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The petrochemical industry has made the economic development of many local communities possible, increasing employment opportunities and generating a complex network of closely-related secondary industries. However, it is known that petrochemical industries emit air pollutants, which have been related to different negative effects on mental health. In addition, many people around the world are being exposed to highly stressful situations deriving from the COVID-19 pandemic and the lockdowns adopted by national and regional governments. The present study aims to analyse the possible differential effects on various psychological outcomes (stress, anxiety, depression and emotional regulation strategies) stemming from the COVID-19 pandemic and consequent lockdown experienced by individuals living near an important petrochemical complex and subjects living in other areas, nonexposed to the characteristic environmental pollutants emitted by these kinds of complex. The sample consisted of 1607 subjects who answered an ad hoc questionnaire on lockdown conditions, the Perceived Stress Scale (PSS), the Hospital Anxiety and Depression Scale (HADS), the Barratt Impulsivity Scale (BIS) and the Emotional Regulation Questionnaire (ERQ). The results indicate that people living closer to petrochemical complexes reported greater risk perception [K = 73.42, p < 0.001, with a medium size effect (η = 0.061)]. However, no significant relationship between psychological variables and proximity to the focus was detected when comparing people living near to or far away from a chemical/petrochemical complex. Regarding the adverse psychological effects of the first lockdown due to COVID-19 on the general population in Catalonia, we can conclude that the conditions included in this survey were mai

Several studies in different countries have reported that part of the population does not fully comply with the measures recommended to prevent COVID-19, and therefore poses a risk to public health. For this reason, several measures have been developed to assess the level of compliance, although many of them have methodological limitations or do not include a comprehensive set of items. The main goal of the current study was to develop a new instrument with suitable psychometric properties, which includes a more complete set of items and controls the impact of acquiescence bias. The participants were 1410 individuals (59.2% women) from Spain, who answered the new questionnaire and several items on sociodemographic and attitudinal issues. Exploratory and confirmatory factor analyses were carried out, and the results suggested that only one content factor was underlying the data. This solution was replicated in a different subsample, which shows the stability of the solution. Furthermore, the relationships between the scores of the new questionnaire and the sociodemographic and attitudinal variables are similar to those obtained in previous studies, which can be regarded as evidence of the validity of the new questionnaire.

We have before us the sixth issue of INSAM Journal of Contemporary Music, Art and Technology. This is the second issue in a row dedicated to the global crisis caused by the Covid-19 pandemic. After the overwhelming response from all over the world to the call for papers and provocative inspections that ensued, here we wanted to discuss the ways in which technology shapes and enables work in the areas of music, arts, humanities, and the education process, this time inviting our collaborators to discuss the shortcomings and struggles of the working processes in these fields. The main theme, “Music, Art and Humanities in the Time of Global Crisis”, expanded from the Main Theme section into the interviews as well.

EXCEPTIUS Corpus v1.0, containing the following data: - raw documents for 21 countries at national level - pre-processed data with spacy-udpipe v1.0 - automatically annotated documents for the identification of exceptional measures at sentence level Country list (ISO 3166-1 alpha-2): AT, BE, HR, CY, CZ, DK, FR, DE, HU, IE, IT, LV, LT, NL, NO, PL, SI, SE, CH, UK Folder structure: each country has a dedicated folder. Inside each folder you will find the following subfolders: - raw_text: the raw text data (.txt format) - processed: the output of the spacy-udpipe v1.0 - each line is a sentence, containing the following info: tokens, lemma, POS, UD dependency relations - model: the predictions of the trained model (XML pre@36 as reported in Table 4 of the paper). Each line is a sentence, separate by 9 tab - each for a exceptional measure class. 1: signals presence of a class. The Italy and Norway folder misses the predictions of the models.

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

Spain is one of the countries that has suffered the most from the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the strain that causes coronavirus disease 2019 (COVID-19). However, there is a lack of information on the characteristics of this disease in the Spanish population. The objective of this study has been to characterize our patients from an epidemiological point of view and to identify the risk factors associated with mortality in our geographical area. We performed a prospective, longitudinal study on 188 hospitalized cases of SARS-Cov-2 infection in Hospital Universitari de Sant Joan, in Reus, Spain, admitted between 15th March 2020 and 30th April 2020. We recorded demographic data, signs and symptoms and comorbidities. We also calculated the Charlson and McCabe indices. A total of 43 deaths occurred during the study period. Deceased patients were older than the survivors (77.7 ± 13.1 vs. 62.8 ± 18.4 years; p < 0.001). Logistic regression analyses showed that fever, pneumonia, acute respiratory distress syndrome, diabetes mellitus and cancer were the variables that showed independent and statistically significant associations with mortality. The Charlson index was more efficient than the McCabe index in discriminating between deceased and survivors. This is one of the first studies to describe the factors associated with mortality in patients infected with SARS-CoV-2 in Spain, and one of the few in the Mediterranean area. We identified the main factors independently associated with mortality in our population. Further studies are needed to complete and confirm our findings.

UIDB/04647/2020 UIDP/04647/2020 Nada faria supor aquando da publicação do anterior volume que o mundo fosse assolado por tão dramática pandemia – a COVID 19 - que num ápice atingiu o mundo inteiro. A nota mais expressiva de pesar pelas mortes registadas que possamos querer deixar patente ficará sempre muito aquém da tragédia humana sentida; sem precedentes, se atendermos ao facto da velocidade inédita a que se propagou, numa escala global e, mediaticamente, monitorada ao minuto. publishersversion published

This essay is comprised of three separate but interconnected sections, each working through at a different level the history of how Southern Illinois came to be called Egypt, and the implications of such a regional nicknaming. In the first, I consider the history of the moniker along with histories of the region through critical discussions of religion, race, and nineteenth century American Egyptomania. In the second, I retrace two cataclysmic events which occurred in Cairo, Illinois, and suggestively implicate by proximity Southern Illinois’ overidentification with Egypt. Finally, I recount and reconsider my own relation to the region and to its history and folklore, and describe my thesis exhibition, which has not yet been mounted due to COVID-19.

The fifth issue of INSAM Journal of Contemporary Music, Art and Technology is the second one we are preparing and publishing in the Covid-19 pandemic. And while the theme for the previous issue was conceived in a world unburdened with what has preoccupied our minds and lives in 2020, the theme for this one is directly shaped by it. During the Spring, when we were taken aback by the governmental measures and the fear of the “invisible enemy” (the use of militant vocabulary is rather prominent in the discourse surrounding the virus), the uncertainty for the future grew strong. However, at that time, we could not predict the longevity, brevity and consequences of the pandemic – in December we are still not certain, but we are getting tired. This is why I would like to thank all the authors for working with us in these trying times, unpacking what can only be a beginning of ‘a global crisis’ during the Summer and Autumn of 2020. The main theme of the issue, Music, Art, and Technology in the Time of Global Crisis, strives to capture this period through the lens of workers in art, music, and academia around the world, focusing on the role and place of arts and technology in our/their relocated institutional realities.

Fake news is not a novel concept but the scale of its spread and the damage it has and continues to cause is alarming. From the US presidential elections in 2016 to the COVID-19 pandemic and today, fake news has been circulating in news media corrupting the public opinion. Fake news alters democratic discussions polarizing people’s opinions sowing distrust in national institutions and setting different groups against each other. It is a threat to democracy and national security. It is crucial to prevent fake news from spreading and one solution is to create an automatic fake news detection system. A solution is researched using natural language processing (NLP) tasks, namely text classification. NLP is a type of artificial intelligence that is essentially taught to understand human language. Using thematic analysis, the main steps and techniques of fake news detection models are described and through a comparative analysis the state-of-the-art models are distinguished. And while there are many potential fake news detection models for English there is not much variety for other languages. So, it is additionally analysed if these benchmark models can be implemented for Finnish language as well. Valeuutiset eivät ole uusi käsite, mutta niiden leviämisen laajuus ja niiden aiheuttamat vahingot ovat huolestuttavia. Yhdysvaltain presidentinvaaleista vuonna 2016 COVID-19-pandemiaan ja nykypäivään asti, uutismediassa on kiertänyt valeuutisia, jotka muokkaavat yleisön mielipidettä. Valeuutiset muuttavat demokraattista keskustelua polarisoimalla ihmisten mielipiteitä kylväen epäluottamusta kansallisiin instituutioihin ja asettaen erilaisia ryhmiä toisiaan vastaan. Se on uhka demokratialle ja kansalliselle turvallisuudelle. On tärkeää estää valeuutisten leviäminen, ja yksi ratkaisu on luoda automaattinen valeuutisten havaitsemisjärjestelmä. Ratkaisua tutkitaan käyttämällä luonnollisen kielen käsittelyn (NLP) tehtäviä, etenkin tekstin luokittelua. NLP on tekoälyn tyyppi, missä tietokone opetetaan ymmärtämään ihmisten kieltä. Temaattisen analyysin avulla kuvataan valeuutisten havaitsemismallien päävaiheet sekä tekniikat, ja vertailevan analyysin avulla valikoidaan uusimmat ja onnistuneimmat mallit. Ja vaikka englannin kielellä on monia mahdollisia valeuutisten havaitsemismalleja, muille kielille ei ole paljon valikoimaa. Lisäksi analysoidaan, voidaanko nämä mallit toteuttaa myös suomen kielelle.