bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transfer function fitting using a continuous Ant Colony Optimization (ACO) algorithm
Authors: Reineix A. , Guiffaut C. 
Published: March 2015

An original approach is proposed in order to achieve the fitting of ultra-wideband complex frequency functions, such as the complex impedances, by using the so-called ACO (Ant Colony Optimization) methods. First, we present the optimization principle of ACO, which originally was dedicated to the combinatorial problems. Further on, the extension to the continuous and mixed problems is explained in more details. The interest in this approach is proved by its ability to define practical constraints and objectives, such as minimizing the number of filters used in the model with respect to a fixed relative error. Finally, the establishment of the model for the first and second order filter types illustrates the power of the method and its interest for the time-domain electromagnetic computation.
The ULF wave foreshock boundary: Cluster observations
Authors: Andrés Nahuel , Meziane Karim , Mazelle Christian , Bertucci Cesar , Gómez Daniel 
Published: March 2015

AbstractThe interaction of backstreaming ions with the incoming solar wind in the upstream region of the bow shock gives rise to a number of plasma instabilities from which ultralow frequency (ULF) waves can grow. Because of their finite growth rate, the ULF waves are spatially localized in the foreshock region. Previous studies have reported observational evidence of the existence of a ULF wave foreshock boundary, whose geometrical characteristics are very sensitive to the interplanetary magnetic field (IMF) cone angle. The statistical properties of the ULF wave foreshock boundary (UWFB) are examined in detail using Cluster data. A new identification of the UWFB is presented using a specific and accurate criterion for identification of boundary crossings. This criterion is based on the degree of IMF rotation as Cluster crosses the boundary. The obtained UWFB is compared with previous results reported in the literature as well as with theoretical predictions. Also, we examined the possible connection between the foreshock boundary properties and the ion emission mechanisms at the bow shock.
Simulation of ULF wave‐modulated radiation belt electron precipitation during the 17 March 2013 storm
Authors: Brito T. , Hudson M. K. , Kress B. , Paral J. , Halford A. , Millan R. , Usanova M. 
Published: March 2015

AbstractBalloon‐borne instruments detecting radiation belt precipitation frequently observe oscillations in the millihertz frequency range. Balloons measuring electron precipitation near the poles in the 100 keV to 2.5 MeV energy range, including the MAXIS, MINIS, and most recently the Balloon Array for Relativistic Radiation belt Electron Losses balloon experiments, have observed this modulation at ULF wave frequencies. Although ULF waves in the magnetosphere are seldom directly linked to increases in electron precipitation since their oscillation periods are much larger than the gyroperiod and the bounce period of radiation belt electrons, test particle simulations show that this interaction is possible. Three‐dimensional simulations of radiation belt electrons were performed to investigate the effect of ULF waves on precipitation. The simulations track the behavior of energetic electrons near the loss cone, using guiding center techniques, coupled with an MHD simulation of the magnetosphere, using the Lyon‐Fedder‐Mobarry code, during a coronal mass ejection (CME)‐shock event on 17 March 2013. Results indicate that ULF modulation of precipitation occurs even without the presence of electromagnetic ion cyclotron waves, which are not resolved in the MHD simulation. The arrival of a strong CME‐shock, such as the one simulated, disrupts the electric and magnetic fields in the magnetosphere and causes significant changes in both components of momentum, pitch angle, and L shell of radiation belt electrons, which may cause them to precipitate into the loss cone.
Current block motions and strain accumulation on active faults in the Caribbean
Authors: Symithe S. , Calais E. , de Chabalier J. B. , Robertson R. , Higgins M. 
Published: March 2015

AbstractThe Caribbean plate and its boundaries with north and south America, marked by subduction and large intra‐arc strike‐slip faults, are a natural laboratory for the study of strain partitioning and interseismic plate coupling in relation to large earthquakes. Here we use most of the available campaign and continuous GPS measurements in the Caribbean to derive a regional velocity field expressed in a consistent reference frame. We use this velocity field as input to a kinematic model where surface velocities results from the rotation of rigid blocks bounded by locked faults accumulating interseismic strain, while allowing for partial locking along the Lesser Antilles, Puerto Rico, and Hispaniola subduction. We test various block geometries, guided by previous regional kinematic models and geological information on active faults. Our findings refine a number of previously established results, in particular slip rates on the strike‐slip faults systems bounding the Caribbean plate to the north and south, and the kinematics of the Gonave microplate. Our much‐improved GPS velocity field in the Lesser Antilles compared to previous studies does not require the existence of a distinct Northern Lesser Antilles block and excludes more than 3 mm/yr of strain accumulation on the Lesser Antilles‐Puerto Rico subduction plate interface, which appears essentially uncoupled. The transition from a coupled to an uncoupled subduction coincides with a transition in the long‐term geological behavior of the Caribbean plate margin from compressional (Hispaniola) to extensional (Puerto Rico and Lesser Antilles), a characteristics shared with several other subduction systems.
Methane emissions in East Asia for 2000–2011 estimated using an atmospheric Bayesian inversion
Authors: Thompson R. L. , Stohl A. , Zhou L. X. , Dlugokencky E. , Fukuyama Y. , Tohjima Y. , Kim S.‐Y. , Lee H. , Nisbet E. G. , Fisher R. E. , Lowry D. , Weiss R. F. , Prinn R. G. , O'Doherty S. , Young D. , White J. W. C. 
Published: March 2015

AbstractWe present methane (CH4) emissions for East Asia from a Bayesian inversion of CH4 mole fraction and stable isotope (δ13C‐CH4) measurements. Emissions were estimated at monthly resolution from 2000 to 2011. A posteriori, the total emission for East Asia increased from 43 ± 4 to 59 ± 4 Tg yr−1 between 2000 and 2011, owing largely to the increase in emissions from China, from 39 ± 4 to 54 ± 4 Tg yr−1, while emissions in other East Asian countries remained relatively stable. For China, South Korea, and Japan, the total emissions were smaller than the prior estimates (i.e., Emission Database for Global Atmospheric Research 4.2 FT2010 for anthropogenic emissions) by an average of 29%, 20%, and 23%, respectively. For Mongolia, Taiwan, and North Korea, the total emission was less than 2 Tg yr−1 and was not significantly different from the prior. The largest reductions in emissions, compared to the prior, occurred in summer in regions important for rice agriculture suggesting that this source is overestimated in the prior. Furthermore, an analysis of the isotope data suggests that the prior underestimates emissions from landfills and ruminant animals for winter 2010 to spring 2011 (no data available for other times). The inversion also found a lower average emission trend for China, 1.2 Tg yr−1 compared to 2.8 Tg yr−1 in the prior. This trend was not constant, however, and increased significantly after 2005, up to 2.0 Tg yr−1. Overall, the changes in emissions from China explain up to 40% of the increase in global emissions in the 2000s.
A negative phase shift of the winter AO/NAO due to the recent Arctic sea‐ice reduction in late autumn
Authors: Nakamura Tetsu , Yamazaki Koji , Iwamoto Katsushi , Honda Meiji , Miyoshi Yasunobu , Ogawa Yasunobu , Ukita Jinro 
Published: March 2015

AbstractThis paper examines the possible linkage between the recent reduction in Arctic sea‐ice extent and the wintertime Arctic Oscillation (AO)/North Atlantic Oscillation (NAO). Observational analyses using the ERA interim reanalysis and merged Hadley/Optimum Interpolation Sea Surface Temperature data reveal that a reduced (increased) sea‐ice area in November leads to more negative (positive) phases of the AO and NAO in early and late winter, respectively. We simulate the atmospheric response to observed sea‐ice anomalies using a high‐top atmospheric general circulation model (AGCM for Earth Simulator, AFES version 4.1). The results from the simulation reveal that the recent Arctic sea‐ice reduction results in cold winters in mid‐latitude continental regions, which are linked to an anomalous circulation pattern similar to the negative phase of AO/NAO with an increased frequency of large negative AO events by a factor of over two. Associated with this negative AO/NAO phase, cold air advection from the Arctic to the mid‐latitudes increases. We found that the stationary Rossby wave response to the sea‐ice reduction in the Barents Sea region induces this anomalous circulation. We also found a positive feedback mechanism resulting from the anomalous meridional circulation that cools the mid‐latitudes and warms the Arctic, which adds an extra heating to the Arctic air column equivalent to about 60% of the direct surface heat release from the sea‐ice reduction. The results from this high‐top model experiment also suggested a critical role of the stratosphere in deepening the tropospheric annular mode and modulation of the NAO in mid to late winter through stratosphere‐troposphere coupling.
Multiple scattering in observations of the GPM dual‐frequency precipitation radar: Evidence and impact on retrievals
Authors: Battaglia A. , Tanelli S. , Mroz K. , Tridon F. 
Published: March 2015

AbstractThis paper illustrates how multiple scattering signatures affect Global Precipitation Measuring (GPM) Mission Dual‐Frequency Precipitation Radar (DPR) Ku and Ka band reflectivity measurements and how they are consistent with prelaunch assessments based on theoretical considerations and confirmed by airborne observations. In particular, in the presence of deep convection, certain characteristics of the dual‐wavelength reflectivity profiles cannot be explained with single scattering, whereas they are readily explained by multiple‐scattering theory. Examples of such signatures are the absence of surface reflectivity peaks and anomalously small reflectivity slopes in the lower troposphere. These findings are relevant for DPR‐based rainfall retrievals and stratiform/convective classification algorithms when dealing with deep convective regions. A path to refining the rainfall inversion problem is proposed by adopting a methodology based on a forward operator which accounts for multiple scattering. A retrieval algorithm based on this methodology is applied to a case study over Africa, and it is compared to the standard DPR products obtained with the at‐launch version of the standard algorithms.
Bayesian model averaging to explore the worth of data for soil‐plant model selection and prediction
Authors: Wöhling Thomas , Schöniger Anneli , Gayler Sebastian , Nowak Wolfgang 
Published: March 2015

AbstractA Bayesian model averaging (BMA) framework is presented to evaluate the worth of different observation types and experimental design options for (1) more confidence in model selection and (2) for increased predictive reliability. These two modeling tasks are handled separately because model selection aims at identifying the most appropriate model with respect to a given calibration data set, while predictive reliability aims at reducing uncertainty in model predictions through constraining the plausible range of both models and model parameters. For that purpose, we pursue an optimal design of measurement framework that is based on BMA and that considers uncertainty in parameters, measurements, and model structures. We apply this framework to select between four crop models (the vegetation components of CERES, SUCROS, GECROS, and SPASS), which are coupled to identical routines for simulating soil carbon and nitrogen turnover, soil heat and nitrogen transport, and soil water movement. An ensemble of parameter realizations was generated for each model using Monte‐Carlo simulation. We assess each model's plausibility by determining its posterior weight, which signifies the probability to have generated a given experimental data set. Several BMA analyses were conducted for different data packages with measurements of soil moisture, evapotranspiration (ETa), and leaf area index (LAI). The posterior weights resulting from the different BMA runs were compared to the weight distribution of a reference run with all data types to investigate the utility of different data packages and monitoring design options in identifying the most appropriate model in the ensemble. We found that different (combinations of) data types support different models and none of the four crop models outperforms all others under all data scenarios. The best model discrimination was observed for those data where the competing models disagree the most. The data worth for reducing prediction uncertainty depends on the prediction to be made. LAI data have the highest utility for predicting ETa, while soil moisture data are better for predicting soil water drainage. Our study illustrates, that BMA provides an objective framework for data worth analysis with respect to both model discrimination and model calibration for a wide range of applications.
Ordinal Data
Authors: Agresti Alan 
Published: March 2015

AbstractAn ordinal variable is one that has a natural ordering of its possible values, but for which the distances between the values are undefined. For simple bivariate analyses, measures of association such as generalizations ofKendall's tausummarize the strength of positive or negative trend in the data by evaluating relative frequencies ofconcordantand discordant pairs of observations.Log‐linear modelswith scores for the categories can describe the structure of the joint distribution. Generalizations of the binarylogistic regression modeldescribe the dependence of an ordinal response variable on a set of qualitative and/or quantitative explanatory variables. Most commonly used is thecumulative logit model, which applies the logit link function to the set of cumulative probabilities. Theproportional oddsversion of the model assumes the same effect on the logit of each cumulative probability. Extensions of the model apply to correlated multivariate ordinal responses; such extensions often occur in longitudinal studies. These extensions includemarginal models, which simultaneously describe effects of explanatory variables on each marginal response, andgeneralized linear mixed modelswithrandom effects.
Multivariate Matching Methods
Authors: Rosenbaum Paul R. 
Published: March 2015

AbstractIn the absence of random assignment to treatment or control, multivariate matching often permits the comparison of treated and control subjects who appeared similar before treatment in terms of a vectorXof observed covariates. Matching selects and divides treated and control subjects to produce disjoint matched pairs containing one treated and one control subject, disjoint matched sets with multiple controls containing one treated subject and one or more controls, or some more flexible structure. Tools for multivariate matching are reviewed, including (i) the propensity score, (ii) fine balance constraints, (iii) optimal or minimum distance matching, (iv) full matching, (v) use of network optimization algorithms, (vi) limited covariate overlap, (vii) matching to strengthen an instrumental variable, and (viii) available software packages in R.
Geographic Information Systems
Authors: Griffith Daniel A. 
Published: March 2015

AbstractA geographic information system (GIS) is a computer hardware and software information system designed to capture, edit, manage, house, retrieve, manipulate, analyze, and display georeferenced data. It comprises high‐resolution scientific visualization capabilities, large‐capacity electronic storage devices, efficient and effective structures for data storage and retrieval, high‐volume communication channels, specialized algorithms for data integration and reliability analysis, and specialized query languages. Its reference point is a digital map. The first GIS was developed by the Canadian government and implemented in 1964. A differentiating feature among GISs is whether the underlying map involves a raster or vector surface partitioning. Spatial statistics requires attribute data, a map, and the tagging of each data observation to a location on a map, items furnished by a GIS; GISs increasingly include spatial statistical procedures in their toolboxes. A GIS also supplies a practical and useful way to reveal spatial and temporal relationships among data. The UCGIS is an organization whose mission is to serve as an effective unified voice for the community of GIS users.
Environmental Justice, Statistics in
Authors: Waller Lance A. 
Published: March 2015

Abstract
We briefly review the historical, legal, and regulatory background of environmental justice assessments, paying particular attention to the role of statistical techniques in such assessments. We stress the difference between “disparate impact” and intentional discrimination and the role of statistical comparisons as defined in the associated regulatory statutes. We contrast two general approaches, the first comparing demographic proportions within exposure categories and the second comparing exposure (or exposure surrogate) distributions between demographic categories.
Data Mining, Evaluation Techniques in
Authors: Gür Ali Özden F. 
Published: March 2015

Abstract

In this article, we focus on evaluation techniques for data mining models after feature selection and parameter estimation have already been performed. Although performance evaluation techniques for data mining models are typically independent of the data mining task, quality measures
are
task specific. We discuss the rationale, and the pros and cons of different quality measures by data mining task using illustrative examples to explain the concepts. We cover the following data mining tasks: classification, regression, rank prediction, clustering, and market basket analysis. We provide detailed discussions for accuracy measures such as misclassification rate, confusion matrix, misclassification cost, quadratic and informational loss,
ROC
curve and lift chart,
RMSE
,
MAD
,
AIC
,
BIC
, Spearman's
ρ
, and Kendall's
τ
, and quality measures for unsupervised learning tasks, including within and between cluster distances. The following performance estimation techniques are discussed: resubstitution, holdout,
v
‐fold cross‐validation, leave‐one‐out/jackknife, and bootstrap. Further, we cover statistical hypothesis testing for comparing performance of two algorithms for the same data mining task. Moreover, references for further reading on the topics covered in this article are provided.
Sampling in Data Mining
Authors: Weiß Christian H. 
Published: March 2015

AbstractAfter a brief review of basic terms and concepts of knowledge discovery in databases (KDD) and data mining, this article investigates aspects of sampling in data mining. A general scheme of sampling, and particular techniques used in data mining are discussed. The main objective of this article is to provide an overview on areas of KDD and data mining, where sampling approaches are applied.
Nonresponse
Authors: Kviz Frederick J. 
Published: March 2015

AbstractSurvey nonresponserefers to the condition in which researchers are unsuccessful in collecting data from one or more units targeted for a survey. Nonresponse is a potential source of error in survey estimates because it may introduce bias and it reduces statistical power. This article reviews the nature of nonresponse and its potential impact on survey estimates, the causes of survey nonresponse, and strategies for minimizing and treating nonresponse.
Independent Component Analysis
Authors: Stone James V. 
Published: March 2015

Abstract

Given a set of
M
signal mixtures (e.g., microphone outputs), each of which is a different mixture of a set of
M
statistically independent source signals (e.g., voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically independent and that they have non‐Gaussian distributions. Different physical processes usually generate statistically independent and non‐Gaussian signals, so that, in the process of extracting such signals from a set of signal mixtures, ICA effectively recovers the underlying physical causes for a given set of measured signal mixtures.
Structural Equation Modeling: Nontraditional Alternatives
Authors: Rigdon Edward E. 
Published: March 2015

AbstractThe most widely known approach for modeling relations between unobservable conceptual variables with multiple observed variable indicators involves using common factors as proxies for the conceptual variables. An important class of alternative approaches uses composites (weighted sums of observed variables) for the same purpose. Among composite‐based approaches, partial least squares path modeling is the most widely used. Generalized structured component analysis is a newer composite‐based alternative with some advanced capabilities. Regression component analysis uses the parameter estimation tools of factor analysis but renders a solution in terms of composites rather than common factors, thus avoiding factor indeterminacy. Researchers seeking to find models, rather than testing a prespecified model, might employ Tetrad. Confirmatory tetrad analysis allows researchers to test structural equation models without estimating model parameters.
Novel poly(vinylidene fluoride)/thermoplastic polyester elastomer composite membrane prepared by the electrospinning of nanofibers onto a dense membrane substrate for protective textiles
Authors: Zhu Fanglong , Xin Qun , Feng Qianqian , Zhou Yu , Liu Rangtong 
Published: March 2015

ABSTRACTConventionally, electrospun thin‐film composite (TFC) membranes used in protective clothing are fabricated on the basis of an electrospinning substrate coated with a nonporous hydrophilic polymer layer. However, the incompatibility of common coating solvents with inertia substrate materials requires that the support material should be surface‐modified to promote the crosslinking of the dense and porous layers. In this study, we attempted to fabricate a novel TFC membrane for use in protective clothing by the one‐step deposition of poly(vinylidene fluoride) (PVDF) nanofibers onto a dense, hydrophilic thermoplastic polyester elastomer (TPEE) membrane substrate via electrospinning. The proposed approach has the advantage of no need for further surface modification of the substrate membrane. The optimized TFC membrane exhibited a better tensile strength than the dense substrate TPEE film. The testing results for the moisture resistance show that the new TFC membrane had a comparatively high water vapor transmission rate, notwithstanding that the value was slightly lower than that of the single‐layered TPEE film. Simultaneously, a resistance‐in‐series model based on Henis–Tripodi's model for explaining the water vapor permeation behavior of the TFC membrane was proposed. The moisture resistance values predicted by the analyzed model were in good agreement with these experimental data. This verified the validity of this in‐series moisture resistance model for the TFC membrane. The influence of the PVDF porosity on the water vapor permeation resistance throughout the TFC membrane was calculated and is discussed. More work is needed to establish the applicability of the new TFC membrane for lamination with nonwoven fabric to form the multilayered fabrics used in firefighters’ protective clothing. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42170.