Giovanni Giuffrida

The United Nations adopted the Sustainable Development Goals (SDGs) on September 25, 2015, to address a wide range of global challenges and promote sustainable development worldwide. Since its adoption, there has been a growing interest among businesses, organizations, and governments in using the SDGs as a framework for sustainable development. One way this interest has manifested is through an increase in the number of documents and reports published by organizations that mention the SDGs. This happened for several reasons. Many organizations recognize that sustainable development is essential not only for the well-being of society and the planet but also for their long-term success and profitability. In addition, reporting on the SDGs can help organizations communicate their sustainability performance to stakeholders, including customers, employees, investors, and regulators. Investors and stakeholders are increasingly interested in the sustainability performance of companies and organizations. As a result, the number of documents published by companies and organizations mentioning SDGs has grown exponentially. All while their claims far exceed their actual sustainability initiatives. This means that much of the sustainability marketing out there is often overstated. Given all these documents, it is valuable to detect the SDGs they refer to. However, this assessment is difficult and labor-intensive to do manually, mainly when dealing with large organizations or referring to specific economic sectors or national and international public institutions. Therefore, specific algorithms can be used to quantitatively assess the alignment of a document to the SDGs, automatically identifying the extent to which the document mentions the SDGs and the degree to which it aligns with the goals. In this context, this chapter explores and reflects on the possibility of using a classification algorithm to analyze and assess companies’ and organizations’ SDG-related communication profiles. To this end, a recently developed classification algorithm is presented and tested to link text documents to their mentioned SDGs automatically.

The persistent volcanic activity of Stromboli Volcano, Aeolian Islands, Italy, is interrupted by paroxysmal eruptions on adecadal interval. In 2019, two strong paroxysms on July 3rd and August 28th, ended a more than a decade long period ofregular strombolian activity. During normal strombolian activity the volcano erupts highly porphyritic scoria and lava (HP)20with a shoshonitic basalt composition. In paroxysmal eruptions the HP material is mingled with low porphyritic (LP)pumices. Here, we provide new in-situ chemical measurements of the glass phases of LP and HP materials. We also providethe first radiogenic isotope data on the bulk compositions of the LP and HP components erupted on July 3rd, 2019. Thesedata show a remarkable shift in the Sr isotope ratio of the LP reservoir to more radiogenic compositions, not observed sincethe paroxysm in 1930.

We study the concept of free will by defining a mathematical model that extends Newton's laws of motion, in such a way that bodies are replaced with agents endowed with free will. In our model the free will of agents is not entirely free, but is bound by the Golden Rule, an ethic found in almost all cultures, which states that one should wish upon others as one wishes upon himself.

Magmatic fluids entering Mt. Etna volcano system from depth exceed those outgassed into atmosphere during inter-eruptive phases.

In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which misleading or even harmful, that has widely spread around the globe. Although Social Media are increasingly being used as information source, Web Search Engines, like Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through Search Engines. By using a real-world dataset associated with recent CoViD-19 pandemic, we first apply re-sampling techniques for class imbalance, then we use existing Machine Learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated Uniform Resource Locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URLs detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URLs features can improve the effectiveness of fake news detection methods.