Formation and characterization of carbon-radical precursors in char steam gasification

Highly reactive radicals play an important role in high-temperature gasification processes. However, the effect of radicals on gasification has not been systematically investigated. In the present study, the formation of carbon-radical precursors using atomic radicals such as OH, O, and H and molecules such as H₂ and O₂ was characterized, and the effect of the precursors on the adsorption step of steam char gasification was studied using quantum chemistry methods. The results revealed that the radicals can be chemisorbed exothermically on char active sites, and the following order of reactivity was observed: O > H₂ > H > OH > O ₂. Moreover, hydrogen bonds are formed between steam molecules and carbon-radical complexes. Steam molecule adsorption onto carbon-O and carbon-OH complexes is easier than adsorption onto clean carbon surfaces. Alternatively, adsorption on carbon-O₂, carbon-H₂, and carbon-H complexes is at the same level with that of clean carbon surfaces; thus, OH and O radicals accelerate the physical adsorption of steam onto the char surface, H radical and O₂ and H₂ molecules do not have a significant effect on adsorption. © 2010 American Chemical Society.

Neural basis of language switching in the brain: fMRI evidence from Korean-Chinese early bilinguals

Using fMRI, we conducted two types of property generation task that involved language switching, with early bilingual speakers of Korean and Chinese. The first is a more conventional task in which a single language (L1 or L2) was used within each trial, but switched randomly from trial to trial. The other consists of a novel experimental design where language switching happens within each trial, alternating in the direction of the L1/L2 translation required. Our findings support a recently introduced cognitive model, the 'hodological' view of language switching proposed by Moritz-Gasser and Duffau. The nodes of a distributed neural network that this model proposes are consistent with the informative regions that we extracted in this study, using both GLM methods and Multivariate Pattern Analyses: the supplementary motor area, caudate, supramarginal gyrus and fusiform gyrus and other cortical areas. 

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Selection and characterisation of geological materials for use as geopolymer precursors

Geopolymer binders are generally formed by reacting powdered aluminosilicate precursors with alkali silicate activators. Most research to date has concentrated on using either pulverised fuel ash or high purity dehydroxylated kaolin (metakaolin) in association with ground granulated blast furnace slag as the main precursor material. However, recently, attention has turned to alternative calcined clays that are abundant throughout the globe and have lower kaolinite contents than commercially available metakaolins. Due to the lack of clear and simple screening protocols enabling assessment of such geological resources for use as precursors in geopolymer systems, the present paper presents results from experimental work that was carried out to develop a functional binder using materials containing kaolinite taken from the Interbasaltic Formation of Northern Ireland. The influence of mineralogy has been examined, and a screening process, using three Interbasaltic materials as examples, that will assist in the rapid selection of suitable geopolymeric precursors from such materials is outlined.

Solventless synthesis of acyl phosphonamidates, precursors to masked bisphosphonates

A series of acyl phosphonamidates, the synthetic precursors to bisphosphonates, have been readily prepared from phosphoramidite type reagents and a range of acid chlorides. These reactions were performed using solventless conditions, where purification was easily achieved using column chromatography with yields ranging from 71-90%. Furthermore, we have demonstrated that these acyl phosphonamidates could be used for the preparation of unsymmetrical bisphosphonates, which do date are scarcely reported in the literature.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Neural dysfunction following respiratory viral infection as a cause of chronic cough hypersensitivity

Respiratory viral infections are a common cause of acute coughing, an irritating symptom for the patient and an important mechanism of transmission for the virus. Although poorly described, the inflammatory consequences of infection likely induce coughing by chemical (inflammatory mediator) or mechanical (mucous) activation of the cough-evoking sensory nerves that innervate the airway wall. For some individuals, acute cough can evolve into a chronic condition, in which cough and aberrant airway sensations long outlast the initial viral infection. This suggests that some viruses have the capacity to induce persistent plasticity in the neural pathways mediating cough. In this brief review we present the clinical evidence of acute and chronic neural dysfunction following viral respiratory tract infections and explore possible mechanisms by which the nervous system may undergo activation, sensitization and plasticity.

Low luminosity TypeII supernovae-II. Pointing towards moderate mass precursors

We present new data for five underluminous Type II-plateau supernovae (SNe IIP), namely SN 1999gn, SN 2002gd, SN 2003Z, SN 2004eg and SN 2006ov. This new sample of lowluminosity SNe IIP (LL SNe IIP) is analysed together with similar objects studied in the past. All of them show a flat light-curve plateau lasting about 100 d, an underluminous late-time exponential tail, intrinsic colours that are unusually red, and spectra showing prominent and narrow P Cygni lines. A velocity of the ejected material below 10³ km s^-1 is inferred from measurements at the end of the plateau. The 56Ni masses ejected in the explosion are very small (≤10^-2 M⊙). We investigate the correlations among ⁵⁶Ni mass, expansion velocity of the ejecta and absolute magnitude in the middle of the plateau, confirming the main findings of Hamuy, according to which events showing brighter plateau and larger expansion velocities are expected to produce more 56Ni. We propose that these faint objects represent the LL tail of a continuous distribution in parameters space of SNe IIP. The physical properties of the progenitors at the explosion are estimated through the hydrodynamical modelling of the observables for two representative events of this class, namely SN 2005cs and SN 2008in. We find that the majority of LL SNe IIP, and quite possibly all, originate in the core collapse of intermediate-mass stars, in the mass range 10-15 M⊙. 

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.