The CAE flight training services and flight simulators provide the future pilots with digital immersion experience and operational support solutions. Currently, additive manufacturing is found to be a valid way to fabricate the cockpit interior components with lower lead time and accelerate the product development.

This project covers the development and validation of electronics designed to operate large arrays of superconducting sensors used for experimental astrophysics experiments. These sensors have strict requirements for signal processing and noise, making them challenging to operate at scale. However, the next generation of astrophysics instruments will require tens-to-hundreds of thousands of such sensors. The research to be carried out in this proposal includes the prototyping and validation of hardware and signal processing algorithms intended to meet these challenges.

Forecasting the number of passengers using the data from ticket booking systems is critical for railway companies to manage revenue and allocate resources. The booking demand data have a unique hierarchical correlation structure that cannot be appropriately modeled by conventional time series approaches. Therefore, this project aims to develop state-of-the-art probabilistic forecasting models for booking demand in train networks. The project will incorporate the hierarchical data correlations, train capacity, and the impact of cancellation into the model to improve forecasting accuracy.

The project aims to facilitate the research and development of new drugs by employing machine learning methods to generate new molecules. This includes understanding various properties about known molecules by training deep learning models for the purpose of molecular generation. The gained understanding of molecules will be used to improve existing models and generate novel molecules with high likelihood of satisfying given properties.

Major sustainability challenges, such as climate change, are global; thus, addressing them requires active transnational and cross-sectoral approaches to knowledge sharing for collective action. However, the dominant practice of flying thousands of scientists and delegates to attend climate summits and international meetings is responsible for a significant share of carbon emissions. The question, then, is how to scale up global action while simultaneously reducing the environmental impacts of such operations.

The COVID-19 pandemic has highlighted the public need for molecular diagnostic tools to identify pathogen panels through nucleic acid amplification tests (NAAT). Many clinical labs worldwide have now established infrastructures for routine molecular assays using classic quantitative PCR (qPCR). In order to further advance this – now routine – tool for in vitro diagnostics (IVD), it has become imperative to develop underlying technologies for robust, specific, and sensitive detection of pathogens, namely next-generation oligonucleotide probes.

Whey and milk ultrafiltration (UF) permeate are usually considered as environmental pollutants due to their elevated organic load. Increase of whey production owing to high demand of milk-derived products creates a huge disposal problem for dairy industries. The conversion of lactose, which is main component of whey and milk UF permeate, to value-added ingredients is advantageous. In this project, we will employ a cost-effective enzymatic method to convert lactose into bioactive LBA using whey and milk UF permeate as substrates.

The learning of bimanual skills is very important in spine surgery since errors can result in poor patient outcomes. Surgical trainees develop these skills by watching expert surgeons do operations and as they acquire more experience, they are given more responsibility to perform complex operations. The limitations of this learning method are the low numbers of expert surgeons to teach these skills and patients may be at increased risks during this traditional approach to training students.

We spend money everyday. How can we make sure that those purchases are responsible? The financial power of consumer purchases could fund our transition to a sustainable society. However, a lack of transparency regarding the inputs, processes, and impacts of our purchasing decisions makes it difficult to choose sustainable options. This project will develop an evaluation system which combines accuracy and simplicity to help people buy sustainably via a user-friendly app and website.

Recent publications have surveyed cannabis flower microbiological communities, detecting several concerning genera like Aspergillus spp., Penicillium spp. Clostridium, Eschericia, Salmonella and Staphyloccus. There are several documented cannabis complications and even fatalities due to Aspergillosis in immuno-compromised patients. One must also recognize that strict and nonspecific microbial regulations can eliminate the use of beneficial microorganisms in agriculture and deliver unforeseen consequences in the marketplace.