Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance

During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the frst European countries to be severely afected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using diferent lexicons-based methods, we fnd a breakpoint corresponding to the date of the frst established case of COVID-19 in Italy that causes a relevant change in sentiment scores used as a proxy of the country’s reputation. Next, we demonstrate that sentiment scores about Italy are associated with the values of the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as early detection signals of changes in the values of FTSE-MIB. Lastly, we evaluate whether diferent machine learning classifers were able to determine the polarity of tweets posted before and after the outbreak with a diferent level of accuracy

Cytogenetic studies in Pentatomidae (Heteroptera): A review

The suborder Heteroptera constitutes one of the most important insect groups because most species are plants feeders and cause damage on many plants of economic importance. One of the most important cytogenetic characteristics of Heteroptera is the holokinetic nature of the chromosomes. One particular feature of some species of Pentatomidae is the regular presence of an abnormal meiosis in one testicular lobe (harlequin lobe). From the 28 species cytogenetically analysed from Argentine material, 21 present the diploid number 2n ¼ 14, four species present a reduced number (2n ¼ 12) and another three species possess an increased diploid number (2n ¼ 16); among all these only three present an harlequin lobe. In the present work, a bibliographic review of the chromosome number and sex determining system of 294 species and subspecies belonging to 121 genera within the subfamilies Asopinae, Discocephalinae, Edessinae, Pentatominae, Phyllocephalinae and Podopinae is presented. The male diploid numbers range from six to 27 with a mode in 14 chromosomes; this last diploid number is present in 85% of the species. The sex chromosome determining system is XY/XX except in three species: Macropygium reticulare (Fabricius, 1803), Rhytidolomia senilis (Say, 1832) and Thyanta calceata (Say, 1832) which present derived sex chromosome systems. Furthermore, the cytogenetic relationships with the other families of Pentatomoidea are discussed

Global attractors for a three-dimensional conserved phase-field system with memory

We consider a conserved phase-field system on a tridimensional bounded domain. The heat conduction is characterized by memory effects depending on the past history of the (relative) temperature [\vartheta] . These effects are represented through a convolution integral whose relaxation kernel [k] is a summable and decreasing function. Therefore the system consists of a linear integrodifferential equation for [\vartheta] which is coupled with a viscous Cahn-Hilliard type equation governing the order parameter [\chi] . The latter equation contains a nonmonotone nonlinearity [\phi] and the viscosity effects are taken into account by the term [-\alpha \Delta\chi_t] , for some [\alpha \geq 0] . Thus, we formulate a Cauchy-Neumann problem depending on [\alpha ] . Assuming suitable conditions on [k] , we prove that this problem generates a dissipative strongly continuous semigroup [S^\alpha (t)] on an appropriate phase space accounting for the past histories of [\vartheta] as well as for the conservation of the spatial means of the enthalpy [\vartheta+\chi] and of the order parameter. We first show, for any [\alpha \geq 0] , the existence of the global attractor [\mathcal A_\alpha ] . Also, in the viscous case ( [\alpha > 0] ), we prove the finiteness of the fractal dimension and the smoothness of [\mathcal A_\alpha ]

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Threshold-based Naïve Bayes classifier

The Threshold-based Naive Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naive Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests' reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Advances in geometrical parametrization and reduced order models and methods for computational fluid dynamics problems in applied sciences and engineering: Overview and perspectives

Several problems in applied sciences and engineering require reduction techniques in order to allow computational tools to be employed in the daily practice, especially in iterative procedures such as optimization or sensitivity analysis. Reduced order methods need to face increasingly complex problems in computational mechanics, especially into a multiphysics setting. Several issues should be faced: stability of the approximation, efficient treatment of nonlinearities, uniqueness or possible bifurcations of the state solutions, proper coupling between fields, as well as offline-online computing, computational savings and certification of errors as measure of accuracy. Moreover, efficient geometrical parametrization techniques should be devised to efficiently face shape optimization problems, as well as shape reconstruction and shape assimilation problems. A related aspect deals with the management of parametrized interfaces in multiphysics problems, such as fluid-structure interaction problems, and also a domain decomposition based approach for complex parametrized networks. We present some illustrative industrial and biomedical problems as examples of recent advances on methodological developments