A semantic modelling approach to knowledge based statistical software

The topic of this thesis is the development of knowledge based statistical software. The shortcomings of conventional statistical packages are discussed to illustrate the need to develop software which is able to exhibit a greater degree of statistical expertise, thereby reducing the misuse of statistical methods by those not well versed in the art of statistical analysis. Some of the issues involved in the development of knowledge based software are presented and a review is given of some of the systems that have been developed so far. The majority of these have moved away from conventional architectures by adopting what can be termed an expert systems approach. The thesis then proposes an approach which is based upon the concept of semantic modelling. By representing some of the semantic meaning of data, it is conceived that a system could examine a request to apply a statistical technique and check if the use of the chosen technique was semantically sound, i.e. will the results obtained be meaningful. Current systems, in contrast, can only perform what can be considered as syntactic checks. The prototype system that has been implemented to explore the feasibility of such an approach is presented, the system has been designed as an enhanced variant of a conventional style statistical package. This involved developing a semantic data model to represent some of the statistically relevant knowledge about data and identifying sets of requirements that should be met for the application of the statistical techniques to be valid. Those areas of statistics covered in the prototype are measures of association and tests of location.

An interdisciplinary approach to designing and evaluating a hybrid solar-biomass power plant

Purpose: Energy security is a major concern for India and many rural areas remain un-electrified. Thus, innovations in sustainable technologies to provide energy services are required. Biomass and solar energy in particular are resources that are widely available and underutilised in India. This paper aims to provide an overview of a methodology that was developed for designing and assessing the feasibility of a hybrid solar-biomass power plant in Gujarat. Design/methodology/approach: The methodology described is a combination of engineering and business management studies used to evaluate and design solar thermal collectors for specific applications and locations. For the scenario of a hybrid plant, the methodology involved: the analytical hierarchy process, for solar thermal technology selection; a cost-exergy approach, for design optimisation; quality function deployment, for designing and evaluating a novel collector - termed the elevation linear Fresnel reflector (ELFR); and case study simulations, for analysing alternative hybrid plant configurations. Findings: The paper recommended that for a hybrid plant in Gujarat, a linear Fresnel reflector of 14,000 m2 aperture is integrated with a 3 tonne per hour biomass boiler, generating 815 MWh per annum of electricity for nearby villages and 12,450 tonnes of ice per annum for local fisheries and food industries. However, at the expense of a 0.3 ¢/kWh increase in levelised energy costs, the ELFR can increase savings of biomass (100 t/a) and land (9 ha/a). Research limitations/implications: The research reviewed in this paper is primarily theoretical and further work will need to be undertaken to specify plant details such as piping layout, pump sizing and structure, and assess plant performance during real operational conditions. Originality/value: The paper considers the methodology adopted proved to be a powerful tool for integrating technology selection, optimisation, design and evaluation and promotes interdisciplinary methods for improving sustainable engineering design and energy management. © Emerald Group Publishing Limited.

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Inventory control in complex manufacturing environments encounters various sources of uncertainity and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modeled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large numbers of examples. The results of three representative cases are summarized. Finally a comparison between the developed fuzzy knowledge-based and traditional, probabilistic approaches is discussed.

An incremental tri-partite approach to ontology learning

In this paper we present a new approach to ontology learning. Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies. The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium. As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources. Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology. We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions. We present an overview of the algorithm and its functionalities.

A novel approach to modelling and exploiting uncertainty in stochastic control systems

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

Selling, resistance and reconciliation:a critical discursive approach to subsidiary role evolution in MNEs

Studies of political dynamics between multinational enterprise (MNE) parents and subsidiaries during subsidiary role evolution have focused largely on control and resistance. This paper adopts a critical discursive approach to enable an exploration of subtle dynamics in the way that both headquarters and subsidiaries subjectively reconstruct their independent-interdependent relationships with each other during change. We draw from a real-time qualitative study of a revealing case of charter change in an important European subsidiary of an MNE attempting to build closer integration across European country operations. Our results illustrate the role of three discourses – selling, resistance and reconciliation – in the reconstruction of the subsidiary–parent relationship. From this analysis we develop a process framework that elucidates the important role of these three discourses in the reconstruction of subsidiary roles, showing how resistance is not simply subversive but an important part of integration. Our findings contribute to a better understanding of the micro-level political dynamics in subsidiary role evolution, and of how voice is exercised in MNEs. This study also provides a rare example of discourse-based analysis in an MNE context, advancing our knowledge of how discursive methods can help to advance international business research more generally.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.

A modular holistic approach to prosody modelling for Standard Yorùbá speech synthesis

This paper presents a novel prosody model in the context of computer text-to-speech synthesis applications for tone languages. We have demonstrated its applicability using the Standard Yorùbá (SY) language. Our approach is motivated by the theory that abstract and realised forms of various prosody dimensions should be modelled within a modular and unified framework [Coleman, J.S., 1994. Polysyllabic words in the YorkTalk synthesis system. In: Keating, P.A. (Ed.), Phonological Structure and Forms: Papers in Laboratory Phonology III, Cambridge University Press, Cambridge, pp. 293–324]. We have implemented this framework using the Relational Tree (R-Tree) technique. R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. The underlying assumption of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combine acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. To implement the intonation dimension, fuzzy logic based rules were developed using speech data from native speakers of Yorùbá. The Fuzzy Decision Tree (FDT) and the Classification and Regression Tree (CART) techniques were tested in modelling the duration dimension. For practical reasons, we have selected the FDT for implementing the duration dimension of our prosody model. To establish the effectiveness of our prosody model, we have also developed a Stem-ML prosody model for SY. We have performed both quantitative and qualitative evaluations on our implemented prosody models. The results suggest that, although the R-Tree model does not predict the numerical speech prosody data as accurately as the Stem-ML model, it produces synthetic speech prosody with better intelligibility and naturalness. The R-Tree model is particularly suitable for speech prosody modelling for languages with limited language resources and expertise, e.g. African languages. Furthermore, the R-Tree model is easy to implement, interpret and analyse.

Peer mentoring in higher education:a multidimensional approach to conceptualising the role of peer mentoring programmes in enhancing the first year undergraduate experience

Across the literature researchers agree that the concept of mentoring results in positive outcomes for both mentors and mentees alike (Enrich et al, 2004). From a pedagogical perspective, student focused mentoring activities in Higher Education are generally perceived to comprise dyadic or triadic relationships that encapsulate a diverse range of learning strategies and/or support mechanisms. Whilst there exists a significant amount of literature regarding the wider value of Peer Mentoring in Higher Education, there remains a notable gap in knowledge about the value of such programmes in enhancing the first year undergraduate experience and thus promoting a smooth transition to University. Using the emergent study findings of a large international project, a multidimensional conceptual framework bringing together the theoretical, conceptual and contextual determinants of Peer Mentoring is proposed. This framework makes a distinctive contribution to current pedagogical theory and practice – particularly in relation to the first year experience.

Putting policy initiatives into practice:adopting an “honest broker” approach to adapting small businesses against flooding

Purpose: Amidst the current economic climate, which places many constraints on expensive flood defence schemes, the policy makers tend to favour schemes that are sympathetic to the needs of small and medium-sized enterprises (SMEs) and which promote empowering local communities based on their individual local contexts. Research has shown that although several initiatives are in place to create behavioural change among SMEs in undertaking adaptation approaches against flooding, they often tend to delay their responses by means of a "wait and see" attitude. The paper aims to discuss these issues. Design/methodology/approach: This paper argues that unless there are conscious efforts in the policy-making community to undertake explicit measures to engage with SMEs in a collaborative way, the uptake of adaptation measures will not be achieved as intended. With the use of the "honest broker" approach the paper provides a conceptual way forward of how a sense of collaboration can be instigated in an engagement process between the policy makers and SMEs, so that the scientific knowledge is translated in an appropriately rational way, which best meets the expectations of the SMEs. Findings: The paper proposes a conceptual model for engaging SMEs that will potentially increase the uptake of flood adaptation measures by SMEs. This could be a useful model with which to kick start a collaborative engagement process that could escalate to wider participation in other areas to improve impact of policy initiatives. Originality/value: The paper lays the conceptual foundation for a new theoretical base in the area, which will encourage more empirical investigations that will potentially enhance the practicality of some of the existing policies. © Emerald Group Publishing Limited.

Using cognitive load theory to interpret student difficulties with a problem-based learning approach to engineering education:a case study

This article reports on an investigationwith first year undergraduate ProductDesign and Management students within a School of Engineering and Applied Science. The students at the time of this investigation had studied fundamental engineering science and mathematics for one semester. The students were given an open ended, ill-formed problem which involved designing a simple bridge to cross a river.They were given a talk on problemsolving and given a rubric to follow, if they chose to do so.They were not given any formulae or procedures needed in order to resolve the problem. In theory, they possessed the knowledge to ask the right questions in order tomake assumptions but, in practice, it turned out they were unable to link their a priori knowledge to resolve this problem. They were able to solve simple beam problems when given closed questions. The results show they were unable to visualize a simple bridge as an augmented beam problem and ask pertinent questions and hence formulate appropriate assumptions in order to offer resolutions.

Developing an intervention to equip nurses for acute life threatening events (ALTEs) in hospital:a phenomenological approach to healthcare research

Objectives: To understand staff's experiences of acute life threatening events (ALTEs) in a pediatric hospital setting. These data will inform an intervention to equip nurses with clinical and emotional skills for dealing with ALTEs. Method: A mixed design was used in the broader research program; this paper focuses on phenomenon-focused interviews analyzed using interpretative phenomenological analysis (IPA). Results: Emerging themes included staff's relationships with patients and the impact of personhood on their ability to perform competently in an emergency. More experienced nurses described "automatic" competence generated through increased exposure to ALTEs and were able to recognize "fumbling and shaking" as a normal stress response. Designating a role was significant to staff experience of effectiveness. Key to nurses' learning experience was reflection and identifying experiences as "teachable moments." Findings were considered alongside existing theories of self-efficacy, reflective thought, and advocacy inquiry to create an experiential learning intervention involving a series of clinical and role-related scenarios. Conclusion: The phenomenological work facilitated an in-depth reading of experience. It accentuated the importance of exposure to ALTEs giving nurses experiential knowledge to prepare them for the impact of these events. Challenges included bracketing the personhood of child patients, shifting focus to clinical tasks during the pressured demands of managing an ALTE, normalizing the physiological stress response, and the need for a forum and structure for reflection and learning. An intervention will be designed to provide experiential learning and encourage nurses to realize and benefit from their embodied knowledge.

Making hiring decisions:a grounded theory approach to a practise-based study of filtering in selection

This thesis objective is to discover “How are informal decisions reached by screeners when filtering out undesirable job applications?” Grounded theory techniques were employed in the field to observe and analyse informal decisions at the source by screeners in three distinct empirical studies. Whilst grounded theory provided the method for case and cross-case analysis, literature from academic and non-academic sources was evaluated and integrated to strengthen this research and create a foundation for understanding informal decisions. As informal decisions in early hiring processes have been under researched, this thesis contributes to current knowledge in several ways. First, it locates the Cycle of Employment which enhances Robertson and Smith’s (1993) Selection Paradigm through the integration of stages that individuals occupy whilst seeking employment. Secondly, a general depiction of the Workflow of General Hiring Processes provides a template for practitioners to map and further develop their organisational processes. Finally, it highlights the emergence of the Locality Effect, which is a geographically driven heuristic and bias that can significantly impact recruitment and informal decisions. Although screeners make informal decisions using multiple variables, informal decisions are made in stages as evidence in the Cycle of Employment. Moreover, informal decisions can be erroneous as a result of a majority and minority influence, the weighting of information, the injection of inappropriate information and criteria, and the influence of an assessor. This thesis considers these faults and develops a basic framework of understanding informal decisions to which future research can be launched.

A Bayesian approach to modeling the conditional density of the inverse controller

The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems. For illustration purposes, the proposed methodology is applied to linear Gaussian systems. © 2004 IEEE.