Academic literature on the topic 'Functional mixed-effects model'
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Journal articles on the topic "Functional mixed-effects model"
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Qin, L. "Functional mixed-effects model for periodic data." Biostatistics 7, no. 2 (August 3, 2005): 225–34. http://dx.doi.org/10.1093/biostatistics/kxj003.
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Maiti, Tapabrata, Samiran Sinha, and Ping-Shou Zhong. "Functional Mixed Effects Model for Small Area Estimation." Scandinavian Journal of Statistics 43, no. 3 (March 15, 2016): 886–903. http://dx.doi.org/10.1111/sjos.12218.
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Chen, Huaihou, and Yuanjia Wang. "A Penalized Spline Approach to Functional Mixed Effects Model Analysis." Biometrics 67, no. 3 (December 14, 2010): 861–70. http://dx.doi.org/10.1111/j.1541-0420.2010.01524.x.
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Kang, Hakmook, Hernando Ombao, Crystal Linkletter, Nicole Long, and David Badre. "Spatio-Spectral Mixed-Effects Model for Functional Magnetic Resonance Imaging Data." Journal of the American Statistical Association 107, no. 498 (June 2012): 568–77. http://dx.doi.org/10.1080/01621459.2012.664503.
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Chen, Ji, David Ohlssen, and Yingchun Zhou. "Functional Mixed Effects Model for the Analysis of Dose-Titration Studies." Statistics in Biopharmaceutical Research 10, no. 3 (June 22, 2018): 176–84. http://dx.doi.org/10.1080/19466315.2018.1458649.
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Su, Yu-Ru, Chongzhi Di, Stephanie Bien, Licai Huang, Xinyuan Dong, Goncalo Abecasis, Sonja Berndt, et al. "A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics." American Journal of Human Genetics 102, no. 5 (May 2018): 904–19. http://dx.doi.org/10.1016/j.ajhg.2018.03.019.
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Rakêt, Lars Lau, Stefan Sommer, and Bo Markussen. "A nonlinear mixed-effects model for simultaneous smoothing and registration of functional data." Pattern Recognition Letters 38 (March 2014): 1–7. http://dx.doi.org/10.1016/j.patrec.2013.10.018.
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Liu, Zhuqing, Andreas J. Bartsch, Veronica J. Berrocal, and Timothy D. Johnson. "A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data." Statistical Methods in Medical Research 28, no. 4 (January 15, 2018): 1203–15. http://dx.doi.org/10.1177/0962280217752378.
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Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensures a relatively high signal-to-noise ratio. However, for pre-surgical functional magnetic resonance imaging analysis, where spatial accuracy is paramount, high-resolution functional magnetic resonance imaging may play an important role with its greater spatial resolution. High spatial resolution comes at the cost of a smaller signal-to-noise ratio. This begs the question as to whether we can leverage the higher signal-to-noise ratio of a standard functional magnetic resonance imaging study with the greater spatial accuracy of a high-resolution functional magnetic resonance imaging study in a pre-operative patient. To answer this question, we propose to regress the statistic image from a high resolution scan onto the statistic image obtained from a standard resolution scan using a mixed-effects model with spatially varying coefficients. We evaluate our model via simulation studies and we compare its performance with a recently proposed model that operates at a single spatial resolution. We apply and compare the two models on data from a patient awaiting tumor resection. Both simulation study results and the real data analysis demonstrate that our newly proposed model indeed leverages the larger signal-to-noise ratio of the standard spatial resolution scan while maintaining the advantages of the high spatial resolution scan.
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Levshina, Natalia. "When variables align: A Bayesian multinomial mixed-effects model of English permissive constructions." Cognitive Linguistics 27, no. 2 (May 1, 2016): 235–68. http://dx.doi.org/10.1515/cog-2015-0054.
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AbstractThis paper is a quantitative multifactorial study of the near-synonymous constructionslet+V,allow+toV andpermit+toV based on the British National Corpus. The study investigates the differences between these constructions with the help of 23 formal, semantic, social and collostructional variables. A Bayesian multinomial mixed-effects model reveals a remarkable alignment of the variables that represent different dimensions of variation, namely, the linguistic distance between the predicates, the conceptual distance between the events they represent, the distance between the speaker and the Permitter and Permittee on the animacy/entrenchment/empathy hierarchy, the social and communicative distance between the interlocutors, as well as the strength of collostructional attraction between the constructions and second verb slot fillers. The paper offers several possible explanations for this alignment from a cognitive, functional and historical perspective.
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Silk, Matthew J., Xavier A. Harrison, and David J. Hodgson. "Perils and pitfalls of mixed-effects regression models in biology." PeerJ 8 (August 12, 2020): e9522. http://dx.doi.org/10.7717/peerj.9522.
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Biological systems, at all scales of organisation from nucleic acids to ecosystems, are inherently complex and variable. Biologists therefore use statistical analyses to detect signal among this systemic noise. Statistical models infer trends, find functional relationships and detect differences that exist among groups or are caused by experimental manipulations. They also use statistical relationships to help predict uncertain futures. All branches of the biological sciences now embrace the possibilities of mixed-effects modelling and its flexible toolkit for partitioning noise and signal. The mixed-effects model is not, however, a panacea for poor experimental design, and should be used with caution when inferring or deducing the importance of both fixed and random effects. Here we describe a selection of the perils and pitfalls that are widespread in the biological literature, but can be avoided by careful reflection, modelling and model-checking. We focus on situations where incautious modelling risks exposure to these pitfalls and the drawing of incorrect conclusions. Our stance is that statements of significance, information content or credibility all have their place in biological research, as long as these statements are cautious and well-informed by checks on the validity of assumptions. Our intention is to reveal potential perils and pitfalls in mixed model estimation so that researchers can use these powerful approaches with greater awareness and confidence. Our examples are ecological, but translate easily to all branches of biology.
Dissertations / Theses on the topic "Functional mixed-effects model"
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Wang, Wei. "Linear mixed effects models in functional data analysis." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/253.
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Regression models with a scalar response and
a functional predictor have been extensively
studied. One approach is to approximate the
functional predictor using basis function or
eigenfunction expansions. In the expansion,
the coefficient vector can either be fixed or
random. The random coefficient vector
is also known as random effects and thus the
regression models are in a mixed effects
framework.
The random effects provide a model for the
within individual covariance of the
observations. But it also introduces an
additional parameter into the model, the
covariance matrix of the random effects.
This additional parameter complicates the
covariance matrix of the observations.
Possibly, the covariance parameters of the
model are not identifiable.
We study identifiability in normal linear
mixed effects models. We derive necessary and
sufficient conditions of identifiability,
particularly, conditions of identifiability
for the regression models with a scalar
response and a functional predictor using
random effects.
We study the regression model using the
eigenfunction expansion approach with random
effects. We assume the random effects have a
general covariance matrix
and the observed values of the predictor are
contaminated with measurement error.
We propose methods of inference for the
regression model's functional coefficient.
As an application of the model, we analyze a
biological data set to investigate the
dependence of a mouse's wheel running
distance on its body mass trajectory.
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Ribbing, Jakob. "Covariate Model Building in Nonlinear Mixed Effects Models." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7923.
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Alves-Soares, Leonardo. "Investigating the Portuguese-English Bilingual Mental Lexicon: Crosslinguistic Orthographic and Phonological Overlap in Cognates and False Friends." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41153.
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This dissertation investigates how cognates are organized in the bilingual mental lexicon and examines whether orthography in one language, via phonological representations, influences the processing of cognates and false friends in the other language. In light of the framework of two well-known models of bilingual visual word recognition, the Bilingual Interactive Activation (BIA) and the Bilingual Interactive Activation Plus (BIA+), the premise is that there is activation from orthography to phonology across a bilingual’s two languages and that this activation is modulated by the degree of orthographic and phonological code overlap. Two objective metrics were used to assess crosslinguistic similarity of Portuguese-English cognates and false friends that were selected for a cross-language lexical decision task with masked priming. Dynamic time warping (DTW), an algorithm that was originally conceived to compare different speech patterns in automatic speech recognition and to measure acoustic similarity between two time-dependent sequences, was used to compute crosslinguistic phonological similarity. The Normalized Levenshtein Distance (NLD), an algorithm that calculates the minimum number of single-character insertions, deletions or substitutions required to change one word into another and normalizes the result by their lengths, was used to compute crosslinguistic orthographic similarity. Portuguese-English bilinguals who acquired their second language after reaching puberty, and English functional monolinguals who grew up speaking primarily English were recruited to participate in the experimental task. Based on collected reaction time and accuracy data, mixed-effects models analyses are used to estimate the individual effects of crosslinguistic orthographic, phonological and semantic similarity and the role each of them, along with English proficiency, word frequency and length play in the organization of the Portuguese-English bilingual mental lexicon.
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"Examination of Mixed-Effects Models with Nonparametrically Generated Data." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53768.
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abstract: Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional mixed-effects models. A simulation study is then presented where the mean and random effects structure of the simulated data were generated using B-splines. The accuracy of recovered curves was examined under various conditions including sample size, number of time points per curve, and measurement design. Results showed the functional mixed-effects models recovered the underlying mean curve more accurately than the nonlinear mixed-effects models. In general, the functional mixed-effects models recovered the underlying individual curves more accurately than the nonlinear mixed-effects models. Progesterone cycle data from Brumback and Rice (1998) were then analyzed to demonstrate the utility of both models. Both models were shown to perform similarly when analyzing the progesterone data.Dissertation/Thesis
Doctoral Dissertation Psychology 2019
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"Handling Sparse and Missing Data in Functional Data Analysis: A Functional Mixed-Effects Model Approach." Master's thesis, 2016. http://hdl.handle.net/2286/R.I.40749.
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abstract: This paper investigates a relatively new analysis method for longitudinal data in the framework of functional data analysis. This approach treats longitudinal data as so-called sparse functional data. The first section of the paper introduces functional data and the general ideas of functional data analysis. The second section discusses the analysis of longitudinal data in the context of functional data analysis, while considering the unique characteristics of longitudinal data such, in particular sparseness and missing data. The third section introduces functional mixed-effects models that can handle these unique characteristics of sparseness and missingness. The next section discusses a preliminary simulation study conducted to examine the performance of a functional mixed-effects model under various conditions. An extended simulation study was carried out to evaluate the estimation accuracy of a functional mixed-effects model. Specifically, the accuracy of the estimated trajectories was examined under various conditions including different types of missing data and varying levels of sparseness.Dissertation/Thesis
Masters Thesis Psychology 2016
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Li, Li. "Model Selection via Minimum Description Length." Thesis, 2011. http://hdl.handle.net/1807/31834.
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The minimum description length (MDL) principle originated from data compression literature and has been considered for deriving statistical model selection procedures. Most existing methods utilizing the MDL principle focus on models consisting of independent data, particularly in the context of linear regression. The data considered in this thesis are in the form of repeated measurements, and the exploration of MDL principle begins with classical linear mixed-effects models. We distinct two kinds of research focuses: one concerns the population parameters and the other concerns the cluster/subject parameters. When the research interest is on the population level, we propose a class of MDL procedures which incorporate the dependence structure within individual or cluster with data-adaptive penalties and enjoy the advantages of Bayesian information criteria. When the number of covariates is large, the penalty term is adjusted by data-adaptive structure to diminish the under selection issue in BIC and try to mimic the behaviour of AIC. Theoretical justifications are provided from both data compression and statistical perspectives. Extensions to categorical response modelled by generalized estimating equations and functional data modelled by functional principle components are illustrated. When the interest is on the cluster level, we use group LASSO to set up a class of candidate models. Then we derive a MDL criterion for this LASSO technique in a group manner to selection the final model via the tuning parameters. Extensive numerical experiments are conducted to demonstrate the usefulness of the proposed MDL procedures on both population level and cluster level.
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Chen, Yakuan. "Methods for functional regression and nonlinear mixed-effects models with applications to PET data." Thesis, 2017. https://doi.org/10.7916/D87W6QJ9.
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The overall theme of this thesis focuses on methods for functional regression and nonlinear mixed-effects models with applications to PET data.
The first part considers the problem of variable selection in regression models with functional responses and scalar predictors. We pose the function-on-scalar model as a multivariate regression problem and use group-MCP for variable selection. We account for residual covariance by "pre-whitening" using an estimate of the covariance matrix, and establish theoretical properties for the resulting estimator. We further develop an iterative algorithm that alternately updates the spline coefficients and covariance. Our method is illustrated by the application to two-dimensional planar reaching motions in a study of the effects of stroke severity on motor control.
The second part introduces a functional data analytic approach for the estimation of the IRF, which is necessary for describing the binding behavior of the radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject's IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which shrinkage and roughness penalties are incorporated to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and non-negativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data.
The third part discusses a nonlinear mixed-effects modeling approach for PET data analysis under the assumption of a compartment model. The traditional NLS estimators of the population parameters are applied in a two-stage analysis, which brings instability issue and neglects the variation in rate parameters. In contrast, we propose to estimate the rate parameters by fitting nonlinear mixed-effects (NLME) models, in which all the subjects are modeled simultaneously by allowing rate parameters to have random effects and population parameters can be estimated directly from the joint model. Simulations are conducted to compare the power of detecting group effect in both rate parameters and summarized measures of tests based on both NLS and NLME models. We apply our NLME approach to clinical PET data to illustrate the model building procedure.
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YANG, YU-CHEN, and 楊郁成. "Mixtures of t Linear Mixed-effects Model with Logistic Link Function of Covariates." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/uwny46.
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碩士逢甲大學
統計學系統計與精算碩士班
106
The issues of model-based clustering and classification of longitudinal data have received increased attention in recent years. The finite mixtures of t linear mixed-effects model (FM-tLMM) has become one of the commonly used analytical tools for performing this work when data contain outliers. In this thesis, we propose an extended finite mixtures of t linear mixed-effects model (Extended FM-tLMM), where the categorical latent variables (component labels) are assumed to be influenced by the fixed observed covariates. As compared with the traditional FM-tLMM which assumes the mixing proportions to be fixed but unknown, our proposed Extended FM-tLMM utilizes a logistic regression model to link the relationship between prior classification probabilities and the covariates of interest. Therefore, the Extended FM-tLMM can offer more accurate estimates of model parameters and better classification performance. In order to obtain maximum likelihood estimates of model parameters, we reformulate the model into hierarchical structures and develop the alternating expectation conditional maximization (AECM) algorithm. Meanwhile, we calculate the standard errors of the parameter estimators by an information-based method. The issue of clustering individuals under the fitted model is also investigated. We utilize a real data example concerning the AIDS clinical trials and simulation studies to investigate the performance of the proposed model and compare it with the existing models in terms of model selection, parameter estimation and classification.
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Karaganis, Milana. "Small Area Estimation for Survey Data: A Hierarchical Bayes Approach." 2009. http://hdl.handle.net/1993/3207.
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Model-based estimation techniques have been widely used in small area estimation. This thesis focuses on the Hierarchical Bayes (HB) estimation techniques in application to small area estimation for survey data.
We will study the impact of applying spatial structure to area-specific effects and utilizing a specific generalized linear mixed model in comparison with a traditional Fay-Herriot estimation model. We will also analyze different loss functions with applications to a small area estimation problem and compare estimates obtained under these loss functions. Overall, for the case study under consideration, area-specific geographical effects will be shown to have a significant effect on estimates. As well, using a generalized linear mixed model will prove to be more advantageous than the usual Fay-Herriot model. We will also demonstrate the benefits of using a weighted balanced-type loss function for the purpose of balancing the precision of estimates with their closeness to the direct estimates.
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Maas, Bea. "Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yield." Doctoral thesis, 2013. http://hdl.handle.net/11858/00-1735-0000-0022-5E77-5.
Full textBooks on the topic "Functional mixed-effects model"
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James, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.
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This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects models and local smoothing techniques, as well as specific methodologies for dealing with sparse functional data in the principal components, clustering, classification, and regression settings. Finally, it describes two approaches for performing regressions involving a functional predictor and a scalar response: SASDA (sequential algorithm for selecting design automatically) and FLiRTI (Functional Linear Regression That’s Interpretable).
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Touchon, Justin C. Applied Statistics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869979.001.0001.
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Whether at the undergraduate, graduate, or post-graduate level, Applied Statistics with R: A Practical Guide for the Life Sciences teaches readers to properly analyze data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner. Readers will come away with the knowledge of which analyses they should use and when they should use them, an important skill in an age when the statistical analyses used in the life-sciences are becoming increasingly advanced. This book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the ‘go-to’ stats program throughout the life-sciences. Written around a single real-world dataset, Applied Statistics with R which encourages readers to become deeply familiar with an imperfect but realistic set of data, much like they themselves might collect. Early chapters are designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.
Book chapters on the topic "Functional mixed-effects model"
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Vannucci, Giulia, Anna Gottard, Leonardo Grilli, and Carla Rampichini. "Random effects regression trees for the analysis of INVALSI data." In Proceedings e report, 29–34. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.07.
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Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem & Larocque, 2011; Sela & Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci & Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.
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Luo, Xinchao, Lixing Zhu, Linglong Kong, and Hongtu Zhu. "Functional Nonlinear Mixed Effects Models for Longitudinal Image Data." In Lecture Notes in Computer Science, 794–805. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19992-4_63.
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Gałecki, Andrzej, and Tomasz Burzykowski. "Fitting Linear Mixed-Effects Models: The lme()Function." In Springer Texts in Statistics, 275–301. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_14.
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Gałecki, Andrzej, and Tomasz Burzykowski. "Fitting Linear Mixed-Effects Models: The lmer() Function." In Springer Texts in Statistics, 303–26. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_15.
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More, Shammi, Simon B. Eickhoff, Julian Caspers, and Kaustubh R. Patil. "Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67670-4_1.
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AbstractMachine learning (ML) methods are increasingly being used to predict pathologies and biological traits using neuroimaging data. Here controlling for confounds is essential to get unbiased estimates of generalization performance and to identify the features driving predictions. However, a systematic evaluation of the advantages and disadvantages of available alternatives is lacking. This makes it difficult to compare results across studies and to build deployment quality models. Here, we evaluated two commonly used confound removal schemes–whole data confound regression (WDCR) and cross-validated confound regression (CVCR)–to understand their effectiveness and biases induced in generalization performance estimation. Additionally, we study the interaction of the confound removal schemes with Z-score normalization, a common practice in ML modelling. We applied eight combinations of confound removal schemes and normalization (pipelines) to decode sex from resting-state functional MRI (rfMRI) data while controlling for two confounds, brain size and age. We show that both schemes effectively remove linear univariate and multivariate confounding effects resulting in reduced model performance with CVCR providing better generalization estimates, i.e., closer to out-of-sample performance than WDCR. We found no effect of normalizing before or after confound removal. In the presence of dataset and confound shift, four tested confound removal procedures yielded mixed results, raising new questions. We conclude that CVCR is a better method to control for confounding effects in neuroimaging studies. We believe that our in-depth analyses shed light on choices associated with confound removal and hope that it generates more interest in this problem instrumental to numerous applications.
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Berk, Maurice, Cheryl Hemingway, Michael Levin, and Giovanni Montana. "Longitudinal Analysis of Gene Expression Profiles Using Functional Mixed-Effects Models." In Advanced Statistical Methods for the Analysis of Large Data-Sets, 57–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_6.
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Mufti, Siraj I., and I. Glenn Sipes. "A Reduction in Mixed Function Oxidases and in Tumor Promoting Effects of Ethanol in a NDEA-Initiated Hepatocarcinogenesis Model." In Advances in Experimental Medicine and Biology, 347–52. Boston, MA: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4684-5877-0_45.
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Mayer, Helmut. "An Integrated Approach To Forward Modeling Carbonate Platform Development." In Computers in Geology - 25 Years of Progress. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195085938.003.0019.
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The forward model presented here is designed to simulate stratigraphic and geometric development of carbonate platforms. Starting from an initial basement geometry, the effects of a number of key variables on water depth are combined for each time increment. This procedure is repeated in an iterative fashion for subsequent time steps. The variables considered include subsidence, carbonate production, sediment redistribution, compaction, isostatic compensation, and eustatic sea-level change. Time- or depth-dependent functions are developed for these variables. Free parameters in these functions allow fitting to realistic magnitudes. A sample simulation demonstrates the characteristics of the model and indicates its usefulness in case studies and predictions. In recent years a number of studies on the modeling of sediment accumulation in various basin settings has been published. Most of them are concerned with clastic basin fill or do not discriminate lithologies (e.g., Turcotte and Kenyon, 1984; Kenyon and Turcotte, 1985; Tetzlaff, 1986; Bitzer and Harbaugh, 1987; Flemings and Jordan, 1987, 1989; Tetzlaff and Harbaugh, 1989; Jervey, 1989), while only few focus on mixed clastic/carbonate systems (e.g., Aigner et al., 1989; Lawrence et al., 1990) or carbonate platforms (e.g., Lerche et al., 1987; Bice, 1988; Demicco and Spencer, 1989; Scaturo et al., 1989). Sediment accumulation and distribution on a carbonate platform and the adjacent slope represent a highly complex system of numerous interdependent factors which in concert determine the development of the stratigraphy and geometry of the platform. The goal of this study is to develop a model that yields a "best compromise" between two principal targets: representation of all important variables in geologically reasonable functional relationships on the one hand, and simplicity on the other. Forward modeling of sedimentary systems serves to simulate the stratigraphic and geometric evolution of the system, dependent on variations in the input parameters. The purpose of this approach is to establish the critical variables and parameters which dominate the system and to produce a geologically reasonable generic stratigraphic pattern. The next step then would be to use the model to reproduce known patterns of actual modern or ancient sedimentary systems (inverse modeling).
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Jakob, Christian. "Ice Clouds in Numerical Weather Prediction Models: Progress, Problems, and Prospects." In Cirrus. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195130720.003.0020.
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The properties of cirrus, as well as the role ice clouds play in the atmosphere, have been extensively described in the previous chapters. To represent the effects of cirrus in atmospheric models, several intimately linked processes need to be described. These processes include the generation and dissipation of ice clouds as well as their interaction with the radiative fluxes throughout the atmosphere. In this chapter the cloud parameterization aspects of this problem (i.e., the treatment of the generation and dissipation of ice clouds), are discussed in the context of global numerical weather prediction (NWP) models. Aspects of the radiative transfer in ice clouds can be found in chapter 13. The main focus of the current chapter is on the cloud parameterization used in the global forecast model of the European Centre for Medium-Range Weather Forecasts (ECMWF). This parameterization will serve as an example in highlighting the progress made, the problems encountered, and the prospects for improving the representation of ice clouds in atmospheric models. The principles of representing clouds in global NWP models are identical to those in general circulation models (GCMs) used for climate research (see chapter 15). Although ice clouds are the focus of this book, a substantial part of this chapter will be concerned with the overall treatment of clouds in numerical models of the atmosphere. In fact, many models used in NWP today distinguish ice clouds from mixed-phase and water clouds only as a function of temperature. Cloud parameterizations in GCMs have evolved rapidly over the last few years. Section 16.2 is a general overview of the progress made. Section 16.3 will describe the cloud parameterization that is currently used in the ECMWF forecast model as a specific example for a state-of-the-art cloud parameterization in NWP. General aspects of the simulation of ice clouds with this model will be presented. GCM simulations of the atmosphere are very sensitive to the treatment of clouds in general (Senior and Mitchell 1993; Rasch and Kristjansson 1998) and to assumptions about cloud ice in particular (Fowler et al. 1996; Jakob and Morcrette 1995). Section 16.4 gives an example of those sensitivities and the model design problems that can arise when model sensitivities exist in combination with a lack of observations, as noted for cloud ice by Stephens et al. (1998).
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Ailshire, Jennifer, and Katrina M. Walsemann. "Education Differences in the Adverse Impact of PM2.5 on Incident Cognitive Impairment Among U.S. Older Adults." In Advances in Alzheimer’s Disease. IOS Press, 2021. http://dx.doi.org/10.3233/aiad210023.
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Background: Air pollution is linked to worse cognitive function in older adults, but whether differences in this relationship exist by education, a key risk factor for cognitive decline, remains unknown. Objective: To determine if the association between fine particulate matter air pollution (PM2.5) and incident cognitive impairment varies by level of education in two cohorts assessed a decade apart. Methods: We used data on adults ages 60 and older from the nationally representative Health and Retirement Study (HRS) linked with tract-level annual average PM2.5. We used mixed-effects logistic regression models to examine education differences in the association between PM2.5 and incident cognitive impairment in two cohorts: 2004 (n = 9,970) and 2014 (n = 9,185). Cognitive impairment was determined with tests of memory and processing speed for self-respondents and proxy and interviewer assessments of cognitive functioning in non-self-respondents. Results: PM2.5 was unrelated to incident cognitive impairment among those with 13 or more years of education, but the probability of impairment increased with greater concentrations of PM2.5 among those with 8 or fewer years of education. The interaction between education and PM2.5 was only found in 2004, possibly because PM2.5 concentrations were much lower in 2014. Conclusion: Education is a key determinant of cognitive decline and impairment, and in higher pollution contexts may serve as a protective factor against the harms of air pollution on the aging brain. Additionally, because air pollution is ubiquitous, and particularly harmful to vulnerable populations, even small improvements in air quality may have large impacts on population health.
Conference papers on the topic "Functional mixed-effects model"
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Befekadu, G. K., M. G. Tadesse, and H. W. Ressom. "A bayesian based functional mixed-effects model for analysis of LC-MS data." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332859.
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Brun, Klaus, and Rainer Kurz. "Analysis of the Effects of Pulsations on the Operational Stability of Centrifugal Compressors in Mixed Reciprocating and Centrifugal Compressor Stations." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50540.
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Mixed operation with both centrifugal and reciprocating compressors in a compression plant poses significant operational challenges as pressure pulsations and machine mismatches lead to centrifugal compressors’ instabilities or poor performance. Arrangements with reciprocating compressors placed in series with centrifugal compressors generally lead to higher suction/discharge pulsations on the centrifugal compressor than conventional parallel operation. This paper demonstrates that by properly analyzing and designing the interconnecting piping between the compressors, utilizing pulsation attenuation devices, and matching the compressors’ volumetric-flow rates, a satisfactory functional compression system design can be achieved for even the worst cases of mixed centrifugal and reciprocating compressor operation. However, even small analysis errors, design deviations, or machine mismatches result in a severely limited (or even inoperable) compression system. Also, pulsation attenuation often leads to a significant pressure loss in the interconnect piping system. Utilizing analysis tools in the design process that can accurately model the transient fluid dynamics of the piping system, the pulsation attenuation devices and the compressor machine behaviors is critical to avoid potentially costly design mistakes and minimize pressured losses. This paper presents the methodology and examples of such an analysis using a 1-D transient Navier-Stokes code for complex compression piping networks. The code development, application, and example results for a set of mixed operational cases are discussed. This code serves as a design tool to avoid critical piping layout and compressor matching mistakes early in the compressor station design process.
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Chakraborty, Arindam, and Sharif Rahman. "Stochastic Fracture Analysis of Three-Dimensional Functionally Graded Materials Employing Concurrent Multiscale Model." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-78021.
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This article presents a stochastic multiscale fracture analysis of three-dimensional functionally graded, particle-matrix composites with random microstructures employing a moment-modified polynomial dimensional decomposition method. The stochastic model is based on a level-cut, inhomogeneous, filtered Poisson random field for representing spatially-varying random microstructures and a moment-modified polynomial dimensional decomposition for calculating the probabilistic characteristics of crack-driving forces. The decomposition involves Fourier-polynomial expansions of component functions by orthonormal polynomial bases, an additive control variate in conjunction with Monte Carlo simulation for calculating the expansion coefficients, and a moment-modified random output to account for the effects of particle locations and geometry. A numerical problem involving a horizontally placed, planar edge-crack in a three-dimensional functionally graded specimen under a mixed-mode deformation was efficiently calculated by the univariate dimensional decomposition to calculate the statistical moments and distribution functions of crack-driving forces and the conditional probability of fracture initiation. The fracture results show significant variation of the stress intensity factors and fracture initiation probability along the crack front of the fracture specimen.
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Cavallaro, Paul V., Andrew Hulton, Mahmoud Salama, and Melvin W. Jee. "Effects of Crimped Fiber Paths on Mixed Mode Delamination Behaviors in Woven Fabric Composites." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65646.
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This research investigated the fracture toughness and crack propagation behaviors of woven fabric reinforced polymer (WFRP) composite laminates subjected to single and mixed mode loadings using numerical models. The objectives were to characterize the fracture behaviors and toughness properties at the fiber/matrix interfaces and to identify mechanisms that can be exploited for increasing delamination resistance. The mode-I and mode-II strain energy release rates GI and GII, the effective critical strain energy release rate, Gc_eff, (also known as the mixed mode fracture toughness) and crack growth stabilities were determined as functions of crimped fiber paths using meso-scale, 2D multi-continuum finite element models. Three variations of a plain-woven fabric architecture were considered; each having different crimped fiber paths. The presence of mixed-mode strain energy release rates at the meso-scale due to the curvilinear fiber paths was shown to influence the interlaminar fracture toughness and was explored for pure single-mode and mixed-mode global loadings. It was concluded that woven fabric composites provided a Fracture Toughness Conversion Mechanism (FTCM) and their toughness properties were dependent upon and varied with positon along the crimped fiber paths. The FTCM was identified as an advanced tailoring mechanism that can be further utilized to improve toughness and damage tolerance thresholds especially when the mode-II fracture toughness GIIc is greater than the mode-I fracture toughness GIc.
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Pavlou, Dimitrios G. "Loading Sequence Effects on Fatigue Damage Accumulation of Offshore Structures: A Deterministic Approach." In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/omae2017-61733.
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Offshore structures are subjected to irregular loading spectra due to their exposure to waves and wind. The environmental loads cause variable amplitude stress histories on critical spots of the structures. The existing engineering methodology (adopted by most of the national standards) to estimate the accumulated fatigue damage is based on Miner’s rule for crack initiation. Paris rule and its modifications are used for crack propagation prediction. However, Miner’s rule is a linear model and does not take into account the sequence effect of loading blocks with different stress amplitude. On the other hand, the widely used Paris rule does not take into account the load interaction effects (e.g. overload-induced crack growth retardations). The prediction of the crack growth rate and the crack growth direction of mixed mode cracks is an important issue as well. Aim of the present paper is the analysis of the weaknesses of the engineering tools for fatigue analysis, and the demonstration of the advantages of non-linear damage functions and crack propagation models. A review of models for fatigue crack initiation and growth (for mode I or mixed mode loading) developed by the author is presented. Representative results are discussed and commented.
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Long, X., and F. Delale. "The Mixed Mode Crack Problem in a FGM Layer." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-43863.
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Functionally graded materials (FGMs) are multiphase composites whose composition, microstructure and properties vary gradually. They can be tailored to meet the requirements encountered in practice through the design of their constituents. In this paper, analytical expressions for stress intensity factors off mixed-mode cracks in a FGM strip have been derived for the first time. A parametric study, by varying both the geometric and material parameters, is conducted to determine their effects on the stress intensity factors.
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Ferrero, Laura, and Ugo Icardi. "Optimization of Multi-Core Sandwich Composites Undergoing Impact Loads." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42851.
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In this paper, multiple cores sandwich composites undergoing impact loads are optimized in order to improve their resistance to the impact-induced delamination. This peculiar type of composites is characterized by one internal face splitting the core in two parts. Owing to their architecture with an intermediate and two external faces, their additional tailoring capability offers potential advantages in terms of energy absorption capability and damage tolerance behavior over conventional sandwich composites. Obviously, an accurate assessment of the interfacial stress fields, of their damage accumulation mechanisms and of their post-failure behavior are fundamental to fully exploit their potential advantages. Despite it is evident that structural models able to accurately describe the local behavior are needed to accomplish this task, the analysis is commonly still carried out using simplified sandwich models which postulate the overall variation of displacements and stresses across the thickness, because more detailed models could make the computational effort prohibitively large. No attempt is here made to review the ample literature about the sandwich composite models, since a plenty of comprehensive bibliographical review papers and monographs are available in the specialized literature. Likewise, no attempt is made for reviewing the methods used to model the damage. It is just remarked that the models to date available range from detailed models which discretize the real structure of the core, to FEM models by brick elements, to discrete-layer models and to sublaminate models. In these paper, two different models are used, to achieve a compromise between accuracy and limitation of costs. The time history of the contact force is computed by a C° eight-node plate element based on a 3D zig-zag model, in order to achieve the best accuracy using a plate model with the customary five functional d.o.f. This model is also used in the optimization process, since it is mathematically easily treatable and accurately describes the strain energy. In addition, it enables a comparison with the classical plate models, since they can be particularized from it. The counterpart plate element of this zig-zag model, which is obtained from a standard C° plate element through a strain energy updating (which successfully described the impact induced damage as shown by the comparison with the damage detected by c-scanning in a previous paper), is used for computing the contact force time history, to reach a good compromise between accuracy and computational costs. A mixed brick element with the three displacements and the three interlaminar stresses as nodal d.o.f. is used to compute the damage at each time step. The onset of damage is predicted in terms of matrix and fibers failure, cracks, delamination, rippling, wrinkling and face damping using different stress-based criteria. In this paper the effects of the accumulated damage are accounted for through the ply-discount theory, i.e. using reduced elastic properties for the layers and the cores that failed, although it is known that some cases exist for which this material degradation model could be unable to describe the real loss of load carrying capacity. The optimization technique recently proposed by the authors is used in this paper for optimizing the energy absorption properties of multi-core sandwiches undergoing impact loads. The effect of this technique is to act as an energy absorption tuning, since it minimizes or maximizes the amount of energy absorbed by specific modes through a suited in-plane variation of the plate stiffness properties (e.g., bending, in-plane and out-of-plane shears and membrane energies). The appropriate in-plane variable distributions of stiffness properties, making certain strain energy contributions of interest extremal, are found solving the Euler-Lagrange equations resulting from assumption of the laminate stiffness properties as the master field and setting to zero the first variation of wanted and unwanted strain energy contributions (e.g., bending, in-plane and out-of-plane shears and membrane energies). Our purpose is to minimize the energy absorbed through unwanted modes (i.e., involving interlaminar strengths) and maximize that absorbed through desired modes (i.e., involving membrane strengths). The final result is a ply with variable stiffness coefficient over its plane which is able to consistently reduce the through-the-thickness interlaminar stress concentrations, with beneficial effects on the delamination strength. All the solutions proposed can be obtained either varying the orientation of the reinforcement fibers, the fiber volume rate or the constituent materials by currently available manufacturing processes. The coefficients of the involved stiffness terms are computed enforcing conditions which range from the thermodynamic constraints, to imposition of the mean stiffness, to the choice of a convex or a concave shape (in order to minimize or maximize the energy contributions of interest). Two solutions of technical interest will be proposed, which both are based on a parabolic distribution of stiffness coefficients. The former reduces the bending of a lamina with moderately increasing the shear stresses, the second one reduces these stresses with a low increment in the bending contribution. The effects of the incorporation of these layers (with the same mean properties of the layers they replace) is shown hereafter.
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Lakshminarayana, Ramaprasad E., and Shun Takai. "Effects of Non-Geometric Features and Incentive Schemes on Manual Assembly of System Variants: An Experimental Study." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42419.
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Although numerous firms have been shifting toward automated assembly, most still rely on manual assembly when complex assembly operation is required for large-scaled systems. Furthermore, because firms design variants of a system to satisfy diverse customer needs, they may manufacture these system variants in the same assembly line. This type of operation, called mixed model assembly, may improve the utilization of existing manufacturing facilities; however, it may also increase assembly errors due to interchanging geometrically similar parts between system variants. Design for Assembly (DFA) is a design guideline that assists engineers in designing systems that are easier to assemble. However, because DFA guidelines group geometrically similar parts in the same part category, it may be impossible to distinguish geometrically similar but functionally different parts (modules) used in different systems. This paper proposes experimenting how cognitive effects of non-geometric part features influence the productivity and quality in mixed model assembly operations. Furthermore, because the productivity and quality of manual assembly may be influenced by the motivation of operators, this paper examines how productivity and quality may be influenced by different incentive schemes.
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Onishi, A., K. Akashi, M. Hashimoto, M. Furu, W. Yamamoto, M. Katayama, R. Hara, et al. "OP0152 A longitudinal study of the effects of disease activity on renal function in patients with rheumatoid arthritis utilizing linear mixed effect models - answer cohort study -." In Annual European Congress of Rheumatology, 14–17 June, 2017. BMJ Publishing Group Ltd and European League Against Rheumatism, 2017. http://dx.doi.org/10.1136/annrheumdis-2017-eular.4544.
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Jocsak, Jeffrey, Victor W. Wong, and Tian Tian. "The Effects of Cylinder Liner Finish on Piston Ring-Pack Friction." In ASME 2004 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/icef2004-0952.
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This paper presents enhancements to a previously developed mixed-lubrication ring-pack model that has been used extensively in the automotive industry in predicting piston-ring/liner oil film thickness, friction and oil-transport processes along the liner. The previous model considers three lubrication regimes, shear thinning of the lubricant, and the unsteady wetting conditions of the rings at the leading and trailing edges. The model incorporates the effects of surface roughness by using Patir and Cheng’s average flow model and the Greenwood and Tripp statistical asperity contact model, assuming a Gaussian distribution of surface roughness. However, as a result of the methods used to machine a cylinder liner and the wear-in process observed in engines, the cylinder liner finish is highly non-Gaussian. The purpose of this current study is to understand the effects of additional surface parameters other than Gaussian root-mean-square surface roughness on piston ring-pack friction in the context of a natural gas reciprocating engine ring/liner interface. In general, the surface roughness of a cylinder liner is negatively skewed. Applying similar methodology published in the literature, a wide variety of non-Gaussian probability density functions were generated in terms of the skewness of the cylinder liner surface. These probability density functions were implemented into the Greenwood and Tripp asperity contact model, and subsequently into the traditional MIT ring-pack friction model. The effects of surface skewness on flow were approximated using Gaussian flow factors and a simple truncation method. The enhanced model was studied in conjunction with results from an existing ring-pack dynamic model that provided the dynamic twists of the rings relative to the liner and inter-ring pressures. In this manner, a detailed analysis of the effects of engineered cylinder liner finish on reducing friction losses was performed.
Reports on the topic "Functional mixed-effects model"
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Carney, Nancy, Tamara Cheney, Annette M. Totten, Rebecca Jungbauer, Matthew R. Neth, Chandler Weeks, Cynthia Davis-O'Reilly, et al. Prehospital Airway Management: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), June 2021. http://dx.doi.org/10.23970/ahrqepccer243.
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Objective. To assess the comparative benefits and harms across three airway management approaches (bag valve mask [BVM], supraglottic airway [SGA], and endotracheal intubation [ETI]) by emergency medical services in the prehospital setting, and how the benefits and harms differ based on patient characteristics, techniques, and devices. Data sources. We searched electronic citation databases (Ovid® MEDLINE®, CINAHL®, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, and Scopus®) from 1990 to September 2020 and reference lists, and posted a Federal Register notice request for data. Review methods. Review methods followed Agency for Healthcare Research and Quality Evidence-based Practice Center Program methods guidance. Using pre-established criteria, studies were selected and dual reviewed, data were abstracted, and studies were evaluated for risk of bias. Meta-analyses using profile-likelihood random effects models were conducted when data were available from studies reporting on similar outcomes, with analyses stratified by study design, emergency type, and age. We qualitatively synthesized results when meta-analysis was not indicated. Strength of evidence (SOE) was assessed for primary outcomes (survival, neurological function, return of spontaneous circulation [ROSC], and successful advanced airway insertion [for SGA and ETI only]). Results. We included 99 studies (22 randomized controlled trials and 77 observational studies) involving 630,397 patients. Overall, we found few differences in primary outcomes when airway management approaches were compared. • For survival, there was moderate SOE for findings of no difference for BVM versus ETI in adult and mixed-age cardiac arrest patients. There was low SOE for no difference in these patients for BVM versus SGA and SGA versus ETI. There was low SOE for all three comparisons in pediatric cardiac arrest patients, and low SOE in adult trauma patients when BVM was compared with ETI. • For neurological function, there was moderate SOE for no difference for BVM compared with ETI in adults with cardiac arrest. There was low SOE for no difference in pediatric cardiac arrest for BVM versus ETI and SGA versus ETI. In adults with cardiac arrest, neurological function was better for BVM and ETI compared with SGA (both low SOE). • ROSC was applicable only in cardiac arrest. For adults, there was low SOE that ROSC was more frequent with SGA compared with ETI, and no difference for BVM versus SGA or BVM versus ETI. In pediatric patients there was low SOE of no difference for BVM versus ETI and SGA versus ETI. • For successful advanced airway insertion, low SOE supported better first-pass success with SGA in adult and pediatric cardiac arrest patients and adult patients in studies that mixed emergency types. Low SOE also supported no difference for first-pass success in adult medical patients. For overall success, there was moderate SOE of no difference for adults with cardiac arrest, medical, and mixed emergency types. • While harms were not always measured or reported, moderate SOE supported all available findings. There were no differences in harms for BVM versus SGA or ETI. When SGA was compared with ETI, there were no differences for aspiration, oral/airway trauma, and regurgitation; SGA was better for multiple insertion attempts; and ETI was better for inadequate ventilation. Conclusions. The most common findings, across emergency types and age groups, were of no differences in primary outcomes when prehospital airway management approaches were compared. As most of the included studies were observational, these findings may reflect study design and methodological limitations. Due to the dynamic nature of the prehospital environment, the results are susceptible to indication and survival biases as well as confounding; however, the current evidence does not favor more invasive airway approaches. No conclusion was supported by high SOE for any comparison and patient group. This supports the need for high-quality randomized controlled trials designed to account for the variability and dynamic nature of prehospital airway management to advance and inform clinical practice as well as emergency medical services education and policy, and to improve patient-centered outcomes.
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Al-Qadi, Imad, Egemen Okte, Aravind Ramakrishnan, Qingwen Zhou, and Watheq Sayeh. Truck Platooning on Flexible Pavements in Illinois. Illinois Center for Transportation, May 2021. http://dx.doi.org/10.36501/0197-9191/21-010.
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Truck platoons have many benefits over traditional truck mobility. Truck platoons have the potential to improve safety and reduce fuel consumption between 5% and 15%, based on platoon configuration. In Illinois, trucks carry more than 50% of freight tonnage and constitute 25% of the traffic on interstates. Therefore, expected fuel savings would be significant for trucks. Deployment of truck platoons within interstate highways may have a direct effect on flexible pavement performance, as the time between consecutive axle loads (i.e., resting time) is expected to decrease significantly. Moreover, platoons could potentially accelerate pavement damage accumulation due to trucks’ channelized position, decreasing pavement service life and increasing maintenance and rehabilitation costs. The main objective of this project was to quantify the effects of truck platoons on pavements and to provide guidelines to control corresponding potential pavement damage. Finite-element models were utilized to quantify the impact of rest period on pavement damage. Recovered and accumulated strains were predicted by fitting exponential functions to the calculated strain profiles. The results suggested that strain accumulation was negligible at a truck spacing greater that 10 ft. A new methodology to control pavement damage due to truck platoons was introduced. The method optimizes trucks’ lateral positions on the pavements, and an increase in pavement service life could be achieved if all platoons follow this optimization method. Life cycle assessment and life cycle cost analysis were conducted for fully autonomous, human-driven, and mixed-traffic regimes. For example, for an analysis period of 45 years, channelized truck platoons could save life cycle costs and environmental impacts by 28% and 21% compared with human-driven trucks, respectively. Furthermore, optimum truck platoon configuration could reduce life cycle costs and environmental impacts by 48% and 36%, respectively, compared with human-driven trucks. In contrast, channelized traffic could increase pavement roughness, increasing fuel consumption by 15%, even though platooning vehicles still benefit from reduction in air drag forces. Given that truck platoons are expected to be connected only in the first phase, no actions are required by the agency. However, in the second phase when truck platoons are also expected to be autonomous, a protocol for driving trends should be established per the recommendation of this study.