The present article studies the properties of the phrase Como si no hubiera (un) mañana (‘As if there were no tomorrow’), which more frequently occurs in European Spanish than in other diatopic varieties of that language. As it is shown in the article, the phrase modifies verbs and verbal predicates, being able to perform intensifying quantification.

According to the usual, simplified picture of the Meinong‑Russell controversy, Meinong’s semantics is structurally amazingly simple but ontologically very expensive, while Russell’s theory contains some counter‑intuitive syntactic complica-tions, but to make up for this expense it releases us from almost all ontological troubles. Now the reality is much more complex. On the one hand it appears that the alleged ontological innocence of Russell’s solution has been highly exaggerated. In particular it assumes a Platonic ontology of universal properties. At the same time, if we look a bit closer, also Meinong’s theory turns out to be much more complicated than it looks at the first sight. It involves a hierarchy of objects exhibiting different degrees of completeness and in the later period of Meinong’s thought the structure of intentional reference takes a form very similar to that which has been proposed by Russell in his On Denoting.

The main goal of the studies was to collect information on the impact of the identified risk factors on the amount of costs incurred in the life cycle of buildings. The own studies were focused especially on residential and non-residential buildings. The studies consisted in obtaining expert opinions on the subject of the research involves in the non-random (arbitrary) selection of a sample of respondents from among specialists corresponding to the industry purpose of the studies and the research material. The research used the expert questionnaire method. The studies were divided into three stages. In the first stage, the identification and division of risk factors in the life cycle of buildings was performed. Then, experts assessed 45 selected risk factors that may affect the amount of costs incurred in the life cycle of buildings. In the last step, the research results were developed in the form of a checklist knowledge base, containing information about the potential impact of the identified risk factors in the life cycle of buildings on the amount of the corresponding components of life cycle costs.

This paper presents a probabilistic machine learning approach to approximate wavelength values for unmeasured positions on an opto-semiconductor wafer after epitaxy. Insufficient information about optical and opto-electronic properties may lead to undetected specification violations and, consequently, to yield loss or may cause product quality issues. Collection of information is restricted because physical measuring points are expensive and in practice samples are only drawn from 120 specific positions. The purpose of the study is to reduce the risk of uncertainties caused by sampling and measuring inaccuracy and provide reliable approximations. Therefore, a Gaussian process regression is proposed which can determine a point estimation considering measuring inaccuracy and further quantify estimation uncertainty. For evaluation, the proposed method is compared with radial basis function interpolation using wavelength measurement data of 6-inch InGaN wafers. Approximations of these models are evaluated with the root mean square error. Gaussian process regression with radial basis function kernel reaches a root mean square error of 0.814 nm averaged over all wafers. A slight improvement to 0.798 nm could be achieved by using a more complex kernel combination. However, this also leads to a seven times higher computational time. The method further provides probabilistic intervals based on means and dispersions for approximated positions.