A possibilistic approach to latent structure analysis for symmetric fuzzy data.

In many situations the available amount of data is huge and can be intractable. When the data set is single valued, latent structure models are recognized techniques, which provide a useful compression of the information. This is done by considering a regression model between observed and unobserved (latent) fuzzy variables. In this paper, an extension of latent structure analysis to deal with fuzzy data is proposed. Our extension follows the possibilistic approach, widely used both in the cluster and regression frameworks. In this case, the possibilistic approach involves the formulation of a latent structure analysis for fuzzy data by optimization. Specifically, a non-linear programming problem in which the fuzziness of the model is minimized is introduced. In order to show how our model works, the results of two applications are given.Latent structure analysis, symmetric fuzzy data set, possibilistic approach.

A least squares approach to Principal Component Analysis for interval valued data

Principal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA) recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.Principal Component Analysis, Least squares approach, Interval valued data, Chemical data

A Tissue Engineering product development pathway

Tissue engineering is a field of inquiry and research that uses engineering techniques and principles of biological sciences to develop functional substitutes for reconstruction of damaged organs. Commercial translation of tissue engineering products is currently in progress all over the world. Many companies are moving their interest towards this market segment that grows by 6% per year. Aim of this thesis is to probe the possibility of developing tissue engineering products in the most cost-effective way, minimizing the industrial risk and developing a specific fund raising model. Tissue engineering is based on three main features: cells, scaffolds and bioreactors. Cells are seeded on a scaffold and cultured in a bioreactor in order to obtain a tissue engineering product. Nevertheless, developing cell carrying products is hampered by certification claims ("advanced therapies" certification rules) that unbearably increase R&D and certification costs and can be faced by either big companies or start-ups of big companies and spin-offs of complex aggregates of research centers involved in advanced cell research. On the other hand, scaffolds (certification class IIb) and bioreactors for tissue engineering (certification class I) can be developed with a lower economic effort, being the competition based on innovation, since their market is in the "growth phase" for scaffolds and in the "introduction phase" for bioreactors in the Levitt's product life cycle theory. Purpose of this thesis is to basically study scaffold and bioreactor features, then to preliminarily design some models of bioreactors and, eventually, to set a business model, based on private and public fund raising, aimed to the development of scaffolds for dental implantology and of bioreactors for cardiovascular and bone tissue engineering. Finally, a business plan of a company being spin-off of Politecnico di Torino and industrial start-up has been elaborate

Damage identification on spatial Timoshenko arches by means of genetic algorithms

In this paper a procedure for the dynamic identification of damage in spatial Timoshenko arches is presented. The proposed approach is based on the calculation of an arbitrary number of exact eigen-properties of a damaged spatial arch by means of the Wittrick and Williams algorithm. The proposed damage model considers a reduction of the volume in a part of the arch, and is therefore suitable, differently than what is commonly proposed in the main part of the dedicated literature, not only for concentrated cracks but also for diffused damaged zones which may involve a loss of mass. Different damage scenarios can be taken into account with variable location, intensity and extension of the damage as well as number of damaged segments. An optimization procedure, aiming at identifying which damage configuration minimizes the difference between its eigen-properties and a set of measured modal quantities for the structure, is implemented making use of genetic algorithms. In this context, an initial random population of chromosomes, representing different damage distributions along the arch, is forced to evolve towards the fittest solution. Several applications with different, single or multiple, damaged zones and boundary conditions confirm the validity and the applicability of the proposed procedure even in presence of instrumental errors on the measured data.Comment: 34 pages, 19 figure