Rapid annulation of tropolone units via a pyrylium ylide 1,3-dipolar cycloaddition reaction

The tropolone subunit of the naturally occurring alkaloid rubrolone aglycon is synthesized via a short reaction sequence starting with a 1,3-dipolar cycloaddition of a pyrylium ylide and indenone, followed by enone oxidation, oxygen bridge elimination and finally hydroxy group oxidation.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.

A national survey of the mangement of delirium in UK intensive care units

Background: Delirium is an acute organ dysfunction common amongst patients treated in intensive care units. The associated morbidity and mortality are known to be substantial. Previous surveys have described which screening tools are used to diagnose delirium and which medications are used to treat delirium, but these data are not available for the United Kingdom. Aim: This survey aimed to describe the UK management of delirium by consultant intensivists. Additionally, knowledge and attitudes towards management of delirium were sought. The results will inform future research in this area. Methods: A national postal survey of members of the UK Intensive Care Society was performed. A concise two page questionnaire survey was sent, with a second round of surveys sent to non-respondents after 6 weeks. The questionnaire was in tick-box format. Results: Six hundred and eighty-one replies were received from 1308 questionnaires sent, giving a response rate of 52%. Twenty-five percent of respondents routinely screen for delirium, but of these only 55% use a screening tool validated for use in intensive care. The majority (80%) of those using a validated instrument used the Confusion Assessment Method for the Intensive Care Unit. Hyperactive delirium is treated pharmacologically by 95%; hypoactive delirium is treated pharmacologically by 25%, with haloperidol the most common agent used in both. Over 80% of respondents agreed that delirium prolongs mechanical ventilation and hospital stay and requires active treatment. Conclusions: This UK survey demonstrates screening for delirium is sporadic. Pharmacological treatment is usually with haloperidol in spite of the limited evidence to support this practice. Hypoactive delirium is infrequently treated pharmacologically.

Unlocking synergies between business units: internal value creation at Royal Vopak.

This paper examines the process of creating and exploiting synergies between business units of a multi-unit corporation and the creation of internal value by combining and exploiting knowledge. It offers a framework to create and manage such synergies and undertakes an empirical test through in-depth study across three business units of Royal Vopak, a Dutch-based global multi-unit corporation. Finally, it offers lessons for corporate managers trying to create and manage cross-unit synergies.

Social capital at work as a predictor of employee health: Multilevel evidence from work units in Finland

The majority of previous research on social capital and health is limited to social capital in residential neighborhoods and communities. Using data from the Finnish 10-Town study we examined social capital at work as a predictor of health in a cohort of 9524 initially healthy local government employees in 1522 work units, who did not change their work unit between 2000 and 2004 and responded to surveys measuring social capital at work and health at both time-points. We used a validated tool to measure social capital with perceptions at the individual level and with co-workers' responses at the work unit level. According to multilevel modeling, a contextual effect of work unit social capital on self-rated health was not accounted for by the individual's socio-demographic characteristics or lifestyle. The odds for health impairment were 1.27 times higher for employees who constantly worked in units with low social capital than for those with constantly high work unit social capital. Corresponding odds ratios for low and declining individual-level social capital varied between 1.56 and 1.78. Increasing levels of individual social capital were associated with sustained good health. In conclusion, this longitudinal multilevel study provides support for the hypothesis that exposure to low social capital at work may be detrimental to the health of employees. (c) 2007 Elsevier Ltd. All rights reserved.

An optimisation of Gaussian mixture models for integer processing units

This paper investigates sub-integer implementations of the adaptive Gaussian mixture model (GMM) for background/foreground segmentation to allow the deployment of the method on low cost/low power processors that lack Floating Point Unit (FPU). We propose two novel integer computer arithmetic techniques to update Gaussian parameters. Specifically, the mean value and the variance of each Gaussian are updated by a redefined and generalised "round'' operation that emulates the original updating rules for a large set of learning rates. Weights are represented by counters that are updated following stochastic rules to allow a wider range of learning rates and the weight trend is approximated by a line or a staircase. We demonstrate that the memory footprint and computational cost of GMM are significantly reduced, without significantly affecting the performance of background/foreground segmentation.