76 resultados para Premerger Review System


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Introduction Asthma is now one of the most common long-term conditions in the UK. It is therefore important to develop a comprehensive appreciation of the healthcare and societal costs in order to inform decisions on care provision and planning. We plan to build on our earlier estimates of national prevalence and costs from asthma by filling the data gaps previously identified in relation to healthcare and broadening the field of enquiry to include societal costs. This work will provide the first UK-wide estimates of the costs of asthma. In the context of asthma for the UK and its member countries (ie, England, Northern Ireland, Scotland and Wales), we seek to: (1) produce a detailed overview of estimates of incidence, prevalence and healthcare utilisation; (2) estimate health and societal costs; (3) identify any remaining information gaps and explore the feasibility of filling these and (4) provide insights into future research that has the potential to inform changes in policy leading to the provision of more cost-effective care.

Methods and analysis Secondary analyses of data from national health surveys, primary care, prescribing, emergency care, hospital, mortality and administrative data sources will be undertaken to estimate prevalence, healthcare utilisation and outcomes from asthma. Data linkages and economic modelling will be undertaken in an attempt to populate data gaps and estimate costs. Separate prevalence and cost estimates will be calculated for each of the UK-member countries and these will then be aggregated to generate UK-wide estimates.

Ethics and dissemination Approvals have been obtained from the NHS Scotland Information Services Division's Privacy Advisory Committee, the Secure Anonymised Information Linkage Collaboration Review System, the NHS South-East Scotland Research Ethics Service and The University of Edinburgh's Centre for Population Health Sciences Research Ethics Committee. We will produce a report for Asthma-UK, submit papers to peer-reviewed journals and construct an interactive map.

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The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

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The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

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Integrated energy system and Energy Internet are two hot topics in the field of energy. In this paper, these two concepts and their focuses are summarized and analyzed. Typical research in the areas of integrated energy system and Energy Internet is reviewed. The research concerns and technologies of these two concepts are then outlined respectively. The differences and similarities of these two concepts and their future development are discussed.

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Utilization of renewable energy sources and energy storage systems is increasing with fostering new policies on energy industries. However, the increase of distributed generation hinders the reliability of power systems. In order to stabilize them, a virtual power plant emerges as a novel power grid management system. The VPP has a role to make a participation of different distributed energy resources and energy storage systems. This paper defines core technology of the VPP which are demand response and ancillary service concerning about Korea, America and Europe cases. It also suggests application solutions of the VPP to V2G market for restructuring national power industries in Korea.

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Exam timetabling is one of the most important administrative activities that takes place in academic institutions. In this paper we present a critical discussion of the research on exam timetabling in the last decade or so. This last ten years has seen an increased level of attention on this important topic. There has been a range of significant contributions to the scientific literature both in terms of theoretical andpractical aspects. The main aim of this survey is to highlight the new trends and key research achievements that have been carried out in the last decade.We also aim to outline a range of relevant important research issues and challenges that have been generated by this body of work.

We first define the problem and review previous survey papers. Algorithmic approaches are then classified and discussed. These include early techniques (e.g. graph heuristics) and state-of-the-art approaches including meta-heuristics, constraint based methods, multi-criteria techniques, hybridisations, and recent new trends concerning neighbourhood structures, which are motivated by raising the generality of the approaches. Summarising tables are presented to provide an overall view of these techniques. We discuss some issues on decomposition techniques, system tools and languages, models and complexity. We also present and discuss some important issues which have come to light concerning the public benchmark exam timetabling data. Different versions of problem datasetswith the same name have been circulating in the scientific community in the last ten years which has generated a significant amount of confusion. We clarify the situation and present a re-naming of the widely studied datasets to avoid future confusion. We also highlight which research papershave dealt with which dataset. Finally, we draw upon our discussion of the literature to present a (non-exhaustive) range of potential future research directions and open issues in exam timetabling research.