Voltage-gated sodium channels are key contributors to excitability in living organisms; hence, modulation of these channels through mutations and/or environmental triggers can often lead to serious disorders. Having an enriched understanding of the sodium channel electrophysiology may result in uncovering potential therapeutic targets. The general aims of our research include studying the effects of various modulators on sodium channels through well-established electrophysiological techniques.

The proposed project aims to address several challenges that the nursery robots made by AIS Inc. is grappling with. The project tasks are divided into two subprojects: 1) optimizing the electrical and control systems for the AIS robots, and 2) designing a multi-agent system to allow collaboration among the robots. The first subproject consists of estimating the state of charge of the robot's battery, and designing and building appropriate self-tuning PID controller for the motor drives installed in robots.

Mutations in the enzyme glucocerebrosidase (GBA1) are the most common genetic risk factor for development of Parkinson’s disease (PD). PD is characterized by the buildup of abnormal protein deposits in the brain, followed by progressive loss of neurons and behavioural symptoms. Numerous studies have noted a correlation between reduced GBA1 activity and increased levels of these abnormal protein deposits in the brain, but the relationship remains poorly understood. The aim of this project is to create an inhibitor that can enter the brain and be used to determine GBA1 in the brain.

In this project, using such mainstream cryptocurrencies as BitCoin and Ethereum as representatives, the intern will analyze the transaction collection strategies of their mining pools, and then collect transactions and the corresponding blocks data to build a large dataset, from which the computing power of different mining pools and their proportions will be analyzed, together with the transaction latencies of pool mining. We will also identify potential enhancement through the analysis and measurement, particularly on energy and delay optimization.

Portable Document Format or PDF is the de facto standard for presenting textual-visual content. In this project, we aim to develop a machine learning framework for PDF document understanding. Despite the recent proliferation of deep learning-based methods for the analysis and processing of natural images, there have been considerably less efforts on designing similar approaches for highly structured data such as documents. Our project will explore two novel ideas.

Salt marshes are intertidal ecosystems found on sheltered temperate marine coastlines which are known to provide a range of ecosystem services. These services include storm surge and flood protection, and carbon storage, which have been identified as valuable services to help coastal communities prepare for and fight against climate change. Salt marshes are good sinks for atmospheric carbon dioxide relative to their small size due to their ability to trap and bury organic matter in their soils.

Electrochemical water splitting into hydrogen and oxygen gas is a technology of growing importance in the clean energy storage and conversion sector. While this technology has been operating successfully for decades using liquid electroytes, emerging technology uses membranes to provide physical separation of the cathode and anode compartments and thereby separation of the product gases, while allowing ions to flow between electrolytes in order for the electrochemical reactions to occur. The membranes used in electrolyzers are typically acidic, proton exchange membranes (PEM), e.g., Nafion.

In the backcountry, the best powder skiing can be found in terrain that is susceptible to snow avalanches. Travelers are responsible for managing this threat by choosing terrain that will minimize this threat, and they must strike a balance between minimizing risk and finding an enjoyable skiing experience. Perception plays a fundamental role when selecting terrain, which means that these choices are influenced by a suite of psychological factors.

The joint objective of the consortium is to undertake R&D necessary to produce a scalable, cost-effective combined hydrogen storage and fuel cell solution for UAV’s that addresses weight and volume and improves refueling logistics. The novel hydrogen storage system will be combined with a high-power density optimized fuel cell stack for UAV’s that integrates with the low pressure, volumetrically efficient, hydrogen storage solution.

Our proposed research investigates how K-12 teachers learn and customize digital classroom tools and learning management systems and how they share this information with each other. In particular, we will be working with our partner Microsoft to investigate the use and customization of the recently developed OneNote Class Notebooks software that is increasingly being used by teachers for various content delivery and content management tasks.