Light coloured and draining wearing courses for low-impact urban pavements

Urban health and well-being are becoming current issues of modern cities due to local climate change and environmental noise. The Urban Heat Island and the Urban Noise Island have a direct impact on the economic, social, and environmental aspects of urban life, negatively affecting the well-being of worldwide citizens. The present research is focused on the study of innovative materials employed in the production of wearing course mixtures aiming to mitigate these phenomena. In particular, a synthetic transparent binder substituting bitumen and recycled aggregates produced from construction and demolition waste. Four mixtures were analysed. Among them, Mix 1 and Mix 2 are conventional wearing courses. The first is exclusively made of natural aggregates, while the second is constituted of 45 % of recycled aggregates (RA). Mix 3 and Mix 4 are draining wearing courses and, in this case, Mix 4 was produced by using 55 % of RA. Laboratory tests were required to fully characterize all the produced samples, allowing a proper comparison of results. Overall, all the mixtures studied provide prominent results suggesting potential applications of these innovative wearing courses in cycle lanes, historical centres, plazas, and parking lots. Among the conventional mixtures, Mix 2 is the most likely to assure the best performance in terms of road safety, efficiency, and durability while as far as the draining mixtures are concerned, Mix 4 is preferable due to its high content of recycled aggregates.

Bitext alignment: building and evaluating a bilingual corpus and translation memory of academic course descriptions

Following the internationalization of contemporary higher education, academic institutions based in non-English speaking countries are increasingly urged to produce contents in English to address international prospective students and personnel, as well as to increase their attractiveness. The demand for English translations in the institutional academic domain is consequently increasing at a rate exceeding the capacity of the translation profession. Resources for assisting non-native authors and translators in the production of appropriate texts in L2 are therefore required in order to help academic institutions and professionals streamline their translation workload. Some of these resources include: (i) parallel corpora to train machine translation systems and multilingual authoring tools; and (ii) translation memories for computer-aided tools. The purpose of this study is to create and evaluate reference resources like the ones mentioned in (i) and (ii) through the automatic sentence alignment of a large set of Italian and English as a Lingua Franca (ELF) institutional academic texts given as equivalent but not necessarily parallel (i.e. translated). In this framework, a set of aligning algorithms and alignment tools is examined in order to identify the most profitable one(s) in terms of accuracy and time- and cost-effectiveness. In order to determine the text pairs to align, a sample is selected according to document length similarity (characters) and subsequently evaluated in terms of extent of noisiness/parallelism, alignment accuracy and content leverageability. The results of these analyses serve as the basis for the creation of an aligned bilingual corpus of academic course descriptions, which is eventually used to create a translation memory in TMX format.