A hybrid layout algorithm for sub-quadratic multidimensional scaling

Many clustering and layout techniques have been used for structuring and visualising complex data. This paper is inspired by a number of such contemporary techniques and presents a novel hybrid approach based upon stochastic sampling, interpolation and spring models. We use Chalmers' 1996 O(N/sup 2/) spring model as a benchmark when evaluating our technique, comparing layout quality and run times using data sets of synthetic and real data. Our algorithm runs in O(N/spl radic/N) and executes significantly faster than Chalmers' 1996 algorithm, whilst producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data

Visualisation techniques for users and designers of layout algorithms

Visualisation systems consisting of a set of components through which data and interaction commands flow have been explored by a number of researchers. Such hybrid and multistage algorithms can be used to reduce overall computation time, and to provide views of the data that show intermediate results and the outputs of complementary algorithms. In this paper we present work on expanding the range and variety of such components, with two new techniques for analysing and controlling the performance of visualisation processes. While the techniques presented are quite different, they are unified within HIVE: a visualisation system based upon a data-flow model and visual programming. Embodied within this system is a framework for weaving together our visualisation components to better afford insight into data and also deepen understanding of the process of the data's visualisation. We describe the new components and offer short case studies of their application. We demonstrate that both analysts and visualisation designers can benefit from a rich set of components and integrated tools for profiling performance

Coordinating views for data visualisation and algorithmic profiling

A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper, we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system

Understanding and responding when things go wrong: key principles for primary care educators

Learning from events with unwanted outcomes is an important part of workplace based education and providing evidence for medical appraisal and revalidation. It has been suggested that adopting a ‘systems approach’ could enhance learning and effective change. We believe the following key principles should be understood by all healthcare staff, especially those with a role in developing and delivering educational content for safety and improvement in primary care. When things go wrong, professional accountability involves accepting there has been a problem, apologising if necessary and committing to learn and change. This is easier in a ‘Just Culture’ where wilful disregard of safe practice is not tolerated but where decisions commensurate with training and experience do not result in blame and punishment. People usually attempt to achieve successful outcomes, but when things go wrong the contribution of hindsight and attribution bias as well as a lack of understanding of conditions and available information (local rationality) can lead to inappropriately blame ‘human error’. System complexity makes reduction into component parts difficult; thus attempting to ‘find-and-fix’ malfunctioning components may not always be a valid approach. Finally, performance variability by staff is often needed to meet demands or cope with resource constraints. We believe understanding these core principles is a necessary precursor to adopting a ‘systems approach’ that can increase learning and reduce the damaging effects on morale when ‘human error’ is blamed. This may result in ‘human error’ becoming the starting point of an investigation and not the endpoint

Understanding patient safety performance and educational needs using the ‘Safety-II’ approach for complex systems

Participation in projects to improve patient safety is a key component of general practice (GP) specialty training, appraisal and revalidation. Patient safety training priorities for GPs at all career stages are described in the Royal College of General Practitioners’ curriculum. Current methods that are taught and employed to improve safety often use a ‘find-and-fix’ approach to identify components of a system (including humans) where performance could be improved. However, the complex interactions and inter-dependence between components in healthcare systems mean that cause and effect are not always linked in a predictable manner. The Safety-II approach has been proposed as a new way to understand how safety is achieved in complex systems that may improve quality and safety initiatives and enhance GP and trainee curriculum coverage. Safety-II aims to maximise the number of events with a successful outcome by exploring everyday work. Work-as-done often differs from work-as-imagined in protocols and guidelines and various ways to achieve success, dependent on work conditions, may be possible. Traditional approaches to improve the quality and safety of care often aim to constrain variability but understanding and managing variability may be a more beneficial approach. The application of a Safety-II approach to incident investigation, quality improvement projects, prospective analysis of risk in systems and performance indicators may offer improved insight into system performance leading to more effective change. The way forward may be to combine the Safety-II approach with ‘traditional’ methods to enhance patient safety training, outcomes and curriculum coverage

Appendix for "Perceptual Variation and Relativism"

In "Perceptual Variation and Relativism" I criticized two proposals for combining perceptual relativism with perceptual atomism. In this appendix I consider and reject several other proposals. I conclude that perceptual relativism should not be combined with perceptual atomism

The dependence of the hydrogen sorption capacity of single-walled carbon nanotubes on the concentration of catalyst

The adsorption of hydrogen on single-walled carbon nanotubes was measured using micro-gravimetric nitrogen and hydrogen adsorption isotherms at 77 K for gas pressures of up to 1 bar (nitrogen) and 12 bar (hydrogen). Results show that surface area and hydrogen uptake depend on the concentration of the iron catalyst used for making the nanotubes. Langmuir fits to the hydrogen uptake curves clearly show two adsorption energies for each sample which we attribute to the groove site for the higher adsorption energy and to the convex tube surface for the lower energy. We also present calculations of the binding energy of hydrogen on these same sites on SWCNTs and confirm that the groove site has a significantly higher (radius-dependent) binding energy than the surface site, consistent with the experimental values. This suggests that the use of the Langmuir model is appropriate to the adsorption of H2 on activated carbons for the temperature and pressure range investigated and could be used as a rapid way of estimating the average tube radius in the sample

Visual Noise Due to Quantum Indeterminacies

We establish that, due to certain quantum indeterminacies, there must be foundational colours that do not reliably cause any particular experience. This report functions as an appendix to Morrison's "Colour in a Physical World.

A systematic review and meta-analysis of the effectiveness of pharmacist-led medication reconciliation in the community after hospital discharge

BACKGROUND Pharmacists’ completion of medication reconciliation in the community after hospital discharge is intended to reduce harm due to prescribed or omitted medication and increase healthcare efficiency, but the effectiveness of this approach is not clear. We systematically review the literature to evaluate intervention effectiveness in terms of discrepancy identification and resolution, clinical relevance of resolved discrepancies and healthcare utilisation, including readmission rates, emergency department attendance and primary care workload. DESIGN Systematic literature review and meta-analysis of extracted data. METHODS Medline, CINHAL, EMBASE, AMED, ERIC, SCOPUS, NHS evidence and the Cochrane databases were searched using a combination of Medical Subject Heading (MeSH) terms and free text search terms. Controlled studies evaluating pharmacist-led medication reconciliation in the community after hospital discharge were included. Study quality was appraised using CASP. Evidence was assessed through meta-analysis of readmission rates. Discrepancy identification rates, emergency department attendance and primary care workload were assessed narratively. RESULTS Fourteen studies were included comprising five RCTs, six cohort studies and three pre-post intervention studies. Twelve studies had a moderate or high risk of bias. Increased identification and resolution of discrepancies was demonstrated in the four studies where this was evaluated. Reduction in clinically relevant discrepancies was reported in two studies. Meta-analysis did not demonstrate a significant reduction in readmission rate. There was no consistent evidence of reduction in emergency department attendance or primary care workload. CONCLUSIONS Pharmacists can identify and resolve discrepancies when completing medication reconciliation after hospital discharge but patient outcome or care workload improvements were not consistently seen. Future research should examine the clinical relevance of discrepancies and potential benefits on reducing healthcare team workload

Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional method