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The accessory genome of Pseudomonas syringae pv. actinidiae (PSA) strains from China, New Zealand, Chile, Italy and Korea.
No full textApprendere il mondo viaggiando: il ferrarese Luigi Pio di Savoia sulle strade d'Europa a metà Seicento
No full textProduction of wood pellets from poplar trees managed as coppices with Different harvesting cycles
No full textHigh-density biomass plantations have played a key role in the national energy landscape in Italy since the 1990s but, to date, an inversion of tendency and a significant reduction of cultivated areas has been noted. Despite this, the existing plantations have seen their coppicing rotation become significantly lengthened, resulting in large quantities of biomass per hectare. This study aimed to identify the best raw material suitable for pellet production using whole trees or stems without branches from poplar plantations at the end of the third, sixth and ninth year of age. All types of pellets made reach the requirements of class A1 for diameter, length, moisture content, ash melting point, lower heating value, as well as nitrogen (N), sulfur (S), and heavy metals. None of the theses satisfied the bulk density parameters while for ashes and mechanical durability, a great variability was observed according to the different raw materials used. An improvement in terms of heating value was observed by transforming the poplar wood chips refined into pellets. The pelletizing process using high density poplar plantation as a raw material highlights the possibility of obtaining a product that meets many of the quality standards required on the market. These aspects are closely related to the innovation carried out in the agro-forestry sector for effective energetic sustainability.5n
Le relazioni fra sistemi economici e ambiente: dall’approccio neoclassico al modello bioeconomico
No full textLingua e norme dell'italiano. Alcune considerazioni a partire dalle lettere fra Cinque e Settecento
No full textSpatiotemporal modification of Mediterranean mountain secondary rangelands in relation to small ruminant grazing management condition
No full text
A day ahead energy load forecasting: Machine learning based model application on an Italian large enterprise
No full textElectric energy costs reduction is a critical aspect for industrial enterprise management. Short-term load forecast is a very important activity both for enterprises and for electric grid manager. Applying a short-term load forecasting method, enterprises can cut energy costs. Furthermore, such an application contributes to the reduction of grid manager interventions to minimize imbalance problems. In this context, industrial sites able to self-produce more than their energy need, have to adopt suitable load forecasting systems both to control energy consumption and to limit dispatching burden due to the feed of power into the grid. Correlation between industrial site energy consumption and industrial productions has encouraged the authors to develop a methodology that provide short-term electric load forecasting, based on machine learning, applicable in a generalized manner using available production plan data. To develop such a complex model, a tool composed of several parts has been implemented. Forecasting model structure is composed of 2 parts, one for prediction and one for imbalance calculation. Neural networks have been used in prediction phases because of their possibility to manage large dataset and to find nonlinear correlation between available variables. Application of developed methodology on real industrial gathered data has provided important results. Forecasting method, although calculated imbalances have reached high values, has led to get around 28% saving on balancing costs compared to enterprise previously applied forecasting method