In this project, we are trying to expose our existing core system functionalities to a public user interface. We will design, implement and test a research prototype user interface that can provide simple and flexible content sharing over a web-based user interface (UI) for organizations such as healthcare or surveillance systems that require a mechanism for (patrial-)sharing content between users in a simple way while providing a chain of custody.
This project aims to design sustainable new microsystems to power data monitoring buoys that can provide a sustainable power source using the ocean waves dynamics. The proposed project will result in significant advances in three long-standing challenges in energy harvesters-based micro devices powering the data monitoring buoys using the ocean waves. These include low frequencies of the ocean waves, mechanical failures due to (high mechanical stress) of the ocean waves, and the efficiency of the energy harvesters-based microdevices.
Hydroponic greenhouse agriculture is an excellent method of producing crops throughout the year in agro-climatologically challenging remote provinces such as Newfoundland and Labrador. However, energy requirements for heating, ventilation and light may reduce profitability of greenhouses even with high local demand for products. This pre-feasibility study investigates the potential of using mini-hydroelectric power to generate the power needed for Growing for Life’s hydroponic greenhouses HVAC systems located in Western Newfoundland.
One of the main concerns in the world is rising energy demand, which forces industries to introduce the products with the lowest energy consumption. Cooling systems often employ single-phase flow and gas-liquid two-phase flow in many applications. These types are well-demonstrated that have lower cooling performance than liquid-liquid two-phase flow. Therefore, customers would pay more as the electricity price goes up. Another pain that customers experience is a higher maintenance cost to keep the product in good condition.
The demand for medical and therapeutic applications with terpene for human and agriculture wellness is booming with a projected market size of $1.01 billion by 2028. With over 30,000 known terpenes, the potential breakthroughs and impact on health and medicine is enormous. However, current standards are limited to the number of terpenes we can categorically identify and quantify for studying with average 21 terpene components in a single mixture.
This proposed project is to develop efficient sensing materials and devices that can detect trace amounts of reactive oxygen species (ROS), in particular a type of activated oxygen, namely singlet oxygen. Detection of singlet oxygen is an important but very challenging task in biological and pharmaceutical applications. The main difficulty in detecting singlet oxygen is due to its very low stability and trace amounts. Certain molecules can capture singlet oxygen through rapid chemical reactions, which result in emission of fluorescence light.
Omega-3 PUFAs are dietary components that have been extensively recognized for their therapeutic value and have shown diverse therapeutic effects including anti-inflammatory, antiarrhythmic, antithrombotic, immunomodulatory, and antineoplastic activities. We observe, NL canola oil possesses high concentrations of the unique essential omega-3 PUFAs and other bioactive phytonutrients conferring important health promotive benefits. We seek to develop a suite of innovative health products capitalizing on the unique functional ingredients of NL Canola to improve consumers health outcome.
Network attacks are becoming more complex every day. It is crucial that we use tools that can detect these sophisticated attacks on networks so that we can identify malicious behavior and prevent attacks and intrusions. The use of machine learning to create intrusion detection engines is great, and we need enough data to train these engines. The purpose of this project is to analyze the problems of existing public datasets and the challenges involved in finding the right machine learning techniques and settings for them.
The beer industry is one of the oldest and high demand industries in the world. The major limitation associated with beer can recycling with its content is that it produces large volume of liquid waste that cannot be discharged to water bodies. The expired beer is unsuitable for to be converted as animal feed due to the health safety aspects. There is a need for recycling this liquid waste as it contains valuable nutrients and energy. There is potential for using the waste beer as substrate for biogas production thereby recovering energy as biogas and nutrients as organic fertilizer.
This research focuses on solving the reporting problem in online proctoring platforms by introducing “behavioral reports” from data collected during an online test. We propose a Machine Learning and artificial intelligence-powered “clarity” report module. A state-of-the-art reporting module that generates behavioral patterns that will help employers make unbiased and informed decisions. Reveal to educators their students’ strengths and weaknesses as well. The behavioral report will be paired with student performance data to help the employer/educators make smarter decisions.