During the excavations carried out in Via di Mercurio (Regio VI, 9, 3) in Pompeii, in 2015, some red, green, black, and brown wall painting fragments were found in the preparatory layer of an ancient pavement which was probably built after the 62 AD earthquake. These fragments, derived from the rubble, were used as coarse aggregate to prepare the mortar for building the pavement. The wall painting fragments are exceptionally well preserved, which is an uncommon occurrence in the city of Pompeii. However, as they were enclosed in the mortar, the wall painting fragments were protected from the high temperatures (probably ranging between 180 °C and 380 °C) produced by the eruption in 79 AD. The pigmented outer surface of each sample was analyzed using a non-destructive multi-analytical approach, by combining spectrophotometric colorimetry and portable X-ray fluorescence with micro-Raman spectroscopy. The compositional characterization of the samples revealed the presence of cuprorivaite, goethite, and celadonite in the green pigments; hematite in the red pigments; goethite in the brown pigment; and charcoal in the black pigment. These data probably provide us with the most “faithful picture” of the various red, green, black, and brown pigments used in Pompeii prior to the 79 AD eruption.

The tribological behavior of a new composite material containing graphene nanosheets (GNS) is presented. The composite material was obtained by Friction Stir Processing, using as metallic matrix the AA1050-H24 alloy. Different tool rotation and advancing speeds were tested in friction stir processing (FSP). The worn surfaces of obtained materials were analyzed by Scanning Electron Microscopy (SEM). Raman spectroscopy demonstrated that graphene reinforcements are successfully mixed into the aluminum matrix. The results proved the feasibility of using GNSs to obtain nanocomposites by FSP. The coefficient of friction of the aluminum alloy was 0.57, decreasing to 0.38 for the nanocomposite GNSs/AA1050. These values decrease for samples obtained at lower tool rotation speeds. The weight losses of the composites are less than that of unreinforced AA1050-H24 alloy for conditions with lower advancing speeds (40 and 60 mm/min) and 1120 rpm.

This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution.