On the construction of experimental designs for a given task by jointly optimizing several quality criteria: Pareto-optimal experimental designs

Experimental designs for a given task should be selected on the base of the problem being solved and of some criteria that measure their quality. There are several such criteria because there are several aspects to be taken into account when making a choice. The most used criteria are probably the so-called alphabetical optimality criteria (for example, the A-, E-, and D-criteria related to the joint estimation of the coefficients, or the I- and G-criteria related to the prediction variance). Selecting a proper design to solve a problem implies finding a balance among these several criteria that measure the performance of the design in different aspects. Technically this is a problem of multi-criteria optimization, which can be tackled from different views. The approach presented here addresses the problem in its real vector nature, so that ad hoc experimental designs are generated with an algorithm based on evolutionary algorithms to find the Pareto-optimal front. There is not theoretical limit to the number of criteria that can be studied and, contrary to other approaches, no just one experimental design is computed but a set of experimental designs all of them with the property of being Pareto-optimal in the criteria needed by the user. Besides, the use of an evolutionary algorithm makes it possible to search in both continuous and discrete domains and avoid the need of having a set of candidate points, usual in exchange algorithms.Projects CTQ2011-26022(SpanishMinisteriodeEconomíayCompetitividad)andBU108A11-2(JuntadeCastillayLeón)

How do tsetse recognise their hosts? The role of shape in the responses of tsetse (Glossina fuscipes and G. palpalis) to artificial hosts

Palpalis-group tsetse, particularly the subspecies of Glossina palpalis and G. fuscipes, are the most important transmitters of human African trypanomiasis (HAT), transmitting .95% of cases. Traps and insecticide-treated targets are used to control tsetse but more cost-effective baits might be developed through a better understanding of the fly’s host-seeking behaviour.Electrocuting grids were used to assess the numbers of G. palpalis palpalis and G. fuscipes quanzensis attracted to and landing on square or oblong targets of black cloth varying in size from 0.01 m2 to 1.0 m2. For both species, increasing the size of a square target from 0.01 m2 (dimensions = 0.1 x 0.1 m) to 1.0 m2 (1.0 x 1.0 m) increased the catch ,4x however the numbers of tsetse killed per unit area of target declined with target size suggesting that the most cost efficient targets are not the largest. For G. f. quanzensis, horizontal oblongs, (1 m wide x 0.5 m high) caught, 1.8x more tsetse than vertical ones (0.5 m wide x 1.0 m high) but the opposite applied for G. p. palpalis. Shape preference was consistent over the range of target sizes. For G. p. palpalis square targets caught as many tsetse as the oblong; while the evidence is less strong the same appears to apply to G. f. quanzensis. The results suggest that targets used to control G. p. palpalis and G. f. quanzensis should be square, and that the most cost-effective designs, as judged by the numbers of tsetse caught per area of target, are likely to be in the region of 0.25 x 0.25 m2. The preference of G. p. palpalis for vertical oblongs is unique amongst tsetse species, and it is suggested that this response might be related to its anthropophagic behaviour and hence importance as a vector of HAT

Asymptotic existence of orthogonal designs

v, 115 leaves ; 29 cmAn orthogonal design of order n and type (si,..., se), denoted OD(n; si,..., se), is a square matrix X of order n with entries from {0, ±x1,..., ±xe}, where the Xj’s are commuting variables, that satisfies XX* = ^ ^g=1 sjx^j In, where X* denotes the transpose of X, and In is the identity matrix of order n. An asymptotic existence of orthogonal designs is shown. More precisely, for any Atuple (s1,..., se) of positive integers, there exists an integer N = N(s1,..., se) such that for each n > N, there is an OD(2n(s1 + ... + se); 2ns1,..., 2nse). This result of Chapter 5 complements a result of Peter Eades et al. which in turn implies that if the positive integers s1, s2,..., se are all highly divisible by 2, then there is a full orthogonal design of type (s1, s2,..., se). Some new classes of orthogonal designs related to weighing matrices are obtained in Chapter 3. In Chapter 4, we deal with product designs and amicable orthogonal designs, and a construction method is presented. Signed group orthogonal designs, a natural extension of orthogonal designs, are introduced in Chapter 6. Furthermore, an asymptotic existence of signed group orthogonal designs is obtained and applied to show the asymptotic existence of orthogonal designs

A Practical Primer To Power Analysis for Simple Experimental Designs

Power analysis is an important tool to use when planning studies. This contribution aims to remind readers what power analysis is, emphasize why it matters, and articulate when and how it should be used. The focus is on applications of power analysis for experimental designs often encountered in psychology, starting from simple two-group independent and paired groups and moving to one-way analysis of variance, factorial designs, contrast analysis, trend analysis, regression analysis, analysis of covariance, and mediation analysis. Special attention is given to the application of power analysis to moderation designs, considering both dichotomous and continuous predictors and moderators. Illustrative practical examples based on G*Power and R packages are provided throughout the article. Annotated code for the examples with R and dedicated computational tools are made freely available at a dedicated web page (https://github.com/mcfanda/primerPowerIRSP). Applications of power analysis for more complex designs are briefly mentioned, and some important general issues related to power analysis are discussed

Effects of Instrument Handle Design on Dental Hygienists\u27 Forearm Muscle Activity During Scaling

Purpose: The purpose of this study was to determine the effects of 4 different commercially available instrument handle designs (A. 16 grams and 12.7 mm diameter, B. 23 grams and 11.1 mm diameter, C. 21 grams and 7.9 mm diameter and D. 18 grams and 6.35 mm diameter) on the muscle activity of four forearm muscles during a simulated scaling experience. Methods: A convenience sample of 27 (n=27) dental hygienists used a Columbia 13/14 curet with four different instrument handles to scale artificial calculus from typodont teeth. Each participant\u27s muscle activity was measured using surface electromyography (sEMG). Results: Similar muscle activity was generated when scaling with instruments at 16, 18, and 21 grams with varying diameter handles. Instrument B generated significantly more muscle activity when compared to each of the other instrument handle designs (p=0.001, p=0.002, p=0.039). The lower left quadrant displayed significantly less muscle activity during scaling than the upper and lower right quadrants (p=0.026, p=0.000), although no significant interaction effect was found with instruments within quadrants. Most participants (62.96%) preferred instrument A, which was rated more comfortable based on weight when compared to the other instruments tested. Conclusions: Instrument handle design has an effect on forearm muscle activity when scaling in a simulated environment. The heaviest instrument with a relatively large diameter (B 11.1 mm and 23 g) generated significantly more overall mean muscle activity compared to the other three instruments. Similar amounts of muscle activity were produced by instruments weighing between 16 and 21 g. Participants\u27 instrument preferences were more affected by handle diameter than weight. Results support the need for further research to determine the impact of these findings on muscle load related to risk of musculoskeletal disorders in a real-world setting

Efficient Generalized Least Squares Method for Mixed Population and Family‐based Samples in Genome‐wide Association Studies

Genome‐wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed‐sample data, but few methods have been proposed for the analysis of genotype‐by‐environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family‐based designs. We propose a novel method—generalized least squares (GLX)—to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran–Armitage trend test, and the W QLS and M QLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome‐wide interaction with insecticide exposure—rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107571/1/gepi21811.pd