Innovative Projects Realized

An analysis of non-structural flood-management measures in Shanghai, China

This research project will involve analyzing non-structural flood management measures in Shanghai, China – one of the world’s most flood vulnerable cities. The Chinese government has invested heavily into structural barriers to flooding, such as the Three Gorges Dam, but there is no fail-safe in times of extreme flood levels. Despite extensive research, there is no information in English literature on the use of public-education and outreach by the government to better prepare Shanghai’s most vulnerable residents. It has been proven that effective flood-management must involve both structural and non-structural risk reduction measures.

Dynamic Control of Robotic Arm

In the design of robotic mechanisms, dynamic balancing i.e. force and moment balancing is an important issue. The robotic mechanism’s accuracy and efficiency are affected because of the unbalance. Moreover, the forces and moments cause fatigue, noise and disturbance in its structural base.

Previous research done by Prof. Chris Zhang include a force balancing method that is termed as adjusting kinematic parameters (AKP) for robotic mechanisms or real-time controllable (RTC) mechanisms. This research includes dynamic balancing method for planar mechanisms only i.e. two dimensional. This method is compared with counter-balance method (CW). It is shown that AKP-method performed better than CW-method in dynamic balancing of robotic mechanism.

My MSc thesis topic is ‘dynamic control of robotic arm’. My research includes furthering the AKP-method in terms of dynamic balance and optimization for spatial (X, Y & Z) 6-DoF mechanism. My research also includes dynamic balancing application to oscillators and robots.

Modeling of packing processes for ellipsoidal particles of arbitrary size

Liquefaction is a destructive phenomenon which usually takes place after an earthquake in areas with water-saturated soil or sand. During the liquefaction process, soil loses its strength and can no longer support structures and buildings which often leads to their destruction. To prevent damages associated with liquefaction, it is critical to study this phenomenon and understand its underlying mechanisms. One approach to study liquefaction is through computer simulation using the discrete element method. In this method, individual soil particles and their contact forces are computed to simulate the displacement of the grains during packing. In this project, soil particles are simplified by ellipsoids. Hydro-Quebec has been developing SiGran, a software to simulate liquefaction, and this project will extend its current sphere packing to ellipsoidal packing. The results of this project will be compared with experimental data to evaluate its accuracy and efficiency.

Universal surface modification method to encourage cell patterning

Surface modification to promote the patterning of mammalian cells is an important tool in cellular biology. It aids in the development of different tissue engineering scaffolds, biosensors, and validation of high throughput screening assays. Surface modification protocols must be tailored to each individual material which limits the utility of a successful antifouling strategy to individual materials. The cost to modify individual materials for multi-material devices, often used in biomedical development, is high. Therefore, a single antifouling method is highly desirable. Through collaboration with leading experts in this field at Soochow University, we hope to gain experience in polyethylene glycol grafting methods to apply to surface modification. Their expertise will improve high throughput screening assays as well as a number of other assays used to study biological interactions and characteristics of cells. This research opportunity provided by Mitacs Globalink will facilitate international collaboration for the development of a surface modification tool in extraordinarily high demand.

Soutenir le transfert des savoirs d’expérience dans le secteur aéronautique : le cas des mécaniciens chez Lockheed Martin

Le vieillissement de la population générale amène inévitablement un vieillissement de la population active, signifiant ainsi une vague de départ pour la retraite dans plusieurs secteurs de l’industrie. Les travailleurs ayant oeuvrés une partie de leur vie dans un certain domaine d’expertise ont accumulé au fil de ces années un bagage de connaissances significatif. Ces connaissances représentent une richesse inestimable. Le transfert de connaissance et de l’expertise est une étape critique avant qu’un quitte vers la retraite. Par contre, les limites de temps, les limites budgétaires, les barrières structurelles dans un contexte de haute productivité peuvent
effectivement venir nuire au transfert de connaissance optimal.
Le but de la présente recherche est l’investigation par l’ergonome des mécanismes qui étaye le transfert de
connaissance dans le milieu de l’aéronautique. Utilisant la démarche d’intervention ergonomique, l’ergonome
ciblera es situations de travail les plus critiques ou l’utilisation d’un logiciel nommé Quantum rend le processus
de transfert de savoirs plus difficile. L’analyse de l’Activité de travail dans ce contexte permettra de faire ressortir
les leviers et les obstacles à un transfert de connaissance idéal. TO BE CONT.

Enhancing Lateness Management in Cross-docking

Today’s marketplace is moving faster than ever, and companies are challenged to distribute their products more quickly, efficiently and cost-effectively. This has led to the rise of cross-docking in the global supply chain to help keep pace with customer demand. Cross-docking refers to the practice of unloading goods or materials from an incoming vehicle (e.g., train car, truck, vessel container) and then loading them directly onto outbound vehicles with no storage in between. A common form of cross-docking operations corresponds to single or multi-item pallets, which are unloaded, sorted based on their destination, and placed directly onto outbound trucks. This strategy allows transportation companies to move towards more proactive, agile and flexible supply chains, with shorter product cycles and easier product customization.
The objectives of the project are to improve the existing software tools that plans the scheduling of the incoming/outgoing vehicles of a crossdocking facility in order to reduce the lateness (tardiness/earliness) of the goods deliveries. In addition, we will explore the integration of machine learning tools in order to enhance those software tools.

Enhancing Lateness Management in Cross-docking

Today’s marketplace is moving faster than ever, and companies are challenged to distribute their products more quickly, efficiently and cost-effectively. This has led to the rise of cross-docking in the global supply chain to help keep pace with customer demand. Cross-docking refers to the practice of unloading goods or materials from an incoming vehicle (e.g., train car, truck, vessel container) and then loading them directly onto outbound vehicles with no storage in between. A common form of cross-docking operations corresponds to single or multi-item pallets, which are unloaded, sorted based on their destination, and placed directly onto outbound trucks. This strategy allows transportation companies to move towards more proactive, agile and flexible supply chains, with shorter product cycles and easier product customization.
The objectives of the project are to improve the existing software tools that plans the scheduling of the incoming/outgoing vehicles of a crossdocking facility in order to reduce the lateness (tardiness/earliness) of the goods deliveries. In addition, we will explore the integration of machine learning tools in order to enhance those software tools.

Estimation and Prediction of Censored Arrival Processes with Censoring for Replenishable Item Purchases

The aim of the project is to predict future customer demand for repeat-buying items based on available customer purchase records. However, the purchase history for a single customer may not be sufficient to base predictions on. Also, some purchase records might be missing due to sales events at competitors’ locations. Thus, treating each customer as a replicant of the average customer and averaging inter-purchase times to predict future demand will likely be an inadequate approach. For this project, a generalization of traditional models in marketing research will be studied and a more flexible model that accounts for time-varying model features will be investigated to better model the data generation process to provide accurate forecasts that will bring foreseeable benefits in logistical efficiency.

Génération d’un modèle de prédiction de présence mycélienne combinant des données géomatiques et génomiques et application au territoire des Premières Nations visant la facilitation de la récolte (et de la commercialisation) de champignons comestibles

Une bonne part du travail du stagiaire consistera à faire du travail d’échantillonnage sur le terrain pour prélever des échantillons de sol de la région du Témiscamingue et de la Haute Mauricie. Ces échantillons seront ensuite rapportés à l’université (UQTR), différentes analyses seront faites et l’ADN contenu dans le sol sera extrait. L’ADN correspondant aux champignons présents dans le sol sera amplifié et séquencé. Les espèces identifiées à partir du séquençage seront corrélées aux données propres à chacun des sites d’échantillonnages pour faciliter le développement d’un modèle mathématique permettant de prédire la présence des champignons, dans le but d’en favoriser l’exploitation rentable.

Analysis of Narrative Driven Social Media Content Use Within Successful Social Media Campaigns Across Multiple Social Platforms To Develop a Campaign Strategy.

The proposed research will take a look at how Non-profit organizations can use storytelling to affect the outcome of their social media campaigns. The intern will be taking a look at narrative driven social content, which is social media content whether videos, audio, infographics, images, etc., that are created to tell a story and its use in social media campaigns and analyze how these types of story driven content affects the success of the campaigns. This research will allow the partner organization to be able to deploy social media campaigns more strategically to increase the donation to be able to finance their social programs. The partner current operating revenue from donation is 35,370 and hope to increase this by 20% after implementing the recommendations from this research.

Coupling event sampling to ColiMinder® high-frequency monitoring of E. coli for improved microbial risk assessment in source waters (COLIRISK)

Safe drinking water supply is a daily need but it can be seriously threatened by microbial hazards originating from fecal contamination of source water, especially following periods of intense rainfall. In order to assess drinking water intakes (DWIs) vulnerability to fecal pollution and to take cost-effective decisions in case of hazardous events, it is urgent to implement early-warning systems. A recent enzyme-based technology, ColiMinder® enables to measure E. coli in water at high temporal resolution (every 30 minutes). In order to implement it for effective microbial risk assessment at DWIs, research first needs to clarify how E. coli signals are representative of the prevalent microbial risk (i.e. of pathogen loads). The present project thus aims at developing a simple and original system that integrates the ColiMinder® with an automated sampling device for event-based sampling and analysis of pathogens and fecal source tracers. This integrated system should prove useful in the implementation of the ColiMinder® at Canadian DWIs with the purpose to improve the assessment of their vulnerability to fecal pollution.

Energy metabolism modelling with sensitivity to activity thermogenesis tracking data

This research initiative aims to develop more accurate mathematical models of human energy metabolism. The
company FitMyLife Health Analytics aims to use the models to help its customers establish behaviour patterns
to achieve health goals, such as increased fitness, increased health and weight loss. The models ensure that
customers’ decisions are rooted in an accurate understanding of the unique aspects of their own body’s metabolism and their individual physical activities. Commonly available fitness tracking devices (such as fitbits,
for example) and newly developed dietary tracking apps provide detailed information that has not been
accessible in previous models. This research project will use these new technologies to develop an improved
tool for human fitness management.