EXPO2013, the heir of EXPO2009, has been enriched by a variety of new algorithms and graphical tools aiming at strengthening the individual steps of the powder structure solution pathway. Particular attention has been addressed to the procedures devoted to improving structural models provided by direct methods in ab initio approaches. In addition, a new procedure has been implemented, working in direct space, which may be chosen by the user as an alternative to the traditional simulated annealing algorithm.

QUALX2.0 is the new version of QUALX, a computer program for qualitative phase analysis by powder diffraction data. The previous version of QUALX was able to carry out phase identification by querying the PDF‐2 commercial database. The main novelty of QUALX2.0 is the possibility of querying also a freely available database, POW_COD. POW_COD has been built up by starting from the structure information contained in the Crystallography Open Database (COD). The latter is a growing collection of diffraction data, freely downloadable from the web, corresponding to inorganic, metal–organic, organic and mineral structures. QUALX2.0 retains the main capabilities of the previous version: (a) automatically estimating and subtracting the background; (b) locating the experimental diffraction peaks; (c) searching the database for single‐phase pattern(s) best matching to the experimental powder diffraction data; (d) taking into account suitable restraints in the search; (e) performing a semi‐quantitative analysis; (f) enabling the change of default choices and strategies via a user‐friendly graphic interface. The advances of QUALX2.0 with respect to QUALX include (i) a wider variety of types of importable ASCII file containing the experimental diffraction pattern and (ii) new search–match options. The program, written in Fortran and C++, runs on PCs under the Windows operating system. The POW_COD database is exported in SQLite3 format.

Smart learning environments can be defined as systems aimed at proposing innovative uses of emerging pedagogical approaches and technologies to support effective learning experiences. In the past years, rather than designing and developing even more advanced technological solutions attention has been focused on defining environments that adopt appropriate strategies to sustain student motivation and engagement. Game-based learning and gamification approaches could be a promising solution, since there is much experimental evidence that proves their effect. In this context, our research aims at defining and developing a Smart Learning Environment able to improve engagement and motivation by means of game-based learning and gamification approaches. In particular, the paper presents two serious games that, using the gamification dimensions, aim at sustaining engagement and motivation in learning processes in medical contexts. In particular, the games involve both the patients, who have to acquire knowledge and skills about their disease, in order to become responsible for their choices, and the medical and paramedical staff, who must acquire knowledge and skills about diagnostic procedures, therapeutic interventions and follow-up of patients. Some results of a user test show that the games enhance student motivation and this means improvement also in knowledge acquisition

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In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.