Journal articles: 'Multivariate Response Surface'

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Moslemi, Amir, and Mirmehdi Seyyed-Esfahani. "A novel robust multivariate regression approach to optimize multiple surfaces." RAIRO - Operations Research 52, no. 4-5 (October 2018): 1233–43. http://dx.doi.org/10.1051/ro/2018016.

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Response surface methodology involves relationships between different variables, specifically experimental inputs as controllable factors, and a response or responses by incorporating uncontrollable factors named nuisance. In order to optimize these response surfaces, we should have accurate response models. A common approach to estimate a response surface is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Most problems face with more than one response which are mostly correlated, that are called multi-response problem. This paper presents a new approach which takes the benefits of robust multivariate regression to cope with the mentioned difficulties. After estimating accurate response surfaces, optimization phase should be applied in order to have proper combination of variables and optimum solutions. Global criterion method of multi-objective optimization has also been used to reach a compromise solution which improves all response variables simultaneously. Finally, the proposed approach is described analytically by a numerical example.

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Bratchell, N. "Multivariate response surface modelling by principal components analysis." Journal of Chemometrics 3, no. 4 (September 1989): 579–88. http://dx.doi.org/10.1002/cem.1180030406.

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Moslemi, Amir, and Mirmehdi Seyyed-Esfahani. "Robust optimization of multistage process: response surface and multi-response optimization approaches." International Journal of Nonlinear Sciences and Numerical Simulation 23, no. 2 (November 26, 2021): 163–75. http://dx.doi.org/10.1515/ijnsns-2017-0003.

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Abstract A multistage system refers to a system contains multiple components or stages which are necessary to finish the final product or service. To analyze these problems, the first step is model building and the other is optimization. Response surfaces are used to model multistage problem as an efficient procedure. One regular approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. OLS method is very sensitive to outliers, so some multivariate robust estimation methods have been discussed in the literature in order to estimate the response surfaces accurately such as multivariate M-estimators. In optimization phase, multi-response optimization methods such as global criterion (GC) method and ε-constraints approaches are different methods to optimize the multi-objective-multistage problems. An example of the multistage problem had been estimated considering multivariate robust approaches, besides applying multi-response optimization approaches. The results show the efficiency of the proposed approaches.

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Kumar, Rupak, and Meega Reji. "Response surface methodology (RSM): An overview to analyze multivariate data." Indian Journal of Microbiology Research 9, no. 4 (January 15, 2023): 241–48. http://dx.doi.org/10.18231/j.ijmr.2022.042.

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In recent years, the fascinating range of Response surface methodology (RSM) applications has captured the interest of many researchers and engineers worldwide. RSM is entirely based on well-known regression principles and variance analysis principles that enable the user to improve, develop and optimize the process or product under study. An overview of the theoretical principles of RSM, the experimental strategy and its tools and components, along with the applications and pros and cons, are described in this paper. Some of the widely used experimental designs of RSM compared in terms of its characteristics and efficiency are included, which helps to point out the importance of design of experiments (DOE) in optimization using RSM. The live demonstrations of a few optimization examples using response surface methodology in different research manuscripts included in this paper also provide a better understanding of the characteristics of RSM in different scenarios.

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Patel, Trina, Donatello Telesca, Saji George, and André E. Nel. "Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling." Annals of Applied Statistics 6, no. 4 (December 2012): 1707–29. http://dx.doi.org/10.1214/12-aoas563.

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Flandrois, C., C. Lahet, D. Feldmann, J. M. Gabastou, A. Gonnon, and I. Maire. "Urinary alanine aminopeptidase assay improved as result of multivariate response-surface analysis." Clinical Chemistry 34, no. 5 (May 1, 1988): 954–57. http://dx.doi.org/10.1093/clinchem/34.5.954.

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Abstract Optimization of determination of alanine aminopeptidase in urine by univariate study led to a method involving pretreatment of urine with Sephadex G50. Re-examination of the optimization by multivariate study led us to recommend higher optimal concentrations: 5.8 mmol/L for the substrate and 300 mmol/L for the Tris buffer. Under these new conditions, pretreatment of urine was no longer necessary and the assay could be completely automated.

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Gatley-Montross, Caitlyn M., John A. Finlay, Nick Aldred, Harrison Cassady, Joel F. Destino, Beatriz Orihuela, Michael A. Hickner, et al. "Multivariate analysis of attachment of biofouling organisms in response to material surface characteristics." Biointerphases 12, no. 5 (December 2017): 051003. http://dx.doi.org/10.1116/1.5008988.

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Li, Yingjie, Xiangliang Liu, Biaojun Zhang, Qun Zhao, Ping Ning, and Senlin Tian. "Aquatic photochemistry of sulfamethazine: multivariate effects of main water constituents and mechanisms." Environmental Science: Processes & Impacts 20, no. 3 (2018): 513–22. http://dx.doi.org/10.1039/c7em00548b.

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Ghattas, Badih, and Diane Manzon. "Machine Learning Alternatives to Response Surface Models." Mathematics 11, no. 15 (August 4, 2023): 3406. http://dx.doi.org/10.3390/math11153406.

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In the Design of Experiments, we seek to relate response variables to explanatory factors. Response Surface methodology (RSM) approximates the relation between output variables and a polynomial transform of the explanatory variables using a linear model. Some researchers have tried to adjust other types of models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vector machines, neural networks and also direct multi-output approaches. We survey the subject and illustrate the use of ten such approaches using simulations and a real use case. In our simulations, the underlying model is linear in the explanatory factors for one response and nonlinear for the others. We focus on the advantages and disadvantages of the different approaches and show how their hyperparameters may be tuned. Our simulations show that even when the underlying relation between the response and the explanatory variables is linear, the RSM approach is outperformed by the direct neural network multivariate model, for any sample size (<50) and much more for very small samples (15 or 20). When the underlying relation is nonlinear, the RSM approach is outperformed by most of the machine learning approaches for small samples (n ≤ 30).

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Murakami, Kenya, Tatsuya Iizaka, Tomoji Kanno, Tetsuro Matsui, Makoto Shimosawa, and Akihiro Takano. "Improvement of Solar Cells Efficiency by Response Surface Method based on Multivariate Analysis Models." IEEJ Transactions on Electronics, Information and Systems 131, no. 8 (2011): 1424–30. http://dx.doi.org/10.1541/ieejeiss.131.1424.

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Pérez-Mateos, M., and Pilar Montero. "Response surface methodology multivariate analysis of properties of high-pressure-induced fish mince gel." European Food Research and Technology 211, no. 2 (July 4, 2000): 79–85. http://dx.doi.org/10.1007/s002179900115.

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Su, Pei-Lan, and Yun-Shiow Chen. "Implementation of a genetic algorithm on MD-optimal designs for multivariate response surface models." Expert Systems with Applications 39, no. 3 (February 2012): 3207–12. http://dx.doi.org/10.1016/j.eswa.2011.09.007.

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He, Fei, Derek J. Posselt, Naveen N. Narisetty, Colin M. Zarzycki, and Vijayan N. Nair. "Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones." Monthly Weather Review 146, no. 7 (June 29, 2018): 2065–88. http://dx.doi.org/10.1175/mwr-d-17-0265.1.

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Abstract This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

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Heinzle, J., S. Anders, S. Bode, C. Bogler, Y. Chen, R. M. Cichy, K. Hackmack, et al. "Multivariate decoding of fMRI data." e-Neuroforum 18, no. 1 (January 1, 2012): 1–16. http://dx.doi.org/10.1007/s13295-012-0026-9.

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AbstractThe advent of functional magnetic resonance imaging (fMRI) of brain function 20 years ago has provided a new methodology for non-invasive measurement of brain function that is now widely used in cognitive neuroscience. Traditionally, fMRI data has been analyzed looking for overall activity changes in brain regions in response to a stimulus or a cognitive task. Now, recent developments have introduced more elaborate, content-based analysis techniques. When multivariate decoding is applied to the detailed patterning of regionally-specific fMRI signals, it can be used to assess the amount of information these encode about specific task-variables. Here we provide an overview of several developments, spanning from applications in cognitive neuroscience (perception, attention, reward, decision making, emotional communication) to methodology (information flow, surface-based searchlight decoding) and medical diagnostics.

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Behera, Amar Kumar, Jun Gu, Bert Lauwers, and Joost R. Duflou. "Influence of Material Properties on Accuracy Response Surfaces in Single Point Incremental Forming." Key Engineering Materials 504-506 (February 2012): 919–24. http://dx.doi.org/10.4028/www.scientific.net/kem.504-506.919.

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The ability to manufacture accurate parts in single point incremental forming is dependent on the capability to properly predict accuracy response surfaces of individual features and feature interaction combinations formed using uncompensated tool paths. Recent studies show that the accuracy profiles obtained are dependent on the choice of material used for forming, in terms of magnitude, geometric shape and nature of errors (under forming and over forming). In this paper, an attempt is made to capture the effect of material properties on the accuracy response surfaces. The response surfaces are modeled using Multivariate Adaptive Regression Splines (MARS), which is a non-parametric multivariate regression technique that helps generating continuous response surfaces. The MARS functions are based on process and feature specific geometric parameters. A set of features and feature interactions for which the response surface dependence on material properties is well predicted is used to illustrate the applicability of the MARS method for predicting the accuracy. An in-process stereo camera system is used to measure the displacement fields for different materials using digital image correlation (DIC) and understand the material dislocation mechanism. Improvements in accuracy for different sheet metal materials based on the predicted response surfaces are then discussed.

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Majumder, Himadri, and Kalipada Maity. "Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach." Journal of Advanced Manufacturing Systems 16, no. 02 (June 2017): 81–99. http://dx.doi.org/10.1142/s0219686717500068.

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This paper represents a multivariate hybrid approach, combining Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Principal Component Analysis (PCA) to optimize different correlated responses during Wire Electrical Discharge Machining (WEDM) process of titanium grade 6. The response parameters selected are the average cutting speed, average Kerf width and average surface roughness (Ra). All of them have been studied in terms of pulse-ON time, pulse-OFF time, wire feed and wire tension. As indicated by Taguchi’s signal-to-noise ratio, the optimum process parameters were achieved for the desired average cutting speed, average Kerf width and average surface roughness, respectively. At last, the optimum combination of process parameters was validated by affirmation test which gave considerably improved various quality characteristics. Confirmation test outcome revealed that multivariate hybrid approach MOORA coupled with PCA was a competent strategy to decide available cutting parameters for a desired response quality for WEDM of titanium grade 6.

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Fuchs, David, Steven Sherwood, and Daniel Hernandez. "An Exploration of Multivariate Fluctuation Dissipation Operators and Their Response to Sea Surface Temperature Perturbations." Journal of the Atmospheric Sciences 72, no. 1 (January 1, 2015): 472–86. http://dx.doi.org/10.1175/jas-d-14-0077.1.

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Abstract The fluctuation–dissipation theorem (FDT) has been proposed as a method of calculating the mean response of the atmosphere to small external perturbations. This paper explores the application of the theory under time and space constraints that approximate realistic conditions. To date, most applications of the theory in the climate context used univariate, low-dimensional-state representations of the climate system and an arbitrarily long sample size. The authors explore high-dimensional multivariate FDT operators and the lower bounds of sample size needed to construct skillful operators. It is shown that the skill of the operator depends on the selection of variables and features representing the climate system and that these features change once memory (slab ocean) is added to the system. In addition, it is found that the FDT operator has skill in estimating the response to realistic sea surface temperature (SST) patterns, such as El Niño–Southern Oscillation (ENSO), despite the fact that these patterns were not part of the data used to produce the operator. The response of clouds is also studied; for variables that represent cloud properties, the decrease in skill in relation to decrease in sample size still maintains the key features of the response.

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Lao, Yi, John David, Arman Torosian, Wensha Yang, and Richard Tuli. "A novel morphologic and metabolic feature fused treatment response evaluation pipeline for pancreatic adenocarcinoma patients." Journal of Clinical Oncology 36, no. 4_suppl (February 1, 2018): 311. http://dx.doi.org/10.1200/jco.2018.36.4_suppl.311.

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311 Background: Adaptive radiation therapy for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment. Traditional RECIST criteria poorly characterize tumors with complex morphological features, while PET imaging inefficiently detects tumors with intrinsically low standardized uptake value (SUV). Here, we performed regional comparisons of 3D intact PA surfaces pre and post chemoradiotherapy (CRT) utilizing surface measurements containing both morphological and metabolic features to better assess response. Methods: Twenty-one locally advanced PA patients with pre- and 6-8 week post-CRT 18F FDG-PET/CT scans were evaluated. Boundaries of initial and post-CRT tumors were manually defined on respective CT images. On each of the tumors, 3D meshes were generated, followed by surface based registration to achieve vertex-wise correspondence. For each surface vertex, a multivariate vector was formed from two components: anatomic (deformation tensors resulted from surface registration), and metabolic (regional SUV obtained from radius to surface projections). To assess tumor response, paired mahabanobis distance (Mdist) between pre- and post-CRT tumor surfaces with previously formed multivariate vectors were calculated for each patient. Mdist was evaluated using Cox analysis correlated with overall survival (OS) and compared with measurements based on serum CA19-9, volume, SUVmax and SUVmean. Results: Among all the tested parameters, Mdist is the best predictor of OS, with a hazard ratio of 0.437 (p = 0.036). Post-CRT versus pre-CRT ratios based on volume and SUVmax both reached borderline significance (p = 0.0769 and 0.0799, respectively), while CA19-9 and SUVmean failed in predicting OS in our small cohort of patients. Conclusions: We introduced a PET/CT-based novel morphologic and metabolic pipeline for post-CRT response evaluation in locally advanced PA. The fused Mdist outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction. The presented fused model may serve as a new biomarker to better characterize the heterogeneity of tumor response to CRT and a predictive marker for surgical resection.

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Birhanu, Yohanis, and Seyoum Leta. "Multivariate Optimization of Pb2+ Adsorption onto Ethiopian Low-Cost Odaracha Soil Using Response Surface Methodology." Molecules 26, no. 21 (October 27, 2021): 6477. http://dx.doi.org/10.3390/molecules26216477.

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Lead pollution is a severe health concern for humankind. Utilizing water contaminated with lead can cause musculoskeletal, renal, neurological, and fertility impairments. Therefore, to remove lead ions, proficient, and cost-effective methods are imperative. In this study, the Odaracha soil which is traditionally used by the local community of the Saketa District was used as a novel low-cost technology to adsorb lead ions. Odaracha adsorbent was characterized by scanning electron microscopy and Fourier transform infrared spectroscopy. The adsorption process followed the batch adsorption experiment. The response surface method was implemented to derive the operating variables’ binary interaction effect and optimize the process. According to the study’s experimental result, at optimum experimental conditions Odaracha adsorbent removes 98.17% of lead ions. Based on the result of the central composite design model, the Pb2+ ion removal efficiency of Odaracha was 97.193%, indicating an insignificant dissimilarity of the actual and predicted results. The coefficient of determination (R2) for Pb2+ was 0.9454. According to the factors’ influence indicated in the results of the central composite design model, all individual factors and the interaction effect between contact time and pH has a significant positive effect on lead adsorption. However, other interaction effects (contact time with dose and pH with dose) did not significantly influence the removal efficiency of lead ions. The adsorption kinetics were perfectly fitted with a pseudo-second-order model, and the adsorption isotherm was well fitted with the Freundlich isotherm model. In general, this study suggested that Odaracha adsorbent can be considered a potential adsorbent to remove Pb2+ ions and it is conceivable to raise its effectiveness by extracting its constituents at the industrial level.

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Balakrishnan, Narayanaswamy, Hong Qin, and Kashinath Chatterjee. "Multivariate Bayesian U-type asymmetric designs for non parametric response surface prediction under correlated errors." Communications in Statistics - Theory and Methods 46, no. 9 (May 10, 2016): 4226–39. http://dx.doi.org/10.1080/03610926.2015.1080843.

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Ahmad, Tanvir, and Munir Akhtar. "Efficient Response Surface Designs for the Second-Order Multivariate Polynomial Model Robust to Missing Observation." Journal of Statistical Theory and Practice 9, no. 2 (July 21, 2014): 361–75. http://dx.doi.org/10.1080/15598608.2014.910479.

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Qin, Li, Wei Lin Peng, and Wei Han. "High-Order RSM for Dynamic Reliability of UHV Transmission Tower." Applied Mechanics and Materials 799-800 (October 2015): 1295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.1295.

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As transmission tower system has the characteristics of large-span and spatial truss structure, the study of the reliability is also developed on the basis of space truss research and large-span structure system. As simple and suitable, RSM (response surface method), in particular, quadratic response surface without considering the cross term is often used in reliability calculation. However, strong nonlinear limit that corresponding to a complex surface, the accuracy of quadratic response surface is inadequate, causing greater reliability estimation error. High-order response surface solves it well. To this end, based on univariate analysis of multivariate function, a reasonable algorithm as to assure that the form of higher-order response surface is proposed; on the algorithm is verified by given examples, the result shows better accuracy and efficiency .

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De Benedetto, Giuseppe, Sabrina Di Masi, Antonio Pennetta, and Cosimino Malitesta. "Response Surface Methodology for the Optimisation of Electrochemical Biosensors for Heavy Metals Detection." Biosensors 9, no. 1 (February 13, 2019): 26. http://dx.doi.org/10.3390/bios9010026.

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Herein, we report the application of a chemometric tool for the optimisation of electrochemical biosensor performances. The experimental design was performed based on the responses of an amperometric biosensor developed for metal ions detection using the flow injection analysis. The electrode preparation and the working conditions were selected as experimental parameters, and thus, were modelled by a response surface methodology (RSM). In particular, enzyme concentration, flow rates, and number of cycles were reported as continuous factors, while the sensitivities of the biosensor (S, µA·mM−1) towards metals, such as Bi3+ and Al3+ were collected as responses and optimised by a central composite design (CCD). Bi3+ and Al3+ inhibition on the Pt/PPD/GOx biosensor response is for the first time reported. The optimal enzyme concentration, scan cycles and flow rate were found to be 50 U·mL−1, 30 and, 0.3 mL·min−1, respectively. Descriptive/predictive performances are discussed: the sensitivities of the optimised biosensor agreed with the experimental design prediction. The responses under the optimised conditions were also tested towards Ni2+ and Ag+ ions. The multivariate approach used in this work allowed us to obtain a wide working range for the biosensor, coupled with a high reproducibility of the response (RSD = 0.72%).

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Cevheroğlu Çıra, Sümeyra, Ahmet Dağ, and Askeri Karakuş. "Application of Response Surface Methodology and Central Composite Inscribed Design for Modeling and Optimization of Marble Surface Quality." Advances in Materials Science and Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/2349476.

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Factors affecting the final surface quality of polished marble are not yet fully understood. Clarifying these factors for optimization of multivariate polishing process by trial and error method is difficult, time-consuming, and costly task. In this study, the empirical practices were carried out using an experimental design, specifically, a central composite inscribed (CCI) design. The factors considered in CCI design were belt speed, rotational speed, and pressure of the polishing head, and the responses were surface glossiness and roughness. Mathematical models describing responses were produced using experimental datasets, and analysis of variance (ANOVA) was used to assess the fit of the models generated with the experimental data. For process optimization, desirability function analysis (DFA) was used. This study has shown that the CCI could efficiently be applied for the modelling of polishing machine for surface quality of marble strips. Better surface quality generally resulted from lower belt speeds, which increased contact time between the abrasives and strips. Optimized surface quality for marble specimen was established.

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Iqbal, M., I. Ahmad, S. M. Hussain, R. A. Khera, T. H. Bokhari, and M. A. Shehzad. "Optimization of pre-sowing magnetic field doses through RSM in pea." International Agrophysics 27, no. 3 (September 1, 2013): 265–73. http://dx.doi.org/10.2478/v10247-012-0094-7.

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Abstract Seed pre-sowing magnetic field treatment was reported to induce biochemical and physiological changes. In the present study, response surface methodology was used for deduction of optimal magnetic field doses. Improved growth and yield responses in the pea cultivar were achieved using a rotatable central composite design and multivariate data analysis. The growth parameters such as root and shoot fresh masses and lengths as well as yield were enhanced at a certain magnetic field level. The chlorophyll contents were also enhanced significantly vs. the control. The low magnetic field strength for longer duration of exposure/ high strength for shorter exposure were found to be optimal points for maximum responses in root fresh mass, chlorophyll ‘a’ contents, and green pod yield/plant, respectively and a similar trend was observed for other measured parameters. The results indicate that the magnetic field pre-sowing seed treatment can be used practically to enhance the growth and yield in pea cultivar and response surface methodology was found an efficient experimental tool for optimization of the treatment level to obtain maximum response of interest.

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de Albuquerque de Carvalho, Marcelle Lins, Daniele Fernandes Carvalho, Edelvio de Barros Gomes, Roberto Nobuyuki Maeda, Lidia Maria Melo Santa Anna, Aline Machado de Castro, and Nei Pereira. "Optimisation of Cellulase Production by Penicillium funiculosum in a Stirred Tank Bioreactor Using Multivariate Response Surface Analysis." Enzyme Research 2014 (June 25, 2014): 1–8. http://dx.doi.org/10.1155/2014/703291.

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Increasing interest in the production of second-generation ethanol necessitates the low-cost production of enzymes from the cellulolytic complex (endoglucanases, exoglucanases, and β-glucosidases), which act synergistically in cellulose breakdown. The present work aimed to optimise a bioprocess to produce these biocatalysts from the fungus Penicillium funiculosum ATCC11797. A statistical full factorial design (FFD) was employed to determine the optimal conditions for cellulase production. The optimal composition of culture media using Avicel (10 g·L−1) as carbon source was determined to include urea (1.2 g·L−1), yeast extract (1.0 g·L−1), KH2PO4 (6.0 g·L−1), and MgSO4·7H2O (1.2 g·L−1). The growth process was performed in batches in a bioreactor. Using a different FFD strategy, the optimised bioreactor operational conditions of an agitation speed of 220 rpm and aeration rate of 0.6 vvm allowed the obtainment of an enzyme pool with activities of 508 U·L−1 for FPase, 9,204 U·L−1 for endoglucanase, and 2,395 U·L−1 for β-glucosidase. The sequential optimisation strategy was effective and afforded increased cellulase production in the order from 3.6 to 9.5 times higher than production using nonoptimised conditions.

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Sylvi, Pismia, Purhadi Purhadi, Kartika Fithriasari, and Sutikno Sutikno. "The t-Distribution Approach to the Second-Order Multiresponse Surface Model of Paracetamol Tablets Quality Characteristics." Applied Sciences 13, no. 6 (March 20, 2023): 3951. http://dx.doi.org/10.3390/app13063951.

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The normal distribution approach is often used in regression analysis at the Response Surface Methodology (RSM) modeling stage. Several studies have shown that the normal distribution approach has drawbacks compared to the more robust t-distribution approach. The t-distribution approach is found to control size much more successfully in small samples compared to existing methods in the presence of moderately heavy tails. In many RSM applications, there is more than one response (multiresponse), which is usually correlated with each other (multivariate). On the other hand, the actual response surface habitually indicates the curve by the optimal value, so the second-order model is used. This paper aims to develop the second-order multiresponse surface model using a multivariate t-distribution approach. This work also provides the parameter estimation procedure and hypothesis testing for the significance of the parameter. First, the parameter estimation is performed using the Maximum Likelihood Estimation (MLE), followed by the Expectation–Maximization algorithm as an iterative method to find (local) maximum likelihood. Next, the Likelihood Ratio Test (LRT) method is used to test the parameters simultaneously. The model obtained uses this approach to determine the conditions of input variables that optimize the Paracetamol tablets’ physical quality characteristics.

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Puiu, Mihaela, Lucian-Gabriel Zamfir, Valentin Buiculescu, Angela Baracu, Cristina Mitrea, and Camelia Bala. "Significance Testing and Multivariate Analysis of Datasets from Surface Plasmon Resonance and Surface Acoustic Wave Biosensors: Prediction and Assay Validation for Surface Binding of Large Analytes." Sensors 18, no. 10 (October 19, 2018): 3541. http://dx.doi.org/10.3390/s18103541.

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In this study, we performed uni- and multivariate data analysis on the extended binding curves of several affinity pairs: immobilized acetylcholinesterase (AChE)/bioconjugates of aflatoxin B1(AFB1) and immobilized anti-AFB1 monoclonal antibody/AFB1-protein carriers. The binding curves were recorded on three mass sensitive cells operating in batch configurations: one commercial surface plasmon resonance (SPR) sensor and two custom-made Love wave surface-acoustic wave (LW-SAW) sensors. We obtained 3D plots depicting the time-evolution of the sensor response as a function of analyte concentration using real-time SPR binding sensograms. These “calibration” surfaces exploited the transient periods of the extended kinetic curves, prior to equilibrium, creating a “fingerprint” for each analyte, in considerably shortened time frames compared to the conventional 2D calibration plots. The custom-made SAW sensors operating in different experimental conditions allowed the detection of AFB1-protein carrier in the nanomolar range. Subsequent statistical significance tests were performed on unpaired data sets to validate the custom-made LW-SAW sensors.

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Liu, Shuangshuang, Min Yao, Shiyue Chen, and Xingzhong Yuan. "Surface Sediment Diatom Assemblages Response to Water Environment in Dongping Lake, North China." Water 13, no. 3 (January 29, 2021): 339. http://dx.doi.org/10.3390/w13030339.

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The relationship between the diatom taxa preserved in surface lake sediments and environmental variables in Dongping Lake was explored using multivariate statistical methods. The statistical analysis showed that the lake was eutrophicated in all seasons. Transparency, chlorophyll a (Chla) and total phosphorus (TP) were the dominant environmental factors in spring and summer, and NH4+-N and chemical oxygen demand (COD) were the dominant environmental factors in autumn and winter. Sixteen genera and 43 species of diatom were found in the surface sediments, and the dominant diatom genera were Aulacoseira, Ulnaria, Cyclotella, Navicula and Fragilaria. A redundancy analysis (RDA) and Monte Carlo permutation 20 test revealed that COD, pH, TP, conductivity and transparency were significant factors influencing diatom assemblage change, meaning that the distribution of the diatom assemblages were mostly influenced by nutrient composition, light intensity and ion concentrations.

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Isiyaka, Hamza Ahmad, Khairulazhar Jumbri, Nonni Soraya Sambudi, Zakariyya Uba Zango, Nor Ain Fathihah Abdullah, Bahruddin Saad, and Adamu Mustapha. "Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies." RSC Advances 10, no. 70 (2020): 43213–24. http://dx.doi.org/10.1039/d0ra07969c.

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Shea, J. M., F. S. Anslow, and S. J. Marshall. "Hydrometeorological relationships on Haig Glacier, Alberta, Canada." Annals of Glaciology 40 (2005): 52–60. http://dx.doi.org/10.3189/172756405781813465.

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AbstractWe investigate the relationships between meteorological, hydrological and glaciological data collected at Haig Glacier, Alberta, Canada, for the 2002 and 2003 ablation seasons. Correlation, lag cross-correlation and multivariate regression analyses are employed to assess the seasonal evolution of relationships between temperature, temperature residuals, total daily radiation, albedo, accumulation-area ratio (AAR) and total daily discharge (Qi). Early-season melt is temperature-dependent, when AAR remains high and the hydraulic properties of the snowpack limit both diurnal discharge variability and a rapid hydrologic response. As the melt season progresses, a decreasing AAR and ripening of the snowpack induce a glacier-wide decrease in albedo, and a structured radiation–discharge response is observed. Radiation-detrended temperature values offer modest improvements over physical temperature values in multivariate regression models estimating daily discharge values. Using a detrended-temperature indexed melt model, we assess the transport efficiency of the glacial hydrologic system through a comparison of total modelled daily melt and observed discharge. Transport efficiency values support the notion of a purge effect during freezing events and at the end of the ablation season, and suggest that it is the evolution of the supraglacial drainage system that controls diurnal discharge variability.

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Wu, Fengfang, Zhiwei Wu, Xiaoyan Wang, Yunliang Liu, and Qing Ye. "High-Precision Detection of Cellular Drug Response Based on SERS Spectrum and Multivariate Statistical Analysis." Biosensors 13, no. 2 (February 8, 2023): 241. http://dx.doi.org/10.3390/bios13020241.

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The rapid development of personalized medicine places high demands on the control of drug dose and cellular drug response to provide patients with better curative effects and low side effects. To solve the problem of low detection accuracies of the cell-counting kit-8 (CCK8) method, a detection method based on surface-enhanced Raman spectroscopy (SERS) of cell-secreted proteins was adopted to evaluate the concentration of the anticancer drug cisplatin and the cellular drug response of nasopharyngeal carcinoma. CNE1 and NP69 cell lines were used to evaluate cisplatin response. The results showed that the combination of the SERS spectrum with principal component analysis–linear discriminant analysis could detect the difference in the response of cisplatin with a concentration difference of 1 μg/mL, which considerably exceeded that of CCK8. In addition, the SERS spectral peak intensity of the cell-secreted proteins strongly correlated with the cisplatin concentration. Furthermore, the mass spectrum of the secreted proteins of the nasopharyngeal carcinoma cells was analyzed to verify the results obtained using the SERS spectrum. The results demonstrated that SERS of secreted proteins has great potential for high-precision detection of chemotherapeutic drug response.

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Rossel, R. A. Viscarra, and A. B. McBratney. "A response-surface calibration model for rapid and versatile site-specific lime-requirement predictions in south-eastern Australia." Soil Research 39, no. 1 (2001): 185. http://dx.doi.org/10.1071/sr99131.

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The development of response-surface calibration models that may be used in conjunction with the lime requirement buffer methods is described. The buffer methods tested were the Woodruff, New Wooruff, Mehlich, and Shoemaker, McLean and Pratt lime-requirement buffers. Model predictions were compared with those obtained from multivariate models and buffer methods calibrated using conventional linear regressions. The multivariate models described lime requirement as a function of a number of soil variables. All of the models were validated against soil : CaCO 3 incubations using a statistical jackknifing procedure for error and bias estimations. The advantages of the derived response-surface models were their improved prediction accuracy and flexibility, with a choice of any target pH CaCl 2 value between 5.5 and 7 without need for individual calibrations. The response-surface model for the Woodruff buffer method produced the most accurate predictions of lime requirement. The uncertainty of its lime requirement predictions for acid soil in an agricultural field at Kelso, New South Wales, Australia, measured by 95% confidence intervals, was 0.22 Mg/ha. A spatial analysis of lime requirement for the field showed a range of 4–11 Mg/ha. This range provides a reason for site-specific lime applications. Under- and over-applications resulting from a ‘blanket’ 7.13 Mg/ha single-rate application of lime over the field were estimated to range from –4 to 2.9 Mg/ha.

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Mullen, Steven F. "Toward a predictive theoretical model for osmolality rise with non-humidified incubation: a randomized, multivariate response-surface study." Human Reproduction 36, no. 5 (February 9, 2021): 1230–41. http://dx.doi.org/10.1093/humrep/deab015.

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Abstract STUDY QUESTION What factors associated with embryo culture techniques contribute to the rate of medium osmolality change over time in an embryo culture incubator without added humidity? SUMMARY ANSWER The surface area-to-volume ratio of culture medium (surface area of the medium exposed to an oil overlay), as well as the density and height of the overlaying oil, all interact in a quantitative way to affect the osmolality rise over time. WHAT IS KNOWN ALREADY Factors such as medium volume, different oil types, and associated properties, individually, can affect osmolality change during non-humidified incubation. STUDY DESIGN, SIZE, DURATION Several experimental designs were used, including simple single-factor completely randomized designs, as well as a multi-factor response surface design. Randomization was performed at one or more levels for each experiment. Osmolality measurements were performed over 7 days, with up to 8 independent osmolality measurements performed per treatment group over that time. For the multi-factor study, 107 independent combinations of factor levels were assessed to develop the mathematical model. PARTICIPANTS/MATERIALS, SETTING, METHODS This study was conducted in a research laboratory setting. Commercially available embryo culture medium and oil was used. A MINC incubator without water for humidification was used for the incubation. Osmolality was measured with a vapor pressure osmometer after calibration. Viscometry and density were conducted using a rheometer, and volumetric flasks with an analytical balance, respectively. Data analyses were conducted with several commercially available software programs. MAIN RESULTS AND THE ROLE OF CHANCE Preliminary experiments showed that the surface area-to-volume ratio of the culture medium, oil density, and oil thickness above the medium all contributed significantly (P < 0.05) to the rise in osmolality. A multi-factor experiment showed that a combination of these variables, in the form of a truncated cubic polynomial, was able to predict the rise in osmolality, with these three variables interacting in the model (P < 0.05). Repeatability, as measured by the response of identical treatments performed independently, was high, with osmolality values being ± 2 of the average in most instances. In the final mathematical model, the terms of the equation were significant predictors of the outcome, with all P-values being significant, and only one P-value > 0.0001. LIMITATIONS, REASONS FOR CAUTION Although the range of values for the variables were selected to encompass values that are expected to be encountered in usual embryo culture conditions, variables outside of the range used may not result in accurate model predictions. Although the use of a single incubator type and medium type is not expected to affect the conclusions, that remains an uncertainty. WIDER IMPLICATIONS OF THE FINDINGS Using this predictive model will help to determine if one should be cautious in using a specific system and will provide guidance on how a system may be modified to provide improved stability during embryo culture. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by Cook Medical. The author is a Team Lead and Senior Scientist at Cook Medical. The author has no other conflicts of interest to declare TRIAL REGISTRATION NUMBER N/A.

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Tolcha, Teshome, Tura Gemechu, Said Al-Hamimi, Negussie Megersa, and Charlotta Turner. "High Density Supercritical Carbon Dioxide for the Extraction of Pesticide Residues in Onion with Multivariate Response Surface Methodology." Molecules 25, no. 4 (February 24, 2020): 1012. http://dx.doi.org/10.3390/molecules25041012.

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The excessive use of pesticides is a serious health problem due to their toxicity and bioaccumulation through the food chain. Due to the complexity of foods, the analysis of pesticides is challenging often giving large matrix effects and co-extracted compounds. To overcome this problem, a selective and “green” supercritical fluid extraction method was developed, using neat carbon dioxide as a solvent at pressures of up to 800 bars. A Box–Behnken response surface experimental design was used, with the independent variables of density (0.70−1.0 g mL−1), temperature (40−70 °C), and volume (10−40 mL) of solvent, and the dependent variable of extracted amount of pesticides. The optimum extraction condition was found at the use of 29 mL of supercritical CO2 at 0.90 g mL−1 and 53 °C (corresponding to 372 bars of pressure). It was observed that increasing the density of CO2 significantly increased the extraction recovery of endrin and 2,4′-dichlorodiphenyldichloroethane. Matrix-matched calibration curves showed satisfactory linearity (R2 ≥ 0.994), and LODs ranged from 0.2 to 2.0 ng g−1. Precision was lower than 11% and recoveries between 80%–103%. Thus, the developed method could efficiently be used for trace analysis of pesticides in complex food matrices without the use of organic solvents.

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Attimarad, Mahesh, Katharigatta Narayanaswamy Venugopala, Muhammad S. Chohan, Pottathil Shinu, Marysheela David, Effren II Plaza Molina, Anroop Balachandran Nair, Nagaraja Sreeharsha, Abdulrahman Ibrahim Altaysan, and Abdulmalek Ahmed Balgoname. "Multivariate Optimization of Chromatographic Conditions for Rapid Simultaneous Quantification of Antidiarrheal Drugs in Formulation Using Surface Response Methodology." Separations 9, no. 5 (April 21, 2022): 103. http://dx.doi.org/10.3390/separations9050103.

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A combination of antibiotics and antiprotozoal and antisecretory medicines has been prescribed for the treatment of diarrhea. A rapid, reproducible liquid chromatographic procedure was established for the concurrent analysis of metronidazole (MET), ofloxacin (OFL), and racecadotril (RAC) in suspension. The Box–Behnken design, a full factorial multivariate optimization technique, was utilized to optimize chromatographic parameters with fewer runs. The separation of MET, OFL, and RAC was accomplished within 3.2 min, using a Zorbax C18 high-performance liquid chromatography column with a simple mobile phase comprising acetonitrile (55 vol.%): methanol (10 vol.%):20 mM phosphate buffer (35 vol.%, pH 6, regulated with ortho-phosphoric acid). The mobile phase was pumped in the isocratic mode at a rate of 1.4 mL/min at ambient temperature. Analytes were monitored by adjusting the wavelength at 295 nm for MET and OFL and 231 nm for RAC. Validation of the proposed HPLC method exhibited linearity in the concentration of 20–250 µg/mL, 10–150 µg/mL, and 5–80 µg/mL for MET, OFL, and RAC respectively, along with an excellent regression coefficient (r2 > 0.999). The accuracy and precision of the chromatographic procedure were also evidenced by the low percent relative error and relative standard deviation. A Pareto chart developed by the two-factor interaction (2FI) study confirmed that the method was robust, as the slight variation in a single factor had no significant influence on the assay outcomes. Lastly, the developed HPLC process was utilized for the concurrent quantification of MET, OFL, and RAC in liquid oral preparation. Furthermore, when the assay results were compared to the described techniques, it was discovered that there was no significant difference in the accuracy and precision of the results. Hence, the developed rapid HPLC method could be employed for the quality control study of a preparation comprising of MET, OFL, and RAC in industries and regulatory authority laboratories.

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Liu Qimeng, 刘其蒙, 黄俊 Huang Jun, 王克鸿 Wang Kehong, and 周琦 Zhou Qi. "Multivariate Nonlinear Regression Model of Laser Fusion in vitro Skin Tissue Incision Performance Based on Response Surface Methodology." Chinese Journal of Lasers 45, no. 8 (2018): 0807002. http://dx.doi.org/10.3788/cjl201845.0807002.

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Lynch, Amanda, David McGinnis, William L. Chapman, and Jeffrey S. Tilley. "A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model." Annals of Glaciology 25 (1997): 127–31. http://dx.doi.org/10.3189/s0260305500013914.

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Different vegetation models impact the atmospheric response of a regional climate model in different ways, and hence have an impact upon the ability of that model to match an observed climatology. Using a multivariate principal-component analysis, we investigate the relationships between several land-surface models (BATS, LSM) coupled to a regional climate model, and observed climate parameters over the North Slope of Alaska. In this application, annual cycle simulations at 20 km spatial resolution are compared with European Centre for Medium-Range Weather Forecasts (ECMWF) climatology. Initial results demonstrate broad agreement between all models; however, small-scale regional variations between land-surface models indicate the strengths and weaknesses of the land-surface treatments in a climate system model. Specifically, we found that the greater surface-moisture availability and temperature-dependent albedo formulation of the LSM model allow for a higher proportion of low-level cloud, and a later, more rapid transition from the winter to the summer regime. Crucial to this transition is the seasonal cycle of incoming solar radiation. These preliminary results indicate the importance of the land-surface hydrologic cycle in modelling the seasonal transitions.

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Lynch, Amanda, David McGinnis, William L. Chapman, and Jeffrey S. Tilley. "A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model." Annals of Glaciology 25 (1997): 127–31. http://dx.doi.org/10.1017/s0260305500013914.

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Different vegetation models impact the atmospheric response of a regional climate model in different ways, and hence have an impact upon the ability of that model to match an observed climatology. Using a multivariate principal-component analysis, we investigate the relationships between several land-surface models (BATS, LSM) coupled to a regional climate model, and observed climate parameters over the North Slope of Alaska. In this application, annual cycle simulations at 20 km spatial resolution are compared with European Centre for Medium-Range Weather Forecasts (ECMWF) climatology. Initial results demonstrate broad agreement between all models; however, small-scale regional variations between land-surface models indicate the strengths and weaknesses of the land-surface treatments in a climate system model. Specifically, we found that the greater surface-moisture availability and temperature-dependent albedo formulation of the LSM model allow for a higher proportion of low-level cloud, and a later, more rapid transition from the winter to the summer regime. Crucial to this transition is the seasonal cycle of incoming solar radiation. These preliminary results indicate the importance of the land-surface hydrologic cycle in modelling the seasonal transitions.

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Mao, Hu Ping, Yi Zhong Wu, and Li Ping Chen. "Data Driven Multivariate Adaptive Regression Splines Based Simulation Optimization." Applied Mechanics and Materials 44-47 (December 2010): 3800–3806. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3800.

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This paper proposes a data driven based optimization approach which combines augmented Lagrangian method, MARS with effective data processing. In the approach, an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations have been performed. MARS is a self-adaptive regression process, which fits in with the multidimensional problems, and uses a modified recursive partitioning strategy to simplify high-dimensional problems into smaller yet highly accurate models. Combining the local response surface of MARS and augmented Lagrangian method improve sequential approximation optimization and reduce simulation times by effective data processing, yet maintain a low computational cost. The approach is applied to a six dimensional test function, a ten dimensional engineering problem and a two dimensional global test functions to demonstrate its feasibility and convergence, and yet some limiting properties.

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FROST, F. J., T. R. KUNDE, T. B. MULLER, G. F. CRAUN, L. M. KATZ, A. J. HIBBARD, and R. L. CALDERON. "Serological responses to Cryptosporidium antigens among users of surface- vs. ground-water sources." Epidemiology and Infection 131, no. 3 (December 2003): 1131–38. http://dx.doi.org/10.1017/s0950268803001341.

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Cryptosporidium oocysts are commonly detected in surface-derived drinking water. However, the public health significance of these findings is unclear. This study compared serological responses to two Cryptosporidium antigen groups for blood donors and college students using chlorinated and filtered river water vs. ground-water sources. The surface water received agricultural and domestic sewage discharges upstream. Participants from the surface-water city had a higher relative prevalence (RP) of a serological response to the 15/17-kDa antigen group (72·3 vs. 52·4%, RP=1·36, P<0·001) and to the 27-kDa antigen group (82·6 vs. 72·5%, RP=1·14, P<0·02). Multivariate logistic regression analysis found that the people with a shorter duration of residence or drinking bottled water also had a lower seropositivity for each marker. Use of private wells was associated with a higher prevalence of response to the 15/17-kDa markers. Seroconversion to the 15/17-kDa antigen group was more common in the residents of the city using surface water. These findings are consistent with an increased risk of Cryptosporidium infection for users of surface-derived drinking water compared with users of municipal ground-water-derived drinking water. Users of private well water may also have an increased risk of infections.

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Wang, Yan, Li Wang, and Xiyang Zhang. "Modeling of processing parameters for the cutting of rolled tungsten plates via abrasive water jet." AIP Advances 12, no. 9 (September 1, 2022): 095103. http://dx.doi.org/10.1063/5.0114050.

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Tungsten is a promising candidate for plasma-facing material in tokamaks. This study aims at determining whether the cut-surface roughness of rolled tungsten plates (RTPs) can be improved using an abrasive water jet (AWJ) through tests combined with modeling of the cutting-process parameters. Based on the key factors affecting the cut-surface roughness, the magnitude and type of test factors were increased, compared with the previous study. A batch test was designed and conducted to investigate the use of an AWJ to cut an RTP and obtain more comprehensive sample data. In addition, the multivariate linear regression method was employed to establish a multivariate quadratic response surface regression model of the relationship between the cutting-process parameters and the cut-surface roughness based on the roughness sample data. Moreover, a backpropagation (BP) artificial neural network (ANN) model was developed based on the cutting-process parameters to predict the cut-surface roughness. The two models were compared and experimentally validated, which showed that the BP ANN model exhibits relatively high accuracy. Thus, the established model can help to improve cut-surface quality and cutting efficiency.

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Peng, Jiyu, Lanhan Ye, Tingting Shen, Fei Liu, Kunlin Song, and Yong He. "Fast Determination of Copper Content in Tobacco (Nicotina tabacum L.) Leaves Using Laser-Induced Breakdown Spectroscopy with Univariate and Multivariate Analysis." Transactions of the ASABE 61, no. 3 (2018): 821–29. http://dx.doi.org/10.13031/trans.12393.

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Abstract. Fast and effective measures to determine heavy metals play an important role in ensuring food quality and safety. In this experiment, laser-induced breakdown spectroscopy (LIBS) was used to detect copper content (Cu) in tobacco ( L.) leaves. The experimental parameters for detection, including laser energy, delay time, and camera gate width, were optimized by response surface methodology (RSM). Univariate analysis and multivariate analysis, including partial least squares regression (PLSR) and extreme learning machine (ELM), were used to establish calibration models. In addition, different preprocessing methods were used to eliminate the signal variations and further improve the calibration performance, including baseline correction, background normalization, area normalization, and standard normal variate (SNV) normalization. The results showed that LIBS combined with both univariate and multivariate methods could be used to detect copper content in tobacco leaves. SNV and area normalization were efficient in dealing with signal variations and improving the calibration performance. The ELM model with SNV normalized variables in the spectral region of 324.02 to 325.98 nm achieved the best performance (R2 = 0.9552 and RMSE = 4.8416 mg kg-1 in the testing set). The results provide the first proof-of-principle data for fast determination of copper content in tobacco leaves. Keywords: Copper content, Laser-induced breakdown spectroscopy, Multivariate calibration, Response surface methodology, Tobacco leaves, Univariate calibration.

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Dandamudi, Santhoshi Priya, Ananda Kumar Chettupalli, Shanthi Priya Dargakrishna, Mounika Nerella, Ramkoteswar Rao Amara, and Vinod Kumar Yata. "Response Surface Method for the Simultaneous Estimation of Atorvastatin and Olmesartan." Trends in Sciences 19, no. 18 (August 28, 2022): 5799. http://dx.doi.org/10.48048/tis.2022.5799.

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The simultaneous estimation of Atorvastatin Calcium (ATS) and Olmesartan Medoxomil (OLM) in bulk and pharmaceutical dosage forms, a new, quick, and cost-effective Reverse Phase High Performance Liquid Chromatography (RP-HPLC) method has been developed. The experimental design was used to achieve multivariate optimization of the RP-HPLC experimental conditions. Three independent variables were used to create mathematical models: Acetonitrile content in the mobile phase composition, buffer pH, and flow rate. Here, the applied model was central composite design (CCD) to research the response surface methodology and study the effects of independent factors. The Shimadzu (LC 20 AT VP) HPLC system with Spinchrom software has been used. Zodiac, C18 (250×4.6 ID) 5μm column, phosphate buffers, and acetonitrile were used as mobile phase in the ratio 40:60 v/v with a flow rate 1.15 mL/min. The eluent was monitored at 212 nm using the Prominence UV-Visible detector. The retention time for OLM and ATS was 2.673 and 3.717, respectively. The optimized procedure was validated as per ICH guidelines. The correlation coefficient of OLM and ATS was 0.9869 and 0.9832. The % of recovery was 98.59, 99.68 %. OLM had a LOD of 17.568 μg/mL, while ATS had a LOD of 12.88 μg/mL. OLM had a LOQ of 53.24 μg/mL, while ATS had a LOQ of 39.04 μg/mL. The pH aqueous phase, solvent composition, and flow rate were the most stringent variables affecting the responses, according to the 3D response surface graphs. A new accurate and precise RP-HPLC approach has been developed and validated and used to regularly analyse OLM and ATS. HIGHLIGHTS A new, quick, and cost-effective RP-HPLC method for the simultaneous estimation of Atorvastatin Calcium and Olmesartan Medoxomil in bulk and pharmaceutical dosage forms RP-HPLC method developed by experimental design and response surface method Developed method was validated as per ICH guidelines GRAPHICAL ABSTRACT

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Dasari, Siva Krishna, Abbas Cheddad, and Petter Andersson. "Predictive modelling to support sensitivity analysis for robust design in aerospace engineering." Structural and Multidisciplinary Optimization 61, no. 5 (January 3, 2020): 2177–92. http://dx.doi.org/10.1007/s00158-019-02467-5.

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AbstractThe design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

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Révelard, Adèle, Claude Frankignoul, and Young-Oh Kwon. "A Multivariate Estimate of the Cold Season Atmospheric Response to North Pacific SST Variability." Journal of Climate 31, no. 7 (April 2018): 2771–96. http://dx.doi.org/10.1175/jcli-d-17-0061.1.

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The Generalized Equilibrium Feedback Analysis (GEFA) is used to distinguish the influence of the Oyashio Extension (OE) and the Kuroshio Extension (KE) variability on the atmosphere from 1979 to 2014 from that of the main SST variability modes, using seasonal mean anomalies. Remote SST anomalies are associated with each single oceanic regressor, but the multivariate approach efficiently confines their SST footprints. In autumn [October–December (OND)], the OE meridional shifts are followed by a North Pacific Oscillation (NPO)-like signal. The OE influence is not investigated in winter [December–February (DJF)] because of multicollinearity, but a robust response with a strong signal over the Bering Sea is found in late winter/early spring [February–April (FMA)], a northeastward strengthening of the Aleutian low following a northward OE shift. A robust response to the KE variability is found in autumn, but not in winter and late winter when the KE SST footprint becomes increasingly small and noisy as regressors are added in GEFA. In autumn, a positive PDO is followed by a northward strengthening of the Aleutian low and a southward shift of the storm track in the central Pacific, reflecting the surface heat flux footprint in the central Pacific. In winter, the PDO shifts the maximum baroclinicity and storm track southward, the response strongly tilts westward with height in the North Pacific, and there is a negative NAO-like teleconnection. In late winter, the North Pacific NPO-like response to the PDO interferes negatively with the response to the OE and is only detected when the OE is represented in GEFA. A different PDO influence on the atmospheric circulation is found from 1958 to 1977.

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Zhang, Wenlin, Zhiqiang Huang, Minqiang Kang, MingJiang Shi, Rong Deng, Yonghong Yan, and Qihua Zhu. "Research on multivariate nonlinear regression model of specific energy of rock with laser drilling based on response surface methodology." Optics Communications 489 (June 2021): 126865. http://dx.doi.org/10.1016/j.optcom.2021.126865.

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Khataee, A. R., M. Safarpour, A. Naseri, and M. Zarei. "Photoelectro-Fenton/nanophotocatalysis decolorization of three textile dyes mixture: Response surface modeling and multivariate calibration procedure for simultaneous determination." Journal of Electroanalytical Chemistry 672 (May 2012): 53–62. http://dx.doi.org/10.1016/j.jelechem.2012.03.010.

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Halios, Christos H., Costas G. Helmis, Helena A. Flocas, Stephan Nyeki, and Dimosthenis N. Assimakopoulos. "On the variability of the surface environment response to synoptic forcing over complex terrain: a multivariate data analysis approach." Meteorology and Atmospheric Physics 118, no. 3-4 (September 18, 2012): 107–15. http://dx.doi.org/10.1007/s00703-012-0209-5.

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Zhuang, Ming, Wei Yao, Lijun Han, Yingying Bi, Chengkui Qiao, Xinru Lv, Mengyuan Cao, and Hanzhong Xie. "Multivariate response surface methodology assisted modified QuEChERS method for the rapid determination of 39 pesticides and metabolites in medlar." Ecotoxicology and Environmental Safety 261 (August 2023): 115102. http://dx.doi.org/10.1016/j.ecoenv.2023.115102.

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Journal articles on the topic 'Multivariate Response Surface'

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