Dual Response Approach Research Articles

Gustation-Inspired Dual-Responsive Hydrogels for Taste Sensing Enabled by Machine Learning.

Human gustatory system recognizes salty/sour or sweet tastants based on their different ionic or nonionic natures using two different signaling pathways. This suggests that evolution has selected this detection dualism favorably. Analogically, this work constructs herein bioinspired stimulus-responsive hydrogels to recognize model salty/sour or sweet tastes based on two different responses, that is, electrical and volumetric responsivities. Different compositions of zwitter-ionic sulfobetainic N-(3-sulfopropyl)-N-(methacryloxyethyl)-N,N-dimethylammonium betaine (DMAPS) and nonionic 2-hydroxyethyl methacrylate (HEMA) are co-polymerized to explore conditions for gelation. The hydrogel responses upon adding model tastant molecules are explored using electrical and visual de-swelling observations. Beyond challenging electrochemical impedance spectroscopy measurements, naive multimeter electrical characterizations are performed, toward facile applicability. Ionic model molecules, for example, sodium chloride and acetic acid, interact electrostatically with DMAPS groups, whereas nonionic molecules, for example,D(-)fructose, interact by hydrogen bonding with HEMA. The model tastants induce complex combinations of electrical and volumetric responses, which are then introduced as inputs for machine learning algorithms. The fidelity of such a trained dual response approach is tested for a more general taste identification. This work envisages that the facile dual electric/volumetric hydrogel responses combined with machine learning proposes a generic bioinspired avenue for future bionic designs of artificial taste recognition, amply needed in applications.

Text highlighting: Three methodological studies to inform guidelines for implementation

Text highlighting was recently introduced as a method for exploring consumer attitudes and perceptions. It consists of asking participants to read a piece of text on a digital device while using highlighting functions to indicate those parts of the text that they ‘like’/feel positive about, as well as those parts they ‘dislike’/feel negative about. As with any new method, methodological research helps to develop recommendations for best practice in use. In this regard, the present research addressed three research questions (RQs) in three studies with participants from New Zealand, United Kingdom, Singapore and Malaysia (ntotal = 5381). In a qualitative approach, Study 1 (RQ1) validated the notion that the text highlighting task is not expected to induce bias. Study 2 addressed RQ2 and explored whether the process of completing the text highlighting task influence consumers’ topic- and product-related responses? There was minimal evidence of this, meaning that text highlighting can reasonably be used together with other types of consumer responses without much the risk of biasing the latter. RQ3 (Study 3) investigated whether the type of highlighting task performed influence text selection. To date the method has been implemented as a dual approach (i.e., selecting both ‘like’ and ‘dislike’ aspects of the text), however, this could tentatively bias either or both responses compared to a single response task where participants select only ‘like’ highlights or only ‘dislike’ highlights. Study 3 found no effect for ‘like’ highlights when comparing dual- and single-response highlighting. However, for ‘dislike’ highlighting, significantly fewer words were selected in the dual-response approach (although the difference in frequency of selection was < 10%). The findings were established in case studies pertaining to kiwifruit and biodynamic agriculture but require confirmation in studies with different products, texts and participants.

Stackelberg game approach to a bi-objective robust design optimization

Robust design has received a great deal of attention from quality researchers in recent years, and a number of optimization methodologies based on the dual response format have been proposed. The majority of existing bi-objective optimization models concentrate on the trade-offs between the process mean and variability functions without investigating the interactions between control factors and quality characteristics. The primary objective of this research is to integrate the Stackelberg leadership model into the robust design procedure and propose a Stackelberg game-based robust design (SGRD) method to determine appropriate control factor settings by minimizing the values of desired optimization targets based on an analysis of possible combinations of input and output quality parameters. Herein, first, a bi-objective robust design optimization problem is formulated as a dual response model using response surface methodology (RSM). Second, the proposed SGRD model is developed via decomposition into two leader-follower game models. Finally, the mean square error (MSE) criterion is applied to evaluate models, and select non-dominated solutions in various situations. Numerical examples are used to demonstrate that the proposed method provides significant solutions in cases containing unidentified priorities between the dual responses and undiscovered correlations among several inputs and outcomes. In addition, according to the case study analysis, the proposed method is more efficient than the conventional dual response approach when dealing with bi-objective robust design optimization problems.

A Hierarchical Bayes Approach to Modeling Heterogeneity in Discrete Choice Experiments: An Application to Public Preferences for Prenatal Screening.

Previous studies assessing preferences for prenatal screening have focused on preferences of the affected population and have largely assumed homogeneous preferences. We aimed to estimate public preferences and willingness to pay for prenatal screening and diagnosis from a Canadian general population sample, and to model preferences at the individual level. A discrete choice experiment was used to elicit preferences for different aspects of prenatal screening and diagnostic strategies. Strategies differed in five attributes: timing of the results, false-negative rate, false-positive rate, risk of miscarriage, and out-of-pocket cost. Respondents made forced and unforced choices using a dual response approach. Hierarchical Bayes analysis was applied to estimate individual-level part-worth utilities. Individual probability and expected uptake of prenatal screening under different scenarios were also assessed. Subgroup analyses were conducted using individual-level preferences. The final analyses were based on a sample of 4601 respondents. Results showed that the two most important attributes were false-negative rate and miscarriage risk. There was significant heterogeneity in preferences among respondents. Individuals' perception of the risk of pregnancy with chromosomal abnormalities affected their preferences for screening. The relatively high uptake of safe and accurate screening among all groups of respondents indicated people's desire for information about the health of their unborn baby regardless of their decision to continue the pregnancy. Our findings are consistent with previous studies based on affected-population preferences. This concordance should be reassuring from a policy perspective and can inform the design of publicly funded prenatal screening programs.

Dual Response Approach in Process Capability based on Hybrid Neural Network-Genetic Algorithms

Process capability has long been recognized as an important performance measure to prove how well the process meets the requirements. Process capability can be improved by applying dual response approach, to determine optimal input factors. Using of artificial intelligence can optimize the prediction of the best input combination with a limited number of experiments. This study proposes an alternatives procedure using a dual response approach and artificial intelligence. One of the most common robust design models has been formulated to minimize variability while maintaining the mean on the desired target. A study case was selected to implement the proposed approach and compare it with conventional optimization models to show the improvement in procedures.

Robust dual-response optimization

ABSTRACTThis article presents a robust optimization reformulation of the dual-response problem developed in response surface methodology. The dual-response approach fits separate models for the mean and the variance and analyzes these two models in a mathematical optimization setting. We use metamodels estimated from experiments with both controllable and environmental inputs. These experiments may be performed with either real or simulated systems; we focus on simulation experiments. For the environmental inputs, classic approaches assume known means, variances, or covariances and sometimes even a known distribution. We, however, develop a method that uses only experimental data, so it does not need a known probability distribution. Moreover, our approach yields a solution that is robust against the ambiguity in the probability distribution. We also propose an adjustable robust optimization method that enables adjusting the values of the controllable factors after observing the values of the environmental factors. We illustrate our novel methods through several numerical examples, which demonstrate their effectiveness.

인공신경망과 유전알고리즘 기반의 쌍대반응표면분석에 관한 연구

Tritiya R. Arungpadang․Young Jin KimDepartment of Systems Management and Engineering, Pukyong National UniversityPrediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from phar-maceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

Dual-Response Approach to Work Stress: An Investigation of Stressors and Wellbeing Outcomes

Dual-Response Approach to Work Stress: An Investigation of Stressors and Wellbeing Outcomes

A fluorescence ratiometric sensor for hypochlorite based on a novel dual-fluorophore response approach

A fluorescence ratiometric sensor for OCl− has been developed based on a novel dual fluorophore response approach. The sensor molecule contains a coumarin fluorophore and a rhodamine fluorophore, and the two fluorophores are directly linked to an OCl− recognition group. The structure of the sensor was characterized by ESI-MS, NMR, and X-ray crystallographic analysis. Upon treatment with OCl−, both fluorophores in the sensor responded simultaneously at two separate optical windows, with large enhancement of the fluorescence ratio (I578/I501) from 0.01 to 39.55. The fluorescence ratios for the sensor showed a good linearity with the concentration of OCl− in the range of 0.2–40μM and the detection limits is 0.024μM (SN−1=3). Investigation of reaction products indicated that the sensor reaction with OCl− produced two new fluorescent molecules, which were responsible for the fluorescence changes in two optical windows. In addition, the sensor showed high selectivity to OCl− over other reactive oxygen species, reactive nitrogen species, cations, and anions. The sensor has also been successfully applied to detection of OCl− in natural water samples with satisfactory recovery.

인공신경망을 이용한 로버스트설계에 관한 연구

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design

The robust parameter design of industrial processes and products on the basis of the concept of building quality into a design has attracted much attention from researchers and practitioners for many years, and several methods have been studied in the research community. Dual response surface methodology is one of the most commonly used approaches for simultaneously optimizing the mean and the variance of response in quality engineering. Nevertheless, when the relationship between influential input factors and output quality characteristics of a process is very complex (e.g. highly nonlinear and noisy), traditional approaches have their limitations. In this article, we introduced support vector regression, kriging model, and radial basis function, which are commonly used in computer experiments, into robust parameter design, and especially introduced a new strategy that builds the dual response surface using the ensemble of surrogates, which can provide a more robust approximation model. We demonstrated the advantages of kriging, support vector regression, radial basis function, and the ensemble of surrogates by reinvestigating the dual response approach on the basis of parametric, nonparametric, and semiparametric approaches, and a simulation experiment is studied. The results show that our presented models can achieve more desirable results than parametric, nonparametric, and semiparametric approaches in terms of fitting and predictive accuracy, and the optimal operating conditions recommended by our presented models are similar to those recommended in literature, which indicates the validation of our presented models. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Optimization of the Soldering Process With ECAs in Electronic Equipment: Characterization Measurement and Experimental Design

With the introduction of the European Directives restrictions of hazardous substances, electrically conductive adhesives (ECAs) have received great attention in the field of electronics as a possible replacement of the traditional tin-lead soldering technology. So, in this new context, the analysis and the characterization of these alternative materials represent a fundamental topic, above all, from the industrial point of view. Nevertheless, studies on ECAs have rarely been reported in the literature, but more recently research has started to focus on this specific topic. After a comparative assessment concerning soldering materials, this paper focuses attention on their characterization through measurement in order to verify the electrical behavior of the isotropic silver conductive adhesive. In addition, since the soldering process is affected by a large number of variables such as the thickness of the conductive adhesive film, radial velocity, and curing temperature, the optimal selection of the factors is carried out through experimental design theory and the dual-response approach by means of generalized linear models. In this paper, the experimental and comparative studies on soldering made up of epoxy adhesives are carried out, in particular, the adhesives constituted by metallic particles (silver), normally in the form of flakes, in a polymer matrix are considered. The novelty of the kind of adhesive considered is the Ag filler loadings of 50-65% by volume. At these loadings, the materials achieve the percolation threshold and are electrically conductive in all directions after the materials are cured. Two different types of conductive adhesives, characterized by different chemical structures and compositions, are experimented and tested. Then, since the lead-free soldering process is characterized by several critical factors, a statistical approach is used to optimize this process. Experimental results obtained by testing samples with ECA materials prove the validity of this paper. The value of this paper is in the application of a statistical approach to these adhesive materials in order to achieve the optimization of the soldering process with a small number of treatment combinations, satisfying at the same time the stringent requirements and achieving robust electrical interconnections.

Optimization of Triple Response Systems by Using the Dual Response Approach and the Hooke-Jeeves Search Method

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

Studies on a biobjective robust design optimization problem

The vast majority of the multiobjective robust design research reported in the literature has been performed under the assumption of multiple quality characteristics. This paper differs in that the process mean and variance are considered to be a biobjective problem since the primary goal of robust design is to determine the optimal robust design factor settings by minimizing performance variability and deviation from a target value of a product. A more comprehensive set of solutions is developed using a lexicographic weighted Tchebycheff approach to the biobjective robust design model rather than the approaches traditionally used in the dual-response approach to obtain efficient solutions. Numerical examples show that the proposed model is far more effective than the traditional weighted sum approach. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix with mathematical proof, figures, and tables.]

Response surface modeling of 1-stearoyl-3(2)-oleoyl glycerol production in a pilot packed-bed immobilized Rhizomucor miehei lipase reactor

A dual response approach using diacylglycerol (DAG) and triacylglycerol (TAG) as responses for optimization of 1-stearoyl-3(2)-oleoyl glycerol-enriched DAG synthesis using response surface methodology (RSM) was investigated. Four variables from a lipase-catalyzed esterification reaction were optimized using a central composite rotatable design. The following optimized conditions yielded 51 wt.% DAG and 22 wt.% TAG: reaction temperature of 55 °C, enzyme dosage of 9.5 wt.%, fatty acid/glycerol molar ratio of 2.1 and reaction time of 3 h. Results were repeatable at 10 kg production scale in a pilot packed-bed enzyme reactor. No significant losses in enzyme activity or changes in fatty acid selectivity on DAG synthesis were observed during the five pilot productions. Lipozyme RM IM showed selectivity towards the production of stearic acid enriched DAG. The purity of DAG oil after purification was 90 wt.%.

Another view of dual response surface modeling and optimization in robust parameter design

Robust parameter design (RPD) based on the concept of building quality into a design has received much attention from researchers and practitioners for years, and a number of methodologies have been studied in the research community. There have been many attempts to integrate RPD principles with well-established statistical techniques, such as response surface methodology, in order to model the response directly as a function of control factors. In this paper, we reinvestigate the dual response approach based on quadratic models Vinning and Myers (J Qual Technol 22:38–45), which is often referred to in the RPD literature and demonstrate that higher-order polynomial models may be more effective in finding better RPD solutions than the commonly-used quadratic model. We also propose optimization models for each of the three classes of quality characteristics (i.e., nominal-the-best, larger-the-better, and smaller-the-better). The optimal solutions obtained using the proposed models are compared with the solutions obtained using the RPD techniques in the current literature.

Multiple-input dual-output adjustment scheme for semiconductor manufacturing processes using a dynamic dual-response approach

This paper presents an optimization-based, multiple-input dual-output (MIDO) run-to-run (R2R) controller for general semiconductor manufacturing processes. This controller, termed ‘adaptive dual-response optimizing controller’ (ADROC), can serve as a recipe regulator between consecutive runs of wafer fabrication. In ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual-response approach is used to seek the optimum recipe within the acceptability region for the execution of next run. In addition, a ‘responsive-type’ adjustment method is devised, serving as a post hoc filter to alleviate the over-control problem and maintain a better trade-off between two potentially conflicting process responses in MIDO R2R situations. Typical applications of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing are demonstrated through simulated processes to illustrate ADROC. The procedure is compared to three benchmark methods (OAQC, single- and double-EWMA controllers) in a simulation experiment.

Diacylglycerol and Triacylglycerol as Responses in a Dual Response Surface-Optimized Process for Diacylglycerol Production by Lipase-Catalyzed Esterification in a Pilot Packed-Bed Enzyme Reactor

Diacylglycerol (DAG) and triacylglycerol (TAG) as responses on optimization of DAG production using a dual response approach of response surface methodology were investigated. This approach takes the molecular equilibrium of DAG into account and allows for the optimization of reaction conditions to achieve maximum DAG and minimum TAG yields. The esterification reaction was optimized with four factors using a central composite rotatable design. The following optimized conditions yielded 48 wt % DAG and 14 wt % TAG: reaction temperature of 66.29 degrees C, enzyme dosage of 4 wt %, fatty acid/glycerol molar ratio of 2.14, and reaction time of 4.14 h. Similar results were achieved when the process was scaled up to a 10 kg production in a pilot packed-bed enzyme reactor. Lipozyme RM IM did not show any significant activity losses or changes in fatty acid selectivity on DAG synthesis during the 10 pilot productions. However, lipozyme RM IM displayed higher selectivity toward the production of oleic acid-enriched DAG. The purity of DAG oil after purification was 92 wt %.

The no-choice option and dual response choice designs

Choice set designs that include a constant or no-choice option have increased efficiency, better mimic consumer choices, and allow one to model changes in market size. However, when the no-choice option is selected no information is obtained on the relative attractiveness of the available alternatives. One potential solution to this problem is to use a dual response format in which respondents first choose among a set of available alternatives in a forced-choice task and then choose among the available alternatives and a no-choice option. This paper uses a simulation to demonstrate and confirm the possible gains in efficiency of dual response over traditional choice-based conjoint tasks when there are different proportions choosing the no-choice option. Next, two choice-based conjoint analysis studies find little systematic violation of IIA with the addition/deletion of a no-choice option. Further analysis supports the hypothesis that selection of the no-choice option is more closely related to choice set attractiveness than to decision difficulty. Finally, validation evidence is presented. Our findings show that researchers can employ the dual response approach, taking advantages of the increased power of estimation, without concern for systematically biasing the resulting parameter estimates. Hence, we argue this is a valuable approach when there is the possibility of a large number of no-choices and preference heterogeneity.

Development of robust design optimization using incomplete data

In robust design, the main goal is to select the levels of the controllable factors to obtain the optimal operating conditions. To this end, Taguchi recommends that statistical experimental design methods be employed. To adopt his approach, we need to observe a number of replicated observations at each design point. A commonly used assumption behind the data collection procedure is that all the data are fully observed.However, in many industrial experiments, interval-censored observations are frequently available in addition to the fully observed observations. For example, the products are often inspected by a go or no-go inspection system which typically provides interval-censored data. Even though fully observed observations are preferred, only partially observed or interval-censored observations are available in practice owing to inherent limitations or time/cost considerations. When a data set consists of both partially and fully observed observations, it is commonly referred to as in the statistics, literature.In this paper, we calculate the optimal operating conditions for the process based on a dual response approach using incomplete data. For robust design optimization problems, the dual response approach is a commonly used technique but the novel approach in this study is that we estimate the process mean and variance with incomplete data using the EM algorithm. Thus, it is possible to find the optimal operating conditions using all of the information available. The applicability of the proposed method is illustrated for a case study incorporating incomplete data. The performance of the proposed method is compared with the ordinary method through Monte Carlo simulations and this substantiates the proposed method.