Study of the Santista denim for the applications in the textile antennas

Orientador: Hugo Enrique Hernández FigueroaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A caracterização eletromagnética do tecido brim Santista, através de medições experimentais e cálculos analíticos da constante dielétrica e tangente de perda desse material foi apresentada de forma pioneira. Para esse propósito foi implementado um confiável e rigoroso método de medição que é exposto em detalhes. A possibilidade da utilização do tecido brim Santista como material dielétrico em dispositivos de radio frequência foi demonstrada de forma irrefutável pelo desenvolvimento, projeto e construção de uma antena têxtil utilizando como substrato este material. O adequado desempenho do protótipo da antena é também mostradoAbstract: The electromagnetic characterization of Santista denim fabric, through experimental measurements and analytical calculations of the dielectric constant and loss tangent of this material was introduced for the first time. For this purpose it was implemented a reliable and accurate measurement method that is exposed in detail. The possibility of the use of Santista denim fabric as dielectric material in radio frequency devices has been irrefutably demonstrated by the development, design and construction of a textile antenna composed of the substrate with Santista denim, whose proper performance is shown hereMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétric

Regional Public Opinions on LGBTI People Equal Opportunities in Employment: Evidence from the Eurobarometer Programme using Small Area Estimation

In recent years, the attention to lesbian, gay, bisexual, transgender and intersex (LGBTI) people’ rights from institutions, society and scientific bodies has clearly progressed. Although equal opportunities in employment are promoted within European countries and by the EU legislation, there are still evident discriminations in Europe. Many LGBTI people still face bullying and anti-LGBTI discrimination in the workplace and job market. Considerably more progress must be made before every LGBTI person feels accepted and comfortable for who they are in the workplace. Importantly, views on equal opportunities in employment are characterised by spatial heterogeneity at a sub-national level. Therefore, it is necessary to disaggregate estimates of relevant indicators, at least, at a regional level. This is crucial to identify the regions requiring more attention by policy makers. However, large-scale sample surveys are not designed to produce precise and accurate sub-national estimates. Small area estimation methods offer powerful tools in this context. Here, we produce regional estimates of three indicators measuring views of discrimination in employment of people from LGBTI communities in Europe. The analyses are based on the Eurobarometer 91.4 2019. Our empirical evidence shows that the estimates produced by small area estimation are reliable, giving important information to policy makers

Multivariate Small Area Estimation of Social Indicators: the Case of Continuous and Binary Variables

Large-scale sample surveys are not designed to produce reliable estimates for small areas. Here, small area estimation methods can be applied to estimate population parameters of target variables to detailed geographic scales. Small area estimation for noncontinuous variables is a topic of great interest in the social sciences where such variables can be found. Generalized linear mixed models are widely adopted in the literature. Interestingly, the small area estimation literature shows that multivariate small area estimators, where correlations among outcome variables are taken into account, produce more efficient estimates than do the traditional univariate techniques. In this article, the author evaluate a multivariate small area estimator on the basis of a joint mixed model in which a small area proportion and mean of a continuous variable are estimated simultaneously. Using this method, the author “borrows strength” across response variables. The author carried out a design-based simulation study to evaluate the approach where the indicators object of study are the income and a monetary poverty (binary) indicator. The author found that the multivariate approach produces more efficient small area estimates than does the univariate modeling approach. The method can be extended to a large variety of indicators on the basis of social surveys

Estimation of Small Area Proportions Under a Bivariate Logistic Mixed Model

A variety of data is of geographic interest but is not available at a small area level from large-scale national sample surveys. Small area estimation can be used to estimate parameters of target variables to detailed geographical scales based on relationships between the target variables and relevant auxiliary information. Small area estimation of proportions is a topic of great interest in many fields of study, where binary variables are diffused, such as in labour force, business, and social exclusion surveys. The univariate generalised mixed model with logit link function is widely adopted in this context. The small area estimation literature has shown that multivariate small area estimators, where correlations among response variables are taken into account, provide more efficient estimates than the traditional univariate approaches. However, the estimation problem of multivariate proportions has not been studied yet. In this article, we propose a bivariate small area estimator of proportions based on a bivariate generalised mixed model with logit link function. A simulation study and an application are presented to evaluate the good properties of the bivariate estimator compared to its univariate setting. We found that the extent of the improved efficiency of the bivariate over the univariate approach is associated with the degree of correlation of the area-specific random effects and the intraclass correlation, whereas it is not strongly related to the area sample size

Commentary to "Critical Limitations of Digital Epidemiology: Why COVID-19 Apps Are Useless"

editorial reviewe

Improving Probabilistic Record Linkage Using Statistical Prediction Models

Record linkage brings together information from records in two or more data sources that are believed to belong to the same statistical unit based on a common set of matching variables. Matching variables, however, can appear with errors and variations and the challenge is to link statistical units that are subject to error. We provide an overview of record linkage techniques and specifically investigate the classic Fellegi and Sunter probabilistic record linkage framework to assess whether the decision rule for classifying pairs into sets of matches and non-matches can be improved by incorporating a statistical prediction model. We also study whether the enhanced linkage rule can provide better results in terms of preserving associations between variables in the linked data file that are not used in the matching procedure. A simulation study and an application based on real data are used to evaluate the methods

Improving Statistical Matching when Auxiliary Information is Available

There is growing interest within National Statistical Institutes in combining available datasets containing information on a large variety of social domains. Statistical matching approaches can be used to integrate data sources through a common set of variables where each dataset contains different units that belong to the same target population. However, a common problem is related to the assumption of conditional independence among variables observed in different data sources. In this context, an auxiliary dataset containing all the variables jointly can be used to improve the statistical matching by providing information on the correlation structure of variables observed across different datasets. We propose modifying the prediction models from the auxiliary dataset through a calibration step and show that we can improve the outcome of statistical matching in a variety of settings. We evaluate the proposed approach via simulation and an application based on the European Union Statistics for Income and Living Conditions and Living Costs and Food Survey for the United Kingdom

Carbon Risk Premium and Worries about Climate Change

This paper sheds light on the impact of public attitudes towards climate change on the pricing of emission (carbon-intensive) and clean (low-emission) stocks. We develop a regional indicator of worries about climate change using data from the European Social Survey Round 8. We classify European regions as little worried, worried and very worried. We confirm previous evidence that emission stocks tend to have higher returns than clean stocks. However, when we focus on stocks quoted in exchange markets located in regions with low level of worries about climate change, we do not find evidence of a carbon risk premium. Conversely, the emission premium in worried regions is significant for medium-high quantiles of the return distribution