Breast cancer teams:the impact of constitution, new cancer workload and methods of operation on their effectiveness

National guidance and clinical guidelines recommended multidisciplinary teams (MDTs) for cancer services in order to bring specialists in relevant disciplines together, ensure clinical decisions are fully informed, and to coordinate care effectively. However, the effectiveness of cancer teams was not previously evaluated systematically. A random sample of 72 breast cancer teams in England was studied (548 members in six core disciplines), stratified by region and caseload. Information about team constitution, processes, effectiveness, clinical performance, and members' mental well-being was gathered using appropriate instruments. Two input variables, team workload (P=0.009) and the proportion of breast care nurses (P=0.003), positively predicted overall clinical performance in multivariate analysis using a two-stage regression model. There were significant correlations between individual team inputs, team composition variables, and clinical performance. Some disciplines consistently perceived their team's effectiveness differently from the mean. Teams with shared leadership of their clinical decision-making were most effective. The mental well-being of team members appeared significantly better than in previous studies of cancer clinicians, the NHS, and the general population. This study established that team composition, working methods, and workloads are related to measures of effectiveness, including the quality of clinical care. © 2003 Cancer Research UK.

Bayesian methods for neural networks

Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.

Bayesian methods for neural networks

Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a potential solution to the problem of over-fitting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.

New numerical model of stimulated Brillouin scattering in optical fibres with nonuniformity

A new numerical model which incorporates Brillouin shift frequency variations arising from fibre inhomogeneities has been developed for stimulated Brillouin scattering in optical fibres. This enables simulations of backscattered and transmitted power as functions of input power based only on known physical and material parameters as well as the polarisation factor and the measured Brillouin gain linewidth for the fibre. Agreement between modelled and experimental power characteristics for a CW input is excellent.

Implications of bank ownership for the credit channel of monetary policy transmission:evidence from India

Using bank-level data from India, we examine the impact of ownership on the reaction of banks to monetary policy, and also test whether the reaction of different types of banks to monetary policy changes is different in easy and tight policy regimes. Our results suggest that there are considerable differences in the reactions of different types of banks to monetary policy initiatives of the central bank, and that the bank lending channel of monetary policy is likely to be much more effective in a tight money period than in an easy money period. We also find differences in impact of monetary policy changes on less risky short-term and more risky medium-term lending. We discuss the policy implications of the findings.

A new DEA model for technology selection in the presence of ordinal data

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach. © 2012 Springer-Verlag London Limited.

A new inventory model for aircraft spares

A special inventory problem is presented: aircraft spares that are repaired and returned to spares, called rotable inventory. Rotable inventory is not consumed so does not change in the medium term, but is rotated through operational, maintenance and stock phases. The objective for inventory performance is fleet Service Level (SL), which effects aircraft dispatch performance. A model is proposed where the fleet SL drives combined stock levels such that cost is optimized. By holding greater numbers of lower-cost items and holding lower levels of more expensive items, it is possible to achieve substantial cost savings while maintaining performance. This approach is shown to be an advance over the current literature and is tested with case data, with conclusive results.

UK industrial firms' dept decision under monetary policy transmission

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Dynamic supply chain alignment:a new business model for peak performance in enterprise supply chains across all geographies

Dynamic supply chain alignment: a new business model for peak performance in enterprise supply chains across all geographies John Gattorna and friends, Farnham, Gower Publishing, 2009, 440pp., £60, ISBN 978-0-566-08822-3.

Monetary policy preferences of the EMU and the UK

We estimate the central bank policy preferences for the European Monetary Union and for the UK. In doing so, we extend the theoretical framework suggested by Cecchetti etal. (TheManchesterSchool, Vol. 70 (2002), pp. 596-618), by assuming that policy preferences change across different regimes. Our empirical results suggest that the weight that policy makers put on inflation is typically profound. Furthermore, it appears that volatility shifts of the economic disturbances are the main factor, which generates variation in policy preferences.

A new VRPPD model and a hybrid heuristic solution approach for e-tailing

We analyze a business model for e-supermarkets to enable multi-product sourcing capacity through co-opetition (collaborative competition). The logistics aspect of our approach is to design and execute a network system where “premium” goods are acquired from vendors at multiple locations in the supply network and delivered to customers. Our specific goals are to: (i) investigate the role of premium product offerings in creating critical mass and profit; (ii) develop a model for the multiple-pickup single-delivery vehicle routing problem in the presence of multiple vendors; and (iii) propose a hybrid solution approach. To solve the problem introduced in this paper, we develop a hybrid metaheuristic approach that uses a Genetic Algorithm for vendor selection and allocation, and a modified savings algorithm for the capacitated VRP with multiple pickup, single delivery and time windows (CVRPMPDTW). The proposed Genetic Algorithm guides the search for optimal vendor pickup location decisions, and for each generated solution in the genetic population, a corresponding CVRPMPDTW is solved using the savings algorithm. We validate our solution approach against published VRPTW solutions and also test our algorithm with Solomon instances modified for CVRPMPDTW.

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.

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942.

Two Bayesian treatments of the n-tuple recognition method

Two probabilistic interpretations of the n-tuple recognition method are put forward in order to allow this technique to be analysed with the same Bayesian methods used in connection with other neural network models. Elementary demonstrations are then given of the use of maximum likelihood and maximum entropy methods for tuning the model parameters and assisting their interpretation. One of the models can be used to illustrate the significance of overlapping n-tuple samples with respect to correlations in the patterns.