Journal articles on the topic 'Complex factorial experiments'
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1
Drinkwater, Laurie E. "Cropping Systems Rsearch: Reconsidering Agricultural Experimental Approaches." HortTechnology 12, no. 3 (January 2002): 355–61. http://dx.doi.org/10.21273/horttech.12.3.355.
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Systems approaches to research can be used to study characteristics of agricultural systems that cannot be addressed using conventional factorial experiments. The goal of a factorial experiment is to break down a complex system in order to isolate and study specific components and identify cause-effect relationships. In contrast, systems experiments aim to understand how a complex system functions as a whole and thus requires that intact systems be studied. Two approaches have been successfully applied to agricultural systems research: 1) field station experiments where simulated cropping systems are established in replicated plots and 2) studies of intact agroecosystems using commercial farms as study sites. These two approaches have complementary strengths and limitations and have made significant contributions to our understanding of ecological processes in agricultural systems. The development of sustainable agroecosystems will be best accomplished using an integrated research approach combining systems experiments with appropriately designed factorial experiments.
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Cervantes, Michel J., and T. Fredrik Engstro¨m. "Factorial Design Applied to CFD." Journal of Fluids Engineering 126, no. 5 (September 1, 2004): 791–98. http://dx.doi.org/10.1115/1.1792277.
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Factorial design, a statistical method widely used for experiments, and its application to CFD are discussed. The aim is to propose a systematic, objective, and quantitative method for engineers to design a set of simulations in order to evaluate main and joint effects of input parameters on the numerical solution. The input parameters may be experimental uncertainty on boundary conditions, unknown boundary conditions, grid, differencing schemes, and turbulence models. The complex flow of the Turbine-99 test case, a hydropower draft tube flow, is used to illustrate the method, where four factors are chosen to perform a 24 factorial design. The radial velocity at the inlet (not measured) is shown to have an important influence on the pressure recovery (7%) and the energy loss factor (49%).
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Łacka, Agnieszka. "NRC Designs—New Tools for Successful Agricultural Experiments." Agronomy 11, no. 12 (November 25, 2021): 2406. http://dx.doi.org/10.3390/agronomy11122406.
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In a nested row–column design (NRC), the experimental units in each of n blocks are grouped into n1 rows and n2 columns. Due to its structure, this experimental design allows full control of the experimental material and a relatively simple feedback loop within the “statistical triangle”. By applying such designs in agricultural experiments, we provide an insurance policy against future unexpected problems. Until now, the cost of this policy has been a complex statistical analysis of experimental data. This paper proposes a new “direct” approach to ANOVA based on the latest literature on the subject. The paper provides the theoretical foundations of this approach, indicates the possibility of applying it to factorial and near-factorial experiments, and supplements the theory with a familiar letter-based representation of all-pairwise comparisons, which has so far been lacking in the literature. The methodology is illustrated by the analysis of a field experiment carried out to improve the use of fungicides against late blight in tomato processing. The presented analytical tools are supplemented with code in R.
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Düval, Sabine, and Thomas Hinz. "Different Order, Different Results? The Effects of Dimension Order in Factorial Survey Experiments." Field Methods 32, no. 1 (November 19, 2019): 23–37. http://dx.doi.org/10.1177/1525822x19886827.
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Factorial surveys are widely used in the social sciences to measure respondents’ attitudes, beliefs, or behavioral intentions. In such surveys, respondents evaluate short descriptions of hypothetical situations, persons, or objects that vary across several dimensions. An important prerequisite of the method’s validity is that respondents are able to deal with the highly complex task created by the need to consider several variable dimensions within one coherent judgment. We analyze the effects of the order in which dimensions are presented in running text vignettes. An experimental setup with four order treatments was randomly allocated to 787 respondents (based on a random sample of register data), yielding 3,119 vignette evaluations. The analyses compare respondent groups across age, education, and response speed. Overall, there is no strong evidence for order effects. However, we find a slight tendency for fast responders to be more prone to recency effects.
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Schaarschmidt, Frank, and Lea Vaas. "Analysis of Trials with Complex Treatment Structure Using Multiple Contrast Tests." HortScience 44, no. 1 (February 2009): 188–95. http://dx.doi.org/10.21273/hortsci.44.1.188.
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Experiments with complex treatment structures are not uncommon in horticultural research. For example, in augmented factorial designs, one control treatment is added to a full factorial arrangement, or an experiment might be arranged as a two-factorial design with some groups omitted because they are practically not of interest. Several statistical procedures have been proposed to analyze such designs. Suitable linear models followed by F-tests provide only global inference for main effects and their interactions. Orthogonal contrasts are demanding to formulate and cannot always reflect all experimental questions underlying the design. Finally, simple mean comparisons following global F-tests do not control the overall error rate of the experiment in the strong sense. In this article, we show how multiple contrast tests can be used as a tool to address the experimental questions underlying complex designs while preserving the overall error rate of the conclusions. Using simultaneous confidence intervals allows for displaying the direction, magnitude, and relevance of the mean comparisons of interest. Along with application in statistical software, shown by two examples, we discuss the possibilities and limitations of the proposed approach.
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Aramesh, Saeed, and Ali Ghorbanian. "Multi-objective Optimization for a Complex Intersection Using Design of Experiments and Simulation." Journal Européen des Systèmes Automatisés 53, no. 6 (December 23, 2020): 791–802. http://dx.doi.org/10.18280/jesa.530605.
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Considering the importance of time in today's world and the rising traffic congestion in urban areas, using methods to reduce wait times and air pollution can have a significant impact on promoting urban management. Given the uncertainty in the number of vehicles and the emission rate of vehicles, a complex T intersection with three traffic lights was simulated in this study. Three objective functions were defined for the mean of wait time, average queue length, and aggregate pollutant emission of the vehicles in queue. First, regression equations for each of the variables were obtained by a full factorial design and analysis of variance, and the optimal period for each traffic light was then computed with a utility function approach. Finally, the results were compared to the results obtained from the optimization of each response variable OptQuest for Arena software.
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Jensen, Signe M., Christian Andreasen, Jens C. Streibig, Eshagh Keshtkar, and Christian Ritz. "A note on the analysis of germination data from complex experimental designs." Seed Science Research 27, no. 4 (September 18, 2017): 321–27. http://dx.doi.org/10.1017/s0960258517000228.
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AbstractIn recent years germination experiments have become more and more complex. Typically, they are replicated in time as independent runs and at each time point they involve hierarchical, often factorial experimental designs, which are now commonly analysed by means of linear mixed models. However, in order to characterize germination in response to time elapsed, specific event-time models are needed and mixed model extensions of these models are not readily available, neither in theory nor in practice. As a practical workaround we propose a two-step approach that combines and weighs together results from event-time models fitted separately to data from each germination test by means of meta-analytic random effects models. We show that this approach provides a more appropriate appreciation of the sources of variation in hierarchically structured germination experiments as both between- and within-experiment variation may be recovered from the data.
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Stenhouse, Neil, and Richard Heinrich. "Breaking Negative Stereotypes of Climate Activists: A Conjoint Experiment." Science Communication 41, no. 3 (May 13, 2019): 339–68. http://dx.doi.org/10.1177/1075547019848766.
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We tested 44 variations in profiles of climate change activists to see what affected willingness to associate with them. The largest effects were from activists’ perspectives on climate change, how often they pressure others, gun control views, and party affiliation. If implemented as a traditional factorial experiment, this experiment would require 648,000 conditions and an infeasibly large sample. We obtained our results much more efficiently via an experimental design rare in communication research. Conjoint experiments will be useful to science communication researchers who wish to simultaneously test many factors of complex stimuli, such as individuals, organizations, technologies, or policies.
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Cangussu, Nara, Ana Mafalda Matos, Paula Milheiro-Oliveira, and Lino Maia. "Modelling and Predicting Self-Compacting High Early Age Strength Mortars Properties: Comparison of Response Models from Full, Fractioned and Small Central Composite Designs." Applied Sciences 13, no. 14 (July 20, 2023): 8413. http://dx.doi.org/10.3390/app13148413.
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The mixture design of cement-based materials can be complex due to the increasing number of constituent raw materials and multiple requirements in terms of engineering performance and economic and environmental efficiency. Designing experiments based on factorial plans has shown to be a powerful tool for predicting and optimising advanced cement-based materials, such as self-compacting high-early-strength cement-based mortars. Nevertheless, the number of factor interactions required for factor scheduling increases considerably with the number of factors. Consequently, the probability that the interactions do not significantly affect the answer also increases. As such, fractioned factorial plans may be an exciting option. For the first time, the current work compares the regression models and the predicting capacity of full, fractionated (A and B fractions) and small factorial designs to describe self-compacting high-early-strength cement-based mortars’ properties, namely, the funnel time, flexure and compressive strength at 24 h for the function of the mixture parameters Vw/Vc, Sp/p, Vw/Vp, Vs/Vm and Vfs/Vs for the different factorial designs. We combine statistical methods and regression analysis. Response models were obtained from the full, fractionated, and small plans. The full and fractionated models seem appropriate for describing the properties of self-compacting high-early-strength cement-based mortars in the experimental region. Moreover, the predicting ability of the full and fractionated factorial designs is very similar; however, the small design predictions reveal some concerns. Our results confirm the potentiality of fractioned plans to reduce the number of experiments and consequently reduce the cost and time of experimentation when designing self-compacting high-early-strength cement-based mortars.
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Jankovic, Aleksandar, Gaurav Chaudhary, and Francesco Goia. "Designing the design of experiments (DOE) – An investigation on the influence of different factorial designs on the characterization of complex systems." Energy and Buildings 250 (November 2021): 111298. http://dx.doi.org/10.1016/j.enbuild.2021.111298.
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Oll, Josua, Rüdiger Hahn, Daniel Reimsbach, and Peter Kotzian. "Tackling Complexity in Business and Society Research: The Methodological and Thematic Potential of Factorial Surveys." Business & Society 57, no. 1 (April 22, 2016): 26–59. http://dx.doi.org/10.1177/0007650316645337.
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Factorial surveys (FSs) integrate elements of survey research and classical experiments. Using a large number of respondents in a controlled setting, FSs approximate complex and realistic judgment situations through so-called vignettes—that is, carefully designed descriptions of hypothetical people, social situations, or scenarios. Despite being rooted, and predominantly applied, in sociology, FSs are particularly promising for business and society (B&S) scholars. Given the multiplicity, inherent complexity, and sometimes fuzziness of B&S research objects, conventional research methods inevitably reach their limits. This article, therefore, systematically presents methodological and thematic opportunities for FS studies in B&S research. It is argued that FSs are well suited to dealing with the complex interplay of societal-, organizational-, and individual-level factors in B&S research and to studying the principles underlying human perceptions, attitudes, values, social norms, and (anticipated) behavior. The application of the FS method is illustrated based on a showcase example in the realm of socially responsible investments (SRIs). As the literature on the conceptualization of FSs is limited, methodological challenges are addressed to guide B&S researchers past the common methodological pitfalls.
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Lakens, Daniël, and Aaron R. Caldwell. "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs." Advances in Methods and Practices in Psychological Science 4, no. 1 (January 2021): 251524592095150. http://dx.doi.org/10.1177/2515245920951503.
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Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental designs. We have created the R package Superpower and online Shiny apps to enable researchers without extensive programming experience to perform simulation-based power analysis for ANOVA designs of up to three within- or between-participants factors. Predicted effects are entered by specifying means, standard deviations, and, for within-participants factors, the correlations. The simulation provides the statistical power for all ANOVA main effects, interactions, and individual comparisons. The software can plot power across a range of sample sizes, can control for multiple comparisons, and can compute power when the homogeneity or sphericity assumption is violated. This Tutorial demonstrates how to perform a priori power analysis to design informative studies for main effects, interactions, and individual comparisons and highlights important factors that determine the statistical power for factorial ANOVA designs.
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Allen Hammer, P. "Reply to Viewpoint." HortScience 24, no. 3 (June 1989): 419. http://dx.doi.org/10.21273/hortsci.24.3.419.
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Abstract The problem of statistics in horticultural research as reported in HortScience and the Journal is not unique to ASHS. Other societies are fighting the same battle. And certainly no one can disagree with Padaki’s comments. However, the question of inconsistencies in the use of statistics is a great deal more complex than he has stated. If all experiments could be easily analyzed as regression, multiple comparison and contrasts, and factorial experiments, the statistical issue would be greatly simplified. However, add to that list sampling, time (years), nonhomogeneity of variance, outliers, percent, transformation, subsamples, split-plots, interactions, unequal observations, dead plants, poorly designed experiments, lack of statistical consultants, poor access to statistical packages, etc. There is “no cookbook” to follow for every experiment nor do statisticians always agree on a single “correct” procedure. The problem is not easily solved.
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de Souza, Caio Henrique Pereira, Márcia Izumi Sakamoto, Paulo Cesar Pozza, Tatiana Carlesso dos Santos, Humberto Marques Lipori, Alceu Kazuo Hirata, and Alice Eiko Murakami. "Enzyme complex addition in barley or rye broiler diets with two energy levels fed from 1 to 21 days." Canadian Journal of Animal Science 102, no. 1 (March 1, 2022): 64–74. http://dx.doi.org/10.1139/cjas-2020-0201.
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Two experiments were conducted to evaluate diet digestibility, performance, digestive parameters, and blood parameters when an enzyme complex (EC) was used in barley- and rye-based diets with different energy levels. In the digestibility assay (exp. I), 108 seventeen-day-old Cobb male broilers were distributed in a completely randomized design in 2 × 2 × 2 + 1 factorial arrangement with two feeds (barley or rye), two EC levels (0% and 0.02%), and two energy levels [3025 and 3125 kcal apparent metabolizable energy (AME)·kg−1], plus a control treatment. In exp. II, 1080 one-day-old Cobb male broilers were distributed in a completely randomized design in 2 × 2 × 2 + 1 factorial arrangement with two feeds (barley or rye), two EC levels (0% and 0.02%), and two energy levels (2875 and 2975 kcal AME·kg−1). No interactions were observed for any variables (exp. I and II). Enzyme complex improved the apparent metabolizable coefficient of gross energy (P = 0.0432) of diets. The EC provided greater weight gain (P = 0.0003) and better feed conversion (P = 0.0025). Intestinal viscosity at 21 d was reduced (P < 0.0001) with the addition of the EC. The EC improved nutrient digestibility and performance, but the effects of energy reduction on performance could not be overcome.
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Stopher, Peter R., and David A. Hensher. "Are More Profiles Better Than Fewer?: Searching for Parsimony and Relevance in Stated Choice Experiments." Transportation Research Record: Journal of the Transportation Research Board 1719, no. 1 (January 2000): 165–74. http://dx.doi.org/10.3141/1719-22.
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Transportation planners increasingly include a stated choice (SC) experiment as part of the armory of empirical sources of information on how individuals respond to current and potential travel contexts. The accumulated experience with SC data has been heavily conditioned on analyst prejudices about the acceptable complexity of the data collection instrument, especially the number of profiles (or treatments) given to each sampled individual (and the number of attributes and alternatives to be processed). It is not uncommon for transport demand modelers to impose stringent limitations on the complexity of an SC experiment. A review of the marketing and transport literature suggests that little is known about the basis for rejecting complex designs or accepting simple designs. Although more complex designs provide the analyst with increasing degrees of freedom in the estimation of models, facilitating nonlinearity in main effects and independent two-way interactions, it is not clear what the overall behavioral gains are in increasing the number of treatments. A complex design is developed as the basis for a stated choice study, producing a fractional factorial of 32 rows. The fraction is then truncated by administering 4, 8, 16, 24, and 32 profiles to a sample of 166 individuals (producing 1, 016 treatments) in Australia and New Zealand faced with the decision to fly (or not to fly) between Australia and New Zealand by either Qantas or Ansett under alternative fare regimes. Statistical comparisons of elasticities (an appropriate behavioral basis for comparisons) suggest that the empirical gains within the context of a linear specification of the utility expression associated with each alternative in a discrete choice model may be quite marginal.
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Patil, Laxmikant R., Gururaj N. Bhadri, Anil R. Shet, and Veeranna S. Hombalimath. "Active and collaborative learning approach in Unit Operations laboratory by Open-ended experiments using the concept of Design of Experiments (DOE)." Journal of Engineering Education Transformations 36, S2 (January 1, 2023): 581–89. http://dx.doi.org/10.16920/jeet/2023/v36is2/23089.
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Unit Operations is the hallmark course of Chemical Engineering and allied disciplines. This involves the separation of products by physical means such as Distillation, Extraction, Gas absorption, Crystallization, Evaporation, Filtration, etc. Unit Operations course is usually offered in the second year of the undergraduate program in Biotechnology. The conventional pedagogy practiced to date was not so effective in the attainment of certain Program Outcomes (PO). To address this issue, categorization of the laboratory was done into four types: Demonstration, Exercise, Structural inquiry, and Open-ended experiments was made. The experiments were carefully designed to address some of the major Technical and Professional outcomes. Assessment criteria for each kind of experiment were mapped to Program outcomes, Outcome elements, and Performance Indicators. This helps in the evaluation of the attainment of Program Outcomes more accurately. The present study deals with an active and collaborative learning approach in the Unit Operations laboratory by framing Open-ended experiments using the concept of Design of Experiments (DoE). Full Factorial Design(FFD) and Response Surface Methodology(RSM) concepts were used to study the effect of various parameters on Liquid-Liquid Extraction operation. The study helped in addressing the Program Outcome-4 defined by the National Board of Accreditation, which is related to conducting investigations of complex problems. The evaluation of the students’ performance was done using rubrics-based methodology, and attainment of the graduate attributes addressed were documented. Quality students’s feedback was taken to identify scope for further improvement in the teaching-learning process. Keywords—Collaborative learning; Design of experiments; Open-ended experiment; Program outcome
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Moreland, Jeffrey C., Julia L. Sharp, and Philip J. Brown. "Lab-Scale Fiber Spinning Experimental Design Cost Comparison." Journal of Engineered Fibers and Fabrics 5, no. 1 (March 2010): 155892501000500. http://dx.doi.org/10.1177/155892501000500105.
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Many statistical experimental designs are too costly or require too much raw material to be feasible for lab-scale fiber spinning experiments. In this study a four-factor response surface design is presented to study the fiber spinning process in detail at the lab scale. The time, cost, and amount of raw material required to execute the proposed design are compared to the typical completely randomized 24 factorial design used in fiber spinning experiments and also to a standard four-factor response surface design. Sample fiber data as well as analysis from a typical statistical software package is provided to further demonstrate the differences between each design. By designating some treatment factors in the design as hard-to-change, split-plotting is used to reduce the time, cost, and amount of raw material required to complete the experiment. The proposed split-plot design is faster and less expensive than a typical factorial design and has the advantage of fitting a more complex second-order model to the system. When compared to a standard response surface design, the proposed split-plot design provides the same second-order modeling capabilities but reduces the cost of the experiment by 53%, the total time by 36%, and the amount of polymer required by 24%. Thus, a split-plot response surface design based on hard-to-change factors is recommended in lab-scale spinning.
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Perkins, Jordan T., Michael C. Petriello, Li Xu, Arnold Stromberg, and Bernhard Hennig. "An open-sourced statistical application for identifying complex toxicological interactions of environmental pollutants." Reviews on Environmental Health 32, no. 1-2 (March 1, 2017): 23–26. http://dx.doi.org/10.1515/reveh-2016-0044.
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Abstract The rising number of chemicals that humans are exposed to on a daily basis, as well as advances in biomonitoring and detection technologies have highlighted the diversity of individual exposure profiles (complex body burdens). To address this, the toxicological sciences have begun to shift away from examining toxic agents or stressors individually to focusing on more complex models with multiple agents or stressors present. Literature on interactions between chemicals is fairly limited in comparison with dose-response studies on individual toxicants, which is largely due to experimental and statistical challenges. Experimental designs capable of identifying these complex interactions are often avoided or not evaluated to their fullest potential because of the difficulty associated with appropriate analysis as well as logistical factors. To assist with statistical analysis of these types of experiments, an online, open-sourced statistical application was created for investigators to use to analyze and interpret potential toxicant interactions in laboratory experimental data using a full-factorial three-way analysis of variance (ANOVA). This model utilizes backward selection on interaction terms to model main effects and interactions.
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Kozak, Marcin. "Analyzing one-way experiments: a piece of cake of a pain in the neck?" Scientia Agricola 66, no. 4 (August 2009): 556–62. http://dx.doi.org/10.1590/s0103-90162009000400020.
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Statistics may be intricate. In practical data analysis many researchers stick to the most common methods, not even trying to find out whether these methods are appropriate for their data and whether other methods might be more useful. In this paper I attempt to show that when analyzing even simple one-way factorial experiments, a lot of issues need to be considered. A classical method to analyze such data is the analysis of variance, quite likely the most often used statistical method in agricultural, biological, ecological and environmental studies. I suspect this is why this method is quite often applied inappropriately: since the method is that common, it does not require too much consideration-this is how some may think. An incorrect analysis may provide false interpretation and conclusions, so one should pay careful attention to which approach to use in the analysis. I do not mean that one should apply difficult or complex statistics; I rather mean that one should apply a correct method that offers what one needs. So, various problems concerned with the analysis of variance and other approaches to analyze such data are discussed in the paper, including checking within-group normality and homocedasticity, analyzing experiments when any of these assumptions is violated, outliers presence, multiple comparison procedures, and other issues.
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Murray, Christopher, and Hannes Baumann. "You Better Repeat It: Complex CO2 × Temperature Effects in Atlantic Silverside Offspring Revealed by Serial Experimentation." Diversity 10, no. 3 (July 20, 2018): 69. http://dx.doi.org/10.3390/d10030069.
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Concurrent ocean warming and acidification demand experimental approaches that assess biological sensitivities to combined effects of these potential stressors. Here, we summarize five CO2 × temperature experiments on wild Atlantic silverside, Menidia menidia, offspring that were reared under factorial combinations of CO2 (nominal: 400, 2200, 4000, and 6000 µatm) and temperature (17, 20, 24, and 28 °C) to quantify the temperature-dependence of CO2 effects in early life growth and survival. Across experiments and temperature treatments, we found few significant CO2 effects on response traits. Survival effects were limited to a single experiment, where elevated CO2 exposure reduced embryo survival at 17 and 24 °C. Hatch length displayed CO2 × temperature interactions due largely to reduced hatch size at 24 °C in one experiment but increased length at 28 °C in another. We found no overall influence of CO2 on larval growth or survival to 9, 10, 15 and 13–22 days post-hatch, at 28, 24, 20, and 17 °C, respectively. Importantly, exposure to cooler (17 °C) and warmer (28 °C) than optimal rearing temperatures (24 °C) in this species did not appear to increase CO2 sensitivity. Repeated experimentation documented substantial inter- and intra-experiment variability, highlighting the need for experimental replication to more robustly constrain inherently variable responses. Taken together, these results demonstrate that the early life stages of this ecologically important forage fish appear largely tolerate to even extreme levels of CO2 across a broad thermal regime.
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Gomes, Wyllerson, Beatriz Martini Rodrigues, Juliana Andréa Franco Burguim, David Mendez Soares, Augusto Etchegaray, Renata Kelly Mendes Valente, and Alessandra Borin Nogueira. "OPTIMIZATION TRENDS IN TOTAL LIPOPEPTIDE PRODUCTION BY Bacillus velezensis 0G REVEALED A SUSTAINABLE SUBMERGED FERMENTATION METHOD USING SWEET POTATO PEELS." Revista Tecnológica 30, no. 1 (March 3, 2022): 107–23. http://dx.doi.org/10.4025/revtecnol.v30i1.55243.
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Bacillus velezensis are plant-growth promoting microorganisms. They produce an interesting class of cyclic peptides; surfactin, which have antifungal and surfactant properties. Lipopeptide of the surfactin family are very potent biosurfactants with important applications for environmental remediation and chemical industries. In the present work, the complex production of lipopeptide (mostly surfactins) by B. velezensis 0G, was evaluated under different growth conditions using factorial design 23 with central and axial points. All experiments were carried out based on sustainable submerged fermentation containing sweet potato peels. The response surface trends demonstrate a maximum biosurfactant production at inoculum volume of 32 mL; combined broth volume of 46 mL, and incubation time of 24h. These observations were based on the analysis of supernatant by HPLC.
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Xing, Lijuan, and Andreas Westphal. "Interaction of Fusarium solani f. sp. glycines and Heterodera glycines in Sudden Death Syndrome of Soybean." Phytopathology® 96, no. 7 (July 2006): 763–70. http://dx.doi.org/10.1094/phyto-96-0763.
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Sudden death syndrome (SDS) of soybean is caused by the soilborne Fusarium solani f. sp. glycines (synonym F. virguliforme). In a sequential approach, two multifactor factorial-design microplot experiments were conducted to investigate the effects of fungal infestation levels and soil moisture on both root necrosis and foliar SDS severity, and the interaction between F. solani f. sp. glycines and Heterodera glycines in fumigated versus nonfumigated soil. In 2003, soybean cv. Spencer was grown in nonfumigated or methyl bromide-fumigated soil and infested with increasing levels of F. solani f. sp. glycines, either under rainfall or irrigated after growth stage V6/R1. In 2004, interactions between F. solani f. sp. glycines and H. glycines were explored in a factorial inoculation design in fumigated or nonfumigated soil, planted to Williams 82 or Cyst-X20-18. In both years, higher levels of foliar SDS severity and root necrosis were found in F. solani f. sp. glycines-infested soils with H. glycines than in soils without the nematode on the soybean cultivars susceptible to both pathogens. Both natural infestations of H. glycines in 2003 and artificially amended populations of H. glycines in 2004 contributed to higher foliar SDS severity. More severe foliar SDS symptoms always were associated with more root necrosis, but elevated levels of root necrosis did not predict severe leaf symptoms. In contrast to the critical role of H. glycines, increasing fungal infestation levels had no significant effects on increasing either foliar SDS symptoms or root necrosis. Effects of moisture regime and fungal infestation levels also were examined in factorial greenhouse and growth chamber experiments. High soil moisture resulted in higher levels of SDS root necrosis. In the greenhouse, root necrosis increased at a higher rate in low soil moisture than the rate in high soil moisture. The two pathogens acted as a complex and the disease development was strongly dependent on high soil moisture.
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Zhang, C., H. Tian, S. Pan, G. Lockaby, and A. Chappelka. "Multi-factor controls on terrestrial carbon dynamics in urbanised areas." Biogeosciences Discussions 10, no. 11 (November 11, 2013): 17597–631. http://dx.doi.org/10.5194/bgd-10-17597-2013.
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Abstract. As urban land cover and populations continue rapidly increasing across the globe, much concern has been raised that urbanization may significantly alter terrestrial carbon dynamics that affects atmospheric CO2 concentration and climate. Urbanization involves complex changes in land structure and multiple environmental factors. Relative contribution of these and their interactive effects need be quantified to better understand urbanization effects on regional C dynamics as well as assess the effectiveness of C sequestration policies focusing on urban green space development. In this study, we analyzed the factors that may control the urbanization effect on ecosystem C dynamics, and proposed a numeric experimental scheme, i.e. scenarios design, to conduct factorial analysis on the effects of different factors. Then as a case study, a dynamic land ecosystem model (DLEM) was applied to quantify the urbanization effect on the C dynamics of the Southern US (SUS) from 1945–2007, and to analyze the relative contributions from each environmental factor and their interactive effects. We found the effect of urban land conversion dominated the C dynamics in the SUS, resulting in about 0.37 Pg C lost from 1945–2007. However, urban ecosystem management and urban-induced environmental changes enhanced C sequestration by 0.12 Pg and 0.03 Pg, respectively. Their C sequestration effects, which amounted to 40% of the magnitude of land conversion effect, partially compensated for the C loss during urbanization. Numeric experiments and factorial analyses indicated complex interactive effects among different factors and between various land covers and environmental controls, findings need to be further confirmed by field studies. The proposed numeric experimental scheme provides a quantitative approach for understanding the complex mechanisms controlling C dynamics, and defining best development practices in urbanised areas.
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Tarlochan, Faris. "Design Optimization of Bi-Tubular Thin Walled Columns for Crashworthiness Application." Key Engineering Materials 462-463 (January 2011): 1218–23. http://dx.doi.org/10.4028/www.scientific.net/kem.462-463.1218.
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Improving crashworthiness of vehicles is considered to be one of the main concerns in traffic safety. The study focused on new designs that can be incorporated into existing vehicles. The paper presents a crashworthiness design of a bi-tubular thin walled column for different dimension profiles. To formulate the complex crashworthiness design problem, the response surface method (RSM) was utilized. The design of experiments of the factorial design is used to construct the response surface for the specific energy absorption (SEA). This surface was optimized for SEA. Results from a finite element analysis of elastic plastic square bi-tubular steel tubes subjected to dynamic axial impact load are reported. The bi-tubular design has shown good potential as an efficient energy absorber in comparison to existing conventional energy absorbing structures.
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Spinozzi, Eleonora, Marta Ferrati, Desiree Lo Giudice, Eugenio Felicioni, Riccardo Petrelli, Giovanni Benelli, Filippo Maggi, and Marco Cespi. "Microwave-Assisted Hydrodistillation of the Insecticidal Essential Oil from Carlina acaulis: A Fractional Factorial Design Optimization Study." Plants 12, no. 3 (January 31, 2023): 622. http://dx.doi.org/10.3390/plants12030622.
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Recently, microwave-assisted hydrodistillation (MAH) has been reported as an innovative technique leading to increased essential oil (EO) extraction yield, coupled with reduced extraction time and energy costs. The EO of Carlina acaulis L. (Asteraceae), mainly constituted by carlina oxide (>95%) and conventionally obtained through traditional hydrodistillation (HD), has been reported as extremely effective against several arthropod vectors and pests of medical and economic importance with limited impact on non-target species, including mammals. This study aimed to the optimization of the EO extraction through MAH by using a one-step design of experiments (DoE) approach that allowed us to relate the characteristics of the produced EOs with the applied experimental conditions using mathematical models. The preliminary screening allowed us to optimize the protocol only by the extraction time, skipping complex data analysis. Moreover, the comparison of the optimized MAH conditions with traditional HD pointed out the higher efficiency of MAH in terms of EO yield (0.65 and 0.49% for MAH and HD, respectively) and extraction time (210 min for MAH). The results obtained confirmed the promising role that MAH could have in C. acaulis EO extraction, with increased yield and reduced extraction time, water consumption, and energy costs, and being employable on an industrial scale, with special reference to insecticidal and acaricidal formulations.
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Huffman, Jeff C., Rachel A. Millstein, Christopher M. Celano, Brian C. Healy, Elyse R. Park, and Linda M. Collins. "Developing a Psychological–Behavioral Intervention in Cardiac Patients Using the Multiphase Optimization Strategy: Lessons Learned From the Field." Annals of Behavioral Medicine 54, no. 3 (October 5, 2019): 151–63. http://dx.doi.org/10.1093/abm/kaz035.
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Abstract Background The Multiphase Optimization Strategy (MOST) is an approach to systematically and efficiently developing a behavioral intervention using a sequence of experiments to prepare and optimize the intervention. Purpose Using a 6 year MOST-based behavioral intervention development project as an example, we outline the results—and resulting decision-making process—related to experiments at each step to display the practical challenges present at each stage. Methods To develop a positive psychology (PP) based intervention to promote physical activity after an acute coronary syndrome (N = 255 across four phases), we utilized qualitative, proof-of-concept, factorial design, and randomized pilot experiments, with iterative modification of intervention content and delivery. Results Through this multiphase approach, we ultimately developed a 12 week, phone-delivered, combined PP-motivational interviewing intervention to promote physical activity. Across stages, we learned several important lessons: (a) participant and interventionist feedback is important, even in later optimization stages; (b) a thoughtful and systematic approach using all information sources is required when conflicting results in experiments make next steps unclear; and (3) new approaches in the field over a multiyear project should be integrated into the development process. Conclusions A MOST-based behavioral intervention development program can be efficient and effective in developing optimized new interventions, and it may require complex and nuanced decision-making at each phase.
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Sedghi, Mohammad, Abolghasem Golian, Ali Afsar, and Reza Mirshekar. "Optimisation of branched-chain amino acid requirements for 15–21-day-old broiler chickens by using the Taguchi method." Animal Production Science 62, no. 5 (February 17, 2022): 430–39. http://dx.doi.org/10.1071/an21238.
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Context There are complex inter-relationships among the branched-chain amino acids (BCAAs), making the use of full factorial design experiments more complicated, costly and time-consuming when attempting to estimate the optimal levels of the BCAAs. Aims Two experiments were conducted to determine optimal concentrations of BCAAs by using the Taguchi method and mathematical models for broiler chickens during 15–21 days of age. Methods In the first experiment, the optimal concentrations of BCAAs were determined for broiler chickens, and the accuracy of the results was evaluated in the second experiment. The orthogonal array layout of L16 (43) was applied to test four concentrations of leucine (Leu), isoleucine (Ile) and valine (Val). To test the validity of the results from 16 diets selected by the Taguchi method and four additional diets from the full factorial design method, a total of 960 broiler chickens was assigned to these 20 experimental diets, with six replicates of eight birds each. The Taguchi method, the multi-objective genetic algorithm (MOGA) and the random search algorithm (RSA) were used to determine the best concentration of BCAAs on the basis of optimal bird performance, which was measured by bodyweight gain, feed intake, and feed conversion ratio (FCR). In the second experiment, six dietary treatments were formulated to establish the reliability of the predictions obtained from the first experiment (Experiment 1). Key results No exceptional concentrations of BCAAs were achieved for a maximum average daily gain and a minimum FCR; however, the diet containing 1.177–1.353% standardised ileal digestible (SID) Leu, 0.742–0.810% SID Ile, and 0.832–0.915% SID Val showed the best performance. The results of the second experiment indicated that the Taguchi method could be used instead of a full factorial design to optimise the amino acid requirements of broiler chickens. Conclusions The requirements of BCAAs were overestimated using the MOGA and RSA methods compared with the Taguchi method. Implications In commercial diets containing high levels of SID Leu, supplementation of an extra 5% SID Ile and Val may improve broiler performance during 15–21 days of age.
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Roberto Chaves Neto, José, Ricardo Boscaini, Renato Carnellosso Guerra, Nívea Raquel Ledur, Maurivan Travessini, Mayara Torres Mendonça, and Ivan Francisco Dressler da Costa. "RESPOSTADA IRRIGAÇÃO E DA APLICAÇÃO DE FUNGICIDAS SOBRE A QUALIDADE SANITÁRIA E PRODUTIVIDADE DE GRÃOS DE MILHO." COLLOQUIUM AGRARIAE 14, no. 4 (December 1, 2018): 121–34. http://dx.doi.org/10.5747/ca.2018.v14.n4.a255.
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The objective of this study was to evaluate the response of irrigation and foliar application of fungicide on the burned grain complex, fungal incidence (Blotter test) and grain yield of two maize hybrids. The experiments were conducted in the agricultural crop 2014/15, in Cruz Alta, RS. For both experiments, the treatments were arranged in a randomized block design and arranged in factorial scheme:2 (commercial hybrids) x 4 (3 fungicides and 1 control), with 4 replicates. In the laboratory, the grains were evaluated, as percentage of burned grains, grain sanity by Blotter test method, weight of one thousand grains and grain yield. There was influence of the use of irrigation, the hybrid, and the application of fungicides on grain yield and the incidence of burned grains and phytopathogenic fungi. Irrigation favored a higher incidence of burned grains and fungi Fusariumsp. and Aspergillussp. The hybrid BG7060HR provided a lower incidence of fungi and burned grains, parallel to a greater mass of a thousand grains and productivity when the fungicide Azoxystrobin + Benzovindiflupir was applied. The application of fungicides in the V8 stage of the culture reduced the incidence of fungi Fusariumsp.and Aspergillussp., especially the fungicide Azoxystrobin + Benzovindiflupir.
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Traynor, Carlos, Tarjinder Sahota, Helen Tomkinson, Ignacio Gonzalez-Garcia, Neil Evans, and Michael Chappell. "Imputing Biomarker Status from RWE Datasets—A Comparative Study." Journal of Personalized Medicine 11, no. 12 (December 13, 2021): 1356. http://dx.doi.org/10.3390/jpm11121356.
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Missing data is a universal problem in analysing Real-World Evidence (RWE) datasets. In RWE datasets, there is a need to understand which features best correlate with clinical outcomes. In this context, the missing status of several biomarkers may appear as gaps in the dataset that hide meaningful values for analysis. Imputation methods are general strategies that replace missing values with plausible values. Using the Flatiron NSCLC dataset, including more than 35,000 subjects, we compare the imputation performance of six such methods on missing data: predictive mean matching, expectation-maximisation, factorial analysis, random forest, generative adversarial networks and multivariate imputations with tabular networks. We also conduct extensive synthetic data experiments with structural causal models. Statistical learning from incomplete datasets should select an appropriate imputation algorithm accounting for the nature of missingness, the impact of missing data, and the distribution shift induced by the imputation algorithm. For our synthetic data experiments, tabular networks had the best overall performance. Methods using neural networks are promising for complex datasets with non-linearities. However, conventional methods such as predictive mean matching work well for the Flatiron NSCLC biomarker dataset.
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Quintana-Najera, Jessica, A. John Blacker, Louise A. Fletcher, Douglas G. Bray, and Andrew B. Ross. "The Influence of Biochar Augmentation and Digestion Conditions on the Anaerobic Digestion of Water Hyacinth." Energies 15, no. 7 (March 30, 2022): 2524. http://dx.doi.org/10.3390/en15072524.
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The augmentation of biochar (BC) during anaerobic digestion (AD) has been identified as a potential strategy for improving the AD of complex feedstocks. This study evaluates the influence of oak wood biochar 450 °C and fermentation conditions during the AD of the invasive aquatic plant, water hyacinth (WH). Factorial 22 design of experiments (DOE) allowed the evaluation of the effect of the crucial processing conditions, inoculum-to-substrate ratio (ISR) and biochar load. Further optimisation was performed to identify the best processing conditions for the AD of WH, at an ideal ISR of 1. The contour plots suggested that methane yield is favoured at biochar loads of ≤0.5%, whereas the production rate is favoured by increasing biochar loads. However, biochar addition offered no further improvement or significant effect on the digestion of WH. The subsequent AD of WH samples collected from different locations in India and Uganda exhibited variable biochemical methane potential (BMP) yields. BC addition had little effect on BMP performance, and in some cases, it even reduced the BMP. This study concludes that the amendment potential of biochar is influenced by digestion conditions and the substrate, particularly when working with complex substrates.
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Dabwan, Abdulmajeed, Adham E. Ragab, Mohamed A. Saleh, Saqib Anwar, Atef M. Ghaleb, and Ateekh Ur Rehman. "Study of the Effect of Process Parameters on Surface Profile Accuracy in Single-Point Incremental Sheet Forming of AA1050-H14 Aluminum Alloy." Advances in Materials Science and Engineering 2020 (January 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/7265941.
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Single-point incremental forming is an innovative flexible and inexpensive technique to form sheet products when prototypes or small batches are required. The process allows complex geometries to be produced using a computer numerical control machine, eliminating the need for a special die. This study reports on the effects of four important single-point incremental forming process parameters on produced surface profile accuracies. The profile accuracy was estimated by measuring the side angle errors and surface roughness and also waviness and circularity of the product inner surface. Full factorial design of experiments was used to plan the study, and the analysis of variance was used to analyze and interpret the results. The results indicate that the tool diameter (d), step depth (s), and sheet thickness (t) have significant effects on the produced profile accuracy, while the feed rate (f) is not significant. As a general rule, thin sheets with greater tool diameters yielded the best surface quality. The results also show that controlling all surface quality features is complex because of the contradicting effects of, and interactions between, a number of the process parameters.
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Rajput, Kiransinh N., Kamlesh C. Patel, and Ujjval B. Trivedi. "Screening and Selection of Medium Components for Cyclodextrin Glucanotransferase Production by New Alkaliphile Microbacterium terrae KNR 9 Using Plackett-Burman Design." Biotechnology Research International 2016 (February 3, 2016): 1–7. http://dx.doi.org/10.1155/2016/3584807.
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Cyclodextrin glucanotransferase (CGTase, EC 2.4.1.19) production using new alkaliphile Microbacterium terrae KNR 9 was investigated by submerged fermentation. Statistical screening for components belonging to different categories, namely, soluble and raw starches as carbon sources, complex organic and inorganic nitrogen sources, minerals, a buffering agent, and a surfactant, has been carried out for CGTase production using Plackett-Burman factorial design. To screen out k (19), number of variables, k+1 (20), number of experiments, were performed. Among the fourteen components screened, four components, namely, soluble starch, corn flour, yeast extract, and K2HPO4, were identified as significant with reference to their concentration effect and corresponding p value. Although soluble starch showed highest significance, comparable significance was also observed with corn flour and hence it was selected as a sole carbon source along with yeast extract and K2HPO4 for further media optimization studies. Using screened components, CGTase production was increased to 45% and 87% at shake flask level and laboratory scale fermenter, respectively, as compared to basal media.
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Glass, Jordan R., and Zachary R. Stahlschmidt. "Should I stay or should I go? Complex environments influence the developmental plasticity of flight capacity and flight-related trade-offs." Biological Journal of the Linnean Society 128, no. 1 (June 5, 2019): 59–69. http://dx.doi.org/10.1093/biolinnean/blz073.
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Abstract Complex environments, characterized by co-varying factors (e.g. temperature and food availability) may cause animals to invest resources differentially into fitness-related traits. Thus, experiments manipulating multiple environmental factors concurrently provide valuable insight into the role of the environment in shaping not only important traits (e.g. dispersal capacity or reproduction), but also trait–trait interactions (e.g. trade-offs between traits). We used a multi-factorial design to manipulate variation in temperature (constant 28 °C vs. 28 ± 5 °C daily cycle) and food availability (unlimited vs. intermittent access) throughout development in the sand field cricket (Gryllus firmus). Using a univariate approach, we found that temperature variability and unlimited food availability promoted survival, development, growth, body size and/or reproductive investment. Using principal components as indices of resource allocation strategy, we found that temperature variability and unlimited food reduced investment into flight capacity in females. Thus, we detected a sex-specific trade-off between flight and other life-history traits that was developmentally plastic in response to variation in temperature and food availability. We develop an experimental and statistical framework to reveal shifts in correlative patterns of investment into different life-history traits. This approach can be applied to a range of biological systems to investigate how environmental complexity influences traits and trait trade-offs.
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Sharma, Sunil, and Anuradha R. Chetiya. "Simplifying the Six Sigma Toolbox through Application of Shainin DOE Techniques." Vikalpa: The Journal for Decision Makers 34, no. 1 (January 2009): 13–30. http://dx.doi.org/10.1177/0256090920090102.
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The success of a Six Sigma programme in an organization depends to a large extent on the success of the Six Sigma projects, which in turn depends on how the team handles the problem and whether the right combination of tools is being applied to address the root cause. The Six Sigma toolbox consists of a wide range of tools comprising, on the one hand, simple and commonly used tools like flow charts, Pareto analysis, and cause-and-effect diagram and the more advanced statistical tools like design of experiments, regression analysis and many more, on the other hand. While the simple tools are easy to apply, understand, and analyse, engineers perceive the more advanced tools difficult to comprehend. Design of experiments (DOE) is one such tool. Two well-known approaches of design of experiments are the Classical DOE, pioneered by Sir Ronald A Fisher and the Taguchi approach, pioneered by Dr Genichii Taguchi. A third approach to experimental design—the Shainin DOE techniques, offered by Dr Dorian Shainin—can be considered as a very good alternative to the other approaches. They are much simpler than the factorial designs, response surface designs, and orthogonal arrays of the conventional approaches of DOE, but at the same time are recognized as being very powerful and effective in solving the chronic quality problems that plague most manufacturers. Shainin DOE basically works at eliminating suspected process variables by mostly using seven different tools, viz., Multi-Vari Charts Component Search Paired Comparison Variable Search Full Factorials B vs. C (Better vs. Current) Analysis Scatter Plots or Realistic Tolerance Parallelogram Plots. Though not very well documented, these tools have proved to be the key drivers in the success of many companies, e.g., Motorola. This article examines two projects of a leading automotive and general lighting lamp manufacturing company, in which a combination of the standard Six Sigma tools and Shainin tools has been successfully used to address the root cause of the problems. The advantage of using Shainin tools is that: Very small sample sizes are required to analyse the problem. Often samples as small as 2 or 3 are enough to make statistically valid conclusions. Statistical software is not required to analyse the data. In fact, Shainin DOE does not even require knowledge of complex statistical tools. It involves employees at all levels, including workers and junior staff in problem solving that was hitherto a domain of senior technical experts. Also, the success of the projects had a very positive effect on the morale of the employees in terms of convincing them that Six Sigma is not all about using complex statistical tools.
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Shaitor, Nikolay, Michal Kelemen, and Boris Yakimovich. "Analysis and Synthesis in the Design of Magnetic Switching Electric Machines." Actuators 10, no. 7 (July 16, 2021): 164. http://dx.doi.org/10.3390/act10070164.
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A systematic approach to the design of electrical machines is implemented by solving problems of analysis and synthesis in various combinations at different stages and stages of design. The questions of the formulation and implementation of synthesis and analysis problems in the study and design of modular-type magnetically commutated electrical machines are considered. They are aimed at reducing weight, size and cost while improving the performance of these newly designed machines. A complex method of parametric synthesis and an automated program containing a calculated mathematical model of an electric machine has been developed. On the basis of numerical full factorial experiments, the optimization parameter is determined, and a regression analysis is performed with the construction of an optimization model. It allows you to find a narrow range of variation of significant factors, at which the optimization parameter satisfies the specified conditions. On the example of studying an inductor generator of axial-radial configuration, new approaches to the formulation and solution of typical problems of analysis and synthesis of modular-type electrical machines are shown. The use of complex parametric synthesis makes it possible to significantly reduce the masses of the designed modular machines in comparison with drum-type inductor machines of the same power.
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Terenteva, O. A., E. V. Flisyuk, D. Yu Ivkin, and I. A. Narkevich. "Development of the Composition and Technology of New Neuroprotective Drug Tablets Using Fractional Factorial Design." Drug development & registration 9, no. 1 (February 26, 2020): 18–22. http://dx.doi.org/10.33380/2305-2066-2020-9-1-18-22.
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Introduction. The creating an effective and safe domestic neuroprotective medicines with a complex of pleiotropic effects realized through specific orphan receptors (SUCNR1, HCA2) of glial cells, is a pressing problem of modern pharmacology and a promising possibility of pharmacotherapy of brain injury and cerebrovascular diseases. Ideally, the medicine should helps restore lost cognitive functions and physical performance after damage to the central nervous system, and its use should improve the quality of life of patients and reduce the risk of complications.Aim. To develop composition and technology of a new medicine tablets with neuroprotective effect, using fractional factorial design of experiment and the Harrington desirability function.Materials and methods. The shape and size of particles, physicochemical (solubility, melting point) and technological properties (bulk density, compressibility factor, fractional composition, hygroscopicity) of the pharmaceutical substance of DEAE derivative were studied according to the methods described in the State Pharmacopoeia of the Russian Federation (14th edition). A three-factor fractional plan based on the 4 × 4 Latin square design for selection a scientifically based composition of solid dosage form was chosen. 16 experiments to test the significance of the factors were carried out. The tablets obtained according to the planning matrix were investigated for disintegration; tablet crush resistance, friability, and hygroscopicity. To optimize the quality of the tablets, the generalized Harrington desirability function was used.Results and discussion. The study of the physicochemical and technological properties of the DEAE derivative substance showed that it is a highly hygroscopic, amorphous, white or pale yellow, odourless powder, prone to the formation of agglomerates. The powder is very easily soluble in water. Taking into account the values of the general desirability function, the best result was shown by sample № 4 consisting of mannitol, calcium stearate, and partially pregelatinized maize starch.Conclusion. The physicochemical and technological properties of the DEAE derivative were studied. The DEAE derivative is a highly hygroscopic substance. Via the method of mathematical planning of the experiment the composition of DEAE derivative tablets was selected and scientifically grounded: DEAE derivative 60 mg, mannitol, partially pregelatinized maize starch, calcium stearate. The average tablet weight is 300 mg. The parameters of pressing tablets were selected.
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Patel, Jalpa, and Dhaval Mori. "Application of 32 Full Factorial Design and Desirability Function for Optimizing The Manufacturing Process for Directly Compressible Multi-Functional Co-Processed Excipient." Current Drug Delivery 17, no. 6 (August 6, 2020): 523–39. http://dx.doi.org/10.2174/1567201817666200508094743.
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Background: Developing a new excipient and obtaining its market approval is an expensive, time-consuming and complex process. Compared to that, the co-processing of already approved excipients has emerged as a more attractive option for bringing better characteristic excipients to the market. The application of the Design of Experiments (DoE) approach for developing co-processed excipient can make the entire process cost-effective and rapid. Objective: The aim of the present investigation was to demonstrate the applicability of the DoE approach, especially 32 full factorial design, to develop a multi-functional co-processed excipient for the direct compression of model drug - cefixime trihydrate using spray drying technique. Methods: The preliminary studies proved the significant effect of atomization pressure (X1) and polymer ratio (microcrystalline cellulose: mannitol - X2) on critical product characteristics, so they were selected as independent variables. The angle of repose, Carr’s index, Hausner’s ratio, tensile strength and Kuno’s constant were selected as response variables. Result: The statistical analysis proved a significant effect of both independent variables on all response variables with a significant p-value < 0.05. The desirability function available in Design Expert 11® software was used to prepare and select the optimized batch. The prepared co-processed excipient had better compressibility than individual excipients and their physical mixture and was able to accommodate more than 40 percent drug without compromising the flow property and compressibility. Conclusion: The present investigation successfully proved the applicability of 32 full factorial design as an effective tool for optimizing the spray drying process to prepare a multi-functional co-processed excipient.
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Knight, Andrew W., Eric S. Eitrheim, Andrew W. Nelson, and Michael K. Schultz. "A calculation model for liquid-liquid extraction of protactinium by 2,6-dimethyl-4-heptanol." Nukleonika 60, no. 4 (December 1, 2015): 837–45. http://dx.doi.org/10.1515/nuka-2015-0154.
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Abstract Reprocessing of spent nuclear fuel usually employs the solvent extraction technique to recover fissile material, isolate other valuable radionuclides, recover precious metals, and remove contaminants. Efficient recovery of these species from highly radioactive solutions requires a detailed understanding of reaction conditions and metal speciation that leads to their isolation in pure forms. Due to the complex nature of these systems, identification of ideal reaction conditions for the efficient extraction of specific metals can be challenging. Thus, the development of experimental approaches that have the potential to reduce the number of experiments required to identify ideal conditions are desirable. In this study, a full-factorial experimental design was used to identify the main effects and variable interactions of three chemical parameters on the extraction of protactinium (Pa). Specifically we investigated the main effects of the anion concentration (NO3-, Cl-) extractant concentration, and solution acidity on the overall extraction of protactinium by 2,6-dimethyl-4-heptanol (diisobutylcarbinol; DIBC) from both HCl and HNO3 solutions. Our results indicate that in HCl, the extraction of protactinium was dominated by the solution acidity, while in nitric acid the extraction was strongly effected by the [DIBC]. Based on our results, a mathematical model was derived, that describes the relationship between concentrations of anions, extractant, and solution acidity and the expected values of Pa distribution coefficients in both HCl and HNO3. This study demonstrates the potential to predict the distribution coefficient values, based upon a mathematical model generated by a full-factorial experimental design.
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Baksh, Dolores, John E. Davies, and Peter W. Zandstra. "Soluble factor cross-talk between human bone marrow-derived hematopoietic and mesenchymal cells enhances in vitro CFU-F and CFU-O growth and reveals heterogeneity in the mesenchymal progenitor cell compartment." Blood 106, no. 9 (November 1, 2005): 3012–19. http://dx.doi.org/10.1182/blood-2005-01-0433.
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AbstractThe homeostatic adult bone marrow (BM) is a complex tissue wherein physical and biochemical interactions serve to maintain a balance between the hematopoietic and nonhematopoietic compartments. To focus on soluble factor interactions occurring between mesenchymal and hematopoietic cells, a serum-free adhesion-independent culture system was developed that allows manipulation of the growth of both mesenchymal and hematopoietic human BM-derived progenitors and the balance between these compartments. Factorial experiments demonstrated a role for stem cell factor (SCF) and interleukin 3 (IL-3) in the concomitant growth of hematopoietic (CD45+) and nonhematopoietic (CD45–) cells, as well as their derivatives. Kinetic tracking of IL-3α receptor (CD123) and SCF receptor (CD117) expression on a sorted CD45– cell population revealed the emergence of CD45–CD123+ cells capable of osteogenesis. Of the total fibroblast colony-forming units (CFU-Fs) and osteoblast colony-forming units (CFU-O), approximately 24% of CFU-Fs and about 22% of CFU-Os were recovered from this population. Cell-sorting experiments demonstrated that the CD45+ cell population secreted soluble factors that positively affect the survival and proliferation of CFU-Fs and CFU-Os generated from the CD45– cells. Together, our results provide insight into the intercellular cytokine network between hematopoietic and mesenchymal cells and provide a strategy to mutually culture both mesenchymal and hematopoietic cells in a defined scalable bioprocess.
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Yasui, Ryota, Keisuke Sekine, and Hideki Taniguchi. "Clever Experimental Designs: Shortcuts for Better iPSC Differentiation." Cells 10, no. 12 (December 15, 2021): 3540. http://dx.doi.org/10.3390/cells10123540.
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For practical use of pluripotent stem cells (PSCs) for disease modelling, drug screening, and regenerative medicine, the cell differentiation process needs to be properly refined to generate end products with consistent and high quality. To construct and optimize a robust cell-induction process, a myriad of cell culture conditions should be considered. In contrast to inefficient brute-force screening, statistical design of experiments (DOE) approaches, such as factorial design, orthogonal array design, response surface methodology (RSM), definitive screening design (DSD), and mixture design, enable efficient and strategic screening of conditions in smaller experimental runs through multifactorial screening and/or quantitative modeling. Although DOE has become routinely utilized in the bioengineering and pharmaceutical fields, the imminent need of more detailed cell-lineage specification, complex organoid construction, and a stable supply of qualified cell-derived material requires expedition of DOE utilization in stem cell bioprocessing. This review summarizes DOE-based cell culture optimizations of PSCs, mesenchymal stem cells (MSCs), hematopoietic stem cells (HSCs), and Chinese hamster ovary (CHO) cells, which guide effective research and development of PSC-derived materials for academic and industrial applications.
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Cahill, Abigail E., and Spencer A. Koury. "Larval settlement and metamorphosis in a marine gastropod in response to multiple conspecific cues." PeerJ 4 (July 28, 2016): e2295. http://dx.doi.org/10.7717/peerj.2295.
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Larvae of the marine gastropodCrepidula fornicatamust complete a transition from the plankton, where they are highly dispersed, to an aggregated group of benthic adults. Previous research has shown that selective settlement of larvae on conspecific adults is mediated by a water-borne chemical cue. However, variable experimental conditions have been used to study this cue, and standardization is needed in order to investigate factors that may have weak effects on settlement. In this study, we developed a time-course bioassay based on a full-factorial design with temporal blocking and statistical analysis of larval settlement rates in the lab. We tested this bioassay by examining settlement in the presence of an abiotic cue (KCl), and biotic cues (water conditioned with adult conspecifics and conspecific pedal mucus). Results confirmed settlement in the presence of both KCl and adult-conditioned water, and discovered the induction of settlement by pedal mucus. This optimized, standardized bioassay will be used in future experiments to characterize the complex process of larval settlement inC. fornicata, particularly to measure components of potentially small effect.
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Wiss, Johanna, David Andersson, Paul Slovic, Daniel Västfjäl, and Gustav Tinghög. "The influence of identifiability and singularity in moral decision making." Judgment and Decision Making 10, no. 5 (September 2015): 492–502. http://dx.doi.org/10.1017/s1930297500005623.
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AbstractThere is an increased willingness to help identified individuals rather than non-identified, and the effect of identifiability is mainly present when a single individual rather than a group is presented. However, identifiability and singularity effects have thus far not been manipulated orthogonally. The present research uses a joint evaluation approach to examine the relative contribution of identifiability and singularity in moral decision-making reflecting conflicting values between deontology and consequentialism. As in trolley dilemmas subjects could either choose to stay with the default option, i.e., giving a potentially life-saving vaccine to a single child, or to actively choose to deny the single child the vaccine in favor of five other children. Identifiability of the single child and the group of children was varied between-subjects in a 2x2 factorial design. In total 1,232 subjects from Sweden and the United States participated in three separate experiments. Across all treatments, in all three experiments, 32.6% of the subjects chose to stay with the deontological default option instead of actively choosing to maximize benefits. Results show that identifiability does not always have a positive effect on decisions in allocation dilemmas. For single targets, identifiability had a negative or no effect in two out of three experiments, while for the group of targets identifiability had a more stable positive effect on subjects’ willingness to allocate vaccines. When the effect of identifiability was negative, process data showed that this effect was mediated by emotional reactance. Hence, the results show that the influence of identifiability is more complex than it has been previously portrayed in the literature on charitable giving.
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Kalla, Subhash, and Christopher David White. "Efficient Design of Reservoir Simulation Studies for Development and Optimization." SPE Reservoir Evaluation & Engineering 10, no. 06 (December 1, 2007): 629–37. http://dx.doi.org/10.2118/95456-pa.
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Summary Development studies examine geologic, engineering, and economic factors to formulate and optimize production plans. If there are many factors, these studies are prohibitively expensive unless simulation runs are chosen efficiently. Experimental design and response models improve study efficiency and have been widely applied in reservoir engineering. To approximate nonlinear oil and gas reservoir responses, designs must consider factors at more than two levels—not just high and low values. However, multilevel designs require many simulations, especially if many factors are being considered. Partial factorial and mixed designs are more efficient than full factorials, but multilevel partial factorial designs are difficult to formulate. Alternatively, orthogonal arrays (OAs) and nearly-orthogonal arrays (NOAs) provide the required design properties and can handle many factors. These designs span the factor space with fewer runs, can be manipulated easily, and are appropriate for computer experiments. The proposed methods were used to model a gas well with water coning. Eleven geologic factors were varied while optimizing three engineering factors. An NOA was specified with three levels for eight factors and four levels for the remaining six factors. The proposed design required 36 simulations compared to 26,873,856 runs for a full factorial design. Kriged response surfaces are compared to polynomial regression surfaces. Polynomial-response models are used to optimize completion length, tubinghead pressure, and tubing diameter for a partially penetrating well in a gas reservoir with uncertain properties. OAs, Hammersley sequences (HSs), and response models offer a flexible, efficient framework for reservoir simulation studies. Complexity of Reservoir Studies Reservoir studies require integration of geologic properties, drilling and production strategies, and economic parameters. Integration is complex because parameters such as permeability, gas price, and fluid saturations are uncertain. In exploration and production decisions, alternatives such as well placement, artificial lift, and capital investment must be evaluated. Development studies examine these alternatives, as well as geologic, engineering, and economic factors to formulate and optimize production plans (Narayanan et al. 2003). Reservoir studies may require many simulations to evaluate the many factor effects on reservoir performance measures, such as net present value (NPV) and breakthrough time. Despite the exponential growth of computer memory and speed, computing accurate sensitivities and optimizing production performance is still expensive, to the point that it may not be feasible to consider all alternative models. Thus, simulation runs should be chosen as efficiently as possible. Experimental design addresses this problem statistically, and along with response models, it has been applied in engineering science (White et al. 2001; Peng and Gupta 2004; Peake et al. 2005; Sacks et al. 1989a) toMinimize computational costs by choosing a small but statistically representative set of simulation runs for predicting responses (e.g., recovery)Decrease expected error compared with nonoptimal simulation designs (i.e., sets of sample points)Evaluate sensitivity of responses to varying factorsTranslate uncertainty in input factors to uncertainty in predicted performance (i.e., uncertainty analysis)Estimate value of information to focus resources on reducing uncertainty in factors that have the most significant effect on response uncertainty to help optimize engineering factors.
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Abhang, Laxman B., Mohd Iqbal, and M. Hameedullah. "Optimization of Machining Process Parameters Using Moora Method." Defect and Diffusion Forum 402 (July 2020): 81–89. http://dx.doi.org/10.4028/www.scientific.net/ddf.402.81.
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A multi-response optimization is a popular tool in many economic, managerial, constructional, manufacturing, process design, product design technologies, machinery and system, devices, process parameters etc. This research paper demonstrates the application of a simple multi-objective optimization on the basis of ratio analysis (MOORA) method to solve the multi-criteria (objective) optimization problem in the machining process. In this paper, the chip-tool interface temperature, main cutting force, and tool wear rate were investigated in various machining conditions in turning operations. Various machining parameters, such as the cutting speed, feed rate, and depth of cut and effective tool inserts nose radius, were considered. Composite factorial design (24+8) was used for experimentation. Multiple response values were obtained using actual experimentation. By using these experiments, two different methods were proposed. Machining parameters were optimized by minimizing chip-tool interface temperatures, tool wear rate, and main cutting force during machining of alloy steel. The results obtained using the MOORA method almost agree with the grey relational analysis method which shows the authenticate applicability, potentiality, and flexibility of MOORA method for solving various complex decision-making problems in present-day manufacturing industries.
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B, Elumalai, Gowri S, Hariharan P, and Arun Pillai KV. "The effect of discharge energy and nano Al2O3 on μED milling of Inconel 718." Surface Topography: Metrology and Properties 9, no. 4 (November 1, 2021): 045017. http://dx.doi.org/10.1088/2051-672x/ac3203.
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Abstract Micro EDM is a well-recognized machining process for complex and precise three-dimensional micro features. However, the disadvantages are high machining time, poor surface finish, and a thick recast layer that restricts the wide establishments. Powder mixed EDM (PMEDM) is a recent advancement in the EDM process that enhances the sparking frequency and inter-electrode gap. This paper presents the effect of Al2O3 nanopowder on micro ED milling of Inconel 718 at chosen levels of discharge energy (3.2 μJ to 7.2 μJ, 32 μJ to 72 μJ and 320 μJ to 720 μJ). To understand the dynamics of the process, comprehensive experimental research was carried out using a full factorial design with 60 experiments. Material removal rate, tool wear rate, and surface finish were evaluated. The surface topography and material migration to the machined surface was also studied. The results showed significant improvement of 157%, 20%, and 21% in MRR, TWR, and surface roughness respectively in nanopowder mixed micro-EDM when compared with conventional micro-EDM. The addition of Al2O3 nanopowder reduced crater size in all three discharge energy ranges and reduced carbon deposition on the machined surface.
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Yao, Zhi Hui, Liang Gao, Mi Xiao, and Lei Yang. "DOE-Based Numerical Investigation on Factors Affecting Temperature Field during Line Heating." Advanced Materials Research 314-316 (August 2011): 620–25. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.620.
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Line heating is a complex thermal-mechanical process as many factors affect the final shape of a processed plate. Generally, the temperature field of the processed plate determines the stress field and strain field. To predict the deformation of the processed plate, this paper investigates the effects of different factors on the temperature field during line heating by using design of experiment (DOE). Firstly, a three dimensional thermal elasto-plastic finite element method (FEM) is developed to calculate the temperature field induced by the single-pass oxygen-acetylene line heating. Secondly, the temperature field is analyzed by using fractional factorial design, in which the maximum temperature is selected as the response, and a fishbone diagram is used to overview all influencing factors. After performing a series of numerical experiments selected by using an ortho-gonal array, three main influencing factors are screened out: plate thickness, flow of acetylene and velocity of heat source. Next, the main effects of these factors are discussed. Finally, analytical re-sults indicate that there exist interaction effects among the three main influencing factors. This in-vestigation demonstrates that DOE is an efficient method for study of the temperature field during line heating.
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Gunzburger, M. S., and J. Travis. "Effects of multiple predator species on green treefrog (Hyla cinerea) tadpoles." Canadian Journal of Zoology 83, no. 7 (July 1, 2005): 996–1002. http://dx.doi.org/10.1139/z05-093.
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Prey species that occur across a range of habitats may be exposed to variable communities of multiple predator species across habitats. Predicting the combined effects of multiple predators can be complex. Many experiments evaluating the effects of multiple predators on prey confound either variation in predator density with predator identity or variation in relative predator frequency with overall predation rates. We develop a new experimental design of factorial predator combinations that maintains a constant expected predation rate, under the null hypothesis of additive predator effects. We implement this design to evaluate the combined effects of three predator species (bass, aeshnid and libellulid odonate naiads) on mortality rate of a prey species, Hyla cinerea (Schneider, 1799) tadpoles, that occurs across a range of aquatic habitats. Two predator treatments (libellulid and aeshnid + libellulid) resulted in lower tadpole mortality than any of the other predator treatments. Variation in tadpole mortality across treatments was not related to coarse variation in microhabitat use, but was likely due to intraguild predation, which occurred in all predator treatments. Hyla cinerea tadpoles have constant, low survival values when exposed to many different combinations of predator species, and predation rate probably increases linearly with predator density.
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Symanowski, J. T., Y. Giraud, P. Dino, P. Atadja, N. Vogelzang, and S. Sharma. "Design and analysis methods for assessing drug synergy: Illustration using histone deacetylase (HDAC) inhibitor LBH589 combined with CDDP in mesothelioma (meso) cell lines." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 18108. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.18108.
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18108 Background: LBH589 (Novartis) is an investigational agent belonging to a cinnamic hydroxamic acid class of compounds that was shown to inhibit HDAC enzyme activity. LBH589 is in phase I clinical testing and combinations with various agents are being explored in the laboratory. Common experimental designs used for in-vitro cell viability experiments to assess drug synergy in cancer cell lines include complete factorial design (CFD) and fixed dose ratio design (FRD). While CFD provides data for the entire response surface, it can result in unfeasibly large experiments and complex synergy calculations, especially when 3 or more drugs are under evaluation. We investigated the FRD and alternative analysis methods to estimate and test synergy between two or more drugs. Methods: Experiments in meso cell lines were conducted using a colorimetric MTS assay. The FRD included 4 LBH concentrations (10 to 100 nM) and 4 CDDP concentrations (.5 to 5 μM), for each drug alone and in combination at a 50:1 CDDP:LBH ratio. Data were analyzed by applying the median effect equations (Chou, Adv Enzyme Regul, 1984) to the joint modeling approach by Straetemans, Biom. J., 2005. This method provides a straightforward estimate of the combination index (CI) and a statistical test for synergy (CI values less than 1.0 indicate synergy). Results: Results showed CI values significantly less than 1 for cell lines 2052 and MSTO for low to mid effect levels. Additive results were seen in cell line 2452. IC50’s were not greatly exceeded; therefore, CI’s for higher effect levels were not estimated. Conclusions: LBH appears to be synergistic with CDDP in some meso cell lines. The FRD is an efficient method to assess drug synergy and will be used for larger experiments to study 3 drug combinations involving LBH589. We propose a straightforward analysis method based on the median effect equations that jointly models the data, providing confidence intervals and a statistical test for synergy. [Table: see text] No significant financial relationships to disclose.
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Loderer, Andreas, Bogdan Galovskyi, Wito Hartmann, and Tino Hausotte. "Qualifying Measuring Systems by Using Six Sigma." Key Engineering Materials 637 (February 2015): 37–43. http://dx.doi.org/10.4028/www.scientific.net/kem.637.37.
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The technology of sheet-bulk metal forming enables the production of complex workpieces with filigree surface structures in only a few forming steps. In order to provide a rapid and production-related workpiece inspection of not only large workpiece features, but also small features in an appropriate quality, a multi-sensor optical measurement system with different resolutions is required. Workpiece features of medium size can be measured by two types of fringe projection sensors. With a structured approach according to Six Sigma, which is based on the five phases design, measure, analyze, improve and control complex tasks are divided into smaller individual problems. In each phase the Six Sigma method recommends tools for solving the individual problems effectively. With the support of the Six Sigma guideline an exemplary sheet-bulk metal forming workpiece feature is used in order to qualify the two measuring systems for a production-related measurement. After defining the explicit goal for the investigations, a detailed analysis of the measurement process leads to a couple of relevant influences. These are input factors for the design of experiments. By a full factorial design, not only an influence of a factor itself, also the interactions between multiple factors can be detected. In the analyze-phase, these results are calculated by different statistical methods. To present the results in a comprehensible way several types of diagrams are used. The shown approach gives an example for a traceable and methodical way to qualify a measurement system for challenging measurement tasks.
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Hoefer, Kevin. "Correlations between Process and Geometric Parameters in Additive Manufacturing of Austenitic Stainless Steel Components Using 3DPMD." Applied Sciences 11, no. 12 (June 17, 2021): 5610. http://dx.doi.org/10.3390/app11125610.
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The additive manufacturing of components is characterized by a layered build-up. The stability of the build-up process with regard to the component geometry and the layer thickness is essential for the success of the entire system. A prerequisite for this is the exact knowledge of the interrelationships between the process, construction parameters and the resulting component geometry, respectively. These correlations are determined within the study using the 3D Plasma Metal Deposition Process (3DPMD). For this purpose, the process is first subjected to a system analysis. Possible influencing variables were identified with regard to the question “Which system parameters influence the component geometry?” and then prioritized. Then, the influence of control factors (welding current intensity, welding speed, and powder mass flow) was investigated according to the specifications of the Design of Experiments (DOE) method by means of a full-factorial experimental design and evaluated on the basis of metallographic cross-sections. As a result, it was determined that the system parameter powder mass flow only influences the layer thickness and not the wall thickness and is, therefore, available as a process control variable. In sum, comprehensive knowledge of complex relationships between the control parameters and the component geometry in additive manufacturing using 3DPMD was achieved and forms the basis for further scientific work.