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Under the influence of the health emergency triggered by the COVID-19 pandemic, many brands changed their communication strategy and included more or less explicit references to the principles of solidarity and fraternity in their TV commercials to boost the confidence and hope of Italian families during the lockdown. The traditional attitudes of the advertising format, which focused on product characteristics, were relegated to the background in order to reinforce the “brand image” through words, signs, hashtags and music that spread empathetic messages to all those who needed to regain hope and trust in a time of extreme emotional fragility. The objective of this paper is to identify the emotions and brand awareness during the lockdown using text mining techniques by measuring customer sentiment expressed on the Twitter social network. Our proposal starts from an unstructured corpus of 20,982 tweets processed with text data mining techniques to identify patterns and trends in people’s posts related to specific hashtags and TV ads produced during the COVID-19 pandemic. The innovations in the brand’s advertising among consumers seem to have triggered some sense of appreciation and gratitude, as well as a strong sense of belonging that was not present before, as the TV ads were perceived as a disruptive element in consumers’ tweets. Although this effect is clearly documented, in this paper we demonstrate its transitory nature, in the sense that the frequency of occurrence of terms associated with an emotional dimension peaks during the weeks of lockdown, and then gradually decreases.

In recent years, environmental problems have become a serious issue worldwide due to the increasing damage caused by climate change. People’s environmental awareness has grown, and public opinion is now demanding effective action from governments. Young people around the world are playing an important role in this, with the Fridays For Future movement, calling on policymakers to make environmental protection one of their political priorities. Through a survey of 1,975 high school students, this paper aims to contribute to the study of young people’s sustainable behaviors and their awareness to take effective action against environmental degradation, to explore their concerns and opinions about environmental issues, and to find out what ecological practices they are willing to adopt in their daily lives. Data analysis is conducted using tree-based methods to examine the sustainable behaviors and define the key practices that constitute them. The results of classification tree show that sustainable behaviors impact lifestyles, whether through less demanding actions such as turning off the faucet or appliances, or willingness to work as a volunteer, among others. The Random Forest provides us with a ranked list of sustainable behaviors that young people engage in to reduce and stop environmental degradation. The results of this study may be of interest to policy makers who need to plan educational pathways for students from elementary school to university, as environmental culture must be a cornerstone of our society.

In recent years, the increasing number of natural disasters has raised concerns about the sustainability of our planet’s future. As young people comprise the generation that will suffer from the negative effects of climate change, they have become involved in a new climate activism that is also gaining interest in the public debate thanks to the Fridays for Future (FFF) movement. This paper analyses the results of a survey of 1,138 young people in a southern Italian region to explore their perceptions of the extent of environmental problems and their participation in protests of green movements such as the FFF. The statistical analyses perform an ordinal classification tree using an original impurity measure considering both the ordinal nature of the response variable and the heterogeneity of its ordered categories. The results show that respondents are concerned about the threat of climate change and participate in the FFF to claim their right to a healthier planet and encourage people to adopt environmentally friendly practices in their lifestyles. Young people feel they are global citizens, connected through the Internet and social media, and show greater sensitivity to the planet’s environmental problems, so they are willing to take effective action to demand sustainable policies from decision-makers. When planning public policies that will affect future generations, it is important for policymakers to know the demands and opinions of key stakeholders, especially young people, in order to plan the most appropriate measures, such as climate change mitigation.

In this paper, well known disnormality or kurtosis indexes are decomposed into contributions to the measure of disnormality of the center, sides and tails of distributions. The results of this stud...

Following the introduction of performance management in Italian universities, Business intelligence has become a strategical means to use administrative data in support of governance and decision-making processes. The aim of this paper is to show the important role played by a decision support system inside an organization by evaluating the outcomes of the fair tuition fee policy of the University of Bari, in favour of low-income students, stated as a priority in its mission. A longitudinal analysis is carried out on the cohort of first-year students enrolled in the academic year 2015–2016, searching for a predictive model of their performance given some explicative variables. The usefulness of adopting a periodic monitoring system, investigating data by means of suitable statistical techniques (logistic regression, survival analysis, Cox regression model), allows to early detect those factors to be modified in order to achieve optimal results with respect to student expectations and quality of higher education.

The purpose of this paper is to broaden the knowledge of mean difference and, in particular, of an important distribution model known as Tukey lambda, which is generally used to choose a model to fit data. We have obtained compact formulas, which are not yet reported in literature, of mean deviation and mean difference related to the said distribution model. These results made it possible to analyze the relationships among variability indexes, namely standard deviation, mean deviation and mean difference, regarding Tukey lambda model.

Choosing the University to attend is an important decision that is made once or twice in a lifetime and has relevant effects for a person’s entire life. In such a process, advice from others, especially current students, is a powerful influencing factor. Therefore, understanding the factors that lead students to become active advocates for their university is strategically important. Social identity theory states that when students choose a university, the image of the institution becomes part of their identity. In case of strong positive identification, the resulting sense of pride enhances their own self-identity and brings positive benefits beyond simply obtaining an education, which are then passed on to everyone. The current study focuses on brand experience and brand reputation and uses a moderated mediation analysis to investigate the mechanisms by which current students can be tools for university choice. Stimulating word-of-mouth (WOM) implies the institution to have and maintain a good reputation and engage students to develop a positive brand experience and pride. This research contributes to the development of a greater strategic awareness of universities’ appeal to better tailor their orientation activities to current or prospective students.

Background: Benralizumab is effective in severe eosinophilic asthma (SEA), but suboptimal responses are observed in some patients. Although several factors have been associated with benralizumab response, no cluster analysis has yet been undertaken to identify different responsiveness sub-phenotypes. Objective: To identify SEA sub-phenotypes with differential responsiveness to benralizumab. Methods: One hundred and five patients diagnosed with SEA who had completed 6months of benralizumab treatment were included in a hierarchical cluster analysis based on a set of clinical variables that can be easily collected in routine practice (age, age at disease onset, disease length, allergen sensitization status, blood eosinophil count, IgE levels, FEV1% predicted, nasal polyposis, bronchiectasis). Results: Four clusters were identified: Clusters 2 and 3 included patients with high levels of both IgE and eosinophils (type-2 biomarkers high), whereas Clusters 1 and 4 included patients with only one type-2 biomarker at a high level: IgE in Cluster 1 and eosinophils in Cluster 4. Clusters 2 and 3 (both type-2 biomarkers high) showed the highest response rate to benralizumab in terms of elimination of exacerbations (79% and 80% respectively) compared to Clusters 1 and 4 (52% and 60% respectively). When super-response (the absence of exacerbation without oral corticosteroid use) was assessed, Cluster 2, including patients with more preserved lung function than the other clusters, but comparable exacerbation rate, oral corticosteroid use and symptom severity, was the most responsive cluster (87.5% of patients). Conclusions: Our cluster analysis identified benralizumab differential response sub-phenotypes in SEA, with the potential of improving disease treatment and precision management.

Objective FMF is an inherited autoinflammatory syndrome caused by mutations in the MEFV gene. MEFV variants are still largely classified as acvariant of uncertain significance, or with unresolved classification, posing significant challenges in FMF diagnosis. Rare Exome Variant Ensemble Learner (REVEL) is a recently developed variant metapredictor tool. To reduce the number of MEFV variants with ambiguous classification, we extracted REVEL scores for all missense variants present in the INFEVERS database, and analysed its correlation with expert-based classification and localization in the MEFV-encoded pyrin functional domains. Methods The data set of 216 MEFV missense variants was divided into four categories (likely benign, variant of uncertain significance, likely pathogenic and unresolved). Variants were plotted onto the pyrin protein, the distribution of REVEL scores in each category was computed and means, confidence intervals, and area under the receiver operating curve were calculated. Results We observed a non-random distribution of pathogenic variants along the pyrin functional domains. The REVEL scores demonstrated a good correlation with the consensus classification of the International Study Group for Systemic Autoinflammatory Diseases. Sensitivity, specificity and accuracy were calculated for different cut-off values of REVEL scores and a gene-specific-threshold of 0.298 was computed with confidence boundary limits. This cut-off value allowed us to propose a reclassification of 96 MEFV gene variants, thus reducing the variant of uncertain significance proportion from 61.6% to 17.6%. Conclusion The combination of available expert information with sensitive predictor tools could result in a more accurate interpretation of clinical consequences of MEFV gene variants, and to a better genetic counselling and patient management.

It is well known that, in classification problems, the predictive capacity of any decision-making model decreases rapidly with increasing asymmetry of the target variable (Sonquist et al., 1973; Fielding 1977). In particular, in segmentation analysis with a categorical target variable, very poor improvements of purity are obtained when the least represented modality counts less than 1/4 of the cases of the most represented modality. The same problem arises with other (theoretically more exhaustive) techniques such as Artificial Neural Networks. Actually, the optimal situation for classification analyses is the maximum uncertainty, that is, equidistribution of the target variable. Some classification techniques are more robust, by using, for example, the less sensitive logit transformation of the target variable (Fabbris & Martini 2002); however, also the logit transformation is strongly affected by the distributive asymmetry of the target variable. In this paper, starting from the results of a direct survey in which the target (binary) variable was extremely asymmetrical (10% vs. 90%, or greater asymmetry), we noted that also the logit model with the most significant parameters had very reduced fitting measures and almost zero predictive power. To solve this predictive issue, we tested post-stratification techniques, artificially symmetrizing a training sample. In this way, a substantially increase of fitting and predictive capacity was achieved, both in the symmetrized sample and, above all, in the original sample. In conclusion of the paper, an application of the same technique to a dataset of very different nature and size is described, demonstrating that the method is stable even in the case of analysis executed with all data of a population.

In the last decade, environmental issues have become a major topic of public opinion. People have begun to develop increasing environmental awareness and positive attitudes toward ecological matters. In particular, young people have begun to actively participate in environmental challenges by organizing green movements and engaging with policymakers. In this perspective, this study explores two main aspects: 1) awareness of the potential impacts of climate change and of environmental issues in each individual's daily life, and 2) the relationship between attitudes and pro-environmental actions. To this end, we analyse qualitative data obtained from a tailored online survey involving either students from a selected group of Italian High schools or volunteer respondents from the students' acquaintances. A total of 1834 responses are analysed using statistical tools to assess the multiple objectives of the survey. We distinguish between young and adult respondents and examine the relationships between awareness, personal experience, and risk perception in the two groups. We then propose a Multiple Correspondence Analysis that focuses on youth attitudes to examine the factors that influence their propensity to environmental issues and to determine the impact of global green movements on the onset or acceleration of their environmental awareness. Although the results show a more pessimistic view of the threats posed by environmental crises, our results highlight that younger generations have a deeper awareness of environmental conditions and are committed to environmental recovery by adopting “good ecological practices” and “active ecological behaviors."

Familial Mediterranean Fever (FMF) is an inherited autoinflammatory syndrome caused by mutations in the MEFV gene. MEFV variants are still in large part classified as variant of uncertain significance (VOUS), or with classification unresolved, posing significant challenges in the clinical diagnosis of Familial Mediterranean Fever (FMF). REVEL is a recently developed variant metapredictor tool. To reduce the number of MEFV variants with ambiguous classification we extracted the REVEL score for all missense variants reported at the locus specific database INFEVERS, and analyzed its correlation with expert-based classification and localization in the MEFV-encoded pyrin protein functional domains.The data set of 216 missense variants was divided in four classification categories (BENIGN, VOUS, PATHOGENIC and UNRESOLVED). MEFV variants were plotted onto the pyrin protein, the distribution of REVEL scores in each category was computed and means, confidence intervals, and area under the receiver operating curve were calculated.We observed a non-random distribution of pathogenic variants along the functional domains of the pyrin protein. The REVEL scores demonstrated a good correlation with the consensus classification of the International Study Group for Systemic Autoinflammatory Diseases (INSAID). Sensitivity, specificity, and accuracy were calculated for different cutoff values of REVEL scores and a gene-specific threshold was computed with confidence boundary limits. A REVEL score of 0.298 was the best performing cut-off to reclassify 96 MEFV gene variants previously of uncertain significance or unsolved thus reducing their proportion from 61.6% to 17.6%.In conclusion, the combination of available expert information with highly sensitive predictor tools yields to more accurate interpretation of clinical consequences of MEFV gene variants. This approach should bring to a better genetic counseling and patient management.Author summaryWe aimed to refine MEFV gene variants classification using the metapredictor REVEL. We demonstrate that a gene-specific threshold is effective for accurate variants’ classification. Using this threshold, we reduced significantly the proportion of MEFV variants with an ambiguous classification. The proposed classification could represent a useful resource for variant interpretation in the context of FMF diagnosis.

The aim of this paper is to examine the relations between the mean difference and the mean deviation with reference to the main continuous distribution models. At present, the analytical expressions of the mean difference, in a more or less compact form, have been developed for almost all the continuous distribution models. The numerical calculation of the mean difference is, instead, always possible for any distribution model. The distribution models without the shape parameters, those with only one shape parameter and those with two shape parameters have been considered. The perfect rank correlation between the values of the two indexes for the models without shape parameters have been ensured. In the case of models with only one shape parameter, it has been observed that, when the shape parameter changes, the two indexes are both increasing or both decreasing, so that the relation between the same is growing. The relation between the two indexes has allowed detection of the intervals in which one index is greater than the other and that in which it is less. Similar findings emerged when dealing with models with two shape parameters determining the region in which one index is greater than the other and as well as the complementary one.

The calculation of the mean difference for the inverse normal distribution can be obtained by a transformation of variable or a hard integration by parts. This paper shows a simpler formula of the mean difference of the inverse normal distribution that highlights the role of the two parameters on the mean difference of the model. It makes it easier to study the relation of the mean difference with the other indexes of variability for the inverse normal distribution.

PurposeIn order to face the new competitive scenario, Italian universities are involved in a change process and are trying to adopt an entrepreneurial approach to better serve their customers/students. The paper aims to assess university performance by testing student satisfaction. It tries to provide universities with some solutions to improve its performance.Design/methodology/approachThe case of the University of Bari has been analysed by interviewing a random (but well stratified) sample of students in order to test their satisfaction concerning services provided and quality perceived. Reasons why students decide to enrol at the University of Bari are also investigated.FindingsUniversities have to concentrate their efforts on the improvement of quality of teaching and non‐teaching services, in order to promptly respond to the target, and foster a stronger relationship with surrounding economic and productive systems.Research implications/limitationsUniversities need to adopt a customer centric approach. Future research should analyse the reform period experienced by the Italian University system, in order to investigate student satisfaction concerning recent changes. Moreover, a cross‐country analysis of student satisfaction, through the servqual questionnaire, would be interesting.Practical implicationsStudent satisfaction at the University of Bari is higher for working students that are not interested in “additional” services. On the contrary, several improvements are perceived as urgent concerning non‐core services provided, in which regular students are very interested.Originality/valueThis paper provides a wide analysis of the services perception tested and viewed through student eyes.

The Survey on illegal migration in Italy (SIMI henceforth) aims to analyse the phenomenon of clandestines migrating to or through Italy. SIMI contains information concerning the main demographic, economic and social characteristics of a sample of 920 clandestines crossing Italian borders and apprehended during 2003. Individual motivations to migrate, migrants’ intentions to return, their expectations about income at destination and their intended remittance rates are collected within SIMI and discussed in this paper. Evidence generally corroborates the predictions of the pertinent literature on development economics with respect to the decision to migrate. Apprehended illegal migrants expected to earn about 8-10 times their income at home, had they reached final destination. The median cost of the trip is more than a yearly average income. One of the conclusions of the survey is that future migratory flows may be massive, as the interviewees (typically in the mid-20s and with family and friends at home that are ready to join in the future) perceive themselves as frontrunners among nationals in their age cohort.

The Survey on illegal migration in Italy (SIMI henceforth) aims to analyse the phenomenon of clandestines migrating to or through Italy. SIMI contains information concerning the main demographic, economic and social characteristics of a sample of 920 clandestines crossing Italian borders and apprehended during 2003. Migrants' motivations, intention to send remittance and expectations about the future are collected within SIMI and reported in this paper.