Proposta di sottotitolaggio del social movie italy in a day

The aim of this thesis is to analyse the main translating issues related to the subtitling of the Italian social movie Italy in a day into English: Italy in a day is a crowdsourced film, comprising a selection of video clips sent by ordinary people, showing occurrences of everyday life on a single day, October 26th, 2013. My dissertation consists of four chapters. The first provides a general overview of audiovisual translation, from the description of the characteristics of filmic products to a summary of the most important audiovisual translation modes; a theoretical framework of the discipline is also provided, through the analysis of the major contributions of Translations Studies and the multidisciplinary approach proposed by the scholar Frederic Chaume. The second chapter offers insight into the subtitling practice, examining its technical parameters, the spatial and temporal constraints, together with the advantages and pitfalls of this translation mode. The main criteria for quality assessment are also outlined, as well as the procedures carried out in the creation of subtitles within a professional environment, with a particular focus on the production of subtitles for the DVD industry. In the third chapter a definition of social movie is provided and the audiovisual material is accurately described, both in form and content. The creation of the subtitling project is here illustrated: after giving some information about the software employed, every step of the process is explained. In the final chapter the main translation challenges are highlighted. In the first part some text reduction techniques in the shift from oral to written are presented; then the culture-specific references and the linguistic variation in the film are analysed and the compensating strategies adopted to fill the linguistic and cultural gap are commented on and justified taking into account the needs and expectations of the target audience.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Geometrical optimization of a short-stem hip implant for the reduction of proximal stress shielding (ottimizzazione geometrica di uno stelo corto di protesi d?anca per la riduzione della schermatura del carico prossimale)

Nowadays the number of hip joints arthroplasty operations continues to increase because the elderly population is growing. Moreover, the global life expectancy is increasing and people adopt a more active way of life. For this reasons, the demand of implant revision operations is becoming more frequent. The operation procedure includes the surgical removal of the old implant and its substitution with a new one. Every time a new implant is inserted, it generates an alteration in the internal femur strain distribution, jeopardizing the remodeling process with the possibility of bone tissue loss. This is of major concern, particularly in the proximal Gruen zones, which are considered critical for implant stability and longevity. Today, different implant designs exist in the market; however there is not a clear understanding of which are the best implant design parameters to achieve mechanical optimal conditions. The aim of the study is to investigate the stress shielding effect generated by different implant design parameters on proximal femur, evaluating which ranges of those parameters lead to the most physiological conditions.

Catalytic reduction of organic pollutants using supported metal nanoparticles

Metal nanoparticle catalysts have in the last decades been extensively researched for their enhanced performance compared to their bulk counterpart. Properties of nanoparticles can be controlled by modifying their size and shape as well as adding a support and stabilizing agent. In this study, preformed colloidal gold nanoparticles supported on activated carbon were tested on the reduction of 4-nitrophenol by NaBH4, a model reaction for evaluating catalytic activity of metal nanoparticles and one with high significance in the remediation of industrial wastewaters. Methods of wastewater remediation are reviewed, with case studies from literature on two major reactions, ozonation and reduction, displaying the synergistic effects observed with bimetallic and trimetallic catalysts, as well as the effects of differences in metal and support. Several methods of preparation of nanoparticles are discussed, in particular, the sol immobilization technique, which was used to prepare the supported nanoparticles in this study. Different characterization techniques used in this study to evaluate the materials and spectroscopic techniques to analyze catalytic activities of the catalyst are reviewed: ultraviolet-visible (UV-Vis) spectroscopy, dynamic light scattering (DLS) analysis, X-ray diffraction (XRD) analysis and transmission electron microscopy (TEM) imaging. Optimization of catalytic parameters was carried out through modifications in the reaction setup. The effects of the molar ratio of reactants, stirring, type and amount of stabilizing agent are explored. Another important factor of an effective catalyst is its reusability and long-term stability, which was examined with suggestions for further studies. Lastly, a biochar support was newly tested for its potential as a replacement for activated carbon.

ICE Emissions Estimation through Machine-learning techniques to support real-world R&D experiments

The emissions estimation, both during homologation and standard driving, is one of the new challenges that automotive industries have to face. The new European and American regulation will allow a lower and lower quantity of Carbon Monoxide emission and will require that all the vehicles have to be able to monitor their own pollutants production. Since numerical models are too computationally expensive and approximated, new solutions based on Machine Learning are replacing standard techniques. In this project we considered a real V12 Internal Combustion Engine to propose a novel approach pushing Random Forests to generate meaningful prediction also in extreme cases (extrapolation, very high frequency peaks, noisy instrumentation etc.). The present work proposes also a data preprocessing pipeline for strongly unbalanced datasets and a reinterpretation of the regression problem as a classification problem in a logarithmic quantized domain. Results have been evaluated for two different models representing a pure interpolation scenario (more standard) and an extrapolation scenario, to test the out of bounds robustness of the model. The employed metrics take into account different aspects which can affect the homologation procedure, so the final analysis will focus on combining all the specific performances together to obtain the overall conclusions.

Synthetic generation of amyloid PET images by non-linear dimensionality reduction inversion

Privacy issues and data scarcity in PET field call for efficient methods to expand datasets via synthetic generation of new data that cannot be traced back to real patients and that are also realistic. In this thesis, machine learning techniques were applied to 1001 amyloid-beta PET images, which had undergone a diagnosis of Alzheimer’s disease: the evaluations were 540 positive, 457 negative and 4 unknown. Isomap algorithm was used as a manifold learning method to reduce the dimensions of the PET dataset; a numerical scale-free interpolation method was applied to invert the dimensionality reduction map. The interpolant was tested on the PET images via LOOCV, where the removed images were compared with the reconstructed ones with the mean SSIM index (MSSIM = 0.76 ± 0.06). The effectiveness of this measure is questioned, since it indicated slightly higher performance for a method of comparison using PCA (MSSIM = 0.79 ± 0.06), which gave clearly poor quality reconstructed images with respect to those recovered by the numerical inverse mapping. Ten synthetic PET images were generated and, after having been mixed with ten originals, were sent to a team of clinicians for the visual assessment of their realism; no significant agreements were found either between clinicians and the true image labels or among the clinicians, meaning that original and synthetic images were indistinguishable. The future perspective of this thesis points to the improvement of the amyloid-beta PET research field by increasing available data, overcoming the constraints of data acquisition and privacy issues. Potential improvements can be achieved via refinements of the manifold learning and the inverse mapping stages during the PET image analysis, by exploring different combinations in the choice of algorithm parameters and by applying other non-linear dimensionality reduction algorithms. A final prospect of this work is the search for new methods to assess image reconstruction quality.