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Summary: In this paper, we study a supercapacitor model represented by an equivalent RC circuit considering five different types of derivatives: Caputo, Caputo‐Fabrizio, and Atangana‐Baleanu fractional derivatives and the conformable and integer‐order derivatives. A set of experimental data from six commercial supercapacitors are used to estimate the parameter values for each derivative model by applying interior point optimization. The results show that the most accurate approach is achieved with the conformable derivative followed by the Caputo fractional derivative. [ABSTRACT FROM AUTHOR]

Abstract: Two likelihood ratio (LR) approaches are presented to evaluate the strength of evidence of MDMA tablet comparisons. The first one is based on a more ‘traditional’ comparison of MDMA tablets by using distance measures (e.g., Pearson correlation distance or a Euclidean distance). In this approach, LRs are calculated using the distribution of distances between tablets of the same-batch and that of different-batches. The second approach is based on methods used in some other fields of forensic comparison. Here LRs are calculated based on the distribution of values of MDMA tablet characteristics within a specific batch and from all batches. The data used in this paper must be seen as examples to illustrate both methods. In future research the methods can be applied to other and more complex data. In this paper, the methods and their results are discussed, considering their performance in evidence evaluation and several practical aspects. With respect to evidence in favor of the correct hypothesis, the second method proved to be better than the first one. It is shown that the LRs in same-batch comparisons are generally higher compared to the first method and the LRs in different-batch comparisons are generally lower. On the other hand, for operational purposes (where quick information is needed), the first method may be preferred, because it is less time consuming. With this method a model has to be estimated only once in a while, which means that only a few measurements have to be done, while with the second method more measurements are needed because each time a new model has to be estimated. [Copyright &y& Elsevier]

Objective. The objective of this paper is to compare, using a cross-company data set, several Bayesian Network (BN) models for Web effort estimation. Method. Eight BNs were built; four automatically using Hugin and PowerSoft tools with two training sets, each containing data on 130 Web projects from the Tukutuku database; four using a causal graph elicited by a domain expert, with parameters obtained by automatically fitting the graph to the same training sets used in the automated elicitation (hybrid models). The accuracy of all eight models was measured using two validation sets, each containing data on 65 projects, and point estimates. As a benchmark, the BN-based estimates were also compared to estimates obtained using Manual Stepwise Regression (MSWR), Case- Based Reasoning (CBR), and mean and median-based effort models. Results. MSWR presented significantly better predictions than any of the BN. models built herein and, in addition, was the only technique to provide significantly superior predictions to a median-based effort model. Two BN models, BNAuHu and BNHyHu, presented similar to, or significantly better accuracy than, the mean-based effort model and similar accuracy to the median-based effort model; however, both showed significantly worse accuracy than MSWR: The other two BN models showed worse accuracy than at least mean-based predictions. Conclusions. This paper investigated data-driven and hybrid BN models using project data from the Tukutuku database. Our results suggest that the use of simpler models, such as the median effort, can outperform more complex models, such as BNs. In addition, MSWR seemed to be the only effective technique for Web effort estimation. [ABSTRACT FROM AUTHOR]

In this paper, we derive the growth pinching estimates for potential functions of τ -quasi-Einstein metrics on complete noncompact connected manifolds, based on the estimates for the scalar curvature and the using of the weighted measure comparison theorem. Our results show that the estimates for potential functions rely on the sign of constants λ and μ . [ABSTRACT FROM AUTHOR]

In this paper we study global distance estimates and uniform local volume estimates in a large class of sub-Riemannian manifolds. Our main device is the generalized curvature dimension inequality introduced by the first and the third author in [3] and its use to obtain sharp inequalities for solutions of the sub-Riemannian heat equation. As a consequence, we obtain a Gromov type precompactness theorem for the class of sub-Riemannian manifolds whose generalized Ricci curvature is bounded from below in the sense of [3] . [ABSTRACT FROM AUTHOR]

This paper describes a new approach to global smoothing problems for dispersive and non-dispersive evolution equations based on the global canonical transforms and the underlying global microlocal analysis. For this purpose, the Egorov-type theorem is established with canonical transformations in the form of a class of Fourier integral operators, and their weighted L2-boundedness properties are derived. This allows us to globally reduce general dispersive equations to normal forms in one or two dimensions. Then, a new comparison principle for evolution equations is introduced. In particular, it allows us to relate different smoothing estimates by comparing certain expressions involving their symbols. As a result, it is shown that the majority of smoothing estimates for different equations are equivalent to each other. Moreover, new estimates as well as several refinements of known results are obtained. The proofs are considerably simplified. A comprehensive analysis is presented for smoothing estimates for dispersive equations. Applications are given to the detailed description of smoothing properties of the Schrödinger, relativistic Schrödinger, wave, Klein–Gordon and other equations. [ABSTRACT FROM PUBLISHER]

In this paper, I study educational integration of students with migration background using data from five international student assessment studies. First, Blinder-Oaxaca decompositions are used to allow for a comparison of integration of migrant students across countries and time. In a second step, integration is related to institutional characteristics of the schooling system. Pooled, country-group and country fixed effects estimations show that time in school and early education are positively related to the integration of students with migration background. Furthermore, in the OECD countries, educational integration in science is positively related to external student assessment policies. [ABSTRACT FROM AUTHOR]

Abstract: This paper is mainly devoted to an empirical study of the legal and real independence of the Tunisian Central Bank as well as to estimating the correlation between the inflationary bias and the real independence of the emerging countries while applying new data sources. Our contribution consists, particularly, in measuring the indicators of legal and real Central Bank independence through applying, respectively, the Jacome (2001) and Cukierman''s (1992) methods. In a second part, we are carrying out a descriptive and comparative analysis of inflation relative to the Maghreb countries designed to check the inflationary bias reduction. However, the third part is consecrated to the study of correlation between the real independence and the inflationary bias, performed over a sample of emerging countries with a panel estimation ranging over the period 1971–2004. Our results conform those achieved by Cukierman (1992), showing an acceptable proxy of the real and legal independence as well as the beneficial effects stemming from inflation. These findings conform those of De Haan (2007) and confirm a positive and non-significant correlation between real independence and inflation. [Copyright &y& Elsevier]

Non-Gaussian processes may require not only the information provided by first two moments, but also that given by the higher-order statistics, in particular, by the third- and fourth-order moments or cumulants. This paper addresses a fourth-order cumulant estimation problem for real discrete-time random non-i.i.d. signal, that can be approximated as an MA stochastic process. An unbiased estimator is proposed, studied and compared to two other frequently used estimators of the fourth-order cumulant (natural estimator and fourth k-statistics). Statistical comparative studies are undertaken from both bias and MSE points of view, for different distribution laws and MA filters. Algorithms, aiming to reduce computational complexity of the proposed estimator, as well as that of the fourth k-statistics bias, are also provided. [ABSTRACT FROM PUBLISHER]

Abstract: High heating value (HHV) is an important property which characterises the energy content of a fuel such as solid, liquid and gaseous fuels. The previous assertion is particularly important for vegetable oils and biodiesels fuels which are expected to replace fossil oils. Estimation of the HHV of vegetable oils and biodiesels by using their fatty acid composition is the aim of this paper. The comparison between the HHVs predicted by the method and those obtained experimentally gives an average bias error of −0.84% and an average absolute error of 1.71%. These values show the utility, the validity and the applicability of the method to vegetable oils and their derivatives. [ABSTRACT FROM AUTHOR]

Abstract: Missing-data methods attempt to improve robust speech recognition by distinguishing between reliable and unreliable data in the time–frequency (T–F) domain. Such methods require a binary mask to label speech-dominant T–F regions of a noisy speech signal as reliable and the rest as unreliable. Current methods for computing the mask are based mainly on bottom-up cues such as harmonicity and produce labeling errors that degrade recognition performance. In this paper, we propose a two-stage recognition system that combines bottom-up and top-down cues in order to simultaneously improve both mask estimation and recognition accuracy. First, an n-best lattice consistent with a speech separation mask is generated. The lattice is then re-scored by expanding the mask using a model-based hypothesis test to determine the reliability of individual T–F units. Systematic evaluations of the proposed system show significant improvement in recognition performance compared to that using speech separation alone. [Copyright &y& Elsevier]

Abstract: This paper proposes a noise robust voice activity detection (VAD) technique called PARADE (PAR based Activity DEtection) that employs the periodic component to aperiodic component ratio (PAR). Conventional noise robust features for VAD are still sensitive to non-stationary noise, which yields variations in the signal-to-noise ratio, and sometimes requires a priori noise power estimations, although the characteristics of environmental noise change dynamically in the real world. To overcome this problem, we adopt the PAR, which is insensitive to both stationary and non-stationary noise, as an acoustic feature for VAD. By considering both periodic and aperiodic components simultaneously in the PAR, we can mitigate the effect of the non-stationarity of noise. PARADE first estimates the fundamental frequencies of the dominant periodic components of the observed signals, decomposes the power of the observed signals into the powers of its periodic and aperiodic components by taking account of the power of the aperiodic components at the frequencies where the periodic components exist, and calculates the PAR based on the decomposed powers. Then it detects the presence of target speech signals by estimating the voice activity likelihood defined in relation to the PAR. Comparisons of the VAD performance for noisy speech data confirmed that PARADE outperforms the conventional VAD algorithms even in the presence of non-stationary noise. In addition, PARADE is applied to a front-end processing technique for automatic speech recognition (ASR) that employs a robust feature extraction method called SPADE (Subband based Periodicity and Aperiodicity DEcomposition) as an application of PARADE. Comparisons of the ASR performance for noisy speech show that the SPADE front-end combined with PARADE achieves significantly higher word accuracies than those achieved by MFCC (Mel-frequency Cepstral Coefficient) based feature extraction, which is widely used for conventional ASR systems, the SPADE front-end without PARADE, and other standard noise robust front-end processing techniques (ETSI ES 202 050 and ETSI ES 202 212). This result confirmed that PARADE can improve the performance of front-end processing for ASR. [Copyright &y& Elsevier]

Abstract: Accurate performance simulation and estimation of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design-point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design-point performance of an engine may be slightly different from its actual performance. In this paper, a Genetic Algorithm (GA) based non-linear gas turbine design-point performance adaptation approach has been presented to best estimate the unknown component parameters and match available design-point engine performance. In the approach, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, by-pass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power and thermal efficiency for industrial engines, gas path pressures and temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity analysis is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The adaptation approach has been applied to an industrial gas turbine engine to test the effectiveness of the approach. The approach has also been compared with a non-linear Influence Coefficient Matrix (ICM) based adaptation method and the advantages and disadvantages of the two adaptation methods have been compared with each other. The application shows that the sensitivity analysis is very useful in the selection of the to-be-adapted component parameters and the GA-based adaptation approach is able to produce good quality engine models at design-point. Compared with the non-linear ICM-based method, the GA-based performance adaptation method is more robust but slower in computation and relatively less accurate. [Copyright &y& Elsevier]

Abstract: Good control charts for high quality processes are often based on the number of successes between failures. Geometric charts are simplest in this respect, but slow in recognizing moderately increased failure rates p. Improvement can be achieved by waiting until failures have occurred, i.e. by using negative binomial charts. In this paper we analyze such charts in some detail. On the basis of a fair comparison, we demonstrate how the optimal r is related to the degree of increase of p. As in practice p will usually be unknown, we also analyze the estimated version of the charts. In particular, simple corrections are derived to control the nonnegligible effects of this estimation step. [Copyright &y& Elsevier]

Abstract: Statistical comparison of organ weights between treated and untreated animals have traditionally been used to predict potential toxicity for patients. The manner of presentation of organ weight data, and the value of statistical analyses have been topics of discussion. Historically, a decision tree approach has been applied for statistical comparison of organ weights which does not control the overall error rate and can lead to different statistical tests being used by chance for identical settings causing confusion. This paper proposes a simple nonparametric approach for assessing treatment effects on organ weights in terms of ratios based on the Hodges–Lehmann estimator. This allows for simple interpretation of results and aids in the identification of potential target organs as the evaluation is based on effect sizes and not on p-values allowing a robust proof of effect as well as a robust proof of no effect. The proposed estimate and the corresponding nonparametric confidence interval applied to a rank-sum score can be used as a confirmatory test for difference and as a confirmatory test for equivalence. Exploratory analyses can be performed calculating the proposed estimates for each organ separately to be summarized graphically in a confidence interval plot. [Copyright &y& Elsevier]

Abstract: In a recent paper, García-Mainar and Montuenga-Gómez [García-Mainar, I. & Montuenga-Gómez, V. M. (2005). Education returns of wage earners and self-employed workers: Portugal vs. Spain. Economics of Education Review, 24(2), 161−170] apply the generalized IV model of Hausman and Taylor to estimate education returns of wage earners and the self-employed in Portugal and in Spain. Our examination reveals several problems which relate to the validity and documentation of the instrumental variables, as well as the robustness of the results. [Copyright &y& Elsevier]

Summary: This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. [Copyright &y& Elsevier]

Summary: This paper proposes the application of evolutionary fuzzy models (EFMs) for suspended sediment concentration estimation. The EFMs are improved by the combination of two methods, fuzzy logic and differential evolution. The accuracy of EFMs is compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment data belonging to two stations, Quebrada Blanca Station and Rio Valenciano Station, operated by the US Geological Survey (USGS) are used as case studies. The mean square errors and determination coefficient statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the EFMs give better estimates than the other techniques. [Copyright &y& Elsevier]

This paper develops a framework for estimating previous illegal experience among annual cohorts of new legal immigrants to the United States – using public-use administrative microdata alone, survey data alone, and the two jointly – and provides estimates for the FY 1996 cohort of new immigrants, based on both administrative and survey data. Our procedures enable assessment of type of illegal experience, including entry without inspection, visa overstay, and unauthorized employment. We compare our estimates of previous illegal experience to estimates that would be obtained using administrative data alone; examine the extent of previous illegal experience by country of birth, immigrant class of admission, religion, and geographic residence in the United States; and estimate multivariate models of the probability of having previous illegal experience. To further assess origins and destinations, we carry out two kinds of contrasts, comparing formerly illegal new legal immigrants both to fellow immigrants who do not have previous illegal experience and also to the broader unauthorized population, the latter using estimates developed by the U.S. Department of Homeland Security (2002 ), Passel (2002 ), and Costanzo et al. (2002 ). [ABSTRACT FROM AUTHOR]

Son preference is strong in Vietnam, according to attitudinal surveys and studies of contraceptive prevalence and birth hazards. These techniques assume a single model is valid for all families, but it is more plausible that son preference is found for some, but not all, families. Heterogeneous preferences may be addressed with a mixture model. This paper specifies and estimates a two-Weibull regression model, applied to the interval between the second and third births. The data come from the Vietnam Living Standards Survey of 1992–93. Applying information criteria, graphs, and martingale-based residuals, the two-Weibull model is found to fit better than a one-Weibull model. Roughly half of parents have son preference and, curiously, a propensity for fewer children. The other group has more children, no son preference, and is colourless in the sense that the birth interval is difficult to predict on the basis of the regressors used. [ABSTRACT FROM PUBLISHER]

Abstract: Satellite remote sensing offers new means of quantifying particulate organic carbon, POC, concentration over large oceanic areas. From SeaWiFS ocean color, we derived 10-year data of POC concentration in the surface waters of the global ocean. The 10-year time series of the global and basin scale average surface POC concentration do not display any significant long-term trends. The annual mean surface POC concentration and its seasonal amplitude are highest in the North Atlantic and lowest in the South Pacific, when compared to other ocean basins. POC anomalies in the North Atlantic, North Pacific, and global concentrations seem to be inversely correlated with El Niño index, but longer time series are needed to confirm this relationship. Quantitative estimates of POC reservoir in the oceanic surface layer depend on the choice of what should represent this layer. Global average POC biomass is 1.34gm−2 if integrated over one optical depth, 3.62gm−2 if integrated over mixed layer depth, and up to 6.41gm−2 if integrated over 200-m layer depth (when assumed POC concentration below MLD is 20mgm−3). The global estimate of total POC reservoir in the surface 200-m layer of the ocean is 228.61×1013 g. We expect that future estimates of POC reservoir may be even larger, when more precise calculations account for deep-water organic-matter maxima in oligotrophic regions, and POC biomass located just below the seasonal mixed layer in spring and summer in the temperate regions. [Copyright &y& Elsevier]