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Philomena M. Bluyssen

Bio: Philomena M. Bluyssen is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Indoor air quality & Medicine. The author has an hindex of 27, co-authored 111 publications receiving 2994 citations. Previous affiliations of Philomena M. Bluyssen include Technical University of Denmark & Netherlands Organisation for Applied Scientific Research.


Papers
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TL;DR: It is argued that existing evidence is sufficiently strong to warrant engineering controls targeting airborne transmission as part of an overall strategy to limit infection risk indoors, and that the use of engineering controls in public buildings would be an additional important measure globally to reduce the likelihood of transmission.

924 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present results and conclusions of the audit in 56 buildings in Europe, in each of nine countries, six or more office buildings were select-ed, and the building characteristics were described by use of a check-list.
Abstract: A European project started at the end of 1992, in which, in addition to current methods, trained sensory panels were used to investigate office buildings all over Europe. The main aim of this EC-Audit was to develop assessment procedures and guid-ance on ventilation and source control, to help optimize energy use in buildings while assuring good indoor air quality. In each of nine countries, six or more office buildings were select-ed. Measurements were performed at five selected locations in each building. The buildings were studied while normally occu-pied and ventilated to identify the pollution sources in the spaces and to quantify the total pollution load caused by the occupants and their activities, as well as the ventilation systems. The investi-gation included physical and chemical measurements, assessment of the perceived air quality in the spaces by a trained sensory pan-el, and measurement of the outdoor air supply to the spaces. A questionnaire for evaluating retrospective and immediate symp-toms and perceptions was given to the occupants of the buildings. The building characteristics were described by use of a check-list. The annual energy consumption of the buildings and the weather conditions were registered. This paper presents results and conclusions of the audit in 56 buildings in Europe. However, the analysis and discussions of the results are a summary of the work done, and are focused mainly on comparison between sensory assessments and the other meas-urements performed. Furthermore, this paper brings the results of the study based on a two-factor analysis. A paper dealing with results on a multifacto-rial analysis is in preparation.

230 citations

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TL;DR: In this paper, the authors used principal component analysis (PCA), reliability analyses, and linear regression analysis to study the relationship between personal, social and building factors and perceived comfort.

228 citations

Journal ArticleDOI
TL;DR: In this article, a new olf unit was introduced in 20 randomly selected offices and assembly halls in Copenhagen to quantify pollution sources in the air and to determine pollution caused by occupants and smoking.

163 citations


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01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

14,604 citations

Journal ArticleDOI
TL;DR: The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models to enable researchers and students alike to reproduce the analyses and learn by doing.
Abstract: The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

1,679 citations