Characterization of the Residence Time Distribution in Spray Dryers

In this work it is presented a study on the residence time distribution (RTD) of particles in a co-current pilot-plant spray dryer operated with a rotary atomization system. A nuclear technique is applied to investigate the RTD responses of spray dryers. The methodology is based on the injection of a radioisotope tracer in the feed stream followed by the monitoring of its concentration at the outlet stream. The experiments were performed during the drying of aqueous suspensions of gadolinium oxide. The RTD responses obtained experimentally presented good reproducibility, indicating that the technique applied is well suited to investigating fluid-dynamics of spray dryers. In addition to the experimental investigation, a mathematical model was used to describe the RTD experimental curves.     

Ranking Pages by Topology and Popularity within Web Sites

We compare two link analysis ranking methods of web pages in a site. The first, called Site Rank, is an adaptation of PageRank to the granularity of a web site and the second, called Popularity Rank, is based on the frequencies of user clicks on the outlinks in a page that are captured by navigation sessions of users through the web site. We ran experiments on artificially created web sites of different sizes and on two real data sets, employing the relative entropy to compare the distributions of the two ranking methods. For the real data sets we also employ a nonparametric measure, called Spearman's footrule, which we use to compare the top-ten web pages ranked by the two methods. Our main result is that the distributions of the Popularity Rank and Site Rank are surprisingly close to each other, implying that the topology of a web site is very instrumental in guiding users through the site. Thus, in practice, the Site Rank provides a reasonable first order approximation of the aggregate behaviour of users within a web site given by the Popularity Rank.     

Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

The Quantitative Structure-Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798; and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (π_k) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the π_k(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on π_k(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.     

Synthesis and physicochemical characterization of a well-defined poly(butadiene 1,3)-block-poly(dimethylsiloxane) copolymer

For the first time, order-order and order-disorder transitions were detected and characterized in a model diblock copolymer of poly(butadiene-1,3) and poly(dimethylsiloxane) (PB-b-PDMS). This model PB-b-PDMS copolymer was synthesized by the sequential anionic polymerization (high vacuum techniques) of butadiene 1,3 (B) and hexamethylciclotrisiloxane (D₃), and subsequently characterized by nuclear magnetic resonance (¹H and ¹³C NMR), size exclusion chromatography (SEC), Fourier Transform infrared spectroscopy (FTIR), Small-Angle X-ray scattering (SAXS) and rheology. SAXS combined with rheological experiments shows that the order-order and order-disorder transitions are thermoreversible. This fact indicates that the copolymer has sufficient mobility at the timescale and at the temperatures of interest to reach their equilibrium morphologies.     

Probabilistic Wind Farms Generation Model for Reliability Studies Applied to Brazilian Sites

This paper presents a computer model for the probabilistic representation of wind farms generation for reliability studies, which can provide an annual estimation of energy production and calculate several performance indexes. The model combines the stochastic characteristics of wind speed with the operational information of the turbines, such as the failure and repair rates, representing the wind farm by a Markov process. The simulations are made with real time series of wind speed of several Brazilian regions and actual turbine models. The influence of some wind farm and installation site characteristics on the results are evaluated, such as the wind speed statistical clustering technique, the number and type of the turbines, and the failure and repair rates. The results obtained reproduce successfully the behavior of the components considered in the model     

Genotoxic effect of Physalis angulata L. (Solanaceae) extract on human lymphocytes treated in vitro

Physalis angulata L (Solanaceae) is a medicinal plant from North of Brazil, whose different extracts and infusions are commonly used in the popular medicine for the treatment of malaria, asthma, hepatitis, dermatitis and rheumatism. However, the genotoxic effects of P. angulata on human cells is not well known. The main purpose of the present study was to evaluate the in vitro genotoxic effects of aqueous extract of P. angulata using the comet assay and the micronucleus assay in human lymphocytes provided from 6 healthy donors. Treatments with P. angulata extracts were performed in vitro in order to access the extent of DNA damage. The comet assay has shown that treatments with P. angulata at 0.5, 1.0, 2.0, 3.0 and 6.0 microg/mL in culture medium were genotoxic. Lymphocytes treated with P. angulata at the concentrations of 3.0 and 6.0 microg/mL in culture medium showed a statistically significant increase in the frequency of micronucleus (p<0.05), however, the cytokinesis blocked proliferation index (CBPI) was not decreased after P. angulata treatment. In conclusion, the present work demonstrated the genotoxic effects of P. angulata extract on human lymphocytes in vitro.     

A survey on approaches to gridification

The Grid shows itself as a globally distributed computing environment, in which hardware and software resources are virtualized to transparently provide applications with vast capabilities. Just like the electrical power grid, the Grid aims at offering a powerful yet easy‐to‐use computing infrastructure to which applications can be easily ‘plugged’ and efficiently executed. Unfortunately, it is still very difficult to Grid‐enable applications, since current tools force users to take into account many details when adapting applications to run on the Grid. In this paper, we survey some of the recent efforts in providing tools for easy gridification of applications and propose several taxonomies to identify approaches followed in the materialization of such tools. We conclude this paper by describing common features among the proposed approaches, and by pointing out open issues and future directions in the research and development of gridification methods. Copyright © 2007 John Wiley & Sons, Ltd.     

Resistivity as a function of composition in the superconducting Nb-Pt A15 phase

The electrical resistivity of the A15 compound Nb_100–x Pt _x (20x28.9) is measured as a function of compositionx from room temperature to the superconducting transition temperatureT _c The residual resistivity ₀ is observed to be nearly independent of composition on the Nb-rich side of stoichiometry, but strongly dependent on the Pt-rich side. A closeT _c correlation with the resistivity sloped/dT at high temperatures and with the electronic specific heat coefficient , which are proportional to the superconducting coupling parameter , is noted. The results can be understood in terms of an order-disorder model.     

Why does language matter to artificial intelligence?

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored.