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Quantitative experiments are essential for investigating, uncovering, and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation-based parameter estimation for rapid, automated assessment and ranking of experiment designs. Using two kinetic model systems with distinct dynamical features, we show that PARSEC-based experiments improve the parameter estimation of a complex system. By its inherent formulation, PARSEC can account for experimental restrictions and parameter variability. Moreover, we demonstrate that there is a strong correlation between sample size and the optimal number of PS clusters in PARSEC, offering a novel method to determine the ideal sampling for experiments. This validates our argument for employing parameter sensitivity in experiment design and illustrates the potential to leverage both model architecture and system dynamics to effectively explore the experimental design space.

The tools and technique used in the Design of Experiments (DOE) have been proved successful in meeting the challenge of continuous improvement over the last 15 years. However, research has shown that applications of these techniques in small and medium-sized manufacturing companies are limited due to a lack of statistical knowledge required for their effective implementation. Although many books have been written in this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers.Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as by those using statistical methods and readers will find the concepts in this book both familiar and easy to understand. The book treats Planning, Communication, Engineering, Teamwork and Statistical Skills in separate chapters and then combines these skills through the use of many industrial case studies. Design of Experiments forms part of the suite of tools used in Six Sigma.Key features:* Provides essential DOE techniques for process improvement initiatives* Introduces simple graphical techniques as an alternative to advanced statistical methods - reducing time taken to design and develop prototypes, reducing time to reach the market* Case studies place DOE techniques in the context of different industry sectors* An excellent resource for the Six Sigma training programThis book will be useful to engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic.Dr Jiju Anthony is Senior Teaching Fellow at the International Manufacturing Unit at Warwick University. He is also a trainer and consultant in DOE and has worked as such for a number of companies including Motorola, Vickers, Procter and Gamble, Nokia, Bosch and a large number of SMEs.

Experimentation is a powerful methodology that enables scientists to establish causal claims empirically. However, one important criticism is that experiments merely provide a black box view of causality and fail to identify causal mechanisms. Specifically, critics argue that, although experiments can identify average causal effects, they cannot explain the process through which such effects come about. If true, this represents a serious limitation of experimentation, especially for social and medical science research that strives to identify causal mechanisms. We consider several experimental designs that help to identify average natural indirect effects. Some of these designs require the perfect manipulation of an intermediate variable, whereas others can be used even when only imperfect manipulation is possible. We use recent social science experiments to illustrate the key ideas that underlie each of the designs proposed.

We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables, or uncontrollable environmental variables. To accomplish this, we characterize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknown response surface. We use tractable examples to demonstrate why it is critical to characterize both types of uncertainty, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.

Experimenter demand effects refer to changes in behavior by experimental subjects due to cues about what constitutes appropriate behavior. We argue that they can either be social or purely cognitive, and that, when they may exist, it crucially matters how they relate to the true experimental objectives. They are usually a potential problem only when they are positively correlated with the true experimental objectives' predictions, and we identify techniques such as non-deceptive obfuscation to minimize this correlation. We discuss the persuasiveness or otherwise of defenses that can be used against demand effects criticisms when such correlation remains an issue.

In light of an increasing interest in experimental work, we provide a review of some of the general issues involved in the design of experiments and illustrate their relevance to sociology and to other areas of social science of interest to sociologists. We provide both an introduction to the principles of experimental design and examples of influential applications of design for different types of social science research. Our aim is twofold: to provide a foundation in the principles of design that may be useful to those planning experiments and to provide a critical overview of the range of applications of experimental design across the social sciences.

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.

This article proposes a method for constructing a new type of space-filling design, called a sliced Latin hypercube design, intended for running computer experiments. Such a design is a special Latin hypercube design that can be partitioned into slices of smaller Latin hypercube designs. It is desirable to use the constructed designs for collective evaluations of computer models and ensembles of multiple computer models. The proposed construction method is easy to implement, capable of accommodating any number of factors, and flexible in run size. Examples are given to illustrate the method. Sampling properties of the constructed designs are examined. Numerical illustration is provided to corroborate the derived theoretical results.

his study optimised the conditions for ultrasound-assisted extraction (UAE) of defatted wheat germ protein (WGP) and evaluated its effect on the functional properties. Single-factor and orthogonal experiment designs were combined to optimise the UAE extraction condition. The extraction of WGP reached the highest level, at 88.66%, with a solid : liquid ratio of 1 : 25 g·mL–1, pH value of 9.0, ultrasonic time of 10 min, and ultrasonic power at 400 W. Under these conditions, albumin, globulin, prolamin, and glutenin accounted for 32.26, 28.52, 5.42, and 22.40% of total protein, respectively. In addition, this study compared the functional properties of WGP extracted by UAE with the results based on a commercially available soy protein (SP) isolate (SPI). The UAE of WGP had better oil absorption, foaming, and emulsifying properties. Therefore, UAE is a promising technique for food protein extraction because it can change the protein efficiencies and properties of the extract.

In experimental economics, where subjects participate in different sessions, observations across subjects of a given session might exhibit more correlation than observations across subjects in different sessions. The main goal of this paper is to clarify what are session-effects: what can cause them, what forms they can take, and what are the potential problems. It will be shown that standard solutions are at times inadequate, and that their properties are sometimes misunderstood.