Search

Energy production is reorganised to mitigate the pressure on the global environment. This reorganisation leads to an increase in the production of wood ash (WA). Multivariate modelling was used to identify the link between production parameters and the physiochemicalcharacteristics of different WAs and to determine which production parameters result in the WAs most suitable for utilisation in cement-based materials. Based on the multivariate model partial least square, WA originating from circulating fluidised bed combustion of wood chips made from whole trees is the optimal type of WA when utilised as a supplementary cementing material with pozzolanic activity. WA originating from the combustion of wood chips made from whole trees is the optimal type of WA when utilised as a supplementary cementing material with hydraulic activity. Furthermore, the combustion method and type of ash were seen to have the largest influence on the physiochemical characteristics of WAs compared to the other production parameters included in this study. [ABSTRACT FROM AUTHOR]

This research is devoted to a systematic review of multivariate models in the context of their application to short-term traffic flow forecasting. A set of discussed models includes autoregressive integrated moving average models (ARIMA and VARMA), error correction models (VECM and EC-VARMA), space-time ARMA (STARMA), and multivariate autoregressive space state models (MARSS). All these models are based on different assumptions about a structure of interrelationships in traffic data (in time, in space or between different traffic characteristics). We discussed base assumptions of these models (such as stationary of traffic flows and spatial independence of data) and their importance in the domain of transport flows. The discussion is supplemented with an empirical application of the models to forecasting of traffic speeds for a small road segment. Empirical conclusions and projected research directions are also presented. [ABSTRACT FROM AUTHOR]

Abstract: In order to ascertain which school characteristics can explain the differences in effectiveness between schools, important methodological choices have to be made in school effectiveness research. One of these choices relates to the criterion or criteria the researcher wishes to use to compare schools. Should, for example, a school be deemed effective if it achieves high performances in mathematics, or in mother tongue or in both? In order to safeguard the construct validity of the concept ‘school effectiveness’ this type of research should include multiple effectiveness criteria and thus reduce the risk of ‘mono-operation bias’. In that case two analysis strategies can be used: a series of univariate analyses (one for each output measure); or a multivariate model in which different dependent variables are modelled at the same time. In this methodological review we will examine what the implications of these different analysis methods are for research results. The dataset is derived from a large-scale effectiveness study conducted into technical and vocational secondary education in Flanders. Based on the results of this study we will make methodological suggestions for researchers in the field of educational effectiveness and school leadership. [Copyright &y& Elsevier]