Projets novateurs réalisés

Children Privacy Protection Engine for Smart Anthropomorphic Toys

A smart anthropomorphic toy is defined as a device consisting of a physical toy component in a humanoid form that connects to a computing system with online services through networking and sensory technologies to enhance the functionality of a traditional toy such as Mattel’s Hello Barbie and Cognitoys Dino. Many studies found that anthropomorphic toys serve a purpose, as children trusted such designs and felt at ease disclosing private information. Online privacy for children has been a great concern in this environment, particularly when the child’s private information is involved and can be potentially shared with other parties. Privacy can result in physical safety of child user, e.g., child predators. While parents strive to ensure their child’s physical and online safety and privacy, there is no standardized child protection engine for parental control in this paradigm. Parental control is a feature in a smart toy for the parents to restrict the content the children can provide to the toy. The main objective of this research is on developing a privacy-aware context data model for smart toys to support a standardized child protection framework with parental controls.

Treatment of Pulp and Paper Wastewater Using Aerobic Granulation Technology

The pollutants from pulp and paper mill effluents are toxic to plant/marine life. Harsh plans on tackling this issue by treating wastewater that comes out of these mills before it has a chance to affect the aquatic life in fresh water resources. Pulp and paper mill effluent, much like any other industrial wastewater, contains trace contaminants in large amounts that are extremely difficult to remove with traditional physical or chemical methods. Aerobic granulation is a great solution to deal with this problem as the microorganisms in the granules are tuned to tackle these pollutants. The biogranules contain a mixed and diverse microbial community; therefore, it is a much better solution than traditional biotreatment technologies used in industry today. Because of the granules’ ability to simultaneously remove contaminants that are organic and inorganic in nature, Harsh’s theory is that pulp mill effluent can be treated much more efficiently with the use of this technology. Partnering with IIT Bombay, he plans on implementing this innovative technology into current treatment practices. The ultimate goal of Harsh’s research is to highlight the benefits of aerobic granulation technology in improving water quality around the world.

Investigation of antioxidant and cardioprotective effects of melatonin in diabetic animals

The growing prevalence of diabetes mellitus, a group of metabolic diseases, is evident worldwide. Diabetes causes a number of acute complications, many leading to cardiovascular disease, which can be partly attributed to the presence of reactive oxygen species (ROS), leading to oxidative stress in the hyperglycemic state. The circadian hormone melatonin has been recognized as having strong antioxidant effects; thus the objective of this study is to evaluate the cardioprotective effect of melatonin on diabetic animals. The main hypothesis is these properties of melatonin will be important to modulate heart tissue oxidative stress (increasing the antioxidant system and decreasing oxidative damages), with a consequent improvement in cardiac metabolism and remodeling. An acute high intensity exercise will be used to challenge animals’ cardiovascular metabolism, function and antioxidant system, in order to clearly evaluate all benefits of melatonin treatment. Unravelling the key effects of melatonin in this respect will provide new knowledge on its potential as a part of the therapeutic repertoire of drugs for cardiomyopathy.

The mobile application market in developing country

The research project I propose concerns the mobile application market in a developing country. In order to provide a complete analysis, I must define what makes Curitiba city favourable to a mobile application market and what people are seeking through mobile applications on the market. Furthermore, this project must reveal how the demand meets the offer concerning mobile applications in terms of price, interrelationship, logistics by obtaining the requirements to create a mobile application in Curitiba. In addition, I want to analyze if the growing economy of Curitiba influences the growth of the mobile application industry. Most of the existing studies concern the mobile phone use but not mobile applications while it could contribute to increase the economic growth.

Connectivité hydrologique et biogéochimique dans le continuum aquifère-rivière

Les budgets globaux de carbone considèrent que les rivières transportent passivement le carbone terrigène vers les océans. Seulement, les rivières sont un lieu de transformation biologique et de transfert vertical du carbone du sédiment à l’atmosphère. Des échanges avec les eaux souterraines y ont également été mis en évidence mais sont encore peu ou mal quantifiés. L’objectif principale de ce projet est de déterminer le rôle de la morphologie des cours d’eau à la fois dans le continuum aquifère-rivière et dans les échanges de CO2 en milieu tropical humide. Ce projet mettra plus particulièrement l’accent sur le rôle d’un paramètre particulier lié à la morphologie des cours d’eau : le débit. Pour répondre à cet objectif, une méthodologie innovante combinant des analyses du 222Rn (traceur des eaux souterraines) et de la pression partielle en CO2 sera mise en place sur deux rivières : le Rio Solimões et le Rio Negro.

Youth Usage of Comude Sports Units in Guadalajara, Mexico

The vast number of positive outcomes associated with participation in sport and physical activity (PA) has led to community initiatives to enhance youth participation in sport and PA. In Guadalajara Mexico, municipalities are offering free sport and PA programs and spaces, but uptake by youth in some areas of the city remains quite poor. Thus, the question my research will address is: what are the barriers and facilitators to youth usage of Comude sports units in Guadalajara, Mexico? I will use a community based participatory research (CBPR) framework, which ensures social and cultural relevance and scientific rigour by involving community members in all stages of the research process. Phase one will involve assembling a local advisory committee by building on currently established connections at the University of Guadalajara. Phase two will engage youth (14-18 years) participating at approximately 12 local sport units through focus groups in which they can share their personal experiences regarding the barriers and facilitators to engaging in sport and PA at the centers. TO BE CONT.

Mixed Ni-Fe-oxide cathodes for hydrogen production by water electrolysis

Traditional energy sources, such as fossil fuels, are rapidly being depleted. Furthermore, CO2, one of the byproducts of burning these fuels, increases environmental risk such as climate change. Therefore, novel renewable energy paths are being developed to reduce the dependency on fossil fuels. Hydrogen, a clean energy carrier, is considered to be the best alternative to the current fossil fuel energy economy. Hydrogen can be produced by electrolysis of water applying energy which split the water molecule and forms hydrogen on the cathode surface. Taking into account this information, the goal of this project is to develop new materials for the cathode, in this case particularly, nickel-iron oxides, in other to achieve a higher hydrogen production by water electrolysis at a lower cost, since the materials currently use are very expensive. In addition, these new cathodes are expected to have a better long-term stability and to perform well in the alkaline and acidic media, and seawater.

Longitudinal Weak Labeling for Lung Cancer Prognosis and Treatment Response Prediction

This project aims at evaluating whether recent results in deep learning models, trained to exploit weak labels (Hwang, 2016) can serve to extract meaningful lesion localizations from image-level labels, either from individual scans or given a (longitudinal) sequence thereof. To this end, we will scale up existing models that have been shown to work on 2D images to a 3D context, studying labeling performance as the dataset size grows. If successful, this work will assert the usefulness of DCNNs to provide a general modeling framework to integrate imaging with other clinical patient data into a predictive system that could help support clinical decisions and ultimately improve patient care. The proposed research project fits within the partner’s scientific roadmap, which is to develop deep learning models suitable to processing clinical data that arises in a sequential fashion at the patient level (longitudinal data), wherein the set of available clinical modalities can be highly variable (heteromodality). The industrial partner has an existing team of full-time researchers dedicated to studying these questions; the intern will attack complementary questions with the help of the team. TO BE CONT.

Non-destructive Thermographic Stress Analysis of a New Composite Plate for Femur Fracture Fixation

The present aim of this study is to use an infrared thermography technique to non-destructively

measure the three-dimensional surface stress field in a synthetic femur fracture model repaired

with a new composite plate vs a clinically-used metal plate. To this end, there are three main

phases of this study. First, the infrared thermography system will be calibrated, which is needed

because the composite plate is made of several layers of woven material which have differing

material properties resulting in differing thermographic properties. Second, peak stress on the

new composite plate and the host femur will be identified as sites for potential mechanical

failure, thereby allowing the optimal repair method to be determined. Third, the mechanical

performance will be compared of a standard metal fracture plate vs. the new protoype composite

plate made from a polymer-based composite material.

Technology and Tools for Quantitative Neurodiagnostics Using Ultra-High Resolution Magnetic Resonance Imaging

The project aims to translate developments in ultra-sensitive MRI sensors to a clinically-relevant setting. To create high-sensitivity sensors for better images, we aim to create a tight-fitting system which places the sensors—akin to antennas—closer to the brain. This will improve the quality of the signals that we can extract from the brain, and allow us to use these improvements to capture images that have higher resolution and better contrast. Using this imaging improvement, we aim to then create a large normative dataset of grey matter thicknesses. This dataset will tell us what is “normal” for thickness in every part of the brain, and let us capture differences accurately and sensitivity. Ultimately, we aim to become sensitive to even subtle changes in grey matter loss, which may permit us to detect certain neurodegenerative diseases earlier, allowing us to treat them better.

Bioprinting an implantable knee meniscus

Each knee contains two menisci, crescent-shaped pieces of cartilage that play a crucial role in absorbing shock and providing nutrition to the joint. The meniscus is one of the most commonly damaged areas of the knee, unfortunately the body cannot easily repair meniscal injuries, leaving patients with reduced mobility and severe pain. Surgical removal of all or part of the damaged meniscus relieves acute pain, but often leads to osteoarthritis (OA) of the knee. Meniscus implants are a potential solution, however none of the currently available replacements prevent the development of OA.
Bioprinting is the fabrication of 3D structures from biocompatible materials. In this project, we will use novel bioprinting methods to build biocompatible meniscus-like tissues and validate their suitability for surgical implantation into the knee. Success of this project will enable Aspect (partner organisation) to progress to the next phase of meniscus implant development by testing printed tissues in animal models.

Learning representations through stochastic gradient descent by minimizing the cross-validation error

Representations are fundamental to Artificial Intelligence. Typically, the performance of a learning system depends on its data representation. These data representations are usually hand-engineered based on some prior domain knowledge regarding the task. More recently, the trend is to learn these representations through deep neural networks as these can produce significant performance improvements over hand-engineered data representations. Learning representations reduces the human labour involved in any system design, and this allows in scaling of a learning system for difficult problems. In this project, we propose to design a new incremental learning algorithm, called crossprop, for learning representations based on prior learning experiences. Specifically, the algorithm considers the influences of all the past weights while minimizing the current squared error, and uses this gradient for incrementally learning the weights in a neural network. This algorithm is called crossprop because it learns to shape the weights in a neural network through leave-one-out cross-validation procedure.