In addition to the environmental burden of its construction and demolition activities, Flemish housing market faces a structural affordability challenge. As one possible answer, this research explores potential so-called expandable houses, being built increasingly often. Through specific design choices that enable disassembly future reuse individual components so align with idea circular econom...

Purpose This study aims to examine the most influential drivers, both product-specific and consumer-specific, affecting Asian consumers' willingness pay (WTP) for organic olive oil. Design/methodology/approach To individuate drivers of WTP products assess their effect, in terms configurational paths consumer profiles, this sequentially employs explorative factor analysis approach a fuzzy-set qu...

This paper discusses the use of explorative data mining tools that allow the educator to explore new relationships between reported learning experiences and actual activities, even if there are multiple dimensions with a large number of measured items. The underlying technology is based on the so-called Compendium Platform for Reproducible Computing (http://www.freestatistics.org) which was bui...

This paper aims to improve our understanding of how exploitative and explorative learning of firms is enhanced through their social capital. Both types of learning differ considerably from each other and we argue that the distinction between them may be an important contingency factor in explaining the value of direct, indirect and (non-)redundant technology-based alliances. In particular we ar...

Dynamic structural responses via time history analysis are highly dependent to characteristics of selected records as the seismic excitation. Ground motion scaling is a well-known solution to reduce such a dependency and increase reliability to the dynamic results. The present work, formulate a twofold problem for optimal spectral matching and performing consequent sizing optimization based on ...

Explorative data analysis 3 Data import . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Gene Probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Data visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Actb quality control . . . . . . . ....