Studies in stoichiometry with compositional data

Geochemical data that is derived from the whole or partial analysis of various geologic materials represent a composition of mineralogies or solute species. Minerals are composed of structured relationships between cations and anions which, through atomic and molecular forces, keep the elements bound in specific configurations. The chemical compositions of minerals have specific relationships that are governed by these molecular controls. In the case of olivine, there is a well-defined relationship between Mn-Fe-Mg with Si. Balances between the principal elements defining olivine composition and other significant constituents in the composition (Al, Ti) have been defined, resulting in a near-linear relationship between the logarithmic relative proportion of Si versus (MgMnFe) and Mg versus (MnFe), which is typically described but poorly illustrated in the simplex. The present contribution corresponds to ongoing research, which attempts to relate stoichiometry and geochemical data using compositional geometry. We describe here the approach by which stoichiometric relationships based on mineralogical constraints can be accounted for in the space of simplicial coordinates using olivines as an example. Further examples for other mineral types (plagioclases and more complex minerals such as clays) are needed. Issues that remain to be dealt with include the reduction of a bulk chemical composition of a rock comprised of several minerals from which appropriate balances can be used to describe the composition in a realistic mineralogical framework. The overall objective of our research is to answer the question: In the cases where the mineralogy is unknown, are there suitable proxies that can be substituted? Kew words: Aitchison geometry, balances, mineral composition, oxides

Recerca versus docència en una experiència d'internalització dels estudis de Geografia a la Universitat de Girona: The faces of landscape, Catalonia and North Karelia = Research versus teaching in one experience of internationalization of the Geography degree studies in the University of Girona: The faces of landscape, Catalonia and North Karelia

Un dels reptes cabdals de la Universitat és enllaçar l’experiència de recerca amb la docència, així com promoure la internacionalització dels estudis, especialment a escala europea, tenint present que ambdues poden actuar com a catalitzadores de la millora de la qualitat docent. Una de les fórmules d’internacionalització és la realització d’assignatures compartides entre universitats de diferents països, fet que suposa l’oportunitat d’implementar noves metodologies docents. En aquesta comunicació es presenta una experiència en aquesta línia desenvolupada entre la Universitat de Girona i la Universitat de Joensuu (Finlàndia) en el marc dels estudis de Geografia amb la realització de l’assignatura 'The faces of landscape: Catalonia and North Karelia'. Aquesta es desenvolupa al llarg de dues setmanes intensives, una en cadascuna de les Universitats. L’objectiu és presentar i analitzar diferents significats del concepte paisatge aportant també metodologies d’estudi tant dels aspectes físics i ecològics com culturals que s’hi poden vincular i que són les que empren els grups de recerca dels professors responsables de l’assignatura. Aquesta part teòrica es completa amb una presentació de les característiques i dinàmiques pròpies dels paisatges finlandesos i catalans i una sortida de camp. Per a la part pràctica es constitueixen grups d’estudi multinacionals que treballen a escala local algun dels aspectes en els dos països, es comparen i es realitza una presentació i defensa davant del conjunt d’estudiants i professorat. La llengua vehicular de l’assignatura és l’anglès

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Internal test sets studies in a group of antimalarials

Topological indices have been applied to build QSAR models for a set of 20 antimalarial cyclic peroxy cetals. In order to evaluate the reliability of the proposed linear models leave-n-out and Internal Test Sets (ITS) approaches have been considered. The proposed procedure resulted in a robust and consensued prediction equation and here it is shown why it is superior to the employed standard cross-validation algorithms involving multilinear regression models

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.