Structural design, manufacturing and testing of the new wing for the CSIR's Modular UAS in composite materials

In the framework of an international collaboration with South Africa CSIR, the structural design, manufacturing and testing of the new wing for the Modular UAS in composite materials has been performed.

In-situ DRIFT/MS study of the conversion of ethanol on mixed Mg/Si oxides

Due to the high price of natural oil and harmful effects of its usage, as the increase in emission of greenhouse gases, the industry focused in searching of sustainable types of the raw materials for production of chemicals. Ethanol, produced by fermentation of sugars, is one of the more interesting renewable materials for chemical manufacturing. There are numerous applications for the conversion of ethanol into commodity chemicals. In particular, the production of 1,3-butadiene whose primary source is ethanol using multifunctional catalysts is attractive. With the 25% of world rubber manufacturers utilizing 1,3-butadiene, there is an exigent need for its sustainable production. In this research, the conversion of ethanol in one-step process to 1,3-butadiene was studied. According to the literature, the mechanisms which were proposed to explain the way ethanol transforms into butadiene require to have both acid and basic sites. But still, there are a lot of debate on this topic. Thus, the aim of this research work is a better understanding of the reaction pathways with all the possible intermediates and products which lead to the formation of butadiene from ethanol. The particular interests represent the catalysts, based on different ratio Mg/Si in comparison to bare magnesia and silica oxides, in order to identify a good combination of acid/basic sites for the adsorption and conversion of ethanol. Usage of spectroscopictechniques are important to extract information that could be helpful for understanding the processes on the molecular level. The diffuse reflectance infrared spectroscopy coupled to mass spectrometry (DRIFT-MS) was used to study the surface composition of the catalysts during the adsorption of ethanol and its transformation during the temperature program. Whereas, mass spectrometry was used to monitor the desorbed products. The set of studied materials include MgO, Mg/Si=0.1, Mg/Si=2, Mg/Si=3, Mg/Si=9 and SiO2 which were also characterized by means of surface area measurements.

By-products of the dairy farming as raw material for the biotechnology production of polyhydroxyalkanoates

I Poliidrossialcanoati (PHA) sono poliesteri completamente biodegradabili, prodotti da microrganismi come fonte di energia e di carbonio per la sintesi di nuovo materiale cellulare, utilizzando come substrato materie prime rinnovabili. Questi poliesteri sono considerati potenziali candidati per la sostituzione delle materie plastiche convenzionali. Tuttavia, i più alti costi di produzione dei PHA in confronto a quelli delle materie plastiche derivanti dal petrolio, rappresentano il principale ostacolo per la parziale sostituzione di questi ultimi con i biopolimeri. Gli alti costi sono principalmente dovuti all'utilizzo di colture microbiche pure (in cui sia presente un solo ceppo batterico) e substrati puri e costosi. Nell'ultimo decennio è stato sviluppato un processo di produzione a tre stadi alternativo e potenzialmente a minor costo, basato sull'utilizzo di colture microbiche miste (Mixed Microbials Culture, MMC) e una varietà di substrati organici a costo contenuto o nullo, quali alcuni rifiuti dell’industria agro-alimentare. Il presente studio si è concentrato sulla prima fase del processo di produzione dei PHA da colture miste, la fermentazione acidogenica, utilizzando siero di latte come fonte di carbonio per produrre acidi organici. In particolare questo lavoro ha avuto come obiettivo quello di studiare come diverse condizioni operative utilizzate nella fase di fermentazione acidogenica possono influenzare la concentrazione e il profilo degli acidi organici prodotti. Sono stati valutati anche gli effetti dei diversi profili degli acidi organici sulla fase di selezione della coltura microbica, in termini di capacità di stoccaggio di PHA e composizione polimerica.

Neural Machine Translation in the Field of Law. Analysis and Evaluation against Human Reference of NMT Raw Outputs of an Atto Costitutivo of S.r.l. from Italian into German.

Artificial Intelligence (AI) is gaining ever more ground in every sphere of human life, to the point that it is now even used to pass sentences in courts. The use of AI in the field of Law is however deemed quite controversial, as it could provide more objectivity yet entail an abuse of power as well, given that bias in algorithms behind AI may cause lack of accuracy. As a product of AI, machine translation is being increasingly used in the field of Law too in order to translate laws, judgements, contracts, etc. between different languages and different legal systems. In the legal setting of Company Law, accuracy of the content and suitability of terminology play a crucial role within a translation task, as any addition or omission of content or mistranslation of terms could entail legal consequences for companies. The purpose of the present study is to first assess which neural machine translation system between DeepL and ModernMT produces a more suitable translation from Italian into German of the atto costitutivo of an Italian s.r.l. in terms of accuracy of the content and correctness of terminology, and then to assess which translation proves to be closer to a human reference translation. In order to achieve the above-mentioned aims, two human and automatic evaluations are carried out based on the MQM taxonomy and the BLEU metric. Results of both evaluations show an overall better performance delivered by ModernMT in terms of content accuracy, suitability of terminology, and closeness to a human translation. As emerged from the MQM-based evaluation, its accuracy and terminology errors account for just 8.43% (as opposed to DeepL’s 9.22%), while it obtains an overall BLEU score of 29.14 (against DeepL’s 27.02). The overall performances however show that machines still face barriers in overcoming semantic complexity, tackling polysemy, and choosing domain-specific terminology, which suggests that the discrepancy with human translation may still be remarkable.