The impact of drug information on the prescribing of drugs

Using prescription analyses and questionnaires, the way drug information was used by general medical practitioners during the drug adoption process was studied. Three new drugs were considered; an innovation and two 'me-too' products. The innovation was accepted by general practitioners via a contagion process, information passing among doctors. The 'me-too' preparations were accepted more slowly and by a process which did not include the contagion effect. 'Industrial' information such as direct mail was used more at the 'awareness' stage of the adoption process while 'professional' sources of information such as articles in medical journals were used more to evaluate a new product. It was shown that 'industrial' information was preferred by older single practice doctors who did not specialise, had a first degree only and who did not dispense their own prescriptions. Doctors were divided into early and late-prescribers by using the date they first prescribed the innovatory drug. Their approach to drug information sources was further studied and it was shown that the early-prescriber issued slightly more prescriptions per month, had a larger list size, read fewer journals and generally rated industrial sources of information more highly than late-prescribers. The prescribing habits of three consultant rheumatologists were analysed and compared with those of the general practitioners in the community which they served. Very little association was noted and the influence of the consultant on the prescribing habits of general practitioners was concluded to be low. The consultants influence was suggested to be of two components, active and passive; the active component being the most influential. Journal advertising and advertisement placement were studied for one of the 'me-too' drugs. It was concluded that advertisement placement should be based on the reading patterns of general practitioners and not on ad-hoc data gathered by representatives as was the present practice. A model was proposed relating the 'time to prescribe' a new drug to the variables suggested throughout this work. Four of these variables were shown to be significant. These were, the list size, the medical age of the prescriber, the number of new preparations prescribed in a given time and the number of partners in the practice.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.

Message-passing algorithms for optimal utilization of cognitive radio networks

Cognitive Radio has been proposed as a key technology to significantly improve spectrum usage in wireless networks by enabling unlicensed users to access unused resource. We present new algorithms that are needed for the implementation of opportunistic scheduling policies that maximize the throughput utilization of resources by secondary users, under maximum interference constraints imposed by existing primary users. Our approach is based on the Belief Propagation (BP) algorithm, which is advantageous due to its simplicity and potential for distributed implementation. We examine convergence properties and evaluate the performance of the proposed BP algorithms via simulations and demonstrate that the results compare favorably with a benchmark greedy strategy. © 2013 IEEE.

The need for wider supply chain management adoption:empirical results from Ireland

Purpose - This paper aims to provide empirical results which suggest that there is a need for more widespread adoption of supply chain management among Irish firms. Design/methodology/approach - The Republic of Ireland is a small, open, trade-dependent economy and is one of the fastest growing economies in the developed world. However, due to rising costs, there is an increasing trend in Ireland to outsource lower function manufacturing processes to lower-cost locations but to retain high-skill functions (such as R&D). This trend, together with other factors such as its peripheral location, suggests that supply chain management is critical from an Irish perspective. In order to gain unique insights of current levels of awareness/adoption of SCM and the potential impact SCM could have on competitiveness, a survey was conducted among 776 Irish firms. Findings - Overall, the findings suggest that many firms in Ireland pay lip-service to the importance of SCM elements and objectives but the majority of firms, about two thirds, have only a passing understanding of what constitutes SCM. Only 25 per cent adopt SCM programmes and only 9 per cent of Irish companies have a specialised SCM or logistics manager. The gaps in their understanding of SCM are matched by the gaps in their awareness of key costs (e.g. 59 per cent of companies do not know their total supply chain costs). While there are supply chain management adopters in Ireland that are already well up the s-curve of innovation transfer, it is the larger group of less aware companies that must become better at how they manage their supply chains. Originality/value - The paper offers a useful insight into supply chain management and its role in Irish industry. © Emerald Group Publishing Limited.

Message-passing for inference and optimization of real variables on sparse graphs

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.

Profit, productivity and price performance changes in the water and sewerage industry:an empirical application for England and Wales

This paper aims to analyse the impact of regulation in the financial performance of the Water and Sewerage companies (WaSCs) in England and Wales over the period 1991–2008. In doing so, a panel index approach is applied across WaSCs over time to decompose unit-specific index number-based profitability growth as a function of the profitability, productivity and price performance growth achieved by benchmark firms, and the catch up to the benchmark firm achieved by less productive firms. The results indicated that after 2000 there is a steady decline in average price performance, while productivity improves resulting in a relatively stable economic profitability. It is suggested that the English and Welsh water regulator is now more focused on passing productivity benefits to consumers, and maintaining stable profitability than it was in earlier regulatory periods. This technique is of great interest for regulators to evaluate the effectiveness of regulation and companies to identify the determinants of profit change and improve future performance, even if sample sizes are limited.

Message passing for distributed optimisation of power allocation with renewable resources

The increase in renewable energy generators introduced into the electricity grid is putting pressure on its stability and management as predictions of renewable energy sources cannot be accurate or fully controlled. This, with the additional pressure of fluctuations in demand, presents a problem more complex than the current methods of controlling electricity distribution were designed for. A global approximate and distributed optimisation method for power allocation that accommodates uncertainties and volatility is suggested and analysed. It is based on a probabilistic method known as message passing [1], which has deep links to statistical physics methodology. This principled method of optimisation is based on local calculations and inherently accommodates uncertainties; it is of modest computational complexity and provides good approximate solutions.We consider uncertainty and fluctuations drawn from a Gaussian distribution and incorporate them into the message-passing algorithm. We see the effect that increasing uncertainty has on the transmission cost and how the placement of volatile nodes within a grid, such as renewable generators or consumers, effects it.

Nonlinear refraction in optical lattices induced by the wave-soliton interference

In the framework of 1D Nonlinear Shrödinger Equation (NSE) we demonstrate how one can control the refractive angle of a fundamental soliton beam passing through an optical lattice, by adjusting either the shape of an individual waveguide or the relative positions of waveguides. Even for a single scatterer its shape has a nontrivial effect on the refraction direction. In the case of shallow modulation we provide an analytical description based of the effect on the soliton perturbation theory. When one considers a lattice of scatterers, there emanates an additional form factor in the radiation density (RD) of emitted waves referring to the wave-soliton beating and interference inside the lattice. We concentrate on the results for two cases: periodic lattice and disordered lattice of scattering shapes. © 2011 IEEE.

On the dual-cone nature of the conical refraction phenomenon

In conical refraction (CR), a focused Gaussian input beam passing through a biaxial crystal and parallel to one of the optic axes is transformed into a pair of concentric bright rings split by a dark (Poggendorff) ring at the focal plane. Here, we show the generation of a CR transverse pattern that does not present the Poggendorff fine splitting at the focal plane, i.e., it forms a single light ring. This light ring is generated from a nonhomogeneously polarized input light beam obtained by using a spatially inhomogeneous polarizer that mimics the characteristic CR polarization distribution. This polarizer allows modulating the relative intensity between the two CR light cones in accordance with the recently proposed dual-cone model of the CR phenomenon. We show that the absence of interfering rings at the focal plane is caused by the selection of one of the two CR cones. (C) 2015 Optical Society of America