Understanding the physical processes of pollutant build-up and wash-off on roof surfaces

Pollutants originating with roof runoff can have a significant impact to urban stormwater quality. This signifies the importance of understanding pollutant processes on roof surfaces. Additionally, knowledge of pollutant processes on roof surfaces is important as roofs are used as the primary catchment surface for domestic rainwater harvesting. In recent years, rainwater harvesting has become one of the primary sustainable water management techniques to counteract the growing demand for potable water. Similar to all impervious services, pollutants associated with roof runoff undergo two primary processes: build-up and wash-off. The knowledge relating to these processes is limited. This paper presents outcomes of an in-depth research study into pollutant build-up and wash-off for roof surfaces. The knowledge will be important in order to develop appropriate strategies to safeguard rainwater users from possible health risks.

Controlled flexibility and lifecycle management of business processes through extensibility

Vendors provide reference process models as consolidated, off-the-shelf solutions to capture best practices in a given industry domain. Customers can then adapt these models to suit their specific requirements. Traditional process flexibility approaches facilitate this operation, but do not fully address it as they do not sufficiently take controlled change guided by vendors' reference models into account. This tension between the customer's freedom of adapting reference models, and the ability to incorporate with relatively low effort vendor-initiated reference model changes, thus needs to be carefully balanced. This paper introduces process extensibility as a new paradigm for customizing reference processes and managing their evolution over time. Process extensibility mandates a clear recognition of the different responsibilities and interests of reference model vendors and consumers, and is concerned with keeping the effort of customer-side reference model adaptations low while allowing sufficient room for model change.

Is the sum more than the corporate entrepreneurship parts? the different effects of social capital on innovation, venturing, and renewal processes

Principal Topic Although corporate entrepreneurship is of vital importance for long-term firm survival and growth (Zahra and Covin, 1995), researchers still struggle with understanding how to manage corporate entrepreneurship activities. Corporate entrepreneurship consists of three parts: innovation, venturing, and renewal processes (Guth and Ginsberg, 1990). Innovation refers to the development of new products, venturing to the creation of new businesses, and renewal to redefining existing businesses (Sharma, and Chrisman, 1999; Verbeke et al., 2007). Although there are many studies focusing on one of these aspects (cf. Burgelman, 1985; Huff et al., 1992), it is very difficult to compare the outcomes of these studies due to differences in contexts, measures, and methodologies. This is a significant lack in our understanding of CE, as firms engage in all three aspects of CE, making it important to compare managerial and organizational antecedents of innovation, venturing and renewal processes. Because factors that may enhance venturing activities may simultaneously inhibit renewal activities. The limited studies that did empirically compare the individual dimensions (cf. Zahra, 1996; Zahra et al., 2000; Yiu and Lau, 2008; Yiu et al., 2007) generally failed to provide a systematic explanation for potential different effects of organizational antecedents on innovation, venturing, and renewal. With this study we aim to investigate the different effects of structural separation and social capital on corporate entrepreneurship activities. The access to existing and the development of new knowledge has been deemed of critical importance in CE-activities (Floyd and Wooldridge, 1999; Covin and Miles, 2007; Katila and Ahuja, 2002). Developing new knowledge can be facilitated by structurally separating corporate entrepreneurial units from mainstream units (cf. Burgelman, 1983; Hill and Rothaermel, 2003; O'Reilly and Tushman, 2004). Existing knowledge and resources are available through networks of social relationships, defined as social capital (Nahapiet and Ghoshal, 1998; Yiu and Lau, 2008). Although social capital has primarily been studied at the organizational level, it might be equally important at top management level (Belliveau et al., 1996). However, little is known about the joint effects of structural separation and integrative mechanisms to provide access to social capital on corporate entrepreneurship. Could these integrative mechanisms for example connect the separated units to facilitate both knowledge creation and sharing? Do these effects differ for innovation, venturing, and renewal processes? Are the effects different for organizational versus top management team integration mechanisms? Corporate entrepreneurship activities have for example been suggested to take place at different levels. Whereas innovation is suggested to be a more bottom-up process, strategic renewal is a more top-down process (Floyd and Lane, 2000; Volberda et al., 2001). Corporate venturing is also a more bottom-up process, but due to the greater required resource commitments relative to innovation, it ventures need to be approved by top management (Burgelman, 1983). As such we will explore the following key research question in this paper: How do social capital and structural separation on organizational and TMT level differentially influence innovation, venturing, and renewal processes? Methodology/Key Propositions We investigated our hypotheses on a final sample of 240 companies in a variety of industries in the Netherlands. All our measures were validated in previous studies. We targeted a second respondent in each firm to reduce problems with single-rater data (James et al., 1984). We separated the measurement of the independent and the dependent variables in two surveys to create a one-year time lag and reduce potential common method bias (Podsakoff et al., 2003). Results and Implications Consistent with our hypotheses, our results show that configurations of structural separation and integrative mechanisms have different effects on the three aspects of corporate entrepreneurship. Innovation was affected by organizational level mechanisms, renewal by integrative mechanisms on top management team level and venturing by mechanisms on both levels. Surprisingly, our results indicated that integrative mechanisms on top management team level had negative effects on corporate entrepreneurship activities. We believe this paper makes two significant contributions. First, we provide more insight in what the effects of ambidextrous organizational forms (i.e. combinations of differentiation and integration mechanisms) are on venturing, innovation and renewal processes. Our findings show that more valuable insights can be gained by comparing the individual parts of corporate entrepreneurship instead of focusing on the whole. Second, we deliver insights in how management can create a facilitative organizational context for these corporate entrepreneurship activities.

Effective communication in innovation processes

Innovation processes are rarely smooth and disruptions often occur at transition points were one knowledge domain passes the technology on to another domain. At these transition points communication is a key component in assisting the smooth hand over of technologies. However for smooth transitions to occur we argue that appropriate structures have to be in place and boundary spanning activities need to be facilitated. This paper presents three case studies of innovation processes and the findings support the view that structures and boundary spanning are essential for smooth transitions.

The influence of instructions and body-scaling as constraints on decision-making processes in team sports

Team games conceptualized as dynamical systems engender a view of emergent decision-making behaviour under constraints, although specific effects of instructional and body-scaling constraints have yet to be verified empirically. For this purpose, we studied the effects of task and individual constraints on decision-making processes in basketball. Eleven experienced female players performed 350 trials in 1 vs. 1 sub-phases of basketball in which an attacker tried to perturb the stable state of a dyad formed with a defender (i.e. break the symmetry). In Experiment 1, specific instructions (neutral, risk taking or conservative) were manipulated to observe effects on emergent behaviour of the dyadic system. When attacking players were given conservative instructions, time to cross court mid-line and variability of the attacker's trajectory were significantly greater. In Experiment 2, body-scaling of participants was manipulated by creating dyads with different height relations. When attackers were considerably taller than defenders, there were fewer occurrences of symmetry-breaking. When attackers were considerably shorter than defenders, time to cross court mid-line was significantly shorter than when dyads were composed of athletes of similar height or when attackers were considerably taller than defenders. The data exemplify how interacting task and individual constraints can influence emergent decision-making processes in team ball games.

Conscious and unconscious processes in human desire

Elaborated Intrusion theory (Kavanagh, Andrade & May 2005) distinguishes between unconscious, associative processes as the precursors of desire, and controlled processes of cognitive elaboration that lead to conscious sensory images of the target of desire and associated affect. We argue that these mental images play a key role in motivating human behavior. Consciousness is functional in that it allows competing goals to be compared and evaluated. The role of effortful cognitive processes in desire helps to explain the different time courses of craving and physiological withdrawal.

Automating human thought processes for a UAV forced landing

This paper describes the current status of a program to develop an automated forced landing system for a fixed-wing Unmanned Aerial Vehicle (UAV). This automated system seeks to emulate human pilot thought processes when planning for and conducting an engine-off emergency landing. Firstly, a path planning algorithm that extends Dubins curves to 3D space is presented. This planning element is then combined with a nonlinear guidance and control logic, and simulated test results demonstrate the robustness of this approach to strong winds during a glided descent. The average path deviation errors incurred are comparable to or even better than that of manned, powered aircraft. Secondly, a study into suitable multi-criteria decision making approaches and the problems that confront the decision-maker is presented. From this study, it is believed that decision processes that utilize human expert knowledge and fuzzy logic reasoning are most suited to the problem at hand, and further investigations will be conducted to identify the particular technique/s to be implemented in simulations and field tests. The automated UAV forced landing approach presented in this paper is promising, and will allow the progression of this technology from the development and simulation stages through to a prototype system

Business Creation Processes in Australia : What Start-Up Attempts Get Up and Running, and Why? - A Preliminary Assessment

The Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) is the largest study of new firm formation that has ever been undertaken in Australia. CAUSEE follows the development of several samples of new and emerging firms over time. In this report we focus on the drivers of outcomes – in terms of reaching an operational stage vs. terminating the effort – of 493 randomly selected nascent firms whose founders have been comprehensively interviewed on two occasions, 12 months apart. We investigate the outcome effects of three groups of variables: Characteristics of the Venture; Resources Used in the Start-Up Process and Characteristics of the Start-Up Process Itself.

Krylov subspace methods for approximating functions of symmetric positive definite matrices with applications to applied statistics and anomalous diffusion

Matrix function approximation is a current focus of worldwide interest and finds application in a variety of areas of applied mathematics and statistics. In this thesis we focus on the approximation of A^(-α/2)b, where A ∈ ℝ^(n×n) is a large, sparse symmetric positive definite matrix and b ∈ ℝ^n is a vector. In particular, we will focus on matrix function techniques for sampling from Gaussian Markov random fields in applied statistics and the solution of fractional-in-space partial differential equations. Gaussian Markov random fields (GMRFs) are multivariate normal random variables characterised by a sparse precision (inverse covariance) matrix. GMRFs are popular models in computational spatial statistics as the sparse structure can be exploited, typically through the use of the sparse Cholesky decomposition, to construct fast sampling methods. It is well known, however, that for sufficiently large problems, iterative methods for solving linear systems outperform direct methods. Fractional-in-space partial differential equations arise in models of processes undergoing anomalous diffusion. Unfortunately, as the fractional Laplacian is a non-local operator, numerical methods based on the direct discretisation of these equations typically requires the solution of dense linear systems, which is impractical for fine discretisations. In this thesis, novel applications of Krylov subspace approximations to matrix functions for both of these problems are investigated. Matrix functions arise when sampling from a GMRF by noting that the Cholesky decomposition A = LL^T is, essentially, a `square root' of the precision matrix A. Therefore, we can replace the usual sampling method, which forms x = L^(-T)z, with x = A^(-1/2)z, where z is a vector of independent and identically distributed standard normal random variables. Similarly, the matrix transfer technique can be used to build solutions to the fractional Poisson equation of the form ϕn = A^(-α/2)b, where A is the finite difference approximation to the Laplacian. Hence both applications require the approximation of f(A)b, where f(t) = t^(-α/2) and A is sparse. In this thesis we will compare the Lanczos approximation, the shift-and-invert Lanczos approximation, the extended Krylov subspace method, rational approximations and the restarted Lanczos approximation for approximating matrix functions of this form. A number of new and novel results are presented in this thesis. Firstly, we prove the convergence of the matrix transfer technique for the solution of the fractional Poisson equation and we give conditions by which the finite difference discretisation can be replaced by other methods for discretising the Laplacian. We then investigate a number of methods for approximating matrix functions of the form A^(-α/2)b and investigate stopping criteria for these methods. In particular, we derive a new method for restarting the Lanczos approximation to f(A)b. We then apply these techniques to the problem of sampling from a GMRF and construct a full suite of methods for sampling conditioned on linear constraints and approximating the likelihood. Finally, we consider the problem of sampling from a generalised Matern random field, which combines our techniques for solving fractional-in-space partial differential equations with our method for sampling from GMRFs.

Primary students' group metacognitive processes in a computer supported collaborative learning environment

The current understanding of students’ group metacognition is limited. The research on metacognition has focused mainly on the individual student. The aim of this study was to address the void by developing a conceptual model to inform the use of scaffolds to facilitate group metacognition during mathematical problem solving in computer supported collaborative learning (CSCL) environments. An initial conceptual framework based on the literature from metacognition, cooperative learning, cooperative group metacognition, and computer supported collaborative learning was used to inform the study. In order to achieve the study aim, a design research methodology incorporating two cycles was used. The first cycle focused on the within-group metacognition for sixteen groups of primary school students working together around the computer; the second cycle included between-group metacognition for six groups of primary school students working together on the Knowledge Forum® CSCL environment. The study found that providing groups with group metacognitive scaffolds resulted in groups planning, monitoring, and evaluating the task and team aspects of their group work. The metacognitive scaffolds allowed students to focus on how their group was completing the problem-solving task and working together as a team. From these findings, a revised conceptual model to inform the use of scaffolds to facilitate group metacognition during mathematical problem solving in computer supported collaborative learning (CSCL) environments was generated.

Context change archetypes : understanding the impact of context change on business processes

The emergence of Enterprise Resource Planning systems and Business Process Management have led to improvements in the design, implementation, and overall management of business processes. However, the typical focus of these initiatives has been on internal business operations, assuming a defined and stable context in which the processes are designed to operate. Yet, a lack of context-awareness for external change leads to processes and supporting information systems that are unable to react appropriately and timely enough to change. To increase the alignment of processes with environmental change, we propose a conceptual framework that facilitates the identification of context change. Based on a secondary data analysis of published case studies about process adaptation, we exemplify the framework and identify four general archetypes of context-awareness. The framework, in combination with the learning from the case analysis, provides a first understanding of what, where, how, and when processes are subjected to change.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Unstructured strategic decision-making processes : CRE decision-making in the Italian consulting industry

This thesis aims at developing a better understanding of unstructured strategic decision making processes and the conditions for achieving successful decision outcomes. Specifically it focuses on the processes used to make CRE (Corporate Real Estate) decisions. The starting point for this thesis is that our knowledge of such processes is incomplete. A comprehensive study of the most recent CRE literature together with Behavioural Organization Theory has provided a research framework for the exploration of CRE recommended =best practice‘, and of how organizational variables impact on and shape these practices. To reveal the fundamental differences between CRE decision-making in practice and the prescriptive =best practice‘ advocated in the CRE literature, a study of seven Italian management consulting firms was undertaken addressing the aspects of content and process of decisions. This thesis makes its primary contribution by identifying the importance and difficulty of finding the right balance between problem complexity, process richness and cohesion to ensure a decision-making process that is sufficiently rich and yet quick enough to deliver a prompt outcome. While doing so, this research also provides more empirical evidence to some of the most established theories of decision-making while reinterpreting their mono-dimensional arguments in a multi-dimensional model of successful decision-making.