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Journal articles on the topic 'Mixed models design'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Zhao, Y., J. Staudenmayer, B. A. Coull, and M. P. Wand. "General Design Bayesian Generalized Linear Mixed Models." Statistical Science 21, no. 1 (February 2006): 35–51. http://dx.doi.org/10.1214/088342306000000015.

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2

Santos, Aladir Horacio, Eduardo Bearzoti, Daniel Furtado Ferreira, and João Luís da Silva Filho. "Simulation of mixed models in augmented block design." Scientia Agricola 59, no. 3 (September 2002): 483–89. http://dx.doi.org/10.1590/s0103-90162002000300012.

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The augmented block design is widely used in breeding programs, with non-replicated treatments generally being selection units, and replicated treatments being standard cultivars. Originally, an intrablock analysis (fixed model) was proposed. Although non-replicated treatments and/or blocks can be considered of random nature, mixed linear models could be used instead. This work evaluated such an approach, using computer simulation. Populations consisted of sets of randomly generated inbred lines. Molecular marker data were also simulated to allow the estimation of the genetic covariance matrix. Different conditions were considered, varying heritability and the coefficient b of Smith of soil heterogeneity. For each condition 100 simulations were performed, considering four linear models, varying respectively the nature of the effects of blocks and non-replicated treatments (fixed - F, or random - R): FF, FR, RF and RR. In relation to FF, the mixed models were more efficient under low to intermediate heritability and high b. Mixed models could improve inference in breeding programs using the augmented block design and the choice of the model should rely on the kind of selection. If this is truncated, the RF model should be preferred; if it is not, then the RR model would be more suitable.

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3

Robinson, Timothy J., Shaun S. Wulff, Douglas C. Montgomery, and Andre I. Khuri. "Robust Parameter Design Using Generalized Linear Mixed Models." Journal of Quality Technology 38, no. 1 (January 2006): 65–75. http://dx.doi.org/10.1080/00224065.2006.11918585.

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4

Evangelou, Evangelos, and Zhengyuan Zhu. "Optimal predictive design augmentation for spatial generalised linear mixed models." Journal of Statistical Planning and Inference 142, no. 12 (December 2012): 3242–53. http://dx.doi.org/10.1016/j.jspi.2012.05.008.

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5

FÃ¼genschuh, Armin, and Alexander Martin. "Mixed-integer models for topology optimization in sheet metal design." PAMM 7, no. 1 (December 2007): 2060049–50. http://dx.doi.org/10.1002/pamm.200700566.

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6

Staudenmayer, J., E. E. Lake, and M. P. Wand. "Robustness for general design mixed models using the t-distribution." Statistical Modelling: An International Journal 9, no. 3 (October 2009): 235–55. http://dx.doi.org/10.1177/1471082x0800900304.

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7

Garroppo, Rosario G., Stefano Giordano, Gianfranco Nencioni, and Maria Grazia Scutellà. "Mixed Integer Non-Linear Programming models for Green Network Design." Computers & Operations Research 40, no. 1 (January 2013): 273–81. http://dx.doi.org/10.1016/j.cor.2012.06.014.

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8

Ryan, Elizabeth G., Christopher C. Drovandi, and Anthony N. Pettitt. "Simulation-based fully Bayesian experimental design for mixed effects models." Computational Statistics & Data Analysis 92 (December 2015): 26–39. http://dx.doi.org/10.1016/j.csda.2015.06.007.

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9

Nyberg, Joakim, Richard Höglund, Martin Bergstrand, Mats O. Karlsson, and Andrew C. Hooker. "Serial correlation in optimal design for nonlinear mixed effects models." Journal of Pharmacokinetics and Pharmacodynamics 39, no. 3 (March 14, 2012): 239–49. http://dx.doi.org/10.1007/s10928-012-9245-5.

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Nyberg, Joakim, Sebastian Ueckert, Eric A. Strömberg, Stefanie Hennig, Mats O. Karlsson, and Andrew C. Hooker. "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool." Computer Methods and Programs in Biomedicine 108, no. 2 (November 2012): 789–805. http://dx.doi.org/10.1016/j.cmpb.2012.05.005.

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Seurat, Jérémy, Yuxin Tang, France Mentré, and Thu Thuy Nguyen. "Finding optimal design in nonlinear mixed effect models using multiplicative algorithms." Computer Methods and Programs in Biomedicine 207 (August 2021): 106126. http://dx.doi.org/10.1016/j.cmpb.2021.106126.

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12

Prus, Maryna. "Equivalence theorems for multiple-design problems with application in mixed models." Journal of Statistical Planning and Inference 217 (March 2022): 153–64. http://dx.doi.org/10.1016/j.jspi.2021.07.012.

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13

Shim, Minjung, Burke Johnson, Joke Bradt, and Susan Gasson. "A Mixed Methods–Grounded Theory Design for Producing More Refined Theoretical Models." Journal of Mixed Methods Research 15, no. 1 (June 8, 2020): 61–86. http://dx.doi.org/10.1177/1558689820932311.

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Current literature lacks explication of how traditional grounded theory and mixed methods–grounded theory (MM-GT) are similar/different and specific explication of how to construct MM-GT designs—our purpose is to do this. We illustrate the design process using a published study. Exploratory Phase 1 involves creation of a formative–theoretical model based on multiple implicit or explicit models identified in the literature, which are then combined into a single model using meta-modeling integration. Also, in Phase 1, a traditional grounded theory is developed “independently” using interview data. These two models are integrated into a combined/meta-model at the end of Phase 1. Confirmatory Phase 2 involves testing of the final Phase 1 meta-model using a mixed methods experiment. In Phase 3, the Phase 1 and Phase 2 results are integrated, producing the “final” meta-model. This article contributes to the field of mixed methods research by showing how to design an MM-GT study that is focused on theory development and testing.

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14

Rosa, Guilherme J. M., Juan P. Steibel, and Robert J. Tempelman. "Reassessing Design and Analysis of two-Colour Microarray Experiments Using Mixed Effects Models." Comparative and Functional Genomics 6, no. 3 (2005): 123–31. http://dx.doi.org/10.1002/cfg.464.

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Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.

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15

Guggemos, Fabien, and Yves Tillé. "Penalized calibration in survey sampling: Design-based estimation assisted by mixed models." Journal of Statistical Planning and Inference 140, no. 11 (November 2010): 3199–212. http://dx.doi.org/10.1016/j.jspi.2010.04.010.

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16

Zhao, Qingling, Zonghua Gu, and Haibo Zeng. "Design optimization for AUTOSAR models with preemption thresholds and mixed-criticality scheduling." Journal of Systems Architecture 72 (January 2017): 61–68. http://dx.doi.org/10.1016/j.sysarc.2016.08.003.

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17

Huang, Yangxin, Jeong-Gun Park, and Yiliang Zhu. "Design of Long-Term HIV Dynamic Studies Using Semiparametric Mixed-Effects Models." Biometrical Journal 50, no. 4 (August 2008): 528–40. http://dx.doi.org/10.1002/bimj.200710440.

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18

Wang, Yi, Kent M. Eskridge, and Saralees Nadarajah. "Optimal Design of Mixed-Effects PK/PD Models Based on Differential Equations." Journal of Biopharmaceutical Statistics 22, no. 1 (December 28, 2011): 180–205. http://dx.doi.org/10.1080/10543406.2010.513465.

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19

Ye, Yixin, Ignacio E. Grossmann, and Jose M. Pinto. "Mixed-integer nonlinear programming models for optimal design of reliable chemical plants." Computers & Chemical Engineering 116 (August 2018): 3–16. http://dx.doi.org/10.1016/j.compchemeng.2017.08.013.

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20

Power, Vincent JG. "The Relative Merits of Courts and Agencies in Competition Law—Institutional Design: Administrative Models; Judicial Models; and Mixed Models." European Competition Journal 6, no. 1 (April 2010): 91–127. http://dx.doi.org/10.5235/ecj.v6n1.91.

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21

Boivin, Jacquelynne Anne. "The Importance of Qualitative Methods in Mixed Methods Studies." International Journal of Adult Education and Technology 12, no. 2 (April 2021): 1–17. http://dx.doi.org/10.4018/ijaet.2021040101.

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This article presents a thorough review of the literature dedicated to learning more about mixed methods research design. Explanatory sequential mixed methods studies and exploratory sequential mixed methods studies are the two types of mixed methods research design models that this article presents in detail. To contextualize different ideas related to research design, the author provides examples of research studies that exemplify different research designs falling within mixed methods. The main objective of this article is to highlight the important role that qualitative research design plays in mixed methods research. Such a presentation of the literature aims to argue that qualitative data substantiates quantitative data as a means to heighten the regard to which qualitative methodology receives.

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22

McKenzie, F. Ellis, Roger C. Wong, and William H. Bossert. "Discrete-Event Models of Mixed-Phenotype Plasmodium falciparum Malaria." SIMULATION 73, no. 4 (October 1999): 213–17. http://dx.doi.org/10.1177/003754979907300403.

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23

Seurat, Jérémy, Thu Thuy Nguyen, and France Mentré. "Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes." Statistical Methods in Medical Research 29, no. 3 (May 27, 2019): 934–52. http://dx.doi.org/10.1177/0962280219850588.

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To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.

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24

Deng, Jun, Liang Zhou, Xiao Zong Huang, Xu Huang, Yu Jing Li, Lin Tao Liu, and Yi Tao. "Study of System Modeling and Simulation Based on Mixed Domain for Analog-Digital Mixed SoC." Applied Mechanics and Materials 423-426 (September 2013): 2688–92. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2688.

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The design of digital-analog mixed SoC involves RF/analog and digital domains, how to effectively improve the design reliability and to reduce the development cycles has become a research hotspot. This paper establishes the appropriate behavioral models of RF / analog / digital IP modules, and carries out the behavioral simulation based on the built mixed-domain simulation platform and the behavioral libraries of RF/analog/digital IP module, which enhances the reliability and stability of mixed SoC design, and reduces the design cycle. Those explorations may be helpful to the designers of digital-analog mixed SoC.

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25

Alì, G., A. Bartel, M. Günther, and C. Tischendorf. "Elliptic Partial Differential-Algebraic Multiphysics Models in Electrical Network Design." Mathematical Models and Methods in Applied Sciences 13, no. 09 (September 2003): 1261–78. http://dx.doi.org/10.1142/s0218202503002908.

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In refined network analysis, a compact network model is combined with distributed models for semiconductor devices in a multiphysics approach. For linear RLC networks containing diodes as distributed devices, we construct a mathematical model that combines the differential-algebraic network equations of the circuit with elliptic boundary value problems modeling the diodes. For this mixed initial-boundary value problem of partial differential-algebraic equations a first existence result is given.

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26

Mendes, Luís A. M., and Luís M. S. S. Castro. "Hybrid-mixed stress finite element models in elastoplastic analysis." Finite Elements in Analysis and Design 45, no. 12 (October 2009): 863–75. http://dx.doi.org/10.1016/j.finel.2009.06.021.

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27

Voss, Daniel T., and Jason C. Hsu. "Multiple comparisons for an unbalanced a × b design under mixed models with interaction." Journal of Statistical Planning and Inference 67, no. 2 (April 1998): 297–309. http://dx.doi.org/10.1016/s0378-3758(97)00116-x.

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28

Han, Cong, and Kathryn Chaloner. "Bayesian Experimental Design for Nonlinear Mixed‐Effects Models with Application to HIV Dynamics." Biometrics 60, no. 1 (March 2004): 25–33. http://dx.doi.org/10.1111/j.0006-341x.2004.00148.x.

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29

Bazzoli, Caroline, Sylvie Retout, and France Mentré. "Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0." Computer Methods and Programs in Biomedicine 98, no. 1 (April 2010): 55–65. http://dx.doi.org/10.1016/j.cmpb.2009.09.012.

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30

St-Cyr, Olivier, and Catherine M. Burns. "Mental Models and Ecological Interface Design: An Experimental Investigation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (September 2002): 270–74. http://dx.doi.org/10.1177/154193120204600311.

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We studied the impact of Ecological Interface Design (EID) displays on mental models using the DURESS II simulator. To assess mental models we used a card sorting exercise, a network building exercise based on the Abstraction Hierarchy, and a system behavior prediction exercise. Participants in the EID condition performed significantly better on the card sorting and network building exercises, but poorer on the prediction exercise. Our results are mixed, suggesting that EID may improve the understanding of means- end links and yet, not improve the ability to predict system behavior. The relatively limited experience of participants with the interface as well as our selection of knowledge elicitation techniques may have influenced these results. More research is needed to fully evaluate the impact of EID on mental models.

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31

Loh, Han Tong, and P. Y. Papalambros. "A Sequential Linearization Approach for Solving Mixed-Discrete Nonlinear Design Optimization Problems." Journal of Mechanical Design 113, no. 3 (September 1, 1991): 325–34. http://dx.doi.org/10.1115/1.2912786.

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Design optimization models of often contain variables that must take only discrete values, such as standard sizes. Nonlinear optimization problems with a mixture of discrete and continuous variables are very difficult, and existing algorithms are either computationally intensive or applicable to models with special structure. A new approach for solving nonlinear mixed-discrete problems with no particular structure is presented here, motivated by its efficiency for models with extensive monotonicities of the problem’s objective and constraint functions with respect to the design variables. It involves solving a sequence of mixed-discrete linear approximations of the original nonlinear model. In this article, a review of previous approaches is followed by description of the resulting algorithm, its convergence properties and limitations. Several illustrative examples are given. A sequel article presents a detailed algorithmic implementation and extensive computational results.

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32

POP, PETRICA C., and CORINA POP SITAR. "New models of the generalized fixed-charge network design problem." Carpathian Journal of Mathematics 28, no. 1 (2012): 143–50. http://dx.doi.org/10.37193/cjm.2012.01.04.

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We consider in this paper the generalized fixed-charge network design (GFCND) problem in which we are interested to find the cheapest backbone network connecting exactly one hub from each of the given clusters. The GFCND problem belongs to the class of generalized combinatorial optimization problems. We describe two mixed integer programming formulations of the GFCND problem. Based on one of the new proposed formulations, we solve the GFCND problem to optimality using CPLEX for problems with up to 30 clusters and 200 nodes. Computational results are reported and compared with those from the literature.

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33

Norrie, Caroline, Martin Stevens, Katherine Graham, Jill Manthorpe, Jo Moriarty, and Shereen Hussein. "Investigating models of adult safeguarding in England – a mixed-methods approach." Journal of Adult Protection 16, no. 6 (December 2, 2014): 377–88. http://dx.doi.org/10.1108/jap-11-2013-0045.

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Purpose – The purpose of this paper is to describe the methodology being used in a study exploring the organisation of adult safeguarding. Design/methodology/approach – A mixed-methods study is presented which describes how the research team is seeking to identify models of adult safeguarding and then compare them using a quasi-experimental study design. Findings – Close examination of this study's methodology highlights the potential value of mixed-method research approaches. Research limitations/implications – Anticipated study challenges include difficulties with gaining agreement from study sites and recruitment of people who have been the subject of a safeguarding referral. Originality/value – This will be the first study in England to identify and compare different models of adult safeguarding in depth. Outlining and discussing current methodology is likely to be of interest to practitioners, managers and other researchers and policy makers.

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34

Jayasuriya, Suhada. "Frequency Domain Design for Robust Performance Under Parametric, Unstructured, or Mixed Uncertainties." Journal of Dynamic Systems, Measurement, and Control 115, no. 2B (June 1, 1993): 439–51. http://dx.doi.org/10.1115/1.2899084.

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This article looks at direct frequency domain design for satisfying robust performance objectives in uncertain, linear time invariant (LTI) plants embedded in a single feedback loop. The uncertain plants may be described by parametric, nonparametric (or unstructured), or mixed uncertain models. Quantitative Feedback Theory (QFT) is one frequency domain design methodology that is direct and is equally effective with any of these models. It can be separated from other frequency domain robust control methods such as H∞ optimal control, μ synthesis, and LQG/LTR for at least (i) its emphasis on cost of feedback measured in terms of controller bandwidth, (ii) its ability to deal nonconservatively with parametric, nonparametric and mixed uncertainty models, and (iii) its utilization of both amplitude and phase of the loop transfer function, pointwise in frequency, for the quantification of robust performance. An exposition of these attributes, unique to QFT, and the basic design methodology, coupled with a recently developed mathematical framework and some existence results for the standard single-loop QFT problem are the salient features of this paper.

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35

Robinson, T. T., C. G. Armstrong, and R. Fairey. "Automated mixed dimensional modelling from 2D and 3D CAD models." Finite Elements in Analysis and Design 47, no. 2 (February 2011): 151–65. http://dx.doi.org/10.1016/j.finel.2010.08.010.

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36

Yang, R. C. "Towards understanding and use of mixed-model analysis of agricultural experiments." Canadian Journal of Plant Science 90, no. 5 (September 1, 2010): 605–27. http://dx.doi.org/10.4141/cjps10049.

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Despite the presence of both fixed and random effects in most agricultural experiments, many crop researchers have continued use of the conventional analysis of variance (ANOVA) model or general linear model (GLM) that provides a correct analysis only if all the effects are fixed. Ignoring or mistreating random effects may have inadvertently led to inappropriate analyses and thus to dubious conclusions appearing in the scientific literature. The objective of this paper is to provide a tutorial account of the mixed-model methodology and its applications to the analysis of agricultural experiments. The description and discussion on ANOVA vs. mixed-effect models center on the use of PROC GLM vs. PROC MIXED of the SAS® System. This paper points out the need for mixed-model analysis, describes and discusses key new features and properties of mixed-model analysis that would facilitate the understanding and use of PROC MIXED. Additionally, it analyzes and interprets three examples: comparison between two samples, and analyses of randomized complete design and split-plot design. Appendices include SAS code and theory underlying mixed-model analysis which will help gain hands-on experiences and ensure correct interpretation of SAS outputs by PROC MIXED. Such a comparative assessment of GLM vs. MIXED procedures will help to underscore the key advantages of PROC MIXED and to convince GLM users to make a true transition towards the increased and appropriate use of PROC MIXED in agricultural experiments.Key words: Analysis of variance, fixed vs. random effects, general linear models, inference spaces, mixed models, randomized complete block design, split-plot design

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37

Manjarin, Rodrigo, Magdalena A. Maj, Michael R. La Frano, and Hunter Glanz. "%polynova_2way: A SAS macro for implementation of mixed models for metabolomics data." PLOS ONE 15, no. 12 (December 15, 2020): e0244013. http://dx.doi.org/10.1371/journal.pone.0244013.

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The generation of large metabolomic data sets has created a high demand for software that can fit statistical models to one-metabolite-at-a-time on hundreds of metabolites. We provide the %polynova_2way macro in SAS to identify metabolites differentially expressed in study designs with a two-way factorial treatment and hierarchical design structure. For each metabolite, the macro calculates the least squares means using a linear mixed model with fixed and random effects, runs a 2-way ANOVA, corrects the P-values for the number of metabolites using the false discovery rate or Bonferroni procedure, and calculate the P-value for the least squares mean differences for each metabolite. Finally, the %polynova_2way macro outputs a table in excel format that combines all the results to facilitate the identification of significant metabolites for each factor. The macro code is freely available in the Supporting Information.

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38

White, Nicole M., and Adrian G. Barnett. "Analysis of multisite intervention studies using generalized linear mixed models." Infection Control & Hospital Epidemiology 40, no. 8 (June 21, 2019): 910–17. http://dx.doi.org/10.1017/ice.2019.114.

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AbstractMultisite intervention studies have become increasingly common in infection control, for example, looking for a change in hospital infection rates after a regional policy change. The design of these studies can take various forms, from pre–post observational studies to randomized trials, in which sites are randomly assigned to the intervention or in which the intervention is sequentially introduced to different sites over time. Data collected under these settings are clustered by hospital and/or ward, consist of repeated measurements and, in some cases, exhibit temporal and/or seasonal patterns. Failure to account for these features in data analysis could well result in biased estimates of intervention effectiveness and impact on the generalizability of model results.

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39

Berni, Rossella, and Matteo Burbui. "Process Optimization of a Superfinishing Machine through Experimental Design and Mixed Response Surface Models." Quality Engineering 26, no. 4 (August 20, 2014): 404–15. http://dx.doi.org/10.1080/08982112.2013.872794.

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40

Ostrovsky, Guennadi M., Luke E. K. Achenie, and Manish Sinha. "On the solution of mixed-integer nonlinear programming models for computer aided molecular design." Computers & Chemistry 26, no. 6 (November 2002): 645–60. http://dx.doi.org/10.1016/s0097-8485(02)00049-9.

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41

Nguyen, Thu Thuy, Caroline Bazzoli, and France Mentré. "Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models." Statistics in Medicine 31, no. 11-12 (October 3, 2011): 1043–58. http://dx.doi.org/10.1002/sim.4390.

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42

Propato, Marco. "Contamination Warning in Water Networks: General Mixed-Integer Linear Models for Sensor Location Design." Journal of Water Resources Planning and Management 132, no. 4 (July 2006): 225–33. http://dx.doi.org/10.1061/(asce)0733-9496(2006)132:4(225).

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43

Girling, Alan J., and Karla Hemming. "Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models." Statistics in Medicine 35, no. 13 (January 7, 2016): 2149–66. http://dx.doi.org/10.1002/sim.6850.

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44

Dou, Jianping, Chun Su, and Xia Zhao. "Mixed integer programming models for concurrent configuration design and scheduling in a reconfigurable manufacturing system." Concurrent Engineering 28, no. 1 (January 22, 2020): 32–46. http://dx.doi.org/10.1177/1063293x19898727.

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A reconfigurable manufacturing system can evolve its configuration to offer exactly the capacity and functionality needed for every demand period. For the reconfigurable manufacturing system with multi-part flow-line configuration simultaneously producing multiple parts within the same family, the production cost and the delivery time are closely related to its configuration and corresponding scheduling for certain demand period. Although studies on multi-part flow-line configuration design are abundant, studies on concurrent optimization of configuration design and scheduling for reconfigurable manufacturing system are scarce. First, a generic mixed integer nonlinear programming model for concurrent configuration design and scheduling is established to relax the limitation of the existing model, and then a mixed integer linear programming model is derived. The decisions of the two generalized models are to decide the amount of stations, the amount of identical machines and machines’ configuration for every station, and assign parts to machines along the multi-part flow line together with sequencing assigned parts for each machine. Based on the mixed integer linear programming model, an exact ε-constraint method is developed to obtain the Pareto optimal solutions with tradeoffs between cost and tardiness. The validation of two models and the ε-constraint method is verified against two cases adapted from the literature.

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45

Wett, B., J. A. Jimenez, I. Takács, S. Murthy, J. R. Bratby, N. C. Holm, and S. G. E. Rönner-Holm. "Models for nitrification process design: one or two AOB populations?" Water Science and Technology 64, no. 3 (August 1, 2011): 568–78. http://dx.doi.org/10.2166/wst.2011.499.

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Models for engineering design of nitrifying systems use one ammonia oxidizer biomass (AOB) state variable. A simple extension using two AOB populations allows a more accurate prediction of nitrification systems at switching process environments. These two AOB subpopulations are characterized by two different sets of kinetic parameters. Selection pressure and competition between the two functional AOB populations are determined by process conditions as demonstrated by three case studies: Case study I describes dynamics of two AOB populations showing different temperature sensitivities (modified Arrhenius term on growth and decay) when bioaugmented from the warm sidestream treatment environment to the cold mainstream and vice-versa. Case study II investigates competition between fast growing μ-strategists and k-strategists adjusted to low ammonia levels depending on the internal mixed liquor recycle rate (IMLR). Case study III shows that AOB transferred from the waste activated sludge of an SBR to the parallel continuous flow system with different decay kinetics can overgrow or coexist with the original population.

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Quiroz, Juan Carlos, Elena Geangu, and Min Hooi Yong. "Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study." JMIR Mental Health 5, no. 3 (August 8, 2018): e10153. http://dx.doi.org/10.2196/10153.

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Background Research in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data. Objective The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual’s emotional state. We present our findings of a user study with 50 participants. Methods The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual “movie clips” and audio “music clips”). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual’s emotion. Results Overall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<.006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness. Conclusions Our findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.

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Bowen, Judy, Anke Dittmar, and Benjamin Weyers. "Task Modelling for Interactive System Design." Proceedings of the ACM on Human-Computer Interaction 5, EICS (May 27, 2021): 1–22. http://dx.doi.org/10.1145/3461736.

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Task models have been used for decades in interactive system design and there are several mature modelling approaches with corresponding tool support. However, in our own work, we have also experienced their limitations, especially in situations where task models are partial ancillary models and not primary artifacts. This was one of the motivations for this paper, which presents a systematic examination of literature to better understand the current place of task models in the continual evolution of user-centred software development practices. While overview work in this domain typically focuses on the analysis of representative task modelling notations and/or tools and relies on foundation papers, we apply a mixed top-down and bottom-up approach to identify relevant themes and trends in the use of task models over the last twenty-years. The paper identifies and discusses dominant patterns of use as well as gaps. It provides a comprehensive framing of both past and present trends in task modelling and supports those who want to incorporate task modelling in their own work. From this we identify areas of research that should receive greater attention in order to address future considerations.

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Bansal, Vikrant, John D. Perkins, and Efstratios N. Pistikopoulos. "A Case Study in Simultaneous Design and Control Using Rigorous, Mixed-Integer Dynamic Optimization Models." Industrial & Engineering Chemistry Research 41, no. 4 (February 2002): 760–78. http://dx.doi.org/10.1021/ie010156n.

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Ellison, Misoo C. "Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials, by Toshiro Tango." Journal of Biopharmaceutical Statistics 27, no. 6 (October 17, 2017): 1121–22. http://dx.doi.org/10.1080/10543406.2017.1362625.

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Li, Fangyi, Jie Liu, Guilin Wen, and Jianhua Rong. "Extending SORA method for reliability-based design optimization using probability and convex set mixed models." Structural and Multidisciplinary Optimization 59, no. 4 (October 31, 2018): 1163–79. http://dx.doi.org/10.1007/s00158-018-2120-2.

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