The appraisal and management of multi-storey dwelling blocks using large concrete panel systems

This exploratory study is concerned with the integrated appraisal of multi-storey dwelling blocks which incorporate large concrete panel systems (LPS). The first step was to look at U.K. multi-storey dwelling stock in general, and under the management of Birmingham City Council in particular. The information has been taken from the databases of three departments in the City of Birmingham, and rearranged in a new database using a suite of PC software called `PROXIMA' for clarity and analysis. One hundred of their stock were built large concrete panel system. Thirteen LPS blocks were chosen for the purpose of this study as case-studies depending mainly on the height and age factors of the block. A new integrated appraisal technique has been created for the LPS dwelling blocks, which takes into account the most physical and social factors affecting the condition and acceptability of these blocks. This appraisal technique is built up in a hierarchical form moving from the general approach to particular elements (a tree model). It comprises two main approaches; physical and social. In the physical approach, the building is viewed as a series of manageable elements and sub-elements to cover every single physical or environmental factor of the block, in which the condition of the block is analysed. A quality score system has been developed which depends mainly on the qualitative and quantitative conditions of each category in the appraisal tree model, and leads to physical ranking order of the study blocks. In the social appraisal approach, the residents' satisfaction and attitude toward their multi-storey dwelling block was analysed in relation to: a. biographical and housing related characteristics; and b. social, physical and environmental factors associated with this sort of dwelling, block and estate in general.The random sample consisted of 268 residents living in the 13 case study blocks. Data collected was analysed using frequency counts, percentages, means, standard deviations, Kendall's tue, r-correlation coefficients, t-test, analysis of variance (ANOVA) and multiple regression analysis. The analysis showed a marginally positive satisfaction and attitude towards living in the block. The five most significant factors associated with the residents' satisfaction and attitude in descending order were: the estate, in general; the service categories in the block, including heating system and lift services; vandalism; the neighbours; and the security system of the block. An important attribute of this method, is that it is relatively inexpensive to implement, especially when compared to alternatives adopted by some local authorities and the BRE. It is designed to save time, money and effort, to aid decision making, and to provide ranked priority to the multi-storey dwelling stock, in addition to many other advantages. A series of solution options to the problems of the block was sought for selection and testing before implementation. The traditional solutions have usually resulted in either demolition or costly physical maintenance and social improvement of the blocks. However, a new solution has now emerged, which is particularly suited to structurally sound units. The solution of `re-cycling' might incorporate the reuse of an entire block or part of it, by removing panels, slabs and so forth from the upper floors in order to reconstruct them as low-rise accommodations.

Development of a computerised library system in the construction industry

This thesis examines the ways that libraries have employed computers to assist with housekeeping operations. It considers the relevance of such applications to company libraries in the construction industry, and describes more specifically the development of an integrated cataloguing and loan system. A review of the main features in the development of computerised ordering, cataloguing and circulation control systems shows that fully integrated packages are beginning to be completed, and that some libraries are introducing second generation programs. Cataloguing is the most common activity to be computerised, both at national and company level. Results from a sample of libraries in the construction industry suggest that the only computerised housekeeping system is at Taylor Woodrow. Most of the firms have access to an in-house computer, and some of the libraries, particularly those in firms of consulting engineers, might benefit from computerisation, but there are differing attitudes amongst the librarians towards the computer. A detailed study of the library at Taylor Woodrow resulted in a feasibility report covering all the areas of its activities. One of the main suggestions was the possible use of a computerised loans and cataloguing system. An integrated system to cover these two areas was programmed in Fortran and implemented. This new system provides certain benefits and saves staff time, but at the cost of time on the computer. Some improvements could be made by reprogramming, but it provides a general system for small technical libraries. A general equation comparing costs for manual and computerised operations is progressively simplified to a form where the annual saving from the computerised system is expressed in terms of staff and computer costs and the size of the library. This equation gives any library an indication of the savings or extra cost which would result from using the computerised system.

Bayesian invariant measurements of generalisation

The problem of evaluating different learning rules and other statistical estimators is analysed. A new general theory of statistical inference is developed by combining Bayesian decision theory with information geometry. It is coherent and invariant. For each sample a unique ideal estimate exists and is given by an average over the posterior. An optimal estimate within a model is given by a projection of the ideal estimate. The ideal estimate is a sufficient statistic of the posterior, so practical learning rules are functions of the ideal estimator. If the sole purpose of learning is to extract information from the data, the learning rule must also approximate the ideal estimator. This framework is applicable to both Bayesian and non-Bayesian methods, with arbitrary statistical models, and to supervised, unsupervised and reinforcement learning schemes.

Individual spirit at work and its relationship with employee work attitudes and organisational outcomes:An empirical examination in corporate Thailand

The purpose of this thesis is to conduct empirical research in corporate Thailand in order to (1) validate the Spirit at Work Scale (2) investigate the relationships between individual spirit at work and three employee work attitudinal variables (job satisfaction, organisational identification and psychological well-being) and three organisational outcomes (in-role performance, organisational citizenship behaviours (OCB), and turnover intentions) (3) further examine causal relations among these organisational behaviour variables with a longitudinal design (4) examine three employee work attitudes as mediator variables between individual spirit at work and three organisational outcomes and (5) explore the potential antecedents of organisational conditions that foster employee experienced individual spirit at work. The two pilot studies with 155 UK and 175, 715 Thai samples were conducted for validation testing of the main measure used in this study: Spirit at Work Scale (Kinjerski & Skrypnek, 2006a). The results of the two studies including discriminant validity analyses strongly provided supportive evidence that Spirit at Work Scale (SAWS) is a sound psychometric measure and also a distinct construct from the three work attitude constructs. The final model of SAWS contains a total of twelve items; a three factor structure (meaning in work, sense of community, and spiritual connection) in which the sub-factors loaded on higher order factors and also had very acceptable reliability. In line with these results it was decided to use the second-order of SAWS model for Thai samples in the main study and subsequent analysis. The 715 completed questionnaires were received from the first wave of data collection during July - August 2008 and the second wave was conducted again within the same organisations and 501 completed questionnaires were received during March - April 2009. Data were obtained through 49 organisations which were from three types of organisations within Thailand: public organisations, for-profit organisations, and notfor-profit organisations. Confirmatory factor analysis of all measures used in the study and hypothesised model were tested with structural equation modelling techniques. The results were greatly supportive for the direct structural model and partially supportive for the fully mediated model. Moreover, there were different findings across self report and supervisor rating on performance and OCB models. Additionally, the antecedent conditions that fostered employees experienced individual spirit at work and the implications of these findings for research and practice are discussed.

The n-tuple network: coping with sub-optimality

The subject of this thesis is the n-tuple net.work (RAMnet). The major advantage of RAMnets is their speed and the simplicity with which they can be implemented in parallel hardware. On the other hand, this method is not a universal approximator and the training procedure does not involve the minimisation of a cost function. Hence RAMnets are potentially sub-optimal. It is important to understand the source of this sub-optimality and to develop the analytical tools that allow us to quantify the generalisation cost of using this model for any given data. We view RAMnets as classifiers and function approximators and try to determine how critical their lack of' universality and optimality is. In order to understand better the inherent. restrictions of the model, we review RAMnets showing their relationship to a number of well established general models such as: Associative Memories, Kamerva's Sparse Distributed Memory, Radial Basis Functions, General Regression Networks and Bayesian Classifiers. We then benchmark binary RAMnet. model against 23 other algorithms using real-world data from the StatLog Project. This large scale experimental study indicates that RAMnets are often capable of delivering results which are competitive with those obtained by more sophisticated, computationally expensive rnodels. The Frequency Weighted version is also benchmarked and shown to perform worse than the binary RAMnet for large values of the tuple size n. We demonstrate that the main issues in the Frequency Weighted RAMnets is adequate probability estimation and propose Good-Turing estimates in place of the more commonly used :Maximum Likelihood estimates. Having established the viability of the method numerically, we focus on providillg an analytical framework that allows us to quantify the generalisation cost of RAMnets for a given datasetL. For the classification network we provide a semi-quantitative argument which is based on the notion of Tuple distance. It gives a good indication of whether the network will fail for the given data. A rigorous Bayesian framework with Gaussian process prior assumptions is given for the regression n-tuple net. We show how to calculate the generalisation cost of this net and verify the results numerically for one dimensional noisy interpolation problems. We conclude that the n-tuple method of classification based on memorisation of random features can be a powerful alternative to slower cost driven models. The speed of the method is at the expense of its optimality. RAMnets will fail for certain datasets but the cases when they do so are relatively easy to determine with the analytical tools we provide.

Studies on the generalisation of Gaussian processes and Bayesian neural networks

The assessment of the reliability of systems which learn from data is a key issue to investigate thoroughly before the actual application of information processing techniques to real-world problems. Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in order to estimate the amount of data required to guarantee a certain level of generalisation performance. In this thesis we analyse the effects on the bounds and the learning curve induced by the smoothness of stochastic processes described by four different covariance functions. We also explain the early, linearly-decreasing behaviour of the curves and we investigate the asymptotic behaviour of the upper bounds. The effect of the noise and the characteristic lengthscale of the stochastic process on the tightness of the bounds are also discussed. The analysis is supported by several numerical simulations. The generalisation error of a Gaussian process is affected by the dimension of the input vector and may be decreased by input-variable reduction techniques. In conventional approaches to Gaussian process regression, the positive definite matrix estimating the distance between input points is often taken diagonal. In this thesis we show that a general distance matrix is able to estimate the effective dimensionality of the regression problem as well as to discover the linear transformation from the manifest variables to the hidden-feature space, with a significant reduction of the input dimension. Numerical simulations confirm the significant superiority of the general distance matrix with respect to the diagonal one.In the thesis we also present an empirical investigation of the generalisation errors of neural networks trained by two Bayesian algorithms, the Markov Chain Monte Carlo method and the evidence framework; the neural networks have been trained on the task of labelling segmented outdoor images.

Balancing expectations in engineering education:enhancing student experiences through empirical research - a comparative analysis

This paper draws upon the findings of an empirical study comparing the expectations and concerns of engineering students with students enrolled on business and management programs. It argues that whilst the two groups of students have very similar expectations, motivations and concerns before their start their studies, once at university, engineering students are twice as likely to drop-out than are their compatriots in business studies. Drawing upon the study findings, recommendations are made as to what might be done to counteract this. The conclusion argues that there is a need for more in-depth research to be conducted in this area in order to identify the reasons behind the different attrition rates and to further enhance engineering undergraduate experience.

Quality management (QM) implementation in the Kuwaiti Oil Industry:An empirical study and a proposed generic framework

In an Arab oil producing country in the Middle East such as Kuwait, Oil industry is considered as the main and most important industry of the country. This industry’s importance emerged from the significant role it plays in both country’s national economy and also global economy. Moreover, Oil industry’s criticality comes from its interconnectivity with national security and power in the Middle East region. Hence, conducting this research in this crucial industry had certainly added values to companies in this industry as it investigated thoroughly the main components of the TQM implementation process and identified which components affects significantly TQM’s implementation and its gained business results. In addition, as the Oil sector is a large sector that is known for its richness of employees with different national cultures and backgrounds. Thus, this culture-heterogeneous industry seems to be the most appropriate environment to address and satisfy a need in the literature to investigate the national culture values’ effects on TQM implementation process. Furthermore, this research has developed a new conceptual model of TQM implementation process in the Kuwaiti Oil industry that applies in general to operations and productions organizations at the Kuwaiti business environment and in specific to organizations in the Oil industry, as well it serves as a good theoretical model for improving operations and production level of the oil industry in other developing and developed countries. Thus, such research findings minimized the literature’s gap found the limited amount of empirical research of TQM implementation in well-developed industries existing in an Arab, developing countries and specifically in Kuwait, where there was no coherent national model for a universal TQM implementation in the Kuwaiti Oil industry in specific and Kuwaiti business environment in general. Finally, this newly developed research framework, which emerged from the literature search, was validated by rigorous quantitative analysis tools including SPSS and Structural Equation Modeling. The quantitative findings of questionnaires collected were supported by the qualitative findings of interviews conducted.

State–society relations in a dynamic framework:the case of the Far East and Sub-Saharan Africa

According to the textbook approach, the developmental states of the Far East have been considered as strong and autonomous entities. Although their bureaucratic elites have remained isolated from direct pressures stemming from society, the state capacity has also been utilised in order to allocate resources in the interest of the whole society. Yet, society – by and large –has remained weak and subordinated to the state elite. On the other hand, the general perception of Sub-Saharan Africa (SSA) has been just the opposite. The violent and permanent conflict amongst rent-seeking groups for influence and authority over resources has culminated in a situation where states have become extremely weak and fragmented, while society – depending on the capacity of competing groups for mobilising resources to organise themselves mostly on a regional or local level (resulting in local petty kingdoms) – has never had the chance to evolve as a strong player. State failure in the literature, therefore, – in the context of SSA – refers not just to a weak and captured state but also to a non-functioning, and sometimes even non-existent society, too. Recently, however, the driving forces of globalisation might have triggered serious changes in the above described status quo. Accordingly, our hypothesis is the following: globalisation, especially the dynamic changes of technology, capital and communication have made the simplistic “strong state–weak society” (in Asia) and “weak state–weak society” (in Africa) categorisation somewhat obsolete. While our comparative study has a strong emphasis on the empirical scrutiny of trying to uncover the dynamics of changes in state–society relations in the two chosen regions both qualitatively and quantitatively, it also aims at complementing the meaning and essence of the concepts and methodology of stateness, state capacity and state-society relations, the well-known building blocks of the seminal works of Evans (1995), Leftwich (1995), Migdal (1988) or Myrdal (1968).

Bayesian invariant measurements of generalisation for continuous distributions

A family of measurements of generalisation is proposed for estimators of continuous distributions. In particular, they apply to neural network learning rules associated with continuous neural networks. The optimal estimators (learning rules) in this sense are Bayesian decision methods with information divergence as loss function. The Bayesian framework guarantees internal coherence of such measurements, while the information geometric loss function guarantees invariance. The theoretical solution for the optimal estimator is derived by a variational method. It is applied to the family of Gaussian distributions and the implications are discussed. This is one in a series of technical reports on this topic; it generalises the results of ¸iteZhu95:prob.discrete to continuous distributions and serve as a concrete example of a larger picture ¸iteZhu95:generalisation.

On the relationship between Bayesian error bars and the input data density

We investigate the dependence of Bayesian error bars on the distribution of data in input space. For generalized linear regression models we derive an upper bound on the error bars which shows that, in the neighbourhood of the data points, the error bars are substantially reduced from their prior values. For regions of high data density we also show that the contribution to the output variance due to the uncertainty in the weights can exhibit an approximate inverse proportionality to the probability density. Empirical results support these conclusions.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

General bounds on Bayes errors for regression with Gaussian processes

Based on a simple convexity lemma, we develop bounds for different types of Bayesian prediction errors for regression with Gaussian processes. The basic bounds are formulated for a fixed training set. Simpler expressions are obtained for sampling from an input distribution which equals the weight function of the covariance kernel, yielding asymptotically tight results. The results are compared with numerical experiments.

Environmental reporting in developing countries:empirical evidence from Bangladesh

Most environmental reporting studies have focused on developed countries. Only a handful number of studies are available on the developing countries, concentrating on the newly industrialized countries and African countries. No studies are available from South Asia except the widely quoted one of Singh and Ahuja (1983). Against this background, it is argued that an empirical study on environmental reporting practices in Bangladesh would make a significant contribution to the environmental reporting literature from the context of developing countries in general, and South Asian countries in particular. The study covers 30 recent annual reports of Bangladeshi companies relating to the year 1996. It shows that very limited environmental disclosure has been made. Although we have noted that 90% of companies made some environmental disclosures, the percentage of companies disclosing environmental information comes down to only 20 if we exclude disclosure related to expenditure on energy usage. In general, the quantity and the quality of disclosures seem to be inadequate and poor as compared to the environmental disclosures in the developed countries. The study concludes with an urge for further research in this regard.

Empirical analysis of human capital development and economic growth in Europe

Recent discussion of the knowledge-based economy draws increasingly attention to the role that the creation and management of knowledge plays in economic development. Development of human capital, the principal mechanism for knowledge creation and management, becomes a central issue for policy-makers and practitioners at the regional, as well as national, level. Facing competition both within and across nations, regional policy-makers view human capital development as a key to strengthening the positions of their economies in the global market. Against this background, the aim of this study is to go some way towards answering the question of whether, and how, investment in education and vocational training at regional level provides these territorial units with comparative advantages. The study reviews literature in economics and economic geography on economic growth (Chapter 2). In growth model literature, human capital has gained increased recognition as a key production factor along with physical capital and labour. Although leaving technical progress as an exogenous factor, neoclassical Solow-Swan models have improved their estimates through the inclusion of human capital. In contrast, endogenous growth models place investment in research at centre stage in accounting for technical progress. As a result, they often focus upon research workers, who embody high-order human capital, as a key variable in their framework. An issue of discussion is how human capital facilitates economic growth: is it the level of its stock or its accumulation that influences the rate of growth? In addition, these economic models are criticised in economic geography literature for their failure to consider spatial aspects of economic development, and particularly for their lack of attention to tacit knowledge and urban environments that facilitate the exchange of such knowledge. Our empirical analysis of European regions (Chapter 3) shows that investment by individuals in human capital formation has distinct patterns. Those regions with a higher level of investment in tertiary education tend to have a larger concentration of information and communication technology (ICT) sectors (including provision of ICT services and manufacture of ICT devices and equipment) and research functions. Not surprisingly, regions with major metropolitan areas where higher education institutions are located show a high enrolment rate for tertiary education, suggesting a possible link to the demand from high-order corporate functions located there. Furthermore, the rate of human capital development (at the level of vocational type of upper secondary education) appears to have significant association with the level of entrepreneurship in emerging industries such as ICT-related services and ICT manufacturing, whereas such association is not found with traditional manufacturing industries. In general, a high level of investment by individuals in tertiary education is found in those regions that accommodate high-tech industries and high-order corporate functions such as research and development (R&D). These functions are supported through the urban infrastructure and public science base, facilitating exchange of tacit knowledge. They also enjoy a low unemployment rate. However, the existing stock of human and physical capital in those regions with a high level of urban infrastructure does not lead to a high rate of economic growth. Our empirical analysis demonstrates that the rate of economic growth is determined by the accumulation of human and physical capital, not by level of their existing stocks. We found no significant effects of scale that would favour those regions with a larger stock of human capital. The primary policy implication of our study is that, in order to facilitate economic growth, education and training need to supply human capital at a faster pace than simply replenishing it as it disappears from the labour market. Given the significant impact of high-order human capital (such as business R&D staff in our case study) as well as the increasingly fast pace of technological change that makes human capital obsolete, a concerted effort needs to be made to facilitate its continuous development.