The crystal structure of sacrofanite, the 74 Å phase of the cancrinite–sodalite supergroup

Sacrofanite, a = 12.903(2) Å, c = 74.284(8) Å, space group View the MathML source, belongs to the cancrinite–sodalite supergroup of minerals, and displays a 28-layer stacking sequence along the c axis. Its stacking sequence is ABCABACACABACBACBACABABACABC…, where A, B and C stand for the positions of six-member rings of tetrahedra in each layer. It corresponds to the Zhdanov symbol |12(8)21|12(8)21|, and gives rise to a framework with topological symmetry P63/mmc. The ordering of Si and Al in the tetrahedral sites reduces the symmetry to View the MathML source. The members of this supergroup of minerals belong to the wider ABC-6 family, where also double rings of tetrahedra may occur. They share many structural features with zeolites, showing structural cages hosting extra-framework ions as well as H2O molecules. The crystal structure of sacrofanite has been modelled on the basis of High Resolution Transmission Electron Microscopy (HRTEM) images. The resulting model has been successfully refined by using both single-crystal synchrotron radiation and laboratory data. The refinements converged to R = 0.083 for 4228 unique reflections, and to R = 0.096 for 15,795 unique reflections, respectively. The resulting framework is formed by eight cancrinite and four sodalite cages superimposed along [0, 0, z], whereas one cancrinite, four sodalite, two losod, and one liottite cages occur along [1/3, 2/3, z] and [2/3, 1/3, z]. The structural formula of sacrofanite, as obtained from the refinement and by crystal chemical considerations, is (Na61K19Ca32)∑=112(Si84Al84O336)(SO4)26Cl2F6·2H2O

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeco-nomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems

Design of Cloud Robotic Services for Senior Citizens to Improve Independent Living and Personal Health Management

A cloud robotics solution was designed and initially tested with a mobile robotic platform and a smart environment, in order to provide health-care management services to senior citizens and improve their independent living. The solution was evaluated in terms of Quality of Service (QoS) and tested in the realistic scenario of the DomoCasa Living Lab, Peccioli, Italy. In particular, a medication reminding service, a remote home monitoring and a user indoor localization algorithm were outsourced in the cloud and provided to the robots, users and carers. The system acquired data from a smart environment and addressed the robot to the user for service delivery. Experiments showed a service's Reliability of Response at least of the 0.04 % and a Time of Response of the same order of magnitude of the processing time required by the user localization algorithm

The antecedents and consequences of health literacy in an ecological perspective: Results from an experimental analysis

This study analyses the relationship between the antecedents and consequences of health literacy (HL) at the ecological level among the nations involved in the European Health Literacy Survey (HLS-EU). The antecedents and consequences were investigated by means of proxy indicators. The HL was measured using the 47-item HLS-EU questionnaire (HLS-EUQ47) and the Newest Vital Sign (NVS). The two measures stood in significant correlation to the outcomes of the sub-discipline of the Euro Health Consumer Index (r = 0.790 for HLS-EUQ47; r = 0.789 for NVS). The HLS-EUQ47 also stood in correlation to the percentage of population with post-secondary education (r = 0.810), the reading performance for 15-year-old students (r = 0.905), the presence of a national screening program for breast (r = 0.732) or cervical cancer (r = 0.873). The NVS stood in correlation with the unemployment rate (r = −0.778), the Gross Domestic Product (r = 0.719), the Gini coefficient (r = −0.743), the rank of the Euro Patient Empowerment Index (r = −0.826), the expenditure on social protection (r = 0.814), the Consumer Empowerment Index (r = 0.898), the percentage of adults using the internet for seeking health information (r = 0.759), the prevalence of overweight individuals (r = −0.843), the health expenditure (r = 0.766), as well as the percentage of individuals using the internet for interacting with public authorities (r = 0.755). This study provides some preliminary considerations regarding alternative means by which to study HL and proposes new methods for experimentation. The methods and the results could offer a means by which the relationship between society and overall healthcare protection could be strengthened

Is higher education more important for firms than research? Disentangling university spillovers

The paper is the first attempt to integrate microdata on universities and firms across most European countries in order to disentangle the impact of knowledge spillovers from human capital (graduates) and intellectual capital (codified research output) on the performance of firms. Data cover all Higher Education Institutions (HEIs) registered in the official European Tertiary Education Register (ETER). Data on performance of firms are from ORBIS and refer to change in the 2011–2015 period in turnover, total assets, intangible assets, and employment. Firms are georeferred and the spillovers from all HEIs located at a given distance are summed and integrated. The findings suggest that, among knowledge spillovers, the creation of human capital via education of students has a larger impact than the circulation of research knowledge. Moreover, the two factors seem to be complements rather than substitutes. Spatial proximity is important for embodied knowledge spillovers (i.e. educated people), while for codified and disembodied spillovers (citations to publications) the spatial dimension is less relevant. The findings have important managerial and policy-making consequences

Cloud-based Wizard of Oz as a service

The paper deals with theoretical and experimental issues of an idea towards a cloud-based Wizard of Oz in the Microsoft Azure cloud environment. Wizard of Oz is a common tool in social robotics and especially in specific applications like mental illness treatment, ambient assisted living, and many others. The final goal is to create a system with the ability to learn and replace a human wizard by an intelligent software agent, which simulates the behavior of the human. � 2015 IEEE