bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Annealing on Gas Sensing Performance of Nanostructured Zno Thick Film Resistors
Authors: Shinde Sarika D. , Patil G. E. , Kajale D. D. , Wagh V. G. , Gaikwad V. B. , Jain G. H. 
Published: November 2017

Abstract
ZnO nano-particles have been synthesized by simple chemical route using a starting solution consisting of zinc acetate and citric acid as a surfactant agent. The structural properties of the prepared ZnO nano-particles annealed at different temperatures have been characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM) analyses. The XRD patterns show ZnO-wurtize phase in the nano-powders, and size of crystals increases by increasing the annealing temperatures. The TEM images show nano-particles as clusters with size in the range of 10-20 nm. Electron diffraction pattern of nano-powders annealed at 900°C temperature shows a well distribution of spherical particles due to the effect of citric acid as surfactant in chemical process. Thick films prepared by screen printing technique from zinc oxide nano-powders annealed at different temperatures (500–900 °C), characterized by SEM analysis and tested for various gases. The film prepared from ZnO powder annealed at 700°C shows the higher sensitivity to H2S gas for 10 ppm gas concentration.
Breath Analysis for Medical Diagnosis
Authors: Hill Darryl , Binions Russell 
Published: November 2017

Abstract
The purpose of this review is to highlight the advances in technology and understanding in the field of breath analysis for medical diagnosis. A critical review of the methods of breath collection, treatment, and analysis is given, highlighting the problems facing the field and areas where promising advancement has been made. One particular area of interest is centered around electronic noses, ideally, portable devices which aim to mimic biological olifactory systems in analysing gases to produce odor fingerprints. Furthermore, recent work has shown it is possible to modify the basic sensor materials to both improve their performance, increase their tolerance to factors such as water vapour interferance which often leave the sensor system de-sensitized to the gaseous biomarkers, and enhance their selectivity. It will be shown how it is possible to accurately quantify concentrations of VOC’s and make disease diagnosis from analysis of the collected data which compare favorably with traditional medical diagnostic techniques.
Optimizing Perceived Heaviness and Motion for Lifting Objects with a Power Assist Robot System Considering Change in Time Constant
Authors: Rahman S. M. Mizanoor , Ikeura Ryojun 
Published: November 2017

Abstract
Power assist robots are usually used for disabled and elderly people to augment their abilities and skills. This paper proposes to use these robots to handle heavy objects in industries, and thus brings a novelty in the applications of power assist robots. However, it is difficult to optimize perceived heaviness and motion either independently or simultaneously for lifting objects with power-assist. Hence, this paper investigates the techniques to optimize perceived heaviness and motion following bionic and psychophysical approaches. We developed two systems-one was used to lift objects manually, and another was a power assist system to lift objects with it. Several hypotheses and strategies related to weight perception and time constant were adopted. Humans lifted objects manually and with power-assist independently. Analyses showed that load force rate for power-assisted lifting were lower than that for manual lifting. We hypothesized that time constant of the assist system might be responsible for this. We changed time constant and found that increase in time constant reduced perceived heaviness and load force. Then, objects were lifted with power-assist in some selected conditions pertaining to time constant. Analyses showed that perceived heaviness was related to load force rate while object motion (acceleration) was related to load force magnitude. It was then demonstrated how to independently optimize perceived heaviness and motion by optimizing load force rate and its magnitude respectively. Techniques for simultaneous optimization of motion and perceived heaviness were also presented. Finally, we proposed to use the findings to develop power assist robots for manipulating heavy objects in industries that may enhance interactions with humans in terms of maneuverability, safety etc.
Smoking as an early risk factor for problematic parenting practices
Authors: Tandon Mini , Grant Julia D. , Madden Pamela A.F. , Bucholz Kathleen K. , Heath Andrew C. 
Published: November 2017

Abstract
Parenting among those who use non-illicit and more common drugs such as cigarettes remains an understudied area for investigation. Secondary data analyses were performed in 2015 on a prospective study with parent and twin data available on n=3,009 individual members of female twin pairs born between July 1975 and June 1985 identified from Missouri-state birth records. Maternal smoking when the twins were 3+ years of age remained a significant predictor of offspring report of childhood sexual abuse (OR, 1.40; 95% CI, 1.02-1.92) after controlling for key covariates. Given limited public health resources, mothers who smoke during child’s preschool years may be targeted for tailored parenting intervention.
Joint Calibration Estimator for Dual Frame Surveys
Authors: Elkasabi Mahmoud A. , Heeringa Steven G. , Lepkowski James M. 
Published: November 2017

AbstractMany dual frame estimators have been proposed in the statistics literature. Some of these estimators are theoretically optimal but hard to apply in practice, whereas others are applicable but have larger variances than the first group. In this paper, a Joint Calibration Estimator (JCE) is proposed that is simple to apply in practice and meets many desirable properties for dual frame estimators. The JCE is asymptotically design unbiased conditional on the strong relationship between the estimation variable and the auxiliary variables employed in the calibration. The JCE achieves better performance when the auxiliary variables can fully explain the variability in the study variables or at least when the auxiliary variables are strong correlates of the estimation variables. As opposed to the standard dual frame estimators, the JCE does not require domain membership information. Even if included in the JCE auxiliary variables, the effect of the randomly misclassified domains does not exceed the random measurement error effect. Therefore, the JCE tends to be robust for the misclassified domains if included in the auxiliary variables. Meanwhile, the misclassified domains can significantly affect the unbiasedness of the standard dual frame estimators as proved theoretically and empirically in this paper.
Multinomial Logistic Regression Approach for the Evaluation of Binary Diagnostic Test in Medical Research
Authors: Dwivedi Alok Kumar , Mallawaarachchi Indika , Figueroa-Casas Juan B. , Morales Angel M. , Tarwater Patrick 
Published: November 2017

Abstract
Evaluating the effect of variables on diagnostic measures (sensitivity, specificity, positive, and negative predictive values) is often of interest to clinical researchers. Logistic regression (LR) models can be used to predict diagnostic measures of a screening test. A marginal model framework using generalized estimating equation (GEE) with logit/log link can be used to compare the diagnostic measures between two or more screening tests. These individual modeling approaches to each diagnostic measure ignore the dependency among these measures that might affect the association of covariates with each diagnostic measure. The diagnostic measures are computed using joint distribution of screening test result and reference test result which generates a multinomial response data. Thus, multinomial logistic regression (MLR) is a more appropriate approach to modeling these diagnostic measures. In this study, the validity of LR and GEE approaches as compared to MLR model was assessed for modeling diagnostic measures. All methods provided unbiased estimates of diagnostic measures in the absence of any covariate. LR and GEE methods produced more biased estimates as compared to MLR approach especially for small sample size studies. No bias was obtained in predicting sensitivity measure using MLR method for one screening test. Our proposed MLR method is robust for modeling diagnostic measures of a screening test as opposed to LR method. MLR method and GEE method produced similar estimates of diagnostic measures for comparing two screening tests in large sample size studies. The proposed MLR model for diagnostic measures is simple, and available in common statistical software. Our study demonstrates that MLR method should be preferred as an alternative for modeling diagnostic measures.
Functional Structure of Polish Regions in the Period 2004-2013 – Measurement Via HHI Index, Florence’s Coefficient of Localization and Cluster Analysis
Authors: Obrębalski Marek , Walesiak Marek 
Published: November 2017

Abstract
The article addresses the measurement and identification problems covering particular social and economic areas (referred to as functions) in the regions of the country, based on the employment structure analysis and assessment by the sectors of the economy. The Herfindahl-Hirschman index was applied to measure sectoral concentration and Florence’s coefficient of localization to determine regional functional specialization. Finally, cluster analysis was conducted to produce the functional typology of regions.
The Office for National Statistics Experience of Collecting and Measuring Subjective Well-Being
Authors: Tinkler Lucy 
Published: November 2017

Abstract
The UK Office for National Statistics (ONS) started measuring subjective well-being in 2011 as part of the ONS Measuring National Well-being programme. The aim of the Measuring National Well-being programme is to measure the quality of life and progress of the UK. This article explores the development of the ONS subjective well-being measures, data collection methods, data presentational considerations, overview of findings, and latest developments. It discusses the way in which user engagement has been key to the development of the ONS subjective well-being statistics.
An Initial Research on Output Well-Being Index Applied to Residents in Wealthy Counties From China
Authors: Xing Zhanjun , Qu Xiaxia 
Published: November 2017

Abstract
Sampling from three wealthy counties in Shandong province (n = 855), this research examined the characteristics of an output well-being index, and the index shows good internal consistency reliability and ideal construct validity. It could be used as an instrument to evaluate the quality of life of Chinese citizens. The index was applied to an analysis of the quality of life of the residents from three wealthy counties. It shows that the level of the quality of life of the rural residents is higher than that of the urban residents, but the level of some indicators is imbalanced, and the levels of subjective and objective indicators are consistent. These characteristics of the well-being index of the rural residents from the wealthy counties are closely related with the level of the local economy and social development. It is advised that the output well-being index could be used to evaluate the degree of citizen’s need that was met and the degree of human being’s all-over development that was realized. It could also be used as an important policy instrument for the policy makers’ good governance.
Borrowing Information Over Time in Binomial/Logit Normal Models for Small Area Estimation
Authors: Franco Carolina , Bell William R. 
Published: November 2017

Abstract
Linear area level models for small area estimation, such as the Fay-Herriot model, face challenges when applied to discrete survey data. Such data commonly arise as direct survey estimates of the number of persons possessing some characteristic, such as the number of persons in poverty. For such applications, we examine a binomial/logit normal (BLN) model that assumes a binomial distribution for rescaled survey estimates and a normal distribution with a linear regression mean function for logits of the true proportions. Effective sample sizes are defined so variances given the true proportions equal corresponding sampling variances of the direct survey estimates. We extend the BLN model to bivariate and time series (first order autoregressive) versions to permit borrowing information from past survey estimates, then apply these models to data used by the U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program to predict county poverty for school-age children. We compare prediction results from the alternative models to see how much the bivariate and time series models reduce prediction error variances from those of the univariate BLN model. Standard conditional variance calculations for corresponding linear Gaussian models that suggest how much variance reduction will be achieved from borrowing information over time with linear models agree generally with the BLN empirical results.
Small Area Estimates of the Population Distribution by Ethnic Group in England: A Proposal Using Structure Preserving Estimators
Authors: Luna Angela , Zhang Li-Chun , Whitworth Alison , Piller Kirsten 
Published: November 2017

Abstract
This paper addresses the problem of producing small area estimates of Ethnicity by Local Authority in England. A Structure Preserving approach is proposed, making use of the Generalized Structure Preserving Estimator. In order to identify the best way to use the available aggregate information, three fixed effects models with increasing levels of complexity were tested. Finite Population Mean Square Errors were estimated using a bootstrap approach. However, more complex models did not perform substantially better than simpler ones. A mixed-effects approach does not seem suitable for this particular application because of the very small sample sizes observed in many areas. Further research on a more flexible fixed-effects estimator is proposed.
Bayesian Inference for State Space Model with Panel Data
Authors: Pandey Ranjita , Chaturvedi Anoop 
Published: November 2017

Abstract
The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the state of art in prediction-based decision making.
Decomposition of Differences in Income Distributions Using Quantile Regression
Authors: Landmesser Joanna Małgorzata 
Published: November 2017

Abstract
The paper deals with microeconometric techniques useful for the study of differences between groups of objects, methods that go beyond simple comparison of average values. Techniques for the decomposition of differences in distributions by constructing counterfactual distributions were considered. Using the Machado-Mata quantile regression approach the empirical decomposition of the inequalities in income distributions of one-person households in urban and rural areas was performed. We employed data from the Household Budget Survey for Poland in 2012. It was found that the tendency towards increased income inequalities between urban and rural residents when moving to the right of the income distribution can be observed. The rural residents are at a disadvantage. The decomposition of the inequalities revealed a growing share of the part explained by different characteristics of people and a declining share of the unexplained part, associated with the evaluation of those characteristics.
An Extension of the Classical Distance Correlation Coefficient for Multivariate Functional Data with Applications
Authors: Górecki Tomasz , Krzyśko Mirosław , Ratajczak Waldemar , Wołyński Waldemar 
Published: November 2017

AbstractThe relationship between two sets of real variables defined for the same individuals can be evaluated by a few different correlation coefficients. For the functional data we have one important tool: canonical correlations. It is not immediately straightforward to extend other similar measures to the context of functional data analysis. In this work we show how to use the distance correlation coefficient for a multivariate functional case.The approaches discussed are illustrated with an application to some socio-economic data.
A New Median Based Ratio Estimator for Estimation of the Finite Population Mean
Authors: Subramani J. 
Published: November 2017

AbstractThe present paper deals with a new median based ratio estimator for the estimation of finite population means in the absence of an auxiliary variable. The bias and mean squared error of the proposed median based ratio estimator are obtained. The performance of the median based ratio estimator is compared with that of the SRSWOR sample mean, ratio estimator and linear regression estimator for certain natural population. It is shown from the numerical comparisons that the proposed median based ratio estimator outperforms the SRSWOR sample mean, ratio estimator and also the linear regression estimator.
A New Family of Estimators of the Population Variance using Information on Population Variance of Auxiliary Variable in Sample Surveys
Authors: Singh Housila P. , Pal Surya K. 
Published: November 2017

Abstract
This paper proposes a family of estimators of population variance S
2
y of the study variable y in the presence of known population variance S
2
x of the auxiliary variable x. It is identified that in addition to many, the recently proposed classes of estimators due to Sharma and Singh (2014) and Singh and Pal (2016) are members of the proposed family of estimators. Asymptotic expressions of bias and mean squared error (MSE) of the suggested family of estimators have been obtained. Asymptotic optimum estimator (AOE) in the family of estimators is identified. Some subclasses of estimators of the proposed family of estimators have been identified along with their properties. We have also given the theoretical comparisons among the estimators discussed in this paper.
Estimation of the Central Moments of a Random Vector Based on the Definition of the Power of a Vector
Authors: Budny Katarzyna 
Published: November 2017

Abstract
The moments of a random vector based on the definition of the power of a vector, proposed by J. Tatar, are scalar and vector characteristics of a multivariate distribution. Analogously to the univariate case, we distinguish the uncorrected and the central moments of a random vector. Other characteristics of a multivariate distribution, i.e. an index of skewness and kurtosis, have been introduced by using the central moments of a random vector. For the application of the mentioned quantities for the analysis of multivariate empirical data, it appears desirable to construct their respective estimators.
This paper presents the consistent estimators of the central moments of a random vector, for which essential characteristics have been found, such as a mean vector and a mean squared error. In these formulas, the relevant orders of approximation have been taken into account.
Sequential Data Weighting Procedures for Combined Ratio Estimators in Complex Sample Surveys
Authors: Alkaya Aylin , Ayhan H. Öztaş , Esin Alptekin 
Published: November 2017

Abstract
In sample surveys weighting is applied to data to increase the quality of estimates. Data weighting can be used for several purposes. Sample design weights can be used to adjust the differences in selection probabilities for non-self weighting sample designs. Sample design weights, adjusted for nonresponse and noncoverage through the sequential data weighting process. The unequal selection probability designs represented the complex sampling designs. Among many reasons of weighting, the most important reasons are weighting for unequal probability of selection, compensation for nonresponse, and post-stratification. Many highly efficient estimation methods in survey sampling require strong information about auxiliary variables, x. The most common estimation methods using auxiliary information in estimation stage are regression and ratio estimator. This paper proposes a sequential data weighting procedure for the estimators of combined ratio mean in complex sample surveys and general variance estimation for the population ratio mean. To illustrate the utility of the proposed estimator, Turkish Demographic and Health Survey 2003 real life data is used. It is shown that the use of auxiliary information on weights can considerably improve the efficiency of the estimates.
Determination of Migration of Six Phthalate Esters in Plastic Toys by GC-MS/MS Coupled with Solid-Phase Extraction Using Cucurbit[n]urils as Adsorbent
Authors: Lv Qing , Li Haiyu , Wang Zhijuan , Bai Hua , Zhang Qing 
Published: November 2017

Object:
This paper presents a method for the determination of migration of six phthalate esters in plastic toys based on gas chromatography-tandem mass spectrometry (GC-MS/MS) coupled with solid phase extraction using cucurbiturils (CB6-8) as adsorbent.

Method:
On the base of self-made migration device, toy samples were migrated for 10 min to 24 h in simulated saliva at 37 °C. The analytes were adsorbed by cucurbiturils SPE cartridges, eluted with ethyl acetate, then determined by GC-MS/MS and quantified by external standard method. The eluting solvents as well as the type and amount of the extraction materials were carefully optimized, then the reusability of the SPE cartridges was investigated. The limits of quantification (LOQs) ranged from 2.5 µg/L to 12.5 µg/L. Average recoveries of target analytes (spiked at three concentration levels) were in the range of 82.7% to 94.4%. Intraday repeatability and interday repeatability of the method varied from 2.3% to 6.6% and from 5.6% to 10.8%, respectively.

Conclusion:
The proposed method was finally used to explore the migration behavior of phthalate esters in commercial plastic toys.
CaRiSMA 1.0: Cardiac Risk Self-Monitoring Assessment
Authors: Agostinelli Angela , Morettini Micaela , Sbrollini Agnese , Maranesi Elvira , Migliorelli Lucia , Di Nardo Francesco , Fioretti Sandro , Burattini Laura 
Published: November 2017

Background:Sport-related sudden cardiac death (SRSCD) can only be fought through prevention.Objective:The aim of this study is to propose an innovative software application, CaRiSMA 1.0 (Cardiac Risk Self-Monitoring Assessment), as a potential tool to help contrasting SRSCD and educating to a correct training.Methods:CaRiSMA 1.0 analyzes the electrocardiographic and heart-rate (HR) signals acquired during a training session through wearable sensors and provides intuitive graphical outputs consisting of two traffic lights, one related to cardiac health, based on resting QTc (a parameter quantifying the duration of ventricular contraction and subsequent relaxation), and one related to training, based on exercise HR. Safe and worthwhile training sessions have green traffic lights. A red QTc traffic light indicates the need of a medical consultation, whereas a red HR traffic light indicate the need of a reduction of training intensity. By way of example, CaRiSMA 1.0 was applied to sample data acquired in 10 volunteers (age= 27±11 years; males/females 3/7).Results:Two acquisitions (20.0%) were rejected because too noisy, indicating that wearable sensors may record poor quality signals. The QTc traffic light was red in 1 case, indicating that people practicing sport may not be aware of being at risk. The HR traffic light was red in 0 cases.Conclusion:CaRiSMA 1.0 is a software application that, for the first time in the sport context, uses QTc, the most important index of cardiac risk in clinics. Thus, it has the potential for giving a contribution in the fight against SRSCD.