Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

Moving superhydrophobic surfaces toward real-world applications

Abstract: Superhydrophobic surfaces have received rapidly increasing research interest since the late 1990s because of their tremendous application potential in areas such as self-cleaning and anti-icing surfaces, drag reduction, and enhanced heat transfer ( 1 – 3 ). A surface is considered superhydrophobic if a water droplet beads up (with contact angles >150°), and moreover, if the droplet can slide away from the surface readily (i.e., it has small contact angle hysteresis). Two essential features are generally required for superhydrophobicity: a micro- or nanostructured surface texture and a nonpolar surface chemistry, to help trap a thin air layer that reduces attractive interactions between the solid surface and the liquid ( 4 , 5 ). However, such surface textures are highly susceptible to mechanical wear, and abrasion may also alter surface chemistry. Both processes can lead to loss of liquid repellency, which makes mechanical durability a central concern for practical applications ( 6 , 7 ). Identifying the most promising avenues to mechanically robust superhydrophobic materials calls for standardized characterization methods.

Variability in the analysis of a single neuroimaging dataset by many teams

Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.