The main objective of this work is to propose new procedures for the general dynamic factor analysis introduced by Forni et al. (2000). First, we develop an identification method for determining the number of common shocks in the general dynamic factor model. Sufficient conditions for consistency of the criterion are provided for large n (number of series) and T (the series length). We believe that our procedure can shed light on the ongoing debate on the number of factors driving the US or Eurozone economy. Second, we show how the dynamic factor analysis method proposed in Forni et al. (2000), combined with our identification method, allows for identifying and estimating joint and block-specific common factors. This leads to a more sophisticated analysis of the structures of dynamic interrelations within and between the blocks in suchdatasets. Besides the framework of the general dynamic factor model we also propose a consistent lag window spectral density estimator based on multivariate M-estimators by Maronna (1976) when the underlying data are coming from the alpha mixing stationary Gaussian process.

The components of SAP signals of low- frequency (LF: 0.08-0.15 Hz) and very low- frequency (VLF: 0.00-0.08 Hz) related with vasomotor tone that reflects the activity of sympathetic premotor neuron in the nucleus reticularis ventrolateralis (NRVL). The Sprague-Dawely male rats with acute endotoxemia (lipopolysaccharide, LPS; 15 mg/Kg i.v.) induced a reduction phase (phase¢¹), followed by partial recovery (phase¢º) and a secondary decrease (phase ¢»). The rats with acute endotoxemia display three phases based on change in the power density of LF and VLF component. Pretreatment with microinjection of NMDA receptor antagonist, MK-801, and non-NMDA receptor antagonist, CNQX into the bilaterial NRVL prolong the survival time and prolong the duration time of phase¢º and phase¢». Pretreatment with high concentration MK-801 (200 pmol) and CNQX (10 pmol) hold the MSAP and heart rate in the steady state and decrease the slope of MSAP falling during endotoxemia.The power density of pretreatment with high concentration MK-801 (200 pmol) and CNQX (10 pmol) was deceease. We conclude that the rat during experimental endotoxemia decrease the duration time of NMDA and non-NMDA receptor activity in NRVL. The NMDA receptor and non-NMDA receptor activity in NRVL during endotoxemia contribute the slope of MSAP falling and cause to death.

Part I - ESR spectra of pure coals, oils and tars are presented; their g values and linewidths are calculated. Almost all the spectra are single, broad resonances; but one coal, Hucknall Coal, exhibits a two line spectrum, a narrow line superimposed on a broad line. On admission of oxygen the narrow line is reversibly lost. On the addition of various solvents to the samples, in most cases, an irreversible loss in ESR signal intensity was observed. There seems to be no direct correlation between which solvent is added to which coal and the effect on the ESR signal intensity. Infra-red spectra of pure coals are studied, both as pressed discs and thin films, and a method for the preparation of these discs and films is given. Solvent addition experiments were undertaken and the results show the breaking of weak coal/water hydrogen bonds and the formation of stronger coal/solvent hydrogen bonds. Part II - ESR spectra of the radical cations of several mono and dicarbonyl compounds are presented and interpretation of these spectra are given. For most compounds the parent radical cation is seen, with the spin on oxygen. The cyclic dicarbonyls show the σ-bonded structure for the cation with coupling to the protons δ to the spin. Some non-aldehydic dicarbonyls show a rearranged structure with the spin on carbon. The aldehydic dicarbonyls all show, in addition to the parent radical cation, lines due to an acetal type species, as yet unidentified. Some compounds containing nitrogen or sulphur in addition to oxygen have the spin localised onto these alternative heteroatoms.

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The application of inductively-coupled plasma (ICP) spectrometry for the analysis of solutions and slurries is reviewed. The critical parameters which affect analytical performance are identified and methods for the optimisation of the ICP for such applications demonstrated. Particle size of the suspended sample is shown to be the most important factor in the analysis of slurry samples. It is demonstrated that only particles less than 8 um in diameter reach the plasma even when the maximal diameter injector tube (3 mm i.d.) is used. Accordingly various methods for the reduction of particle size have been investigated and milling shown to be the preferable approach. A low cost method using blown zirconia spheres in a screw-cap plastic bottle agitated on a flask shaker was developed. Using this method it proved possible to analyse a wide variety of solid samples using slurry atomisation using both ICP- atomic emission spectrometry (ICP-AES) and ICP-mass spectrometry (ICP-MS) with aqueous calibration. Results are presented for the elemental analysis of certified reference material (CRM) soils, catalyst samples, a zeolite, titanium dioxide, P.T.F.E. powder, and unfiltered natural waters by slurry atomisation ICP-AES. Comparison by other techniques showed excellent accuracy for major, minor and trace elements and equivalent precision to solution analysis. The CRM soils, catalysts and the zeolite were also analysed by ICP-MS using slurry atomisation with similar success and enhanced accuracy at the trace level. Aluminium proved to be an exception and generally low recoveries (80-90%) were observed for this element using slurry atomisation. Finally the uses of the simplex optimisation algorithm to improve analytical performance, particularly in ICP-AES, is discussed. A computer program to facilitate the use of simplex optimisation for a variety of instrumentation was written and its use in three different analytical techniques demonstrated. The necessary hardware and software modifications to a commercially available ICP spectrometer to enable automated simplex optimisation are described. The prospects for such intelligent self-optimising instrumentation are discussed as is the potential extension of the application of slurry analysis by ICP-AES and ICP-MS.

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This thesis is concerned with various aspects of stationary and nonstationary time series analysis. In the nonstationary case, we study estimation of the Wold-Cram'er evolutionary spectrum, which is a time-dependent analogue of the spectrum of a stationary process. Existing estimators of the Wold-Cram'er evolutionary spectrum suffer from several problems, including bias in boundary regions of the time-frequency plane, poor frequency resolution, and an inability to handle the presence of purely harmonic frequencies. We propose techniques to handle all three of these problems. We propose a new estimator of the Wold-Cram'er evolutionary spectrum (the BCMTFSE) which mitigates the first problem. Our estimator is based on an extrapolation of the Wold-Cram'er evolutionary spectrum in time, using an estimate of its time derivative. We apply our estimator to a set of simulated nonstationary processes with known Wold-Cram'er evolutionary spectra to demonstrate its performance. We also propose an estimator of the Wold-Cram'er evolutionary spectrum, valid for uniformly modulated processes (UMPs). This estimator mitigates the second problem, by exploiting the structure of UMPs to improve the frequency resolution of the BCMTFSE. We apply this estimator to a simulated UMP with known Wold-Cram'er evolutionary spectrum. To deal with the third problem, one can detect and remove purely harmonic frequencies before applying the BCMTFSE. Doing so requires a consideration of the aliasing problem. We propose a frequency-domain technique to detect and unalias aliased frequencies in bivariate time series, based on the observation that aliasing manifests as nonlinearity in the phase of the complex coherency between a stationary process and a time-delayed version of itself. To illustrate this ``unaliasing'' technique, we apply it to simulated data and a real-world example of solar noon flux data. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2009-02-05 10:18:13.476

The ability to probe molecular electronic and vibrational structures gives rise to optical absorption spectroscopy, which is a credible tool used in molecular quantification and classification with high sensitivity, low limit of detection (LoD), and immunity to electromagnetic noises. Spectra are sensitive to slight analyte variations, so they are often used to identify a sample’s components. This thesis proposes several methods for quick classification and quantification of analysts based on their absorbance spectra. functional Principal Component Analysis (fPCA) is employed for feature extraction and dimension reduction. For 1,000-pixel spectra data, fPCA can capture the majority variance with as few output scores as the number of expected analytes. This reduces the amount of calculation required for the following machine learning algorithms. Further, the output scores are fed into XGBoost and logistic regression for classification, and fed into XGBoost and linear regression for quantification. Our models were tested on both synthesized datasets and experimentally acquired dataset. Our models demonstrated similar performance compared to deep learning but with much faster processing speeds. For the synthesized 30 dB dataset, our model XGBoost with fPCA could reach a micro-averaged f1 score of 0.9551 ± 0.0008, while FNN-OT [1] could obtain 0.940±0.001. fPCA helped the algorithms extract the feature of each analyte; furthermore, the output scores nearly had a linear relationship with their concentrations. It was much easier for the algorithm to find the mapping function between the inputs and the outputs with fPCA, which shortened the training and testing time. / Graduate

Yes / We develop a novel approach to estimate the n unknown constituent frequencies of a noiseless signal that comprises of unknown number, n, of sinusoids of unknown phases and unknown amplitudes. The new two way approach uses two constraints to accurately estimate the unknown frequencies of the sinusoidal components in a signal. The new approach serves as a verification test for the estimated unknown frequencies through the estimated count of the unknown number of frequencies. The Hankel matrix, of the time domain samples of the signal, is used as a basis for further analysis in the Pisarenko harmonic decomposition. The new constraints, the Existence Factor (EF) and the Component Factor (CF), have been introduced in the methodology based on the relationships between the components of the sinusoidal signal and the eigenspace of the Hankel matrix. The performance of the developed approach has been tested to correctly estimate any number of frequencies within a signal with or without a fixed unknown bias. The method has also been tested to accurately estimate the very closely spaced low frequencies. / Innovate UK