<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Candila, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Morelli, Giacomo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Risks</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2227-9091/9/8/144</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vasiliki Voukelatou</style></author><author><style face="normal" font="default" size="100%">Gabrielli, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Miliou, Ioanna</style></author><author><style face="normal" font="default" size="100%">Cresci, Stefano</style></author><author><style face="normal" font="default" size="100%">Sharma, Rajesh</style></author><author><style face="normal" font="default" size="100%">Tesconi, Maurizio</style></author><author><style face="normal" font="default" size="100%">Pappalardo, Luca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring objective and subjective well-being: dimensions and data sources</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics (JDSA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baltakiene, M</style></author><author><style face="normal" font="default" size="100%">Baltakys, K</style></author><author><style face="normal" font="default" size="100%">Cardamone, D</style></author><author><style face="normal" font="default" size="100%">Parisi, F</style></author><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author><author><style face="normal" font="default" size="100%">Torricelli, M</style></author><author><style face="normal" font="default" size="100%">de Jeude, JA</style></author><author><style face="normal" font="default" size="100%">Saracco, F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Maximum entropy approach to link prediction in bipartite networks</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1805.04307</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>45</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Massimiliano de Leoni</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Models and Logs used in the paper ‘Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning’ accepted for ICAPS 2018</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computation Theory and Mathematics</style></keyword><keyword><style  face="normal" font="default" size="100%">Event Logs</style></keyword><keyword><style  face="normal" font="default" size="100%">Petri nets</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://data.4tu.nl/repository/uuid:a02afec8-b7c7-42b7-8dff-36d3de3032be</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">TU Eindhoven</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>