The Properties of Satellite Galaxies in External Systems. I. Morphology and Structural Parameters

We present the first results of an ongoing project to study the morphological, kinematical, dynamical, and chemical properties of satellite galaxies of external giant spiral galaxies. The sample of objects has been selected from the catalogue by Zaritsky et al. (1997). The paper analyzes the morphology and structural parameters of a subsample of 60 such objects. The satellites span a great variety of morphologies and surface brightness profiles. About two thirds of the sample are spirals and irregulars, the remaining third being early-types. Some cases showing interaction between pairs of satellites are presented and briefly discussed.Comment: Accepted for publication in Astrophys. Journal Supp. Se

Body condition assessment using digital images.

This project assessed the ability to assign a body condition score (BCS) to a dairy cow from digital photographs or videos. Images were taken from the rear of the cow at a 0 to 20 degrees angle relative to the tail head. Four observers assigned a BCS to each of 57 cows at a farm visit (live, farm 1) and later from a photograph (photo). Means +/- standard deviations of BCS by method and observer were as follows: live = 3.25 +/- 0.51, 3.42 +/- 0.49, 3.32 +/- 0.58, 3.13 +/- 0.62; photo = 3.36 +/- 0.52, 3.32 +/- 0.43, 3.44 +/- 0.62, 3.14 +/- 0.6 for observers 1 to 4, respectively. Body condition score means differed across observers for live (observer 2 higher and observer 4 lower, compared with observers 1 and 3) and photo methods (observer 3 lower, compared with observers 1, 2, and 3); however, within observer, the mean live BCS did not differ from the mean photo BCS. Correlation coefficients between BCS assigned live and from photos were 0.84, 0.82, 0.82, and 0.90 for observers 1 to 4, respectively. Subsequently, observer 1 visited 2 farms, assigned a live BCS, and digitally photographed 187 cows (56 and 131 cows from farms 2 and 3, respectively). Observers 2, 3, and 4 assigned a BCS from the photographs. Means +/- standard deviations of BCS by observer (method) were 1 (live) 3.35 +/- 0.55; 2 (photo) 3.33 +/- 0.49; 3 (photo) 3.60 +/- 0.54; and 4 (photo) 3.26 +/- 0.62. The mean BCS for observer 3 was higher and that for observer 4 was lower than for observers 1 and 2. Correlation coefficients between observer 1 and observers 2 through 4 were 0.78, 0.76, and 0.79, respectively. Observer 1 assigned a BCS to 41 cows at a farm visit and 3 wk later assessed the BCS of cows from a video taken at a farm visit by a different individual. Cows were restrained in headlocks at a feed bunk when assessing BCS and for video production. No difference was detected for the mean BCS, for the standard deviation of the mean BCS, or in the distribution of BCS between the live and video assessments. Mean and SD for 17 groups of Holstein cows from 20 farms were used to generate 10,000 random samples of BCS. Groups of 25, 50, 100, and 150 cows were created from the random samples, and estimates of mean BCS were determined by sampling 3 to 80% of the group. Estimates of mean BCS with a sample size of 30% or more from a group of cows fell within the 95% confidence limit of the true mean more than 98% of the time. Digital photographs provide adequate imaging for assessment of BCS. Sampling 30% of a group should be adequate to assess the mean BCS. Video imaging allowed a rapid assessment of BCS but did not permit identification of individual cows

Italica (Seville, Spain): use of local marble in Augustan age

This study concerns 51 marble finds made of \u201cCipollino verde\u201d coming from the ancient city of Italica (north of the modern city of Santiponce, 9 km NW of Seville, Spain), the earliest Roman settlement in Spain, founded in 206 B.C. The aim of this work was to determine their provenance from Greek and Italian quarries or from local quarries worked in the Iberian Peninsula. Thin-section optical microscopy, X-ray powder diffraction, bulk rock chemistry and O, C and Sr stable isotope analyses were carried out. Results were compared with literature data on \u201cCipollino verde\u201d marbles quarried in southern Euboea (Greece), Apuan Alps (Italy), Almer\ueda, Extremoz, Seville (Spain) and \uc9vora (Portugal). These comparisons indicated twenty-five marble samples consistent with an origin from Italy (Corchia and Arni districts, Apuan alps), twenty from Greece (Styra and Pyrgari districts, southern Euboea) and only six from Spain (Macael, Almer\ueda province)

Forecasting macroseismic scenarios through anisotropic attenuation: a Bayesian approach

In this work we aim at two objects: quantifying, by a binomial-beta probabilistic model, the uncertainty involved in the assessment of the intensity decay, an ordinal quantity often incorrectly treated as real variable, and, given the finite dimension of the fault, modelling non-symmetric decays but exploiting information collected from previous studies on symmetric cases. To this end we transform the plane so that the ellipse having the fault length as maximum axis is changed into a circle with fixed diameter. We start from an explorative analysis of a set of macroseismic fields representative of the Italian seismicity among which we identify three different decay trends by applying a hierarchical clustering method. Then we focus on the exam of the seismogenic area of Etna volcano where some fault structures are well recognizable as well as the anisotropic trend of the attenuation. As in volcanic zones the seismic attenuation is much quicker than in other zones, we first shrink and then transform the plane so that the decay becomes again symmetric. Following the Bayesian paradigm we update the model parameters and associate the estimated values of the intensity at site with the corresponding locations in the original plane. Backward validation and comparison with the deterministic law are also presented

Probabilistic procedure to estimate the macroseismic intensity attenuation in the Italian volcanic districts

In this work we apply a probabilistic procedure to estimate the macroseismic intensity attenuation in the volcanic areas of Italy which allows to exploit additional information on historical earthquakes following the Bayesianapproach. The method starts from the intensity data points of the selected earthquakes and arrives at theassessment of the probability distribution for the intensity at a site given the epicentral intensity and thesite-epicenter distance. The CMTE local earthquake catalogue has been used for the Etna region while for theother Italian volcanic districts (Aeolian Islands, Ischia, Vesuvius and Albani Hills) the CPTI04 Italian seismic catalogue and the DBMI04 associated database have been considered. For the analysis, subsets of earthquakeswith epicentral intensity I0 ≥ VII MCS and I0 ≥ VI MCS were used for the Etna region and for the other Italian volcanic districts, respectively. Only earthquakes with more than 10 felt observations have been considered. The results show a specific attenuation trend for the Etna region compared with the other Italian volcanic areas

Probability distribution of the macroseismic intensity attenuation in the Italian volcanic districts

We present the probabilistic version of the analysis performed in Azzaro et al. (2006a) on the attenuation of the seismic intensity in Italian volcanic districts. The main results are the estimate of the probability distribution of the intensity at site IS, conditioned on the site-epicenter distance d and on I0, and then, assuming the mode of this distribution as estimator of IS, the forecasting of future macroseismic fields given I0. To this end we have modified the method presented in Rotondi and Zonno (2004) by inserting the following innovative elements: identification of possible different trends and exploitation of knowledge from prior experience or data. Data set. The intensity dataset considered in the present analysis is the same used in the study by Azzaro et al. (2006a), based on a deterministic approach. We consider a total of 38 earthquakes located in the Italian volcanic areas, so distributed: Etna region (24 events), Aeolian Islands (6 events), Vesuvius-Ischia (3 events) and Albani Hills (5 events). The CMTE local earthquake catalogue (Azzaro et al., 2000, 2002, 2006b) has been used for the Etna region while for the other Italian volcanic districts (Aeolian Islands, Ischia, Vesuvius and Albani Hills) the CPTI04 Italian seismic catalogue (Gruppo di lavoro CPTI, 2004) and the DBMI04 associated database (Stucchi et al., 2007) have been considered (Tab. 1). For the analysis, subsets of earthquakes with epicentral intensity I0 ≥ VII MCS and I0 ≥ VI MCS were used for the Etna region and for the other Italian volcanic districts, respectively. Probability model. We cite here the key-elements of the probabilistic method, referring to Rotondi and Zonno (2004) for a detailed description. Instead of adding a gaussian error to deterministic relationships which express the intensity decay as a function of some factors (epicentral intensity, site-epicenter distance, depth, site types, and styles of faulting), we treat the decay as an aleatory variable defined on the domain {0, I0}. Consequently, we assume that the intensity IS is a discrete binomial distributed variable Bin(I0 , p) where pI0 means the probability of null decay, and p belongs to [0,1]. According to the Bayesian approach, p is considered as a random variable following the beta distribution Beta(α, β). Since mean and variance of p are functions of the α, β hyperparameters, we can express our initial knowledge on the decay process through these parameters. To do this, we have divided each macroseismic field in bins of fixed width and the intensity data points in subsets according to this spatial subdivision. For each bin we have repeated the following procedure: a) assessing the prior values to α, β, that is a prior distribution for p; b) updating, through Bayes’ theorem, the hyperparameters on the basis of the current observations; c) estimating the p parameter through the mean of its posterior distribution. By substituting this estimate in the distribution Bin(I0 , p), we obtain an updated binomial distribution indicated as plug-in distribution. Its mode has been assumed as the expected value of the intensity at the sites within the corresponding bin. To predict the intensity at any distance we have smoothed the p’s estimated in the different bins through a monotonically decreasing function; the lowest mean squared error was given by the inverse power function . Hence, the mode of the plug-in distribution obtained by setting p=g(d) provides an expected value for IS at any distance. If, on the contrary, we assume that, from the attenuation viewpoint, the sites inside any bin behave in the same way, we can average over the domain [0,1] of p by integrating the product of the likelihood with respect to the posterior Beta distribution of p. In this way we have obtained the so-called predictive distribution for every bin and its mode is taken as expected value for IS at any site inside that bin. Trends in the intensity decay. We have analysed the macroseismic field of the 38 earthquakes constituting our dataset (Tab. 1) by drawing the decay versus the site-epicenter distance of each data point. A quick look at these graphical representations suggests that these earthquakes do not show an homogeneous decay. To identify different trends in the decay, we have synthetized the information contained in each field by collecting, in a matrix, median, mean, and quartile of each set of distances from the epicenter of the points with the same ΔI. Then we have applied to this matrix a clustering algorithm based on the evaluation of the distance between each pair of rows of the matrix. The dataset has been thus partitioned into two groups of events according to their attenuation trend: the first set mainly formed by the earthquakes of Mt. Etna and Vesuvius-Ischia areas, the second one including the events of the Aeolian Islands and Albani Hills. The set 1 shows an higher decay than the set 2, so two different spatial scales are required: bins of width 1 km for the set 1 and of width 25 km for the set 2. A similar classification analysis was performed in Zonno et al. (2008) on 55 earthquakes representative of the Italian territory; in that case three classes were identified. The probabilistic analysis above described has been separately applied to the two sets, discriminating the events of from those of , and using as a priori distributions for the parameters p’s those indicated in Zonno et al. (2008) for the class of earthquakes with the highest attenuation. The hyperparameters α’s and β’s have been then updated through the observed intensity data points according to the expressions α=α0 + ΣNn=1 IS (n) and β= β0 + ΣNn=1 (I0 - IS (n)). Some results. For each bin the values of the predictive probability function of for the Etna area and Aeolian Islands, are shown in Fig. 1; the squares indicate the values of the intensity decay computed through the logarithmic regressions (Tab. 2) obtained by Azzaro et al. (2006) with the same dataset. These values can be compared with the mode of the predictive function in each bin. The fit between the two methods is good but much more information is provided by the probabilistic approach. In addition to the estimate of the intensity at any site, the probability distribution of IS provides a measure of the uncertainty and its values can be directly used in the software “SASHA” (D’Amico and Albarello, 2007) to calculate the probabilistic seismic hazard at the site. Conclusions. The identification of different decay trends produced by the clustering algorithm matches well with that already presented in the literature (Azzaro et al. 2006), and this suggests that the method could be successfully applied to other cases. Only two earthquakes in Albani Hills - 1876/10/26, I0 VI-VII, 1927/12/26, I0 VII-VIII - are unexpectedly included in the set 1 together with the events of Mt. Etna and Vesuvio-Ischia areas; further, detailed analyses are required to explain such an anomaly. Some problems are still open: a) most of the earthquakes here considered have epicentral intensity I0 VII or VIII, so that we have evaluated the probability functions of IS conditioned on these two values of I0. Also other values of I0 must be used in the analysis; b) the method should be also validated on other earthquakes not included in the dataset of Tab. 1, on the basis of probabilistic measures of the degree to which the model predicts the decay in the data points of a macroseismic field (Rotondi and Zonno, 2004)

Modelling approach to the assessment of biogenic fluxes at a selected Ross Sea site, Antarctica

Several biogeochemical data have been collected in the last 10 years of Italian activity in Antarctica (ABIOCLEAR, ROSSMIZE, BIOSESO-I/II). A comprehensive 1-D biogeochemical model was implemented as a tool to link observations with processes and to investigate the mechanisms that regulate the flux of biogenic material through the water column. The model is ideally located at station B (175° E–74° S) and was set up to reproduce the seasonal cycle of phytoplankton and organic matter fluxes as forced by the dominant water column physics over the period 1990–2001. Austral spring-summer bloom conditions are assessed by comparing simulated nutrient drawdown, primary production rates, bacterial respiration and biomass with the available observations. The simulated biogenic fluxes of carbon, nitrogen and silica have been compared with the fluxes derived from sediment traps data. The model reproduces the observed magnitude of the biogenic fluxes, especially those found in the bottom sediment trap, but the peaks are markedly delayed in time. Sensitivity experiments have shown that the characterization of detritus, the choice of the sinking velocity and the degradation rates are crucial for the timing and magnitude of the vertical fluxes. An increase of velocity leads to a shift towards observation but also to an overestimation of the deposition flux which can be counteracted by higher bacterial remineralization rates. Model results suggest that the timing of the observed fluxes depends first and foremost on the timing of surface production and on a combination of size-distribution and quality of the autochtonous biogenic material. It is hypothesized that the bottom sediment trap collects material originated from the rapid sinking of freshly-produced particles and also from the previous year's production period

PROBABILISTIC PROCEDURE TO ESTIMATE THE MACROSEISMIC INTENSITY ATTENUATION IN THE ITALIAN VOLCANIC DISTRICTS

In Italian volcanic areas, we apply a probabilistic procedure for Macroseismic Intensity Attenuation estimates. The procedure, following the Bayesian approach, allows to exploit additional information on historical earthquakes. The method, given the epicentral intensity and the site epicenter distance, begins from selected earthquakes intensity data points and ends at the assessment of the intensity (Is) probability distribution at a site. Our probabilistic method provides a probability function matrix that can be directly applied for the computation of probabilistic seismic hazard at the site