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The results highlighted that 40 laboratories fall into the "low risk" and the remaining 2 into the "moderate risk" category.Labs with risk factors are a minority. These were properly identified using the proposed methodology.Biomedical research in academic settings is an important issue for Public Health and Environment protection. As workplaces, the facilities for research expose their personnel to different hazards and health risks. The University of L'Aquila (Italy) carried out a field study aimed at creating and applying a checklist intended for laboratory staff.The proposed checklist was derived from the procedure illustrated in the Appendix (procedure followed for the identification of a numerical index of biological risk for university facilities) and consists of 9 items. The study was conducted in 42 laboratories.

In 2017 the Joint Research Centre developed a Power Plant Database [1] for energy systems modelling (JRC-PPDB) in order to support the unit activities in energy systems modelling and knowledge management. As demand for open data is increasingly sought after, an open version (JRC-PPDB-OPEN), based on exclusively open data was designed. The JRC-PPDB-OPEN is primarily based on a collection of all the information published by ENTSO-E[1] on the European power plants at unit level. This information was extended, improved, and where possible corrected using information contained in open datasets published by WRI Powerwatch[2], Global energy observatory[3], FRESNA[4] and the EEA[5]. The JRC-PPDB-OPEN database is a first attempt towards a more detailed and coherent, albeit still incomplete, dataset of European power plants. Further work to expand and improve the information contained therein is called for. To this extend, and in order to facilitate the future involvement of third parties in such efforts, the associations between records in the different datasets (linkage) are included. [1] https://transparency.entsoe.eu/ [2] http://datasets.wri.org/dataset/globalpowerplantdatabase [3] http://globalenergyobservatory.org/ [4] https://github.com/FRESNA/powerplantmatching [5] https://prtr.eea.europa.eu/#/home

JEL, Moneta e Credito, V. 45, N. 180 (1992)

33rd European Photovoltaic Solar Energy Conference and Exhibition; 2485-2491, On regional scale, the estimation of the solar power generation from the environmental conditions and the solar power forecast is essential for Distribution System Operators, Transmission System Operator, energy traders, and aggregators. Indeed, high photovoltaic penetration results in a stochastic variability of the electric demand that could compromise the stability of the grid, increase the amount of energy reserve and the energy imbalance cost. In this context, two upscaling methods and several models were developed and used for estimation and forecast of the photovoltaic distributed generation in a small area of Italy with high photovoltaic penetration. The upscaling methods are based on chains of data-driven models (k-mean clustering, principal component analysis and ensemble of neural networks) that use as inputs satellite derived irradiance and numerical weather prediction. These models allow a complete assessment of the accuracy of the regional power forecast at different horizons, from 0 hour ahead (power estimation) to 48 hours ahead. The power estimation model provided a RMSE of 3% of installed capacity. Intra-day forecast (from 1 to 4 hours) obtained a RMSE of 5% - 7% and a skill score with respect to the clear sky persistence from -8% to 33.6%. The one and two days ahead forecast achieved a RMSE of 7% and 7.5% and a skill score of 39.2% and 45.7%. The ensemble smoothing effect on cluster scale was also studied. It was proved that the RMSE reduction achieved on cluster scale due to the ensemble smoothing follows the same exponential law found for single systems. In this case the smoothing effect reduces the RMSE of day-ahead forecast of 12% with respect to the mean single cluster value. It was also shown that the skill score increases linearly with the size of the region with a rate of 0.1% for km. Furthermore, a method to estimate the forecast error was also developed. It was based on an ensemble neural network model coupled with a probabilistic correction. It can provide a highly reliable computation of the prediction intervals.

32nd European Photovoltaic Solar Energy Conference and Exhibition; 1475-1481, This work aims to compare several data-driven models using different Numerical Weather Prediction (NWP) input data and then to build up an outperforming Multi-Model Ensemble (MME) and its prediction intervals. Statistic, stochastic and hybrid machine-learning algorithms were developed and the NWP data from IFS and WRF models were used as input. It was found that the same machine learning algorithm differs in performance even if it make use of NWP data with comparable accuracy. This apparent inconsistency depends on the capability of the machine learning model to correct the bias error of the input data. The stochastic and the hybrid model using the same WRF input, as well as the stochastic and the non-linear statistic models using the same IFS input, produce very similar results. The MME resulting from the averaging of the best data-driven forecasts, improves the accuracy of the outperforming member of the ensemble, bringing the skill score from 42% to 46%. To reach this performance, the ensemble should include forecasts with similar accuracy but generated with the higher variety of different data-driven technology and NWP input.

CONTEXT: In Italy, the surveillance of congenital hypothyroidism (CH) is performed by the Italian National Registry of Infants with CH (INRICH). Up to now, about 3600 infants with CH are recorded in the INRICH, and a high number of twins are included. OBJECTIVE: Our objective was to estimate the risk of CH in multiple and single deliveries and to compare neonatal features of CH twins with twins from the general population. DESIGN: The Italian population of CH infants recorded in the INRICH from 1989-2000 was investigated. RESULTS: A more than 3-fold higher frequency of twins was found in the CH population than in the general population, and for the first time, it was possible to estimate the CH incidence in multiple (10.1 in 10,000) and single deliveries (3.2 in 10,000 live births). Significantly higher frequencies of in situ gland as well as lower TSH mean level at screening were found in twin than in singleton CH babies. The concordance rate for permanent CH was very low (4.3%) and due to only three concordant couples. However, a high recurrence risk for CH was estimated in siblings of affected babies recorded in the INRICH, including twins considered as siblings. CONCLUSIONS: The high CH incidence observed in twins is worthy of interest for the high number of induced pregnancies in Italy as well as in other Western countries. Moreover, the low concordance rate for CH among twins together with a high recurrence risk for the disease among siblings indicates that environmental risk factors may act as a trigger on a susceptible genetic background in the etiology of the disease

An application of Evolution Strategies (ESs) to the dynamic identification of hybrid seismic isolation systems is presented. It is shown how ESs are highly effective for the optimisation of the test problem defined in previous work for methodology validation. The acceleration records of a number of dynamic tests performed on a seismically isolated building are used as reference data for the parameter identification. The application of CMA-ES to a previously existing model considerably improves previous results but at the same time reveals limitations of the model. To investigate the problem three new mechanical models with higher number of parameters are developed. The application of CMA-ES to the best designed model allows improvements of up to 79% compared to the solutions previously available in literature. © Springer-Verlag Berlin Heidelberg 2013.

CCTs (Italian Treasury credit certificates) were first introduced in 1977 in order to meet the need for more effective management of the public debt. The CCT was accepted in the market as a `centaur': a short-term security in its yield and risk features, but with a long maturity. However, in terms of techniques of financial analysis and portfolio management, the methodological approach to CCTs appears to be generally confused. JEL: G20, PSL Quarterly Review, V. 46, N. 185 (1993)