834 research outputs found
PRIMULE: Privacy risk mitigation for user profiles
No full textThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification
Mario Pratesi and Tuscany
No full textThe subject and main aim of the bachelor thesis, "Mario Pratesi and Tuscany" is to introduce a quite unknown and hardly accessible author writing in the 19th and 20th century, and his native land. The introductory section outlines the historical background and presents the highlights that are related to the unification of Italy. A description of the author's life and a chronological review of his works follow. The next chapters are devoted to Tuscany, the author's native land. We try to find out to what extent Pratesi's creation was influenced by the environment in which he was born and grew up. Next is an analytical part, in which we introduce several major topics that the author uses in his novels (focusing on numerous elements) and according to those we attempt to create a comprehensive picture of Tuscany and its inhabitants at the time. This is based on two major works, the novels: L'Eredità and Perfidie del caso. A brief insight into the language of Pratesi's works with emphasis on regional and dialect influences is considered too. In conclusion we outline the strengths and weaknesses of the author's novels and try to figure out why Mario Pratesi is an author rather less well-known, both in the Czech Republic as in his native country, Italy. We will try to put the author in historical and..
Direct detection of iron clusters in L ferritins through ESI-MS experiments
No full textHuman cytoplasmic ferritins are heteropolymers of H and L subunits containing a catalytic ferroxidase center and a nucleation site for iron biomineralization, respectively. Here, ESI-MS successfully detected labile metal-protein interactions revealing the formation of tetra- and octa-iron clusters bound to L subunits, as previously underscored by X-ray crystallography
Weighted estimation in multilevel ordinal and binary models in the presence of informative sampling designs
No full textMultilevel models are often fitted to survey data gathered with a complex multistage sampling design. However, if such a design is informative, in the sense that the inclusion probabilities depend on the response variable even after conditioning on the covariates, the standard maximum likelihood estimators are biased. In this paper, following the Pseudo Maximum Likelihood approach of Skinner (1989), we propose a probability-weighted estimation procedure for multilevel ordinal and binary models which eliminates the bias generated by the informativeness of the design. The reciprocals of the inclusion probabilities at each sampling stage are used toweight the log-likelihood function and the weighted estimators obtained in this way are tested by means of a simulation study for the simple case of a binary random intercept model with and without covariates. The variance estimators are obtained by a boostrap procedure. The maximization of the weighted log-likelihood of the model is domne by the NLMIXED procedure of the SA, which is based on adaptive Gaussian quadrature. Also the bootstrap estimation of variances is implemented in the SAS environment
Reducing food waste: an investigation on the behaviour of Italian Youths
No full textPurpose â The purpose of this paper is to assess the knowledge of youths concerning food waste as well as to identify factors that influence changes in behaviour concerning food wasted and planning shopping for preventing it. Design/methodology/approach â The data used were collected from a sample of 233 students at Roma-Tre University in Italy. Probit models were specified to identify factors affecting food waste reduction in both pre-shopping and consumption phases. Findings â Results show that the more aware youths are concerning food waste, the more likely they are to reduce leftovers. In contrast, the concern about food freshness increases waste. A greater awareness of the consequences of food wasted increases the likelihood that youths will make a shopping list. Research limitations/implications â The main limitation of the study can be found in the non-probabilistic sampling design used for the collection of data. Practical implications â This study provides information for both social marketers and policy makers. New educational campaigns against food waste should be carried out by providing them with a realistic perception of food waste as well as by teaching young consumers how to recognize the level of freshness of food. Originality/value â This study provides a first insight of the factors that influence food waste reduction as well as the habit of making a shopping list from an individual perspective
Small area estimation via m-quantile geographically weighted regression
No full textThe effective use of spatial information, that is the geographic locations of population units, in a regression model-based approach to small area estimation is an important practical issue. One approach for incorporating such spatial information in a small area regression model is via Geographically Weighted Regression (GWR). In GWR the relationship between the outcome variable and the covariates is characterised by local rather than global parameters, where local is defined spatially. In this paper we investigate GWR-based small area estimation under the M-quantile modelling approach. In particular, we specify an M-quantile GWR model that is a local model for the M-quantiles of the conditional distribution of the outcome variable given the covariates. This model is then used to define a bias-robust predictor of the small area characteristic of interest that also accounts for spatial association in the data. An important spin-off from applying the M-quantile GWR small area model is that it can potentially offer more efficient synthetic estimation for out of sample areas. We demonstrate the usefulness of this framework through both model-based as well as design-based simulations, with the latter based on a realistic survey data set. The paper concludes with an illustrative application that focuses on estimation of average levels of Acid Neutralizing Capacity for lakes in the north-east of the USA.<br/
Bootstrap for estimating the mean squared error of the spatial EBLUP
No full textThis work assumes that the small area quantities of interest follow a Fay-Herriot model with
spatially correlated random area effects. Under this model, parametric and nonparametric
bootstrap procedures are proposed for estimating the mean squared error of the EBLUP (Empirical
Best Linear Unbiased Predictor). A simulation study compares the bootstrap estimates with an
asymptotic analytical approximation and studies the robustness to non-normality. Finally, two
applications with real data are described
Vespa case
No full textSi analizza il caso Vespa Piaggio come esempio emblematico di marketing del Made in Italy. In particolare si esamina l evoluzione del mercato e del brand secondo un ottica prospettica
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