In this paper, we study a model describing the displacement of a linearly elastic flexural shell subjected to given dynamic loads from the computational point of view. The model under consideration takes the form of a set of hyperbolic variational equations posed over the space of admissible linearised inextensional displacements, and a set of initial conditions. Since the original model is not suitable for the implementation of a finite element method, we conduct the experiments on the corresponding penalised model. It was recently shown that the solution to such a penalised model is a good approximation of the solution to the original model. Numerical tests are therefore conducted on the the penalised model; the approximation of the solution to the penalised model is obtained via Newmark’s scheme, which is then implemented and tested for shells having the following middle surfaces: a portion of a cylinder and a portion of a cone. For the sake of completeness, we also present the results of the numerical tests related to a model describing the displacement of a linearly elastic elliptic membrane shell under the action of given dynamic loads.

The selection of texts for second language learning purposes typically relies on teachers' and test developers' individual judgment of the observable qualitative properties of a text. Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility. This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning. A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts. The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.

NLP technologies and components have an increasing diffusion in mass analysis of text based dialogues, such as classifiers for sentiment polarity, trends clustering of online messages and hate speech detection. In this work we present the design and the implementation an automatic classification tool for the evaluation of the complexity of Italian texts as understood by a speaker of Italian as a second language. The classification is done within the Common European Framework of Reference for Languages (CEFR) which aims at classifying speakers language proficiency. Results of preliminary experiments on a data set of real texts, annotated by experts and used in actual CEFR exam sessions, show a strong ability of the proposed system to label texts with the correct language proficiency class and a great potential for its integration in learning tools, such systems supporting examiners in tests design and automatic evaluation of writing abilities.