Inhibition of sulfide on the simultaneous removal of nitrate and p‐cresol by a denitrifying sludge

BACKGROUND: Many industrial discharges, such as those generated from petrochemical refineries, contain large amounts of sulfurous, nitrogenous and organic contaminants. Denitrification has emerged as a suitable technology for the simultaneous removal of these pollutants in a single reactor unit; however, more evidence is demanded to clarify the limitations of denitrification on the simultaneous removal of sulfide and phenolic contaminants and to optimize the biological process. The aim of this study was to evaluate the capacity of a denitrifying sludge to simultaneously convert sulfide and p‐cresol via denitrification. RESULTS: Sulfide was the preferred electron donor over p‐cresol, imposing a 5 h lag phase (required for complete sulfide removal) on organotrophic denitrification. Addition of sulfide (20 mg S^2? L^?1) to p‐cresol‐amended denitrifying cultures also decreased the reduction rate of nitrate and nitrite, as well as the production rate of nitrogen gas. Nitrite reduction rate was the most affected step by sulfide, decreasing from 35 to 21 mg N (g VSS d)^?1. A synergistic inhibitory effect of nitrate and sulfide was also observed on nitrite reduction. Despite the effects of sulfide on the respiratory rates monitored, complete removal of nitrate, sulfide and p‐cresol could be achieved after 48 h of incubation. CONCLUSION: Our results suggest that simultaneous removal of sulfide and p‐cresol could be achieved in denitrifying reactors, but a large hydraulic residence time may be required to sustain an efficient process due to inhibitory effects of sulfide. Copyright © 2008 Society of Chemical Industry     

Nonparametric Analysis of the Order-Statistic Model in Software Reliability

In the literature on statistical inference in software reliability, the assumptions of parametric models and random sampling of bugs have been pervasive. We argue that both assumptions are problematic, the first because of robustness concerns and the second due to logical and practical difficulties. These considerations motivate the approach taken in this paper. We propose a nonparametric software reliability model based on the order-statistic paradigm. The objective of the work is to estimate, from data on discovery times observed within a type I censoring framework, both the underlying distribution F from which discovery times are generated and N, the unknown number of bugs in the software. The estimates are used to predict the next time to failure. The approach makes use of Bayesian nonparametric inference methods, in particular, the beta-Stacy process. The proposed methodology is illustrated on both real and simulated data     

Mapping of Trellises Associated with General Encoders onto High-Performance VLSI Architectures

A rate 1/n binary generic convolutional encoder is a shift-register circuit where the inputs are information bits and the outputs are blocks of n bits generated as linear combinations on the appropriate shift register contents. The decoding of the outputs of a convolutional encoder can be carried out by the well-known Viterbi algorithm. The communication pattern of the Viterbi Algorithm is given as a graph, called trellis, associated to the state diagram of the corresponding encoder. In this paper we present a methodology that permits the efficient mapping of the Viterbi algorithm onto a column of an arbitrary number of processors. This is done through the representation of the data flow by using mathematical operators which present an inmediate hardware projection. A single operator string has been obtained to represent a generic encoder through the study of the data flow of free-forward encoders and feed-back encoders. The formal model developed is employed for the partitioning of the computations among an arbitrary number of processors in such a way that the data are recirculated opimizing the use of the processors and the communications. As a result, we obtain a highly regular and modular architecture suitable for VLSI implementation.     

Effect of polyethylene on osteocalcin, alkaline phosphatase and procollagen secretion by human osteoblastic cells

BACKGROUND AND STUDY AIMS: Anastomotic leakage is a severe complication in gastric surgery and it is associated with a high rate of mortality. Conservative treatment sometimes is not sufficient to stem the leakages and, even when it is sufficient, it takes a long time. The present study describes the first experience in the treatment of anastomotic leakages with endoscopic clipping. PATIENTS AND METHODS: From May 1995 to December 1996, seven patients with postoperative anastomotic leakages after gastric surgery were prospectively treated in our Endoscopy Service. Metallic endoclips (MD 850, Olympus Corp., Tokyo, Japan) with prongs 12 mm long and 6 mm wide were applied, controlling the closure of the leakage by endoscopy, using radiographs to confirm the closure 24 hours later. RESULTS: Complete closure of the leakage was obtained in all seven cases. A single session of endoscopic clipping was needed for five patients while two other required, respectively, two and three sessions. The median time of leakage closure after endoscopic clipping was 2.3 days (range 1-5 days). The clips spontaneously dislodged within 1 month in five patients and within the second month in the other two patients. CONCLUSION: Endoscopic treatment of anastomotic leakages by metallic clips represents a safe and easily repeated method and, compared to conservative treatment, it seems to offer several time and cost advantages. Further studies involving a larger number of patients are needed to verify this finding.     

Optimization techniques for curved path computing

Participating media with an inhomogeneous index of refraction make light follow curved paths. Simulating this in a global illumination environment has usually been neglected due to the complexity of the calculations involved, sacrificing accurate physical simulations for efficient visual results. This paper aims to simulate non-linear media in a more reasonable time than previous works without losing physical correctness. Accuracy is achieved by solving the Eikonal equation of geometrical optics, which describes the path followed by a light beam that traverses a non-linear medium. This equation is used in the context of a photon mapping extension.     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images

The Journal of Supercomputing - The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation....     

Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

Missing data imputation for fuzzy rule-based classification systems

Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.