Innovative Projects Realized

Automatic construction layout marking using ground and aerial vehicles

The proposed research aims to map and locate pollutant sources by deploying a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) after a natural or human induced disaster in order to sense, assess and aid in search-and-rescue operations. We aim to use a heterogeneous team, armed with an array of gaseous and aerosols sensors, in order to assess and map the current state and risks. These heterogeneous robotic teams are better suited to handle tasks in catastrophic events that might cover a large geographical area (e.g., earthquakes) or present complex scenarios (e.g., collapsed building).
The project will use small UAVs, in coordination with a UGV, to detect air pollutants. This will be an initial demonstration which will be done indoors with a motion tracking system and small aerial vehicles all controlled from a central computer. TO BE CONT’D

Large scale parameters controlling convection in the tropics: CAPE, CIN, Moisture, and Vertical Velocity

The representation of moist convection and clouds in global climate models is one of the hardest problems in contemporary meteorology. The problem arises because the phenomenon of convection and cloud processes occur at much smaller spatial scales than current computers allow in terms of horizontal grid resolution. Nonetheless, theoretical and empirical knowledge led the climate modelling community to propose a certain number of convection schemes or parametrizations to deal with these issues. However, the two-way dynamical interactions between these large scale parameters and the convection process, and what they are remain open for debate. In this project, we will use numerical simulations based on the publicly available Weather Research and Forecast code to assess how these parameters influence the initiation, intensity, lifetime, and depth of convection. TO BE CONT’D

A mechanistic study of the association between poverty and executive functions in early childhood: Contributions of early brain development and the early caregiving environment

Poor cognitive and academic outcomes are common consequences for children growing up in economically poor families. Poverty is associated with alterations in brain development, communication and cognitive skills. Children living in poverty are exposed to poverty-related risk factors and stressors, including less language exposure. Little is known about the early development of language and cognitive skills relative to brain structures. We will test whether aspects of children’s early experience known to impact the development of cognitive skills also explain the association between poverty and brain development. An intensive home visit at 6 months of age will include observational and objective measures of caregiver behavior, language exposure (via Language Environment Analysis (LENA) system speech recorders) and sleep hygiene. We assume that poor language interactions would impact children’s brain development. The results of this study will guide early prevention and intervention efforts to enhance the development of children living in poverty.

J.P. Metras Sports Museum: Oral History Project, Preserving the Voices of the Past

This project involves the audio and visual recording of interviews with past and present

athletes, coaches, and administrators of the varsity athletic program in London and the

surrounding areas in Ontario. The over arching objective of the project is to create an

ongoing database of stories to supplment the active archival research already taking place at

the Metras Sports Museum. Because of close ties with the Public History Department, the JP

Metras Sports Musuem has witnessed significant expansion and subsequent exposure. By

creating an oral history research collection, the “w” Club will continue to fulfil its mission in

“The enhancement and promotion of intercollegiate athletics at Western University.”

Furthermore, it is vital to capture these stories before they are lost. This project would make

this possible.

An order-reduced simulation framework for 3D multiscale geophysical electromagnetic problems

The goal of this research is to investigate the extension of upscaling and multiscale methods and their application to efficiently simulate (frequency-dependent and time-dependent) electromagnetic fields in geophysical scenarios that include metallic-cased boreholes and fractures filled with conductive/resistive fluids. Simulating this type of geophysical settings is quite challenging because they consider highly heterogeneous media and features at multiple spatial scales that require a very large mesh to be accurately represented. This results in a system of equations to be solved that often exceeds the limits of average computers. Thus, the key is to reduce the problem size but retain the accuracy of the electromagnetic responses, which is what upscaling and multiscale methods aim to do efficiently. The research-based solution proposed in this project is relevant to advance the prediction and simulation capabilities for geophysical electromagnetic problems in the context of Hydraulic fracturing. TO BE CONT’D

Development of high-dimensional robust split regression models to measure data quality for improved use of sensors and diagnostics in a heavy industrial setting

We propose the development of novel statistical methods to improve upon detection of normal and abnormal behaviours in industrial sensors. The ability to detect subtle changes in sensor performance in a wide range of operational conditions has significant potential for MineSense Technologies Ltd., which operates sensors in challenging conditions. It can be very difficult to perform reactive sensor system maintenance in an active mine, as it may require shutdown or suspension of critical operations. Therefore, the ability to detect abnormal behaviours and thus perform preventative maintenance is valuable. The main objectives of this project are to (1) characterize and monitor system data quality, (2) develop high-dimensional, robust split regression models for sensing system telemetry data, and (3) develop approaches to integrate these models into broader system health monitoring. TO BE CONT’D

Links between aquatic macrophytes, nutrient concentrations and deep-water oxygen in an Ontario lake

Human activity near lakes can have a profound impact on the state of the lake. When nutrient-rich pollution enters the lake it can cause toxic algal blooms, decreased oxygen levels, a loss of diversity, fish kills and degradation of water quality. Aquatic plants may limit nutrients in run-off from entering the main lake. Aquatic plants along the shoreline take up the nutrients that potentially enter the lake before they get to the open water zone. At Chandos Lake, Ontario, the species of aquatic plants living along the shoreline have changed due to an invasive plant entering the area. The effect this shift has had on the nutrient levels of the lake are unclear. To study this relationship, lake sediments (i.e., mud) can be used to infer historical changes in nutrients, oxygen, and shoreline plant abundance. TO BE CONT’D

Understanding the Evolving Nature of Refugee sponsors in Canada

This project examines the type of refugee sponsorship groups, their relation to the sponsored refugees, and the challenges and successes that they have faced. This is primary research, using the data that is at this point available but not organised by the partnership organization. It will benefit the partnership organization in giving it an overall, empirically valid assessment of areas of improvement in public policies that are adapted to changing circumstances.

Local Sustainability Partnerships: Understanding the relationship between partnership structural features and partners’ outcomes

Over 10,000 cities around the world have implemented LA21s through partnerships involving public, private and civil society organizations. Organizing large cross-sector partnerships requires structures for ensuring collaborative action (such as decision-making processes, communication systems, monitoring and reporting systems, etc.). Being involved in the partnership results in outcomes for the partner organizations (e.g., sustainability progress, legitimacy, relationship building, skills development, etc.). This research aims to understand the relationship between partnership implementation structures and partner outcomes.

Data was collected on partner outcomes through a survey from three large sustainability partnerships (Gwangju, South Korea; Montreal, Canada; and Barcelona, Spain), each with over 100 partner organizations. 186 partners completed the survey, for a response rate of 33%. Data was collected on the partnership structures through interviews and documents. Results will show if the design of the partnerships’ structure, and specific structural features are critical for achieving desired outcomes at the partner level. Understanding these partnerships and their partner relationships is critical for addressing sustainable community plans, and thus achieving Sustainable Development Goal (SDG) # 11.

Baseline Carbon Stocks in Nova Scotia Forests: Role of the Forest Floor

The forWater Network, funded by the federal government as well as industry partners and provincial governments, is a national research network looking into the impacts of forest-management strategies on drinking-water source quality and treatability. forWater Network researchers at Dalhousie University (including Duinker, the supervisor in this application) are working with Halifax Water and Westfor Management Inc. to determine how the Pockwock forested watershed can be managed to improve water treatability. A key issue here is the movement of dissolved organic carbon (DOC). DOC movement from the land to the water has increased in recent decades, and that increases the cost of treating drinking water to acceptable standards. The Halifax study centres on integrated simulation modelling to build and analyze long-term scenarios of forest management and climate change and their effects on DOC levels in Lake Pockwock. TO BE CONT’D

Machine learning algorithms utilization for business process optimization and enhanced predictive symbiotic matching within the logistics transportation management ecosystem

In this project, various business challenges within the transportation management industry will be studied to look for ways to optimize and improve the current practices. Artificial intelligence-based algorithms will be developed in order to automate manual intensive processes, optimize route planning and demand management, including price fluctuations, adverse weather events and seasonal impacts, and finally attempt to improve shipper and carrier relationships via improved transparency, dispute resolution and better methods for matching based on a trusted statistically validated ratings engine.

L’intégration des fondations subventionnaires dans l’écosystème de la finance socialement responsable du Québec

Si les fondations sont habituellement reconnues pour les donations qu’elles distribuent aux organismes de bienfaisance ou aux organismes communautaires, plusieurs d’entre elles développent une finance socialement responsable dans le but que leurs investissements financiers produisent eux aussi un impact social. Cette recherche reconstituera les trajectoires de deux fondations québécoises qui ont mis en oeuvre une finance plus responsable et solidaire afin de mieux servir leur mission sociale. Pour ce faire, nous ferons des enquêtes au sein de ces fondations en menant une série d’entretiens auprès des principaux instigateurs de ces stratégies. Par les données recueillies, nous voulons répondre à deux grands questionnements de recherche : (1) en quoi la mise en place de ces stratégies vient-elle redéfinir le rôle des fondations dans la société et (2) comment se présente les processus par lesquels les fondations intègrent l’écosystème de la finance socialement responsable du Québec?