In light of these challenges, online versions of MAP and GMT have recently been developed. The current project aims to evaluate if online versions of MAP and GMT are successful at improving cognitive functioning, cognitive complaints, and the impact of cognitive concerns on daily functioning. Findings will be used for knowledge dissemination and will aid in the commercialization of these evidence-based online cognitive interventions.

Point-of-Need Microfluidic Biosensor for Detecting Airborne Viruses using Molecularly Imprinted Polymers: Towards COVID 19 Virus Monitoring

This project is a partnership between York University and Sixth Wave Innovation Inc. (SIXW). The goal of this partnership is to develop components of a portable and low-cost technology for rapid and on-site air sampling and detection of aerosol viruses in indoor and outdoor environments. Virus capturing will be done by Molecularly Imprinted Polymers (MIPs) under the extensive expertise of the partner company. MIPs are robust materials with pre-made nano-cavities that can capture target objects such as viruses in this project. For this, MIP-based nanoparticles for microwire coating and surface immobilization of viruses will be designed and optimized. MIP-virus conjugation will be investigated in microfluidic devices. Finally, on-chip virus capturing, tagging with detection labels, and quantitative detection will be performed electrically. This project will result in innovative scientific research, mobilization of research from conception to proof of concept stage, and lab-scale development and integration of sample preparation and virus detection devices. These integrated sample preparation and detection systems will be portable, easy to operate, and sensitive for future use by inspectors, businesses, hospitals, special care centers, and police force involved in decision-making to address various challenges associated with airborne pathogen outbreaks and pandemics.

Passive Airborne Sensor Platform

In disaster scenarios involving airborne contaminants, where the dispersal of toxic agents can impact human lives, first responders require fast and accurate dispersal trajectory information. Existing methods that detect the local presence of an agent do not provide insight towards dispersal trajectory, and long range spread is either simulated with sparse reference data or measured long after the dispersion is complete. The lightweight and porous form of the milkweed seed offers natural inspiration for a novel sensor platform. In addition to investigating the market potential for the passive airborne sensor platform, the objective of the project is to quantify the effect of porosity on the response of the sensor platform to rapid changes in wind speed. Understanding how porosity affects the capability of the sensor platform to passively track the flow will assist in scaling the sensor platform design to meet the needs of potential customers.

A novel analgesic in a surgical model of osteoarthritis

Osteoarthritis is the most common form of joint disease affecting over 80% of the human

population above 75 years old and burdening health organizations worldwide. Osteoarthritis is

characterized by progressive joint degeneration resulting in chronic pain and loss of joint

function. Currently there is no cure for osteoarthritis; available treatments are only symptomatic

targeting pain and are associated with significant side effects, emphasizing the need for new

treatments.

Isovaline is a novel analgesic which showed remarkable effects in several pain models

without producing central nervous system side effects. The current study will examine the

analgesic profile of isovaIine in mice; we will assess the efficacy of isovaIine in alleviating the

signs of osteoarthritis and restoring the ability of the mice which underwent surgical

destabilization of the knee joint to exercise voluntarily. The effect of isovaline will be compared

to dicIofenac, and morphine the currently drugs of choice for osteoarthritic pain.

Characterization of powder materials for additive manufacturing (3D printing ability) using machine learning methods

Metal additive manufacturing (AM), also known as 3D Printing, is a technology that produces three-dimensional metallic parts layer by layer as opposed to conventional subtractive techniques. The qualities of 3D parts are significantly influenced by the characteristics of the feedstock materials, which depend on the manufacturing processes and also vary between batches. Powder pack density and flowability are often used to evaluate the 3D printability of the powder. The two metrics are currently determined by experiments and/or numerical simulations. However, the existed methods are often time/labor consuming due to the repeated sampling/testing processes and/or generate poor knowledge in the predictions of a new powder batch due to the sophisticated physics behind particle contact/interaction. The machine learning (ML) methods, especially deep learning (DL) method has already been widely used in object detection and segmentation. In particular, neural networks (NNs) are able to learn the relationship between the input features and output targets based on previous data. Compared with traditional methods (experiments and simulations), ML methods feature high objectivity and prediction accuracy and efficiency. Once the dataset between powder characteristics and processing is established, ML can realize in-situ monitoring of powder characteristics and real-time control the powder manufacturing processes. TOBECONTINUED