State estimators in soft sensing and sensor fusion for sustainable manufacturing

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables which cannot be measured directly (’soft sensing’) or for which only noisy, intermittent, delayed, indirect or unreliable measurements are available, perhaps from multiple sources (’sensor fusion’). In this paper we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes. It is shown that state estimation algorithms can play a key role in manufacturing systems to accurately monitor and control processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and control of distributed manufacturing systems

Well being of obstetric patients on minimal blood transfusions (WOMB trial)

Background: Primary postpartum haemorrhage is an obstetrical emergency often causing acute anaemia that may require immediate red blood cell (RBC) transfusion. This anaemia results in symptoms such as fatigue, whic

Disproportionate Intrauterine Growth Intervention Trial At Term: DIGITAT

Contains fulltext : 65628.pdf ( ) (Open Access)BACKGROUND: Around 80% of intrauterine growth restricted (IUGR) infants are born at term. They have an increase in perinatal mortality and morbidity including behavioral problems, minor developmental delay and spastic cerebral palsy. Management is controversial, in particular the decision whether to induce labour or await spontaneous delivery with strict fetal and maternal surveillance. We propose a randomised trial to compare effectiveness, costs and maternal quality of life for induction of labour versus expectant management in women with a suspected IUGR fetus at term. METHODS/DESIGN: The proposed trial is a multi-centre randomised study in pregnant women who are suspected on clinical grounds of having an IUGR child at a gestational age between 36+0 and 41+0 weeks. After informed consent women will be randomly allocated to either induction of labour or expectant management with maternal and fetal monitoring. Randomisation will be web-based. The primary outcome measure will be a composite neonatal morbidity and mortality. Secondary outcomes will be severe maternal morbidity, maternal quality of life and costs. Moreover, we aim to assess neurodevelopmental and neurobehavioral outcome at two years as assessed by a postal enquiry (Child Behavioral Check List-CBCL and Ages and Stages Questionnaire-ASQ). Analysis will be by intention to treat. Quality of life analysis and a preference study will also be performed in the same study population. Health technology assessment with an economic analysis is part of this so called Digitat trial (Disproportionate Intrauterine Growth Intervention Trial At Term). The study aims to include 325 patients per arm. DISCUSSION: This trial will provide evidence for which strategy is superior in terms of neonatal and maternal morbidity and mortality, costs and maternal quality of life aspects. This will be the first randomised trial for IUGR at term. TRIAL REGISTRATION: Dutch Trial Register and ISRCTN-Register: ISRCTN10363217

Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose

Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes.</p

Introductory overview of identifiability analysis : A guide to evaluating whether you have the right type of data for your modeling purpose

<p>Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes.</p

Operation compared with watchful waiting in elderly male inguinal hernia patients: a review and data analysis.

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