Tracing the source of bottled water using stable isotope techniques

The Indian Summer Monsoon (ISM) precipitation recharges ground water aquifers in a large portion of the Indian subcontinent. Monsoonal precipitation over the Indian region brings moisture from the Arabian Sea and the Bay of Bengal (BoB). A large difference in the salinity of these two reservoirs, owing to the large amount of freshwater discharge from the continental rivers in the case of the BoB and dominating evaporation processes over the Arabian Sea region, allows us to distinguish the isotopic signatures in water originating in these two water bodies. Most bottled water manufacturers exploit the natural resources of groundwater, replenished by the monsoonal precipitation, for bottling purposes. The work presented here relates the isotopic ratios of bottled water to latitude, moisture source and seasonality in precipitation isotope ratios. We investigated the impact of the above factors on the isotopic composition of bottled water. The result shows a strong relationship between isotope ratios in precipitation (obtained from the GNIP data base)/bottled water with latitude. The approach can be used to predict the latitude at which the bottled water was manufactured. The paper provides two alternative approaches to address the site prediction. The limitations of this approach in identifying source locations and the uncertainty in latitude estimations are discussed. Furthermore, the method provided here can also be used as an important forensic tool for exploring the source location of bottled water from other regions. Copyright (C) 2011 John Wiley & Sons, Ltd.

A review of recent developments in parametric based acoustic emission techniques applied to concrete structures

This article presents a review of recent developments in parametric based acoustic emission (AE) techniques applied to concrete structures. It recapitulates the significant milestones achieved by previous researchers including various methods and models developed in AE testing of concrete structures. The aim is to provide an overview of the specific features of parametric based AE techniques of concrete structures carried out over the years. Emphasis is given to traditional parameter-based AE techniques applied to concrete structures. A significant amount of research on AE techniques applied to concrete structures has already been published and considerable attention has been given to those publications. Some recent studies such as AE energy analysis and b-value analysis used to assess damage of concrete bridge beams have also been discussed. The formation of fracture process zone and the AE energy released during the fracture process in concrete beam specimens have been summarised. A large body of experimental data on AE characteristics of concrete has accumulated over the last three decades. This review of parametric based AE techniques applied to concrete structures may be helpful to the concerned researchers and engineers to better understand the failure mechanism of concrete and evolve more useful methods and approaches for diagnostic inspection of structural elements and failure prediction/prevention of concrete structures.

Prediction of Shallow Landslide prone regions in Undulating Terrains

Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

Efficient post-processing techniques for speech enhancement

We address the problem of speech enhancement in real-world noisy scenarios. We propose to solve the problem in two stages, the first comprising a generalized spectral subtraction technique, followed by a sequence of perceptually-motivated post-processing algorithms. The role of the post-processing algorithms is to compensate for the effects of noise as well as to suppress any artifacts created by the first-stage processing. The key post-processing mechanisms are aimed at suppressing musical noise and to enhance the formant structure of voiced speech as well as to denoise the linear-prediction residual. The parameter values in the techniques are fixed optimally by experimentally evaluating the enhancement performance as a function of the parameters. We used the Carnegie-Mellon university Arctic database for our experiments. We considered three real-world noise types: fan noise, car noise, and motorbike noise. The enhancement performance was evaluated by conducting listening experiments on 12 subjects. The listeners reported a clear improvement (MOS improvement of 0.5 on an average) over the noisy signal in the perceived quality (increase in the mean-opinion score (MOS)) for positive signal-to-noise-ratios (SNRs). For negative SNRs, however, the improvement was found to be marginal.

Epoch extraction based on integrated linear prediction residual using plosion index

Epoch is defined as the instant of significant excitation within a pitch period of voiced speech. Epoch extraction continues to attract the interest of researchers because of its significance in speech analysis. Existing high performance epoch extraction algorithms require either dynamic programming techniques or a priori information of the average pitch period. An algorithm without such requirements is proposed based on integrated linear prediction residual (ILPR) which resembles the voice source signal. Half wave rectified and negated ILPR (or Hilbert transform of ILPR) is used as the pre-processed signal. A new non-linear temporal measure named the plosion index (PI) has been proposed for detecting `transients' in speech signal. An extension of PI, called the dynamic plosion index (DPI) is applied on pre-processed signal to estimate the epochs. The proposed DPI algorithm is validated using six large databases which provide simultaneous EGG recordings. Creaky and singing voice samples are also analyzed. The algorithm has been tested for its robustness in the presence of additive white and babble noise and on simulated telephone quality speech. The performance of the DPI algorithm is found to be comparable or better than five state-of-the-art techniques for the experiments considered.

A fast algorithm for speech polarity detection using long-term linear prediction

Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

Sentence comprehension before and after 1970: Topics, debates and techniques

[EN] Language Down the Garden Path traces the lines of research that grew out of Bever's classic paper. Leading scientists review over 40 years of debates on the factors at play in language comprehension, production, and acquisition (the role of prediction, grammar, working memory, prosody, abstractness, syntax and semantics mapping); the current status of universals and narrow syntax; and virtually every topic relevant in psycholinguistics since 1970. Written in an accessible and engaging style, the book will appeal to all those interested in understanding the questions that shaped, and are still shaping, this field and the ways in which linguists, cognitive scientists, psychologists, and neuroscientists are seeking to answer them.

The classification and prediction of macroinvertebrate communities in British rivers

This article describes the progress of the River Communities Project which commenced in 1977. This project aimed to develop a sensitive and practical system for river site classification using macroinvertebrates as an objective means of appraising the status of British rivers. The relationship between physical and chemical features of sites and their biological communities were examined. Sampling was undertaken on 41 British rivers. Ordination techniques were used to analyze data and the sites were classified into 16 groups using multiple discrimination analysis. The potential for using the environmental data to predict to which group a site belonged and the fauna likely to be present was investigated.

Proposal and validation of methodologies for the categorisation of continuous variables in the development of prediction models

158 p.

Predictive techniques for turbulent oscillatory flows

The prediction of turbulent oscillatory flow at around transitional Reynolds numbers is considered for an idealized electronics system. To assess the accuracy of turbulence models, comparison is made with measurements. A stochastic procedure is used to recover instantaneous velocity time traces from predictions. This procedure enables more direct comparison with turbulence intensity measurements which have not been filtered to remove the oscillatory flow component. Normal wall distances, required in some turbulence models, are evaluated using a modified Poisson equation based technique. A range of zero, one and two equation turbulence models are tested, including zonal and a non-linear eddy viscosity models. The non-linear and zonal models showed potential for accuracy improvements.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.

Power combining techniques into unbalanced loads for Class-E and inverse Class-E amplifiers

A power combining strategy for Class-E and inverse Class-E amplifiers operating at high frequencies such that they can operate into unbalanced loads is proposed. This power combining method is particularly important for the inverse Class-E amplifier configuration whose single-stage topology is naturally limited for small-to-medium power applications. Design examples for the power combining synthesis of classical Class-E and then inverse Class-E amplifiers with specification 3 V-1.5 W-2.5 GHz are given. For this specification, it is shown that a three-branch combiner has a natural 50 V output impedance. The resulting circuits are simulated within Agilent Advanced Design Systems environment with good agreement to theoretical prediction. Further the performance of the proposed circuits when operated in a Linear amplification using Nonlinear Components transmitter configuration whereby two-branch amplifiers are driven with constant amplitude conjugate input phase signals is investigated.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.