2018

The inverse Finite Element Method (iFEM) is applied to reconstruct the displacement field of a shell structure which undergoes large deformations using discreet strain measurements as the prescribed data. The iFEM computations are carried out using an incremental procedure where at each load step, the incremental strains are used to evaluate the incremental displacements which in turn update the geometry of the deformed structure.

A crack or a localized damage in a structure provokes a discontinuity in the rotation. Consequently, mode-shapes are nonsmooth at the damage position and the first derivative (strictly related to the rotation) presents a jump discontinuity. Based on this simple concept, a new approach has been developed in order to predict the location of the mode-shape derivative discontinuities, and therefore the location of damage, without the need to directly differentiate.

Monitoring product temperature is mandatory in a freeze-drying process, in particular in the process development stage, as final product quality may be jeopardized when its temperature trespasses a threshold value, that is a characteristic of each product being freeze-dried. To this purpose thermocouples are usually inserted in some of the vials of the batch to track product dynamics.

Occupational accidents are commonly collected in large databases by National Workers Compensation Authorities and companies’ safety and prevention teams. The analysis of the data can be difficult because the database elements are characterized by many parameters, which are not of a numerical nature. Data mining techniques could represent an efficient tool for the identification of useful information in large databases. In 2011, a two-level clustering method, made of SOM and numerical clustering, obtained positive results in identifying critical accident dynamics.

We propose a simple but new method of characterizing multivariate Bernoulli variables belonging to a given class, i.e., with some specified moments. Within a given class, this characterization allows us to generate easily a sample of mass functions. It also provides the bounds that all the moments must satisfy to be compatible and the possibility to choose the best distribution according to a certain criterion. For the special case of the Fréchet class of the multivariate Bernoulli distributions with given margins, we find a polynomial characterization of the class.

We introduce a class of multivariate factor-based processes with the dependence structure of Lévy ραρα-models and Sato marginal distributions. We focus on variance gamma and normal inverse Gaussian marginal specifications for their analytical tractability and fit properties. We explore if Sato models, whose margins incorporate more realistic moments term structures, preserve the correlation flexibility in fitting option data. Since ραρα-models incorporate nonlinear dependence, we also investigate the impact of Sato margins on nonlinear dependence and its evolution over time.

In this paper, we study factor-based subordinated Lévy processes in their variance gamma (VG) and normal inverse Gaussian (NIG) specifications, and focus on their ability to price multivariate exotic derivatives. Both model specifications, calibrated to a data set of multivariate barrier reverse convertibles listed at the Swiss market, demonstrate good ability in capturing smile patterns and recovering empirical correlations. We show how the range of correlations spanned by each model is linked to the process marginal distributions.

This paper applies probabilistic graphical models in a new framework to study association rules driven by consumer choices in a network of Italian museums. The network consists of the museums participating in the programme of Abbonamento Musei Torino Piemonte, which is a yearly subscription managed by Associazione Torino Città Capitale Europea. It is available to people living in the Piemonte region, Italy. Consumers are card-holders, who are allowed entry to all the museums in the network for one year.

The objective of this work is to analyze and then optimize thermal energy consumptions of public buildings located within the mountain community of Lanzo, Ceronda and Casternone Valleys. Some measures have been proposed to reduce energy consumption and consequently the economic cost for energy production, as well as the harmful GHG emissions in the atmosphere.

In urban areas, district heating (DH) represents a valuable technology for providing sanitary water and house heating to buildings, because of its technical and economic strengths and its potentials on reduction of pollutant emissions. In large towns, DH is often an evolving structure. Connection of additional buildings, without any modifications in the existing network, is a frequent option to be considered to avoid further investment costs.