Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Alternative method to evaluate discharge coefficients. Part 2: case study

The purpose of this article is to apply an alternative method whereby discharge coefficients can be estimated for the flow through a poppet valve at various lifts. Presented is the development of an operational quasi-steady flow rig. An engine cylinder head poppet valve was used as the case study. The requirement to directly measure mass flowrates using a standard conventional steady flow apparatus has been eliminated. Transient mass flowrates, pressures and temperatures of air during an inflow test for a poppet valve at various lifts were measured. Mass flowrates were also calculated from measured cylinder gas pressures and corrected for heat transfer. Using both methods to determine the mass flowrates, isentropic discharge coefficients were calculated and shown to compare within +/- 4.0 per cent of steady flow data. A computational fluid dynamics (CFD) validation of the quasi-steady flow rig is also presented.

Development of the kinetic model of platinum catalyzed ammonia oxidation in a microreactor

The ammonia oxidation reaction on supported polycrystalline platinum catalyst was investigated in an aluminum-based microreactor. An extensive set of reactions was included in the chemical reactor modeling to facilitate the construction of a kinetic model capable of satisfactory predictions for a wide range of conditions (NH3 partial pressure, 0.01-0.12 atm; O-2 partial pressure, 0.10-0.88 atm; temperature, 523-673 K; contact time, 0.3-0.7 ms). The elementary surface reactions used in developing the mechanism were chosen based on the literature data concerning ammonia oxidation on a Pt catalyst. Parameter estimates for the kinetic model were obtained using multi-response least squares regression analysis using the isothermal plug-flow reactor approximation. To evaluate the model, the behavior of a microstructured reactor was simulated by means of a complete Navier-Stokes model accounting for the reactions on the catalyst surface and the effect of temperature on the physico-chemical properties of the reacting mixture. In this way, the effect of the catalytic wall temperature non-uniformity and the effect of a boundary layer on the ammonia conversion and selectivity were examined. After further optimization of appropriate kinetic parameters, the calculated selectivities and product yields agree very well with the values actually measured in the microreactor. (C) 2002 Elsevier Science B.V. All rights reserved.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples

A study was undertaken to examine a range of sample preparation and near infrared reflectance spectroscopy (NIPS) methodologies, using undried samples, for predicting organic matter digestibility (OMD g kg(-1)) and ad libitum intake (g kg(-1) W-0.75) of grass silages. A total of eight sample preparation/NIRS scanning methods were examined involving three extents of silage comminution, two liquid extracts and scanning via either external probe (1100-2200 nm) or internal cell (1100-2500 nm). The spectral data (log 1/R) for each of the eight methods were examined by three regression techniques each with a range of data transformations. The 136 silages used in the study were obtained from farms across Northern Ireland, over a two year period, and had in vivo OMD (sheep) and ad libitum intake (cattle) determined under uniform conditions. In the comparisons of the eight sample preparation/scanning methods, and the differing mathematical treatments of the spectral data, the sample population was divided into calibration (n = 91) and validation (n = 45) sets. The standard error of performance (SEP) on the validation set was used in comparisons of prediction accuracy. Across all 8 sample preparation/scanning methods, the modified partial least squares (MPLS) technique, generally minimized SEP's for both OMD and intake. The accuracy of prediction also increased with degree of comminution of the forage and with scanning by internal cell rather than external probe. The system providing the lowest SEP used the MPLS regression technique on spectra from the finely milled material scanned through the internal cell. This resulted in SEP and R-2 (variance accounted for in validation set) values of 24 (g/kg OM) and 0.88 (OMD) and 5.37 (g/kg W-0.75) and 0.77 (intake) respectively. These data indicate that with appropriate techniques NIRS scanning of undried samples of grass silage can produce predictions of intake and digestibility with accuracies similar to those achieved previously using NIRS with dried samples. (C) 1998 Elsevier Science B.V.

Use of a lux-marked rhizobacterium as a biosensor to assess changes in rhizosphere C flow due to pollutant stress

The flow of carbon from plant roots into soil supports a range of microbial processes and is therefore critical to ecosystem function and health. Pollution-induced stress, which influences rhizosphere C flow is of considerable potential importance, and therefore needs to be evaluated. This paper reports on a method, based on reporter gene technology, for quantifying pollutant effects on rhizosphere C flow. The method uses the lux-marked rhizobacterium Pseudomonas fluorescens, where bioluminescence output of this biosensor is directly correlated with the metabolic activity and reports on C flow in root exudate. Plantago lanceolata was treated with paraquat (representing a model pollutant stress) in a simple microcosm system. The lux-biosensor response correlated closely with C concentrations in the exudate and demonstrated that the pollutant stress increased the C flow from the plantago roots, 24 h after application of the herbicide. The lux-reporter system therefore potentially offers a technique for use in assessing the impact of pollutant stress on rhizosphere C flow through the soil microbial biomass.

USE OF UNIDIRECTIONAL DATA FLOW IN BIT-LEVEL SYSTOLIC ARRAY CHIPS.

We show how the architecture of two recently reported bit-level systolic array circuits - a single-bit coefficient correlator and a multibit convolver - may be modified to incorporate unidirectional data flow. This feature has advantages in terms of chip cascadability, fault tolerance and possible wafer-scale integration.

Prenatal and postnatal inflammation in relation to cortisol levels in preterm infants at 18 months corrected age

Objective

To examine whether early inflammation is related to cortisol levels at 18 months corrected age (CA) in children born very preterm.

Study Design

Infants born ≤ 32 weeks gestational age were recruited in the NICU, and placental histopathology, MRI, and chart review were obtained. At 18 months CA developmental assessment and collection of 3 salivary cortisol samples were carried out. Generalized least squares was used to analyze data from 85 infants providing 222 cortisol samples.

Results

Infants exposed to chorioamnionitis with funisitis had a significantly different pattern of cortisol across the samples compared to infants with chorioamnionitis alone or no prenatal inflammation (F[4,139] = 7.3996, P <.0001). Postnatal infections, necrotizing enterocolitis and chronic lung disease were not significantly associated with the cortisol pattern at 18 months CA.

Conclusion

In children born very preterm, prenatal inflammatory stress may contribute to altered programming of the HPA axis.

Keywords: preterm, chorioamnionitis, funisitis, premature infants, hypothalamic-pituitary-adrenal axis, infection, cortisol, stress

Flow due to multiple jets downstream of a barrage: Experiments, 3-D CFD and depth-averaged modelling

The flow through and downstream of a row of seven open draft tubes in a barrage has been investigated through laboratory experiments in a wide flume, a three-dimensional (3D) computational fluid dynamics simulation, and a two-dimensional depth-averaged computation. Agreement between the experiments and the 3D modeling is shown to be good, including the prediction of an asymmetric Coandă effect. One aim is to determine the distance downstream at which depth-averaged modeling provides a reasonable prediction; this is shown to be approximately 20 tube diameters downstream of the barrage. Upstream of this, the depth-averaged modeling inaccurately predicts water level, bed shear, and the 3D flow field. The 3D model shows that bed shear stress can be markedly magnified near the barrage, particularly where the jets become attached.

Brunetta’s reform swan song? An assessment of its success in local governments through the analysis of its tools

The work aims at assessing the success of Brunetta’s reform (Legislative Decree n. 150/2009), a far-reaching reform that aimed at improving both organizational and individual performance in Italian public administration through a specific planning and control process (the performance cycle) and most of all through two new tools, Performance Plan and Performance Report. The success of the reform is assessed, with particular emphasis on local governments, analyzing the diffusion and use of these new tools. The study has been conducted using a deductive-inductive methodology. Thus, after a study of managerial reforms in Italy and performance measurement literature, a possible model (PerformEL Model) local governments could follow to draw up Performance Plan and Report as effective tools for performance measurement has been designed (deductive phase). Performance Plans 2011-2013 and Performance Report 2011 downloaded from Italian big sized municipalities’ websites have been analyzed in the light of PerformEL Model, to assess the diffusion of the documents and their coherence with legal requirements and suggestions from literature (inductive phase). Data arising from the empirical analysis have been studied to evaluate the diffusion and the effectiveness of big sized municipalities’ Performance Plans and Reports as performance measurement tools and thus to assess the success of the reform (feedback phase). The study shows a scarce diffusion of the documents; they are mostly drew up because of their compulsoriness or to gain legitimization. The results testify the failure of Brunetta’s reform, at least with regard to local governments.

Metabolic signatures of human Alzheimer's disease (AD): H-1 NMR analysis of the polar metabolome of post-mortem brain tissue

Brain tissue from so-called Alzheimer's disease (AD) mouse models has previously been examined using H-1 NMR-metabolomics, but comparable information concerning human AD is negligible. Since no animal model recapitulates all the features of human AD we undertook the first H-1 NMR-metabolomics investigation of human AD brain tissue. Human post-mortem tissue from 15 AD subjects and 15 age-matched controls was prepared for analysis through a series of lyophilised, milling, extraction and randomisation steps and samples were analysed using H-1 NMR. Using partial least squares discriminant analysis, a model was built using data obtained from brain extracts. Analysis of brain extracts led to the elucidation of 24 metabolites. Significant elevations in brain alanine (15.4 %) and taurine (18.9 %) were observed in AD patients (p ≤ 0.05). Pathway topology analysis implicated either dysregulation of taurine and hypotaurine metabolism or alanine, aspartate and glutamate metabolism. Furthermore, screening of metabolites for AD biomarkers demonstrated that individual metabolites weakly discriminated cases of AD [receiver operating characteristic (ROC) AUC <0.67; p < 0.05]. However, paired metabolites ratios (e.g. alanine/carnitine) were more powerful discriminating tools (ROC AUC = 0.76; p < 0.01). This study further demonstrates the potential of metabolomics for elucidating the underlying biochemistry and to help identify AD in patients attending the memory clinic

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Cadmium phytotoxicity and trophic transfer to isopods

O cádmio (Cd) é um metal não essencial e é considerado um poluente prioritário pela comunidade europeia. Este metal atinge o ambiente no decurso de várias actividades antropogénicas e tende a concentrar-se nos solos e sedimentos, onde está potencialmente disponível para as plantas, sendo posteriormente transferido através da cadeia trófica. Neste contexto, o principal objectivo da presente dissertação foi o estudo dos efeitos da assimilação e da acumulação de Cd em plantas e as suas consequências para animais consumidores. Numa primeira fase, foram estudados os principais efeitos fisiológicos e genotóxicos do Cd em plantas. As plantas de alface (Lactuca sativa L.) expostas a Cd apresentaram um decréscimo na eficiência fotossintética, aumento de peroxidação lipídica e alterações significativas na actividade de enzimas de stress oxidativo. Estas alterações culminaram num decréscimo do crescimento da parte aérea no final da exposição. As respostas obtidas pelos parâmetros bioquímicos sugerem que estes poderão ser utilizados como eventuais biomarcadores em testes ecotoxicológicos com Cd em abordagens integrantes em conjunto com parâmetros clássicos. Os efeitos mutagénicos de Cd foram avaliados através da determinação da instabilidade de microsatélites (IM). Não foi observada IM, nem nas folhas nem nas raízes de plantas de alface com 5 semanas de idade expostas a 100 μM Cd durante 14 dias, no entanto observou-se IM em raízes de alface exposta a 10 μM Cd durante 28 dias desde a germinação. A idade da planta e a maior acumulação de Cd nas raízes poderão explicar os resultados obtidos. A clastogenicidade de Cd foi analisada em três espécies vegetais com diferentes capacidades de destoxificação e acumulação de metais através de citometria de fluxo. Foram detectadas alterações significativas nos parâmetros analisados em raízes alface, mas não nas espécies Thlaspi caerulescens J & C Presl e Thlaspi arvense L. Estes resultados sugerem que o stress provocado pelo Cd originou clastogenicidade como consequência da perda de porções de cromossomas, uma vez que o conteúdo de ADN nuclear diminuiu. A transferência trófica através da cadeia alimentar permanece muito pouco estudada em termos ecotoxicológicos. A distribuição subcellular de metais num organismo pode ser utilizada para compreender a transferência trófica de um metal na cadeia alimentar. Como tal, numa última parte é estudado de que modo a distribuição subcellular do Cd em plantas com perfis de acumulação de Cd distintos afecta a biodisponibilidade e transferência trófica de Cd para isópodes. A distribuição de Cd entre as 4 fracções subcelulares obtidas através de centrifugação diferencial revelou a existência de diferenças significativas entre as espécies de plantas. Estes resultados em conjunto com a avaliação directa da eficiência de assimilação (EA) de Cd individual de cada uma das quatro fracções subcelulares das plantas em estudo, resultou em informação de grande relevância para a explicação das diferenças observadas na EA de Cd por parte de isópodes alimentados com folhas de diferentes espécies de plantas. Com base nos resultados obtidos, o Cd ligado a proteínas estáveis à temperatura (e.g. metaloteoninas e fitoquelatinas) é o menos biodisponível, sendo assim o que menos contribuiu para a transferência trófica, enquanto que o Cd ligado a proteínas desnaturadas pela temperatura foi a fracção mais disponível para transferência trófica de Cd ao isópode. Estes resultados realçam a relevância ecológica da distribuição subcelular de Cd em plantas que tem influência directa na tranferência trófica deste metal para os consumidores e ainda o facto de que alterações na distribuição subcelular de Cd em plantas devido a diferentes mecanismos de destoxificação poderá ter um impacto directo na transferência trófica de Cd para o animal consumidor.

Contributos para a análise estatística de séries de contagem

A análise das séries temporais de valores inteiros tornou-se, nos últimos anos, uma área de investigação importante, não só devido à sua aplicação a dados de contagem provenientes de diversos campos da ciência, mas também pelo facto de ser uma área pouco explorada, em contraste com a análise séries temporais de valores contínuos. Uma classe que tem obtido especial relevo é a dos modelos baseados no operador binomial thinning, da qual se destaca o modelo auto-regressivo de valores inteiros de ordem p. Esta classe é muito vasta, pelo que este trabalho tem como objectivo dar um contributo para a análise estatística de processos de contagem que lhe pertencem. Esta análise é realizada do ponto de vista da predição de acontecimentos, aos quais estão associados mecanismos de alarme, e também da introdução de novos modelos que se baseiam no referido operador. Em muitos fenómenos descritos por processos estocásticos a implementação de um sistema de alarmes pode ser fundamental para prever a ocorrência de um acontecimento futuro. Neste trabalho abordam-se, nas perspectivas clássica e bayesiana, os sistemas de alarme óptimos para processos de contagem, cujos parâmetros dependem de covariáveis de interesse e que variam no tempo, mais concretamente para o modelo auto-regressivo de valores inteiros não negativos com coeficientes estocásticos, DSINAR(1). A introdução de novos modelos que pertencem à classe dos modelos baseados no operador binomial thinning é feita quando se propõem os modelos PINAR(1)T e o modelo SETINAR(2;1). O modelo PINAR(1)T tem estrutura periódica, cujas inovações são uma sucessão periódica de variáveis aleatórias independentes com distribuição de Poisson, o qual foi estudado com detalhe ao nível das suas propriedades probabilísticas, métodos de estimação e previsão. O modelo SETINAR(2;1) é um processo auto-regressivo de valores inteiros, definido por limiares auto-induzidos e cujas inovações formam uma sucessão de variáveis independentes e identicamente distribuídas com distribuição de Poisson. Para este modelo estudam-se as suas propriedades probabilísticas e métodos para estimar os seus parâmetros. Para cada modelo introduzido, foram realizados estudos de simulação para comparar os métodos de estimação que foram usados.

Metabonomics of human amniotic fluid for prenatal diagnostics

O trabalho apresentado nesta tese teve como principais objectivos contribuir para o conhecimento da composição do líquido amniótico humano (LA), colhido no 2º trimestre de gravidez, assim como investigar possíveis alterações na sua composição devido à ocorrência de patologias pré-natais, recorrendo à metabonómica e procurando, assim, definir novos biomarcadores de doenças da grávida e do feto. Após uma introdução descrevendo o estado da arte relacionado com este trabalho (Capítulo 1) e os princípios das metodologias analíticas usadas (Capítulo 2), seguida de uma descrição dos aspectos experimentais associados a esta tese (Capítulo 3), apresentam-se os resultados da caracterização da composição química do LA (gravidez saudável) por espectroscopia de ressonância magnética nuclear (RMN), assim como da monitorização da sua estabilidade durante o armazenamento e após ciclos de congelamento-descongelamento (Capítulo 4). Amostras de LA armazenadas a -20°C registaram alterações significativas, tornando-se estas menos pronunciadas (mas ainda mensuráveis) a -70°C, temperatura recomendada para o armazenamento de LA. Foram também observadas alterações de composição após 1-2 ciclos de congelamento-descongelamento (a ter em conta aquando da reutilização de amostras), assim como à temperatura ambiente (indicando um período máximo de 4h para a manipulação e análise de LA). A aquisição de espectros de RMN de 1H de alta resolução e RMN acoplado (LC-NMR/MS) permitiu a detecção de 75 compostos no LA do 2º trimestre, 6 dos quais detectados pela primeira vez no LA. Experiências de difusão (DOSY) permitiram ainda a caracterização das velocidades de difusão e massas moleculares médias das proteínas mais abundantes. O Capítulo 5 descreve o estudo dos efeitos de malformações fetais (FM) e de cromossomopatias (CD) na composição do LA do 2º trimestre de gravidez. A extensão deste trabalho ao estudo dos efeitos de patologias no LA que ocorrem no 3º trimestre de gravidez é descrita no Capítulo 6, nomeadamente no que se refere ao parto pré-termo (PTD), pré-eclampsia (PE), restrição do crescimento intra-uterino (IUGR), ruptura prematura de membranas (PROM) e diabetes mellitus gestacional (GDM). Como complemento a estes estudos, realizou-se uma análise preliminar da urina materna do 2º trimestre para o estudo de FM e GDM, descrita no Capítulo 7. Para interpretação dos dados analíticos, obtidos por espectroscopia RMN de 1H, cromatografia líquida de ultra eficiência acoplada a espectrometria de massa (UPLC-MS) e espectroscopia do infravermelho médio (MIR), recorreu-se à análise discriminante pelos métodos dos mínimos quadrados parciais e o método dos mínimos quadrados parciais ortogonal (PLS-DA e OPLS-DA) e à correlação espectral. Após análise por validação cruzada de Monte-Carlo (MCCV), os modelos PLS-DA de LA permitiram distinguir as FM dos controlos (sensibilidades 69-85%, especificidades 80-95%, taxas de classificação 80-90%), revelando variações metabólicas ao nível do metabolismo energético, dos metabolismos dos aminoácidos e glícidos assim como possíveis alterações ao nível do funcionamento renal. Observou-se também um grande impacto das FM no perfil metabólico da urina materna (medido por UPLC-MS), tendo no entanto sido registados modelos PLS-DA com menor sensibilidade (40-60%), provavelmente devido ao baixo número de amostras e maior variabilidade da composição da urina (relativamente ao LA). Foram sugeridos possíveis marcadores relacionados com a ocorrência de FM, incluindo lactato, glucose, leucina, valina, glutamina, glutamato, glicoproteínas e conjugados de ácido glucurónico e/ou sulfato e compostos endógenos e/ou exógenos (<1 M) (os últimos visíveis apenas na urina). No LA foram também observadas variações metabólicas devido à ocorrência de vários tipos de cromossomopatias (CD), mas de menor magnitude. Os perfis metabólicos de LA associado a pré- PTD produziram modelos que, apesar do baixo poder de previsão, sugeriram alterações precoces no funcionamento da unidade fetoplacentária, hiperglicémia e stress oxidativo. Os modelos obtidos para os grupos pré- IUGR pré- PE, pré- PROM e pré-diagnóstico GDM (LA e urina materna) registaram baixo poder de previsão, indicando o pouco impacto destas condições na composição do LA e/ou urina do 2º trimestre. Os resultados obtidos demonstram as potencialidades da análise dos perfis metabólicos do LA (e, embora com base em menos estudos, da urina materna) do 2º trimestre para o desenvolvimento de novos e complementares métodos de diagnóstico, nomeadamente para FM e PTD.