Ricerc@Sapienza

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach.

Longevity risk management is an area of the life insurance business where the use of
Artificial Intelligence is still underdeveloped. The paper retraces the main results of the
recent actuarial literature on the topic to draw attention to the potential of Machine
Learning in predicting mortality and consequently improving the longevity risk quantification
and management, with practical implication on the pricing of life products
with long-term duration and lifelong guaranteed options embedded in pension contracts

To univocally identify mineral species on the basis of their formula, the IMA-CNMNC recommends the use of the dominant-valency rule and/or the site-total-charge approach, which can be considered two procedures complementary to each other for mineral identification. In this regard, several worked examples are provided in this study along with some simple suggestions for a more consistent terminology and a straightforward use of mineral formulae. IMA-CNMNC guidelines subordinate the mineral structure to the mineral chemistry in the hierarchical scheme adopted for classification.

A new, IMA-approved classification scheme for the spinel-supergroup minerals is here reported.

The classification of the tetrahedrite group minerals in keeping with the current IMA-accepted nomenclature rules is discussed. Tetrahedrite isotypes are cubic, with space group symmetry I 4 ̄ 3m. $Ioverline43m.$The general structural formula of minerals belonging to this group can be written as M(2)A6M(1)(B4C2)X(3) D4S(1)Y12S(2)Z, where A = Cu+, Ag+, o (vacancy), and (Ag6)4+ clusters; B = Cu+, and Ag+; C = Zn2+, Fe2+, Hg2+, Cd2+, Mn2+, Cu2+, Cu+, and Fe3+; D = Sb3+, As3+, Bi3+, and Te4+; Y = S2- and Se2-; and Z = S2-, Se2-, and o.

Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers. Despite their unrivaled training speed, MTS classifiers based on a standard RC architecture fail to achieve the same accuracy of fully trainable neural networks.

Featured Application This review will offer a global overview of the chemometric approaches most commonly used in the field of spectroscopy-based food analysis and authentication. Three different scenarios will be surveyed: data exploration, calibration and classification. Basic and simple descriptions of the main multivariate techniques exploited in such a domain along with a comprehensive outline of their most recent and interesting applications will be provided.

Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics.

Promoting and protecting the market of typical national territory’s products are fundamental from a commodity point of view. Often, high-added value foods are subjected to fraud. Chestnuts have extraordinary nutritional and organoleptic qualities and Italy is one of the biggest producers of this product. The purpose of the present study is to develop an analytical method suitable to authenticate the chestnut of Vallerano a PDO agro-food produced in Central-Italy.

Rice is a staple food in Vietnam, and the concern about rice is much greater than that for other foods. Preventing fraud against this product has become increasingly important in order to protect producers and consumers from possible economic losses. The possible adulteration of this product is done by mixing, or even replacing, high-quality rice with cheaper rice. This highlights the need for analytical methodologies suitable for its authentication. Given this scenario, the present work aims at testing a rapid and non-destructive approach to detect adulterated rice samples.