Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Lack of horizontal gene transfer of methicillin-resitance genetic determinants from PBP2a-positive, coagulase-negative Staphylococci to methicillin-sensitive Staphylococcus aureus using transcutaneous electric nerve stimulation (TENS),

Previous research shows that approximately half of the coagulase-negative staphylococci (CNS) isolated from patients in the intensive care unit (ICU) at Belfast City Hospital were resistant to methicillin. The presence of this relatively high proportion of methicillin-resistance genetic material gives rise to speculation that these organisms may act as potential reservoirs of methicillinresistance genetic material to methicillin-sensitive Staphylococcus aureus (MSSA). Mechanisms of horizontal gene transfer from PBP2a-positive CNS to MSSA, potentially transforming MSSA to MRSA, aided by electroporation-type activities such as transcutaneous electrical nerve stimulation (TENS), should be considered. Methicillin-resistant CNS (MR-CNS) isolates are collected over a two-month period from a variety of clinical specimen types, particularly wound swabs. The species of all isolates are confirmed, as well as their resistance to oxacillin by standard disc diffusion assays. In addition, MSSA isolates are collected over the same period and confirmed as PBP2a-negative. Electroporation experiments are designed to mimic the time/voltage combinations used commonly in the clinical application of TENS. No transformed MRSA were isolated and all viable S. aureus cells remained susceptible to oxacillin and PBP2a-negative. Experiments using MSSA pre-exposed to sublethal concentrations of oxacillin (0.25 µg/mL) showed no evidence of methicillin gene transfer and the generation of an MRSA. The study showed no evidence of horizontal transfer of methicillin resistance genetic material from MR-CNS to MSSA. These data support the belief that TENS and the associated time/voltage combinations used do not increase conjugational transposons or facilitate horizontal gene transfer from MR-CNS to MSSA.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

Stimulation of phosphate uptake and polyphosphate accumulation by activated sludge microorganisms in response to sulfite addition

Enhanced phosphate removal from wastewaters is dependent on the synthesis and intracellular accumulation of polyphosphate by sludge microorganisms. However the role played by polyphosphate in microbial metabolism and the factors that trigger its formation remain poorly-understood. Many examples of the accumulation of the biopolymer by environmental microorganisms are documented; these include a recent report of the presence of large polyphosphate inclusions in sulfur-oxidizing marine bacteria. To investigate whether any link might exist outside the marine environment between the presence of reduced sulfur compounds and enhanced levels of microbial phosphate uptake and polyphosphate accumulation, activated sludge cultures were grown under laboratory conditions in media that contained sulfite, thiosulfate, hydrosulfite or tetrathionate. Only in the presence of sulfite was there any evidence of a stimulatory effect; in medium that contained 0.5 mM sodium sulfite some 17% more phosphate was removed by the sludge, whilst there was an almost two-fold increase in intracellular polyphosphate levels. No indications of sulfite toxicity were observed.

Neural network forecasts of input-output technology

A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologies forecasting and updating technology coefficients and multipliers. Prominent among such techniques is the RAS method, while more information demanding econometric methods, as well as other less promising ones, have been proposed. However, there has been little interest expressed in the use of more modern and often more innovative methods, such as neural networks in IO analysis in general. This study constructs, proposes and applies a Backpropagation Neural Network (BPN) with the purpose of forecasting IO technology coefficients and subsequently multipliers. The RAS method is also applied on the same set of UK IO tables, and the discussion of results of both methods is accompanied by a comparative analysis. The results show that the BPN offers a valid alternative way of IO technology forecasting and many forecasts were more accurate using this method. Overall, however, the RAS method outperformed the BPN but the difference is rather small to be systematic and there are further ways to improve the performance of the BPN.

Chloroquine causes lysosomal dysfunction in neural retina and RPE: Implications for retinopathy

Chronic use of chloroquine has been shown to induce numerous pathophysiological defects in the retina. This drug has the ability to alter pH of intracellular compartments and lysosomal function of the retinal pigment epithelium (RPE) and retinal neurons may constitute the basis of chloroquine retinopathy. The aim of the current study was to investigate pathogenic alterations in retinal cells continuously exposed to chloroquine using appropriate in vivo and in vitro models.