Clarification of a wheat straw‐derived medium with ion‐exchange resins for xylitol crystallization

BACKGROUND: Xylitol bioproduction from lignocellulosic residues comprises hydrolysis of the hemicellulose, detoxification of the hydrolysate, bioconversion of the xylose, and recovery of xylitol from the fermented hydrolysate. There are relatively few reports on xylitol recovery from fermented media. In the present study, ion‐exchange resins were used to clarify a fermented wheat straw hemicellulosic hydrolysate, which was then vacuum‐concentrated and submitted to cooling in the presence of ethanol for xylitol crystallization. RESULTS: Sequential adsorption into two anion‐exchange resins (A‐860S and A‐500PS) promoted considerable reductions in the content of soluble by‐products (up to 97.5%) and in medium coloration (99.5%). Vacuum concentration led to a dark‐colored viscous solution that inhibited xylitol crystallization. This inhibition could be overcome by mixing the concentrated medium with a commercial xylitol solution. Such a strategy led to xylitol crystals with up to 95.9% purity. The crystallization yield (43.5%) was close to that observed when using commercial xylitol solution (51.4%). CONCLUSION: The experimental data demonstrate the feasibility of using ion‐exchange resins followed by cooling in the presence of ethanol as a strategy to promote the fast recovery and purification of xylitol from hemicellulose‐derived fermentation media. Copyright © 2008 Society of Chemical Industry     

Coupling GIS with Hydrologic and Hydraulic Flood Modelling

Geographic Information Systems (GIS) have been recognised as a powerful means to integrate and analyse data from various sources in the context of comprehensive floodplain management. As part of this comprehensive approach to floodplain management, it is very important to be able to predict the consequences of different scenarios in terms of flooded areas and associated risk. Hydrologic and hydraulic modelling plays a crucial role and there is much to gain in incorporating these modelling capabilities in GIS. This is still a rather complex task and research is being done on the full integration of these models. Interfacing between these models and GIS may be a very efficient way of overcoming the difficulties and getting very good results in terms of engineering practice. This paper presents results based on the use of Intergraph GIS coupled with Idrisi GIS. Using these two systems substantially increased the flexibility of using GIS as a tool for flood studies. A lumped (XSRAIN) and a distributed (OMEGA) hydrologic models were used to simulate flood hydrographs. The well known HEC-2 Hydraulic model was used to compute flooded areas. These models were applied in the Livramento catchment with very good results. The computation of flooded areas for different flood scenarios, and its representation in GIS, can be used in the assessment of affected property and associated damages. This is a very useful GIS-based approach to floodplain management.     

Influence of the amount of ferromagnetic addition on the rheological properties of spinning solutions,the structure,the strength,and thermal properties of polyacrylonitrile fibres

The rheological properties of spinning solutions of polyacrylonitrile in dimethyl formamide (DMF) containing various amounts of a ferromagnetic nanoaddition were investigated. The porous structure, the strength and thermal properties of fibers obtained from these solutions were assessed, as well as the uniformity of the nanoaddition distribution on the fiber surface was estimated. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Face recognition with lattice independent component analysis and extreme learning machines

We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.     

Degradation of Leaf Litter Phenolics by Aquatic and Terrestrial Isopods

To investigate species-specific decomposition rates of litter from native (Quercus faginea) and introduced (Eucalyptus globulus) tree species in Portugal, we monitored changes in the phenolic signature of leaf litter during decomposition as mediated by an aquatic, Proasellus coxalis (Isopoda: Asellota), and two terrestrial, Porcellio dispar and Eluma caelatum (Isopoda: Oniscidea), detritivores. Although the litter of Eucalyptus and Quercus did not differ in overall protein precipitation capacity, we detected differences in terms of contents of particular phenolic compounds and phenol oxidation products. Accordingly, we observed food-specific consumption rates in Proasellus, but not in the terrestrial isopods. Proasellus digested Eucalyptus at significantly higher rates than Quercus, whereas the opposite was the case for Eluma, and Porcellio digested both litter types equally well. Despite slight differences in detail, effects of Proasellus on changes in the signature of litter phenolics were similar for both litter types, whereas terrestrial isopods—Porcellio and Eluma, although they differed from each other—digestively degraded phenolic compounds in Eucalyptus and Quercus litter, respectively, in different ways. Overall, however, degradation of litter phenolics was similarly effective on both litter types. From these data, we conclude that decomposition of Eucalyptus litter does not proceed more slowly than of litter from native Portuguese trees.     

Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.     

Explanation and reliability of prediction models: the case of breast cancer recurrence

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.     

The Kosko Subsethood Fuzzy Associative Memory (KS-FAM): Mathematical Background and Applications in Computer Vision

Many well-known fuzzy associative memory (FAM) models can be viewed as (fuzzy) morphological neural networks (MNNs) because they perform an operation of (fuzzy) mathematical morphology at every node, possibly followed by the application of an activation function. The vast majority of these FAMs represent distributive models given by single-layer matrix memories. Although the Kosko subsethood FAM (KS-FAM) can also be classified as a fuzzy morphological associative memory (FMAM), the KS-FAM constitutes a two-layer non-distributive model. In this paper, we prove several theorems concerning the conditions of perfect recall, the absolute storage capacity, and the output patterns produced by the KS-FAM. In addition, we propose a normalization strategy for the training and recall phases of the KS-FAM. We employ this strategy to compare the error correction capabilities of the KS-FAM and other fuzzy and gray-scale associative memories in terms of some experimental results concerning gray-scale image reconstruction. Finally, we apply the KS-FAM to the task of vision-based self-localization in robotics.     

Strongdeco: Expansion of analytical,strongly correlated quantum states into a many-body basis

We provide a Mathematica code for decomposing strongly correlated quantum states described by a first-quantized, analytical wave function into many-body Fock states. Within them, the single-particle occupations refer to the subset of Fock–Darwin functions with no nodes. Such states, commonly appearing in two-dimensional systems subjected to gauge fields, were first discussed in the context of quantum Hall physics and are nowadays very relevant in the field of ultracold quantum gases. As important examples, we explicitly apply our decomposition scheme to the prominent Laughlin and Pfaffian states. This allows for easily calculating the overlap between arbitrary states with these highly correlated test states, and thus provides a useful tool to classify correlated quantum systems. Furthermore, we can directly read off the angular momentum distribution of a state from its decomposition. Finally we make use of our code to calculate the normalization factors for Laughlin?s famous quasi-particle/quasi-hole excitations, from which we gain insight into the intriguing fractional behavior of these excitations.Program summaryProgram title: StrongdecoCatalogue identifier: AELA_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AELA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 5475No. of bytes in distributed program, including test data, etc.: 31 071Distribution format: tar.gzProgramming language: MathematicaComputer: Any computer on which Mathematica can be installedOperating system: Linux, Windows, MacClassification: 2.9Nature of problem: Analysis of strongly correlated quantum states.Solution method: The program makes use of the tools developed in Mathematica to deal with multivariate polynomials to decompose analytical strongly correlated states of bosons and fermions into a standard many-body basis. Operations with polynomials, determinants and permanents are the basic tools.Running time: The distributed notebook takes a couple of minutes to run.     

The effect of probiotics (Lactobacillus rhamnosus HN001, Lactobacillus paracasei LPC-37, and Lactobacillus acidophilus NCFM) on the availability of minerals from Dutch-type cheese

The use of probiotic cultures in the production of Dutch-type cheeses did not lead to significant changes in their chemical composition but it lowered their acidity. The availability of calcium and magnesium analyzed by in vitro enzymatic hydrolysis was 19 and 35%, respectively; the availability of phosphorus was significantly higher, at >90%. The use of probiotic cultures significantly increased the availability of calcium (~2.5%), phosphorus (~6%), and magnesium (~18%). The in vitro method supports accurate determination of the effect of the Lactobacillus spp. cultures on the availability of mineral compounds ingested with Dutch-type cheese.