Biomimetic oxidative treatment of spruce wood studied by pyrolysis-molecular beam mass spectrometry coupled with multivariate analysis and (13)C-labeled tetramethylammonium hydroxide thermochemolysis: implications for fungal degradation of wood

In this work, pyrolysis-molecular beam mass spectrometry analysis coupled with principal components analysis and (13)C-labeled tetramethylammonium hydroxide thermochemolysis were used to study lignin oxidation, depolymerization, and demethylation of spruce wood treated by biomimetic oxidative systems. Neat Fenton and chelator-mediated Fenton reaction (CMFR) systems as well as cellulosic enzyme treatments were used to mimic the nonenzymatic process involved in wood brown-rot biodegradation. The results suggest that compared with enzymatic processes, Fenton-based treatment more readily opens the structure of the lignocellulosic matrix, freeing cellulose fibrils from the matrix. The results demonstrate that, under the current treatment conditions, Fenton and CMFR treatment cause limited demethoxylation of lignin in the insoluble wood residue. However, analysis of a water-extractable fraction revealed considerable soluble lignin residue structures that had undergone side chain oxidation as well as demethoxylation upon CMFR treatment. This research has implications for our understanding of nonenzymatic degradation of wood and the diffusion of CMFR agents in the wood cell wall during fungal degradation processes.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

Modeling and analysis of laminate composite plates with embedded active-passive piezoelectric networks

The objective of this work is to present the finite element modeling of laminate composite plates with embedded piezoelectric patches or layers that are then connected to active-passive resonant shunt circuits, composed of resistance, inductance and voltage source. Applications to passive vibration control and active control authority enhancement are also presented and discussed. The finite element model is based on an equivalent single layer theory combined with a third-order shear deformation theory. A stress-voltage electromechanical model is considered for the piezoelectric materials fully coupled to the electrical circuits. To this end, the electrical circuit equations are also included in the variational formulation. Hence, conservation of charge and full electromechanical coupling are guaranteed. The formulation results in a coupled finite element model with mechanical (displacements) and electrical (charges at electrodes) degrees of freedom. For a Graphite-Epoxy (Carbon-Fibre Reinforced) laminate composite plate, a parametric analysis is performed to evaluate optimal locations along the plate plane (xy) and thickness (z) that maximize the effective modal electromechanical coupling coefficient. Then, the passive vibration control performance is evaluated for a network of optimally located shunted piezoelectric patches embedded in the plate, through the design of resistance and inductance values of each circuit, to reduce the vibration amplitude of the first four vibration modes. A vibration amplitude reduction of at least 10 dB for all vibration modes was observed. Then, an analysis of the control authority enhancement due to the resonant shunt circuit, when the piezoelectric patches are used as actuators, is performed. It is shown that the control authority can indeed be improved near a selected resonance even with multiple pairs of piezoelectric patches and active-passive circuits acting simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.

Gait Pattern Adaptation for an Active Lower-Limb Orthosis Based on Neural Networks

This work deals with neural network (NN)-based gait pattern adaptation algorithms for an active lower-limb orthosis. Stable trajectories with different walking speeds are generated during an optimization process considering the zero-moment point (ZMP) criterion and the inverse dynamic of the orthosis-patient model. Additionally, a set of NNs is used to decrease the time-consuming analytical computation of the model and ZMP. The first NN approximates the inverse dynamics including the ZMP computation, while the second NN works in the optimization procedure, giving an adapted desired trajectory according to orthosis-patient interaction. This trajectory adaptation is added directly to the trajectory generator, also reproduced by a set of NNs. With this strategy, it is possible to adapt the trajectory during the walking cycle in an on-line procedure, instead of changing the trajectory parameter after each step. The dynamic model of the actual exoskeleton, with interaction forces included, is used to generate simulation results. Also, an experimental test is performed with an active ankle-foot orthosis, where the dynamic variables of this joint are replaced in the simulator by actual values provided by the device. It is shown that the final adapted trajectory follows the patient intention of increasing the walking speed, so changing the gait pattern. (C) Koninklijke Brill NV, Leiden, 2011

Remote control of CNC machines using the CyberOPC communication system over public networks

During the last few years, the evolution of fieldbus and computers networks allowed the integration of different communication systems involving both production single cells and production cells, as well as other systems for business intelligence, supervision and control. Several well-adopted communication technologies exist today for public and non-public networks. Since most of the industrial applications are time-critical, the requirements of communication systems for remote control differ from common applications for computer networks accessing the Internet, such as Web, e-mail and file transfer. The solution proposed and outlined in this work is called CyberOPC. It includes the study and the implementation of a new open communication system for remote control of industrial CNC machines, making the transmission delay for time-critical control data shorter than other OPC-based solutions, and fulfilling cyber security requirements.

Artificial neural networks analysis of Sao Paulo subway tunnel settlement data

Artificial neural networks have been used to analyze a number of engineering problems, including settlement caused by different tunneling methods in various types of ground mass. This paper focuses on settlement over shotcrete- supported tunnels on Sao Paulo subway line 2 (West Extension) that were excavated in Tertiary sediments using the sequential excavation method. The adjusted network is a good tool for predicting settlement above new tunnels to be excavated in similar conditions. The influence of network training parameters on the quality of results is also discussed. (C) 2007 Elsevier Ltd. All rights reserved.

Performance and molecular evaluation of an anaerobic system with suspended biomass for treating wastewater with high fat content after enzymatic hydrolysis

The effect of a lipase-rich fungal enzymatic preparation, produced by a Penicillium sp. during solid-state fermentation, was evaluated in an anaerobic digester treating dairy wastewater with 1200 mg of oil and grease/L The oil and grease hydrolysis step was carried out with 0.1% (w/v) of solid enzymatic preparation at 30 degrees C for 24 h, and resulted in a final free acid concentration eight times higher than the initial value. The digester operated in sequential batches of 48 h at 30 degrees C for 245 days, and had high chemical oxygen demand (COD) removal efficiencies (around 90%) when fed with pre-hydrolyzed wastewater. However, when the pre-hydrolysis step was removed, the anaerobic digester performed poorly (with an average COD removal of 32%), as the oil and grease accumulated in the biomass and effluent oil and grease concentration increased throughout the operational period. PCR-DGGE analysis of the Bacteria and Archaea domains revealed remarkable differences in the microbial profiles in trials conducted with and without the pre-hydrolysis step, indicating that differences observed in overall parameters were intrinsically related to the microbial diversity of the anaerobic sludge. (C) 2009 Elsevier Ltd. All rights reserved.

Morphological and molecular characterization of cyanobacteria from a Brazilian facultative wastewater stabilization pond and evaluation of microcystin production

The cyanobacterial population in the Cajati waste stabilization pond system (WSP) from Sao Paulo State, Brazil was assessed by cell isolation and direct microscope counting techniques. Ten strains, belonging to five genera (Synechococcus, Merismopedia, Leptolyngbya, Limnothrix, and Nostoc), were isolated and identified by morphological and molecular analyses. Morphological identification of the isolated strains was congruent with their phylogenetic analyses based on 16S rDNA gene sequences. Six cyanobacterial genera (Synechocystis, Aphanocapsa, Merismopedia, Lyngbya, Phormidium, and Pseudanabaena) were identified by direct microscope inspection. Both techniques were complementary, since, of the six genera identified by direct microscopic inspection, only Merismopedia was isolated, and the four other isolated genera were not detected by direct inspection. Direct microscope counting of preserved cells showed that cyanobacteria were the dominant members (> 90%) of the phytoplankton community during both periods evaluated (summer and autumn). ELISA tests specific for hepatotoxicmicrocystins gave positive results for six strains (Synechococcus CENA108, Merismopedia CENA106, Leptolyngbya CENA103, Leptolyngbya CENA112, Limnothrix CENA109, and Limnothrix CENA110), and for wastewater samples collected from raw influent (3.70 mu g microcystins/l) and treated effluent (3.74 mu g microcystins/l) in summer. Our findings indicate that toxic cyanobacteria in WSP systems are of concern, since the treated effluent containing cyanotoxins will be discharged into rivers, irrigation channels, estuaries, or reservoirs, and can affect human and animal health.

Ozone oxidation of pulp and paper wastewater and its impact on molecular weight distribution of organic matter

The impact of ozone oxidation on removing high molecular weight (HMW) organics in order to improve the biodegradability of alkaline bleach plant effluent was investigated using a semi-batch reactor under different initial pH (12 and 7). After the ozonation process, the ratio of BOD5/COD increased from 0.07 to 0.16 and 0.22 for initial pH 12 and 7, respectively. Also, the effluent color decreased by 48% and 61% at initial pH 12 and pH 7, respectively. These changes were primarily driven by reductions of the HMW fractions of the effluent during ozonation.

Molecular characterization of a miraculin-like gene differentially expressed during coffee development and coffee leaf miner infestation

The characterization of a coffee gene encoding a protein similar to miraculin-like proteins, which are members of the plant Kunitz serine trypsin inhibitor (STI) family of proteinase inhibitors (PIs), is described. PIs are important proteins in plant defence against insects and in the regulation of proteolysis during plant development. This gene has high identity with the Richadella dulcifica taste-modifying protein miraculin and with the tomato protein LeMir; and was named as CoMir (Coffea miraculin). Structural protein modelling indicated that CoMir had structural similarities with the Kunitz STI proteins, but suggested specific folding structures. CoMir was up-regulated after coffee leaf miner (Leucoptera coffella) oviposition in resistant plants of a progeny derived from crosses between C. racemosa (resistant) and C. arabica (susceptible). Interestingly, this gene was down-regulated during coffee leaf miner herbivory in susceptible plants. CoMir expression was up-regulated after abscisic acid application and wounding stress and was prominent during the early stages of flower and fruit development. In situ hybridization revealed that CoMir transcripts accumulated in the anther tissues that display programmed cell death (tapetum, endothecium and stomium) and in the metaxylem vessels of the petals, stigma and leaves. In addition, the recombinant protein CoMir shows inhibitory activity against trypsin. According to the present results CoMir may act in proteolytic regulation during coffee development and in the defence against L. coffeella. The similarity of CoMir with other Kunitz STI proteins and the role of CoMir in plant development and plant stress are discussed.