Fungal pathogens of agriculturally significant crops pose a serious threat against global food security. This is exacerbated by the limited classes of fungicides that are commercially available for the farmers and the rapid emergence of resistance against the existing fungicides. Furthermore, resistance against agricultural fungicides can poses serious threat to human health as it can provide cross-resistance to the antifungal drugs that are used in the clinics world-wide.
Vibration analysis is probably the most widely used technique to perform health monitoring of mechanical machinery. Specifically, we are interested in monitoring ‘Vibrating screens’, machines that are for example used by the mining industry to sort aggregate by size. Over the last 10 years the research group of Dr. v. Mohrenschildt has developed hardware, software and theory to accomplish this. The goal is to further the understanding of feature extraction and classification to perform effective predictive maintenance.
In this research, first we will using tensor network techniques speedup the processing in neural network since computational cost is a major bottleneck in neural network based deep learning. Note that the weight matrix in each layer of neural network is with huge size, because of millions of parameters. This may cause large time complexity to calculate all the parameters. To overcome the large time complexity, we could use tensor decomposition to calculate the low-rank weight matrix, to reduce a large amount of parameters, therefore to reduce the time complexity.
Presbyopia is an aging phenomenon that eventually affects 99.9% of the population and Aniridia is a rare genetic disease resulting in the absence of an iris. Currently, no treatment provides a dynamic solution for patients. We are creating a thin, flexible, biocompatible electronic device to rapidly control a bionic iris through a fast, reversible, wireless energy efficient process, providing a responsive and dynamic solution to both diseases. Successful completion of the project will significantly aid the partner organization to commercialize their device.
Formation of aggregates and flocs from fine suspended particles is of great significance in industrial applications involving solid-liquid separation (dewatering) stages, whose objective is to clean and recycle water. Fine particles in waste suspensions are inherently difficult to remove from process water. As a result, a number of steps are taken to increase the size of the particles, typically by aggregation using various chemicals. Resistance of the aggregates (strength) to external forces affects the efficiency of solid-liquid separation.
Flow cytometry is a technique used to detect and measure physical and chemical characteristics of a population of cells or particles. A sample containing cells or particles is suspended in a fluid and injected into the flow cytometer instrument. The sample is focused to ideally flow one cell at a time through a laser beam and the light scattered is characteristic to the cells and their components. Cells are often labeled with fluorescent markers so that light is first absorbed and then emitted in a band of wavelengths.
Volatile Organic Compounds (VOCs) are major contributors to smog, causing harm to both the environment and human health. However, VOCs control faces tremendous challenges. The aggregation of low VOCs concentration emitted by small and medium-sized enterprises (SMEs) have significant environmental and social impacts. However, SMEs find the current “on the market” technologies impractical and too expensive in initial investments and operational maintenance costs. To help alleviate the problem, SunHub Inc.
Microarray testing allows high-volume analysis. This work will develop tools for accelerated analysis and modifications to surfaces used within the partner facilities. The goal is to enhance the performance of current assay designs and to inform and guide the next-generation of assay designs (ie 384 well plates) which will support the partner’s technology leadership position. After implementing a print run and analysis using the current quality control protocols, data will be compared with existing results.
Innovative geosynthetic drainage products have been developed that have the potential to significantly benefit the stability of constructed embankments or reconstructed slopes especially where these are constructed from soil (or soil-like materials such as tailings) that are finer and less permeable (and thus weaker) then free-draining coarse-grained granular fills. Applications include reconstruction and stabilisation of natural slopes, embankments or dams constructed of (or at least partly from) mine tailings or other finer-grained materials.
Reliable monthly and seasonal streamflow predictions are essential for optimal planning of water resources, particularly for reservoir operation and planning applications. Streamflow predictions can also improve water use efficiency and provide early drought and flood warning. The importance of streamflow forecasting is rising with climate change, causing more frequent and hazardous flood and drought events.