Study of the absorption of organic compounds on biopolymers

This work has been conducted in order to determine the solubility and diffusion coefficients of different aromatic substances in two different grades of polylactic acid (PLA), Amorphous (PDLLA) and Crystalline (PLLA); in particular the focus is on the following terpenes: Linalool, α-Pinene, β-Citronellol and L-Linalool. Moreover, further analyses have been carried out with the aim to verify if the use of neat crystalline PLA, (PLLA), a chiral substrate, may lead to an enantioenrichment of absorbed species in order to use it as membrane in enantioselective processes. The other possible applications of PLA, which has aroused interest in carry out the above-mentioned work, concerns its use in food packaging. Therefore, it is interesting and also very important, to evaluate the barrier properties of PLA, focusing in particular on the transport and absorption of terpenes, by the packaging and, hence, by the PLA. PLA films/slabs of one-millimeter thickness and with square shape, were prepared through the Injection Molding process. On the resulting PLA films heat pretreatment processes of normalizing were then performed to enhance the properties of the material. In order to evaluate solubility and diffusion coefficient of the different penetrating species, the absorption kinetics of various terpenes, in the two different types of PLA, were determined by gravimetric methods. Subsequently, the absorbed liquid was extracted with methanol (MeOH), non- solvent for PLA, and the extract analyzed by the use of High Performance Liquid Chromatography (HPLC), in order to evaluate its possible enantiomeric excess. Moreover, PLA films used were subjected to differential scanning calorimetry (DSC) which allowed to measure the glass transition temperature (Tg) and to determine the degree of crystallinity of the polymer (Xc).

Performance Analysis of 5G based Positioning in Railway Applications

Rail transportation has significant importance in the future world. This importance is tightly bounded to accessible, sustainable, efficient and safe railway systems. Precise positioning in railway applications is essential for increasing railway traffic, train-track control, collision avoidance, train management and autonomous train driving. Hence, precise train positioning is a safety-critical application. Nowadays, positioning in railway applications highly depends on a cellular-based system called GSM-R, a railway-specific version of Global System for Mobile Communications (GSM). However, GSM-R is a relatively outdated technology and does not provide enough capacity and precision demanded by future railway networks. One option for positioning is mounting Global Navigation Satellite System (GNSS) receivers on trains as a low-cost solution. Nevertheless, GNSS can not provide continuous service due to signal interruption by harsh environments, tunnels etc. Another option is exploiting cellular-based positioning methods. The most recent cellular technology, 5G, provides high network capacity, low latency, high accuracy and high availability suitable for train positioning. In this thesis, an approach to 5G-based positioning for railway systems is discussed and simulated. Observed Time Difference of Arrival (OTDOA) method and 5G Positioning Reference Signal (PRS) are used. Simulations run using MATLAB, based on existing code developed for 5G positioning by extending it for Non Line of Sight (NLOS) link detection and base station exclusion algorithms. Performance analysis for different configurations is completed. Results show that efficient NLOS detection improves positioning accuracy and implementing a base station exclusion algorithm helps for further increase.

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.