Monopoles, Dirac operator, and index theory for fuzzy SU(3) / (U(1) x U(1))

The intersection of the ten-dimensional fuzzy conifold Y-F(10) with S-F(5) x S-F(5) is the compact eight-dimensional fuzzy space X-F(8). We show that X-F(8) is (the analogue of) a principal U(1) x U(1) bundle over fuzzy SU(3) / U(1) x U(1)) ( M-F(6)). We construct M-F(6) using the Gell-Mann matrices by adapting Schwinger's construction. The space M-F(6) is of relevance in higher dimensional quantum Hall effect and matrix models of D-branes. Further we show that the sections of the monopole bundle can be expressed in the basis of SU(3) eigenvectors. We construct the Dirac operator on M-F(6) from the Ginsparg-Wilson algebra on this space. Finally, we show that the index of the Dirac operator correctly reproduces the known results in the continuum.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

On linear series and a conjecture of D. C. Butler

Let C be a smooth irreducible projective curve of genus g and L a line bundle of degree d generated by a linear subspace V of H-0 (L) of dimension n+1. We prove a conjecture of D. C. Butler on the semistability of the kernel of the evaluation map V circle times O-C -> L and obtain new results on the stability of this kernel. The natural context for this problem is the theory of coherent systems on curves and our techniques involve wall crossing formulae in this theory.

Series-connected substrate-integrated lead-carbon hybrid ultracapacitors with voltage-management circuit

Cell voltage for a fully charged-substrate-integrated lead-carbon hybrid ultracapacitor is about 2.3 V. Therefore, for applications requiring higher DC voltage, several of these ultracapacitors need to be connected in series. However, voltage distribution across each series-connected ultracapacitor tends to be uneven due to tolerance in capacitance and parasitic parallel-resistance values. Accordingly, voltage-management circuit is required to protect constituent ultracapacitors from exceeding their rated voltage. In this study, the design and characterization of the substrate-integrated lead-carbon hybrid ultracapacitor with co-located terminals is discussed. Voltage-management circuit for the ultracapacitor is presented, and its effectiveness is validated experimentally.

Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

Nonvanishing of Koecher-Maass series attached to Siegel cusp forms

We prove a nonvanishing result for Koecher-Maass series attached to Siegel cusp forms of weight k and degree n in certain strips on the complex plane. When n = 2, we prove such a result for forms orthogonal to the space of the Saito-Kurokawa lifts `up to finitely many exceptions', in bounded regions. (C) 2015 Elsevier Inc. All rights reserved.

Cybernetic Modeling Revisited: A Method for Inferring the Cybernetic Variables u(i) from Experimental Data

The cybernetic modeling framework for the growth of microorganisms provides for an elegant methodology to account for the unknown regulatory phenomena through the use of cybernetic variables for enzyme induction and activity. In this paper, we revisit the assumption of limited resources for enzyme induction (Sigma u(i) = 1) used in the cybernetic modeling framework by presenting a methodology for inferring the individual cybernetic variables u(i) from experimental data. We use this methodology to infer u(i) during the simultaneous consumption of glycerol and lactose by Escherichia coli and then model the fitness trade-offs involved in the recently discovered predictive regulation strategy of microorganisms.

On the critical thickness for non-localized to localized plastic flow transition in metallic glasses: A molecular dynamics study

Molecular dynamics simulations were employed to investigate the specimen thickness-dependent tensile behavior of a series of Cu(x)Z(100-x) (x = 20, 40, 50, 64 and 80 at%) metallic glass (MG) films, with a particular focus on the critical thickness, tc, below which non-localized plastic flow takes place. The simulation results reveal that while the transition occurs in all the alloys examined, t(c) is sensitive to the composition. We rationalize t(c) by postulating that the strain energy stored in the sample at the onset of plastic deformation has to be sufficient for the formation of shear bands. The composition-dependence of t(c) was found to correlate with the average activation energy of the atomic level plastic deformation events. (C) 2015 Elsevier Ltd. All rights reserved.

Isotopic disequilibrium in Globigerina bulloides and carbon isotope response to productivity increase in Southern Ocean

Oxygen and carbon isotope ratios in planktonic foraminifera Globigerina bulloides collected from tow samples along a transect from the equatorial Indian ocean to the Southern Ocean (45 degrees E and 80 degrees E and 10 degrees N to 53 degrees S) were analysed and compared with the equilibrium delta O-18 and delta C-13 values of calcite calculated using the temperature and isotopic composition of the water column. The results agree within similar to 0.25% for the region between 10 degrees N and 40 degrees S and 75-200 m water depth which is considered to be the habitat of Globigerina bulloides. Further south (from 40 degrees S to 55 degrees S), however, the measured delta O-18 and delta C-13 values are higher than the expected values by similar to 2% and similar to 1% respectively. These enrichments can be attributed to either a `vital effect' or a higher calcification rate. An interesting pattern of increase in the delta C-13(DIC) value of the surface water with latitude is observed between 35 degrees S and similar to 60 degrees S, with a peak at similar to 42 degrees S. This can be caused by increased organic matter production and associated removal. A simple model accounting for the increase in the delta C-13(DIC) values is proposed which fits well with the observed chlorophyll abundance as a function of latitude.

Stalagmite growth perturbations from the Kumaun Himalaya as potential earthquake recorders

The central part of the Himalaya (Kumaun and Garhwal Provinces of India) is noted for its prolonged seismic quiescence, and therefore, developing a longer-term time series of past earthquakes to understand their recurrence pattern in this segment assumes importance. In addition to direct observations of offsets in stratigraphic exposures or other proxies like paleoliquefaction, deformation preserved within stalagmites (speleothems) in karst system can be analyzed to obtain continuous millennial scale time series of earthquakes. The Central Indian Himalaya hosts natural caves between major active thrusts forming potential storehouses for paleoseismological records. Here, we present results from the limestone caves in the Kumaun Himalaya and discuss the implications of growth perturbations identified in the stalagmites as possible earthquake recorders. This article focuses on three stalagmites from the Dharamjali Cave located in the eastern Kumaun Himalaya, although two other caves, one of them located in the foothills, were also examined for their suitability. The growth anomalies in stalagmites include abrupt tilting or rotation of growth axes, growth termination, and breakage followed by regrowth. The U-Th age data from three specimens allow us to constrain the intervals of growth anomalies, and these were dated at 4273 +/- 410 years BP (2673-1853 BC), 2782 +/- 79 years BP (851-693 BC), 2498 +/- 117 years BP (605-371 BC), 1503 +/- 245 years BP (262-752 AD), 1346 +/- 101 years BP (563-765 AD), and 687 +/- 147 years BP (1176-1470 AD). The dates may correspond to the timings of major/great earthquakes in the region and the youngest event (1176-1470 AD) shows chronological correspondence with either one of the great medieval earthquakes (1050-1250 and 1259-1433 AD) evident from trench excavations across the Himalayan Frontal Thrust.

Reversible jump sampler for autoregressive time series

We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.

Bayesian model selection for time series using Markov chain Monte Carlo

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.