Analytical model to locate the fluidisation interface in a solid-gas vacuum fluidised bed

Vacuum fluidised beds have a distinct advantage of being operated with reduced mass consumption of the fluidising media. However, a low quality of fluidisation reduces the opportunity to utilise the bubbling regime in vacuum fluidised beds. Fluidisation maps are often used to depict the interface between the quiescent, bubbling and slugging regimes inside a fluidised bed. Such maps have been obtained by visual observations of the fluidisation interface in transparent fluidised beds. For beds which are visually inaccessible fluidisation maps are difficult to obtain. The present work therefore attempts to model the interface travel in a vacuum fluidised bed. The pressure gradient due to the bed weight has been determined to be a main contributor for fluidisation/defluidisation under vacuum. A simple analytical model based on the pressure gradient (PG model) is developed to predict the interface location in a vacuum fluidised bed. For a segregated bed, the Gibilaro-Rowe (GR) model is modified and used to predict the jetsam layer growth along with the fluidisation interface. The predictions are compared with the experimental data for minimally and highly segregated particles and it is seen that for non-segregated powders the predictions are quite accurate. Lack of sufficient knowledge of bubble characteristics, however, impeded accurate prediction of the jetsam growth especially at high flow rates. However, an approximate complete fluidisation interface is successfully predicted using the GR-PG model. © 2014 Elsevier B.V.

Towards designing an email classification system using multi-view based semi-supervised learning

The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive task and requires in-depth domain knowledge. Thus, only a very small proportion of the data can be labeled in practice. This bottleneck greatly degrades the effectiveness of supervised email classification systems. In order to address this problem, in this work, we first identify some critical issues regarding supervised machine learning-based email classification. Then we propose an effective classification model based on multi-view disagreement-based semi-supervised learning. The motivation behind the attempt of using multi-view and semi-supervised learning is that multi-view can provide richer information for classification, which is often ignored by literature, and semi-supervised learning supplies with the capability of coping with labeled and unlabeled data. In the evaluation, we demonstrate that the multi-view data can improve the email classification than using a single view data, and that the proposed model working with our algorithm can achieve better performance as compared to the existing similar algorithms.

A framework for cloud-based healthcare services to monitor noncommunicable diseases patient

Monitoring patients who have noncommunicable diseases is a big challenge. These illnesses require a continuous monitoring that leads to high cost for patients' healthcare. Several solutions proposed reducing the impact of these diseases in terms of economic with respect to quality of services. One of the best solutions is mobile healthcare, where patients do not need to be hospitalized under supervision of caregivers. This paper presents a new hybrid framework based on mobile multimedia cloud that is scalable and efficient and provides cost-effective monitoring solution for noncommunicable disease patient. In order to validate the effectiveness of the framework, we also propose a novel evaluation model based on Analytical Hierarchy Process (AHP), which incorporates some criteria from multiple decision makers in the context of healthcare monitoring applications. Using the proposed evaluation model, we analyzed three possible frameworks (proposed hybrid framework, mobile, and multimedia frameworks) in terms of their applicability in the real healthcare environment.

DNA methylation and the social gradient of osteoporotic fracture: a conceptual model

INTRODUCTION: Although there is a documented social gradient for osteoporosis, the underlying mechanism(s) for that gradient remain unknown. We propose a conceptual model based upon the allostatic load theory, to suggest how DNA methylation (DNAm) might underpin the social gradient in osteoporosis and fracture. We hypothesise that social disadvantage is associated with priming of inflammatory pathways mediated by epigenetic modification that leads to an enhanced state of inflammatory reactivity and oxidative stress, and thus places socially disadvantaged individuals at greater risk of osteoporotic fracture. METHODS/RESULTS: Based on a review of the literature, we present a conceptual model in which social disadvantage increases stress throughout the lifespan, and engenders a proinflammatory epigenetic signature, leading to a heightened inflammatory state that increases risk for osteoporotic fracture in disadvantaged groups that are chronically stressed. CONCLUSIONS: Our model proposes that, in addition to the direct biological effects exerted on bone by factors such as physical activity and nutrition, the recognised socially patterned risk factors for osteoporosis also act via epigenetic-mediated dysregulation of inflammation. DNAm is a dynamic modulator of gene expression with considerable relevance to the field of osteoporosis. Elucidating the extent to which this epigenetic mechanism transduces the psycho-social environment to increase the risk of osteoporotic fracture may yield novel entry points for intervention that can be used to reduce individual and population-wide risks for osteoporotic fracture. Specifically, an epigenetic evidence-base may strengthen the importance of lifestyle modification and stress reduction programs, and help to reduce health inequities across social groups. MINI ABSTRACT: Our conceptual model proposes how DNA methylation might underpin the social gradient in osteoporotic fracture. We suggest that social disadvantage is associated with priming of inflammatory signalling pathways, which is mediated by epigenetic modifications, leading to a chronically heightened inflammatory state that places disadvantaged individuals at greater risk of osteoporosis.

How do homeopaths reason and make decisions? Integrating theory, practice, and education

BACKGROUND: Homeopathy is a major modality in complementary and alternative medicine. Significant tensions exist between homeopathic practice and education, evident in the diversity of practice styles and pedagogic models. Utilizing clinical reasoning knowledge in conventional medicine and allied health sciences, this article seeks to identify and critique existing research in this important area. MATERIALS AND METHODS: A literature search utilizing MEDLINE,(®) Allied and Complementary Medicine (AMED), and CINAHL(®) (Cumulative Index to Nursing and Allied Health Literature) was conducted. Key terms including clinical thinking, clinical reasoning, decision-making, homeopathy, and complementary medicine were utilized. A critical appraisal of the evidence was undertaken. RESULTS: Four (4) studies have examined homeopathic clinical reasoning. Two (2) studies sought to measure and quantify homeopathic reasoning. One (1) study proposed a reasoning model, based on pattern recognition, hypothetico-deductive reasoning, intuition, and remedy-matching (PHIR-M), resembling much that has been previously mapped in conventional medical reasoning research. The fourth closely investigated the meaning and use of intuition in homeopathic decision-making. CONCLUSIONS: Taken together, these four studies provide valuable insight into what is currently known about homeopathic clinical reasoning. However, despite the history and breadth of practice, little is known about homeopathic clinical reasoning and decision-making. Building on the research would require viewing clinical reasoning not only as a cognitive phenomenon but also as a situated and interactive one. Further research into homeopathic clinical reasoning is indicated.

A GARCH model for testing market efficiency

In this paper we propose a generalised autoregressive conditional heteroskedasticity (GARCH) model-based test for a unit root. The model allows for two endogenous structural breaks. We test for unit roots in 156 US stocks listed on the NYSE over the period 1980 to 2007. We find that the unit root null hypothesis is rejected in 40% of the stocks, and only in four out of the nine sectors the null is rejected for over 50% of stocks. We conclude with an economic significance analysis, showing that mostly stocks with mean reverting prices tend to outperform stocks with non-stationary prices.

Ocular exposure to UV-B in sunlight: the Melbourne Visual Impairment Project model

Quantification of ocular exposure to ultraviolet-B radiation (UV-B) has become an important public health issue, with reports that the ozone layer is being depleted worldwide. Ocular exposure to UV-B is determined by ambient UV-B levels, the duration of outdoor exposure, the proportion of ambient UV-B that reaches the eye, and the use of ocular protection. We have developed a simplified model for quantifying lifetime ocular UV-B exposure that can be used in large epidemiological surveys. Exposure to UV-B is assessed and quantified using a model based on personal exposure over the six summer months. Data available for a population-based sample of 1150 people in the age range 40-98 years revealed a distribution in average annual lifetime ocular UV-B exposure similar to that reported in a previous study on which this model is based, and also demonstrate that people can recall lifetime personal behaviour related to ocular protection. It takes 12 minutes on average to collect these data. This model can be employed by researchers worldwide for uniform assessment of ocular UV-B exposure.

Hybrid controller with the combination of FLC and neural network-based IMC for nonlinear processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.

The influence of the supply chain agents on the new product development's performance: an analysis based on the multi-group moderation

Este estudo buscou verificar a influencia dos agentes da cadeia de suprimentos no desempenho do desenvolvimento de novos produtos quando os agentes são analisados em conjunto. A motivação desta pesquisa veio de estudos que alertaram para a consideração da integração da cadeia de suprimentos como um constructo multidimensional, englobando o envolvimento da manufatura, fornecedores e clientes no desenvolvimento de novos produtos; e devido à falta de informação sobre as influencias individuais destes agentes no desenvolvimento de novos produtos. Sob essas considerações, buscou-se construir um modelo analítico baseado na Teoria do Capital Social e Capacidade Absortiva, construir hipóteses a partir da revisão da literatura e conectar constructos como cooperação, envolvimento do fornecedor no desenvolvimento de novos produtos (DNP), envolvimento do cliente no DNP, envolvimento da manufatura no DNP, antecipação de novas tecnologias, melhoria contínua, desempenho operacional do DNP, desempenho de mercado do NPD e desempenho de negócio do DNP. Para testar as hipóteses foram consideradas três variáveis moderadoras, tais como turbulência ambiental (baixa, média e alta), indústria (eletrônicos, maquinários e equipamentos de transporte) e localização (América, Europa e Ásia). Para testar o modelo foram usados dados do projeto High Performance Manufacturing que contém 339 empresas das indústrias de eletrônicos, maquinários e equipamentos de transporte, localizadas em onze países. As hipóteses foram testadas por meio da Análise Fatorial Confirmatória (AFC) incluindo a moderação muti-grupo para as três variáveis moderadoras mencionadas anteriormente. Os principais resultados apontaram que as hipóteses relacionadas com cooperação foram confirmadas em ambientes de média turbulência, enquanto as hipóteses relacionadas ao desempenho no DNP foram confirmadas em ambientes de baixa turbulência ambiental e em países asiáticos. Adicionalmente, sob as mesmas condições, fornecedores, clientes e manufatura influenciam diferentemente no desempenho de novos produtos. Assim, o envolvimento de fornecedores influencia diretamente no desempenho operacional e indiretamente no desempenho de mercado e de negócio em baixos níveis de turbulência ambiental, na indústria de equipamentos de transporte em países da Americanos e Europeus. De igual forma, o envolvimento do cliente influenciou diretamente no desempenho operacional e indiretamente no desempenho de mercado e do negócio em médio nível de turbulência ambiental, na indústria de maquinários e em países Asiáticos. Fornecedores e clientes não influenciam diretamente no desempenho de mercado e do negócio e não influenciam indiretamente no desempenho operacional. O envolvimento da manufatura não influenciou nenhum tipo de desempenho do desenvolvimento de novos produtos em todos os cenários testados.

A unified model for the long and high jump

A simple model based on, the maximum energy that an athlete can produce in a small time interval is used to describe the high and long jump. Conservation of angular momentum is used to explain why an athlete should, run horizontally to perform a vertical jump. Our results agree with world records. (c) 2005 American Association of Physics Teachers.

Temporal variability of soil CO2 emission after conventional and reduced tillage described by an exponential decay in time model

A quantificação do impacto das práticas de preparo sobre as perdas de carbono do solo é dependente da habilidade de se descrever a variabilidade temporal da emissão de CO2 do solo após preparo. Tem sido sugerido que as grandes quantidades de CO2 emitido após o preparo do solo podem servir como um indicador das modificações nos estoques de carbono do solo em longo termo. Neste trabalho é apresentado um modelo de duas partes baseado na temperatura e na umidade do solo e que inclui um termo exponencial decrescente do tempo que é eficiente no ajuste das emissões intermediárias após preparo: arado de disco seguido de uma passagem com a grade niveladora (convencional) e escarificador de arrasto seguido da passagem com rolo destorroador (reduzido). As emissões após o preparo do solo são descritas utilizando-se estimativa não linear com um coeficiente de determinação (R²) tão alto quanto 0.98 após preparo reduzido. Os resultados indicam que nas previsões da emissão de CO2 após o preparo do solo é importante considerar um termo exponencial decrescente no tempo após preparo.

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Dowscaling estocástico para extremos climáticos via interpolação espacial

Present day weather forecast models usually cannot provide realistic descriptions of local and particulary extreme weather conditions. However, for lead times of about a small number of days, they provide reliable forecast of the atmospheric circulation that encompasses the subscale processes leading to extremes. Hence, forecasts of extreme events can only be achieved through a combination of dynamical and statistical analysis methods, where a stable and significant statistical model based on prior physical reasoning establishes posterior statistical-dynamical model between the local extremes and the large scale circulation. Here we present the development and application of such a statistical model calibration on the besis of extreme value theory, in order to derive probabilistic forecast for extreme local temperature. The dowscaling applies to NCEP/NCAR re-analysis, in order to derive estimates of daily temperature at Brazilian northeastern region weather stations