Innovative Projects Realized

Évaluation de la valeur du capital de marque en contexte d’organisation sportive à but non lucratif

Dans un contexte financier et budgétaire où les fédérations sportives sont appelées à développer davantage leurs revenues autonomes, l’exploitation d’un actif intangible telle que la marque peut constituer une action stratégique décisive pour ces dernières. Toutefois, le secteur des OBNL sportives, au sein duquel les fédérations peuvent être incluses, a historiquement été négligé quant au développement de méthodologies permettant l’évaluation financière d’une marque. Ce projet voudra ainsi combler cette lacune et offrir à l’organisation partenaire, Hockey Québec en l’occurrence, un outil qui lui permettra de quantifier la valeur à accorder à sa marque.

Towards a Circular, Sustainable, and Zero-Waste Construction Industry: Investigating Innovative and Efficient Solutions for Construction Composite Products Recycling with Technical, Environmental, and Economic Considerations

The motivation for our project is to address the growing concern about construction waste and its environmental impact. By finding efficient and environmentally friendly recycling methods for composite construction, we can reduce waste generation, conserve resources, and minimize the construction industry’s carbon footprint. Our approach involves conducting technical, environmental, and economic assessments to identify the most efficient and sustainable recycling methods. We aim to develop practical solutions that can be implemented by the construction industry, leading to reduced waste and improved sustainability practices. These solutions can help the partner organization, Global Green Construction, enhance its recycling processes, optimize resource utilization, and strengthen its market position. But the impact of this research goes beyond just one company. It will benefit everyone in Canada. In other words, our findings will contribute to improving the Canadian community by promoting sustainable construction practices, minimizing environmental degradation, and supporting Canada’s sustainability goals. The research outcomes will be disseminated through publications, conferences, and industry events, ensuring that the knowledge generated reaches a broad audience and fosters further innovation.

Solving a new variant of the Resource-Constrained Activity Sequencing Problem

In applications arising in project management, warehouse stock management and project deliveries, several
activities that use a common resource need to be executed in a sequential manner in order to keep nonnegative
resource levels. As an example, consider newly produced items entering a warehouse, and the items that are
demanded by the customers leaving the warehouse. In this setting, the demand can only be satisfied if the number
of items is enough. In the classical management problem, the demand is taken as an exogenous factor, and the decision is made on the production plan. In this project, we generalize this problem by also considering the timing
of the demand as a decision. In other words, the sequence of production and demand is to be determined while
ensuring that the resource level during the execution is never below zero.

Learning and routing in last-mile delivery

The vehicle routing problem and its extensions have been successful in optimizing a measure such as time or cost, but only when these measures can be accurately estimated or approximated. However, this is not the case in complex environments. Particularly in urban routing, it is generally complicated, and efficient routing requires tacit knowledge of the drivers. That is the main reason for which the drivers may change the routes that are ‘optimized’ by routing algorithms during the operations. The routes generated by automated systems are also generally modified by experienced staff for optimization purposes. The objective of the project is to capture such tacit knowledge in the complex urban environments, and to incorporate this information along with the changes in operational procedures into routing algorithms, to minimize the human interventions a posteriori to generation of the routes.

Calcium Imaging Data Analysis with Interactive Visualization

With recent advances in microscopy techniques for calcium imaging, scientists are now able to monitor the neurons in large brain areas of living animals. However, this also creates significant challenges for scientists who must make sense of the large and complex datasets generated. As a result, there is a need for new componential methods and systems to augment current workflows used by neuroscientists. This study seeks to address this problem by leveraging advanced visualization techniques, building on the results of our previous successful collaboration with a partner. Specifically, we aim to design, develop, and evaluate an interactive visual system that allows users, who are often not machine learning experts, to better harness complex models and visually discover insights from the data that serve their domain-specific goals. In addition to the visual system, this research will also contribute useful design guidelines, investigations, and experimental results for future research on relevant topics such as calcium imaging and neuro activity analysis.

NTV News Outlet: Assessing Diversity of women’s representation in the news broadcast media

As one of the requirements of the Master’s in Gender Studies at Memorial University of Newfoundland and Labrador, the intern is undertaking an internship to learn the impact of women’s presence in the newsroom on the coverage of women’s financial contributions in the labour market, particularly those from the South Asian region living in NL, Canada. Through this internship, she will assess gender diversity in the newsroom and its impact on media representation of women of colour while also gaining practical experience and insights into the dynamics of news production and journalism practices.
The focus of the project is to contribute to developing a more diverse, intersectional feminist, and inclusive environment in the newsroom. The intern will also participate in the culture and labour practices at NTV, learning pertinent skills and expertise on the job at NTV and offering academic knowledge about gender-based discrimination, feminist, migration, and immigration, and other South Asian issues, especially women. The intern will assess the organization’s structure and whether there is a complaint redress mechanism to ensure accountability, transparency, and monitoring. She will identify areas that require improvement, thereby providing recommendations to us for improvement in how we operate under the EDI lens.

Sweat Club BSI Proposal

Sweat Club is a boutique-style gym located in downtown Fredericton that offers a variety of group fitness classes, including strength training and high-intensity interval training. The gym aims to provide a fun, inclusive, and motivating environment for clients of all fitness levels. However, like any business in the fitness industry, Sweat Club faces several challenges, such as competition from other fitness businesses, scheduling and logistics, marketing and promotion, keeping up with trends and changing preferences, managing costs, and safety and liability concerns. One significant opportunity for the business is to expand its clientele and target different age groups, particularly a younger demographic.

To combat these challenges, Sweat Club plans to hire a summer intern to improve and aid the business in all areas where they are currently facing challenges. The internship offers an opportunity for the intern to gain knowledge in the business field and the fitness industry. Summer interns can offer fresh perspectives and innovative ways to approach problems, which can benefit the business. Additionally, the intern can help increase productivity by taking on tasks that might otherwise be left undone due to time constraints.

Développement d’un système informationnel 360 pour l’optimisation des opérations

Videns Analytics est une compagnie qui approche cinq ans d’existence et qui regroupe une quarantaine de collaborateurs. Pour sa gestion interne, la compagnie utilise plusieurs outils qui touchent différents domaines comme par exemple:
– la gestion des heures des collaborateurs, utile notamment pour la facturation;
– la planification de l’affectation des ressources humaines sur les différents projets;
– les ressources humaines;
– la comptabilité;
– la gestion des clients et prospects (CRM).
L’objectif du projet est de développer un système d’informations analytique qui permettra de rassembler ces données et de les présenter sous un format qui facilitera la prise de décision et permettra d’optimiser la gestion de la compagnie. Le projet couvre:
– l’infrastructure cloud qui va héberger le système d’informations;
– le modèle de données;
– la base de données;
– la génération de code ETL en python qui va extraire et transformer les données des systèmes existants pour les intégrer au système informationnel;
– des tableaux de bord qui vont permettre de présenter les données de façon synthétique.

Transforming Community Economies

Communities across Canada are planning for significant population and job growth. They do this through Official Community Plans and Zoning Bylaws that regulate how land can be used, whether it’s for homes, jobs, shopping, parks, schools, and more.
Many of these communities were once established either as trading posts near a wealth of natural resources that could be shipped across the country and the world, or as bedroom communities within driving distance to the major service centres of bustling cities. This legacy remains an important component of local economies. Though today, a greater awareness of environmental challenges and the climate crisis is forcing communities to explore ways to green their economies.
How best can a community planner support this shift towards a more sustainable and less carbon-intensive economy?

Conception d’un algorithme LSTM de marche instable chez les personnes âgées

Azimut Médical est une entreprise montréalaise spécialisée dans le développement de vêtements gonflables de protection intelligents spécifiquement adaptés à la prévention des fractures de la hanche chez les personnes âgées. En effet, chaque année, plus de 330 000 personnes âgées souffrent d’une fracture à la hanche en Amérique du Nord seulement. Ces fractures coûtent plus de 20 milliards de dollars au système de santé et causent plus de 70 000 morts annuellement. De plus, au Canada seulement, les chutes représentent la cause la plus importante de blessures chez les personnes âgées. La moitié des personnes atteintes d’une fracture de la hanche perd l’habileté de marcher dans l’année qui suit de façon permanente, résultant par un impact majeur sur sa qualité de vie.
La problématique commune d’Azimut Médical et du stagiaire à travers ce mandat est de participer au développement d’outils intelligents permettant la prévention des chutes chez les aînés.
Pour répondre à cette problématique, Azimut Médical développe le produit Air Séqur: un vêtement gonflable novateur et intelligent qui permet une détection anticipée de la chute et séquentiellement, un déploiement de coussins gonflables absorbant l’impact de la chute.

Product Manager

NTWIST’s purpose has at its core the contribution to one of the most significant challenges globally and nationally: the Net-Zero Emissions goal. In this project, we will implement in-house developed digital technologies to help move forward into the industrial sector’s energy transition process. Particularly, we will implement a software solution powered by AI that will leverage energy efficiency in the mining sector significantly.

Contrast/modality-invariant anatomical recognition in clinical scans for multiple sclerosis study

Multiple sclerosis (MS) is a chronic and progressive autoimmune disease that affects the central nervous system (CNS), including the brain and the spinal cord. In MS, the immune system attacks the myelin that covers the neuronal fibers, resulting in inflammation and damage of CNS that leads to a wide range of symptoms concerning movement, sensation, and cognition. Big data analysis with multi-contrast/modality medical imaging can help improve our understanding of the disease. However, when collecting multi-centre clinical brain and spinal cord scans of MS patients, the file names are usually uninformative for the anatomy within the scan, making manual data organization for downstream analysis time-consuming. To tackle these challenges, the project will develop a new algorithm using few-shot learning to allow contrast-invariant anatomical identification. The resulting algorithm can greatly improve the efficiency of data collection and organization for the study of MS and biomarker discovery.