Museums and Crisis Management due to Covid -19. Effects of Pandemic and the Role of Digital and Social Networks Communication

The overall purpose of this study is the effects of lockdown within Italian museums caused by the Coronavirus disease-19 (Covid-19) emergency. During the pandemic, the museums are being closed, and the use of digital content and social networks seems to have been the solution to overcome the closure problem. The study analyzes the role of digital content and tools in communication to manage the shock due to Covid-19. The basic design of the research recurs to the crisis management model used to analyze three stages crossed by museums as preparedness, reaction, and recovery. The study involved several Italian museums grouped according to the art collection, governance, and funding sources. A semi-structured questionnaire was sent to them. The main findings show the presence of digital as a strategy already adopted by museums before the outbreak. However, the results reveal the absence of a preventive phase as requested by the model of crisis management. At the same time, it has emerged the need to adapt the digital content to guarantee the mission of fruition for the public

Searching for diversity. An overview on board of Italian Cultural Organizations.

Diversity takes on different meanings and synonymous; actually, the theme has been strongly debated in the era of globalization, migration and because of the affirmation of human rights and gender policies. Many studies are related to the analysis of board diversity within the forprofit organizations. Indeed, there is paucity of studies that linked the topic to the role of demographic and non-demographic diversity among cultural organizations and NPOs sector. Italian cultural institutions have been grouped together in a website by the private association AICI. The website was used in the present study in order to map organizations and their boards in terms of multiple diversity variables such visible and invisible parameters. Hereby, diversity is explored among board members of 102 private foundations and associations, including dimensions like age, gender, nationality, educational and professional background. One of the main finds of the research highlights how Italian cultural organizations have a low degree of diversity within the boards of directors

How B-Corporations' mobilize IC (unawares) to create value. Evidence from the Italian ecosystem

Purpose – This work investigates the presence and disclosure unawares of Intellectual Capital (IC) within Italian Benefit Corporations and their capability to create value through the mobilization of IC.Design/methodology/approach – The analysis is developed on a double level. In the first part the work recurs to the qualitative Content Analysis to investigate if the certified BCorps mobilize the IC. In the second part is conducted an empirical analysis on sample of the certified B-Corporations located in Italy to detect the degree of awareness about information, meaning and presence of Intellectual Capital.Findings – The BCorps responding to the assessment tools make an aware disclosure of value creation informations, but they are unaware that within this information there are elements of Intellectual Capital.Originality/value – Bcorps are a new reality to discover and there are few academic researches about this topic.Practical Implications – The lack of awareness about the presence and mobilization of Intellectual Capital makes reflect on the fact that for companies that want to become BCorps or want to renew the certification to continue to be in the BCorps ecosystem they must pay attention to the development of IC

Coupling radio propagation and weather forecast models to maximize Ka-band channel transmission rate for interplanetary missions

Deep space (DS) missions for interplanetary explorations are aimed at acquiring information about the solar system and its composition. To achieve this result a radio link is established between the space satellite and receiving stations on the Earth. Significant channel capacity must be guaranteed to such spacecraft-to-Earth link considering their large separation distance as well. Terrestrial atmospheric impairments on the space-to-Earth propagating signals are the major responsible for the signal degradation thus reducing the link’s channel temporal availability. Considering the saturation and the limited bandwidth of the conventional systems used working at X-band (around 8.4 GHz), frequencies above Ku-band (12-18 GHz) are being used and currently explored for next future DS missions. For example, the ESA mission EUCLID, planned to be launched in 2020 to reach Sun-Earth Lagrange point L2, will use the K-band (at 25.5-27 GHz). The BepiColombo (BC) ESA mission to Mercury, planned to be launched in 2016, will use Ka-band (at 32-34 GHz) with some modules operating at X-band too. The W-band is also being investigated for space communications (Lucente et al., IEEE Systems J., 2008) as well as near-infrared band for DS links (Luini at al., 3rd IWOW, 2014; Cesarone et al., ICSOS, 2011). If compared with X-band channels, K-band and Ka-band can provide an appealing data rate and signal-to-noise ratio in free space due to the squared-frequency law increase of antenna directivity within the downlink budget (for the same physical antenna size). However, atmospheric path attenuation can be significant for higher frequencies since the major source of transmission outage is not only caused by convective rainfall, as it happens for lower frequencies too, but even non-precipitating clouds and moderate precipitation produced by stratiform rain events are detrimental. This means that accurate channel models are necessary for DS mission data link design at K and Ka band. A physical approach can offer advanced radiopropagation models to take into account the effects due to atmospheric gases, clouds and precipitation. The objective of this work is to couple a weather forecast numerical model with a microphysically- oriented radiopropagation model, providing a description of the atmospheric state and of its effects on a DS downlink. This work is developed in the framework of the RadioMeteorological Operations Planner (RMOP) program, aimed at performing a feasibility study for the BC mission (Biscarini et al., EuCAP 2014). The RMOP chain for the link budget computation is composed by three modules: weather forecast (WFM), radio propagation (RPM) and downlink budget (DBM). WFM is aimed at providing an atmospheric state vector. Among the available weather forecast models, for RMOP purposes we have used the Mesoscale Model 5. The output of the WFM is the input of the RPM for the computation of the atmospheric attenuation and sky-noise temperature at the receiving ground station antenna. RPM makes use of radiative transfer solver based on the Eddington approximations well as accurate scattering models. Time series of attenuation and sky-noise temperature coming from the RPM are converted into probability density functions and then ingested by the DBM to compute the received data volume (DV). Using the BC mission as a reference test case for the Ka-band ground station at Cebreros (Spain), this work will show the advantages of using a coupled WFM-RPM approach with respect to climatological statistics in a link budget optimization procedure. The signal degradation due to atmospheric effects in DS links in terms of received DV will be also investigated not only at Ka band, but also at X, K and W for intercomparison. The quality of the DS downlink will be given in terms of received DV and the results at different frequencies compared showing the respective advantages and drawbacks

Mesoscale high-resolution meteorological and radiative transfer models for satellite downlink budget design at millimeter-wave frequencies

Deep space (DS) missions for interplanetary explorations are aimed at acquiring information about the solar system and its composition. To achieve this result a radio link is established between the space satellite and receiving stations on the Earth. Significant channel capacity must be guaranteed to such spacecraft-to-Earth link considering their large separation distance as well. Terrestrial atmospheric impairments on the space-to-Earth propagating signals are the major responsible for the signal degradation thus reducing the link’s channel temporal availability. Considering the saturation and the limited bandwidth of the conventional systems used working at X-band (around 8.4 GHz), frequencies above Ku-band (12-18 GHz) are being used and currently explored for next future DS missions. For example, the ESA mission EUCLID, planned to be launched in 2020 to reach Sun-Earth Lagrange point L2, will use the K-band (at 25.5-27 GHz). The BepiColombo (BC) ESA mission to Mercury, planned to be launched in 2016, will use Ka-band (at 32-34 GHz) with some modules operating at X-band too. The W-band is also being investigated for space communications (Lucente et al., IEEE Systems J., 2008) as well as near-infrared band for DS links (Luini at al., 3rd IWOW, 2014; Cesarone et al., ICSOS, 2011). If compared with X-band channels, higher frequency bands can provide an appealing data rate and signal-to-noise ratio in free space due to the squared-frequency law increase of antenna directivity within the downlink budget (for the same physical antenna size). In particular, W-band (75–110 GHz) can be one valid alternative to K- and Ka-bands; theoretically, W-band should provide high channel capacities due to the large bandwidth availability and a more robust immunity to signal interference. However, atmospheric path attenuation can be significant for higher frequencies since the major source of transmission outage is not only caused by convective rainfall, as it happens for lower frequencies too, but even non-precipitating clouds and moderate precipitation produced by stratiform rain events are detrimental. This means that accurate channel models are necessary for DS mission data link design. A physical approach can offer advanced radiopropagation models to take into account the effects due to atmospheric gases, clouds and precipitation. The objective of this work is to couple a weather forecast numerical model with a microphysically-oriented radiopropagation model, providing a description of the atmospheric state and of its effects on a DS downlink. This work is the continuation of a study developed in the framework of the RadioMeteorological Operations Planner (RMOP) program, aimed at performing a feasibility study for the BC mission (Biscarini et al., EuCAP 2014). The RMOP chain for the link budget computation is composed by three modules: weather forecast (WFM), radio propagation (RPM) and downlink budget (DBM). WFM is aimed at providing an atmospheric state vector. Among the available weather forecast models, for RMOP purposes we have used the Mesoscale Model 5. The output of the WFM is the input of the RPM for the computation of the atmospheric attenuation and sky-noise temperature at the receiving ground station antenna. RPM makes use of radiative transfer solver, based on the Eddington approximations well as accurate scattering models. Time series of attenuation and sky-noise temperature coming from the RPM are converted into probability density functions and then ingested by the DBM to compute the received data volume (DV). RMOP project was originally aimed at investigating the Ka-band for DS mission focusing the attention on the advantages of using a coupled WFM- RPM approach with respect to climatological statistics in a link budget optimization procedure. In this work we extended the study to the W- and K- band. The signal degradation, due to atmospheric effects in DS links in terms of received DV, is investigated and a comparison among K-, Ka-, W- and the more commonly used X-band is carried out. The quality of the DS downlink will be given in terms of received DV and the results at different frequencies compared showing the respective advantages and drawbacks

Improving Reading Comprehension and Summarising Skills in Primary School: A Quasi-Experimental Study

MIGLIORARE LA COMPRENSIONE DEL TESTO E LE CAPACITÀ DI SINTESI NELLA SCUOLA PRIMARIA: UNO STUDIO QUASI-SPERIMENTALE Abstract The paper reports the results of the evaluation of a programme aimed at improving reading comprehension and summarising skills of fourth graders through a quasi-experimental study conducted with 671 students (421 in the experimental group and 250 in the control group) in Italian schools. Students assigned to the experimental group received three months of the intervention while students in the control group continued with regular teacher practice. Results showed a statistically significant difference between the two conditions on reading comprehension and summarising skills. Students included in the experimental group outperformed students in the control group in both the measures used (dppc2 = 0.32 in the Summarising Test and dppc2 = 0.54 in the Summary Qualitative Assessment). No differences were found between students with different proficiency vocabulary levels in the experimental group. The implications for research practice and limitations of the study are discussed

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging

Investigating Ka-band science data transfer for BepiColombo mission by using radiometeorological numerical models

Deep space (DS) exploration is aimed at acquiring information about the solar system and its composition, a purpose that can be achieved only if a significant communication capacity can be provided to spacecrafts at very large distances [1]. The Ka-band (at 32-34 GHz) and higher frequency band channels can provide this capacity if compared to the current X-band (around 8.4 GHz) [2]-[4]. Ka-band can offer a striking performance advantage over X-band because of the square-frequency law increase of directivity of the downlink beam for the same physical antenna size. This opens up a possible and useful trade space for Ka-band missions with the same antenna size (and spacecraft constraints) and radio frequency power, since a Ka-band mission can return four times more data than a comparable X–band mission. For the European Space Agency (ESA), the next step in this direction will be the utilisation of Ka-band downlinks both to generate radiometric observables (in combination with X-band uplink) as well as to increase science data transfer [5]. The first satellite mission adopting such frequency operationally will be BepiColombo (BC), the ESA cornerstone mission to Mercury (expected launch in 2015) including the Mercury Orbiter Radio Experiment (MORE) at X-Ka band [6]. The optimal allocation of channel resources above Ku band is limited by the significant impact of radio- meteorological factors which can irremediably degrade the quality of service for fairly high percentage of time [7]. At Ka band, for instance, attenuation due to cloudy and rainy troposphere can be even one order of magnitude larger than at X-band. The major cause of outages at Ka band and above is due to rainfall, as well as non-precipitating clouds. For small carrier-to-noise ratio (CNR), the impact of atmospheric noise temperature can become non-negligible [4]. In order to achieve the optimum data return at Ka-band, a different approach with respect to the link budget computation at lower frequencies (e.g., S or X band) is necessary [2]. Such link analysis is based on the maximization of the expected data return in a probabilistic framework rather than on a specified link statistical availability. Recent methods uses monthly statistics collected at the receiving site with the aim of defining average values of expected received data volume [5] and the exploitation of numerical weather forecasting is also foreseen [3]. This paper introduces the preliminary concept of the RadioMetOP (RadioMeteorological Operations Planner) technique and describes its main modelling components and objectives, limiting the analysis to rainfall effects. Numerical results in terms of received frame data for unconstrained and constrained system scenarios are also described together with a discussion about the possible impact of RadioMetOP methods on BC operations

The influence of invasive jellyfish blooms on the aquatic microbiome in a coastal lagoon (Varano, SE Italy) detected by an Illumina-based deep sequencing strategy

The rapid expansion of multicellular native and alien species outbreaks in aquatic and terrestrial ecosystems (bioinvasions) may produce significant impacts on bacterial community dynamics and nutrient pathways with major ecological implications. In aquatic ecosystems, bioinvasions may cause adverse effects on the water quality resulting from changes in biological, chemical and physical properties linked to significant transformations of the microbial taxonomic and functional diversity. Here we used an effective and highly sensitive experimental strategy, bypassing the efficiency bottleneck of the traditional bacterial isolation and culturing method, to identify changes of the planktonic microbial community inhabiting a marine coastal lagoon (Varano, Adriatic Sea) under the influence of an outbreak-forming alien jellyfish species. Water samples were collected from two areas that differed in their level of confinement inside in the lagoon and jellyfish densities (W, up to 12.4 medusae m−3; E, up to 0.03 medusae m−3) to conduct a snapshot microbiome analysis by a metagenomic approach. After extraction of the genetic material in the environmental water samples, we deep-sequenced metagenomic amplicons of the V5–V6 region of the 16S rRNA bacterial gene by an Illumina MiSeq platform. Experiments were carried out in triplicates, so six libraries of dual indexed amplicons of 420 bp were successfully sequenced on the MiSeq platform using a 2 × 250 bp paired-end sequencing strategy. Approximately 7.5 million paired-end reads (i.e. 15 million total reads) were generated, with an average of 2.5 million reads (1.25 M pairs) per sample replicate. The sequence data, analyzed through a novel bioinformatics pipeline (BioMaS), showed that the structure of the resident bacterial community was significantly affected by the occurrence of jellyfish outbreaks. Clear qualitative and quantitative differences were found between the western and eastern areas (characterized by many or few jellyfish), with 84 families, 153 genera and 324 species in the W samples, and 104 families, 199 genera and 331 species in the E samples. Significant differences between the two sampling areas were particularly detected in the occurrence of 16 families, 22 genera and 61 species of microbial taxa. This is the first time that a NGS platform has been used to screen the impact of jellyfish bioinvasions on the aquatic microbiome, providing a preliminary assessment of jellyfish-driven changes of the functional and structural microbial biodiversity

Psychosocial characteristics and social networks of suicidal prisoners: towards a model of suicidal behaviour in detention

Prisoners are at increased risk of suicide. Investigation of both individual and environmental risk factors may assist in developing suicide prevention policies for prisoners and other high-risk populations. We conducted a matched case-control interview study with 60 male prisoners who had made near-lethal suicide attempts in prison (cases) and 60 male prisoners who had not (controls). We compared levels of depression, hopelessness, self-esteem, impulsivity, aggression, hostility, childhood abuse, life events (including events occurring in prison), social support, and social networks in univariate and multivariate models. A range of psychosocial factors was associated with near-lethal self-harm in prisoners. Compared with controls, cases reported higher levels of depression, hopelessness, impulsivity, and aggression, and lower levels of self-esteem and social support (all p values <0.001). Adverse life events and criminal history factors were also associated with near-lethal self-harm, especially having a prior prison spell and having been bullied in prison, both of which remained significant in multivariate analyses. The findings support a model of suicidal behaviour in prisoners that incorporates imported vulnerability factors, clinical factors, and prison experiences, and underscores their interaction. Strategies to reduce self-harm and suicide in prisoners should include attention to such factors