Distributed multi-generation systems: energy models and analyses

Libro riguardante la caratterizzazione di sistemi per la produzione combinata di energia da sistemi di multi-generazione distribuita (Distributed Multi-Generation, DMG). Il libro tratta componenti, schemi e modelli di sistemi di multi-generazione distribuita, illustra concetti derivanti da proposte originali degli autori in merito all'analisi e alla pianificazione dei sistemi, con i corrispondenti indicatori energetici e ambientali approccio formulati secondo un approccio unificato. Numerosi esempi applicativi inclusi nel libro riguardano in particolare sistemi di cogenerazione e trigenerazion

Economical comparison of CHP systems for industrial user with large steam demand

In this paper cogeneration benefits applied to a user with a high steam demand are analyzed. The methodology for the feasibility study and the economical analysis of the investment is presented under the Italian legislative framework. The methodology is applied to an actual case and a detailed description and discussion of all data input is provided. Especially this last key point will be faced using starting data usually available in these kind of studies (i.e., not very detailed for thermal consumption). Finally a comparison of different CHP technologies and a sensitivity analysis is done

Dietary chia seed (Salvia hispanica L.) rich in a-linolenic acid improves adiposity and normalises hypertriacylglycerolaemia and insulin resistance in dyslipaemic rats

The present study investigates the benefits of the dietary intake of chia seed (Salvia hispanica L.) rich in α-linolenic acid and fibre upon dyslipidaemia and insulin resistance (IR), induced by intake of a sucrose-rich (62.5 %) diet (SRD). To achieve these goals two sets of experiments were designed: (i) to study the prevention of onset of dyslipidaemia and IR in Wistar rats fed during 3 weeks with a SRD in which chia seed was the dietary source of fat; (ii) to analyse the effectiveness of chia seed in improving or reversing the metabolic abnormalities described above. Rats were fed a SRD during 3 months; by the end of this period, stable dyslipidaemia and IR were present in the animals. From months 3-5, half the animals continued with the SRD and the other half were fed a SRD in which the source of fat was substituted by chia seed (SRD+chia). The control group received a diet in which sucrose was replaced by maize starch. The results showed that: (i) dietary chia seed prevented the onset of dyslipidaemia and IR in the rats fed the SRD for 3 weeks - glycaemia did not change; (ii) dyslipidaemia and IR in the long-term SRD-fed rats were normalised without changes in insulinaemia when chia seed provided the dietary fat during the last 2 months of the feeding period. Dietary chia seed reduced the visceral adiposity present in the SRD rats. The present study provides new data regarding the beneficial effect of chia seed upon lipid and glucose homeostasis in an experimental model of dislipidaemia and IR.Fil: Chicco, Adriana Graciela. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Ciencias Biológicas; ArgentinaFil: D'alessandro, Maria Eugenia Guadalupe. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Ciencias Biológicas; ArgentinaFil: Hein, Gustavo Juan. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Ciencias Biológicas; ArgentinaFil: Oliva, Maria Eugenia. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Ciencias Biológicas; ArgentinaFil: Lombardo, Yolanda B.. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Departamento de Ciencias Biológicas; Argentin

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