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This paper evaluates the amplification factors at different locations of a tuff cliff subjected to seismic loading. We present a 1D and 2D modelling of a cliff located in the Sorrento Peninsula, a major Quaternary morpho-structural unit of the western flank of Southern Apennines. The geometry of our model is represented by a tuff cliff of 30 m height, with sand deposits at its base, covered by a 7-m-thick volcanoclastic layer. The geotechnical and geophysical parameters used for the analysis have been estimated via field measurements and laboratory tests. We selected seven scaled seismic events as input motions for the analysis, and the linear equivalent assumption was used in both 1D and 2D modelling. The general trend showed that in the cliff edge area, the amplification factor reaches a maximum value of 10 with a period of 0.11 s, while in other areas, its value decreases down to 2.90 seaside and 4.0 landside. The comparison of the 1D and 2D modelling highlights the importance of the topographic effects connected with a cliff. The average amplification factors (AF) for the 1D and the 2D models, have been computed within different time ranges, consistent with the ones adopted in the Italian microzonation studies (0.1–0.5 s, 0.4–0.8 s, 0.7–1.1 s). [ABSTRACT FROM AUTHOR]

This paper presents the results of numerical analyses of ground motion in the Red Zone sector of Amatrice hill, violently struck by the 2016–2017 Central Italy seismic sequence. The methodologies used in processing the data to define the numerical model are firstly described. The results obtained from the computational analyses are then presented and discussed by comparing them with experimental data set of weak motion recordings. Computational analyses were performed via both a 2D-numerical FEM model and a pseudo-three-dimensional hybrid model (SiSeRHMap) which develops multispectral maps taking into account topographic effects. Starting from available geological data and geophysical measurements, an original and specific subsoil GIS model was developed and utilised to perform the computational analyses. The preliminary map for fundamental periods computed from the subsoil model is in good agreement with the experimental data. A restricted set of weak ground motions acquired from an accelerometric station located in a reference site was used as input for the numerical analyses, while the signals of the corresponding events recorded at the top of the hill were used as targets in the reliability evaluation analysis of the outputs. In the area of Amatrice hill, which is characterized by a complex geological and topographical context, the reliability analysis shows a good performance of the hybrid model compared to the 2D-FEM model in the prediction of seismic response. Agreement generally was also good with regards to the experimental and computational results, both in relation to the amplitude and to the shape of the spectral amplification that change depending on the hill sector. Considering the predictive reliability of the models, a high amplification, due to topographic effects, was observed for the Red Zone by performing a back-simulation of the 24th August 2016 main shock. The analysis results highlight also that the maximum amplification factors, based on the definition of the Housner intensity, occur in the interval of periods 0–0.5 s covering the fundamental period range of the buildings in this area. [ABSTRACT FROM AUTHOR]

Shoulder biomechanics is a fast growing field, which is progressively expanding its focus to include more applied research. The papers included in this Special Issue confirm this trend. After a classification of the papers as dealing with fundamental or applied research through theoretical or experimental methods, in this Editorial we tried to summarize the elements of consensus and the open issues discussed during the last International Shoulder Group meeting, held in Bologna (Italy) in 2008. [ABSTRACT FROM AUTHOR]

Sediments infilling in intermontane basins in areas with high seismic activity can strongly affect ground-shaking phenomena at the surface. Estimates of thickness and density distribution within these basin infills are crucial for ground motion amplification analysis, especially where demographic growth in human settlements has implied increasing seismic risk. We employed a 3D gravity modeling technique (ITerative RESCaling—ITRESC) to investigate the Fucino Basin (Apennines, central Italy), a half-graben basin in which intense seismic activity has recently occurred. For the first time in this region, a 3D model of the Meso-Cenozoic carbonate basement morphology was retrieved through the inversion of gravity data. Taking advantage of the ITRESC technique, (1) we were able to (1) perform an integration of geophysical and geological data constraints and (2) determine a density contrast function through a data-driven process. Thus, we avoided assuming a priori information. Finally, we provided a model that honored the gravity anomalies field by integrating many different kinds of depth constraints. Our results confirmed evidence from previous studies concerning the overall shape of the basin; however, we also highlighted several local discrepancies, such as: (a) the position of several fault lines, (b) the position of the main depocenter, and (c) the isopach map. We also pointed out the existence of a new, unknown fault, and of new features concerning known faults. All of these elements provided useful contributions to the study of the tectono-sedimentary evolution of the basin, as well as key information for assessing the local site-response effects, in terms of seismic hazards. [ABSTRACT FROM AUTHOR]

Background: The main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data., Results: Different machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675., Conclusions: Results revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects.