Potential rockfalls and analysis of slope dynamics in the palatine archaeological area (Rome, Italy)

The Palatine Hill is among the main archaeological sites of Roman antiquity. Today, this place requires continuous care for its safeguarding and conservation. Among the main problems, slope instabilities threaten the southwestern border of the hill flanked by the Velabrum Valley, as also testified by historical documents. The upper part of the investigated slope is characterized by Middle Pleistocene red-brownish tuffs known as "Tufo Lionato". The rock mass is affected by two jointing belts featuring the slope edge and its internal portion with different joint frequency and distribution. The analysis of the geometric relationship between the joint systems and the slope attitude evidenced possible planar sliding and toppling failure mechanisms on the exposed tuff cliffs. Potential rock block failures threatening the local cultural heritage were contrasted with preliminary works for site remediation. In addition, stress-strain numerical modelling verified the hypothesis of a tensile origin for the jointing belts, suggested by fracture characteristics and orientation. A first modelling was limited to the southwestern edge of the Palatine Hill and analysed the present stress-strain condition of the slope, proving the inconsistency with the observed deformation. A second modelling was extended to the Palatine-Velabrum slope-to-valley system to consider the role played by the geomorphological evolution of the area on the local slope dynamics during the late Pleistocene-Holocene. Results demonstrate how original conditions of slope instability, deformation and retreat along the Palatine western edge were determined by deep valley incision, and controlled by deformability contrasts within the slope. Slope instability influenced the site occupation and development during the Roman civilization, as also indicated by the remnants of retaining walls of different ages at the slope base

Active E-Learning by Doing with ALDO

It has been proved how teaching and learning educational processes can largely benefit from the application of ICT-based services within e-learning platforms, such as collaborative editing and advanced data visualizations. However, among state-of-the-art solutions, no one is able to tackle the problem in a comprehensive way. In this extended abstract, we discuss ALDO (Active e-Learning by DOing), a novel, advanced digital framework supporting integrated facilities for effective, active e-learning. ALDO includes an active repository for collecting, sharing, retrieving, and analyzing relevant materials, collaborative editing services, an e-learning platform, and advanced visualization tools to inspect the spatial and temporal dimension of specific data contexts. All such services and tools are made available to teachers/students through a dedicated Web portal. Although the present research was carried out within the H2020 Project DETECt (Detecting Transcultural Identity in European Popular Crime Narratives), by focusing on the specific data context of European crime narrative, the generality of the framework makes it suitable for any type of educational task. The design and creation of above tools and services, together with their uses, are presented and discussed through a series of real examples taken from DETECt

Hygro-thermo-chemo-mechanical coupled discrete model for the self-healing in Ultra High Performance Concrete

Reliable durability predictions and design for advanced cement-based materials cannot disregard the modelling of their inherent self-healing capability. A discrete meso-scale model to simulate the recovery in water tightness, stiffness and strength induced by the (stimulated) autogenous healing of cracks for Ultra High Performance Concrete is presented. In this paper the model is implemented into the numerical framework of the Multiphysics-Lattice Discrete Particle Model (M-LDPM), resulting from the coupling of the Hygro-Thermo-Chemical (HTC) model and Lattice Discrete Particle Model (LDPM). Consistently with experimental evidence, the development of the self-repairing process is modelled as consisting of two independent stages: (a) the healing of matrix cracks, affecting both moisture permeability and fracture strength in the cracked state, and (b) the recovery in terms of fibre bridging action, relying on the adhesion between the healing products and the walls of the tunnel cracks which form during the fibre debonding process. This research activity is framed into the Horizon 2020 project ReSHEALience (GA 760824)

Numerical modelling via a coupled discrete approach of the autogenous healing for Fibre-Reinforced Cementitious Composites (FRCCs)

Aiming to predict long-term performance of advanced cement-based materials and design more durable structures, a reliable modelling of the autogenous healing of cementitious materials is crucial. A dis-crete model for the regain in terms of water tightness, stiffness and strength induced by the autogenous and/or “stimulate" autogenous healing was recently proposed for ordinary plain concrete. The modelling proposal stemmed from the coupling of two models, namely the Hygro-Thermo-Chemical (HTC) model, on one side,and the Lattice Discrete Particle Model (LDPM), on the other side, resulting in the Multiphysics-Lattice Discrete Particle Model (M-LDPM). Being this approach not customised only for ordinary concrete, but for the whole broad category of cementitious materials, in this paper, its application to Fibre-Reinforced Cementitious Composites is presented. To accurately simulate what has been experimentally observed so far, the mechanical model is updated to also include the self-healing of the tunnel cracks at the fibre-matrix interfaces. Therefore,the self-repairing process is modelled to develop on two independent stages: (a) matrix cracks healing, and(b) fibre bridging action restoring. This research activity is part of the modelling tasks framed into the project ReSHEALience, funded from the European Union’s Horizon 2020 Research and Innovation Programme

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

Despite recent advances, fast and reliable Human Activity Recognition in confined space is still an open problem related to many real-world applications, especially in health and biomedical monitoring. With the ubiquitous presence of Wi-Fi networks, the activity recognition and classification problems can be solved by leveraging some characteristics of the Channel State Information of the 802.11 standard. Given the well-documented advantages of Deep Learning algorithms in solving complex pattern recognition problems, many solutions in Human Activity Recognition domain are taking advantage of those models. To improve the time and precision of activity classification of time-series data stemming from Channel State Information, we propose herein a fast deep neural model encompassing concepts not only from state-of-the-art recurrent neural networks, but also using convolutional operators with added randomization. Results from real data in an experimental environment show promising results

R-parity violation in SU(5)

We show that judiciously chosen R-parity violating terms in the minimal renormalizable supersymmetric SU(5) are able to correct all the phenomenologically wrong mass relations between down quarks and charged leptons. The model can accommodate neutrino masses as well. One of the most striking consequences is a large mixing between the electron and the Higgsino. We show that this can still be in accord with data in some regions of the parameter space and possibly falsified in future experiments.Comment: 30 pages, 1 figure. Revised version. To appear in JHE

U and Th content in the Central Apennines continental crust: a contribution to the determination of the geo-neutrinos flux at LNGS

The regional contribution to the geo-neutrino signal at Gran Sasso National Laboratory (LNGS) was determined based on a detailed geological, geochemical and geophysical study of the region. U and Th abundances of more than 50 samples representative of the main lithotypes belonging to the Mesozoic and Cenozoic sedimentary cover were analyzed. Sedimentary rocks were grouped into four main "Reservoirs" based on similar paleogeographic conditions and mineralogy. Basement rocks do not outcrop in the area. Thus U and Th in the Upper and Lower Crust of Valsugana and Ivrea-Verbano areas were analyzed. Based on geological and geophysical properties, relative abundances of the various reservoirs were calculated and used to obtain the weighted U and Th abundances for each of the three geological layers (Sedimentary Cover, Upper and Lower Crust). Using the available seismic profile as well as the stratigraphic records from a number of exploration wells, a 3D modelling was developed over an area of 2^{\circ}x2^{\circ} down to the Moho depth, for a total volume of about 1.2x10^6 km^3. This model allowed us to determine the volume of the various geological layers and eventually integrate the Th and U contents of the whole crust beneath LNGS. On this base the local contribution to the geo-neutrino flux (S) was calculated and added to the contribution given by the rest of the world, yielding a Refined Reference Model prediction for the geo-neutrino signal in the Borexino detector at LNGS: S(U) = (28.7 \pm 3.9) TNU and S(Th) = (7.5 \pm 1.0) TNU. An excess over the total flux of about 4 TNU was previously obtained by Mantovani et al. (2004) who calculated, based on general worldwide assumptions, a signal of 40.5 TNU. The considerable thickness of the sedimentary rocks, almost predominantly represented by U- and Th- poor carbonatic rocks in the area near LNGS, is responsible for this difference.Comment: 45 pages, 5 figures, 12 tables; accepted for publication in GC