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This study aims to determine whether team production among peer groups culturally aggregated can affect the individual student performance in an Italian university. Two further hypotheses were tested in order to verify whether students perform better in team than individually and if students culturally aggregated in team perform better than students differently aggregated. By means of a field experiment, 162 students were involved in the study. The participants had the opportunity to sit a new examination model after responding to a self-reported questionnaire. The new model contemplated that the students performed in team for the first part of the program and carried out the remaining part of examination individually. The groups were composed of individuals linked by homogeneous preferences about cultural activities. The results show that team performance affects positively the final score of exam and the students culturally aggregated pass the exam more than students not culturally aggregated. The aggregative cultural activities influenced the test score more than individual effort or other personal characteristics. We therefore recommend the implementation of a new examination models based on cooperation among students supporting the aggregative cultural activities where sharing action prevails as predictors. [ABSTRACT FROM AUTHOR]

• The observed amount of antidepressant (AD) prescriptions in 2020 measured in number of defined daily doses (DDDs) was higher than predicted, particularly in men, in adults and among the classes of selective serotonin reuptake inhibitors and other ADs. • The greatest increase of AD use was observed in young people, and in the first four and last two months of 2020, prior and during the first and second stricter Italian lockdowns. • The increase in AD prescriptions may indirectly indicate a rise of mental problems and sleeping disorders in the general population, related to the COVID-19 consequences. • The increase of AD use might be also related to a hoarding of prescriptions prior to lockdown restrictions. Few studies investigated the impact of the pandemic on antidepressant (AD) use. The Social and Health Information System of Friuli Venezia Giulia region, Italy, provided data on AD use. Sex, age, AD class and month used the amount of AD prescriptions, measured by defined daily doses (DDD) per 1000 inhabitants per day, to compare AD use in 2020 with the period 2015–2019. A linear trend model predicted AD use for 2020, based on years 2015–2019. AD use was on average 20% higher in each month of 2020 when compared with the same month for the period 2015–2019, with an increase of more than 30% in the first four and in the last two months of 2020. The observed AD use in 2020 was higher than predicted, particularly in men, and in the 30–59 years age group. Descriptive study of AD use without analysis of data at the individual level. No information on psychiatric diagnoses of AD users. AD use was higher in the first year of the COVID-19 pandemic. Further research is warranted to understand if this may be related to a rise in mental disorders in the general population during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The genetic algorithm (GA) technique is applied to obtain optimal parameter values of the standard rating curve model (RCM) for predicting, in real time, event-based flow discharge hydrographs at sites receiving significant lateral inflows. The standard RCM uses the information of discharge and effective cross-sectional flow area at an upstream station and effective cross-sectional flow area wave travel time later at a downstream station to predict the flow rate at this last site. The GA technique obtains the optimal parameter values of the model, here defined as the GA-RCM model, by minimizing the mean absolute error objective function. The GA-RCM model was tested to predict hydrographs at three different stations, located on the Upper Tiber River in central Italy. The wave travel times characterizing the three selected river branches are, on the average, 4, 8, and 12 h. For each river reach, seven events were employed, four for the model parameters’ calibration and three for model testing. The GA approach, employing 100 chromosomes in the initial gene pool, 75% crossover rate, 5% mutation rate, and 10,000 iterations, made the GA-RCM model successfully simulate the hydrographs observed at each downstream section closely capturing the trend, time to peak, and peak rates with, on the average, less than 5% error. The model performance was also tested against the standard RCM model, which uses, on the contrary to the GA-RCM model, different values for the model parameters and wave travel time for each event, thus, making the application of the standard RCM for real time discharge monitoring inhibited. The comparative results revealed that the RCM model improved its performance by using the GA technique in estimating parameters. The sensitivity analysis results revealed that at most two events would be sufficient for the GA-RCM model to obtain the optimal values of the model parameters. A lower peak hydrograph can also be employed in the calibration to predict a higher peak hydrograph. Similarly, a shorter travel time hydrograph can be used in GA to obtain optimal model parameters that can be used to simulate floods characterized by longer travel time. For its characteristics, the GA-RCM model is suitable for the monitoring of discharge in real time, at river sites where only water levels are observed. [ABSTRACT FROM AUTHOR]

On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease. [ABSTRACT FROM AUTHOR]

The Nobel laureate Niels Bohr once said that: "Predictions are very difficult, especially if they are about the future". Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM2.5, PM10, and NO2. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February–July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September–December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September–December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history—and this, too, is science at the service of society. [ABSTRACT FROM AUTHOR]