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Porous metal materials are widely used in heat-absorbing, biomedical parts and other fields because of their excellent mechanical properties and biocompatibility. Further applications of porous metal materials are inseparable from secondary precision machining. However, traditional cutting methods tend to compromise the original porosity of the material surface. Electrical discharge machining (EDM) is a type of non-conventional machining method which is more preferable to overcome these defects. In the present study, the main target is to investigate the effect of four input electrical machining parameters (current, frequency, efficiency and gap voltage) on the five output responses (material removal rate (MRR), electrode wear, surface roughness (SR), noise and time consumption value of environmental particulate matter (PM2.5)) when electrical discharge machining porous 316L stainless steel and compared with that of pure 316L stainless steel. The Taguchi method was employed to determine the relationship between electrical discharge machining parameters and output responses. The analysis of variance (ANOVA) was used to evaluate the effect of input parameters on output responses. The effects of electrical machining parameters on above responses were analyzed by main effects, and the order of influence was obtained, respectively. Finally, the surface morphology was observed by scanning electron microscope (SEM), and the effect of electrical machining parameters on the characteristics and formation of machined surface of porous stainless steel was mainly analyzed. [ABSTRACT FROM AUTHOR]

Supersaturated designs offer cost-effective efficacy in discerning significant factors among a vast array of potential factors, thereby rendering them valuable. The current literature studies several design selection criteria and analysis methods for such designs. For two-level designs, the screening performance of optimal designs constructed under different optimality criteria remains similar, especially when the effect directions are not known in advance. The Gauss-Dantzig Selector (GDS) is the preferred analysis method for two-level designs. For the multi- and mixed-level supersaturated designs, despite the existence of multiple design optimality criteria and design construction methods, the literature lacks guidance for both the design selection and the choice of analysis method. Through extensive simulation studies, we show that the multi- and mixed-level designs constructed using different optimality criteria have equivalent screening performance for the unknown effect directions. For known effect directions, generalized minimum aberration-optimal designs have slightly better screening performance. On the analysis front, however, the story differs from two-level designs. While LASSO and GDS show superior performance among the analysis methods compared, they depend on the parameterization or the coding of factors. Since no single choice of parameterization is best across sparsity levels, scenarios, and designs, we propose using group LASSO, which is invariant to parameterizations. Finally, we characterize the settings in terms of the number of runs, factors, and the effect sparsity, which are too complex to get meaningful results from group LASSO. [ABSTRACT FROM AUTHOR]

Supersaturated designs are used in science and engineering to efficiently explore a large number of factors with a limited number of runs. It is not uncommon in engineering to consider a few, if not all, factors at more than two levels. Multi- and mixed-level supersaturated designs may, therefore, be handy. While the two-level supersaturated designs are widely studied, the literature on multi- and mixed-level designs is still scarce. A recent paper establishes that the group LASSO should be preferred as an analysis method because it can retain the natural group structure of multi- and mixed-level designs. A few optimality criteria for such designs also exist in the literature. These criteria typically aim to find designs that maximize average pairwise orthogonality. However, the literature lacks guidance on the better or ‘right’ optimality criteria from a screening perspective. In addition, the existing optimal designs are often balanced and are rarely available. We propose two new optimality criteria based on the large-sample properties of group LASSO. Our criteria fill the gap in the literature by providing design selection criteria that are directly related to the preferred analysis method. We then construct Pareto-efficient designs on the two new criteria and demonstrate that (a) our optimality criteria can be used to order existing optimal designs on their screening performance, (b) the Pareto-efficient designs are often better than or as good as the existing optimal designs, and (c) the Pareto-efficient designs can be constructed using a coordinate exchange algorithm and are, therefore, available for any choice of the number of runs, factors, and levels. A repository of three- and four-level designs with the number of runs between 8 and 16 is also provided. [ABSTRACT FROM AUTHOR]

Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including emergent properties that arise implicitly from the interaction between simulation components. Antibody-antigen binding is a complex mechanism by which an antibody sequence wraps itself around an antigen with high affinity. In this study, we use a synthetic simulation framework for antibody-antigen folding and binding on a 3D lattice that include full details on the spatial conformation of both molecules. We investigate how emergent properties arise in this framework, in particular the physical proximity of amino acids, their presence on the binding interface, or the binding status of a sequence, and relate that to the individual and pairwise contributions of amino acids in statistical models for binding prediction. We show that weights learnt from a simple logistic regression model align with some but not all features of amino acids involved in the binding, and that predictive sequence binding patterns can be enriched. In particular, main effects correlated with the capacity of a sequence to bind any antigen, while statistical interactions were related to sequence specificity. [ABSTRACT FROM AUTHOR]

A factorial design examines the effects of two independent variables on a single, continuous dependent variable. The statistical test employed to analyze the data is a two-way analysis of variance (ANOVA). This test yields three results: a main effect for each of the independent variables and an interaction effect between the two independent variables. This article explains factorial designs and two-way ANOVA with the help of a worked example using hypothetical data in a spreadsheet provided as a supplementary file. The main effects and interaction effects are explained and illustrated using tables and figures. A short discussion provides general notes about the concepts explained in this article, along with brief notes on repeated measures ANOVA and higher order ANOVAs. Many additional examples, with figures and explanations, are provided in the supplementary materials, which the reader is strongly encouraged to view. [ABSTRACT FROM AUTHOR]

Objective: Abnormal cerebellar development has been implicated in attention-deficit/hyperactivity disorder (ADHD), although cerebro-cerebellar functional connectivity (FC) has yet to be examined in ADHD. Our objective is to investigate the disturbed cerebro-cerebellar FC in children and adolescents with ADHD.Method: We analyzed a dataset of 106 individuals with ADHD (68 children, 38 adolescents) and 62 healthy comparison individuals (34 children, 28 adolescents) from the publicly available ADHD-200 dataset. We identified 7 cerebellar subregions based on cerebro-cerebellar FC and subsequently obtained the FC maps of cerebro-cerebellar networks. The main effects of ADHD and age and their interaction were examined using 2-way analysis of variance.Results: Compared to comparisons, ADHD showed higher cerebro-cerebellar FC in the superior temporal gyrus within the somatomotor network. Interactions of diagnosis and age were identified in the supplementary motor area and postcentral gyrus within the somatomotor network and middle temporal gyrus within the ventral attention network. Follow-up Pearson correlation analysis revealed decreased cerebro-cerebellar FC in these regions with increasing age in comparisons, whereas the opposite pattern of increased cerebro-cerebellar FC occurred in ADHD.Conclusion: Increased cerebro-cerebellar FC in the superior temporal gyrus within the somatomotor network could underlie impairments in cognitive control and somatic motor function in ADHD. In addition, increasing cerebro-cerebellar FC in older participants with ADHD suggests that enhanced cerebellar involvement may compensate for dysfunctions of the cerebral cortex in ADHD. [ABSTRACT FROM AUTHOR]

In this paper, we applied the Sobol’s method on an already existing mathematical model of coronavirus disease 2019 (covid-19). The objectives of this research work are to study the individual effects of involved parameters as well as combine (mutual) effects of parameters on output variables of covid-19 model. The study is also useful to identify the ranking of key model parameters and factors fixing. The ultimate goal is to identify the controlling parameters, which eventually will help decision makers to explore various policy options to control the covid-19 pandemic. For this purpose, first we present the model with its basic properties that are positivity and existence of solution. Then use the Sobol’s method to discuss the individual effects of involved parameters as well as combine (mutual) effects of parameters on output variables of covid-19 model. Finally, we present the results, discussions and concluding remarks about key model parameters and identifying the controlling parameters, which eventually will help decision makers to explore various policy options to control the covid-19 pandemic.

Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of interest. It mainly considers contrasts that test standard main and interaction effects, even though more complex contrasts can in principle be used. Analyses, however, only focus on drawing conclusions about average effects and do not take into consideration interindividual differences in these effects. We propose an alternative approach to RM-ANOVA for analyzing repeated measures data, termed latent repeated measures analysis of variance (L-RM-ANOVA). The new approach is based on structural equation modeling and extends the latent growth components approach. L-RM-ANOVA enables the researcher to not only consider mean differences between different experimental conditions (i.e., average effects), but also to investigate interindividual differences in effects. Such interindividual differences are considered with regard to standard main and interactions effects and also with regard to customized contrasts that allow for testing specific hypotheses of interest. Furthermore, L-RM-ANOVA can include a measurement model for latent variables and can be used for the analysis of complex multi-factorial repeated measures designs. We conclude the presentation by demonstrating L-RM-ANOVA using a minimal repeated measures example. [ABSTRACT FROM AUTHOR]

This Short Methodological Report builds on research about moderation practices by focusing on a marginal effects approach to interpreting how a main effect is informed by the presence of a moderating variable. Following a content analysis of published studies and a survey of management researchers, our findings suggest there is a great deal of confusion about the ways in which to interpret how a main effect may fluctuate owing to a moderating variable. We therefore provide explicit instructions on how to implement and interpret a marginal effects approach that depicts the nature of a main effect in the presence of a moderator. We use different scenarios and examples to illustrate how researchers can employ the marginal effects technique, which provides an indication of the relationship between the independent and dependent variables over different values of the moderator. We argue and demonstrate that the marginal effects approach helps resolve conflicting findings that may arise from using other prevailing techniques to interpret both main effects and moderation. [ABSTRACT FROM AUTHOR]

Moderated regression models include an interaction, or product term, and can be used to assess whether the relationship between a given independent variable (IV) and a dependent variable (DV) depends on a third moderator variable (MV). If the moderation effect is significant, researchers recommend either ignoring main effects completely, or carefully interpreting them as conditional effects. However, when the moderation effect is not significant, this implies that the typical interpretation of main effects as average effects is appropriate. The present study challenges this claim since lack of significance may be due to lack of power rather than to no true population effect. To explore this idea, a simulation study is conducted and analytic illustration provided. Results indicate that when a true moderation effect exists, it may not be detected, implying the potential for misleading interpretation of main effects. To guard against this, applied researchers are encouraged to conduct power analyses prior to a moderation study; to mean-center predictors; to consider exploring the main-effects-only model by omitting the interaction effect; and to consider information criteria approaches to testing effects.

We investigate the role of orthogonal arrays (OAs) and balanced arrays (BAs) in both full and fractional factorial designs with N runs and m three-level quantitative factors. Firstly, due to the non-existence of the OA(18, 8, 3, 2), we find and construct a BA(18, 8, 3, 2) that represents the E-, A-, D-optimal design with N = 18 runs and m = 8 three-level factors under the main-effect model. Also, we are interested in comparing the OA(N, m, 3, 2)s with the BA(N, m, 3, 2)s, when they represent designs with N ≡ 0 (mod 9) runs and m three-level factors with respect to the E-, A-, D-criteria under the second-order model. We provide a generalized definition of balanced arrays. Moreover, we find and construct the OA(N, m, 3, 2)s and the BA(N, m, 3, 2)s that represent the E-, A-, D-optimal designs with N = 9 , 18, 27, 36 runs and m = 2 three-level factors under the second-order model. Furthermore, it is shown that the BA(18, m, 3, 2)s, m = 3 , 4 and a BA(27, 3, 3, 2) perform better than the OA(18, m, 3, 2)s, m = 3 , 4 and the OA(27, 3, 3, 3), respectively, when they represent the corresponding designs with respect to the E-, A-, D-criteria under the second-order model. [ABSTRACT FROM AUTHOR]

In this paper, we applied the Sobol's method on an already existing mathematical model of coronavirus disease 2019 (covid-19). The objectives of this research work are to study the individual effects of involved parameters as well as combine (mutual) effects of parameters on output variables of covid-19 model. The study is also useful to identify the ranking of key model parameters and factors fixing. The ultimate goal is to identify the controlling parameters, which eventually will help decision makers to explore various policy options to control the covid-19 pandemic. For this purpose, first we present the model with its basic properties that are positivity and existence of solution. Then use the Sobol's method to discuss the individual effects of involved parameters as well as combine (mutual) effects of parameters on output variables of covid-19 model. Finally, we present the results, discussions and concluding remarks about key model parameters and identifying the controlling parameters, which eventually will help decision makers to explore various policy options to control the covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014. [ABSTRACT FROM AUTHOR]

Acacia mangium is one of the most important hardwood plantation species in the tropics. The question of how to grow Acacia productively and sustainably remains a major issue for the forestry sector in many tropical countries. We analyzed the productivity of A. mangium plantations across five contrasting ecological zones in Vietnam. These covered gradients in rainfall from 1750 mm to 3060 mm and a mean annual temperature range of 22 – 27 0C. Plantation productivity across these zones varied from an MAI of 6 m3 ha y−1 to 31 m3 ha y−1. A comprehensive suite of statistical methods was utilized for variable and model selection to minimize confounding and multicollinearity issues among predictors and identify determinants of plantation productivity using 27 biophysical variables. These variables encompass plantation age, climate parameters, as well as site and soil properties. Results showed that, apart from plantation age, the factors influencing plantation productivity and explaining a majority of variation in mean annual timber increment are soil organic carbon content and the number of foggy days. Soil carbon is probably an index of overall soil fertility and its significance reflects the fact that the plantations have often been established on relatively degraded sites. The lower productivity at sites having a higher frequency of foggy days (up to 57 days per year in some regions) may arise from lower solar radiation and temperature during fog. The results of this study can inform site selection and plantation management tools aimed at maximizing Acacia mangium productivity in Vietnam and other countries with similar ecological conditions. Planting A. mangium in high-intensity fog areas is not recommended. Further studies on the ecophysiological mechanisms of how fog influences the growth of tropical A. mangium plantations are also needed. • We examined A. mangium plantation productivity across diverse ecological zones. • Rigorous statistical approach for variable and model selection employed. • Soil organic carbon and number of foggy days as key plantation productivity predictors. • Provided site selection and management tools for A. mangium in Vietnam. [ABSTRACT FROM AUTHOR]

To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a regression relationship between tool wear ratio and the process inputs has been developed.

Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure’s construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to manipulate it and little is known about the efficacy of dynamic figures. This present study compared how effectively static and dynamic graphical formats convey relationship information, and in particular variable interactions. Undergraduates (N = 128, 56% female, Mage = 18.9) were given a brief tutorial on main effects and interactions in data and then answered 48 multiple-choice questions about specific graphs. Each question involved one of four figure types and one of four relationship types (main effect only, interaction only, main effect and interaction, or no relationship), with relationship types and graphical formats fully crossed. Multilevel logistic regression analysis revealed that participants were fairly accurate at detecting main effects and null relationships but struggled with interaction effects. Additionally, the static 3D graph lowered performance for detecting main effects, although this negative effect disappeared when participants were allowed to rotate the 3D graph. These results suggest that dynamic figures in digital publications are a potential tool to effectively communicate data, but they are not a panacea. Undergraduates continued to struggle with more complicated relationships (e.g., interactions) regardless of graph type. Future studies will need to examine more experienced populations and additional dynamic graph formats, especially ones tailored for demonstrating interactions (e.g., profiler plots).

Detecting epistatic interaction is a typical way of identifying the genetic susceptibility of complex diseases. Multifactor dimensionality reduction (MDR) is a decent solution for epistasis detection. Existing MDR‐based methods still suffer from high computational costs or poor performance. In this paper, we propose a new solution that integrates a dual screening strategy with MDR, termed as DualWMDR. Particularly, the first screening employs an adaptive clustering algorithm with part mutual information (PMI) to group single nucleotide polymorphisms (SNPs) and exclude noisy SNPs; the second screening takes into account both the single‐locus effect and interaction effect to select dominant SNPs, which effectively alleviates the negative impact of main effects and provides a much smaller but accurate candidate set for MDR. After that, MDR uses the weighted classification evaluation to improve its performance in epistasis identification on the candidate set. The results on diverse simulation datasets show that DualWMDR outperforms existing competitive methods, and the results on three real genome‐wide datasets: the age‐related macular degeneration (AMD) dataset, breast cancer (BC), and celiac disease (CD) datasets from the Wellcome Trust Case Control Consortium, again corroborate the effectiveness of DualWMDR. [ABSTRACT FROM AUTHOR]

Graphs are useful tools to communicate meaningful patterns in data, but their efficacy varies considerably based on the figure's construction and presentation medium. Specifically, a digital format figure can be dynamic, allowing the reader to manipulate it and little is known about the efficacy of dynamic figures. This present study compared how effectively static and dynamic graphical formats convey relationship information, and in particular variable interactions. Undergraduates (N = 128, 56% female, M age = 18.9) were given a brief tutorial on main effects and interactions in data and then answered 48 multiple-choice questions about specific graphs. Each question involved one of four figure types and one of four relationship types (main effect only, interaction only, main effect and interaction, or no relationship), with relationship types and graphical formats fully crossed. Multilevel logistic regression analysis revealed that participants were fairly accurate at detecting main effects and null relationships but struggled with interaction effects. Additionally, the static 3D graph lowered performance for detecting main effects, although this negative effect disappeared when participants were allowed to rotate the 3D graph. These results suggest that dynamic figures in digital publications are a potential tool to effectively communicate data, but they are not a panacea. Undergraduates continued to struggle with more complicated relationships (e.g., interactions) regardless of graph type. Future studies will need to examine more experienced populations and additional dynamic graph formats, especially ones tailored for demonstrating interactions (e.g., profiler plots). [ABSTRACT FROM AUTHOR]

The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This article is about stochastic group weights in ANOVA models – a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments. We propose two new ways to incorporate stochastic group weights in the tests of average effects – one based on the general linear model and one based on multigroup structural equation models (SEMs). We show in simulation studies that our methods have nominal type I error rates in experiments with stochastic group weights while classic approaches show an inflated type I error rate. The SEM approach can additionally deal with heteroscedastic residual variances and latent variables. An easy-to-use software package with graphical user interface is provided. [ABSTRACT FROM AUTHOR]

The article is devoted to the theory of the design of experiments. It introduces a formal definition of factorial models and factorial designs. On this basis, it builds the mathematical foundations of the factorial design of experiments. The presented concept supports many important aspects of experimental design including the main one: the construction of the optimal designs. [ABSTRACT FROM AUTHOR]

The production of edible bird's nest (EBN) is an important income-generating agricultural activity in Malaysia. However, cleanliness inspection during EBN production poses certain difficulties due to issues such as labor dependency, human error, and inconsistency. Automated inspection remains a challenging task as there is no effective algorithm for impurities segmentation. Impurities, such as feathers, have similar color properties as EBN, making segmentation difficult due to the low contrast between the two features. We propose and demonstrate an experimental design-based lighting solution for improving the contrast ratio between the impurities and EBN in a scientific and systematic way. Lighting parameters, such as the configuration, angle, wavelength, and intensity of lighting, are investigated using a full factorial experimental design. The contrast ratio between the impurities and the EBN is chosen as the response variable in the experimental design. The main effects plot of the lighting parameters is obtained to identify the most optimal lighting parameters. The optimal lighting setup yields an improvement of 36.2%, with a contrast ratio of 0.79, compared to the normal fluorescent lighting setup, which has a contrast ratio of 0.58. This improvement would produce distinct intensity regions for impurities and EBN within an EBN image, therefore facilitating the segmentation operation. [ABSTRACT FROM AUTHOR]

Background: Most preference-based instruments producing overall values for health states are devised on the simplifying assumption that the overall effect of distinct health-related quality of life domains (attributes) of the instrument equals the sum of the attributes. Nevertheless, health attributes are often inter-related and depend on each other.Objectives: To investigate whether inclusion of second-order interactions in the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L) value function would result in better fit and lead to different health state values than a model with main effects only.Methods: Using an efficient design, 400 pairs of EQ-5D-3L health states were generated in a pairwise choice format. We analyzed responses of 4000 people from the general population using a conditional logit model, and we tested goodness of fit using pseudo R2, Akaike information criterion, differences in log-likelihood, and likelihood ratio. We compared accuracies of models' predictions based on root mean square error and mean absolute error.Results: The interaction-effects model showed systematically lower values than the main-effects model. Inclusion of interactions resulted only in a slightly better model fit. Interactions comprising mobility and self-care were the most salient.Conclusions: For the EQ-5D-3L, a value function based on interactions produces systematically lower values than a main-effects model, meaning that the effect of two or more health problems combined is stronger than the sum of the individual main effects. [ABSTRACT FROM AUTHOR]

In this paper, we propose two methods of constructing row-column designs for factorial experiments. The constructed designs have orthogonal factorial structure with balance and permits estimation of main effects with full efficiency. [ABSTRACT FROM AUTHOR]

A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model’s parameters, almost all existing estimation strategies are non-robust against outliers. Outliers in the quadratic and interaction effects may deteriorate the model structure more severely than their effects in the main effect. We propose a robust estimation strategy based on the robust functional principal component decomposition of the function-valued variables and τ -estimator. The performance of the proposed method relies on the truncation parameters in the robust functional principal component decomposition of the function-valued variables. A robust Bayesian information criterion is used to determine the optimum truncation constants. A forward stepwise variable selection procedure is employed to determine relevant main, quadratic, and interaction effects to address a possible model misspecification. The finite-sample performance of the proposed method is investigated via a series of Monte-Carlo experiments. The proposed method’s asymptotic consistency and influence function are also studied in the supplement, and its empirical performance is further investigated using a U.S. COVID-19 dataset. [ABSTRACT FROM AUTHOR]

Metformin represents the first line of treatment and is the most widely prescribed antihypergycemic drug in type 2 diabetic patients. It can be used as monotherapy or in combination with other oral antihyperglycemic drugs or insulin. Additionally, it is also prescribed in type 1 diabetic patients, it proved to be effective in prediabetes and also provided beneficial effects in other insulin resistant states, for example in polycystic ovary syndrome. Nevertheless, the exact molecular mechanism of its action remains unknown. It was shown that it inhibits liver gluconeogenesis, facilitates glucose uptake into peripheral tissues, such as striated muscle; it also acts in the gut. Besides antihyperglycemic effects, metformin was also shown to possess several beneficial, protective effects, so-called pleiotropic effects: particularly on the cardiovascular system and in cancer patients. Metformin has only few side effects, the most serious being metformin-associated lactic acidosis. The latter appears in rare clinical cases with pre-existing chronic kidney disease or advanced heart failure with tissue hypoperfusion, which consequently represent relative contraindications for metformin use. In the past, metformin treatment was usually discontinued when performing iodine contrast imaging, however recently there is evidence of its safety even in patients with higher stages of chronic kidney disease. All in all, metformin is a drug with a long tradition and a promising future.

The between-within split of total sum of squares in one-way analysis of variance (ANOVA) is intuitively appealing and computationally simple, whether balanced or not. In the balanced two-factor setting, the same heuristic and computations apply to analyse treatment sum of squares into main effects and interaction effects sums of squares. Accomplishing the same in unbalanced settings is more difficult, requiring development of tests of general linear hypotheses. However, textbooks treat unbalanced settings with proportional subclasss numbers (psn) as essentially equivalent to balanced settings. It is shown here that, while psn permit an ANOVA-like partition of sums of squares, test statistics for main effects of the two factors generally test the wrong hypotheses when the model includes interaction effects. [ABSTRACT FROM AUTHOR]

The multiple non symmetric correspondence analysis (MNSCA) is a useful technique for analyzing a two-way contingency table. In more complex cases, the predictor variables are more than one. In this paper, the MNSCA, along with the decomposition of the Gray–Williams Tau index, in main effects and interaction term, is used to analyze a contingency table with two predictor categorical variables and an ordinal response variable. The Multiple-Tau index is a measure of association that contains both main effects and interaction term. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition, while the interaction effect represents the combined effect of predictor categorical variables on the ordinal response variable. Moreover, for ordinal scale variables, we propose a further decomposition in order to check the existence of power components by using Emerson's orthogonal polynomials. [ABSTRACT FROM AUTHOR]

In recent years, anthocyanins are widely used in food industry as natural dyes and in medicine as compounds with high antioxidant activity. In connection with this, examination of adsorption removal of anthocyanins from extracts of cheap plants containing a lot of these compounds as well as of main and interaction effects which influent on adsorption of anthocyanins is important. In this paper, experiments were conducted according to full factorial experimental design to identify the main and interaction effects of factors and their influence on the removal efficiency of anthocyanins from red cabbage aqueous extracts by bentonite. The factors were investigated at two levels: contact time - 30 and 120 min, temperature - 293 and 323 K, initial anthocyanin concentration - 70 and 130 mg/L, and adsorbent mass - 10 and 25 g/L. The total number of 16 experiments was performed and each experiment was repeated thrice to evaluate an experimental error. Main and interaction effects were analysed using of variance (ANOVA), F-test and P-values to define most important process variables affecting the anthocyanin adsorption. A mathematical model that considers the significant main and interaction effects was suggested. The variance analysis and the statistic data showed that the model was adequate and had high coefficient of determination (R2 = 0.9954). The mathematical model obtained allows prediction of the removal of anthocyanins from red cabbage aqueous extracts with bentonite. We have found that the contact time and adsorbent mass have the positive effect, but the initial concentration of anthocyanins and temperature as well as two-way interaction “contact time-temperature”, three-way interactions “contact time-initial anthocyanin concentration-adsorbent mass” and “temperature-initial concentration of anthocyanins-adsorbent mass” have the negative effect on the removal of anthocyanins from red cabbage extracts by bentonite. The results indicated that higher removal efficiency of anthocynians from red cabbage aqueous extracts was 95 % at 120 min, 293 K, initial anthocyanin concentration 70 mg/L, and adsorbent mass 25 g/L. [ABSTRACT FROM AUTHOR]

For paired choice experiments, two new construction methods of designs are proposed for the estimation of the main effects. In many cases, these designs require about 30-50% fewer choice pairs than the existing designs and at the same time have reasonably high D-efficiencies for the estimation of the main effects. Furthermore, as against the existing efficient designs, our designs have higher D-efficiencies for the same number of choice pairs. [ABSTRACT FROM AUTHOR]

Este trabalho foi realizado com o objetivo de selecionar o arranjo para efeitos fixos e a estrutura de (co)variância residual que melhor representam a variabilidade dos pesos dentro do rebanho e dentro dos indivíduos, considerando-se dados de pesos de bovinos analisados como medidas repetidas. Foram utilizados dados de peso de 3.690 bovinos Canchim, obtidos ao nascimento, à desmama, aos 12 e aos 18 meses de idade. Analisaram-se diferentes arranjos para os efeitos fixos (grupos de contemporâneos e/ou efeitos principais de ano, mês ou época de nascimento e sexo do bezerro) e diferentes estruturas de (co)variâncias para os resíduos, considerando-se ou não alteração da variância residual ao longo da vida do animal e alteração da correlação entre as medidas tomadas em intervalos diferentes. Os resultados indicaram que o arranjo mais adequado dos efeitos fixos para representar a variabilidade dos pesos dos animais dentro do rebanho foi o grupo de contemporâneos formado por ano, mês e sexo do bezerro e que as melhores estruturas de (co)variâncias residuais foram a Fator Analítico de Primeira Ordem e a Não Estruturada, que consideram o aumento das variâncias com o aumento da idade do indivíduo e as correlações diferentes para cada par de medidas de peso.The aim of this work was to evaluate arrays of fixed effects and residual covariance structures that best fit the herd and the animal variability to weights at birth, weaning, twelve and eighteen months of 3,690 Canchim animals. Different arrays of fixed effects (contemporary groups and, or the main effects of year, month or season of birth and sex) and different residual covariance structures (considering or not change of variance and of correlation between weights at different ages) were studied. The results indicated that the most adequate array of fixed effects to fit herd variability was the contemporary group of year, month and sex. The best residual covariance structures were the First-Order Factor Analytic and the Unstructured, which consider increase of residual variance as age increases and different correlation between pair of weights at different ages.

Solanum torvum is a highly vigorous relative of eggplant that is resistant to a number of harmful soil-borne diseases and is compatible for grafting with eggplant. Being a potential rootstock, this plant frequently presents poor and erratic germination, which makes its practical use difficult. We used an L8 (2 7 ) orthogonal array design to evaluate the primary effects of seven factors (soaking of seeds, scarification with sodium hypochlorite (NaClO), application of gibberellic acid (GA 3 ), use of potassium mitrate (KNO 3 ) as a moistening agent, cold stratification, application of a heat shock, and light irradiation during germination) at two levels (L0 and L1) using four germination parameters (early and final germination, germination rate and vigour index) in fresh S. torvum seeds. S. torvum seeds had a strong dormancy with no germination in the untreated seeds and high early and final germination (approximately 100%) in certain treatments. An evaluation of the main effects revealed highly positive effects on germination from seed soaking, and the use of GA 3 , KNO 3 , and light irradiation, whereas NaClO scarification had a negative effect. The application of cold stratification and heat shock treatments also had a positive effect on seed germination but to a lesser extent than the other treatments. An improved proposed protocol that consisted of subjecting seeds to soaking, the application of GA 3 and KNO 3 , cold stratification, heat shock, and light irradiation was validated and demonstrated to be highly effective, with seed germination success greater than 60% being observed at 3 days and final germination reaching a plateau at 6 days. A second validation experiment using a commercial growing substrate also showed a high emergence (approximately 50%) at 7 days and a final germination of approximately 80% was recorded with application of the improved protocol. The seed germination protocol that we have developed will facilitate the use of S. torvum as a rootstock for eggplant and its use in breeding programmes. Our results also reveal that orthogonal array designs are a powerful tool for establishing improved protocols for seed germination. [ABSTRACT FROM AUTHOR]

Computer simulators are used in science and technology to model physical processes or the behavior of real-world systems. Sensitivity analysis provides a useful tool for quantifying the impact of uncertainty in the computer simulator inputs on the computed output. We focus on global sensitivity analysis, which quantifies output uncertainty as all the inputs vary continuously over the input space. The influence of each input and how uncertainty in the output is apportioned among the inputs are determined by calculating the main effects and sensitivity indices of the computer simulator inputs. Typically, these quantities are computed using Monte Carlo methods, which require a large number of computer simulator runs, making the calculations infeasible if the simulator is computationally expensive. Bayesian methods have been used to tackle sensitivity analysis of computationally expensive simulators through building a statistical emulator for the computer simulator output, typically, based on a Gaussian process prior for the simulator output function. In this work, we develop an approach for integrating global sensitivity analysis tools and extending semi-Bayesian approaches to a fully Bayesian methodology. The approach is used to carry out sensitivity analysis of the Leaf–Canopy Model, a radiative transfer model that simulates the interaction of sunlight with vegetation. Matlab codes are available online as supplementary material to this article. [ABSTRACT FROM AUTHOR]

Steel wire quality is measured in terms of tensile strength, yield strength, elongation and relaxation. Minimum requirements for these specifications are defined by international standards. This research intends to assist in setting the steel wire machine by identifying the relationship and appropriate range among manufacturing process variables and parameters. By knowing these relationships, the setting up machine can be done in short time which leading to cost reduction and achieving quality standard. The results reveal that the treatment setting of temperature was the most significant variable followed by tension. In particular, the three mechanical properties of tensile strength, yield strength and elongation achieved when temperature set at 350°C. Mathematical models were derived for the effect of process variables on tensile strength, yield strength and relaxation. Response optimisation was used to find a composite solution to optimise the response variables simultaneously. [ABSTRACT FROM AUTHOR]

Abstract: We consider inference for row effects in the presence of possible interactions in a two-way fixed effects model when the numbers of observations are themselves random variables. Let N ij be the number of observations in the cell, be the probability that a particular observation is in that cell and be the expected value of an observation in that cell. We assume that the have a joint multinomial distribution with parameters n and . Then is the expected value of a randomly chosen observation in the ith row. Hence, we consider testing that the are equal. With the unknown, there is no obvious sum of squares and F-ratio computed by the widely available statistical packages for testing this hypothesis. Let be the sample mean of the observations in the ith row. We show that is an MLE of is consistent and is conditionally unbiased. We then find the asymptotic joint distribution of the and use it to construct a sensible asymptotic size test of the equality of the and asymptotic simultaneous () confidence intervals for contrasts in the . [Copyright &y& Elsevier]

The classical F test for the hypothesis of no main fixed effects in the two-way crossed mixed effects design is derived under model assumptions that include normality, variance homogeneity, and symmetry. The symmetry assumption specifies that the random main and interaction effects are independent. While it is known that the F test is robust against violations of the normality assumption and/or the homoscedasticity assumption in the balanced case, the effects of the symmetry assumption have not been investigated. Our simulations demonstrate that the F test becomes very liberal under violations of the symmetry assumption. A new test procedure is developed under a more flexible model, which does not require the restrictive assumptions of the classical model. By considering the cell sample sizes to be random, the procedure applies equally well to unbalanced designs and to designs with empty cells, provided the missingness is completely at random. The asymptotic theory of the test statistic pertains to designs where the number of levels of both the fixed and random effects is large. The limiting distribution of the proposed test statistic is an infinite weighted sum of independent χ2 1 random variables. An approximation to this limiting distribution is proposed. Extensive simulations indicate that the proposed test procedure achieves levels reasonably close to the nominal ones under violations of the symmetry and other classical assumptions, and under different patterns of missingness. An analysis of a dataset from the Mussel Watch Project is presented. The supplementary materials for this article are available online and contain the proofs pertaining to the asymptotic distribution of the proposed statistic, as well as the results of simulations with nonnormal errors and random effects. [ABSTRACT FROM PUBLISHER]

Scientists in the fields of nutrition and other biological sciences often design factorial studies to test the hypotheses of interest and importance. In the case of two-factorial studies, it is widely recognized that the analysis of factor effects is generally based on treatment means when the interaction of the factors is statistically significant, and involves multiple comparisons of treatment means. However, when the two factors do not interact, a common understanding among biologists is that comparisons among treatment means cannot or should not be made. Here, we bring this misconception into the attention of researchers. Additionally, we indicate what kind of comparisons among the treatment means can be performed when there is a nonsignificant interaction among two factors. Such information should be useful in analyzing the experimental data and drawing meaningful conclusions. [ABSTRACT FROM AUTHOR]

Jones and Nachtsheim (2011) propose a new class of designs for definitive screening. These designs have very nice properties for practical use. Their construction approach requires a computerized search. This short note provides a theoretical basis for design construction, making use of conference matrices. The design construction is straightforward, and the resulting design is always a global optimum definitive screening design. The proposed method only works when the number of factors is even, however. [ABSTRACT FROM AUTHOR]

When interaction terms exist in a two-factor, factorial experiment, the consideration and analysis of main effects are often restricted to those situations where the interaction between factors is not significant. Hinkelman and Kempthorne [4] softened that stance somewhat and advocate testing main effects when the interaction is deemed co-directional but not anti-directional. A test for the main effects in that situation may be pragmatic to the practitioner and appealing to researchers in other disciplines. Intersection-union and union-intersection methods are examined for assessing the directional nature of significant interactions so that the main effects in a two-factor factorial may be evaluated. The tests suggested are conceptually straightforward and practical and maintain the nominal Type-I error rate. Examples are provided to illustrate the methods. [ABSTRACT FROM AUTHOR]

Process models are widely used tools, both for studying fundamental processes themselves and as elements of larger system studies. A radiative transfer model (RTM) simulates the interaction of light with a medium. We are interested in RTMs that model light reflected from a vegetated region. Such an RTM takes as input various biospheric and illumination parameters and computes the upwelling radiation at the top of the canopy. The question we address is as follows: Which of the inputs to the RTM has the greatest impact on the computed observation? We study the leaf canopy model (LCM) RTM, which was designed to study the feasibility of observing leaf chemistry remotely. Its inputs are leaf chemistry variables (chlorophyll, water, lignin, and cellulose) and canopy structural parameters (leaf area index, leaf angle distribution, soil reflectance, and sun angle). We present a statistical approach to the sensitivity analysis of RTMs to answer the question previously posed. The focus is on global sensitivity analysis, studying how the RTM output changes as the inputs vary continuously according to a probability distribution over the input space. The influence of each input variable is captured through the "main effects" and "sensitivity indices." Direct computation requires extensive computationally expensive runs of-the RTM. We develop a Gaussian process approximation to the RTM output to enable efficient computation. We illustrate how the approach can effectively determine the inputs that are vital for accurate prediction. The methods are applied to the LCM with seven inputs and output obtained at eight wavelengths associated with Moderate-resolution Imaging Spectroradiometer bands that are sensitive to vegetation. [ABSTRACT FROM AUTHOR]

Zirconium alloy is the main structural material used in the nuclear fuel production. This material has distinct advantages like less thermal neutron absorption cross-section and better chemical and mechanical properties for the reactor-working conditions. This material is widely used in boiling water and heavy water type reactors. Natural Uranium Dioxide (UO2) pellets are loaded into thin wall zirconium alloy tubes and ends are hermitically sealed by welding zirconium alloy plugs using resistance welding under helium atmosphere. Industrial experimentation is carried out for this welding process and the responses are measured in the form of weld deformation (upset), ultrasonic testing, and metallographic tests. The collected data is statistically analyzed for determining the optimum weld parameters. The impact of variation in the set values of process parameters on weld quality is studied by sensitivity analysis using simulation on the response models developed. Accordingly these values are set as operational control limits and a weld acceptance criterion is developed as an input for Data Acquisition System (DAS) for online monitoring of welding process at a sampling rate of 20 K per sec. [ABSTRACT FROM AUTHOR]

Within the context of choice experimental designs, most authors have proposed designs for the multinomial logit model under the assumption that only the main effects matter. Very little attention has been paid to designs for attribute interaction models. In this article, three types of Bayesian D-optimal designs for the multinomial logit model are studied: main-effects designs, interaction-effects designs, and composite designs. Simulation studies are used to show that in situations where a researcher is not sure whether or not attribute interaction effects are present, it is best to take into account interactions in the design stage. In particular, it is shown that a composite design constructed by including an interaction-effects model and a main-effects model in the design criterion is most robust against misspecification of the underlying model when it comes to making precise predictions. [ABSTRACT FROM AUTHOR]