Chapter Chinese migration(s) to Italy beyond stereotypes and simplistic views: the case of the graphic novels Primavere e Autunni and Chinamen

The current contribution aims at describing some key-aspects of Rocchi and Demonte’s graphic novels “Primavere e Autunni” (2015) and “Chinamen” (2017), especially in relation to: 1) the historical reconstruction of Chinese migration to Italy; 2) the challenge of widespreading negative stereotypes against Chinese migrants, which still characterize dominant public discourse in Italian society. The first paragraph will highlight theoretical aspects of both works, in particular relation to the literature on migration and of migration, with Sinoitalian literature, as well as with macro- and micro-aspects of Chinese migration to Italy. After that, some common points of both works will be underlined, including structure and style, semiotic aspects, communicative functions and multimodality. The third paragraph will specifically focus on a series of key-figures described in the graphic novels, which contribute to draw the attention to specific aspects regarding Chinese historical presence in Milan and in Italy

Labor Law—NLRB\u27s Lack of Remedial Power in a Runaway Plant Situation

Local 57, Garment Workers v. NLRB (Garwin Corp.), 374 F.2d 295 (D.C. Cir. 1967)

Water Distribution System Modeling and Optimization: A Case Study

In the last years, the scientific literature has reported an increasing use of hydraulic models to describe water distribution systems (WDS). Hydraulic models represent tools for managing the complexity of WDSs, and a number of optimization methods have been proposed to improve the performance of these infrastructures. However, because of the lack of available data on WDSs many works have only considered synthetic WDS with idealized behaviour or small-sized WDSs with simple topology and limited complexity. This lack of complex case studies has often hindered the demonstration of the potential of hydraulic models and of the optimization approaches relying on their use. In this work, we present a case study about a real large WDS. The system is composed of approximately 3000 pipes (>170 km) and 3000 demand nodes (corresponding to 50,000 users) that are spread across a hilly area over a 200 m elevation gradient. Water is provided by ten wells and it is distributed by five pumping stations and four tanks at different elevations. Pump operation is partly automatically controlled by water levels in tanks and partly by a fixed temporal schedule. This complexity results in a nontrivial hydraulic behaviour that is well reproduced by our hydraulic model. The model is also used with a multi-objective genetic algorithm solver to identify different operational scenarios that lead to a reduction of energy consumption and water leakages

Community detection as a tool for DMA identification

Water losses, the portion of water introduced in a pipe network but not consumed by users, represent a significant problem in water distribution system (WDS) management. Modern guidelines suggest to divide the pipe network in clusters, in order to compute a water balance and measure water consumption by each group. These clusters are called district metered areas (DMAs). The division of a pipe network in DMAs is usually realized with a visual exam supported by technical experience. This approach, which is convenient for small WDSs, becomes dicult to apply to large WDSs characterized by thousands of user nodes and pipes. Therefore, it is necessary to have an automatic tool to recognize the affinity degree of neighbouring nodes and to decide how to assign a node to a particular DMA. We propose an automated approach to subdivide pipes, that only requires flow rates through the network. The method has been tested to a large WDS often used as benchmark. The approach successfully divides the pipe network in an acceptable number of DMAs. Each resulting DMA is characterized by a low number of external links and by a proper number of users

A text mining framework for Big Data

Text Mining is the ability to generate knowledge (insight) from text. This is a challenging task, especially when the target text databases are very large. Big Data has attracted much attention lately, both from academia and industry. A number of distributed databases, search engines and frameworks have been developed to handle the memory and time constraints, which are required to process a large amount of data. However, there is no open-source end-to-end framework that can combinenearreal-timeandbatchprocessingofingestedbigtextualdataalongwith user-defined options and provision of specific, reliable insight from the data. This is important as this way new unstructured information is made accessible in near real-time, more personalised customer products can be created and novel unusual patterns can be found and actioned on quickly. This work focuses on a proprietary complete near real-time automated classification framework for unstructured data with the use of Natural Language Processing and Machine Learning algorithms on Apache Spark. The evaluation of our framework shows that it achieves a comparable accuracy with respect to some of the best approaches presented in the literature

Chinese Students’ Development of Textual Competence in L2 Italian: A Corpus-Based Study

The majority of studies conducted about Chinese students’ acquisition of L2 Italian since the 1990s have mainly focused on the analysis of learners’ phonological and morphosyntactic competences, usually adopting contrastive methods (Valentini 1992 and Banfi 2003, inter alia). More recent studies have been carried out from the point of view of applied linguistics (Rastelli 2010) and input processing (Rastelli 2013). However, textual and meta-textual competences of this category of learners have not yet been deeply investigated, even though the importance of such competences has already been acknowledged within important documents of language policy, e.g. the Common European Framework of Reference for Languages (CEFR 2001). This article is divided into two main sections. After preliminary theoretical considerations about some key-concepts, I will first emphasize, according to a theoretical background (Scalise and Ceccagno 2005; Diadori and Di Toro 2009 inter alia), the role played by some factors in slowing down, as well as in causing difficulties to Chinese students’ development of textual competence in L2 Italian. Secondly, I will analyze the results of a corpus-based cross-sectional study, the purpose of which was to investigate some aspects involved in the development of Chinese University students’ textual skills in Italian as a Second Languag

Chemistry through tattoo inks: a multilevel approach to a practice on the rise for eliciting interest in chemical education

Within the framework of a nationwide project to boost students’ enrollment in scientific disciplines in Italy, a multilevel science project was designed with a focus on the chemistry of tattoo inks, offering immediate connection with 16−18 year-old high school students. The approach takes into account time constraints, since all sessions have a maximum span of 8 h, and the heterogeneity of the audience, made up of students without background restrictions. Tattoos are perceived as a form of body art and can be conveniently used as the “A” in the STEAM (science, technology, engineering, art, and mathematics) methodology. The project involved active lectures and guided inquiry into the simple chemical concepts related to tattoo inks, addressed in practical units with multioutcome experiments and comparative instrumental analysis. The connections with correlated issues, such as norms regarding tattoo ink composition and verification, were also discussed. The efficacy of the tattoo ink Scientific Degree Plan experience was then evaluated through two types of surveys: one on the enjoyment of the plan and the other on mastery of the chemical concepts at the end of the experience