What exactly is public in a public good game? A lab-in-the field experiment

Are public good games really capturing individuals\u2019 willingness to contribute to real-life public goods? To answer this question, we conducted a lab-in-the-field experiment with communities who own collective goods. In our experiment, subjects voluntarily contribute to a common pool, which can either be subdivided in individual vouchers, as in standard public good games, or used to acquire collective goods, as it happens for real-life public goods. We show that participants\u2019 contributions are larger when the voucher is paid individually, suggesting that individuals\u2019 willingness to contribute to public goods may be overestimated when based on results from laboratory experiments

What exactly is public in a public good game? A lab-in-the field experiment

Are public good games really capturing individuals’ willingness to contribute to real-life public goods? To answer this question, we conducted a lab-in-the-field experiment with communities who own collective goods. In our experiment, subjects voluntarily contribute to a common pool, which can either be subdivided in individual vouchers, as in standard public good games, or used to acquire collective goods, as it happens for real-life public goods. We show that participants’ contributions are larger when the voucher is paid individually, suggesting that individuals’ willingness to contribute to public goods may be overestimated when based on results from laboratory experiments

What exactly is public in a public good game? A lab-in-the field experiment

Are public good games really capturing individuals’ willingness to contribute to real-life public goods? To answer this question, we conducted a lab-in-the-field experiment with communities who own collective goods. In our experiment, subjects voluntarily contribute to a common pool, which can either be subdivided in individual vouchers, as in standard public good games, or used to acquire collective goods, as it happens for real-life public goods. We show that participants’ contributions are larger when the voucher is paid individually, suggesting that individuals’ willingness to contribute to public goods may be overestimated when based on results from laboratory experiments

Design, synthesis and biological activity of selective hCAs inhibitors based on 2-(benzylsulfinyl)benzoic acid scaffold

A large library of derivatives based on the scaffold of 2-(benzylsulfinyl)benzoic acid were synthesised and tested as atypical inhibitors against four different isoforms of human carbonic anhydrase (hCA I, II, IX and XII, EC 4.2.1.1). The exploration of the chemical space around the main functional groups led to the discovery of selective hCA IX inhibitors in the micromolar/nanomolar range, thus establishing robust structure-activity relationships within this versatile scaffold. HPLC separation of some selected chiral compounds and biological evaluation of the corresponding enantiomers was performed along with molecular modelling studies on the most active derivatives

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

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)

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

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

Sustainable rearing for kid meat production in Southern Italy marginal areas: A comparison among three genotypes

Sustainable goat breeding plays an important role in the economy of marginal areas. The present study aimed to compare performances and meat quality traits in kids of a native Apulian genotype (Garganica) in comparison with two Mediterranean breeds (Maltese and Derivata di Siria). Kids suckled dam milk until they were 21 (±2) days old, hence three groups of 12 male kids per each genotype were made. The kids received a pelleted feed ad libitum in addition to dam milk and were slaughtered at 60 days of age. The Maltese kids showed the lowest net cold-dressing percentage, with statistical differences compared to Garganica and Derivata di Siria. Meat obtained from Garganica kids showed a rosy color due to a significantly lower a* index and were also more tender since a lower WBS was recorded in comparison with the other two genotypes. As for the nutritional value of meat, the best n-6/n-3 ratio was found for the Derivata di Siria breed. In conclusion, Garganica kid meat showed the lowest content of SFA and atherogenic index, with potential beneficial effects for human health

Valutazioni di pericolosità sismica in termini di intensità macrosismica utilizzando metodi di sito

Vengono presentate le stime di pericolosità sismica, in termini di intensità macrosismica, ottenute mediante l’approccio probabilistico proposto da Albarello e Mucciarelli (2002), basato sull’impiego dei dati documentari relativi agli effetti locali prodotti dai terremoti passati (storie sismiche di sito). I risultati forniti da questa procedura (approccio “di sito”), in termini di minimo valore di intensità caratterizzato da una probabilità di eccedenza inferiore al 10% in 50 anni (Iref), sono confrontati con quelli ottenuti sul territorio italiano tramite la metodologia “standard” di Cornell-McGuire. Per meglio comprendere i motivi delle differenze osservate tra le due stime e il ruolo di differenti basi informative, sono state prodotte diverse mappe di pericolosità. Stime di pericolosità a scala locale sono state inoltre effettuate nell’area dell’Etna dove, grazie a numerosi studi macrosismici di dettaglio, sono disponibili storie sismiche di sito particolarmente ricche