Multivariate relationships between groundwater chemistry and toxicity in an urban aquifer

Multivariate statistical methods were used to investigate file Causes of toxicity and controls on groundwater chemistry from 274 boreholes in an Urban area (London) of the United Kingdom. The groundwater was alkaline to neutral, and chemistry was dominated by calcium, sodium, and Sulfate. Contaminants included fuels, solvents, and organic compounds derived from landfill material. The presence of organic material in the aquifer caused decreases in dissolved oxygen, sulfate and nitrate concentrations. and increases in ferrous iron and ammoniacal nitrogen concentrations. Pearson correlations between toxicity results and the concentration of individual analytes indicated that concentrations of ammoinacal nitrogen, dissolved oxygen, ferrous iron, and hydrocarbons were important where present. However, principal component and regression analysis suggested no significant correlation between toxicity and chemistry over the whole area. Multidimensional Scaling was used to investigate differences in sites caused by historical use, landfill gas status, or position within the sample area. Significant differences were observed between sites with different historical land use and those with different gas status. Examination of the principal component matrix revealed that these differences are related to changes in the importance of reduced chemical species.

Multivariate analysis of phenol mineralisation by combined hydrodynamic cavitation and heterogeneous advanced Fenton processing

Phenolic compounds in wastewaters are difficult to treat using the conventional biological techniques such as activated sludge processes because of their bio-toxic and recalcitrant properties and the high volumes released from various chemical, pharmaceutical and other industries. In the current work, a modified heterogeneous advanced Fenton process (AFP) is presented as a novel methodology for the treatment of phenolic wastewater. The modified AFP, which is a combination of hydrodynamic cavitation generated using a liquid whistle reactor and the AFP is a promising technology for wastewaters containing high organic content. The presence of hydrodynamic cavitation in the treatment scheme intensifies the Fenton process by generation of additional free radicals. Also, the turbulence produced during the hydrodynamic cavitation process increases the mass transfer rates as well as providing better contact between the pseudo-catalyst surfaces and the reactants. A multivariate design of experiments has been used to ascertain the influence of hydrogen peroxide dosage and iron catalyst loadings on the oxidation performance of the modified AFP. High er TOC removal rates were achieved with increased concentrations of hydrogen peroxide. In contrast, the effect of catalyst loadings was less important on the TOC removal rate under conditions used in this work although there is an optimum value of this parameter. The concentration of iron species in the reaction solution was measured at 105 min and its relationship with the catalyst loadings and hydrogen peroxide level is presented.

Rapid quality assessment of wheat cultivars registered in Poland using the 2-g mixograph and multivariate statistical analysis

Baking and 2-g mixograph analyses were performed for 55 cultivars (19 spring and 36 winter wheat) from various quality classes from the 2002 harvest in Poland. An instrumented 2-g direct-drive mixograph was used to study the mixing characteristics of the wheat cultivars. A number of parameters were extracted automatically from each mixograph trace and correlated with baking volume and flour quality parameters (protein content and high molecular weight glutenin subunit [HMW-GS] composition by SDS-PAGE) using multiple linear regression statistical analysis. Principal component analysis of the mixograph data discriminated between four flour quality classes, and predictions of baking volume were obtained using several selected mixograph parameters, chosen using a best subsets regression routine, giving R-2 values of 0.862-0.866. In particular, three new spring wheat strains (CHD 502a-c) recently registered in Poland were highly discriminated and predicted to give high baking volume on the basis of two mixograph parameters: peak bandwidth and 10-min bandwidth.

Multivariate analysis of the Fugl-Meyer outcome measures assessing the effectiveness of GENTLE/S robot-mediated stroke therapy

Background: Robot-mediated therapies offer entirely new approaches to neurorehabilitation. In this paper we present the results obtained from trialling the GENTLE/S neurorehabilitation system assessed using the upper limb section of the Fugl-Meyer ( FM) outcome measure. Methods: We demonstrate the design of our clinical trial and its results analysed using a novel statistical approach based on a multivariate analytical model. This paper provides the rational for using multivariate models in robot-mediated clinical trials and draws conclusions from the clinical data gathered during the GENTLE/S study. Results: The FM outcome measures recorded during the baseline ( 8 sessions), robot-mediated therapy ( 9 sessions) and sling-suspension ( 9 sessions) was analysed using a multiple regression model. The results indicate positive but modest recovery trends favouring both interventions used in GENTLE/S clinical trial. The modest recovery shown occurred at a time late after stroke when changes are not clinically anticipated. Conclusion: This study has applied a new method for analysing clinical data obtained from rehabilitation robotics studies. While the data obtained during the clinical trial is of multivariate nature, having multipoint and progressive nature, the multiple regression model used showed great potential for drawing conclusions from this study. An important conclusion to draw from this paper is that this study has shown that the intervention and control phase both caused changes over a period of 9 sessions in comparison to the baseline. This might indicate that use of new challenging and motivational therapies can influence the outcome of therapies at a point when clinical changes are not expected. Further work is required to investigate the effects arising from early intervention, longer exposure and intensity of the therapies. Finally, more function-oriented robot-mediated therapies or sling-suspension therapies are needed to clarify the effects resulting from each intervention for stroke recovery.

A note on estimating market–based minimum capital risk requirements: a multivariate GARCH approach

Internal risk management models of the kind popularized by J. P. Morgan are now used widely by the world’s most sophisticated financial institutions as a means of measuring risk. Using the returns on three of the most popular futures contracts on the London International Financial Futures Exchange, in this paper we investigate the possibility of using multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models for the calculation of minimum capital risk requirements (MCRRs). We propose a method for the estimation of the value at risk of a portfolio based on a multivariate GARCH model. We find that the consideration of the correlation between the contracts can lead to more accurate, and therefore more appropriate, MCRRs compared with the values obtained from a univariate approach to the problem.

On the variability of the surface environment response to synoptic forcing over complex terrain: a multivariate data analysis approach

Synoptic climatology relates the atmospheric circulation with the surface environment. The aim of this study is to examine the variability of the surface meteorological patterns, which are developing under different synoptic scale categories over a suburban area with complex topography. Multivariate Data Analysis techniques were performed to a data set with surface meteorological elements. Three principal components related to the thermodynamic status of the surface environment and the two components of the wind speed were found. The variability of the surface flows was related with atmospheric circulation categories by applying Correspondence Analysis. Similar surface thermodynamic fields develop under cyclonic categories, which are contrasted with the anti-cyclonic category. A strong, steady wind flow characterized by high shear values develops under the cyclonic Closed Low and the anticyclonic H–L categories, in contrast to the variable weak flow under the anticyclonic Open Anticyclone category.

Crossbred cow adoption and milk market participation in a multivariate count data framework

Cross-bred cow adoption is an important and potent policy variable precipitating subsistence household entry into emerging milk markets. This paper focuses on the problem of designing policies that encourage and sustain milkmarket expansion among a sample of subsistence households in the Ethiopian highlands. In this context it is desirable to measure households’ ‘proximity’ to market in terms of the level of deficiency of essential inputs. This problem is compounded by four factors. One is the existence of cross-bred cow numbers (count data) as an important, endogenous decision by the household; second is the lack of a multivariate generalization of the Poisson regression model; third is the censored nature of the milk sales data (sales from non-participating households are, essentially, censored at zero); and fourth is an important simultaneity that exists between the decision to adopt a cross-bred cow, the decision about how much milk to produce, the decision about how much milk to consume and the decision to market that milk which is produced but not consumed internally by the household. Routine application of Gibbs sampling and data augmentation overcome these problems in a relatively straightforward manner. We model the count data from two sites close to Addis Ababa in a latent, categorical-variable setting with known bin boundaries. The single-equation model is then extended to a multivariate system that accommodates the covariance between crossbred-cow adoption, milk-output, and milk-sales equations. The latent-variable procedure proves tractable in extension to the multivariate setting and provides important information for policy formation in emerging-market settings

Multivariate analysis of the dielectric response of materials modeled using networks of resistors and capacitors

We discuss the modeling of dielectric responses of electromagnetically excited networks which are composed of a mixture of capacitors and resistors. Such networks can be employed as lumped-parameter circuits to model the response of composite materials containing conductive and insulating grains. The dynamics of the excited network systems are studied using a state space model derived from a randomized incidence matrix. Time and frequency domain responses from synthetic data sets generated from state space models are analyzed for the purpose of estimating the fraction of capacitors in the network. Good results were obtained by using either the time-domain response to a pulse excitation or impedance data at selected frequencies. A chemometric framework based on a Successive Projections Algorithm (SPA) enables the construction of multiple linear regression (MLR) models which can efficiently determine the ratio of conductive to insulating components in composite material samples. The proposed method avoids restrictions commonly associated with Archie’s law, the application of percolation theory or Kohlrausch-Williams-Watts models and is applicable to experimental results generated by either time domain transient spectrometers or continuous-wave instruments. Furthermore, it is quite generic and applicable to tomography, acoustics as well as other spectroscopies such as nuclear magnetic resonance, electron paramagnetic resonance and, therefore, should be of general interest across the dielectrics community.

Evaluating multivariate forecast densities: a comparison of two approaches

We consider methods of evaluating multivariate density forecasts. A recently proposed method is found to lack power when the correlation structure is mis-specified. Tests that have good power to detect mis-specifications of this sort are described. We also consider the properties of the tests in the presence of more general mis-specifications.

Forecasting with Bayesian multivariate vintage-based VARs

We consider the forecasting of macroeconomic variables that are subject to revisions, using Bayesian vintage-based vector autoregressions. The prior incorporates the belief that, after the first few data releases, subsequent ones are likely to consist of revisions that are largely unpredictable. The Bayesian approach allows the joint modelling of the data revisions of more than one variable, while keeping the concomitant increase in parameter estimation uncertainty manageable. Our model provides markedly more accurate forecasts of post-revision values of inflation than do other models in the literature.

Experimental evolution for generalists and specialists reveals multivariate genetic constraints on thermal reaction norms

Theory predicts the emergence of generalists in variable environments and antagonistic pleiotropy to favour specialists in constant environments, but empirical data seldom support such generalist–specialist trade-offs. We selected for generalists and specialists in the dung fly Sepsis punctum (Diptera: Sepsidae) under conditions that we predicted would reveal antagonistic pleiotropy and multivariate trade-offs underlying thermal reaction norms for juvenile development. We performed replicated laboratory evolution using four treatments: adaptation at a hot (31 °C) or a cold (15 °C) temperature, or under regimes fluctuating between these temperatures, either within or between generations. After 20 generations, we assessed parental effects and genetic responses of thermal reaction norms for three correlated life-history traits: size at maturity, juvenile growth rate and juvenile survival. We find evidence for antagonistic pleiotropy for performance at hot and cold temperatures, and a temperature-mediated trade-off between juvenile survival and size at maturity, suggesting that trade-offs associated with environmental tolerance can arise via intensified evolutionary compromises between genetically correlated traits. However, despite this antagonistic pleiotropy, we found no support for the evolution of increased thermal tolerance breadth at the expense of reduced maximal performance, suggesting low genetic variance in the generalist–specialist dimension.