Climate has a selective influence on nasal cavity morphology. Due to the constraints of cranial integration, naturally selected changes in one structure necessitate changes in others in order to maintain structural and functional cohesion. The relationships between climate and skull/nasal cavity morphology have been explored, but the integrative role of nasal variability within the skull as a whole has not. This thesis presents two hypotheses: 1) patterns of craniofacial integration observed in 2D can be reproduced using 3D geometric morphometric techniques; 2) the nasal cavity exhibits a higher level of covariation with the lateral cranial base than with other parts of the skull, since differences in nasal morphology and basicranial breadth have both been linked to climatic variables. The results support the former hypothesis, but not the latter; covariation observed between the nasal cavity and other cranial modules may suggest that these relationships are characterized by a unique integrative relationship. / February 2016

To deal with the increasing information overload and with the structured and unstructured data complexity, many organizations have implemented enterprise content management (ECM) systems. Published research on ECM so far is very limited and reports on ECM implementations have been scarce until recently (Tyrväinen et al. 2006). However, the little available ECM literature shows that many organizations using ECM focus on operational benefits while strategic decision-making benefits are rarely considered. Moreover, the strategic capabilities such as decision making capabilities of ECM are not fully investigated in the current literature. In addition, the literature lacks a strategic management framework (SMF) that links strategies, business objectives, and performance management although there are several published studies that discuss ECM strategy. A strategic management framework would seem essential to effectively manage ECM strategy formulation, implementation, and performance evaluation (Kaplan and Norton 1996; Ittner and Larcker 1997). The absence of an appropriate strategic management framework keeps organizations from effective strategic planning, implementation, and evaluation, which affects the organizational capabilities overall. Therefore, the objective of this dissertation is to determine the decision support capabilities of ECM, and specify how ECM strategies can be formulated, implemented, and evaluated in order to fully utilize the ECM strategic capabilities. Structural equation modeling as well as design science approaches will be adopted to achieve the dissertation objectives.

By testing a fiber beard, certain cotton fiber length parameters can be obtained rapidly. This is the method used by the High Volume Instrument (HVI). This study is aimed to explore the approaches and obtain the inference of length distributions of HVI beard sam- ples in order to develop new methods that can help us find the distribution of original fiber lengths and further improve HVI length measurements. At first, the mathematical functions were searched for describing three different types of length distributions related to the beard method as used in HVI: cotton fiber lengths of the original fiber population before picked by the HVI Fibrosampler, fiber lengths picked by HVI Fibrosampler, and fiber beard's pro-jecting portion that is actually scanned by HVI. Eight sets of cotton samples with a wide range of fiber lengths are selected and tested on the Advanced Fiber Information System (AFIS). The measured single fiber length data is used for finding the underlying theoreti-cal length distributions, and thus can be considered as the population distributions of the cotton samples. In addition, fiber length distributions by number and by weight are dis- cussed separately. In both cases a mixture of two Weibull distributions shows a good fit to their fiber length data. To confirm the findings, Kolmogorov-Smirnov goodness-of-fit tests were conducted. Furthermore, various length parameters such as Mean Length (ML) and Upper Half Mean Length (UHML) are compared between the original distribution from the experimental data and the fitted distributions. The results of these obtained fiber length distributions are discussed by using Partial Least Squares (PLS) regression, where the dis-tribution of the original fiber length from the distribution of the projected one is estimated.

The dynamic structure of the Food Supply Chain (FSC) distinguishes itself from other supply chains. Providing food to customers in a healthy and fresh manner necessitates a significant effort on the part of manufacturers and retailers. In practice, while these partners collaboratively forecast time-sensitive and / or short-life product-groups (e.g. perishable, seasonal, promotional and newly launched products), they confront significant challenges which prevent them from generating accurate forecasts and conducting long-term collaborations. Partners’ challenges are not limited only to the fluctuating demand of time-sensitive product-groups and continuously evolving consumer choices, but are also largely related to their conflicting expectations. Partners’ contradictory expectations mainly occur during the practices of integration, forecasting and information exchange in the FSC. This research specifically focuses on the Collaborative Forecasting (CF) practices in the FSC. However, CF is addressed from the manufacturers’ point of view, when they collaboratively forecast perishable, seasonal, promotional and newly launched products with retailers in the FSC. The underlying reasons are that while there is a paucity of research studying CF from the manufacturers’ standpoint, associated product-groups decay at short notice and their demand is influenced by uncertain consumer behaviour and the dynamic environment of FSC. The aim of the research is to identify factors that have a significant influence on the CF performance. Generating accurate forecasts over the aforementioned product-groups and sustaining long-term collaborations (one year or more) between partners are the two major performance criteria of CF in this research. This research systematically reviews the literature on Collaborative Planning, Forecasting and Replenishment (CPFR), which combines the supply chain practices of upstream and downstream members by linking their planning, forecasting and replenishment operations. The review also involves the research themes of supply chain integration, forecasting process and information sharing. The reason behind reviewing these themes is that partners’ CF is not limited to forecasting practices, it also encapsulates the integration of chains and bilateral information sharing for accurate forecasts. A single semi-structured interview with a UK based food manufacturer and three online group discussions on the business oriented social networking service of LinkedIn enrich the research with pragmatic and qualitative data, which are coded and analysed via software package QSR NVivo 9. Modifying the results of literature review through the qualitative data makes it possible to develop a rigorous conceptual model and associated hypotheses. Then, a comprehensive online survey questionnaire is developed to be delivered to food manufacturers located in the UK & Ireland, North America and Europe. An exploratory data analysis technique using Partial Least Squares (PLS) guides the research to analyse the online survey questionnaire empirically. The most significant contributions of this research are (i) to extend the body of literature by offering a new CF practice, aiming to improve forecast accuracy and long-term collaborations, and (ii) to provide managerial implications by offering a rigorous conceptual model guiding practitioners to implement the CF practice, for the achievement of accurate forecasts and long-term collaborations. In detail, the research findings primarily emphasise that manufacturers’ interdepartmental integration plays a vital role for successful CF and integration with retailers. Effective integration with retailers encourages manufacturers to conduct stronger CF in the FSC. Partners’ forecasting meetings are another significant factor for CF while the role of forecasters in these meetings is crucial too, implying forecasters’ indirect influence on CF. Complementary to past studies, this research further explores the manufacturers’ various information sources that are significant for CF and which should be shared with retailers. It is also significant to maintain the quality level of information whilst information is shared with retailers. This result accordingly suggests that the quality level of information is obliquely important for CF. There are two major elements that contribute to the literature. Firstly, relying on the particular product-groups in the FSC and examining CF from the manufacturers’ point of view not only closes a pragmatic gap in the literature, but also identifies new areas for future studies in the FSC. Secondly, the CF practice of this research demonstrates the increasing forecast satisfaction of manufacturers over the associated product-groups. Given the subjective forecast expectations of manufacturers, due to organisational objectives and market dynamics, demonstrating the significant impact of the CF practice on the forecast satisfaction leads to generalising its application to the FSC. Practitioners need to avail themselves of this research when they aim to collaboratively generate accurate forecasts and to conduct long-term collaborations over the associated product-groups. The benefits of this research are not limited to the FSC. Manufacturers in other industries can benefit from the research while they collaborate with retailers over similar product-groups having a short shelf life and / or necessitating timely and reliable forecasts. In addition, this research expands new research fields to academia in the areas of the supply chain, forecasting and information exchange, whilst it calls the interest of academics to particular product-groups in the FSC for future research. Nevertheless, this research is limited to dyad manufacturer-retailer forecast collaborations over a limited range of product-groups. This is another opportunity for academics to extend this research to different types of collaborations and products.

Os serviços de emergências hospitalares (EH) desempenham um papel fundamental no sistema de saúde, servindo de porta de entrada para hospitais e fornecendo cuidados para pacientes com lesões e doenças graves. No entanto, as EH em todo o mundo sofrem com o aumento da demanda e superlotação. Múltiplos fatores convergem simultaneamente para resultar nessa superlotação, porém a otimização do gerenciamento do fluxo dos pacientes pode auxiliar na redução do problema. Nesse contexto, o tempo de permanência dos pacientes na EH (TPEH) é consolidado na literatura como indicador de qualidade do fluxo de pacientes. O tema desta dissertação é a previsão e gestão da demanda em EH com alto grau de congestionamento, que é abordado através de três artigos científicos. O objeto de estudo é o Hospital de Clínicas de Porto Alegre (HCPA). No primeiro artigo, são aplicados quatro modelos de previsão da procura por atendimento na EH, avaliando-se a influência de fatores climáticos e de calendário. O segundo artigo utiliza a técnica de regressão por mínimos quadrados parciais (PLS – partial least squares) para previsão de quatro indicadores relacionados ao TPEH para hospitais com alto grau de congestionamento. O tempo médio de permanência (TM) na EH resultou em um modelo preditivo com melhor ajuste, com erro médio absoluto percentual (MAPE - mean absolute percent error) de 5,68%. O terceiro artigo apresenta um estudo de simulação para identificação dos fatores internos do hospital que influenciam o TPEH. O número de exames de tomografias e a taxa de ocupação nas enfermarias clínicas e cirúrgicas (ECC) foram as que mais influenciaram. / Emergency departments (ED) play a key role in the health system, serving as gateway to hospitals and providing care for patients with injuries and serious illnesses. However, EDs worldwide suffer from increased demand and overcrowding. Multiple factors simultaneously converge to result in such overcrowding, and the optimization of patient flow management can help reduce the problem. In this context, the length of stay of patients in ED (LSED) is consolidated in the literature as a patient flow quality indicator. This thesis deals with forecast and demand management in EDs with a high degree of congestion. The subject is covered in three scientific papers, all analyzing data from the Hospital de Clínicas de Porto Alegre’s ED. In the first paper we apply four demand forecasting models to predict demand for service in the ED, evaluating the influence of climatic and calendar factors. The second article uses partial least squares (PLS) regression to predict four indicators related to LSED. The mean length of stay in the ED resulted in a model with the best fit, with mean percent absolute error (MAPE) of 5.68%. The third article presents a simulation study to identify the internal hospital factors influencing LSED. The number of CT exams and the occupancy rate in the clinical and surgical wards were the most influential factors.

Predictable, robust genetic parts including constitutive promoters are one of the defining attributes of synthetic biology. Ideally, candidate promoters should cover a broad range of expression strengths and yield homogeneous output, whilst also being orthogonal to endogenous regulatory pathways. However, such libraries are not always readily available in non-model organisms, such as the industrially relevant genus Geobacillus. A multitude of different approaches are available for the identification and de novo design of prokaryotic promoters, although it may be unclear which methodology is most practical in an industrial context. Endogenous promoters may be individually isolated from upstream of well-understood genes, or bioinformatically identified en masse. Alternatively, pre-existing promoters may be mutagenised, or mathematical abstraction can be used to model promoter strength and design de novo synthetic regulatory sequences. In this investigation, bioinformatic, mathematic and mutagenic approaches to promoter discovery were directly compared. Hundreds of previously uncharacterised putative promoters were bioinformatically identified from the core genome of four Geobacillus species, and a rational sampling method was used to select sequences for in vivo characterisation. A library of 95 promoters covered a 2-log range of expression strengths when characterised in vivo using fluorescent reporter proteins. Data derived from this experimental characterisation were used to train Artificial Neural Network, Partial Least Squares and Random Forest statistical models, which quantifiably inferred the relationship between DNA sequence and function. The resulting models showed limited predictive- but good descriptive-power. In particular, the models highlighted the importance of sequences upstream of the canonical -35 and -10 motifs for determining promoter function in Geobacillus. Additionally, two commonly used mutagenic techniques for promoter production, Saturation Mutagenesis of Flanking Regions and error-prone PCR, were applied. The resulting sequence libraries showed limited promoter activity, underlining the difficulty of deriving synthetic promoters in species where understanding of transcription regulation is limited. As such, bioinformatic identification and deep-characterisation of endogenous promoter elements was posited as the most practical approach for the derivation of promoter libraries in non-model organisms of industrial interest.

This thesis consists of one stock return predictability study and two international risk exposure studies. The first study shows that the statistical significance of out-of-sample predictability of market returns given by Kelly and Pruitt (2013), using a partial least squares methodology, constructed from the valuation ratios of portfolios, is overstated for two reasons. Firstly, the analysis is conducted on gross returns rather than excess returns, and this raises the apparent predictability of the equity premium due to the inclusion of predictable movements of interest rates. Secondly, the bootstrap statistics used to assess out-of-sample significance do not account for small-sample bias in the estimated coefficients. This bias is well known to affect in-sample tests of significance and I show that it is also important for out-of-sample tests of significance. Accounting for both these effects can radically change the conclusions; for example, the recursive out-of-sample R2 values for the sample period 1965-2010 are insignificant for the prediction of one-year excess returns, and one-month returns, except in the case of the book-to-market ratios of six size- and value-sorted portfolios which are significant at the 10% level. The second study examines whether U.S. common stocks are exposed to international risks, which I define as shocks to foreign markets that are orthogonal to U.S. market returns. By sorting stocks on past exposure to this risk factor I show that it is possible to create portfolios with an ex-post spread in exposure to international risk. I examine whether the international risk is priced in the cross-section of U.S. stocks, and find that for small stocks an increase in exposure to international risk results in lower returns relative to the Fama-French three-factor model. I conduct similar analysis on a measure of the international value premium and find little evidence of this risk being priced in U.S. stocks. The third study examines whether a portfolios of U.S. stocks can mimic foreign index returns, thereby providing investors with the benefits of international diversification without the need to invest directly in assets that trade abroad. I test this proposition using index data from seven developed markets and eight emerging markets over the period 1975-2013. Portfolios of U.S. stocks are constructed out-of-sample to mimic these international indices using a step-wise procedure that selects from a variety of industry portfolios, stocks of multinational corporations, country funds and American depositary receipts. I also use a partial least squares approach to form mimicking portfolios. I show that investors are able to gain considerable exposure to emerging market indices using domestically traded stocks. However, for developed market indices it is difficult to obtain home-made exposure beyond the simple exposure of foreign indices to the U.S. market factor. Using mean-variance spanning tests I find that, with few exceptions, international indices do not improve over the investment frontier provided by the domestically constructed alternative of investing in the U.S. market index and portfolios of industries and multinational corporations.

With increasing globalisation of the world economy, there is growing interest in international business research among academics, business practitioners and public policy makers. As marketing is usually the first corporate function to internationalise, it occupies the centre-stage in the international strategy debate. The objective of this study is to understand the environmental and organisational factors that drive the desirable outcomes of learning, innovation and performance in multinational firms. By adapting the IO-based, resource-based and contingency theories, the study proposes the environment-conduct-outcome framework and a model of global marketing in MNCs. Using the structural equation modelling-based PLS methodology, the model is estimated with data from a global survey of marketing managers in MNC subsidiaries. The results show that the traditional international marketing strategy and organisational structure constructs of adaptation and autonomy do not have a significant direct effect on MNC performance. Instead, the effects are largely mediated by the networking, learning and innovation constructs that are included in the proposed model. The study also shows that, whereas collaborative decision making has a positive effect on interunit learning, subsidiary autonomy has a significant influence on innovativeness in MNC subsidiaries. Finally, it is found that marketing mix adaptation has an adverse impact on the performance of MNCs facing high global integration pressures but improves the performance of MNCs confronted with low global integration pressures. The findings have important implications for global marketing in MNCs. First, to enhance organisational learning and innovation and ultimately improve corporate performance, MNCs should simultaneously develop the potentially conflicting organisational attributes of collective decision-making among the subsidiaries and greater autonomy to the subsidiaries. Second, to tap local knowledge, MNCs should increasingly regard their country units as 'colleges' or 'seminaries' of learning rather than merely as 'subsidiaries' with secondary or subordinate roles. Finally, to improve MNC performance, the key requirement is to achieve a good fit between the global organisational structure, marketing strategy and business environment. Overall, the results provide partial support for the IO-based and resource-based views and strong support for the contingency perspective in international strategy.

<p>In regression with near collinear explanatory variables, the least squares predictor has large variance. Ordinary least squares regression (OLSR) often leads to unrealistic regression coefficients. Several regularized regression methods have been proposed as alternatives. Well-known are principal components regression (PCR), ridge regression (RR) and continuum regression (CR). The latter two involve a continuous metaparameter, offering additional flexibility.</p><p>For a univariate response variable, CR incorporates OLSR, PLSR, and PCR as special cases, for special values of the metaparameter. CR is also closely related to RR. However, CR can in fact yield regressors that vary discontinuously with the metaparameter. Thus, the relation between CR and RR is not always one-to-one. We develop a new class of regression methods, LSRR, essentially the same as CR, but without discontinuities, and prove that any optimization principle will yield a regressor proportional to a RR, provided only that the principle implies maximizing some function of the regressor's sample correlation coefficient and its sample variance. For a multivariate response vector we demonstrate that a number of well-established regression methods are related, in that they are special cases of basically one general procedure. We try a more general method based on this procedure, with two meta-parameters. In a simulation study we compare this method to ridge regression, multivariate PLSR and repeated univariate PLSR. For most types of data studied, all methods do approximately equally well. There are cases where RR and LSRR yield larger errors than the other methods, and we conclude that one-factor methods are not adequate for situations where more than one latent variable are needed to describe the data. Among those based on latent variables, none of the methods tried is superior to the others in any obvious way.</p>

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