Poly(butylene adipate-co-terephthalate) (PBAT), a biodegradable flexible, and tough polymer is herein used, for the first time, to encapsulate and protect isocyanate derivatives. Isocyanates are essential building blocks widely employed in the chemical industry for the production of high-performing materials. Microencapsulation of isocyanates eliminates the risks associated with their direct handling and protects them from moisture. In light of this, and having in mind eco-innovative products and sustainability, we present a straightforward process to encapsulate isophorone diisocyanate (IPDI) using this biodegradable polymer. Spherical and core-shell microcapsules (MCs) were produced by an emulsion system combined with the solvent evaporation method. The MCs present a regular surface, without holes or cracks, with a thin shell and high isocyanate loadings, up to 79 wt%. Additionally, the MCs showed very good isocyanate protection if not dispersed in organic or aqueous solutions. Effects of various process parameters were systematically studied, showing that a higher stirring speed (1000 rpm) and emulsifier amount (2.5 g), as well as a smaller PBAT amount (1.60 g), lead to smaller MCs and narrower size distribution.

Food choices influence the health of individuals, and supermarkets are the place where part of the world population makes their food choices on a daily basis. Different methods to influence food purchasing habits are used, from promotions to food location. However, very few supermarket chains use social norms, the human need to conform to the perceived behavior of the group, to increase healthy food purchase habits. This research seeks to understand how a social norm nudge, a message conveying fruit and vegetable purchasing norms positioned in strategic places, can effectively change food choices. Using data from an intervention in a Portuguese supermarket, the fruit and vegetable purchase quantities of 1636 customers were measured over three months and compared with the corresponding period of the previous year. The results show that the nudge intervention positively affected those whose purchasing habits are categorized as less healthy, while those with healthy habits were slightly negatively affected. Moreover, a follow-up inferential statistical analysis allows us to conclude that applying this intervention at a larger scale would deliver significant financial results for the supermarket chain in which the study took place, by decreasing the costs related to produce perishability while simultaneously improving the health of the consumer and the sustainability of the planet.

This paper describes our participation on Task 7 of SemEval 2014, which focused on the recognition and disambiguation of medical concepts. We used an adapted version of the Stanford NER system to train CRF models to recognize textual spans denoting diseases and disorders, within clinical notes. We considered an encoding that accounts with noncontinuous entities, together with a rich set of features (i) based on domain specific lexicons like SNOMED CT, or (ii) leveraging Brown clusters inferred from a large collection of clinical texts. Together with this recognition mechanism, we used a heuristic similarity search method, to assign an unambiguous identifier to each concept recognized in the text.Our best run on Task A (i.e., in the recognition of medical concepts in the text) achieved an F-measure of 0.705 in the strict evaluation mode, and a promising F-measure of 0.862 in the relaxed mode, with a precision of 0.914. For Task B (i.e., the disambiguation of the recognized concepts), we achieved less promising results, with an accuracy of 0.405 in the strict mode, and of 0.615 in the relaxed mode.

By extending the traditional store-and-forward mechanism, network coding has the capability to improve a network's throughput, robustness, and security. Given the fundamentally different packet processing required by this new paradigm and the inflexibility of hardware, existing solutions are based on software. As a result, they have limited performance and scalability, creating a barrier to its wide-spread adoption. By leveraging the recent advances in programmable networking hardware, in this paper we propose a random linear network coding data plane written in P4, as a first step towards a productionlevel platform. Our solution includes the ability to combine the payload of multiple packets and of executing the required Galois field operations, and shows promise to be practical even under the strict memory and processing constraints of switching hardware.