54 resultados para Practical Advice to Entrepreneurs


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In this paper, a multiloop robust control strategy is proposed based on H∞ control and a partial least squares (PLS) model (H∞_PLS) for multivariable chemical processes. It is developed especially for multivariable systems in ill-conditioned plants and non-square systems. The advantage of PLS is to extract the strongest relationship between the input and the output variables in the reduced space of the latent variable model rather than in the original space of the highly dimensional variables. Without conventional decouplers, the dynamic PLS framework automatically decomposes the MIMO process into multiple single-loop systems in the PLS subspace so that the controller design can be simplified. Since plant/model mismatch is almost inevitable in practical applications, to enhance the robustness of this control system, the controllers based on the H∞ mixed sensitivity problem are designed in the PLS latent subspace. The feasibility and the effectiveness of the proposed approach are illustrated by the simulation results of a distillation column and a mixing tank process. Comparisons between H∞_PLS control and conventional individual control (either H∞ control or PLS control only) are also made

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Background: Selection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results. Methods: The identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated. Results: Using nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% – 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive. Conclusions: Our approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.

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Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in geographic information systems (GIS) stem from either vague/ambiguous or imprecise/inaccurate/incomplete information and it is necessary for GIS to develop tools and techniques to manage these uncertainties. There is a widespread agreement in the GIS community that although GIS has the potential to support a wide range of spatial data analysis problems, this potential is often hindered by the lack of consistency and uniformity. Uncertainties come in many shapes and forms, and processing uncertain spatial data requires a practical taxonomy to aid decision makers in choosing the most suitable data modeling and analysis method. In this paper, we: (1) review important developments in handling uncertainties when working with spatial data and GIS applications; (2) propose a taxonomy of models for dealing with uncertainties in GIS; and (3) identify current challenges and future research directions in spatial data analysis and GIS for managing uncertainties.

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The incidence of melanoma has increased rapidly over the past 30 years, and the disease is now the sixth most common cancer among men and women in the U.K. Many patients are diagnosed with or develop metastatic disease, and survival is substantially reduced in these patients. Mutations in the BRAF gene have been identified as key drivers of melanoma cells and are found in around 50% of cutaneous melanomas. Vemurafenib (Zelboraf(®) ; Roche Molecular Systems Inc., Pleasanton, CA, U.S.A.) is the first licensed inhibitor of mutated BRAF, and offers a new first-line option for patients with unresectable or metastatic melanoma who harbour BRAF mutations. Vemurafenib was developed in conjunction with a companion diagnostic, the cobas(®) 4800 BRAF V600 Mutation Test. The purpose of this paper is to make evidence-based recommendations to facilitate the implementation of BRAF mutation testing and targeted therapy in patients with metastatic melanoma in the U.K. The recommendations are the result of a meeting of an expert panel and have been reviewed by melanoma specialists and representatives of the National Cancer Research Network Clinical Study Group on behalf of the wider melanoma community. This article is intended to be a starting point for practical advice and recommendations, which will no doubt be updated as we gain further experience in personalizing therapy for patients with melanoma.

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Background: Previous research has highlighted an ambiguity in understanding cooking related terminology and a number of barriers and facilitators to home meal preparation. However, meals prepared in the home still include convenience products (typically high in sugars, fats and sodium) which can have negative effects on health. Therefore, this study aimed to qualitatively explore: (1) how individuals define cooking from ‘scratch’, and (2) their barriers and facilitators to cooking with basic ingredients.
Methods: 27 semi-structured interviews were conducted with participants (aged 18-58 years) living on the island of Ireland, eliciting definitions of ‘cooking from scratch’ and exploring the reasons participants cook in a particular way. The interviews were professionally transcribed verbatim and Nvivo 10 was used for an inductive thematic analysis.
Results: Our results highlighted that although cooking from ‘scratch’ lacks a single definition, participants viewed it as optimal cooking. Barriers to cooking with raw ingredients included: 1) time pressures; (2) desire to save money; (3) desire for effortless meals; (4) family food preferences; and (5) effect of kitchen disasters. Facilitators included: 1) desire to eat for health and well-being; (2) creative inspiration; (3) ability to plan and prepare meals ahead of time; and (4) greater self-efficacy in one’s cooking ability.
Conclusions: Our findings contribute to understanding how individuals define cooking from ‘scratch’, and barriers and facilitators to cooking with raw ingredients. Interventions should focus on practical sessions to increase cooking self-efficacy; highlight the importance of planning ahead and teach methods such as batch cooking and freezing to facilitate cooking from scratch.

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Amendments to secularization theory have brought the issue of public religions to the fore in recent years. In particular, the work of Casanova and Beyer has maintained the importance of functional differentiation whilst pointing to the flow of religious discourses across social boundaries. These issues, however, have received little ethnographic attention, such that many of the problems associated with theories of differentiation and globalization have not been engaged in a sustained manner. Research within black majority London Methodist congregations is drawn upon to suggest ways in which these theories can be reconsidered. Three related issues are focused upon: the continued importance of the nation-state (including national stratifications); the importance of a practical approach to religion, such that discourses are understood as ‘practical discourses’; and the importance of not privileging religion by reifying it in functional terms. These considerations have ramifications not only for secularization theory, but the general field of the sociological study of religion.

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Over the last decade, much new research has appeared on the subject of the Great Irish Famine but, remarkably, a major political event during the famine - the 1847 general election - has received virtually no mention. Recent work on politics in this period has tended to concentrate on political reaction in Britain rather than Ireland. The aim of this article is to examine the response of Irish politicians to the famine during the general election of 1847. The main source has been the political addresses and nomination speeches of most of the 140 candidates. The evidence from this material shows that, although the famine was an important matter in many constituencies, it was not the dominant issue countrywide. Various proposals to deal with the famine emerged, but there was an absence of agreed, practical measures to deal with immediate problems. The parties in Ireland failed to create a common platform to challenge the government over its efforts. Ideological constraints played an important part in these failures. The general election of 1847 represents a lost opportunity to tackle some of the effects of the famine.

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In many domains when we have several competing classifiers available we want to synthesize them or some of them to get a more accurate classifier by a combination function. In this paper we propose a ‘class-indifferent’ method for combining classifier decisions represented by evidential structures called triplet and quartet, using Dempster's rule of combination. This method is unique in that it distinguishes important elements from the trivial ones in representing classifier decisions, makes use of more information than others in calculating the support for class labels and provides a practical way to apply the theoretically appealing Dempster–Shafer theory of evidence to the problem of ensemble learning. We present a formalism for modelling classifier decisions as triplet mass functions and we establish a range of formulae for combining these mass functions in order to arrive at a consensus decision. In addition we carry out a comparative study with the alternatives of simplet and dichotomous structure and also compare two combination methods, Dempster's rule and majority voting, over the UCI benchmark data, to demonstrate the advantage our approach offers. (A continuation of the work in this area that was published in IEEE Trans on KDE, and conferences)

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We present a practical approach to Natural Language Generation (NLG) for spoken dialogue systems. The approach is based on small template fragments (mini-templates). The system’s object architecture facilitates generation of phrases across pre-defined business domains and registers, as well as into different languages. The architecture simplifies NLG in well-understood application contexts, while providing the flexibility for a developer and for the system, to vary linguistic output according to dialogue context, including any intended affective impact. Mini-templates are used with a suite of domain term objects, resulting in an NLG system (MINTGEN – MINi-Template GENerator) whose extensibility and ease of maintenance is enhanced by the sparsity of information devoted to individual domains. The system also avoids the need for specialist linguistic competence on the part of the system maintainer.

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Polypropylene (PP), a semi-crystalline material, is typically solid phase thermoformed at temperatures associated with crystalline melting, generally in the 150° to 160°Celsius range. In this very narrow thermoforming window the mechanical properties of the material rapidly decline with increasing temperature and these large changes in properties make Polypropylene one of the more difficult materials to process by thermoforming. Measurement of the deformation behaviour of a material under processing conditions is particularly important for accurate numerical modelling of thermoforming processes. This paper presents the findings of a study into the physical behaviour of industrial thermoforming grades of Polypropylene. Practical tests were performed using custom built materials testing machines and thermoforming equipment at Queen′s University Belfast. Numerical simulations of these processes were constructed to replicate thermoforming conditions using industry standard Finite Element Analysis software, namely ABAQUS and custom built user material model subroutines. Several variant constitutive models were used to represent the behaviour of the Polypropylene materials during processing. This included a range of phenomenological, rheological and blended constitutive models. The paper discusses approaches to modelling industrial plug-assisted thermoforming operations using Finite Element Analysis techniques and the range of material models constructed and investigated. It directly compares practical results to numerical predictions. The paper culminates discussing the learning points from using Finite Element Methods to simulate the plug-assisted thermoforming of Polypropylene, which presents complex contact, thermal, friction and material modelling challenges. The paper makes recommendations as to the relative importance of these inputs in general terms with regard to correlating to experimentally gathered data. The paper also presents recommendations as to the approaches to be taken to secure simulation predictions of improved accuracy.

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The business angel market is usually identified as a local market, and the proximity of an investment has been shown to be key in the angel's investment preferences and an important filter at the screening stage of the investment decision. This is generally explained by the personal and localized networks used to identify potential investments, the hands-on involvement of the investor and the desire to minimize risk. However, a significant minority of investments are long distance. This paper is based on data from 373 investments made by 109 UK business angels. We classify the location of investments into three groups: local investments ( those made within the same county or in adjacent counties); intermediate investments ( those made in counties adjacent to the 'local' counties); and long-distance investments ( those made beyond this range). Using ordered logit analysis the paper develops and tests a number of hypotheses that relate long-distance investment to investment characteristics and investor characteristics. The paper concludes by drawing out the implications for entrepreneurs seeking business angel finance in investment-deficient regions, business angel networks seeking to match investors to entrepreneurs and firms ( which are normally their primary clients), and for policy-makers responsible for local and regional economic development.

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This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.

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Diabetes mellitus is a chronic illness which affects a significant number of childbearing women. Despite the potential for adverse consequences for both maternal and fetal wellbeing, few women with diabetes plan their pregnancies to ensure that they enter pregnancy in optimal health. Furthermore, whilst adverse pregnancy outcomes are well documented for women with type I diabetes, it is now apparent that an increasing number of women with type II diabetes are becoming pregnant with similar adverse associated risk. There is an increasing recognition that significant adverse pregnancy outcomes are determined prior to a woman initiating pregnancy care, many of which could be minimised with the introduction of preconception care. As formalised preconception care clinics remain scant across the United Kingdom, there is an urgent need to increase the opportunities for the provision of preconception care and advice to women with diabetes. Midwives are ideally placed to provide preconception advice to women and could provide the missing link in terms of preconception advice for women with diabetes.