Central bank macroeconomic forecasting during the global financial crisis: The European Central Bank and Federal Reserve Bank of New York experiences

This paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The paper is the result of a collaborative effort between the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not focus exclusively on point forecast performance. It also examines density forecasts, as well as methodological contributions, including how financial market data could have been incorporated into the forecasting process

On Democracy in Peer-to-Peer systems

The information flow inside a P2P network is highly dependent on the network structure. In order to ease the diffusion of relevant data toward interested peers, many P2P protocols gather similar nodes by putting them in direct contact. With this approach the similarity between nodes is computed in a point-to-point fashion: each peer individually identifies the nodes that share similar interests with it. This leads to the creation of a sort of "private" communities, limited to each peer neighbors list. This "private" knowledge do not allow to identify the features needed to discover and characterize the correlations that collect similar peers in broader groups. In order to let these correlations to emerge, the collective knowledge of peers must be exploited. One common problem to overcome in order to avoid the "private" vision of the network, is related to how distributively determine the representation of a community and how nodes may decide to belong to it. We propose to use a gossip-like approach in order to let peers elect and identify leaders of interest communities. Once leaders are elected, their profiles are used as community representatives. Peers decide to adhere to a community or another by choosing the most similar representative they know about

Correlates of calcaneal quantitative ultrasound parameters in patients with diabetes: the study on the assessment of determinants of muscle and bone strength abnormalities in diabetes

OBJECTIVE: Quantitative ultrasound (QUS) provides an estimate of bone mineral density (BMD) and also evaluates bone quality, which has been related to increased fracture risk in people with diabetes. This study aimed at assessing the correlates of calcaneal QUS parameters in diabetic subjects encompassing various degrees of micro and macrovascular complications and a wide-range of peripheral nerve function. METHODS: Four hundred consecutive diabetic patients were examined by QUS to obtain values of broadband ultrasound attenuation (BUA), the speed of sound (SOS), quantitative ultrasound index (QUI), and BMD. RESULTS: Among surrogate measures of complications, sensory and motor nerve amplitude and heart rate response to cough test and standing correlated with QUS parameters at univariate analysis, together with age, body mass index (BMI), waist circumference, lipid profile, and renal function. Multivariate analysis revealed that BUA, SOS, QUI, and BMD were independently associated with age, male gender, hemoglobin A1c, BMI (or fat, but not fat-free mass), and somatic and autonomic nerve function parameters. CONCLUSIONS: These data indicate that peripheral nerve dysfunction is associated with worse QUS parameters, possibly contributing to increased fracture risk in diabetes. The positive relation of QUS measures with adiposity needs further investigation. This trial is registered with ClinicalTrials.gov (NCT01600924)

GROUP: A Gossip Based Building Community Protocol

The detection of communities of peers characterized by similar interests is currently a challenging research area. To ease the diffusion of relevant data to interested peers, similarity based overlays define links between similar peers by exploiting a similarity function. However, existing solutions neither give a clear definition of peer communities nor define a clear strategy to partition the peers into communities. As a consequence, the spread of the information cannot be confined within a well defined region of an overlay. This paper proposes a distributed protocol for the detection of communities in a P2P network. Our approach is based on the definition of a distributed voting algorithm where each peer chooses the more similar peers among those in a limited neighbourhood range. The identifier of the most representative peer is exploited to identify a community. The paper shows the effectiveness of our approach by presenting a set of experimental results

An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases – from their design to their deployment, to their management –, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks

Finite mixture model-based classification of a complex vegetation system

To propose a Finite Mixture Model (FMM) as an additional approach for classifying large datasets of georeferenced vegetation plots from complex vegetation systems. Study area: The Italian peninsula including the two main islands (Sicily and Sardinia), but excluding the Alps and the Po plain. Methods: We used a database of 5,593 georeferenced plots and 1,586 vascular species of forest vegetation, created in TURBOVEG by storing published and unpublished phytosociological plots collected over the last 30 years. The plots were classified according to species composition and environmental variables using a FMM. Classification results were compared with those obtained by TWINSPAN algorithm. Groups were characterized in terms of ecological parameters, dominant and diagnostic species using the fidelity coefficient. Interpretation of resulting forest vegetation types was supported by a predictive map, produced using discriminant functions on environmental predictors, and by a non\u2010metric multidimensional scaling ordination. Results: FMM clustering obtained 24 groups that were compared with those from TWINSPAN, and similarities were found only at a higher classification level corresponding to the main orders of the Italian broadleaf forest vegetation: Fagetalia sylvaticae, Carpinetalia betuli, Quercetalia pubescenti-petraeae and Quercetalia ilicis. At lower syntaxonomic level, these 24 groups were referred to alliances and sub-alliances. Conclusions: Despite a greater computational complexity, FMM appears to be an effective alternative to the traditional classification methods through the incorporation of modelling in the classificatory process. This allows classification of both the co-occurrence of species and environmental factors so that groups are identified not only on their species composition, as in the case of TWINSPAN, but also on their specific environmental niche

Designing greenhouse subsystems for a lunar mission: the LOOPS - M Project

The 2020s is a very important decade in the space sector, where international cooperation is moving towards the exploration of the Moon and will lead to stable lunar settlements, which will require new, innovative, and efficient technologies. In this context, the project LOOPS–M (Lunar Operative Outpost for the Production and Storage of Microgreens) was created by students from Sapienza University of Rome with the objective of designing some of the main features of a lunar greenhouse. The project was developed for the IGLUNA 2021 campaign, an interdisciplinary platform coordinated by Space Innovation as part of the ESA Lab@ initiative. The LOOPS-M mission was successfully concluded during the Virtual Field Campaign that took place in July 2021. This project is a follow-up of the V-GELM Project, which took part in IGLUNA 2020 with the realization in Virtual Reality of a Lunar Greenhouse: a simulation of the main operations connected to the cultivation module, the HORT3 , which was already developed by ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) during the AMADEE-18 mission inside the HORTSPACE project. This paper will briefly describe the main features designed and developed for the lunar greenhouse and their simulation in a VR environment: an autonomous cultivation system able to handle the main cultivation tasks of the previous cultivation system, a bioconversion system that can recycle into new resources the cultivation waste with the use of insects as a biodegradation system, and a shield able of withstanding hypervelocity impacts and the harsh lunar environment. A wide overview of the main challenges faced, and lessons learned by the team to obtain these results, will be given. The first challenge was the initial inexperience that characterized all the team members, being for most the first experience with an activity structured as a space mission, starting with little to no know-how regarding the software and hardware needed for the project, and how to structure documentation and tasks, which was acquired throughout the year. An added difficulty was the nature of LOOPS-M, which included very different objectives that required different fields of expertise, ranging from various engineering sectors to biology and entomology. During the year, the team managed to learn how to handle all these hurdles and the organizational standpoint, working as a group, even if remotely due to the Covid-19 pandemic. Through careful planning, hard work and the help of supervisors, the activity was carried out through reviews, up to the prototyping phase and the test campaign with a successful outcome in each aspect of the project. By the end of the year everyone involved had acquired new knowledge, both practical and theoretical, and learned how to reach out and present their work to sponsors and to the scientific community

Variability in genes regulating vitamin D metabolism is associated with vitamin D levels in type 2 diabetes

Mortality rate is increased in type 2 diabetes (T2D). Low vitamin D levels are associated with increased mortality risk in T2D. In the general population, genetic variants affecting vitamin D metabolism (DHCR7 rs12785878, CYP2R1 rs10741657, GC rs4588) have been associated with serum vitamin D. We studied the association of these variants with serum vitamin D in 2163 patients with T2D from the "Sapienza University Mortality and Morbidity Event Rate (SUMMER) study in diabetes". Measurements of serum vitamin D were centralised. Genotypes were obtained by Eco™ Real-Time PCR. Data were adjusted for gender, age, BMI, HbA1c, T2D therapy and sampling season. DHCR7 rs12785878 (p = 1 x 10-4) and GC rs4588 (p = 1 x 10-6) but not CYP2R1 rs10741657 (p = 0.31) were significantly associated with vitamin D levels. One unit of a weighted genotype risk score (GRS) was strongly associated with vitamin D levels (p = 1.1 x 10-11) and insufficiency (<30 ng/ml) (OR, 95%CI = 1.28, 1.16-1.41, p = 1.1 x 10-7). In conclusion, DHCR7 rs12785878 and GC rs4588, but not CYP2R1 rs10741657, are significantly associated with vitamin D levels. When the 3 variants were considered together as GRS, a strong association with vitamin D levels and vitamin D insufficiency was observed, thus providing robust evidence that genes involved in vitamin D metabolism modulate serum vitamin D in T2D

Italian Case Study: socio-economic impact analysis of a cyber attack to a power plant in an Italian scenario. Cost and benefit estimation of CIPS standard adoptions. A reduced version

RT 55; The Italian Case study is a comprehensive report in which a possible attack scenario to a thermoelectric power plant is described in the Italian electric grid context and an assessment of social-economic impact is evaluated. A cost-benefit analysis of the adoption of comprehensive CIP standards is estimated. This is a reduced version of a full report in order to have a public deliverable purged from sensitive and confidential informatio