Research into traumatic brain injury (TBI) mechanisms is essential for the development of methods to prevent its occurrence. One of the most common ways to incur a TBI is from falls, especially for the young and very old. The purpose of this thesis was to investigate how the acceleration loading curves influenced the occurrence of different types of TBI, namely: epidural hematoma, subdural hematoma, subarachnoid hemorrhage, and contusion. This investigation was conducted in three parts. The first study conducted reconstructions of 20 TBI cases with varying outcomes using MADYMO, Hybrid III, and finite element methodologies. This study provided a dataset of threshold values for each of the TBI injuries measured in parameters of strain and stress. The results of this study indicated that using a combined reconstructive approach produces results which are in keeping with the literature for TBI. The second study examined how the characteristics of the loading curves which were produced from each reconstruction influenced the outcome using a principal components analysis. It was found that the duration of the event accounted for much of the variance in the results, followed with the acceleration components. Different curve characteristics also accounted for differing amounts of variance in each of the lesion types. Study 3 examined how the dynamic response of the impact influenced where in the brain a subdural hematoma (SDH) could occur. It was found that the largest magnitudes of acceleration produced SDH in the parietal lobe, and the lowest in the occipital lobe. Overall this thesis examined the mechanism of injury for TBI using a large dataset with methodologies which complement each other’s limitations. As a result in depth information of the nature of TBI was attained and information provided which may be used to improve future protection and standard development.

Evidence seems to suggest that with 14 years of unbroken economic growth, the UK’s construction and building services sector is experiencing severe skills crisis of between 40 – 50 per cent retention rate and declining numbers of entrant trainees. More importantly, the level of this severity varies with sub regional and regional peculiarities. To date, most studies on this area have focused on increasing the population of the existing pools of labour rather than harnessing existing ones. Adopting the concept of multiskilling, current techniques of evaluating skills crisis were critically reviewed. While there has been some empirically beneficial application of this concept in the US, it is a rarity in the literature to find previous works on multiskilling in UK’s construction and building services sector. Adopting an action research approach, a Project Steering Group of industry stakeholders served as a research ‘think tank’ for validating empirical results, and in line with the theory of construct validity, instruments of survey were designed and operationalized in a pilot and major surveys of supply and demand sides’ target groups. Employing the relative index ranking technique, the forecast implications of UK’s economic stability are ‘real’ and a demand led system is prescribed as a tentative ‘cushion’ for sustainable but immediate redress. A time series data for the period 1961 – 2004 is explored and systematised quantitative demand led models for evaluating construction output based on aggregated and disaggregated manpower attributes are developed using principal component regression (PCR). Aggregating these models, it is deduced that multiskilling could help redress skills shortage in the long term. A new trade equilibrium framework and a multiskilled focused partnership in training programme are prescribed with response strategies and recommendations.

Following Merton (1942) science studies has moved from the philosophy of science to a more sociologically minded analysis of scientific activity. This largely involves a shift away from questions that bear on the context of justification – a question of rationality and philosophy, to those that deal with the context of discovery. This thesis investigates changes in science studies in three papers: sociocultural evolutionary theories of scientific change; general trends in science studies - especially concerning the sociology of science; and a principle component analysis (PCA) that details the development and interaction between research programmes in science studies. This thesis describes the proliferation of research programmes in science studies and uses evolutionary theory to make sense of the pattern of change.

Quantification of natural organic matter (NOM) in water is limited by the complex and varied nature of compounds found in natural waters. Current characterization techniques, which identify and quantify fractions of NOM, are often expensive and time consuming suggesting the need for rapid and accurate characterization methods. In this work, principal component analysis of fluorescence excitation-emission matrices (FEEM-PCA) was investigated as a NOM characterization technique. Through the use of jar tests and disinfection by-product formation tests, FEEM-PCA was shown to be a good surrogate for disinfection by-product precursors. FEEM-PCA was also applied in order to characterize differences in humic-like, protein-like, and Rayleigh scattering between multiple source waters and due to differing treatment processes. A decrease in Rayleigh scattering influence was observed for a deep lake intake, and multiple processes were found to significantly affect humic-like substances, protein-like, and Rayleigh scattering fractions.

Following Merton (1942) science studies has moved from the philosophy of science to a more sociologically minded analysis of scientific activity. This largely involves a shift away from questions that bear on the context of justification – a question of rationality and philosophy, to those that deal with the context of discovery. This thesis investigates changes in science studies in three papers: sociocultural evolutionary theories of scientific change; general trends in science studies - especially concerning the sociology of science; and a principle component analysis (PCA) that details the development and interaction between research programmes in science studies. This thesis describes the proliferation of research programmes in science studies and uses evolutionary theory to make sense of the pattern of change.

Quantification of natural organic matter (NOM) in water is limited by the complex and varied nature of compounds found in natural waters. Current characterization techniques, which identify and quantify fractions of NOM, are often expensive and time consuming suggesting the need for rapid and accurate characterization methods. In this work, principal component analysis of fluorescence excitation-emission matrices (FEEM-PCA) was investigated as a NOM characterization technique. Through the use of jar tests and disinfection by-product formation tests, FEEM-PCA was shown to be a good surrogate for disinfection by-product precursors. FEEM-PCA was also applied in order to characterize differences in humic-like, protein-like, and Rayleigh scattering between multiple source waters and due to differing treatment processes. A decrease in Rayleigh scattering influence was observed for a deep lake intake, and multiple processes were found to significantly affect humic-like substances, protein-like, and Rayleigh scattering fractions.

Antioxidants are molecules capable of stabilizing and preventing oxidation. Certain peptides, protein hydrolysates, have shown antioxidant capacities, which are obtained once liberated from the native protein structure. Soy protein isolates (SPI) were enzymatically hydrolyzed by pepsin and pancreatin mixtures. The soy protein hydrolysates (SPH) were fractionated with sequential ultrafiltration (UF) and nanofiltration (NF) membrane steps. Heat pre-treatment of SPI at 95 degrees celsius (C) for 5 min prior to enzymatic hydrolysis was investigated for its effect on peptide distribution and antioxidant capacity. SPH were subjected to UF with a 10 kDa molecular weight cut off (MWCO) polysulfone membrane. UF permeate fractions (lower molecular weight than 10 kDa) were fractionated by NF with a thin film composite membrane (2.5 kDa MWCO) at pH 4 and 8. Similar peptide content and antioxidant capacity (α=0.05) were obtained in control and pre-heated SPH when comparing the respective UF and NF permeate and retentate fractions produced. FCR antioxidant capacities of the SPH fractions were significantly lower than their ORAC antioxidant capacities, and the distribution among the UF and NF fractions was generally different. Most UF and NF fractions displayed higher antioxidant capacities when compared to the crude SPI hydrolysates, showing the importance of molecular weight on antioxidant capacity of peptides. The permeate fractions produced by NF at pH 8 displayed the highest antioxidant capacity, expressed in terms of Trolox equivalents (TE) per total solids (TS): 5562 μmol TE/g TS for control SPH, and 5187 μmol TE/g TS for pre-heated SPH. Due to the improvement in antioxidant capacity of peptides by NF at pH 8, the potential for NF as a viable industrial fractionation process was demonstrated. Principal component analysis (PCA) of fluorescence excitation-emission matrix (EEM) data for UF and NF peptide fractions, followed by multi-linear regression analysis, was assessed for its potential to monitor and identify the contributions to ORAC and FCR, two in vitro antioxidant capacity assays, of SPH during membrane fractionation. Two statistically significant principal components (PCs) were obtained for UF and NF peptide fractions. Multi-linear regression models (MLRM) were developed to estimate their fluorescence and PCA-captured ORAC (ORAC-FPCA) and FCR (FCR-FPCA) antioxidant capacities. The ORAC-FPCA and FCR-FPCA antioxidant capacities for NF samples displayed strong, linear relationships at different pH conditions (R-squared>0.99). Such relationships are believed to reflect the individual and relative combined contributions of tryptophan and tyrosine residues present in the SPH fractions to ORAC and FCR antioxidant capacities. Therefore, the proposed method provides a tool for the assessment of fundamental parameters of antioxidant capacities captured by ORAC and FCR assays.

In pattern recognition, when data has different structures in different parts of the input space, fitting one global model can be slow and inaccurate. Learning methods can quickly learn the structure of the data in local regions, consequently, offering faster and more accurate model fitting. Breaking training data set into smaller subsets may lead to curse of dimensionality problem, as a training sample subset may not be enough for estimating the required set of parameters for the submodels. Increasing the size of training data may not be at hand in many situations. Interestingly, the data in local regions becomes more correlated. Therefore, by decorrelation methods we can reduce data dimensions and hence the number of parameters. In other words, we can find uncorrelated low dimensional subspaces that capture most of the data variability. The current subspace modelling methods have proved better performance than the global modelling methods for the given type of training data structure. Nevertheless these methods still need more research work as they are suffering from two limitations 2 There is no standard method to specify the optimal number of subspaces. &sup2 / There is no standard method to specify the optimal dimensionality for each subspace. In the current models these two parameters are determined beforehand. In this dissertation we propose and test algorithms that try to find a suboptimal number of principal subspaces and a suboptimal dimensionality for each principal subspaces automatically.

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This research focuses to develop some new techniques on statistical learning including methodology, computation and application. We also developed statistical quantification in nanomaterials. For a large number of random variables with temporal or spatial structures, we proposed shrink estimates of covariance matrix to account their Markov structures. The proposed method exploits the sparsity in the inverse covariance matrix in a systematic fashion. To deal with high dimensional data, we proposed a robust kernel principal component analysis for dimension reduction, which can extract the nonlinear structure of high dimension data more robustly. To build a prediction model more efficiently, we developed an active learning via sequential design to actively select the data points into the training set. By combining the stochastic approximation and D-optimal designs, the proposed method can build model with minimal time and effort. We also proposed factor logit-models with a large number of categories for classification. We show that the convergence rate of the classifier functions estimated from the proposed factor model does not rely on the number of categories, but only on the number of factors. It therefore can achieve better classification accuracy. For the statistical nano-quantification, a statistical approach is presented to quantify the elastic deformation of nanomaterials. We proposed a new statistical modeling technique, called sequential profile adjustment by regression (SPAR), to account for and eliminate the various experimental errors and artifacts. SPAR can automatically detect and remove the systematic errors and therefore gives more precise estimation of the elastic modulus.