Eurix (Italy)

Measurement of δ(2) H values in non-exchangeable-H (δ(2) H(n)) and δ(18)O values in organic environmental samples are inconsistent among laboratories worldwide due to varied and lengthy approaches in controlling for H isotope exchange (for δ(2)H(n) values) and removal of trace moisture (δ(2)H(n) and δ(18)O values), which undermines the comparability of organic δ(2)H and δ(18)O data produced among different laboratories. An online preparation system was developed for the measurement of the δ(2)H(n) and δ(18)O values of organic samples, coupled to isotope-ratio mass spectrometers. The system features a 50-position autosampler and isolation valve where (1) the samples are held isothermal between ambient to 40-120 ± 0.1 °C for H isotopic exchange experiments (δ(2) H(n)) and drying of hygroscopic samples (δ(2) H(n) and δ(18)O), (2) the samples are evacuated to <5 mbar and flushed with helium for moisture and N(2) removal, and (3) injection of up to 500 μL of H(2)O is possible for controlled vapour exchangeable-H experiments. The system provides highly reproducible and precise δ(2)H(n) isotope estimates for a range of organic keratinous standard powders over a wide range of experimental temperatures. A reproducible sample processing regimen can now be applied to a wider range of organics and hygroscopic samples that are currently hampered by poorly controlled preparative methods amongst laboratories. Rapid and reproducible online vacuum equilibration of samples and standards for the routine measurement of δ(2)H(n) and δ(18)O values is now possible using the online equilibration system, with the added benefit that sample processing times for organic δ(2)H values are reduced from weeks to hours.

Several measures exist to describe similarities between digital contents, especially for what concerns images. Nevertheless, distances based on low-level visual features embedded in a multidimensional linear space are hardly suitable for capturing semantic similarities and recently novel techniques have been introduced making use of hierarchical knowledge bases. While being successfully exploited in specific contexts, the human perception of similarity cannot be easily encoded in such rigid structures. In this paper we propose to represent a knowledge base of semantic concepts as a complex network whose topology arises from free conceptual associations and is markedly different from a hierarchical structure. Images are anchored to relevant semantic concepts through an annotation process and similarity is computed following the related paths in the complex network. We finally show how this definition of semantic similarity is not necessarily restricted to images, but can be extended to compute distances between different types of sensorial information such as pictures and sounds, modeling the human ability to realize synaesthesias.