A novel identification method for ultra trace detection of biomolecules using functionalised Surface Enhanced Raman Spectroscopy (SERS)

This thesis developed a new method for measuring extremely low amounts of organic and biological molecules, using Surface enhanced Raman Spectroscopy. This method has many potential applications, e.g. medical diagnosis, public health, food provenance, antidoping, forensics and homeland security. The method development used caffeine as the small molecule example, and erythropoietin (EPO) as the large molecule. This method is much more sensitive and specific than currently used methods; rapid, simple and cost effective. The method can be used to detect target molecules in beverages and biological fluids without the usual preparation steps.

A finite volume scheme with preconditioned Lanczos method for two-dimensional space-fractional reaction–diffusion equations

Fractional differential equations have been increasingly used as a powerful tool to model the non-locality and spatial heterogeneity inherent in many real-world problems. However, a constant challenge faced by researchers in this area is the high computational expense of obtaining numerical solutions of these fractional models, owing to the non-local nature of fractional derivatives. In this paper, we introduce a finite volume scheme with preconditioned Lanczos method as an attractive and high-efficiency approach for solving two-dimensional space-fractional reaction–diffusion equations. The computational heart of this approach is the efficient computation of a matrix-function-vector product f(A)bf(A)b, where A A is the matrix representation of the Laplacian obtained from the finite volume method and is non-symmetric. A key aspect of our proposed approach is that the popular Lanczos method for symmetric matrices is applied to this non-symmetric problem, after a suitable transformation. Furthermore, the convergence of the Lanczos method is greatly improved by incorporating a preconditioner. Our approach is show-cased by solving the fractional Fisher equation including a validation of the solution and an analysis of the behaviour of the model.

Modelling airport passenger group dynamics using an agent-based method

This thesis investigates the influence of passenger group dynamics on passengers' behaviour in an international airport. A simulation model is built to analyse passengers' behaviour during airport departure processes and during an emergency event. Results from the model showed that passengers' group dynamics have significant influences on the performance and utilisation of airport services. The agent-based model also provides a convenient way to investigate the effectiveness of space design and service allocations, which may contribute to the enhancement of passenger airport experiences.

Nucleic acid hybridizations as radioactive tool for rapid detection of banana bunchy top virus

Banana bunchy top disease (BBTD) caused by banana bunchy top virus (BBTV) was radioactively detected by nucleic acid hybridization techniques. Results showed that, 32P-labelled insert of pBT338 was hybridized with nucleic acid extracts from BBTV-infected plants from Egypt and Australia but not with those from CMV-infected plants from Egypt. Results revealed that BBTV was greatly detected in midrib, roots, meristem, corm, leaves and pseudostem respectively. BBTV was also detected in symptomless young plants prepared from diseased plant materials grown under tissue culture conditions but was not present in those performed from healthy plant materials. The sensitivity of dot blot and Southern blot hybridizations for the detection of BBTV was also performed for the detection of BBTV.

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

Two dynamic discrete choice estimation problems and simulation method solutions

This paper considers two problems that frequently arise in dynamic discrete choice problems but have not received much attention with regard to simulation methods. The first problem is how to simulate unbiased simulators of probabilities conditional on past history. The second is simulating a discrete transition probability model when the underlying dependent variable is really continuous. Both methods work well relative to reasonable alternatives in the application discussed. However, in both cases, for this application, simpler methods also provide reasonably good results.

Polarization of forecast densities : a new approach to time series classification

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

Enhancing Research Productivity of TEFL Academic in China : A Mixed Method Study

As research has become an important indicator of TEFL academics’ overall performance in Chinese higher education institutions, it is critical that TEFL academics are able to meet the expectation of conducting research. This mixed-method study (an initial survey followed by a qualitative collective case study)investigated research productivity of Chinese TEFL academics and associated influences, with the ultimate objective of constructing a framework to help build their research capacity in the future. The findings from this study revealed that the 182 Chinese TEFL academics’ research productivity during 2004-2008 was relatively low. Four influences were identified that impacted on thier research productivity: TEFL disciplinary influences, institutional and departmental research environments, individual characteristics desirable for research, and TEFL academics’ perceptions about research. Drawing upon the above findings, a Framework towards Enhancing Chinese TEFL Academics’ Research Productivity (FECTARP) was constructed. The FECTAR presented a framework for Chinese institutions and TEFL departments to enhance their TEFL academics' research capacity.

The renormalization group: A perturbation method for the graduate curriculum

In this paper the renormalization group (RG) method of Chen, Goldenfeld, and Oono [Phys. Rev. Lett., 73 (1994), pp.1311-1315; Phys. Rev. E, 54 (1996), pp.376-394] is presented in a pedagogical way to increase its visibility in applied mathematics and to argue favorably for its incorporation into the corresponding graduate curriculum.The method is illustrated by some linear and nonlinear singular perturbation problems. Key word. © 2012 Society for Industrial and Applied Mathematics.

A Combined Renormalization Group-Multiple Scale Method for Singularly Perturbed Problems

This paper introduces a straightforward method to asymptotically solve a variety of initial and boundary value problems for singularly perturbed ordinary differential equations whose solution structure can be anticipated. The approach is simpler than conventional methods, including those based on asymptotic matching or on eliminating secular terms. © 2010 by the Massachusetts Institute of Technology.

Plasma-enabled growth of single-crystalline SiC/AlSiC Core–Shell nanowires on porous alumina templates

We report the catalyst-free synthesis of the arrays of core–shell, ultrathin, size-uniform SiC/AlSiC nanowires on the top of a periodic anodic aluminum oxide template. The nanowires were grown using an environmentally friendly, silane-free process by exposing the silicon supported porous alumina template to CH4 + H2 plasmas. High-resolution scanning and transmission electron microscopy studies revealed that the nanowires have a single-crystalline core with a diameter of about 10 nm and a thin (1–2 nm) amorphous AlSiC shell. Because of their remarkable length, high aspect ratio, and very high surface area-to-volume ratio, these unique structures are promising for nanoelectronic and nanophotonic applications that require efficient electron emission, light scattering, etc. A mechanism for nanowire growth is proposed based upon the reduction of the alumina template to nanosized metallic aluminum droplets forming between nanopores. The subsequent incorporation of silicon and carbon atoms from the plasma leads to nucleation and growth from the top of the alumina template.

Nanopore processing in dielectric materials and dielectric template-assisted nanoarray synthesis : using pulsed bias to enhance process throughput and precision

An effective technique to improve the precision and throughput of energetic ion condensation through dielectric nanoporous templates and reduce nanopore clogging by using finely tuned pulsed bias is proposed. Multiscale numerical simulations of ion deposition show the possibility of controlling the dynamic charge balance on the upper template's surface to minimize ion deposition on nanopore sidewalls and to deposit ions selectively on the substrate surface in contact with the pore opening. In this way, the shapes of nanodots in template-assisted nanoarray fabrication can be effectively controlled. The results are applicable to various processes involving porous dielectric nanomaterials and dense nanoarrays.

Pulsed i-PVD of dielectric nanodot arrays using a nanoporous template

The results of multi-scale numerical simulations of pulsed i-PVD template-assisted nanofabrication of ZnO nanodot arrays on a silicon substrate are presented. The ratios and spatial distributions of the ion fluxes deposited on the lateral and bottom surfaces of the nanopores are computed as a function of the external bias and plasma parameters. The results show that the pulsed bias plays a significant role in the ion current distribution inside the nanopores. The results of numerical experiments of this work suggest that by finely adjusting the pulse voltage, the pulse duration and the duty cycle of the external pulsed bias, the nanopore clogging can be successfully avoided during the deposition and the shapes of the deposited ZnO nanodots can be effectively controlled. A figure is presented.

Templated i-PVD of metallic nanodot arrays

The results of numerical simulation of plasma-based, porous, template-assisted nanofabrication of Au nanodot arrays on highly-doped silicon taking into account typical electron density of low-temperature plasma of 1017-1018 m-3 and electron temperature of 2-5 eV are reported here. Three-dimensional microscopic topography of ion flux distribution over the outer and inner surfaces of the nanoporous template is obtained via numerical simulation of Au ion trajectories in the plasma sheath, in the close proximity of, and inside the nanopores. It is shown that, by manipulating the electron temperature, the cross-sheath potential drop, and by additionally altering the structure of the nanoporous template, one can control the ion fluxes within the nanopores, and eventually maximize the ion deposition onto the top surface of the developing crystalline Au nanodots (see top panel in the figure). In the same time, this procedure allows one to minimize amorphous deposits on the sidewalls that clutter and may eventually close the nanopores, thus disrupting the nanodot growth process, as it is shown in the bottom panel in the figure on the right.

Unbalance identification in large steam turbo-generator unit using a model-based method

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.