Aspects of the prevention and repair of chloride-induced corrosion of steel in concrete

Sodium formate, potassium acetate and a mixture of calcium and magnesium acetate (CMA) have all been identified as effective de-icing agents. In this project an attempt has been made to elucidate potentially deleterious effects of these substances on the durability of reinforced concrete. Aspects involving the corrosion behaviour of embedded steel along with the chemical and physical degradation of the cementitious matrix were studied. Ionic diffusion characteristics of deicer/pore solution systems in hardened cement paste were also studied since rates of ingress of deleterious agents into cement paste are commonly diffusion-controlled. It was found that all the compounds tested were generally non-corrosive to embedded steel, however, in a small number of cases potassium acetate did cause corrosion. Potassium acetate was also found to cause cracking in concrete and cement paste samples. CMA appeared to degrade hydrated cement paste although this was apparently less of a problem when commercial grade CMA was used in place of the reagent grade chemical. This was thought to be due to the insoluble material present in the commercial formulation forming a physical barrier between the concrete and the de-icing solution. With the test regimes used sodium formate was not seen to have any deleterious effect on the integrity of reinforced concrete. As a means of restoring the corrosion protective character of chloride-contaminated concrete the process of electrochemical chloride removal has been previously developed. Potential side-effects of this method and the effect of external electrolyte composition on chloride removal efficiency were investigated. It was seen that the composition of the external electrolyte has a significant effect on the amount of chloride removed. It was also found that, due to alterations to the composition of the C3A hydration reaction products, it was possible to remove bound chloride as well as that in the pore solution. The use of an external electrolyte containing lithium ions was also tried as a means of preventing cathodically-induced alkali-silica reaction in concretes containing potentially reactive aggregates. The results obtained were inconclusive and further practical development of this approach is needed.

A comparative analysis of cultural value orientations of Polish and Turkish employees:implications for international human resource management

This study empirically compares and contrasts the cultural value orientations of employees from Poland and Turkey by testing the compatibility of their values in three stages through seven cultural dimensions. The first phase of the study deals with the assessment of inter-country cultural value differences; the second phase investigates the intra-country cultural dynamics between selected demographic groups; and the third phase examines the inter-country cultural differences among the selected demographic groups of employees. The research has been conducted adopting the Maznevski, DiStephano, and Nason's (1995) version of cultural perspectives questionnaire with a sample of 744 (548 Polish and 196 Turkish) respondents. The results show significant cultural differences between Poland and Turkey, a presence of cultural dynamics among certain demographic groups within the country, and a mixture of convergence and divergence in the value systems of certain demographic groups both within and between the two nation(s). The research findings convey important messages to international human resource strategists in order for them to employ an effective and rational employment policy and business negotiation approach(es) to effectively operate in these countries. It also highlights that diversity of cultural values not only requires viewing each of them through cultural dimensions at a macro-level with a cross-country reference, but also requires monitoring their dynamics at the micro-level with reference to controlled demographic groups. © 2013 Taylor & Francis.

Effect of mixing sequence on the workability and performance of asphalt mixtures

In Sweden, during recent years, a new type of mixing protocol has been applied, in which the order of mixing is changed from the conventional method. Improved workability and diminished mixing and compaction energy needs have been important drivers for this. Considering that it is the mastic phase, which is modified by changing the mixing order, it provides an interesting case study for explaining the mechanisms of workability in connection with the mastic phase. To do so, an analytical viscosity framework was combined with a mixture morphology framework to upscale to the mixing level and tribology principles to explain the interaction between the mastic and the aggregates. From the mastic viscosity protocol, it was found that the mixing order significantly affects the resulting mastic viscosity. To analyse the effect of this on the workability and resulting mixture performance, X-ray computed tomography was used to analyse mixtures produced by the two different mixing sequences. Mechanical testing was utilised to determine the long-term mechanical performance. In this part of the study, mastic viscosity as a function of particle concentration and distribution was directly coupled to improved mixture workability and enhanced long-term performance.

Pyrolysis and combustion of municipal solid wastes:evaluation of synergistic effects using TGA-MS

A thermogravimetric methodology was developed to investigate and semi-quantify the extent of synergistic effects during pyrolysis and combustion of municipal solid waste (MSW). Results from TGA-MS were used to compare the pyrolysis and combustion characteristics of single municipal solid waste components (polyvinyl chloride (PVC), polypropylene (PP), polystyrene (PS), branches (BR), leaves (LV), grass (GR), packaging paper (PK), hygienic paper (HP) and cardboard (CB)) and a mixture (MX) of PP, BR and CB. Samples were heated under dynamic conditions at 20°C/min from 25°C to 1000°C with the continuous record of their main evolved fragments. Synergistic effects were evaluated by comparing experimental and calculated weight losses and relative areas of MS peaks. Pyrolysis of the mixture happened in two stages, with the release of H2, CH4, H2O, CO and CO2 between 200 and 415°C and the release of CH4, CxHy, CO and CO2 between 415 and 525°C. Negative synergistic effect in the 1st stage was attributed to the presence of PP where the release of hydrocarbons and CO2 from BR and CB was inhibited, whereas positive synergistic effects were observed during the 2nd degradation stage. In a second part of the study, synergistic effects were related to the dependency of the effective activation energy (Eα) versus the conversion (α). Higher Eαs were obtained for MX during its 1st stage of pyrolysis and lower Eαs for the 2nd stage when compared to the individual components. On the other hand, mostly positive synergistic effects were observed during the combustion of the same mixture, for which lower Eαs were recorded.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

The casing layer in the culture of Agaricus bisporus, the cultivated mushroom

The casing layer is an essential component of the system employed in the culture of Agaricus bisporus. The literature appropriate to the casing layer is fully reviewed, including aspects relating to fructification and morphogenesis in A.bisporus, together with an appraisal of the various media employed, their properties and functions, and the commercial significance of the casing layer. Equipment is described for use in experiments in mushroom culture, based on a scaled-down version of normal growing technique, allowing the analysis of both weights and number of fruitbodies forming, which was useful in assessing the effects of different casing treatments. The basic steps in the production of fruitbodies in A.bisporus.are described, including a photographic study of the colonisation of casing and fructification. Various alterations to the physical structure of peat/chalk casing mixtures were found to have an effect on fructification; those causing an opening-out of the casing structure tended to give better yields, especially in the early stages of production. It was shown that, in order to obtain greater yield through casing amendment, fructification must be stimulated, giving increased numbers of fruitbodies, disproportionate to their total weight and consequently of lower mean weight. A synthetic casing medium based on the light glass-like mineral, perlite, was developed. The best formula obtained was -.1 part perlite: 1 part montmorillonite clay (by weight): 3 parts 0.01% glucose solution. Perlite/montmorillonite casing could be improved by adding compost colonised by mycelium of A.bisporus, or adding a peat-chalk casing extract. Perlite was also found to be suitable for admixture with the standard casing medium and a mixture of equal parts by volume performed as well as the peat/chalk casing normally used.

Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?

In this paper we investigate whether consideration of store-level heterogeneity in marketing mix effects improves the accuracy of the marketing mix elasticities, fit, and forecasting accuracy of the widely-applied SCAN*PRO model of store sales. Models with continuous and discrete representations of heterogeneity, estimated using hierarchical Bayes (HB) and finite mixture (FM) techniques, respectively, are empirically compared to the original model, which does not account for store-level heterogeneity in marketing mix effects, and is estimated using ordinary least squares (OLS). The empirical comparisons are conducted in two contexts: Dutch store-level scanner data for the shampoo product category, and an extensive simulation experiment. The simulation investigates how between- and within-segment variance in marketing mix effects, error variance, the number of weeks of data, and the number of stores impact the accuracy of marketing mix elasticities, model fit, and forecasting accuracy. Contrary to expectations, accommodating store-level heterogeneity does not improve the accuracy of marketing mix elasticities relative to the homogeneous SCAN*PRO model, suggesting that little may be lost by employing the original homogeneous SCAN*PRO model estimated using ordinary least squares. Improvements in fit and forecasting accuracy are also fairly modest. We pursue an explanation for this result since research in other contexts has shown clear advantages from assuming some type of heterogeneity in market response models. In an Afterthought section, we comment on the controversial nature of our result, distinguishing factors inherent to household-level data and associated models vs. general store-level data and associated models vs. the unique SCAN*PRO model specification.

The effect of a curing agent on the thermal degradation of fire retardant brominated epoxy resins

The diglycidyl ether of tetrabromobisphenol A, the diglycidyl ether of bisphenol A and their mixture was cured by 4,4'-diaminodiphenyl methane. The pyrolysis of the obtained epoxy resins was studied by TG, DSC, TG/FTIR as well as FTIR characterization of pyrolysis residues. The gaseous and high boiling pyrolysis products were collected, characterized by GC/MS and their formation is discussed. The brominated epoxy resins are thermally less stable than the non-brominated ones. This effect is caused by the amine-containing hardener. The degradation initiation reaction is associated with the formation of hydrogen bromide which further destabilizes the epoxy network. The effect of the curing agent can be used in recycling of epoxy resins to separate brominated pyrolysis products from non-brominated ones.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

Humans assume a mixture of diffuse and point-source lighting when viewing sinusoidal shading patterns

Observers perceive sinusoidal shading patterns as being due to sinusoidally corrugated surfaces, and perceive surface peaks to be offset from luminance maxima by between zero and 1/4 wavelength. This offset varies with grating orientation. Physically, the shading profile of a sinusoidal surface will be approximately sinusoidal, with the same spatial frequency as the surface, only when: (A) it is lit suitably obliquely by a point source, or (B) the light source is diffuse and hemispherical--the 'dark is deep' rule applies. For A, surface peaks will be offset by 1/4 wavelength from the luminance maxima; for B, this offset will be zero. As the sum of two same-frequency sinusoids with different phases is a sinusoid of intermediate phase, our results suggest that observers assume a mixture of two light sources whose relative strength varies with grating orientation. The perceived surface offsets imply that gratings close to horizontal are taken to be lit by a point source; those close to vertical by a diffuse source. [Supported by EPSRC grants to AJS and MAG].

Sun and sky:does human vision assume a mixture of point and diffuse illumination when interpreting shape-from-shading?

People readily perceive smooth luminance variations as being due to the shading produced by undulations of a 3-D surface (shape-from-shading). In doing so, the visual system must simultaneously estimate the shape of the surface and the nature of the illumination. Remarkably, shape-from-shading operates even when both these properties are unknown and neither can be estimated directly from the image. In such circumstances humans are thought to adopt a default illumination model. A widely held view is that the default illuminant is a point source located above the observer's head. However, some have argued instead that the default illuminant is a diffuse source. We now present evidence that humans may adopt a flexible illumination model that includes both diffuse and point source elements. Our model estimates a direction for the point source and then weights the contribution of this source according to a bias function. For most people the preferred illuminant direction is overhead with a strong diffuse component.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.