COVID-19 pandemic has hit people’s health, economy, and society worldwide. Great confidence in returning to normality has been placed in the vaccination campaign. The knowledge of individual immune profiles and the time required to achieve immunological protection is crucial to choose the best vaccination strategy. We compared anti-S1 antibody levels produced over time by BNT162b2 and AZD1222 vaccines and evaluated the induction of antigen-specific T-cells. A total of 2569 anti-SARS-CoV-2 IgG determination on dried blood spot samples were carried out, firstly in a cohort of 1181 individuals at random time-points, and subsequently, in an independent cohort of 88 vaccinated subjects, up to the seventeenth week from the first dose administration. Spike-specific T-cells were analysed in seronegative subjects between the two doses. AZD1222 induced lower anti-S1 IgG levels as compared to BNT162b2. Moreover, 40% of AZD1222 vaccinated subjects and 3% of BNT162b2 individuals resulted in seronegative during all the time-points, between the two doses. All these subjects developed antigen-specific T cells, already after the first dose. These results suggest that this test represents an excellent tool for a wide sero-surveillance. Both vaccines induce a favourable immune profile guaranteeing efficacy against severe adverse effects of SARS-CoV-2 infection, already after the first dose administration.

Sensitivity and identifiability problems of the modal parameters in the presence of corrosion damage are studied. The first concerns the rate of change of the modal parameters against the damage increase. The second concerns the uncertainty intervals overlapping of the modal parameters between the sound and damaged states. To this end, different testing methods, different identification methods (time, frequency and time-frequency methods), different corrosion levels and different thermal condition (summer-winter) are considered. Prestressed concrete beams of identical geometry endowed with low to high reinforcement ratios are dynamically tested. The free decaying vibrations are used to identify the modal parameters: frequency, modal shapes and damping. The prestressing force is found to be not a variable of the problem. The damage levels range from very low to moderate. It is found that reliable damage identification is possible only for moderate damage. In spite of higher scatter, damping reveals a more stable and sensitive indicator than frequency. Modal shapes shows regular changes, but within the range of the uncertainty intervals.

Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

An experimental campaign named the Pescara benchmark and devoted to study the dynamic behaviour of corroded p.c. joists has been conducted. The steel corrosion reduces the area of the reinforcement and causes cracking of concrete so that r/c members are subjected to loss of strength and stiffness. It is of interest to evaluate the corrosion level at which the damage can be detected through signal processing procedures and how close such level is to the r/c member safety limits. Joists of current industrial production having different steel to concrete ratios are tested in different laboratory conditions. Dynamic tests involve either free vibrations and forced vibrations due to a moving mass simulating actual traffic loads in railway bridges. The paper discusses the rationale of the tests including the set up of the artificial corrosion, the static characterization of the joist and the dynamic tests in the different stages of corrosion experienced.

Cluster analysis has for long been an effective tool for analysing data. Thus, several disciplines, such as marketing, psychology and computer sciences, just to mention a few, did take advantage from its contribution over time. Traditionally, this kind of algorithm concentrates only on numerical or categorical data at a time. In this work, instead, we analyse a dataset composed of mixed data, namely both numerical than categorical ones. More precisely, we focus on profiling visitors of the National Park of Majella in the Abruzzo region of Italy, which observations are characterized by variables such as gender, age, profession, expectations and satisfaction rate on park services. Applying a standard clustering procedure would be wholly inappropriate in this case. Therefore, we hereby propose an unsupervised classification of mixed data, a specific procedure capable of processing both numerical than categorical variables simultaneously, releasing truly precious information. In conclusion, our application therefore emphasizes how cluster analysis for mixed data can lead to discover particularly informative patterns, allowing to lay the groundwork for an accurate customers profiling, starting point for a detailed marketing analysis.