Sports mega-events in the perception of the local community: the case of Itaquera region in São Paulo at the 2014 FIFA World Cup Brazil

Hosting sporting mega-events involve considerable investment in and significant engagement with local communities. This paper contributes to the burgeoning literature about sporting mega event’s socio-geographical impacts on tourism, employment, urban regeneration, and socio-economic and cultural benefits. Within the context of 2014 FIFA World Cup in Brazil, we assess, utilizing interview data from local communities, the impact of the construction of the 2014 World Cup’s opening venue, ‘Arena Corinthians’ soccer stadium, in Itaquera, as well as other infrastructure developments more widely in the East Zone of São Paulo. The results point to the diversity of opinions among the resident community about the real legacies resulting from this sport mega-event.     

American beauty: trade flows and export costs of US movies

This paper estimates a hedonic model of revenues for 1431 American movies in 45 non-American countries over the period 2002–2013. Such an extensive analysis is proven to be useful to estimate the effect of indicators of geographical and cultural distance on total revenue and the number of American films exported to non-American countries once the model specification also controls for relevant film features. It is found that indicators of cultural and geographical distance, in addition to the Human Development Index, have a significant effect on both the number of American films shown and the revenue obtained. Estimation results have been also used to appraise the importance of film features in different groups of countries from the perspective of their degree of development and distance from the USA.     

Ground reaction forces during steeplechase hurdling and waterjumps

Athletes in the 3,000 m steeplechase track and field event negotiate unmovable hurdles and waterjumps. Ground reaction forces (GRF) in the steeplechase were quantified to elucidate injury risks / mechanisms and to inform coaches. Five male and five female steeplechasers participated. GRF were measured during treadmill running, and using specially mounted force platforms, during hurdle and waterjump takeoffs and landings at 5.54 m/s (males) or 5.00 m/s (females). Results are presented as: male mean ± SD / female mean ± SD. Initial and active peaks of vertical GRF during treadmill running were 2.04 ± 0.72 / 2.25 ± 0.28 BW and 3.11 ± 0.27 / 2.98 ± 0.24 BW. Compared to treadmill running, peak vertical forces were greater (p < 0.001) for: hurdle takeoff (initial: 4.25 ± 0.86 / 3.78 ± 0.60 BW, active: 3.82 ± 0.20 / 3.74 ± 0.32 BW), hurdle landing (active: 4.41 ± 1.13 / 4.21 ± 0.21 BW), waterjump takeoff (initial: 4.32 ± 0.67 / 4.56 ± 0.54 BW, active: 4.00 ± 0.24 / 3.83 ± 0.31 BW), and waterjump landing (initial: 3.45 ± 0.34 / #3.78 ± 0.32 BW, active:5.40 ± 0.78 / #6.23 ± 0.74 BW); (#) indicates not statistically compared (n = 2). Based on horizontal impulse, athletes decelerated during takeoff steps and accelerated during landing steps of both hurdling and waterjumps. Vertical GRF peaks and video indicated rearfoot strikes on the treadmill but midfoot strikes during hurdle and waterjump landings. Potentially injurious GRF occur during the steeplechase, particularly during waterjump landings (up to 7.0 BW).     

The white colour in Etruscan polychromes on terracotta: Spectroscopic identification of kaolin

The present work reports on a spectroscopic study of white colour samples collected from Etruscan polychromes on terracotta from the Cerveteri area. In particular, both white-on-red potsherds of the Orientalising period and high-value polychromes of the Archaic period were considered. The white pigment, a fundamental element in this class of artworks, was not clearly identified in previous archaeological and analytical studies, although the use of kaolin was tentatively proposed. Therefore, samples from the Monte Sughereto kaolin quarry in the Cerveteri area were also studied. The results of detailed analyses carried out using micro-Raman spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy first show that the quarry material is a high-purity kaolin containing quartz. Raman results strongly suggest that the same kind of kaolin was employed in the polychromes of the Archaic period. As to white-on red potsherds, infrared results have a key role in showing that the white pigment is a kaolin closely corresponding to the quarry material since a particular kaolin polytype (dickite) is dominant in both cases. The present work thus provides the first definitive identification of the white pigment used in Etruscan polychromes on terracotta from the Cerveteri area, and suggests that the source of kaolin might be the Monte Sughereto quarry.     

Detecting ethnicity-targeted hate speech in Russian social media texts

Ethnicity-targeted hate speech has been widely shown to influence on-the-ground inter-ethnic conflict and violence, especially in such multi-ethnic societies as Russia. Therefore, ethnicity-targeted hate speech detection in user texts is becoming an important task. However, it faces a number of unresolved problems: difficulties of reliable mark-up, informal and indirect ways of expressing negativity in user texts (such as irony, false generalization and attribution of unfavored actions to targeted groups), users’ inclination to express opposite attitudes to different ethnic groups in the same text and, finally, lack of research on languages other than English. In this work we address several of these problems in the task of ethnicity-targeted hate speech detection in Russian-language social media texts. This approach allows us to differentiate between attitudes towards different ethnic groups mentioned in the same text – a task that has never been addressed before. We use a dataset of over 2,6M user messages mentioning ethnic groups to construct a representative sample of 12K instances (ethnic group, text) that are further thoroughly annotated via a special procedure. In contrast to many previous collections that usually comprise extreme cases of toxic speech, representativity of our sample secures a realistic and, therefore, much higher proportion of subtle negativity which additionally complicates its automatic detection. We then experiment with four types of machine learning models, from traditional classifiers such as SVM to deep learning approaches, notably the recently introduced BERT architecture, and interpret their predictions in terms of various linguistic phenomena. In addition to hate speech detection with a text-level two-class approach (hate, no hate), we also justify and implement a unique instance-based three-class approach (positive, neutral, negative attitude, the latter implying hate speech). Our best results are achieved by using fine-tuned and pre-trained RuBERT combined with linguistic features, with F1-hate=0.760, F1-macro=0.833 on the text-level two-class problem comparable to previous studies, and F1-hate=0.813, F1-macro=0.824 on our unique instance-based three-class hate speech detection task. Finally, we perform error analysis, and it reveals that further improvement could be achieved by accounting for complex and creative language issues more accurately, i.e., by detecting irony and unconventional forms of obscene lexicon.     

Irony detection via sentiment-based transfer learning

Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection.     

Universities’ attractiveness to students: The Darwinism effect

Due to significant government cuts to Higher Education funding in Southern European systems, their already underfunded universities were forced to increasingly compete for students as sources of additional revenue. Concurrently, families and students that continued to afford participation in Higher Education became more selective when choosing a university, realising the riskier investment that Higher Education participation had become. Through a competing destinations model and relying on all Italian private and public universities, this study finds that the competition forces characterising universities’ attractiveness over the last decade have changed since the financial crisis of 2008. In a context of lower demand for Higher Education, the competition for students grew and universities in close proximity were better prepared to face the new challenges, leading to the growth of Higher Education clusters.     

Light-driven directional ion transport for enhanced osmotic energy harvesting

Light-driven ion (proton) transport is a crucial process both for photosynthesis of green plants and solar energy harvesting of some archaea. Here, we describe use of a TiO₂/C₃N₄ semiconductor heterojunction nanotube membrane to realize similar light-driven directional ion transport performance to that of biological systems. This heterojunction system can be fabricated by two simple deposition steps. Under unilateral illumination, the TiO₂/C₃N₄ heterojunction nanotube membrane can generate a photocurrent of about 9 μA/cm², corresponding to a pumping stream of ∼5500 ions per second per nanotube. By changing the position of TiO₂ and C₃N₄, a reverse equivalent ionic current can also be realized. Directional transport of photogenerated electrons and holes results in a transmembrane potential, which is the basis of the light-driven ion transport phenomenon. As a proof of concept, we also show that this system can be used for enhanced osmotic energy generation. The artificial light-driven ion transport system proposed here offers a further step forward on the roadmap for development of ionic photoelectric conversion and integration into other applications, for example water desalination.