Experimental Design Optimization Research Articles

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials

Key messageNew forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions.In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (GtimesE). With genomic prediction, GtimesE can be accounted for by modeling the genetic covariance between trials, even when the overall experimental design is highly unbalanced between trials, thanks to the genomic relationship between genotypes. In this study, we propose new forms of the coefficient of determination (CD, i.e., the expected model-based square correlation between a genetic value and its corresponding prediction) that can be used to forecast the genomic prediction reliability of genotypes, both for their trial-specific performance and their mean performance. As the expected prediction reliability based on these new CD criteria is generally a good approximation of the observed reliability, we demonstrate that they can be used to optimize multi-environment trials in the presence of GtimesE. In addition, this reliability may be highly variable between genotypes, especially in unbalanced designs with complex pedigree relationships between genotypes. Therefore, it can be useful for breeders to assess it before selecting genotypes based on their predicted genetic values. Using a wheat population evaluated both for simulated and phenology traits, and two maize populations evaluated for grain yield, we illustrate this approach and confirm the value of our new CD criteria.

Media optimization design towards maximizing biomass production of Tetradesmus obliquus under mixotrophic conditions

Maximizing biomass production is a key factor in developing large-scale microalgae cultivation. One major aspect to address is the cultivation medium composition in regards to the process objective. In this study, the effect of eight important nutrients/factors (glucose, nitrate, phosphate, sulfate, magnesium, iron, manganese, and calcium) and their interactions were investigated on the biomass production of the microalgae Tetradesmus obliquus under mixotrophic conditions. The Taguchi method, a robust tool for experimental design optimization, was used to find the optimum media composition for biomass production, with only a few well-defined experimental sets. Using Taguchi's L18 orthogonal array resulted to a 4-fold enhancement in biomass yields, with a maximum of about 7.98 g/L. Nitrate exhibited the most significant effect. Iron and phosphate showed a strong influence on short-term cultivations (6 to 9 days), while glucose and calcium had more impact on long-term cultivations (15 days). The overall optimum experimental conditions were provided.

Development of a Chiral Capillary Electrophoresis Method for the Enantioseparation of Verapamil Using Cyclodextrins as Chiral Selectors and Experimental Design Optimization

Chirality is a property of asymmetry which determines the pharmacokinetic and pharmacological profiles of optically active pharmaceuticals. Verapamil (VER), a calcium channel blocker phenylalkylamine derivative used in the treatment of cardio-vascular diseases, is a chiral compound, marketed as a racemate, although differences between the pharmacokinetic and pharmacological attributes of the enantiomers have been reported. The aim of our study was to develop a new chiral separation method for VER enantiomers by capillary electrophoresis (CE) using cyclodextrins (CDs) as chiral selectors (CSs). After an initial screening, using different native and derivatized CDs, at four pH levels, heptakis 2,3,6-tri-O-methyl-β-CD (TM-β-CD), a neutral derivatized CD, was identified as the optimum CS. For method optimization, a preliminary univariate approach was applied to characterize the influence of analytical parameters on the separation followed by a Box–Behnken experimental design to establish the optimal separation conditions. Chiral separation of enantiomers was achieved with a resolution of 1.58 in approximately 4 min; the migration order was R-VER followed by S-VER. The method analytical performance was evaluated in terms of precision, linearity, accuracy, and robustness (applying a Plackett–Burnam experimental design). The developed method was applied for the determination of VER enantiomers in pharmaceuticals. Finally, a computer modelling of VER–CD complexes was used to describe host–guest chiral recognition.

Investigation of thermal flow structure and performance heat transfer in three‐dimensional circular pipe using twisted tape based on Taguchi method analysis

AbstractIn the current investigation, the twisted tape inserts are considered as the augmentation thermal technique, the influence of a variety of twisted tape configurations on pressure drop characteristics, temperature differences, thermal performance of fluid flow structure, heat transfer improvement, and friction factor are numerically evaluated. The changed geometrical parameters employed for this study comprise twisted tape width, twisted tape thickness, number of turns, and inward thickness are the input parameters. Design of experiments method is applied to analyze the influence of latter various types of geometrical parameters on hydraulic thermofluid pattern and heat transfer improvement in the twisted tube heat exchanger as the output variables. For the experimental design optimization Taguchi analysis is based on investigate of alterations and performs the orthogonal arrays (OA). Moreover, the OA L16 is chosen as the plan of experimental study. It is found the best design of twisted tape in this study by using computational fluid dynamics numerical methodology complained with Taguchi method the enhancement in heat transfer and hence the overall performance evaluation factor is higher than 1.2.

Implementation of design of experiments for optimization of forced degradation conditions and development of a stability-indicating high-performance liquid chromatography method for sepiwhite.

Sepiwhite is a novel anti-pigmenting agent that is derived from fatty acid and phenylalanine and used for hyperpigmentation induced by light exposure or inflammation. In this study, a simple and validated high-performance liquid chromatography method for the quantitation of sepiwhite was developed. Optimized forced degradation of sepiwhite at thermal, acid/base, photolysis, oxidative, and heavy metal ions conditions were evaluated and the effect of each of them on production of specific 10%-30% degradants was studied by the approach of design of experiments. Sepiwhite accelerated study was conducted and toxicity of sepiwhite at each condition was tested. An optimized high-performance liquid chromatography method was validated by a face-centered central composition design. Ten different degradants were identified from sepiwhite and degradation behavior under different conditions was studied. Sepiwhite and its degradant products show no cytotoxicity. This optimized high-performance liquid chromatography method can be applied for quality control assay and sepiwhite degradation behavior may be considered in the manufacturing of sepiwhite products.

Retraction Note to: Air PM2.5 pollutant prediction and VR environmental art design index based on parameter estimation

The research on the dispersion and speed of environmental pollutants in the air plays an important role, which can help to enhance the impact of environmental pollutants in the surrounding areas on the research city, and carry out accurate evaluation and analysis. This paper studies the judgment of PM2.5 pollution source and VR environmental design index based on various data. In the modern environmental design, the original cultural factors are widely used. In the corresponding environmental design, the traditional culture hidden in the environmental design can be clearly seen through the design. Therefore, in the modern environmental design, we can add some traditional culture through imitation and modification, so as to make the traditional cultural value of environmental art higher. This is not only the work of art that directly characterizes the environment, but also the work of art. This review is an important step in the model building process: parameter estimation and optimization of experimental technology design. The function theorem aims at parameter estimation for finding models and unknown parameters that give the optimal set of experimental data. Optimum design goal is to design experiments to provide a dynamic linear model for subsequent identification, estimation, and/or identifying the maximum information content.

Experimental design optimization of RP-HPLC method for simultaneous estimation of metsulfuron-methyl, chlorantraniliprole and chlorimuron-ethyl residues in stems of Oryza sativa

BackgroundThe study aims to develop a chemometrics optimized D-optimal mixture design approach assisted RP- HPLC method for the determination of pesticide residues of metsulfuron-methyl, chlorantraniliprole, and chlorimuron-ethyl in the stems of Oryza sativa. Chromatographic separation was achieved on a C18 column using a mobile phase consisting of a pH 3.5 phosphate buffer and acetonitrile in the ratio 85:15.ResultsThe optimized HPLC method gave a sharp resolution of metsulfuron-methyl, chlorantraniliprole and chlorimuron-ethyl at a retention time of 2.599 min, 3.805 min and 4.661 min receptively. Linearity was observed in the range 100‒500 µg/mL for metsulfuron-methyl (r2 = 0.999), 4‒20 µg/mL for chlorantraniliprole (r2 = 0.999) and 100‒500 µg/mL for chlorimuron-ethyl (r2 = 0.999). The developed method was validated as per ICH guidelines.ConclusionThe proposed chemometrics optimized RP-HPLC method was found to be successful in the resolution of pesticide residues in the stems of O. sativa. The developed method can be applied to routine quantification of metsulfuron-methyl, chlorantraniliprole and chlorimuron-ethyl.

Orthogonal Design of Experiments for Optimization of Lipid Nanoparticles for mRNA Engineering of CAR T Cells.

Viral engineered chimeric antigen receptor (CAR) T cell therapies are potent, targeted cancer immunotherapies, but their permanent CAR expression can lead to severe adverse effects. Nonviral messenger RNA (mRNA) CAR T cells are being explored to overcome these drawbacks, but electroporation, the most common T cell transfection method, is limited by cytotoxicity. As a potentially safer nonviral delivery strategy, here, sequential libraries of ionizable lipid nanoparticle (LNP) formulations with varied excipient compositions were screened in comparison to a standard formulation for improved mRNA delivery to T cells with low cytotoxicity, revealing B10 as the top formulation with a 3-fold increase in mRNA delivery. When compared to electroporation in primary human T cells, B10 LNPs induced comparable CAR expression with reduced cytotoxicity while demonstrating potent cancer cell killing. These results demonstrate the impact of excipient optimization on LNP performance and support B10 LNPs as a potent mRNA delivery platform for T cell engineering.

Experimental Investigation on Silicon Powder Mixed-EDM of Nimonic-90 Superalloy

Powder mixed electrical discharge machining (PM-EDM) is a technological advancement in electrical discharge machining (EDM) processes where fine powder is added to dielectric to improve the machining rate and surface quality. In this paper, machining of Nimonic-90 was carried out using fabricated PM-EDM, setup by adding silicon powder to kerosene oil. The influence of four input process parameters viz. powder concentration (PC), discharge current (IP), spark on duration (SON), and spark off duration (SOFF) has been investigated on surface roughness and recast layer thickness. L9 Taguchi orthogonal and grey relational analysis have been employed for experimental design and multi-response optimization, respectively. With the addition of silicon powder to kerosene oil, a significant decrease in surface roughness and recast layer thickness was noticed, as compared to pure kerosene. Spark on duration was the most significant parameter for both surface roughness and the recast layer thickness. The minimum surface roughness (3.107 µm) and the thinnest recast layer (14.926 μm) were obtained at optimum process parameters i.e., PC = 12 g/L, IP = 3 A, SON = 35 μs, and SOFF = 49 μs using grey relational analysis.

Investigation and optimization of heat treatment process on tensile behaviour of Ti6Al4V alloy

AbstractThis article examines the microstructure and tensile strength of annealed Ti6Al4V alloy at different temperatures and times. Taguchi based L9 (32) design was used for the experimental design matrix and optimization of the tensile strength of heat treated samples. The optimum parameter combination was at the temperature of 950 °C and soaking time of 1 hour. ANOVA results show that temperature is the most influencing parameter and a regression equation of degree two was developed to predict tensile strength. Results of the microstructure show lamellar structure development within the samples heat‐treated at 1000 °C.

Design of Experiments for Optimization of Polyoxometalate Syntheses

Design of experiments (DOE) is a key method for optimizing physical processes by altering multiple variables at once to assess their effect. In chemistry, DOE explores a wider parameter space than the dominant “One Factor at a Time” (OFAT) method providing greater opportunity to explore the factors that can be used to optimize yield, purity, and to explore chemical space for new compounds. One area of chemistry that suffers from low yields and poor reproducibility but is full of hard to predict and interesting materials is polyoxometalate cluster science. Herein, we developed a DOE analysis methodology to explore the parameter space of polyoxometalate cluster formation to explore the subtle input effects that are known to have an impact on the product discovery, purity, and stability under the preparation conditions. Using a Plackett–Burman screening design, we analyzed the effect of six synthetic parameters in only 12 experiments, following up with a full factorial analysis of the three most significant factors to identify the key parameters in the successful synthesis of each. Based on this, we provide a useful template that produces the input data for automated synthesis based on DOE on other synthetic procedures. In our POM test cases, redox agent stoichiometry was found in three of the four systems studied to be significant factors with pH and temperature, which also found to be commonly important. The insights derived from this analysis were applied to design optimized synthetic procedures and improve the yield of the product by on average >33% from the highest reported literature yield. Thus, the DOE methodology outlined here is shown to yield insights into reaction optimization rapidly with facile experimental design and analysis even for complex multivariate synthetic procedures.

Highly effective pre-concentration of thymol and carvacrol using nano-sized magnetic molecularly imprinted polymer based on experimental design optimization and their trace determination in summer savoury, Origanum majorana and Origanum vulgare extracts

To ascertain thymol and carvacrol in pharmaceutical syrups, a valid and effective magnetic molecular imprinted polymer dispersive solid phase microextraction (MMIP-DSPME) process was developed in this study, which was in combination with a high performance liquid chromatography-ultra violet (HPLC-UV) technique for the assessment of thymol and carvacrol separation and pre-concentration. Contact time, eluent kind and volume, pH, the mass of the MMIP were all taken into consideration as key factors. Design expert and multi-objective response surface methodology (RSM) were used to optimize these variables. The mass of the MMIP, sample pH, eluent kind, time of sorption, the volume of eluent, and time of elution were 10 mg, 6, acetonitrile, 28 min, 200 µL, and 5.5 min, respectively, for the maximum extraction recovery of the analytes. The limit of detection (LOD) was 0.042 ng mL−1 at the optimal conditions, while the value for the limit of quantification (LOQ) was 0.140 ng mL−1. At the optimized conditions for thymol and carvacrol, the suggested MMIP sorbent had sorption capacities of 64.1 and 72.6 mg g−1, respectively. Furthermore, for triplicate measurements, the linear dynamic range (LDR) was 0.40–5000 ng mL−1, and the method's accuracy (RSD %) was 6.26%. The saturation magnetization for the MMIP was 19.0 emu g−1 obtained by VSM, allowing the sorbent to be separated quickly. The sorption experiments confirmed the large sorption capacity of the MMIP for thymol and carvacrol, as well as its homogeneous binding sites. The extraction recovery for thymol and carvacrol was 96.9–103.8% and 96.6–105.4%, respectively, at all spiked amounts (20, 100, 200, and 500 ng mL−1). The findings of seven desorption–regeneration cycles using MMIP demonstrated the high stability of the sorbent. The MMIP revealed a particular behavior of sorption for thymol and carvacrol, implying a selective, simple, effective, and flexible analytical method.

Design of experiments for development and optimization of a liquid chromatography coupled to tandem mass spectrometry bioanalytical assay

Design of experiment (DoE) is a chemometric approach to study the influence of each experimental factor simultaneously at various levels with a predefined number of experiments, considering all possible interactions between the factors. In this tutorial special feature article, Margrét Thorsteinsdóttir and colleague provide an overview of the basic concepts of DoE and a strategy for implementation of DoE for the optimization of a quantitative LC-MS/MS methods. Indeed, DoE is an excellent tool for the development and optimization of hyphenated techniques such as LC-MS/MS, where several experimental factors need to be simultaneously optimized to obtain maximum sensitivity with adequate resolution between closely eluting peaks. The results are expressed as a mathematical function of the experimental conditions providing a mean to predict and estimate results at levels that were not directly studied. The data can be explored by use of counter plots and response surface plots to visualize how the response is affected by the factors studied and for finding a combination of factor settings that will result in optimum analytical conditions. With better designed experiments, flow of measurements to knowledge can proceed in the most cost-effective way. Margrét Thorsteinsdóttir (PhD) is Professor at the Faculty of Pharmaceutical Science at the University of Iceland (Reykjavik, Iceland). Her main research interest is focused on the development of high-performance separation science with applications in clinical MS.

Optimal design of experiments for optimization-based model calibration using Fisher information matrix

Statistical model calibration to infer unknown model parameters and model bias has been widely developed through comparison between simulation response and experimental data. Bayesian-based model calibration typically represented as Kennedy and O'Hagan (KOH) framework and optimization-based model calibration have been proposed, but efforts on optimization of experimental design to reduce the epistemic uncertainty minimizing experimental resources are still limited. Furthermore, when an unknown model parameter has natural and uncontrollable variability, the estimation may be much more difficult since both aleatory and epistemic uncertainties exist. In this paper, we have developed a framework to find the optimal design of experiments (DoE) satisfying the target information gain for inference of unknown model parameters based on optimization-based model calibration. The expected Fisher information matrix is approximated to quantify the expected information gain for the maximum likelihood estimation (MLE). Namely, the necessary number of experiments at each experimental design can be obtained to attain desired precision on estimators while minimizing the overall experimental cost. The numerical study verifies the feasibility of the proposed framework, which means that there is certainly a dominant DoE that gives more information to the inference on specific model parameters.

RETRACTED ARTICLE: Air PM2.5 pollutant prediction and VR environmental art design index based on parameter estimation

The research on the dispersion and speed of environmental pollutants in the air plays an important role, which can help to enhance the impact of environmental pollutants in the surrounding areas on the research city, and carry out accurate evaluation and analysis. This paper studies the judgment of PM2.5 pollution source and VR environmental design index based on various data. In the modern environmental design, the original cultural factors are widely used. In the corresponding environmental design, the traditional culture hidden in the environmental design can be clearly seen through the design. Therefore, in the modern environmental design, we can add some traditional culture through imitation and modification, so as to make the traditional cultural value of environmental art higher. This is not only the work of art that directly characterizes the environment, but also the work of art. This review is an important step in the model building process: parameter estimation and optimization of experimental technology design. The function theorem aims at parameter estimation for finding models and unknown parameters that give the optimal set of experimental data. Optimum design goal is to design experiments to provide a dynamic linear model for subsequent identification, estimation, and/or identifying the maximum information content.

Thin film microextraction based on Co3O4@GO-Nylon‐6 polymeric membrane to extract morin and quercetin and determining them through high performance liquid chromatography-ultraviolet detection

The in-situ synthesis of one-dimensional Co3O4@GO nanosorbent and its fabrication into the layers of Nylon-6 polymer have been introduced in the present study. The extraction performance of the prepared membrane was studied through using the prepared membrane for thin-film microextraction of two selected flavonoids (as the model compounds) from different kinds of juice and black tea. The extracted analytes (morin and quercetin) were quantified via high performance liquid chromatography-ultraviolet detection (HPLC-UV). The experimental design optimization strategy including Central Composite Design (CCD) was employed to optimize different TFME variables. The selected factors that were once thought to be effective on TFME were screened by Plackett–Burman Design before the optimization. The following figures of merit were obtained under the achieved optimized condition: the Linear Dynamic Ranges (LDRs): 5.0–500.0 μg L−1 and 5.0–200.0 μg L−1, Limits of Determination (LODs): 1.3 μg L−1 and 1.6 μg L−1, Limits of Quantification (LOQs): 4.3 μg L−1 and 5.1 μg L−1, the Relative Standard Deviations (RSDs) <4.7% (at two concentration levels of 5.0 and 200.0 µg L−1), Absolute Recovery (AR%): 73.0 and 64.0 and enrichment factors of 73.0 and 64.0 for morin and quercetin, respectively. The above-mentioned method was finally used to extract and determine morin and quercetin in natural orange juice, commercial orange juice, natural pear juice, commercial peach juice and black tea. The obtained results showed satisfactory relative recoveries from 90.0% to 98.0% and acceptable precisions (4.8% < RSD < 7.2%). Therefore, the presented method can be considered as an appropriate alternative to the conventional extraction methods in measuring trace amounts of flavonoids in real samples.

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

Digital Twins (DTs) are receiving considerable attention from multiple disciplines. Much of the literature at this time is dedicated to the conceptualization of digital twins, and associated enabling technologies and challenges. In this paper, we consider these propositions for the specific application of nuclear power. Our review finds that the current DT concepts are amenable to nuclear power systems, but benefit from some modifications and enhancements. Further, some areas of the existing modeling and simulation infrastructure around nuclear power systems are adaptable to DT development, while more recent efforts in advanced modeling and simulation are less suitable at this time. For nuclear power applications, DT development should rely first on mechanistic model-based methods to leverage the extensive experience and understanding of these systems. Model-free techniques can then be adopted to selectively, and correctively, augment limitations in the model-based approaches. Challenges to the realization of a DT are also discussed, with some being unique to nuclear engineering, however most are broader. A challenging aspect we discuss in detail for DTs is the incorporation of uncertainty quantification (UQ). Forward UQ enables the propagation of uncertainty from the digital representations to predict behavior of the physical asset. Similarly, inverse UQ allows for the incorporation of data from new measurements obtained from the physical asset back into the DT. Optimization under uncertainty facilitates decision support through the formal methods of optimal experimental design and design optimization that maximize information gain, or performance, of the physical asset in an uncertain environment.

Design of experiment for optimization of 3D printing parameters of base plate structures in colostomy bag for newborns

ABSTRACT This study introduces a low-cost ostomy bag with sufficient softness for a newborn’s body to improve newborn medical care. Fused-deposition modeling was performed with a thermoplastic elastomer as the 3D printing material. The experimental design of the Taguchi L9(34) orthogonal arrays was used for the parameter design. To assess quality, the hardness was measured, with a focus on the smaller-the-better characteristic of the average hardness. The experimental results showed that the optimal combination of the structural hardness parameters is A1B3C1D1. As per the A scale, the average Shore hardness of the experimental model was 41.9. The signal-to-noise ratio of the experimental sample was −32.4526 dB, which is within the confidence interval, indicating that the factor effect has a reproduction characteristic that represents the reliability of this experimental result. The original material’s Shore hardness was reduced by 44.95%, from 85 A to 46.79 A, which represents an effective improvement in wearability.

Optimization and application of green solvent extraction of natural bioactive coumarins from Lamiaceae and Asteraceae herbal plants

Simple coumarins, 2H-1-benzopyran-2-ones, represent group of natural bioactive compounds. They are considered as secondary metabolites in plants of Lamiaceae and Asteraceae families. In this study, heat assisted extraction with low transition temperature mixtures (LTTM) based on choline chloride and L-lactic acid were used for simultaneous extraction of esculin, umbelliferone, scoparone, 4-methylumbelliferone and herniarin from herbal plants prior their determination by HPLC method with fluorescence detection. The effect of LTTM composition on coumarins extraction was verified. Optimal extraction conditions included choline chloride/L-lactic acid/1,3-propanediol (1:2:1) extraction solvent, solid/liquid ratio 1/40 g/mL, treatment time 15 min, rotation speed 20 rpm and temperature 60 °C. Temperature was the most significant parameter affecting extraction efficiency, the extraction time was an important variable for penetration of solvent in the matrix. Proposed HPLC method was applicable in linear concentration ranges from 5 to 100 ng/mL (for esculin, umbelliferone, scoparone, 4-methylumbelliferone) and from 15 ng/mL to 100 ng/mL (for herniarin) (R2 ≥ 0.998). The limits of detection were 0.2–5.0 ng/mL. The recovery of the method was higher than 87.7% (RSD < 5.8%). The content of umbelliferone and herniarin in herbal plants were 11.49–68.38 μg/g. The use of LTTM extraction solvent, application the response surface methodology and Taguchi orthogonal arrays design of experiment for optimization of extraction conditions, were contribute to the green aspect of the developed method.

Deep Learning Approach for Vibration Signals Applications.

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.