Experimental design and model construction algorithms for radial basis function networks

New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Parameter estimation based on stacked regression and evolutionary algorithms

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (OLS) algorithm. The PRESS prediction errors are used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

Dual-orthogonal radial basis function networks for nonlinear time series prediction

A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.

Metabolism of tea flavonoids in the gastrointestinal tract

There is considerable interest in the bioavailability of flavan-3-ols such as tea catechins and their bioactivity in vivo. Although flavanols such as catechin and epicatechin have long been characterized as powerful antioxidants in vitro, evidence suggests that these compounds undergo significant metabolism and conjugation during absorption in the small intestine and in the colon. In the small intestine these modifications lead primarily to the formation of glucuronide conjugates that are more polar than the parent flavanol and are marked for renal excretion. Other phase II processes lead to the production of O-methylated forms that have reduced antioxidant potential via the methylation of the B-ring catechol. Significant modification of flavanols also occurs in the colon where the resident microflora degrade them to smaller phenolic acids, some of which may be absorbed. Cell, animal and human studies have confirmed such metabolism by the detection of flavanol metabolites in the circulation and tissues. This review will highlight the major sites of flavanol metabolism in the gastrointestinal tract and the processes that give rise to potential bioactive forms of flavan-3-ols in vivo.

Rational expectations? Developer behaviour and development cycles in the central London office market

Purpose – Expectations of future market conditions are acknowledged to be crucial for the development decision and hence for shaping the built environment. The purpose of this paper is to study the central London office market from 1987 to 2009 and test for evidence of rational, adaptive and naive expectations. Design/methodology/approach – Two parallel approaches are applied to test for either rational or adaptive/naive expectations: vector auto-regressive (VAR) approach with Granger causality tests and recursive OLS regression with one-step forecasts. Findings – Applying VAR models and a recursive OLS regression with one-step forecasts, the authors do not find evidence of adaptive and naïve expectations of developers. Although the magnitude of the errors and the length of time lags between market signal and construction starts vary over time and development cycles, the results confirm that developer decisions are explained, to a large extent, by contemporaneous and historic conditions in both the City and the West End, but this is more likely to stem from the lengthy design, financing and planning permission processes rather than adaptive or naive expectations. Research limitations/implications – More generally, the results of this study suggest that real estate cycles are largely generated endogenously rather than being the result of large demand shocks and/or irrational behaviour. Practical implications – Developers may be able to generate excess profits by exploiting market inefficiencies but this may be hindered in practice by the long periods necessary for planning and construction of the asset. Originality/value – This paper focuses the scholarly debate of real estate cycles on the role of expectations. It is also one of very few spatially disaggregate studies of the subject matter.

Optimal hedging with higher moments

This study proposes a utility-based framework for the determination of optimal hedge ratios (OHRs) that can allow for the impact of higher moments on hedging decisions. We examine the entire hyperbolic absolute risk aversion family of utilities which include quadratic, logarithmic, power, and exponential utility functions. We find that for both moderate and large spot (commodity) exposures, the performance of out-of-sample hedges constructed allowing for nonzero higher moments is better than the performance of the simpler OLS hedge ratio. The picture is, however, not uniform throughout our seven spot commodities as there is one instance (cotton) for which the modeling of higher moments decreases welfare out-of-sample relative to the simpler OLS. We support our empirical findings by a theoretical analysis of optimal hedging decisions and we uncover a novel link between OHRs and the minimax hedge ratio, that is the ratio which minimizes the largest loss of the hedged position. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark

Regulation of NF-κB activity in astrocytes: effects of flavonoids at dietary-relevant concentrations

Neuroinflammation plays an important role in the progression of neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. Sustained activation of nuclear transcription factor κB (NF-κB) is thought to play an important role in the pathogenesis of neurodegenerative disorders. Flavonoids have been shown to possess antioxidant and anti-inflammatory properties and we investigated whether flavonoids, at submicromolar concentrations relevant to their bioavailability from the diet, were able to modulate NF-κB signalling in astrocytes. Using luciferase reporter assays, we found that tumour necrosis factor (TNFα, 150 ng/ml) increased NF-κB-mediated transcription in primary cultures of mouse cortical astrocytes, which was abolished on co-transfection of a dominant-negative IκBα construct. In addition, TNFα increased nuclear localisation of p65 as shown by immunocytochemistry. To investigate potential flavonoid modulation of NF-κB activity, astrocytes were treated with flavonoids from different classes; flavan-3-ols ((−)-epicatechin and (+)-catechin), flavones (luteolin and chrysin), a flavonol (kaempferol) or the flavanones (naringenin and hesperetin) at dietary-relevant concentrations (0.1–1 μM) for 18 h. None of the flavonoids modulated constitutive or TNFα-induced NF-κB activity. Therefore, we conclude that NF-κB signalling in astrocytes is not a major target for flavonoids.

The sky is the limit? The determinants and constraints of European airports' commercial revenues

This study investigates the determinants of commercial and retail airport revenues as well as revenues from real estate operations. Cross-sectional OLS, 2SLS and robust regression models of European airports identify a number of significant drivers of airport revenues. Aviation revenues per passenger are mainly determined by the national income per capita in which the airport is located, the percentage of leisure travelers and the size of the airport proxied by total aviation revenues. Main drivers of commercial revenues per passenger include the total number of passengers passing through the airport, the ratio of commercial to total revenues, the national income, the share of domestic and leisure travelers and the total number of flights. These results are in line with previous findings of a negative influence of business travelers on commercial revenues per passenger. We also find that a high amount of retail space per passenger is generally associated with lower commercial revenues per square meter confirming decreasing marginal revenue effects. Real estate revenues per passenger are positively associated with national income per capita at airport location, share of intra-EU passengers and percent delayed flights. Overall, aviation and non-aviation revenues appear to be strongly interlinked, underlining the potential for a comprehensive airport management strategy above and beyond mere cost minimization of the aviation sector.

"Oft expectation fails": a time-series analysis of construction starts in the London office market

Expectations of future market conditions are generally acknowledged to be crucial for the development decision and hence for shaping the built environment. This empirical study of the Central London office market from 1987 to 2009 tests for evidence of adaptive and naive expectations. Applying VAR models and a recursive OLS regression with one-step forecasts, we find evidence of adaptive and naïve, rather than rational expectations of developers. Although the magnitude of the errors and the length of time lags vary over time and development cycles, the results confirm that developers’ decisions are explained to a large extent by contemporaneous and past conditions in both London submarkets. The corollary of this finding is that developers may be able to generate excess profits by exploiting market inefficiencies but this may be hindered in practice by the long periods necessary for planning and construction of the asset. More generally, the results of this study suggest that real estate cycles are largely generated endogenously rather than being the result of unexpected exogenous shocks.

Eco-labeling, rents, sales prices and occupancy rates: do LEED and Energy Star labeled offices obtain multiple premiums?

Drawing upon an updated and expanded dataset of Energy Star and LEED labeled commercial offices, this paper investigates the effect of eco-labeling on rental rates, sale prices and occupancy rates. Using OLS and robust regression procedures, hedonic modeling is used to test whether the presence of an eco-label has a significant positive effect on rental rates, sale prices and occupancy rates. The study suggests that estimated coefficients can be sensitive to outlier treatment. For sale prices and occupancy rates, there are notable differences between estimated coefficients for OLS and robust regressions. The results suggest that both Energy Star and LEED offices obtain rental premiums of approximately 3%. A 17% sale price premium is estimated for Energy Star labeled offices but no significant sale price premium is estimated for LEED labeled offices. Surprisingly, no significant occupancy premium is estimated for Energy Star labeled offices and a negative occupancy premium is estimated for LEED labeled offices.

In vitro fermentation of a red wine extract by human gut microbiota: changes in microbial groups and formation of phenolic metabolites

An in vitro batch culture fermentation experiment was conducted with fecal inocula from three healthy volunteers in the presence and absence of a red wine extract. Changes in main bacterial groups were determined by FISH during a 48 h fermentation period. The catabolism of main flavonoids (i.e., flavan-3-ols and anthocyanins) and the formation of a wide a range of phenolic microbial metabolites were determined by a targeted UPLC-PAD-ESI-TQ MS method. Statistical analysis revealed that catechol/pyrocatechol, as well as 4-hydroxy-5-(phenyl)-valeric, 3- and 4-hydroxyphenylacetic, phenylacetic, phenylpropionic, and benzoic acids, showed the greatest increases in concentration during fermentation, whereas 5-(3′-hydroxyphenyl)-γ-valerolactone, its open form 4-hydroxy-5-(3′-hydroxyphenyl)-valeric acid, and 3,4-dihydroxyphenylacetic acid represented the largest interindividual variations in the catabolism of red wine polyphenols. Despite these changes, microbial catabolism did not produce significant changes in the main bacterial groups detected, although a slight inhibition of the Clostridium histolyticum group was observed.

In vitro fermentation of grape seed flavan-3-ol fractions by human faecal microbiota: changes in microbial groups and phenolic metabolites

With the aim of investigating the potential of flavan-3-ols to influence the growth of intestinal bacterial groups, we have carried out the in vitro fermentation, with human faecal microbiota, of two purified fractions from grape seed extract (GSE): GSE-M (70% monomers and 28% procyanidins) and GSE-O (21% monomers and 78 % procyanidins). Samples were collected at 0, 5, 10, 24, 30 and 48 h of fermentation for bacterial enumeration by fluorescent in situ hybridization and for analysis of phenolic metabolites. Both GSE-M and GSE-O fractions promoted growth of Lactobacillus/Enterococcus and decrease in the Clostridium histolyticum group during fermentation, although the effects were only statistically significant with GSE-M for Lactobacillus/Enterococcus (at 5 and 10 h of fermentation) and GSE-O for C. histolyticum (at 10 h of fermentation). Main changes in polyphenol catabolism also occurred during the first 10 h of fermentation, however no significant correlation coefficients (P>0.05) were found between changes in microbial populations and precursor flavan-3-ols or microbial metabolites. Together these data suggest that the flavan-3-ol profile of a particular food source could affect the microbiota composition and its catabolic activity, inducing changes that could in turn affect the bioavailability and potential bioactivity of these compounds.

Urban Growth Boundaries and their impact on land prices

Investigation of the effects of Urban Growth Boundaries (UGB) on land prices are restricted by a lack of good land market data. However, undeveloped land transactions at the urban fringe of the Melbourne metropolitan area in Australia are recorded in a data set that enables exploration of the impact of its UGB. Estimation can take account of endogeneity issues, while controlling for policy anticipation effects and other potential influences on land prices. OLS and instrumental variable estimates indicate that land prices rose substantially inside the UGB after its enactment in 2003 but did not rise much outside of it.