How a sensitive analysis on the coupling geology and borehole heat exchanger characteristics can improve the efficiency and production of shallow geothermal plants

Knowledge of the thermal behaviour around and throughout borehole heat exchangers (BHEs) is essential for designing a low enthalpy geothermal plant. In particular, the type of grout used in sealing the space between BHE walls and the pipes is fundamental for optimizing the heat transfer and minimizing the thermal resistance, thereby promoting the reduction of total drilling lengths and installation costs. A comparison between grouts with different thermal conductivities coupled with common hydrogeological contexts, was modelled for a typical one-year heating for continental climates. These data have been used for a sensitivity analysis taking into account different flow rates through pipes. The results highlight that in groundwater transient conditions, porous lithologies allow for greater heat power extractions to be obtained with an increasing grout thermal conductivity than limestone or clayey silt deposits do. Moreover, increasing the inlet flow rates through the pipe greatly improves the final heat power extraction. As a result, when the underground allows for high extraction rates, the use of high performing grouts is warmly suggested ensuring greater productions

Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport

Computational algorithms to predict Gene Ontology annotations

Background Gene function annotations, which are associations between a gene and a term of a controlled vocabulary describing gene functional features, are of paramount importance in modern biology. Datasets of these annotations, such as the ones provided by the Gene Ontology Consortium, are used to design novel biological experiments and interpret their results. Despite their importance, these sources of information have some known issues. They are incomplete, since biological knowledge is far from being definitive and it rapidly evolves, and some erroneous annotations may be present. Since the curation process of novel annotations is a costly procedure, both in economical and time terms, computational tools that can reliably predict likely annotations, and thus quicken the discovery of new gene annotations, are very useful. Methods We used a set of computational algorithms and weighting schemes to infer novel gene annotations from a set of known ones. We used the latent semantic analysis approach, implementing two popular algorithms (Latent Semantic Indexing and Probabilistic Latent Semantic Analysis) and propose a novel method, the Semantic IMproved Latent Semantic Analysis, which adds a clustering step on the set of considered genes. Furthermore, we propose the improvement of these algorithms by weighting the annotations in the input set. Results We tested our methods and their weighted variants on the Gene Ontology annotation sets of three model organism genes (Bos taurus, Danio rerio and Drosophila melanogaster ). The methods showed their ability in predicting novel gene annotations and the weighting procedures demonstrated to lead to a valuable improvement, although the obtained results vary according to the dimension of the input annotation set and the considered algorithm. Conclusions Out of the three considered methods, the Semantic IMproved Latent Semantic Analysis is the one that provides better results. In particular, when coupled with a proper weighting policy, it is able to predict a significant number of novel annotations, demonstrating to actually be a helpful tool in supporting scientists in the curation process of gene functional annotations

Technical Solutions and Standards Upgrade for Photovoltaic Systems Operated over 1500 Vdc

This paper deals with photovoltaic (PV) systems with operating voltage increased over the value 1500 V in DC, which represents the limit of the current solutions and the actual standard for the PV plant at utility-scale level. The increase of the DC voltage is aimed at reducing the cable energy losses, the number of components and to optimise the layout of the plants, increasing the competitiveness of Medium Voltage PV (MVPV) solutions with rated powers of hundreds of megawatt. The analysis carried out has identified the possible solutions to adopt in order to reach this target and has remarked that today the International Standards are not covering all the aspects of the technical solutions to be introduced in a MVPV plant. This paper indicates the key issues to be addressed by new Standards on some components in order to enable the deployment of MVPV solutions. Finally, the characteristics of an installation at 1500 V DC and some results of tests carried out on the isolation system of a 1500 V PV plant are discussed

Application of artificial dynamics to represent non-isolated single-input multiple-output DC-DC converters with averaged models

This paper presents for the first time the application of a method based on the transformation of the differential algebraic equations of non-isolated Single-Input Multiple Output (SIMO) DC-DC converters into a set of ordinary differential equations, by using artificial dynamics whose asymptotic convergence to the solution is guaranteed by the satisfaction of the relevant Lyapunov conditions. The mathematical formulation is simpler than in other formulations applied in the literature to study non-isolated SIMO DC-DC converters, and encompasses the use of sensitivity functions. The results show that the proposed solution represents in a fully accurate way the dynamics of the averaged models of Zeta Buck-Boost and Cúk Boost Combination converters