Fracture toughness of SIC, Si3N4, and sialon ceramics using controlled surface flaws

Knoop and Vickers indentation cracks have frequently been used as model 'precracks' in ceramic bend specimens for fracture toughness (K1c) determination. Indentation residual stress reduces the measured K1c but can be removed or accounted for by grinding, annealing, or modelling. Values of K1c are obtained for four materials using Vickers indentations and an improved stress intensity factor. Methods for residual stress removal or incorporation are compared, and the most reliable stress removal alternative is identified for each material. © 1996 The Institute of Materials.

Regularisation of mixture density networks

Mixture Density Networks are a principled method to model conditional probability density functions which are non-Gaussian. This is achieved by modelling the conditional distribution for each pattern with a Gaussian Mixture Model for which the parameters are generated by a neural network. This thesis presents a novel method to introduce regularisation in this context for the special case where the mean and variance of the spherical Gaussian Kernels in the mixtures are fixed to predetermined values. Guidelines for how these parameters can be initialised are given, and it is shown how to apply the evidence framework to mixture density networks to achieve regularisation. This also provides an objective stopping criteria that can replace the `early stopping' methods that have previously been used. If the neural network used is an RBF network with fixed centres this opens up new opportunities for improved initialisation of the network weights, which are exploited to start training relatively close to the optimum. The new method is demonstrated on two data sets. The first is a simple synthetic data set while the second is a real life data set, namely satellite scatterometer data used to infer the wind speed and wind direction near the ocean surface. For both data sets the regularisation method performs well in comparison with earlier published results. Ideas on how the constraint on the kernels may be relaxed to allow fully adaptable kernels are presented.

Managing risk in a large rural electrification programme in India

Rural electrification projects and programmes in many countries have suffered from design, planning, implementation and operational flaws as a result of ineffective project planning and lack of systematic project risk analysis. This paper presents a hierarchical risk-management framework for effectively managing large-scale development projects. The proposed framework first identifies, with the involvement of stakeholders, the risk factors for a rural electrification programme at three different levels (national, state and site). Subsequently it develops a qualitative risk prioritising scheme through probability and severity mapping and provides mitigating measures for most vulnerable risks. The study concludes that the hierarchical risk-management approach provides an effective framework for managing large-scale rural electrification programmes. © IAIA 2007.

Managing healthcare quality using logical framework analysis

Purpose - The purpose of this paper is to develop an integrated quality management model that identifies problems, suggests solutions, develops a framework for implementation and helps to evaluate dynamically healthcare service performance. Design/methodology/approach - This study used the logical framework analysis (LFA) to improve the performance of healthcare service processes. LFA has three major steps - problems identification, solution derivation, and formation of a planning matrix for implementation. LFA has been applied in a case-study environment to three acute healthcare services (Operating Room utilisation, Accident and Emergency, and Intensive Care) in order to demonstrate its effectiveness. Findings - The paper finds that LFA is an effective method of quality management of hospital-based healthcare services. Research limitations/implications - This study shows LFA application in three service processes in one hospital. This very limited population sample needs to be extended. Practical implications - The proposed model can be implemented in hospital-based healthcare services in order to improve performance. It may also be applied to other services. Originality/value - Quality improvement in healthcare services is a complex and multi-dimensional task. Although various quality management tools are routinely deployed for identifying quality issues in healthcare delivery, they are not without flaws. There is an absence of an integrated approach, which can identify and analyse issues, provide solutions to resolve those issues, develop a project management framework to implement those solutions. This study introduces an integrated and uniform quality management tool for healthcare services. © Emerald Group Publishing Limited.

Time series analysis in the assessment of ICT impact at the aggregate level – lessons and implications for the new economy

The major role of information and communication technology (ICT) in the new economy is well documented: countries worldwide are pouring resources into their ICT infrastructure despite the widely acknowledged “productivity paradox”. Evaluating the contribution of ICT investments has become an elusive but important goal of IS researchers and economists. But this area of research is fraught with complexity and we have used Solow's Residual together with time-series analysis tools to overcome some methodological inadequacies of previous studies. Using this approach, we conduct a study of 20 countries to determine if there was empirical evidence to support claims that ICT investments are worthwhile. The results show that ICT contributes to economic growth in many developed countries and newly industrialized economies (NIEs), but not in developing countries. We finally suggest ICT-complementary factors, in an attempt to rectify possible flaws in ICT policies as a contribution towards improvement in global productivity.

Regularisation of mixture density networks

Mixture Density Networks are a principled method to model conditional probability density functions which are non-Gaussian. This is achieved by modelling the conditional distribution for each pattern with a Gaussian Mixture Model for which the parameters are generated by a neural network. This thesis presents a novel method to introduce regularisation in this context for the special case where the mean and variance of the spherical Gaussian Kernels in the mixtures are fixed to predetermined values. Guidelines for how these parameters can be initialised are given, and it is shown how to apply the evidence framework to mixture density networks to achieve regularisation. This also provides an objective stopping criteria that can replace the `early stopping' methods that have previously been used. If the neural network used is an RBF network with fixed centres this opens up new opportunities for improved initialisation of the network weights, which are exploited to start training relatively close to the optimum. The new method is demonstrated on two data sets. The first is a simple synthetic data set while the second is a real life data set, namely satellite scatterometer data used to infer the wind speed and wind direction near the ocean surface. For both data sets the regularisation method performs well in comparison with earlier published results. Ideas on how the constraint on the kernels may be relaxed to allow fully adaptable kernels are presented.

The problem of appraising qualitative research

Qualitative research can make a valuable contribution to the study of quality and safety in health care. Sound ways of appraising qualitative research are needed, but currently there are many different proposals with few signs of an emerging consensus. One problem has been the tendency to treat qualitative research as a unified field. We distinguish universal features of quality from those specific to methodology and offer a set of minimally prescriptive prompts to assist with the assessment of generic features of qualitative research. In using these, account will need to be taken of the particular method of data collection and methodological approach being used. There may be a need for appraisal criteria suited to the different methods of qualitative data collection and to different methodological approaches. These more specific criteria would help to distinguish fatal flaws from more minor errors in the design, conduct, and reporting of qualitative research. There will be difficulties in doing this because some aspects of qualitative research, particularly those relating to quality of insight and interpretation, will remain difficult to appraise and will rely largely on subjective judgement.

Graphene layers produced from carbon nanotubes by frication

Graphene layers have been produced from multi-walled carbon nanotube (MWCNT) bulk materials by friction when polished on ground-glass, offering a novel and effective method to produce graphene layers, which, more importantly, could be transferred to other substrates by rubbing. Field emission scanning electron microscopy, Raman spectroscopy, atomic force microscopy, transmission electron microscopy and selected area electron diffraction confirmed the formation of graphene layers. They were thought to be peeled away from the MWCNT walls due to friction. The reflection spectra showed that absorption of as-produced graphene layers decreased with wavelength in the range of 250–400 nm, compared to the MWCNT bulk material having strong absorption at 350 nm. Nanoscratch test was used to determine the mechanical properties of graphene films, suggesting the tolerance of as-produced graphene film to flaws introduced by scratch.

Quantifying uncertainty in citizen weather data

The sheer volume of citizen weather data collected and uploaded to online data hubs is immense. However as with any citizen data it is difficult to assess the accuracy of the measurements. Within this project we quantify just how much data is available, where it comes from, the frequency at which it is collected, and the types of automatic weather stations being used. We also list the numerous possible sources of error and uncertainty within citizen weather observations before showing evidence of such effects in real data. A thorough intercomparison field study was conducted, testing popular models of citizen weather stations. From this study we were able to parameterise key sources of bias. Most significantly the project develops a complete quality control system through which citizen air temperature observations can be passed. The structure of this system was heavily informed by the results of the field study. Using a Bayesian framework the system learns and updates its estimates of the calibration and radiation-induced biases inherent to each station. We then show the benefit of correcting for these learnt biases over using the original uncorrected data. The system also attaches an uncertainty estimate to each observation, which would provide real world applications that choose to incorporate such observations with a measure on which they may base their confidence in the data. The system relies on interpolated temperature and radiation observations from neighbouring professional weather stations for which a Bayesian regression model is used. We recognise some of the assumptions and flaws of the developed system and suggest further work that needs to be done to bring it to an operational setting. Such a system will hopefully allow applications to leverage the additional value citizen weather data brings to longstanding professional observing networks.

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.