Sequential batch reactor for eucalypt kraft pulp effluent treatment with Trametes versicolor

BACKGROUND: A sequential batch reactor (SBR) was used for eucalypt kraft pulp effluent treatment with Trametes versicolor. A 2³ full factorial design and response surface methodology were applied to optimise the batch fermentation conditions. Effluent concentration, culture medium and inoculum age were the factors selected for this study in order to optimise the reduction of chemical oxygen demand (COD). RESULTS: The presence of Trametes Defined Medium (TDM) in the fermentation was required to obtain a significant COD reduction. Experiments in the batch reactor confirmed, in general, the predicted results of optimisation developed from Erlenmeyer batch assays. The T. versicolor culture remained active during 42 days of study in the SBR, providing approximately 80% of COD reduction. CONCLUSION: Trametes versicolor may be considered as very promising for the biological treatment of effluents from kraft pulp mills in an SBR system instead of the activated sludge mixed cultures traditionally used. Copyright © 2008 Society of Chemical Industry     

Influence of Reaction Parameters on the Hydrogenolysis of Hydroxymatairesinol Over Carbon Nanofibre Supported Palladium Catalysts

The density functional theory (DFT/B3LYP) calculations were applied to investigate the interaction of a Pt₆ particle with the ZSM-5 zeolite framework. The electronic structure of the metal particle is strongly affected by the interaction with basic framework oxygens and acid sites of the zeolite support. Adsorption on basic sites (E_ads = 6 kcal/mol) favors the formation of the electron enriched metal cluster. Interaction of the platinum cluster with the acid site characterized by stabilization energy of 47 kcal/mol results in oxidation of the metal particle and suppression of Brønsted acidity of the support. The hypothesis is put forward that the oxidized platinum particle can function as an active site for the alkane isomerisation on platinum supported high silica zeolites.     

Biophysical Model of Larval Yellow Perch Advection and Settlement in Lake Michigan

Potential for large-scale physical transport processes to affect recruitment of Lake Michigan yellow perch (Perca flavescens) was studied by examining the variation in larval distribution, growth rate, and settlement during June–August 1998–2003 using a 3D particle transport model linked with an individual-based bioenergetics growth model. In all years, virtual larvae were released nearshore in southwestern Lake Michigan, a known and important spawning region for yellow perch. For any given year, the same circulation pattern and water temperature either promoted or reduced yellow perch settlement depending on the consumption rates and settlement size chosen in the growth model. Increased consumption increased the number of settled larvae and expanded the total area where larvae settled, whereas increased settlement size reduced the number of settled larvae and reduced the overall settlement area. Interannual variability in circulation patterns and water temperature also resulted in contrasting larval settlement rates, settlement locations, and size of settlement areas between years. Model predictions were most consistent with field observations of age-0 yellow perch from Illinois and Michigan waters when settlement was assumed to occur at 50 mm. Moreover, our model suggests that larvae originating from southwestern Lake Michigan can recruit anywhere within the southern basin and even in the northern basin. Future model improvement will require information on the relative contribution of various sectors to the larval pool, their distribution with reference to the hydrodynamic landscape, the feeding and growth of yellow perch during their pelagic phase, and the size at transition to demersal stage.     

Preparation of grids for simulations of groundwater flow in fractured porous media

This work presents an extension of grid generation techniques for finite-volume discretizations of density-driven flow in fractured porous media, in which fractures are considered as low-dimensional manifolds and are resolved by sides of grid elements. The proposed technique introduces additional degrees of freedom for the unknowns assigned to the fractures and thus allows to reconstruct jumps of the solution over a fracture. Through the concept of degenerated elements, the proposed technique can be used for arbitrary junctions of fractures but is sufficiently simple regarding the implementation and allows for the application of conventional numerical solvers. Numerical experiments presented at the end of the paper demonstrate the applicability of this technique in two and three dimensions for complicated fracture networks.     

Generalized Transfer Subspace Learning Through Low-Rank Constraint

It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.     

Accuracy estimate and optimization techniques for SimRank computation

The measure of similarity between objects is a very useful tool in many areas of computer science, including information retrieval. SimRank is a simple and intuitive measure of this kind, based on a graph-theoretic model. SimRank is typically computed iteratively, in the spirit of PageRank. However, existing work on SimRank lacks accuracy estimation of iterative computation and has discouraging time complexity. In this paper, we present a technique to estimate the accuracy of computing SimRank iteratively. This technique provides a way to find out the number of iterations required to achieve a desired accuracy when computing SimRank. We also present optimization techniques that improve the computational complexity of the iterative algorithm from O(n ⁴) in the worst case to min(O(nl), O(n ³/ log₂ n)), with n denoting the number of objects, and l denoting the number object-to-object relationships. We also introduce a threshold sieving heuristic and its accuracy estimation that further improves the efficiency of the method. As a practical illustration of our techniques, we computed SimRank scores on a subset of English Wikipedia corpus, consisting of the complete set of articles and category links.     

Optimal allocation–consumption problem for a portfolio with an illiquid asset

During financial crises investors manage portfolios with low liquidity, where the paper-value of an asset differs from the price proposed by the buyer. We consider an optimization problem for a portfolio with an illiquid, a risky and a risk-free asset. We work in the Merton's optimal consumption framework with continuous time. The liquid part of the investment is described by a standard Black–Scholes market. The illiquid asset is sold at a random moment with prescribed distribution and generates additional liquid wealth dependent on its paper-value. The investor has a hyperbolic absolute risk aversion also denoted as HARA-type utility function, in particular, the logarithmic utility function as a limit case. We study two different distributions of the liquidation time of the illiquid asset – a classical exponential distribution and a more practically relevant Weibull distribution. Under certain conditions we show the smoothness of the viscosity solution and obtain closed formulae relevant for numerics.     

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.