Modelling construction client risk performance using organisation behaviour parameters

The role of client in inducing project risk has not been adequately covered and the construction industry. A focus on this aspect of risk should enable construction to square up the ‘risk circle’ for managing projects and contribute to the general development of risk management strategies for construction organisations. The thesis investigates the client role from an organisational behaviour perspective. The aim of the thesis is to determine whether organisational characteristics influence risk management behaviour for the client, and whether these characteristics affect the project risk performance positively or negatively. The objectives of the research that underpin the thesis were three­fold. First it was to explore the organisational characterisation of the construction client in the management of risk within the project environment. Second, it was to establish the effect of the client’s risk behaviour on the project. Third, to identify the dominant parameters which affect client risk management, and to investigate the interaction between the parameters and the client’s risk management practices and attributes. Establishing such interaction will show how the parameters explain the nature and extent of risk transfer from the organisational into the project. It also facilitate the provision of a guidance to define the client organisational attributes that are sensitive to project risk, or those attributes that are not. The study adopted a competing values framework on organisation behaviour that resulted in an elicitation instrument for testing the relationship between organisational characteristics and risk performance. Data was obtained by surveying a sample of client organisations who are actively engaged in procuring projects in the UK construction industry. The outcome of the research showed that the parameters that are represented in the competing values framework (namely, Open system, Rational model, Internal process, and Human resources) affected the risk practices and attributes of the client in different ways. The outcome specifically showed that the Rational Model has a significant positive influence on risk performance while the Internal Process has a significant negative influence on risk performance. Both the Open system and Human resources showed insignificant influence. This supports the notion that construction risk is part of a functional system that extends to the client risk performance and that the client organisational characteristics contribute to the risk behaviour within the construction project. The thesis offers two very significant contributions to the body of knowledge that underpins the management of risk in project and construction organisations: namely, the contribution to the level of risk made by the client organisations should form part of the considerations in any project appraisal; and the risk contributions by the client should address the Rational model and Internal process contexts of their organisation

Segmentation of neuroanatomy in magnetic resonance images

Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account

Theoretical Concepts of Quantum Mechanics

Quantum theory as a scientific revolution profoundly influenced human thought about the universe and governed forces of nature. Perhaps the historical development of quantum mechanics mimics the history of human scientific struggles from their beginning. This book, which brought together an international community of invited authors, represents a rich account of foundation, scientific history of quantum mechanics, relativistic quantum mechanics and field theory, and different methods to solve the Schrodinger equation. We wish for this collected volume to become an important reference for students and researchers

Probabilistic state estimation in regimes of nonlinear error growth

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2005.Includes bibliographical references (p. 273-286).State estimation, or data assimilation as it is often called, is a key component of numerical weather prediction (NWP). Nearly all implementable methods of state estimation suitable for NWP are forced to assume that errors remain in regimes of linear error growth and retain distributions of Gaussian uncertainty, yet nonlinear systems like the atmosphere can readily allow regimes of nonlinear error growth and, in turn, produce distributions of non- Gaussian uncertainty. State-of-the-art, ensemble-based methods of state estimation suitable for NWP are examined to gauge the consequences and relevance of violating the linear error growth assumption. For quite generic sources of non-Gaussian uncertainty, the methods are observed to fail, as they must, and the obtained analyses become probabilistically unreliable before becoming inaccurate. The mispositioning of coherent features is identified as a specific, geophysically relevant source of non-Gaussian uncertainty that can easily cause the state-of-the-art methods of state estimation to fail. However, an understanding of relevant phenomenology sometimes allows these same methods to remain successful owing to an available redefinition of the involved errors. The redefinition is phrased as an alternative error model. It is recognized and exploited that non-Gaussian additive Eulerian errors can come from Gaussian Lagrangian position errors. A two-step, augmented state vector approach is developed that is suitable for use with coherent features and that relies only on implementable methods of state estimation.(cont.) By combining the dual Eulerian and Lagrangian state information into one vector, an ensemble can approximate their covariance, thus allowing each component's uncertainty to be reduced. The first step of the two-step approach reduces the feature position errors in an effort to render the residual additive errors Gaussian, thereby allowing the second step of an implementable state estimation method to proceed successfully. Philosophically, the two-step approach uses physical knowledge of the problem (as phrased by the error model) to compensate for neglected important non-Gaussian uncertainty structure in the state estimation process. The proposed two-step approach successfully allows use of implementable methods of state estimation to obtain probabilistically reliable analyses in regimes of nonlinear error growth, something unavailable using current standards.by W. Gregory Lawson.Ph.D

A structured approach to physically-based modeling for computer graphics

This thesis presents a framework for the design of physically-based computer graphics models. The framework includes a paradigm for the structure of physically-based models, techniques for "structured" mathematical modeling, and a specification of a computer program structure in which to implement the models. The framework is based on known principles and methodologies of structured programming and mathematical modeling. Because the framework emphasizes the structure and organization of models, we refer to it as "Structured Modeling." The Structured Modeling framework focuses on clarity and "correctness" of models, emphasizing explicit statement of assumptions, goals, and techniques. In particular, we partition physically-based models, separating them into conceptual and mathematical models, and posed problems. We control complexity of models by designing in a modular manner, piecing models together from smaller components. The framework places a particular emphasis on defining a complete formal statement of a model's mathematical equations, before attempting to simulate the model. To manage the complexity of these equations, we define a collection of mathematical constructs, notation, and terminology, that allow mathematical models to be created in a structured and modular manner. We construct a computer programming environment that directly supports the implementation of models designed using the above techniques. The environment is geared to a tool-oriented approach, in which models are built from an extensible collection of software objects, that correspond to elements and tasks of a "blackboard" design of models. A substantial portion of this thesis is devoted to developing a library of physically-based model "modules," including rigid-body kinematics, rigid-body dynamics, and dynamic constraints, all built with the Structured Modeling framework. These modules are intended to serve both as examples of the framework, and as potentially useful tools for the computer graphics community. Each module includes statements of goals and assumptions, explicit mathematical models and problem statements, and descriptions of software objects that support them. We illustrate the use of the library to build some sample models, and include discussion of various possible additions and extensions to the library. Structured Modeling is an experiment in modeling: an exploration of designing via strict adherence to a dogma of structure, modularity, and mathematical formality. It does not stress issues such as particular numerical simulation techniques or efficiency of computer execution time or memory usage, all of which are important practical considerations in modeling. However, at least so far as the work carried on in this thesis, Structured Modeling has proven to be a useful aid in the design and understanding of complex physically based models.</p

Knowledge and Management Models for Sustainable Growth

In the last years sustainability has become a topic of global concern and a key issue in the strategic agenda of both business organizations and public authorities and organisations. Significant changes in business landscape, the emergence of new technology, including social media, the pressure of new social concerns, have called into question established conceptualizations of competitiveness, wealth creation and growth. New and unaddressed set of issues regarding how private and public organisations manage and invest their resources to create sustainable value have brought to light. In particular the increasing focus on environmental and social themes has suggested new dimensions to be taken into account in the value creation dynamics, both at organisations and communities level. For companies the need of integrating corporate social and environmental responsibility issues into strategy and daily business operations, pose profound challenges, which, in turn, involve numerous processes and complex decisions influenced by many stakeholders. Facing these challenges calls for the creation, use and exploitation of new knowledge as well as the development of proper management models, approaches and tools aimed to contribute to the development and realization of environmentally and socially sustainable business strategies and practices

STK /WST 795 Research Reports

These documents contain the honours research reports for each year for the Department of Statistics.Honours Research Reports - University of Pretoria 20XXStatisticsBSs (Hons) Mathematical Statistics, BCom (Hons) Statistics, BCom (Hons) Mathematical StatisticsUnrestricte

Queensland University of Technology: Handbook 1997

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT

Queensland University of Technology: Handbook 1996

The Queensland University of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QUT