A hybrid model for mapping simplified seismic response via a GIS-metamodel approach

In earthquake-prone areas, site seismic response due to lithostratigraphic sequence plays a key role in seismic hazard assessment. A hybrid model, consisting of GIS and metamodel (model of model) procedures, was introduced aimed at estimating the 1-D spatial seismic site response in accordance with spatial variability of sediment parameters. Inputs and outputs are provided and processed by means of an appropriate GIS model, named GIS Cubic Model (GCM). This consists of a block-layered parametric structure aimed at resolving a predicted metamodel by means of pixel to pixel vertical computing. The metamodel, opportunely calibrated, is able to emulate the classic shape of the spectral acceleration response in relation to the main physical parameters that characterize the spectrum itself. Therefore, via the GCM structure and the metamodel, the hybrid model provides maps of normalized acceleration response spectra. The hybrid model was applied and tested on the built-up area of the San Giorgio del Sannio village, located in a high-risk seismic zone of southern Italy. Efficiency tests showed a good correspondence between the spectral values resulting from the proposed approach and the 1-D physical computational models. Supported by lithology and geophysical data and corresponding accurate interpretation regarding modelling, the hybrid model can be an efficient tool in assessing urban planning seismic hazard/risk. © Author(s) 2014

An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Abstract. An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data

Modeling L- and X-band backscattering of wheat and tests over fields of Pampas

A discrete scattering model and a detailed set of ground measurements are used to simulate the backscattering coefficients of wheat fields during the whole growth cycle. Simulations are carried out at L- and X-band, and at HH, VV, and HV polarizations. Wheat fields are located in Pampas (Argentina), and are characterized by low values of plant density. Simulations show that the backscattering coefficient is driven by variations of soil moisture at L-band, particularly for HH polarization, with low vegetation effects. Conversely, the attenuation of vegetation is dominant in producing variations of backscattering coefficients at X-band, particularly for VV polarization. Simulations are compared against experimental data collected over the same Pampas region, using airborne SARAT SAR at L-band and COSMO-SKYMED at X-band. Assuming a surface height standard deviation in a 0.4–0.7 cm range, the simulations generally agree with experimental data, with an RMSE lower than about 2 dB at L-band and X-band, except a limited number of cases. Discrepancies observed in specific conditions are discussed. Overall, the results indicate that a joint use of L- and X-band has a good potential to monitor both soil moisture and vegetation growth

Kinematic Segmentation and Velocity in Earth Flows: A Consequence of Complex Basal-slip Surfaces

Abstract We investigated relations between geomorphic structures, movement velocity, and basal-slip surface geometry within individual kinematic domains of two large earth flows in the Apennine Mountains of southern Italy: the "Montaguto" earth flow and the "Mount Pizzuto" earth flow. Our analyses indicated that the earth flows are composed of distinct kinematic zones characterized by specific deformational patterns and longitudinal velocity profiles. Variations in velocity within individual kinematic zones is controlled by the geometry of the basal-slip surface, and, in particular by local variations in slope angle. Slip-surface geometry and slope also seem to control the density of extensional structures in driving earth-flow elements

Narrative-based computational modelling of the Gp130/JAK/STAT signalling pathway.

BACKGROUND: Appropriately formulated quantitative computational models can support researchers in understanding the dynamic behaviour of biological pathways and support hypothesis formulation and selection by "in silico" experimentation. An obstacle to widespread adoption of this approach is the requirement to formulate a biological pathway as machine executable computer code. We have recently proposed a novel, biologically intuitive, narrative-style modelling language for biologists to formulate the pathway which is then automatically translated into an executable format and is, thus, usable for analysis via existing simulation techniques. RESULTS: Here we use a high-level narrative language in designing a computational model of the gp130/JAK/STAT signalling pathway and show that the model reproduces the dynamic behaviour of the pathway derived by biological observation. We then "experiment" on the model by simulation and sensitivity analysis to define those parameters which dominate the dynamic behaviour of the pathway. The model predicts that nuclear compartmentalisation and phosphorylation status of STAT are key determinants of the pathway and that alternative mechanisms of signal attenuation exert their influence on different timescales. CONCLUSION: The described narrative model of the gp130/JAK/STAT pathway represents an interesting case study showing how, by using this approach, researchers can model biological systems without explicitly dealing with formal notations and mathematical expressions (typically used for biochemical modelling), nevertheless being able to obtain simulation and analysis results. We present the model and the sensitivity analysis results we have obtained, that allow us to identify the parameters which are most sensitive to perturbations. The results, which are shown to be in agreement with existing mathematical models of the gp130/JAK/STAT pathway, serve us as a form of validation of the model and of the approach itself

PREDICTION OF DEFORMATION CAUSED BY LANDSLIDES BASED ON GRAPH CONVOLUTION NETWORKS ALGORITHM AND DINSAR TECHNIQUE

Abstract. Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points

Assessment of cyst content using mean gray value for discriminating endometrioma from other unilocular cysts in premenopausal women

Objective To assess whether the analysis of cyst content using mean gray value (MGV) can discriminate ovarian endometriomas from other unilocular ovarian cysts in premenopausal women. Methods Stored three-dimensional (3D) volumes from 54 unilocular ovarian cysts diagnosed in 50 premenopausal women (mean age, 37 (range, 22–50) years) were analyzed to calculate the MGV from cyst content. Cysts with solid components or septations were excluded. MGV was calculated in all cases with the Virtual Organ Computer-aided AnaLysisTM technique. The Bmode presumptive diagnosis based on the examiner’s subjective impression was also recorded. Results Sixteen of the cysts resolved spontaneously and were given a final clinical diagnosis of hemorrhagic functional cyst, while 38 cysts were removed surgically (diagnosed histologically as seven simple cysts, three hemorrhagic cysts, 20 endometriomas, five mucinous cysts and three paraovarian cysts). B-mode diagnoses were as follows: seven simple cysts, 18 hemorrhagic cysts, 24 endometriomas, three mucinous cysts and two paraovarian cysts. MGV was significantly higher in ovarian endometrioma when compared with all other kinds of cyst. The receiver–operating characteristics curve showed that using an MGV cut-off ≥15.560 had a sensitivity of 85% and a specificity of 76.5% for diagnosing ovarian endometrioma (area under the curve, 0.831; 95% CI, 0.718–0.944). These figures were similar to those for B-mode diagnosis (sensitivity, 90%; specificity, 82%) (McNemar test, P = 1.000). Combining B-mode and MGV gave a sensitivity of 80% and a specificity of 91%. Conclusion Cyst content MGV is higher in ovarian endometrioma than it is in other unilocular ovarian cysts. The diagnostic performance of MGV is similar to that of the examiner’s subjective impression. The combination of both criteria achieves the highest specificit

The X-ray Spectrum of the Rapid Burster using the Chandra HETGS

We present observations of the Rapid Burster (RB, also known as MXB 1730-335) using the Chandra High Energy Transmission Grating Spectrometer. The average interval between type II (accretion) bursts was about 40 s. There was one type I (thermonuclear flash) burst and about 20 "mini-bursts" which are probably type II bursts whose peak flux is 10-40% of the average peak flux of the other type II bursts. The time averaged spectra of the type II bursts are well fit by a blackbody with a temperature of kT = 1.6 keV, a radius of 8.9 km for a distance of 8.6 kpc, and an interstellar column density of 1.7e22 per sq. cm. No narrow emission or absorption lines were clearly detected. The 3 sigma upper limits to the equivalent widths of any features are < 10 eV in the 1.1-7.0 keV band and as small as 1.5 eV near 1.7 keV. We suggest that Comptonization destroys absorption features such as the resonance line of Fe XXVI.Comment: 10 pages, 4 figures, accepted for publication in AJ (with minor changes and enhanced discussion of the instrument configuration

Diagnostic accuracy of transvaginal sonography for detecting parametrial involvement in women with deep endometriosis: systematic review and meta-analysis

Objective: To evaluate the accuracy of transvaginal sonography (TVS) for detecting parametrial deep endometriosis, using laparoscopy as the reference standard. Methods: A search was performed in PubMed/MEDLINE and Web of Science for studies evaluating TVS for detecting parametrial involvement in women with suspected deep endometriosis, as compared with laparoscopy, from January 2000 to December 2020. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to evaluate the quality of the studies. Pooled sensitivity, specificity and positive and negative likelihood ratios for TVS in the detection of parametrial deep endometriosis were calculated, and the post-test probability of parametrial deep endometriosis following a positive or negative test was determined. Results: The search identified 134 citations. Four studies, comprising 560 patients, were included in the analysis. The mean prevalence of parametrial deep endometriosis at surgery was 18%. Overall, the pooled estimated sensitivity, specificity and positive and negative likelihood ratios of TVS in the detection of parametrial deep endometriosis were 31% (95% CI, 10–64%), 98% (95% CI, 95–99%), 18.5 (95% CI, 8.8–38.9) and 0.70 (95% CI, 0.46–1.06), respectively. The diagnostic odds ratio was 26 (95% CI, 10–68). Heterogeneity was high. Visualization of a lesion suspected to be parametrial deep endometriosis on TVS increased significantly the post-test probability of parametrial deep endometriosis. Conclusion: TVS has high specificity but low sensitivity for the detection of parametrial deep endometriosis. © 2021 The Authors. Ultrasound in Obstetrics &amp; Gynecology published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology

SUSCEPTIBILITY MAPPING OF SHALLOW LANDSLIDES INDUCING DEBRIS FLOWS: A COMPARISON OF PHYSICS-BASED APPROACHES

The assessment of timing and potential locations of rainfallinduced shallow landslides through mathematical models represents a challenge for the assessment of landslide hazard, especially in cases with limited or not available data. In fact, modeling slope hydrological response and stability requires accurate estimates of unsaturated/saturated hydraulic and geotechnical properties of materials involved in landsliding, as well as climate and topography. Such aspect is relevant for the prediction of location and timing of landslide events, which is greatly needed to reduce their catastrophic effects in terms of economic losses and casualties. To such a scope, we present the comparison of results of two physics-based models applied to the assessment of susceptibility to shallow rainfall-induced landslides in Valtellina region (northern Italy). The analyses were carried out considering effects of availability, resolution and type of data concerning spatial distribution, thickness and properties of soils coverings. For such a scope, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) and the Climatic Rainfall Hydrogeological Modeling Experiment (CHRyME) models were considered. The study emphasizes issues in performing distributed numerical slope stability modeling depending on the availability of spatially distributed soil properties which hamper the quality of physic-based models. Further analyses aimed at the probabilistic assessment of landslide spatial distribution, related to a specific value of rainfall threshold, can be considered as potentially applicable to multi-scale landslide hazard mapping and extendable to other similar mountainous frameworks