Characterisation of porous solids using small-angle scattering and NMR cryoporometry

The characteristics of several porous systems have been studied by the use of small-angle neutron scattering [SANS] and nuclear magnetic resonance [NMR] techniques. The measurements reveal different characteristics for sol-gel silicas, activated carbons and ordered mesoporous silicas of the MCM and SBA type. Good agreement is obtained between gas adsorption measurements and the NMR and SANS results for pore sizes above 10 nm. Recent measurements of the water/ice phase transformation in SBA silicas by neutron diffraction are also presented and indicate a complex relationship that will require more detailed treatment in terms of the possible effects of microporosity in the silica substrate. The complementarity of the different methods is emphasised and there is brief discussion of issues related to possible future developments

Geometric lattice structure of covering and its application to attribute reduction through matroids

The reduction of covering decision systems is an important problem in data mining, and covering-based rough sets serve as an efficient technique to process the problem. Geometric lattices have been widely used in many fields, especially greedy algorithm design which plays an important role in the reduction problems. Therefore, it is meaningful to combine coverings with geometric lattices to solve the optimization problems. In this paper, we obtain geometric lattices from coverings through matroids and then apply them to the issue of attribute reduction. First, a geometric lattice structure of a covering is constructed through transversal matroids. Then its atoms are studied and used to describe the lattice. Second, considering that all the closed sets of a finite matroid form a geometric lattice, we propose a dependence space through matroids and study the attribute reduction issues of the space, which realizes the application of geometric lattices to attribute reduction. Furthermore, a special type of information system is taken as an example to illustrate the application. In a word, this work points out an interesting view, namely, geometric lattice to study the attribute reduction issues of information systems

Thermodynamic analysis of the Quantum Critical behavior of Ce-lattice compounds

A systematic analysis of low temperature magnetic phase diagrams of Ce compounds is performed in order to recognize the thermodynamic conditions to be fulfilled by those systems to reach a quantum critical regime and, alternatively, to identify other kinds of low temperature behaviors. Based on specific heat ($C_m$) and entropy ($S_m$) results, three different types of phase diagrams are recognized: i) with the entropy involved into the ordered phase ($S_{MO}$) decreasing proportionally to the ordering temperature ($T_{MO}$), ii) those showing a transference of degrees of freedom from the ordered phase to a non-magnetic component, with their $C_m(T_{MO})$ jump ($\Delta C_m$) vanishing at finite temperature, and iii) those ending in a critical point at finite temperature because their $\Delta C_m$ do not decrease with $T_{MO}$ producing an entropy accumulation at low temperature. Only those systems belonging to the first case, i.e. with $S_{MO}\to 0$ as $T_{MO}\to 0$, can be regarded as candidates for quantum critical behavior. Their magnetic phase boundaries deviate from the classical negative curvature below $T\approx 2.5$\,K, denouncing frequent misleading extrapolations down to T=0. Different characteristic concentrations are recognized and analyzed for Ce-ligand alloyed systems. Particularly, a pre-critical region is identified, where the nature of the magnetic transition undergoes significant modifications, with its $\partial C_m/\partial T$ discontinuity strongly affected by magnetic field and showing an increasing remnant entropy at $T\to 0$. Physical constraints arising from the third law at $T\to 0$ are discussed and recognized from experimental results

Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file

TRAPID : an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes

Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system

Comparative Analysis of the Recent Financial Crisis' Impact on the Retail Electronic Commerce in the European Union in the USA and in Poland

The aim of this article is to compare and assess the impact of the recent financial crisis on the retail electronic commerce in the economies of the European Union, the USA and Poland. Therefore the selected data from the biggest international companies connected with the retail electronic commerce from the years 2007 and 2008 in comparison to the previous year, and the selected economic data from the economies of the USA, the European Union and Poland till the year 2009, concerning the utilization and value of the electronic commerce trade and the number of people doing shopping online, and conclusions drawn from the analyses of those data are presented and discussed.Celem artykułu jest porównanie oraz ocenienie wpływu ostatniego kryzysu finansowego na detaliczny handel elektroniczny w gospodarkach Unii Europejskiej, Stanów Zjednoczonych oraz Polski. W tym celu wybrane dane z największych międzynarodowych firm związanych z detalicznym handlem elektronicznym z lat 2007 i 2008 w porównaniu do roku poprzedniego oraz wybrane dane ekonomiczne z gospodarek Stanów Zjednoczonych, Unii Europejskiej i Polski do roku 2009, dotyczące wykorzystatnia oraz wartości detalicznego handlu elektronicznego i liczby ludzi korzystających z zakupów internetowych oraz wnioski wyciągnięte z analiz tych danych są zaprezentowane i omówione

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well

Optimizing feature extraction in image analysis using experimented designs, a case study evaluating texture algorithms for describing appearance retention in carpets

When performing image analysis, one of the most critical steps is the selection of appropriate techniques. A huge amount of features can be extracted from several techniques and the selection is commonly performed based on expert knowledge. In this paper we present the theory of experimental designs as a tool for an objective selection of techniques in image analysis domain. We present a study case for evaluating appearance retention in textile floor coverings using texture features. The use of experimental design theory permitted to select an optimal set of techniques for describing the texture changes due to degradation