Measurement and modelling moisture transport processes within porous construction materials

Thesis submitted for the degree of Doctor of Philosophy at the University of LutonMoisture is one of the primary factors connected with the damage observed on the envelope of buildings. The moisture states are normally dominated by moisture transport processes within and between porous building materials from rain penetration, rising damp or infiltration of water vapour that is normally accompanied with heat transfer. The research into moisture transport behaviour of building materials is extremely important for the characterisation of behaviour in connection with durability, waterproofing, degradation of appearance and thermal performance ofbuilding elements. In the first stage of this research, commercial gypsum plasters were experimentally investigated with their moisture transport specifications. The hydraulic parameters including sorptivity, saturated conductivity and permeability of water vapour were determined with new findings related with the dependence of hydraulic parameters on water/plaster ratios, wetting-drying cycles and additives. The results obtained were compared with other porous building materials and recommendations for their manufacture and selection in building construction were made. Secondly, on the basis of comprehensive investigations of the dielectric properties of gypsum plasters, an integrated automatic real-time monitoring system for moisture transport processes was designed and successfully developed utilising a pin-type resistance sensor and sensor array. The data acquisition, data analysis, result display and saving are all integrated with the computer controlled interface. The polarisation effects and temperature effects for various gypsum plaster materials were compensated and realised by control options. The monitoring system developed for moisture monitoring was directly applied in 1-dimension moisture transport processes and can easily be extended to the monitoring of 2 or 3 dimension moisture transport processes by embedding an appropriate sensor array into materials. In the third part of the research, on the basis of experimental investigation of water absorption processes of uniform materials and two-layer composites, the water diffusivity as functions of moisture content were determined from experimental moisture profiles for various gypsum plaster materials. The models governing the moisture transport processes were formed based on extended Darcy's law and experimental diffusivity functions. By applying the finite element method and developed software, the non-linear partial differential equations were numerically solved under specified boundary and initial conditions in absorption processes. The simulation results achieved satisfactory agreement with experimental moisture profiles for various materials and for two-layered composites

Magnetic Resonance imaging (MRI) in detection of _Bifidobacterium longum_ and _Clostridium novyi-NT_ labeled with superparamagnetic iron oxide (SPIO) nanoparticle

*Purpose:* To investigate the MR imaging of _Bifidobacterium longum_ and _Clostridium novyi-NT_ labeling with superparamagnetic iron oxide (SPIO) nanoparticles.

*Materials and methods:* Tubes containing _B. longum_-SPIO, Free-SPIO, _B. longum_ and PYG Medium were incubated under anaerobic condition in _in vitro_ experiment. Transmission electron microscope and Prussian blue staining were used to demonstrate intra-bacteria nanoparticles. R~2~^*^ mapping and R~2~ mapping were reconstructed after MR scanning. _B. longum_-SPIO and _C. novyi_-NT-SPIO were injected respectively _in vivo_ to show whether it might be traced by MR imaging.

*Results:* Magnetosomes in bacteria were observed by electron microscopic and stained by Prussian blue staining. At the same concentration of SPIOs, the R~2~^*^ value of _B. longum_-SPIO was significantly higher than that of Free-SPIO (P<0.001), however, the R~2~ value was lower comparing with Free-SPIO (P<0.001). After injection with _B. longum_-SPIO, they could present in tumor and shorten T~2~^*^.

*Conclusion:* _B. longum_ and _C. novyi_-NT could be labeled by SPIO and then traced by MRI

Measles Rash Identification Using Residual Deep Convolutional Neural Network

Measles is extremely contagious and is one of the leading causes of vaccine-preventable illness and death in developing countries, claiming more than 100,000 lives each year. Measles was declared eliminated in the US in 2000 due to decades of successful vaccination for the measles. As a result, an increasing number of US healthcare professionals and the public have never seen the disease. Unfortunately, the Measles resurged in the US in 2019 with 1,282 confirmed cases. To assist in diagnosing measles, we collected more than 1300 images of a variety of skin conditions, with which we employed residual deep convolutional neural network to distinguish measles rash from other skin conditions, in an aim to create a phone application in the future. On our image dataset, our model reaches a classification accuracy of 95.2%, sensitivity of 81.7%, and specificity of 97.1%, indicating the model is effective in facilitating an accurate detection of measles to help contain measles outbreaks