Real-World Parameter Tuning Using Factorial Design with Parameter Decomposition

Phenolic compounds are common pollutants found particularly in the effluents from petroleum and petrochemical, coal conversion, and phenol producing industries. Due to their toxicity and carcinogenity, water and wastewater containing phenolic compounds must be treated before being used and discharged for receiving water bodies. There is a growing interest in using low-cost and commercially available materials for the adsorption of organic compounds. Clay minerals have great potential to remove phenols in water due to efficient sorbents which can be used as alternative adsorbents to replace the costly activated carbon. They are widely applied in many fields of adsorption technology including the removal of amines, metals, ketones, phosphates, chlorophyll, non-ionic contaminants, and organic pigments/dyes. Montmorillonitic smectites, {(Na,Ca)0.33(Al,Mg)2(Si4O10)-(OH)2.nH2O} are one of the natural clay minerals that have specific surface chemical properties. In this study, a factorial experimental design technique was used to investigate the adsorption of phenol from water solution on montmorillonite. Factorial design of experiments is employed to study the effect of three factors: pH (2.0 and 8.0), montmorillonite dosage (0.1 and 1.0 g/L) and initial low and high concentration (10 and 100 mg/L). The efficiency of phenol adsorption was determined after 60 min of treatment. Main effects and interaction effects of three factors were analyzed by using statistical techniques. A regression model was found to fit the experimental data very well. Through using the Student’s t-test, analysis of variance, F-test and lack of fit to define most important process variables affecting the percentage phenol adsorption.

Statistics for experimenters: design, innovation, and discovery

Pig Longissimus lumborum proteome: Part I. Effects of genetic background, rearing environment and gender

Parameter setting of Evolutionary Algorithms is a time consuming task with two main approaches: parameter tuning and parameter control. In this work we describe a new methodology for tuning parameters of Genetic Programming algorithms using factorial designs, one-factor designs and multiple linear regression. Our experiments show that factorial designs can be used to determine which parameters have the largest effect on the algorithm's performance. This way, parameter setting efforts can focus on them, largely reducing the parameter search space. Two classical GP problems were studied, with six parameters for the first problem and seven for the second. The results show the maximum tree depth as the parameter with the largest effect on both problems. A one-factor design was performed to fine-tune tree depth on the first problem and a multiple linear regression to fine-tune tree depth and number of generations on the second.

Designing and analysing two-level factorial or fractional factorial experiments require all experimental runs should be completed. However, in many actual industrial experiments, a portion of experimental runs cannot be completed due to either infeasibility or budget limitation, resulting in incomplete experiments. In this paper, two effective methods to estimate the factorial effects in incomplete 2 level factorial or fractional factorial experiments are developed. In the first method, each traditional factorial main effect or interaction effect is decomposed as the average of many individual elementary effects. Under this model, missing runs in factorial experiments only affect some individual elementary effects; the main effects or interactions can still be estimated by the partial average of the remaining individual elementary effects. The second method is based on estimation of missing runs by using response surface model on incomplete experimental data set. These two methods are compared with other two existing methods for incomplete factorial experiments on eight testing problems and the results show our two methods perform well.

BackgroundMulti-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs fall under several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent a combination of factorial and MAMS trial designs and can provide increased efficiency relative to fixed, traditional designs. We explore design choices associated with Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs, which represent the combination of factorial and MAMS designs.MethodsSimulation studies were conducted to assess the impact of the type of analyses, the timing of analyses, and the effect size observed across multiple outcomes on trial operating characteristics for a FAST design. Given multiple outcomes types assessed within the hypothetical trial, the primary analysis approach for each assessment varied depending on data type.ResultsThe simulation studies demonstrate that the proposed class of FAST trial designs can offer a framework to potentially provide improvements relative to other trial designs, such as a MAMS or factorial trial. Further, we note that the design implementation decisions, such as the timing and type of analyses conducted throughout trial, can have a great impact on trial operating characteristics.ConclusionsMotivated by a trial currently under design, our work shows that the FAST category of trial can potentially offer benefits similar to both MAMS and factorial designs; however, the chosen design aspects which can be included in a FAST trial need to be thoroughly explored during the planning phase.

This book on experimental design is somewhat similar in flavor to the classic book by G.E

Fractional factorial design to study admixtures used for 3D concrete printing applications

Individualism refers to personal interests while collectivism refers to collective interests. Formal and informal reporting channels are accesses to report wrongdoings. This research aims to test the causality effect between cultural orientation and whistleblowing intention. The second objectives is to test the causality effect between reporting channel to whistleblowing intention. The third objectives is to examines the interaction effect between cultural orientation and reporting channel to whistleblowing intention. These research use 2x2 between subjects’ factorial design. The manipulation of this research is cultural orientation (individualism and collectivism) and reporting channels (formal and informal). The subjects in this research are accounting students of two private universities in Central Java and Bali. The variable measured is whistleblowing intention. The results of the main effects test show that the cultural orientation has an effect on the intention to complain about fraud. The testing of other main effects shows that the reporting channel has an effect on the whistleblowing intention. Testing the interaction effect shows that cultural orientation influences the intentions of whistleblowing intention depending on the reporting channels provided.

Factorial design, formulation, in vitro and in vivo evaluation of rapid orally disintegrating tablets prepared by sublimation technique using captopril as a model drug

Context Few studies have evaluated the effects of providing concentrate pre- or postpartum, or during both of these intervals, to beef cows grazing tropical pastures, even though this information is important for planning breeder herd management. Aims A study was undertaken to examine the effects of supplementation with concentrates before calving (prepartum), after calving (postpartum) or during both these intervals on production and reproductive efficiency in Bos indicus beef cows grazing Urochloa decumbens pasture. Methods Nellore cows, initially 6 months pregnant (mean ± s.e.), weighing 516 ± 1.3 kg and body condition score 4.7 ± 0.15 (1–9 scale), were used in an experiment with four treatments in a 2 × 2 factorial design with 20 animals per treatment. The treatments comprised no supplementation or provision of a supplement (1 kg/cow.day containing 28% crude protein) for 90 days prepartum only, 90 days postpartum only or during the pre- and postpartum intervals. Key results Responses could be described in terms of the main effects of providing the supplement pre- or post-calving; the interactions were not significant (P > 0.05). Prepartum supplements increased the cow bodyweight at calving by 29 kg, and calf birthweight by 3.7 kg (P 4 did not change the response to postpartum supplementation. Implications Prepartum supplementation may increase cow performance and calf weight at birth, whereas postpartum supplementation may improve the reproductive efficiency.

Nonparametric analysis of variance tests for factorial experiments, available in the literature, relate mostly to main effects in two-way layout linear models without interactions. Although the approach of Lehmann (1964) (see also Puri and Sen (1967)) can be adapted to construct tests for interactions, such tests are rather tedious to apply and are only asymptotically distribution-free. To obviate these drawbacks, the theory of permutationally distribution-free rank order tests for main effects, developed independently by the two authors, Sen (1968), Mehra (1968) (see also Mehra and Sarangi (1967)), is further extended in this paper to provide suitable tests for interactions in factorial experiments. The asymptotic power-efficiencies of the proposed tests are also studied. [The results of this paper were obtained independently by the two authors using, respectively, extensions of the Hajek (1962) and the Chernoff-Savage (1958) theorems on the asymptotic normality of rank order statistics.]

An intuitive design pattern for sequentially estimating parameters of a 2k factorial experiment with active confounding avoidance and least treatment combinations

Time trend resistant fractional factorial experiments have often been based on regular fractionated designs where several algorithms exist for sequencing their runs in minimum number of factor-level changes (i.e. minimum cost) such that main effects and/or two-factor interactions are orthogonal to and free from aliasing with the time trend, which may be present in the sequentially generated responses. On the other hand, only one algorithm exists for sequencing runs of the more economical non-regular fractional factorial experiments, namely Angelopoulos et al. [1]. This research studies sequential factorial experimentation under non-regular fractionated designs and constructs a catalog of 8 minimum cost linear trend-free 12-run designs (of resolution III) in 4 up to 11 two-level factors by applying the interactions-main effects assignment technique of Cheng and Jacroux [3] on the standard 12-run Plackett–Burman design, where factor-level changes between runs are minimal and where main effects are orthogonal to the linear time trend. These eight 12-run designs are non-orthogonal but are more economical than the linear trend-free designs of Angelopoulos et al. [1], where they can accommodate larger number of two-level factors in smaller number of experimental runs. These non-regular designs are also more economical than many regular trend-free designs. The following will be provided for each proposed systematic design:(1) The run order in minimum number of factor-level changes.(2) The total number of factor-level changes between the 12 runs (i.e. the cost).(3) The closed-form least-squares contrast estimates for all main effects as well as their closed-form variance–covariance structure.In addition, combined designs of each of these 8 designs that can be generated by either complete or partial foldover allow for the estimation of two-factor interactions involving one of the factors (i.e. the most influential).

Toxicity assessment of organic contaminants: Evaluation of mixture effects in model industrial mixtures using 2 n full factorial design