A heuristic model for the simulation of the deformation of elastic and spongy material for virtual reality applications

This paper presents a practical algorithm for the simulation of interactive deformation in a 3D polygonal mesh model. The algorithm combines the conventional simulation of deformation using a spring-mass-damping model, solved by explicit numerical integration, with a set of heuristics to describe certain features of the transient behaviour, to increase the speed and stability of solution. In particular, this algorithm was designed to be used in the simulation of synthetic environments where it is necessary to model realistically, in real time, the effect on non-rigid surfaces being touched, pushed, pulled or squashed. Such objects can be solid or hollow, and have plastic, elastic or fabric-like properties. The algorithm is presented in an integrated form including collision detection and adaptive refinement so that it may be used in a self-contained way as part of a simulation loop to include human interface devices that capture data and render a realistic stereoscopic image in real time. The algorithm is designed to be used with polygonal mesh models representing complex topology, such as the human anatomy in a virtual-surgery training simulator. The paper evaluates the model behaviour qualitatively and then concludes with some examples of the use of the algorithm.

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.

Effectiveness of different footbath solutions in the treatment of digital dermatitis in dairy cows

Three experiments were conducted to test the effectiveness of different footbath solutions and regimens in the treatment of digital dermatitis (DD) in dairy cows. During the study, groups of cows walked through allocated footbath solutions after milking on 4 consecutive occasions. All cows were scored weekly for DD lesion stage on the hind feet during milking. A “transition grade” was assigned on the basis of whether the DD lesions improved (1) or deteriorated or did not improve (0) from week to week. This grade per cow was averaged for all cows in the group. In experiment 1, 118 cows were allocated to 1 of 3 footbath treatments for 5 wk: (1) 5% CuSO4 each week, (2) 2% ClO- each week, or (3) no footbath (control). The mean transition grade, and proportion of cows without DD lesions at the end of the trial were significantly higher for treatment 1 above (0.36, 0.13, and 0.11, respectively; standard error of the difference, SED=0.057). In experiment 2, 117 cows were allocated to 1 of 4 footbath treatment regimens for 8 wk: (1) 5% CuSO4 each week, (2) 2% CuSO4 each week, (3) 5% CuSO4 each fortnight, or (4) 2% CuSO4 each fortnight. For welfare reasons, cows allocated to the weekly and fortnightly footbath regimens had an average prevalence of >60% and =25% active DD at the start of the trial, respectively. Significantly more cows had no DD lesions (0.53 vs. 0.36, respectively; SED=0.049), and the mean transition grade of DD lesions was higher in the 5% compared with the 2% weekly CuSO4 treatment (0.52 vs. 0.38, respectively; SED=0.066). Similarly, significantly more cows had no DD lesions in the 5% compared with the 2% fortnightly CuSO4 treatments (0.64 vs. 0.47, respectively; SED=0.049). In experiment 3, 95 cows were allocated to 1 of 3 footbath treatments: (1) each week alternating 5% CuSO4 with 10% salt water, (2) each week alternating 5% CuSO4 with water, or (3) 5% CuSO4 each fortnight (control). After 10 wk, more cows had no DD in the salt water treatment than in the control treatment (0.35 vs. 0.26, respectively; SED=0.038), but levels of active lesions were higher for this treatment than in the other 2 treatments (0.17, 0.00, and 0.13, respectively; SED=0.029). Treatment did not affect mean transition grade of DD lesions. In conclusion, CuSO4 was the only footbath solution that was consistently effective for treatment of DD. In cases when DD prevalence was high, a footbath each week using 5% CuSO4 was the most effective treatment.

Remediation of phenol-contaminated water by adsorption using poly(methyl methacrylate) (PMMA)

Recently polymeric adsorbents have been emerging as highly effective alternatives to activated carbons for pollutant removal from industrial effluents. Poly(methyl methacrylate) (PMMA), polymerized using the atom transfer radical polymerization (ATRP) technique has been investigated for its feasibility to remove phenol from aqueous solution. Adsorption equilibrium and kinetic investigations were undertaken to evaluate the effect of contact time, initial concentration (10-90 mg/L), and temperature (25-55 degrees C). Phenol uptake was found to increase with increase in initial concentration and agitation time. The adsorption kinetics were found to follow the pseudo-second-order kinetic model. The intra-particle diffusion analysis indicated that film diffusion may be the rate controlling step in the removal process. Experimental equilibrium data were fitted to five different isotherm models namely Langmuir, Freundlich, Dubinin-Radushkevich, Temkin and Redlich-Peterson by non-linear least square regression and their goodness-of-fit evaluated in terms of mean relative error (MRE) and standard error of estimate (SEE). The adsorption equilibrium data were best represented by Freundlich and Redlich-Peterson isotherms. Thermodynamic parameters such as Delta G degrees and Delta H degrees indicated that the sorption process is exothermic and spontaneous in nature and that higher ambient temperature results in more favourable adsorption. (C) 2011 Elsevier B.V. All rights reserved.

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

A Robust Co-Localisation Measurement Utilising Z-Stack Image Intensity Similarities for Biological Studies

Background: Co-localisation is a widely used measurement in immunohistochemical analysis to determine if fluorescently labelled biological entities, such as cells, proteins or molecules share a same location. However the measurement of co-localisation is challenging due to the complex nature of such fluorescent images, especially when multiple focal planes are captured. The current state-of-art co-localisation measurements of 3-dimensional (3D) image stacks are biased by noise and cross-overs from non-consecutive planes.

Method: In this study, we have developed Co-localisation Intensity Coefficients (CICs) and Co-localisation Binary Coefficients (CBCs), which uses rich z-stack data from neighbouring focal planes to identify similarities between image intensities of two and potentially more fluorescently-labelled biological entities. This was developed using z-stack images from murine organotypic slice cultures from central nervous system tissue, and two sets of pseudo-data. A large amount of non-specific cross-over situations are excluded using this method. This proposed method is also proven to be robust in recognising co-localisations even when images are polluted with a range of noises.

Results: The proposed CBCs and CICs produce robust co-localisation measurements which are easy to interpret, resilient to noise and capable of removing a large amount of false positivity, such as non-specific cross-overs. Performance of this method of measurement is significantly more accurate than existing measurements, as determined statistically using pseudo datasets of known values. This method provides an important and reliable tool for fluorescent 3D neurobiological studies, and will benefit other biological studies which measure fluorescence co-localisation in 3D.

Retrodirective Rotman Lens Constraining Factors

The mechanism by which a retrodirective Rotman lens operates is examined theoretically and prediction is compared with measurement. By deriving the reflection matrix based on the phase delay relationship between the beam ports and the array ports we show that if the phase delay difference between neighbouring ports is constrained in a particular way that the reflection matrix becomes an inverse diagonal matrix and the Rotman lens functions as a Van Atta Array hence can perform retrodirective reflection. Further, the primary factors governing the bandwidth and beam pointing error of the lens are elaborated.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.

A universal measure of chaotropicity and kosmotropicity

Diverse parameters, including chaotropicity, can limit the function of cellular systems and thereby determine the extent of Earth's biosphere. Whereas parameters such as temperature, hydrophobicity, pressure, pH, Hofmeister effects, and water activity can be quantified via standard scales of measurement, the chao-/kosmotropic activities of environmentally ubiquitous substances have no widely accepted, universal scale. We developed an assay to determine and quantify chao-/kosmotropicity for 97 chemically diverse substances that can be universally applied to all solutes. This scale is numerically continuous for the solutes assayed (from +361kJkg^-1mol^-1 for chaotropes to -659kJkg^-1mol^-1 for kosmotropes) but there are key points that delineate (i) chaotropic from kosmotropic substances (i.e. chaotropes =+4; kosmotropes =-4kJkg^-1mol^-1); and (ii) chaotropic solutes that are readily water-soluble (log P<1.9) from hydrophobic substances that exert their chaotropic activity, by proxy, from within the hydrophobic domains of macromolecular systems (log P>1.9). Examples of chao-/kosmotropicity values are, for chaotropes: phenol +143, CaCl₂ +92.2, MgCl₂ +54.0, butanol +37.4, guanidine hydrochloride +31.9, urea +16.6, glycerol [>6.5M] +6.34, ethanol +5.93, fructose +4.56; for kosmotropes: proline -5.76, sucrose -6.92, dimethylsulphoxide (DMSO) -9.72, mannitol -6.69, trehalose -10.6, NaCl -11.0, glycine -14.2, ammonium sulfate -66.9, polyethylene glycol- (PEG-)1000 -126; and for relatively neutral solutes: methanol, +3.12, ethylene glycol +1.66, glucose +1.19, glycerol [<5M] +1.06, maltose -1.43 (kJkg^-1mol^-1). The data obtained correlate with solute interactions with, and structure-function changes in, enzymes and membranes. We discuss the implications for diverse fields including microbial ecology, biotechnology and astrobiology.

The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples

A study was undertaken to examine a range of sample preparation and near infrared reflectance spectroscopy (NIPS) methodologies, using undried samples, for predicting organic matter digestibility (OMD g kg(-1)) and ad libitum intake (g kg(-1) W-0.75) of grass silages. A total of eight sample preparation/NIRS scanning methods were examined involving three extents of silage comminution, two liquid extracts and scanning via either external probe (1100-2200 nm) or internal cell (1100-2500 nm). The spectral data (log 1/R) for each of the eight methods were examined by three regression techniques each with a range of data transformations. The 136 silages used in the study were obtained from farms across Northern Ireland, over a two year period, and had in vivo OMD (sheep) and ad libitum intake (cattle) determined under uniform conditions. In the comparisons of the eight sample preparation/scanning methods, and the differing mathematical treatments of the spectral data, the sample population was divided into calibration (n = 91) and validation (n = 45) sets. The standard error of performance (SEP) on the validation set was used in comparisons of prediction accuracy. Across all 8 sample preparation/scanning methods, the modified partial least squares (MPLS) technique, generally minimized SEP's for both OMD and intake. The accuracy of prediction also increased with degree of comminution of the forage and with scanning by internal cell rather than external probe. The system providing the lowest SEP used the MPLS regression technique on spectra from the finely milled material scanned through the internal cell. This resulted in SEP and R-2 (variance accounted for in validation set) values of 24 (g/kg OM) and 0.88 (OMD) and 5.37 (g/kg W-0.75) and 0.77 (intake) respectively. These data indicate that with appropriate techniques NIRS scanning of undried samples of grass silage can produce predictions of intake and digestibility with accuracies similar to those achieved previously using NIRS with dried samples. (C) 1998 Elsevier Science B.V.

Determining trophic niche width: An experimental test of the stable isotope approach

Determining the trophic niche width of an animal population and the relative degree to which a generalist population consists of dietary specialists are long-standing problems of ecology. It has been proposed that the variance of stable isotope values in consumer tissues could be used to quantify trophic niche width of consumer populations. However, this promising idea has not yet been rigorously tested. By conducting controlled laboratory experiments using model consumer populations (Daphnia sp., Crustacea) with controlled diets, we investigated the effect of individual- and population-level specialisation and generalism on consumer d C mean and variance values. While our experimental data follow general expectations, we extend current qualitative models to quantitative predictions of the dependence of isotopic variance on dietary correlation time, a measure for the typical time over which a consumer changes its diet. This quantitative approach allows us to pinpoint possible procedural pitfalls and critical sources of measurement uncertainty. Our results show that the stable isotope approach represents a powerful method for estimating trophic niche widths, especially when taking the quantitative concept of dietary correlation time into account. © 2012 The Authors.

Investigation of the Human Brain Metabolome to Identify Potential Markers for Early Diagnosis and Therapeutic Targets of Alzheimer’s Disease

A study combining high resolution mass spectrometry (liquid chromatography-quadrupole time-of-flight-mass spectrometry, UPLC-QTof-MS) and chemometrics for the analysis of post-mortem brain tissue from subjects with Alzheimer’s disease (AD) (n = 15) and healthy age-matched controls (n = 15) was undertaken. The huge potential of this metabolomics approach for distinguishing AD cases is underlined by the correct prediction of disease status in 94–97% of cases. Predictive power was confirmed in a blind test set of 60 samples, reaching 100% diagnostic accuracy. The approach also indicated compounds significantly altered in concentration following the onset of human AD. Using orthogonal partial least-squares discriminant analysis (OPLS-DA), a multivariate model was created for both modes of acquisition explaining the maximum amount of variation between sample groups (Positive Mode-R2 = 97%; Q2 = 93%; root mean squared error of validation (RMSEV) = 13%; Negative Mode-R2 = 99%; Q2 = 92%; RMSEV = 15%). In brain extracts, 1264 and 1457 ions of interest were detected for the different modes of acquisition (positive and negative, respectively). Incorporation of gender into the model increased predictive accuracy and decreased RMSEV values. High resolution UPLC-QTof-MS has not previously been employed to biochemically profile post-mortem brain tissue, and the novel methods described and validated herein prove its potential for making new discoveries related to the etiology, pathophysiology, and treatment of degenerative brain disorders.

The application of Near-Infrared Reflectance Spectroscopy (NIRS) to detect melamine adulteration of soya bean meal

Soya bean products are used widely in the animal feed industry as a protein based feed ingredient and
have been found to be adulterated with melamine. This was highlighted in the Chinese scandal of
2008. Dehulled soya (GM and non-GM), soya hulls and toasted soya were contaminated with melamine
and spectra were generated using Near Infrared Reflectance Spectroscopy (NIRS). By applying chemometrics
to the spectral data, excellent calibration models and prediction statistics were obtained. The coefficients
of determination (R2) were found to be 0.89–0.99 depending on the mathematical algorithm used,
the data pre-processing applied and the sample type used. The corresponding values for the root mean
square error of calibration and prediction were found to be 0.081–0.276% and 0.134–0.368%, respectively,
again depending on the chemometric treatment applied to the data and sample type. In addition, adopting
a qualitative approach with the spectral data and applying PCA, it was possible to discriminate
between the four samples types and also, by generation of Cooman’s plots, possible to distinguish
between adulterated and non-adulterated samples.

Quantification of interpoint topographic correlations of threshold values in glaucomatous visual fields

Purpose: The authors sought to quantify neighboring and distant interpoint correlations of threshold values within the visual field in patients with glaucoma. Methods: Visual fields of patients with confirmed or suspected glaucoma were analyzed (n = 255). One eye per patient was included. Patients were examined using the 32 program of the Octopus 1-2-3. Linear regression analysis among each of the locations and the rest of the points of the visual field was performed, and the correlation coefficient was calculated. The degree of correlation was categorized as high (r > 0.66), moderate (0.66 = r > 0.33), or low (r = 0.33). The standard error of threshold estimation was calculated. Results: Most locations of the visual field had high and moderate correlations with neighboring points and with distant locations corresponding to the same nerve fiber bundle. Locations of the visual field had low correlations with those of the opposite hemifield, with the exception of locations temporal to the blind spot. The standard error of threshold estimation increased from 0.6 to 0.9 dB with an r reduction of 0.1. Conclusion: Locations of the visual field have highest interpoint correlation with neighboring points and with distant points in areas corresponding to the distribution of the retinal nerve fiber layer. The quantification of interpoint correlations may be useful in the design and interpretation of visual field tests in patients with glaucoma.

Mathematical Modelling of a Reciprocating Piston Expander

Modern internal combustion (IC) engines reject around two thirds of the energy provided by the fuel as low-grade waste heat. Capturing a portion of this waste heat energy and transforming it into a more useful form of energy could result in a significant reduction in fuel consumption. By using the low-grade heat, an organic Rankine cycle (ORC) can produce mechanical work from a pressurised organic fluid with the use of an expander.
Ideal gas assumptions are shown to produce significant errors in expander performance predictions when using an organic fluid. This paper details the mathematical modelling technique used to accurately model the thermodynamic processes for both ideal and non-ideal fluids within the reciprocating expander. A comparison between the two methods illustrates the extent of the errors when modelling a reciprocating piston expander. Use of the ideal gas assumptions are shown to produce an error of 55% in the prediction of power produced by the expander when operating on refrigerant R134a.