Essays on Entrepreneurship and Founder Pre-entry Knowledge

Thesis (Ph.D.)--University of Washington, 2016-06

Phenolic acids and abscisic acid in Australian Eucalyptus honeys and their potential for floral authentication

Seven phenolic acids related to the botanical origins of nine monofloral Eucalyptus honeys from Australia, along with two abscisic isomers, have been analyzed. The mean content of total phenolic acids ranges from 2.14 mg/100 g honey of black box (Eucalyptus largiflorens) honey to 10.3 mg/100 g honey of bloodwood (Eucalyptus intermedia) honey, confirming an early finding that species-specific differences of phytochemical compositions occur quantitatively among these Eucalyptus honeys. A common profile of phenolic acids, comprising gallic, chlorogenic, coumaric and caffeic acids, can be found in all the Eucalyptus honeys, which could be floral markers for Australian Eucalyptus honeys. Thus, the analysis of phenolic acids could also be used as an objective method for the authentication of botanical origin of Eucalyptus honeys. Moreover, all the honey samples analyzed in this study contain gallic acid as the main phenolic acid, except for stringybox (Eucalyptus globoidia) honey which has ellagic acid as the main phenolic acid. This result indicates that the species-specific differences can also be found in the honey profiles of phenolic acids. Further-more, the analysis of abscisic acid in honey shows that the content of abscisic acid varies from 0.55 mg/100 g honey of black box honey to 4.68 mg/ 100 g honey of bloodwood honey, corresponding to the contents of phenolic acids measured in these honeys. These results have further revealed that the HPLC analysis of honey phytochemical constituents could be used individually and/or jointly for the authentication of the botanical origins of Australian Eucalyptus honeys. (C) 2003 Elsevier Ltd. All rights reserved.

On the equivalence of weighted Country-Product-Dummy (CPD) Method and the Rao-System for multilateral price comparisons

The country-product-dummy (CPD) method, originally proposed in Summers (1973), has recently been revisited in its weighted formulation to handle a variety of data related situations (Rao and Timmer, 2000, 2003; Heravi et al., 2001; Rao, 2001; Aten and Menezes, 2002; Heston and Aten, 2002; Deaton et al., 2004). The CPD method is also increasingly being used in the context of hedonic modelling instead of its original purpose of filling holes in Summers (1973). However, the CPD method is seen, among practitioners, as a black box due to its regression formulation. The main objective of the paper is to establish equivalence of purchasing power parities and international prices derived from the application of the weighted-CPD method with those arising out of the Rao-system for multilateral comparisons. A major implication of this result is that the weighted-CPD method would then be a natural method of aggregation at all levels of aggregation within the context of international comparisons.

CDMA multiuser detection, neural networks, and statistical mechanics

A novel approach, based on statistical mechanics, to analyze typical performance of optimum code-division multiple-access (CDMA) multiuser detectors is reviewed. A `black-box' view ot the basic CDMA channel is introduced, based on which the CDMA multiuser detection problem is regarded as a `learning-from-examples' problem of the `binary linear perceptron' in the neural network literature. Adopting Bayes framework, analysis of the performance of the optimum CDMA multiuser detectors is reduced to evaluation of the average of the cumulant generating function of a relevant posterior distribution. The evaluation of the average cumulant generating function is done, based on formal analogy with a similar calculation appearing in the spin glass theory in statistical mechanics, by making use of the replica method, a method developed in the spin glass theory.

Modelling data and voice traffic over IP networks using continuous-time Markov models

Common approaches to IP-traffic modelling have featured the use of stochastic models, based on the Markov property, which can be classified into black box and white box models based on the approach used for modelling traffic. White box models, are simple to understand, transparent and have a physical meaning attributed to each of the associated parameters. To exploit this key advantage, this thesis explores the use of simple classic continuous-time Markov models based on a white box approach, to model, not only the network traffic statistics but also the source behaviour with respect to the network and application. The thesis is divided into two parts: The first part focuses on the use of simple Markov and Semi-Markov traffic models, starting from the simplest two-state model moving upwards to n-state models with Poisson and non-Poisson statistics. The thesis then introduces the convenient to use, mathematically derived, Gaussian Markov models which are used to model the measured network IP traffic statistics. As one of the most significant contributions, the thesis establishes the significance of the second-order density statistics as it reveals that, in contrast to first-order density, they carry much more unique information on traffic sources and behaviour. The thesis then exploits the use of Gaussian Markov models to model these unique features and finally shows how the use of simple classic Markov models coupled with use of second-order density statistics provides an excellent tool for capturing maximum traffic detail, which in itself is the essence of good traffic modelling. The second part of the thesis, studies the ON-OFF characteristics of VoIP traffic with reference to accurate measurements of the ON and OFF periods, made from a large multi-lingual database of over 100 hours worth of VoIP call recordings. The impact of the language, prosodic structure and speech rate of the speaker on the statistics of the ON-OFF periods is analysed and relevant conclusions are presented. Finally, an ON-OFF VoIP source model with log-normal transitions is contributed as an ideal candidate to model VoIP traffic and the results of this model are compared with those of previously published work.

The domain of organizational cognitive neuroscience:theoretical and empirical challenges

In this editorial, the authors respond to the 2011 article in the Journal of Management by Becker, Cropanzano, and Sanfey, titled “Organizational Neuroscience: Taking Organizational Theory Inside the Neural Black Box.” More specifically, the authors build on the ideas of Becker et al. first to clarify and extend their work and then to explore the critical philosophical issues involved in drawing inferences from neuroscientific research. They argue that these problems are yet to be solved and that organizational researchers who wish to incorporate neuroscientific advances into their work need to engage with them.

A practical phase sensitive amplification scheme for two channel phase regeneration

A "black-box" phase sensitive amplifier is presented achieving simultaneous suppression of deterministic phase distortion on two independent 42.66 Gbit/s DPSK modulated signal wavelengths.

All-optical phase and amplitude regenerator for next-generation telecommunications systems

Fibre-optic communications systems have traditionally carried data using binary (on-off) encoding of the light amplitude. However, next-generation systems will use both the amplitude and phase of the optical carrier to achieve higher spectral efficiencies and thus higher overall data capacities(1,2). Although this approach requires highly complex transmitters and receivers, the increased capacity and many further practical benefits that accrue from a full knowledge of the amplitude and phase of the optical field(3) more than outweigh this additional hardware complexity and can greatly simplify optical network design. However, use of the complex optical field gives rise to a new dominant limitation to system performance-nonlinear phase noise(4,5). Developing a device to remove this noise is therefore of great technical importance. Here, we report the development of the first practical ('black-box') all-optical regenerator capable of removing both phase and amplitude noise from binary phase-encoded optical communications signals.

The discretionary role of the line manager in inspiring work engagement and innovative behaviour:a study of social exchange and job resources in the public sector

This thesis is about the discretionary role of the line manager in inspiring the work engagement of staff and their resulting innovative behaviour examined through the lens of Social Exchange Theory (Blau, 1964) and the Job Demands-Resources theory (Bakker, Demerouti, Nachreiner & Schaufeli, 2001). The study is focused on a large British Public Sector organisation undergoing a major organisational shift in the way in which they operate as part of the public sector. It is often claimed that people do not leave organisations; they leave line managers (Kozlowski & Doherty, 1989). Regardless of the knowledge in the literature concerning the importance of the line manager in organisations (Purcell, 2003), the engagement literature in particular is lacking in the consideration of such a fundamental figure in organisational life. Further, the understanding of the black box of managerial discretion and its relationship to employee and organisation related outcomes would benefit from greater exploration (Purcell, 2003; Gerhart, 2005; Scott, et al, 2009). The purpose of this research is to address these gaps with relation to the innovative behaviour of employees in the public sector – an area that is not typically associated with the public sector (Bhatta, 2003; McGuire, Stoner & Mylona, 2008; Hughes, Moore & Kataria, 2011). The study is a CASE Award PhD thesis, requiring academic and practical elements to the research. The study is of one case organisation, focusing on one service characterised by a high level of adoption of Strategic Human Resource Management activities and operating in a rather unique manner for the public sector, having private sector competition for work. The study involved a mixed methods approach to data collection. Preliminary focus groups with 45 participants were conducted, followed by an ethnographic period of five months embedded into the service conducting interviews and observations. This culminated in a quantitative survey delivered within the wider directorate to approximately 500 staff members. The study used aspects of the Grounded Theory (Glaser & Strauss, 1967) approach to analyse the data and developed results that highlight the importance of the line manager in an area characterised by SHRM and organisational change for engaging employees and encouraging innovative behaviour. This survey was completed on behalf of the organisation and the findings of this are presented in appendix 1, in order to keep the focus of the PhD on theory development. Implications for theory and practice are discussed alongside the core finding. Line managers’ discretion surrounding the provision of job resources (in particular trust, autonomy and implementation and interpretation of combined bundles of SHRM policies and procedures) influenced the exchange process by which employees responded with work engagement and innovative behaviour. Limitations to the research are the limitations commonly attributed to cross-sectional data collection methods and those surrounding generalisability of the qualitative findings outside of the contextual factors characterising the service area. Suggestions for future research involve addressing these limitations and further exploration of the discretionary role with regards to extending our understanding of line manager discretion.

Neural Network Based Approach for Developing the Enterprise Strategy

Modern enterprises work in highly dynamic environment. Thus, the developing of company strategy is of crucial importance. It determines the surviving of the enterprise and its evolution. Adapting the desired management goal in accordance with the environment changes is a complex problem. In the present paper, an approach for solving this problem is suggested. It is based on predictive control philosophy. The enterprise is modelled as a cybernetic system and the future plant response is predicted by a neural network model. The predictions are passed to an optimization routine, which attempts to minimize the quadratic performance criterion.

Classification of Data to Extract Knowledge from Neural Networks

A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is the weights of the neural network trained. This method allows knowledge extraction from neural networks with continuous inputs and output as well as rule extraction. An example of the application is showed. This example is based on the extraction of average load demand of a power plant.

The political discourse and material practice of technology enhanced learning

Technology discloses man’s mode of dealing with Nature, the process of production by which he sustains his life, and thereby also lays bare the mode of formation of his social relations, and of the mental conceptions that flow from them (Marx, 1990: 372) My thesis is a Sociological analysis of UK policy discourse for educational technology during the last 15 years. My framework is a dialogue between the Marxist-based critical social theory of Lieras and a corpus-based Critical Discourse Analysis (CDA) of UK policy for Technology Enhanced Learning (TEL) in higher education. Embedded in TEL is a presupposition: a deterministic assumption that technology has enhanced learning. This conceals a necessary debate that reminds us it is humans that design learning, not technology. By omitting people, TEL provides a vehicle for strong hierarchical or neoliberal, agendas to make simplified claims politically, in the name of technology. My research has two main aims: firstly, I share a replicable, mixed methodological approach for linguistic analysis of the political discourse of TEL. Quantitatively, I examine patterns in my corpus to question forms of ‘use’ around technology that structure a rigid basic argument which ‘enframes’ educational technology (Heidegger, 1977: 38). In a qualitative analysis of findings, I ask to what extent policy discourse evaluates technology in one way, to support a Knowledge Based Economy (KBE) in a political economy of neoliberalism (Jessop 2004, Fairclough 2006). If technology is commodified as an external enhancement, it is expected to provide an ‘exchange value’ for learners (Marx, 1867). I therefore examine more closely what is prioritised and devalued in these texts. Secondly, I disclose a form of austerity in the discourse where technology, as an abstract force, undertakes tasks usually ascribed to humans (Lieras, 1996, Brey, 2003:2). This risks desubjectivisation, loss of power and limits people’s relationships with technology and with each other. A view of technology in political discourse as complete without people closes possibilities for broader dialectical (Fairclough, 2001, 2007) and ‘convivial’ (Illich, 1973) understandings of the intimate, material practice of engaging with technology in education. In opening the ‘black box’ of TEL via CDA I reveal talking points that are otherwise concealed. This allows me as to be reflexive and self-critical through praxis, to confront my own assumptions about what the discourse conceals and what forms of resistance might be required. In so doing, I contribute to ongoing debates about networked learning, providing a context to explore educational technology as a technology, language and learning nexus.

Two-B or not two-B? Design patterns for hybrid metaheuristics

Real world search problems, characterised by nonlinearity, noise and multidimensionality, are often best solved by hybrid algorithms. Techniques embodying different necessary features are triggered at specific iterations, in response to the current state of the problem space. In the existing literature, this alternation is managed either statically (through pre-programmed policies) or dynamically, at the cost of high coupling with algorithm inner representation. We extract two design patterns for hybrid metaheuristic search algorithms, the All-Seeing Eye and the Commentator patterns, which we argue should be replaced by the more flexible and loosely coupled Simple Black Box (Two-B) and Utility-based Black Box (Three-B) patterns that we propose here. We recommend the Two-B pattern for purely fitness based hybridisations and the Three-B pattern for more generic search quality evaluation based hybridisations.

QPSK 3R regenerator using a phase sensitive amplifier

A black box phase sensitive amplifier based 3R regeneration scheme is proposed for non-return to zero quadrature phase shift keyed formatted signals. Performance improvements of more than 2 dB are achieved at the presence of input phase distortion.

Modeling the Impacts of Climate Change on Phytogeographical Units. A Case Study of the Moesz Line

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate data-set, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.