Traditional Experimental Design Research Articles

Power and vertical locations: A socioecological perspective

High-rise construction increasingly dominates the contemporary urban landscape. We show that the physical built environment is linked to lay theories about social power. In five experiments (N = 1091), we comprehensively test the bi-directional causal link between floor location in high-rises and perceived social power. First, using immersive virtual reality (VR) experience of floor location within a physical urban environment (Experiment 1) and traditional experimental designs (Experiments 2–3), we demonstrate that information about a person's floor location affects perceptions regarding that individual's social power. Second, we find that information about a person's social power influences inferences about that individual's location on higher floors (Experiment 4) and encourages greater monetary outlays for the higher locations of powerful employees (Experiment 5). These findings carry important implications for social hierarchies and inequality, individual choices and policy implications in the urban environment, and the use of VR in environmental perception and cognition research.

Assessing green manure impact on wheat productivity through Bayesian analysis of yield monitor data.

Agronomy research traditionally relies on small, controlled trial plots, which may not accurately represent the complexities and variabilities found in larger, real-world settings. To address this gap, we introduce a Bayesian methodology for the analysis of yield monitor data, systematically collected across extensive agricultural landscapes during the 2020/21 and 2021/22 growing seasons. Utilizing advanced yield monitoring equipment, our method provides a detailed examination of the effects of green manure on wheat yields in a real-world context. The results from this comprehensive analysis reveal significant insights into the impact of green manure application on wheat production, demonstrating enhanced yield outcomes across varied landscapes. This evidence suggests that the Bayesian approach to analyzing yield monitor data can offer more precise and contextually relevant information than traditional experimental designs. This research underscores the value of integrating large-scale data analysis techniques in agronomy, moving beyond small-scale trials to offer a broader, more accurate perspective on agricultural practices. The adoption of such methodologies promises to refine farming strategies and policies, ultimately leading to more effective and sustainable agricultural outcomes. The inclusion of a Python script in the appendix illustrates our analytical process, providing a tangible resource for replicating and extending this research within the agronomic community.

Traditional or adaptive design of experiments? A pilot-scale comparison on wood delignification

Traditional design of experiments and response surface methodology are widely used in engineering and process development. Bayesian optimization is an alternative machine learning approach that adaptively selects successive experimental conditions based on a predefined performance measure. Here we compared the two approaches using simulations and empirical experiments on alkaline wood delignification to identify important benefits and drawbacks of Bayesian optimization in the context of design of experiments. The simulations showed that the selection of initial experiments and measurement noise influenced the convergence of the Bayesian optimization algorithm to known optimal conditions. Both methods, however, showed comparable pilot-scale results on optimal digestion conditions, where high cellulose yields were combined with acceptable kappa numbers and pulp viscosities. Bayesian optimization did not enable a decrease in the number of experiments required for reaching these conditions but provided a more accurate model in the vicinity of the optimum based on additional modelling and cross-validation. These results shed light on the practical differences between the two methodologies for process development and are an important contribution to the chemometrics and machine learning communities.

Molecular responses of sponge larvae exposed to partially weathered condensate oil

Anthropogenic inputs of petroleum hydrocarbons into the marine environment can have long lasting impacts on benthic communities. Sponges form an abundant and diverse component of benthic habitats, contributing a variety of important functional roles; however, their responses to petroleum hydrocarbons are largely unknown. This study combined a traditional ecotoxicological experimental design and endpoint with global gene expression profiling and microbial indicator species analysis to examine the effects of a water accommodated fraction (WAF) of condensate oil on a common Indo-Pacific sponge, Phyllospongia foliascens. A no significant effect concentration (N(S)EC) of 2.1 % WAF was obtained for larval settlement, while gene-specific (N(S)EC) thresholds ranged from 3.4 % to 8.8 % WAF. Significant shifts in global gene expression were identified at WAF treatments ≥20 %, with larvae exposed to 100 % WAF most responsive. Results from this study provide an example on the incorporation of non-conventional molecular and microbiological responses into ecotoxicological studies on petroleum hydrocarbons.

Investigation of drag reduction by slurry-like drag-reducing agent in microtube flow using response surface methodology (RSM)

In this study, we investigated the drag reduction property of a premixed slurry drag reducer in a millimeter-scale pipe. The aim of this study is to establish the correlation between Darcy friction factor and drag reducer concentration (C) and volume flow (Q). First, the experimental plan was designed by using the response surface method (RSM), and then the experimental data were processed to establish the quadratic correlation between the response variable and the description variable. After that, ANOVA analysis of variance was used to verify the accuracy of the experimental data and the correlation. Finally, the prediction model is extended to a larger concentration and volume flow range, and it is found that the accuracy between the predicted value of friction coefficient and the experimental value is ± 30%, thus verifying that the correlation is suitable for the small-scale fully turbulent region. Compared with traditional experimental design and correlation methods, the implementation of Response Surface Methodology (RSM) in this study not only reduces the experimental time but also yields a more robust correlation for predicting the friction coefficient.

A two-phase approach optimizing productivity for a mAb-producing CHO cell culture process using dynamic response surface methodology models

Fed-batch cell culture processes are common in the biomanufacturing industry due to their ease of development and simplicity of execution in the cGMP environment. One challenge of fed-batch operation is that the physiochemical conditions within the bioreactor do not remain constant throughout a batch but change over time as the viable cell density changes. It is well accepted that fed-batch cultures are multi-phasic, divided into “growth, stationary, and death phases”. Yet, using empirical modeling methods such as response surface models (RSMs) for optimization is still common. While RSMs can identify local performance maxima, they are limited by the time-invariant factors (input parameters) they use. These factors do not change throughout the run. Dynamic RSM (DRSM) models predict the time-dependent impact of the process inputs allowing for the identification of optimized process operations that change with time. Starting with the data sets generated via a traditional Design of Experiments (DoE) design, the DRSM model successfully optimized the peak cell count during the growth phase of culture and then identified a second set of process parameters that optimized productivity during the stationary phase. The resulting process achieved a harvest titer improvement of 28% relative to the base process condition.

Tensile Test Optimization Using the Design of Experiment and Soft Computing

The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator on the tensile test of brass samples. This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). By combining these techniques, the study aims to leverage their individual strengths and achieve superior results. The results reveal that the area of the contact point exerts the most significant influence on the tensile test, followed by the operator and temperature. The optimal levels of these parameters are determined to be a level of two for the operator, a level of three for the area of the contact point, and a level of one for the temperature. The study demonstrated that the hybrid soft computing method outperformed the traditional DOE method, achieving a substantial improvement in elongation of 32.9%. The optimized combination of machining parameters led to a notable enhancement in the brass samples’ tensile properties, highlighting the effectiveness of the applied methodology. The marginal error of only 0.72% in the hybrid approach showcases its high precision and reliability in determining the optimal levels of machining parameters. These findings underscore the potential of the Taguchi optimization method, ANFIS, and GA in achieving superior results in the tensile testing of materials, particularly in cases where multiple parameters are involved. The research results provide valuable insights for industries relying on precise material characterization, offering a robust methodology for optimizing tensile testing procedures. The study’s success in leveraging a hybrid soft computing approach serves as a promising avenue for future research in the field of material testing and optimization techniques.

Data Informed Model Test Design With Machine Learning – an Example in Nonlinear Wave Load on a Vertical Cylinder

Abstract Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The optimal design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering -- nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The metocean data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several ‘interpretable’ decisions which can be explained with physical insights.

Robust design modeling and optimization for dealing with a non-convex design space

Classic experimental designs effectively generate experimental design points under standard design spaces such as a circle, a cube, or a sphere. However, for non-convex design spaces, traditional experimental designs are not appropriate due to nonstandard experimental design conditions. No attention has been given to developing an algorithm to generate design points for a non-convex design space in the literature. This paper is three-fold. One, this paper presents a novel exchange algorithm based on an inner approximation approach in order to construct optimal design points where the design space is non-convex. Besides, the proposed algorithm efficiently produces experimental design points for non-convex design space. In addition, it is provided a fair comparison of the proposed algorithm performance. Two, the fitted response functions of the mean, standard deviation, and variance are found using the proposed regression model construction technique where the design space is non-convex. Three, a selected optimal design concept-based robust design optimization model is developed to find Pareto optimal solution settings of chosen design factors for the non-convex design space. Finally, an Industry 4.0 concept-based numerical example is provided to illustrate how to apply the proposed methodology effectively for the non-convex design space.

Beauty of Explicit Dispersity (Đ) Equations in Controlled Polymerizations.

Dispersity (Đ) as a critical parameter indicates the level of uniformity of the polymer molar mass or chain length. In the past several decades, the development of explicit equations for calculating Đ experiences a continual revolution. This viewpoint tracks the historical evolution of the explicit equations from living to reversible-deactivation polymerization systems. Emphasis is laid on displaying the charm of explicit Đ equations in batch reversible-deactivation radical polymerization (RDRP), with highlights of the relevant elegant mathematical manipulations. Some representative emerging applications enabled by the existing explicit equations are shown, involving nitroxide-mediated polymerization (NMP), atom transfer radical polymerization (ATRP), and reversible addition-fragmentation chain transfer (RAFT) polymerization systems. Stemming from the several outlined challenges and outlooks, sustained concerns about the explicit Đ equations are still highly deserved. It is expected that these equations will continue to play an important role not only in traditional polymerization kinetic simulation and design of experiments but also in modern intelligent manufacturing of precision polymers and classroom education.

GRRIEn Analysis: A Data Science Cheat Sheet for Earth Scientists Learning from Global Earth Observations

Abstract Globally available environmental observations (EOs), specifically from satellites and coupled Earth system models, represent some of the largest datasets of the digital age. As the volume of global EOs continues to grow, so does the potential of these data to help Earth scientists discover trends and patterns in Earth systems at large spatial scales. To leverage global EOs for scientific insight, Earth scientists need targeted and accessible exposure to skills in reproducible scientific computing and spatiotemporal data science, and to be empowered to apply their domain understanding to interpret data-driven models for knowledge discovery. The Generalizable, Reproducible, Robust, and Interpreted Environmental (GRRIEn) analysis framework was developed to prepare Earth scientists with an introductory statistics background and limited/no understanding of programming and computational methods to use global EOs to successfully generalize insights from local/regional field measurements across unsampled times and locations. GRRIEn analysis is generalizable, meaning results from a sample are translated to landscape scales by combining direct environmental measurements with global EOs using supervised machine learning; robust, meaning that the model shows good performance on data with scale-dependent feature and observation dependence; reproducible, based on a standard repository structure so that other scientists can quickly and easily replicate the analysis with a few computational tools; and interpreted, meaning that Earth scientists apply domain expertise to ensure that model parameters reflect a physically plausible diagnosis of the environmental system. This tutorial presents standard steps for achieving GRRIEn analysis by combining conventions of rigor in traditional experimental design with the open-science movement. Significance Statement Earth science researchers in the digital age are often tasked with pioneering big data analyses, yet have limited formal training in statistics and computational methods such as databasing or computer programming. Earth science researchers often spend tremendous amounts of time learning core computational skills, and making core analytical mistakes, in the process of bridging this training gap, at risk to the reputability of observational geostatistical research. The GRRIEn analytical framework is a practical guide introducing community standards for each phase of the computational research pipeline (dataset engineering, model training, and model diagnostics) to promote rigorous, accessible use of global EOs in Earth systems research.

Unconventional approaches for organic electrosynthesis: Recent progress

Modern organic electrochemistry enables a broad range of synthetic transformations in a convenient and “green” manner. However, the limitations, inherent in the established design of an electrochemical experiment – such as mass and heat transfer and the non-homogeneity of an electric field – may sometimes limit the effectivity of the method. The development of novel unconventional approaches to conduct electrochemical experiments may help to overcome these problems by introducing significant modifications to the traditional experimental design. In this review, the most notable emerging fields in organic electrochemistry are briefly surveyed – microflow electrosynthesis, alternating current electrolysis, and bipolar electrochemistry – with a particular emphasis on recent synthetic progress using these methods.

Factorial Designing – An Essential Tool in Pharmaceutical Optimization

With the ever-increasing techniques of research, the traditional experimental design is not sufficient as well as satisfying to contribute enough to the experiment's robustness. Formulation development by utilising factorial design is a smarter way of experimentation which act as a crucial tool for optimization. Factorial design is more efficient for the type of experiments having effect of two or more factors. Factorial design is more adaptable and provides more significant information about the process and product, as well as a variety of software. This manuscript provides the reader with a thorough understanding of factorial experimental design, its theoretical foundation, and how to utilise the statistical tool in realworld research. Keywords: factorial design, optimization, research

Fused-Filament Fabrication of Short Carbon Fiber-Reinforced Polyamide: Parameter Optimization for Improved Performance under Uniaxial Tensile Loading.

This study intends to contribute to the state of the art of Fused-Filament Fabrication (FFF) of short-fiber-reinforced polyamides by optimizing process parameters to improve the performance of printed parts under uniaxial tensile loading. This was performed using two different approaches: a more traditional 2k full factorial design of experiments (DoE) and multiple polynomial regression using an algorithm implementing machine learning (ML) principles such as train-test split and cross-validation. Evaluated parameters included extrusion and printing bed temperatures, layer height and printing speed. It was concluded that when exposed to new observations, the ML-based model predicted the response with higher accuracy. However, the DoE fared slightly better at predicting observations where higher response values were expected, including the optimal solution, which reached an UTS of 117.1 ± 5.7 MPa. Moreover, there was an important correlation between process parameters and the response. Layer height and printing bed temperatures were considered the most influential parameters, while extrusion temperature and printing speed had a lower influence on the outcome. The general influence of parameters on the response was correlated with the degree of interlayer cohesion, which in turn affected the mechanical performance of the 3D-printed specimens.

A critical analysis of research methods and experimental models to study apical extrusion of debris and irrigants.

During root canal treatment, intra-canal content as well as irrigants and root filling materials may be extruded into the periapical tissues, leading to undesirable consequences such as delay of healing and flare-ups. Although apical extrusion of debris is an inherent occurrence of root canal treatment and no instrument or technique has totally solved this problem, attempts are underway to minimize the harm exerted to periapical tissues with the development of new techniques. This has led researchers to continuously evaluate new instruments and methodologies in terms of apical extrusion. New research on extrusion is frequently published some of which utilize traditional experimental designs and others assessing extrusion using modified or innovative methodologies. This review focuses on the research methods and experimental models to study apical extrusion of debris and irrigants by making a critical analysis of each technique. A PubMed and Scopus search was conducted and 144 articles relevant to apical extrusion were retrieved from 1968 until 2021. The methodologies used in every research article were evaluated and a classification was created. Each methodology is described with the general outlines and advantages and drawbacks of each technique summarized with a critical approach. It appears that the collection of debris in a vial still serves as a method chosen by authors even in most recent studies. On the other hand, development of experimental models that include multiple factors considering the drawbacks of the existing study designs is essential to draw more reliable conclusions on the apical extrusion phenomenon.

A model-based approach to designing developmental toxicology experiments using sea urchin embryos

The key aim of this paper is to suggest a more quantitative approach to designing a dose–response experiment, and more specifically, a concentration–response experiment. The work proposes a departure from the traditional experimental design to determine a dose–response relationship in a developmental toxicology study. It is proposed that a model-based approach to determine a dose–response relationship can provide the most accurate statistical inference for the underlying parameters of interest, which may be estimating one or more model parameters or pre-specified functions of the model parameters, such as lethal dose, at maximal efficiency. When the design criterion or criteria can be determined at the onset, there are demonstrated efficiency gains using a more carefully selected model-based optimal design as opposed to an ad-hoc empirical design. As an illustration, a model-based approach was theoretically used to construct efficient designs for inference in a developmental toxicity study of sea urchin embryos exposed to trimethoprim. This study compares and contrasts the results obtained using model-based optimal designs versus an ad-hoc empirical design.

The Hybrid-Agile Design of Experiments Methodology

A DOE (Design of Experiments) is the laying out of a detailed experimental plan in advance of doing the experiment. Optimal DOEs maximize the amount of information that can be obtained for a given amount of experimental effort. The traditional DOE methodology is waterfall-type methodology implying a sequential and linear life-cycle process. The success of the experiment and usefulness of the results are highly dependent on the initial experimental setup and assumptions, and does not allow to go back and change something that was not well-documented or thought upon in the design stage. The fast-changing software development industry have made it understandable that the traditional waterfall methodology for developing systems, which follows similar patters to the traditional DOE, lacks the agility required for developing robust systems. These limitations have triggered the development of agile: a type of incremental model of software development based on principles that focuses more on flexible responses to change, instead of in-depth planning at the design stage. This paper proposes the hybrid-agile DOE methodology – a methodology that incorporates agile principles in traditional waterfall DOE methodologies – to design effective experimental layouts that allow for improvement during the experimental trial process. The methodology is applied to the natural ageing of adhesives tapes for building applications. This methodology can overcome traditional DOE, by adding agility in the whole process, especially in cases where the investigated products lack prior information and are characterised by large variability.

Multivariate genome-wide association study of leaf shape in a Populus deltoides and P. simonii F1 pedigree.

Leaf morphology exhibits tremendous diversity between and within species, and is likely related to adaptation to environmental factors. Most poplar species are of great economic and ecological values and their leaf morphology can be a good predictor for wood productivity and environment adaptation. It is important to understand the genetic mechanism behind variation in leaf shape. Although some initial efforts have been made to identify quantitative trait loci (QTLs) for poplar leaf traits, more effort needs to be expended to unravel the polygenic architecture of the complex traits of leaf shape. Here, we performed a genome-wide association analysis (GWAS) of poplar leaf shape traits in a randomized complete block design with clones from F1 hybrids of Populus deltoides and Populus simonii. A total of 35 SNPs were identified as significantly associated with the multiple traits of a moderate number of regular polar radii between the leaf centroid and its edge points, which could represent the leaf shape, based on a multivariate linear mixed model. In contrast, the univariate linear mixed model was applied as single leaf traits for GWAS, leading to genomic inflation; thus, no significant SNPs were detected for leaf length, measures of leaf width, leaf area, or the ratio of leaf length to leaf width under genomic control. Investigation of the candidate genes showed that most flanking regions of the significant leaf shape-associated SNPs harbored genes that were related to leaf growth and development and to the regulation of leaf morphology. The combined use of the traditional experimental design and the multivariate linear mixed model could greatly improve the power in GWAS because the multiple trait data from a large number of individuals with replicates of clones were incorporated into the statistical model. The results of this study will enhance the understanding of the genetic mechanism of leaf shape variation in Populus. In addition, a moderate number of regular leaf polar radii can largely represent the leaf shape and can be used for GWAS of such a complicated trait in Populus, instead of the higher-dimensional regular radius data that were previously considered to well represent leaf shape.

Task-driven assessment of experimental designs in diffusion MRI: A computational framework.

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments’ ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline’s task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline’s advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method’s predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

Optimization of muscle cell culture media using nonlinear design of experiments.

Optimizing media for biological processes, such as those used in tissue engineering and cultivated meat production, is difficult due to the extensive experimentation required, number of media components, nonlinear and interactive responses, and the number of conflicting design objectives. Here we demonstrate the capacity of a nonlinear design-of-experiments (DOE) method to predict optimal media conditions in fewer experiments than a traditional DOE. The approach is based on a hybridization of a coordinate search for local optimization with dynamically adjusted search spaces and a global search method utilizing a truncated genetic algorithm using radial basis functions to store and model prior knowledge. Using this method, we were able to reduce the cost of muscle cell proliferation media while maintaining cell growth 48h after seeding using 30 common components of typical commercial growth medium in fewer experiments than a traditional DOE (70vs. 103). While we clearly demonstrated that the experimental optimization algorithm significantly outperforms conventional DOE, due to the choice of a 48h growth assay weighted by medium cost as an objective function, these findings were limited to performance at a single passage, and did not generalize to growth over multiple passages. This underscores the importance of choosing objective functions that align well with process goals.