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Open accessJournal ArticleDOI: 10.1088/1757-899X/263/2/022043

Selection of a design for response surface

01 Nov 2017-Vol. 263, Iss: 2, pp 022043
Abstract: Box-Behnken, Central-Composite, D and I-optimal designs were compared using statistical tools. Experimental trials for all designs were generated. Random uniform responses were simulated for all models. R-square, Akaike and Bayesian Information Criterion for the fitted models were noted. One–way ANOVA and Tukey's multiple comparison test were performed on these parameters. These models were evaluated based on the number of experimental trials generated in addition to the results of the statistical analyses. D-optimal design generated 12 trials in its model, which was lesser in comparison to both Central Composite and Box-Behnken designs. The R-square values of the fitted models were found to possess a statistically significant difference (P<0.0001). D-optimal design not only had the highest mean R-square value (0.7231), but also possessed the lowest means for both Akaike and Bayesian Information Criterion. The D-optimal design was recommended for generation of response surfaces, based on the assessment of the above parameters.

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Journal ArticleDOI: 10.1080/03639045.2019.1593439
Abstract: Growing evidence suggest that Alzheimer’s disease (AD), the most common cause of dementia among the elderly is a metabolic disorder associated with impaired brain insulin signaling. Hence, the diab...

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Topics: Dementia (51%)

24 Citations


Journal ArticleDOI: 10.1007/S11116-019-10018-6
03 Jun 2020-Transportation
Abstract: Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be considered. Furthermore, the computational burden of model systems plays a key role in choosing a calibration approach, and usually forces modellers to calibrate demand-side and network models separately. Also, trial-and-error methods and expert opinion are currently the backbones of transport model calibration, which leaves room for error in the calibrated parameters. This paper addresses these challenges and suggests a structured approach for determining optimal calibrated transport model parameters. This approach involves joint estimation and calibration of demand and network models, with a major focus on avoiding any manipulation of the OD matrix. The approach can be applied to static or dynamic traffic assignments. The approach is applied by calibrating GTAModel—an example of a large-scale agent-based model system from Toronto, Canada.

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Topics: Network model (52%)

7 Citations


Open accessJournal ArticleDOI: 10.29244/JITKT.V13I1.32654
30 Apr 2021-
Abstract: Caulerpa lentillifera merupakan rumput laut hijau yang memiliki potensi besar namun kelimpahannya belum banyak dieksplorasi. Rumput laut tersebut tersebar pada beberapa wilayah perairan di Indonesia. Rumput laut diketahui memiliki kadar lemak yang rendah namun tersusun oleh poli asam lemak tidak jenuh. Ekstraksi lemak pada umumnya hanya menggunakan pelarut organik. Pada proses ekstraksi diperlukan perlakuan awal seperti enzyme assisted extraction untuk mendegradasi dinding sel dan meningkatkan akses pelarut masuk ke dalam sel. Penelitian dirancang untuk mengetahui kondisi optimum proses enzyme assisted extraction lemak rumput laut hijau segar C. lentillifera dengan menggunakan enzim selulase. Proses optimasi dilakukan menggunakan Response Surface Methodology (RSM) model Central Composite Design dengan 15 perlakuan. Perlakuan didapatkan untuk mengetahui pengaruh variabel bebas, diantaranya konsentrasi enzim, suhu hidrolisis, dan waktu hidrolisis terhadap variabel terikat yaitu jumlah ekstrak lemak dan aktivitas antioksidan. Hasil penelitian didapatkan model 2FI dan Linier berturut-turut untuk variabel terikat jumlah lemak dan aktivitas antioksidan. Kondisi optimum yang diperoleh yaitu konsentrasi enzim sebesar 2%, suhu hidrolisis sebesar 30 °C, dan waktu hidrolisis selama 1 jam. Kondisi optimum tersebut kemudian dapat diverifikasi dengan diberikan perlakuan terpilih sebanyak 2 kali ulangan atau lebih hingga mendekati hasil prediksi. Asam lemak yang diperoleh setelah metilasi dan identifikasi dengan GC-MS yaitu asam palmitat dan asam laurat. Optimasi proses ekstraksi lemak memungkinkan potensi pemanfaatan lemak dari rumput laut segar C. lentillifera berdasarkan faktor yang memengaruhi.

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5 Citations


Open accessPosted ContentDOI: 10.1101/2020.06.20.20135186
23 Jun 2020-medRxiv
Abstract: Different agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate different control strategies to control outbreak of the infectious disease. While there are several estimation methods for the disease-specific parameters of COVID-19, they have been used for aggregate level models such as SIR and not for agent-based models. We propose a mathematical structure to determine parameter values of agent-based models considering the mutual effects of parameters. Then, we assess the extent to which different control strategies can intervene the transmission of COVID-19. Accordingly, we consider scenarios of easing social distancing restrictions, opening businesses, speed of enforcing control strategies and quarantining family members of isolated cases on the disease progression. We find the social distancing compliance level in the Sydney greater metropolitan area to be around 85%. Then we elaborate on consequences of easing the compliance level in the disease suppression. We also show that tight social distancing levels should be considered when the restrictions on businesses and activity participations are easing.

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  • Figure 3: A comparison between the influence of implementing the lockdown earlier (in greenish) and later (in reddish) while all the strategies are in place as in the base case scenario. (A) Daily number of cases, and (B) Cumulative number of cases.
    Figure 3: A comparison between the influence of implementing the lockdown earlier (in greenish) and later (in reddish) while all the strategies are in place as in the base case scenario. (A) Daily number of cases, and (B) Cumulative number of cases.
  • Figure 2: A comparison of different SD compliance levels. The settings for other control strategies are the same as in the base scenario. (a) daily number of cases (linear), (b) cumulative cases (linear), (c) daily number of cases (logarithmic), and (d) cumulative cases (logarithmic). Note: Responding to the skewness of large values, (A) and (B) are plotted in logarithmic scale in (C) and (D).
    Figure 2: A comparison of different SD compliance levels. The settings for other control strategies are the same as in the base scenario. (a) daily number of cases (linear), (b) cumulative cases (linear), (c) daily number of cases (logarithmic), and (d) cumulative cases (logarithmic). Note: Responding to the skewness of large values, (A) and (B) are plotted in logarithmic scale in (C) and (D).
  • Figure 1: Power of the calibrated SydneyGMA -based disease spreading model in reproducing the daily number of cases (A), the cumulative number of cases (B) and the number of cases at each state of the pandemic modelling (C) in the base-case scenario.
    Figure 1: Power of the calibrated SydneyGMA -based disease spreading model in reproducing the daily number of cases (A), the cumulative number of cases (B) and the number of cases at each state of the pandemic modelling (C) in the base-case scenario.
  • Figure 4: A comparison of different travel load and its interaction with home quarantine strategy at two social distance compliance levels of 85.9% and 60%. (A) daily number of cases (SD compliance levels is 85.9%), (B) cumulative cases daily number of cases (SD compliance levels is 85.9%), (C) daily number of cases (SD compliance levels is 60%), and (D) cumulative cases daily number of cases (SD compliance levels is 60%). Note: Responding to the skewness of large values, (C) and (D) are plotted in logarithmic scale.
    Figure 4: A comparison of different travel load and its interaction with home quarantine strategy at two social distance compliance levels of 85.9% and 60%. (A) daily number of cases (SD compliance levels is 85.9%), (B) cumulative cases daily number of cases (SD compliance levels is 85.9%), (C) daily number of cases (SD compliance levels is 60%), and (D) cumulative cases daily number of cases (SD compliance levels is 60%). Note: Responding to the skewness of large values, (C) and (D) are plotted in logarithmic scale.

4 Citations


Journal ArticleDOI: 10.1016/J.CEP.2019.107619
Abstract: A combination of electrocoagulation with other methods seems to have garnered much attention in the research area for the past decade to eliminate heavy metal ions from the synthetic and real wastewater effluents. Combining two various methods into a single system appears to be an efficient and promising approach for heavy metal removal, mainly due to their cost-effectiveness, simple operation and suitability for industrial applications. Solar photovoltaic systems have gained much attention because they make use of clean, renewable energy and make the treatment method cost-effective. In this regard, it is imperative to explore the potential of solar photovoltaic systems to remove heavy metals. A response surface methodology based on the central composite design (CCD) was employed to examine the effects of three independent variables such as pH, initial Pb(II) concentration and adsorbent dosage. The results indicated that the highest Pb(II) removal efficiency up to 99.88% can be achieved using the CCD model with the following optimum conditions: (1) pH: 6.01, (2) initial Pb(II) concentration: 15.00 mg/L and (3) adsorbent dosage: 2.50 g/L. Based on the results, the combined system offered an attractive alternative over the single electrocoagulation and adsorption treatment systems as it can produce high Pb(II) removal efficiency.

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Topics: Electrocoagulation (52%)

4 Citations


References
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Open accessJournal ArticleDOI: 10.1109/TAC.1974.1100705
Abstract: The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.

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Topics: Likelihood function (61%), Akaike information criterion (61%), Statistical model (60%) ...read more

42,619 Citations


Open accessJournal ArticleDOI: 10.1214/AOS/1176344136
Abstract: The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.

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Topics: Bayesian information criterion (57%), g-prior (55%), Bayes' theorem (55%) ...read more

35,659 Citations


Open access
01 Jan 2005-
Abstract: The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.

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Topics: Bayes' theorem (56%), Context (language use) (54%), Asymptotic expansion (54%) ...read more

33,801 Citations


Journal ArticleDOI: 10.1177/0049124104268644
Abstract: The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information...

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7,541 Citations


Journal ArticleDOI: 10.1016/J.TALANTA.2008.05.019
15 Sep 2008-Talanta
Abstract: A review about the application of response surface methodology (RSM) in the optimization of analytical methods is presented. The theoretical principles of RSM and steps for its application are described to introduce readers to this multivariate statistical technique. Symmetrical experimental designs (three-level factorial, Box-Behnken, central composite, and Doehlert designs) are compared in terms of characteristics and efficiency. Furthermore, recent references of their uses in analytical chemistry are presented. Multiple response optimization applying desirability functions in RSM and the use of artificial neural networks for modeling are also discussed.

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3,660 Citations


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