Nanofluid suspensions as heat carrier fluids in single U-tube borehole heat exchangers

The borehole heat exchanger (BHE) is a critical component to improve energy efficiency and decreasing environmental impact of ground-source heat pump systems. The lower thermal resistance of the BHE results in the better thermal performance and/or in the lower required borehole length. In the present study, effects of employing a nanofluid suspension as a heat carrier fluid on the borehole thermal resistance are examined. A 3D transient finite element code is adopted to evaluate thermal comportment of nanofluids with various concentrations in single U-tube borehole heat exchangers and to compare their performance with the conventional circuit fluid. The results show, in presence of nanoparticles, the borehole thermal resistance is reduced to some extent and the BHE renders a better thermal performance. It is also revealed that employing nanoparticle fractions between 0.5% and 2 % are advantageous in order to have an optimal decrement percentage of the thermal resistance

Strong Convergence towards self-similarity for one-dimensional dissipative Maxwell models

We prove the propagation of regularity, uniformly in time, for the scaled solutions of one-dimensional dissipative Maxwell models. This result together with the weak convergence towards the stationary state proven by Pareschi and Toscani in 2006 implies the strong convergence in Sobolev norms and in the L^1 norm towards it depending on the regularity of the initial data. In the case of the one-dimensional inelastic Boltzmann equation, the result does not depend of the degree of inelasticity. This generalizes a recent result of Carlen, Carrillo and Carvalho (arXiv:0805.1051v1), in which, for weak inelasticity, propagation of regularity for the scaled inelastic Boltzmann equation was found by means of a precise control of the growth of the Fisher information.Comment: 26 page

A subgraph isomorphism algorithm and its application to biochemical data

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms

A subgraph isomorphism algorithm and its application to biochemical data

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms

PYDBSCAN UN SOFTWARE PER IL CLUSTERING DI DATI

Con il termine clustering si indica il processo mediante il quale è possibile raggruppare oggetti in base a caratteristiche comuni (features). Questo approccio, alla base dei processi di estrazione di conoscenza da insiemi di dati (data mining), riveste notevole importanza nelle tecniche di analisi. Come verrà mostrato in questo lavoro, l’applicazione delle tecniche di clustering consente di analizzare dataset, con l’obiettivo di ricercare strutture che possano fornire informazioni utili circa i dati oggetto dello studio. Gli ambiti in cui tali algoritmi sono impiegati risultano essere eterogenei, a partire dalle analisi di dati biomedici, astrofisici, biologici, fino ad arrivare a quelli geofisici. La letteratura è ricca di vari casi di studio, dai quali il ricercatore può trarre spunto e adattare i differenti approcci alle proprie esigenze. Il software PyDBSCAN, oggetto del presente lavoro, permette di applicare tecniche di clustering basate sul concetto di densità, applicate ad oggetti (o punti) appartenenti ad insiemi definiti in uno spazio metrico. L’algoritmo di base è il DBSCAN (Density Based Spatial Clustering on Application with Noise) [Ester et al., 1996], di cui viene riportata una implementazione ottimizzata al fine di migliorare la qualità del processamento dei dati. Schematicamente, il sistema proposto può essere rappresentato come in Fig. 1. Il software, sviluppato in Python 2.6 [Python ref.], utilizza le librerie scientifiche Numpy [Numpy ref.], Matplotlib [matplotlib ref.] e la libreria grafica PyQt [PyQt ref.] impiegata nella realizzazione dell’interfaccia utente. Python è un linguaggio di programmazione che permette la realizzazione di applicazioni crossplatform in grado di funzionare su diversi sistemi operativi quali Windows, Unix, Linux e Mac OS. Nella prima parte del lavoro verranno brevemente descritte le tecniche oggetto del software presentato, mentre nella seconda parte verrà descritto un esempio di applicazione su dati reali

KAOS: A new automated computational method for the identification of overexpressed genes

Background: Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1-3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions. Results: We propose a computational method aimed at the automatic identification of genes which are selectively over-expressed in a very small fraction of samples within a specific tissue. The method does not require a healthy counterpart or a reference sample for the analysis and can be therefore applied also to transcriptional data generated from cell lines. In our implementation the tool can use gene-expression data from microarray experiments, as well as data generated by RNASeq technologies. Conclusions: The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search). The tool enables the automatic execution of iterative searches for the identification of extreme outliers and for the graphical visualization of the results. Filters can be applied to select the most significant outliers. The performance of the tool was evaluated using a synthetic dataset and compared to state-of-the-art tools. KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background. To validate the method on real case studies, we used publicly available tumor cell line microarray data, and we were able to identify genes which are known to be overexpressed in specific samples, as well as novel ones

Boltzmann equations for mixtures of Maxwell gases: exact solutions and power like tails

We consider the Boltzmann equations for mixtures ofMaxwell gases. It is shown that in certain limiting case the equations admit self-similar solutions that can be constructed in explicit form. More precisely, the solutions have simple explicit integral representations. The most interesting solutions have finite energy and power like tails. This shows that power like tails can appear not just for granular particles (Maxwell models are far from reality in this case), but also in the system of particles interacting in accordance with laws of classical mechanics. In addition, non-existence of positive self-similar solutions with finite moments of any order is proven for a wide class of Maxwell models.Comment: 20 page