Closure to “Characterization of Model Uncertainty for Cantilever Deflections in Undrained Clay” by D. M. Zhang, K. K. Phoon, H. W. Huang, and Q. F. Hu

Zhang, Dong Ming; Phoon, Kok‐Kwang; Huang, Hongwei; Hu, Qunfang

doi:10.1061/(asce)gt.1943-5606.0001396

Cited by 153 publications

(210 citation statements)

References 10 publications

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“…However, the implementation of data science could potentially navigate and reveal such complex matter within catalysis as machine learning can treat multiple factors in high dimensions . In earlier years, neural network was implemented to treat heterogeneous catalysis where such data science techniques are proposed to be effective tools for simulating catalyst properties and the performance of solid materials . Since then, rapid development of machine learning algorithms occurred due to the introduction of random forest and support vector machine, which greatly improve and expand the ways of understanding the data .…”

Section: Introductionmentioning

confidence: 99%

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

2019

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The challenge in catalytic reactions lies within its complexity coming from high dimensional experimental factors. In order to solve such complexity, machine learning is implemented to treat experimental conditions in high dimensions. Oxidative coupling of methane, methane to C2 compounds (ethylene and ethane), is chosen as the prototype reaction where 156 data consisting of various experimental conditions is prepared. Machine learning reveals that the relationship between experimental conditions and C2 yield is non‐linear matter. In particular, extreme tree regression is found to accurately reproduce the experimental data. In addition, machine learning predictions can be a good indicator for designing experiments. Thus, machine learning can be a powerful approach towards understanding and determining experimental conditions in high dimension.

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Section: Introductionmentioning

confidence: 99%

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

2019

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“…Strong intramolecular hydrogen bond association not only affects hydrophilic ability of hydroxyl groups but also leads to the compact crystalline structure in crystalline zone. It is found, in general, that the reaction takes place in amorphous area, but seldom in compact crystalline zone …”

Section: Resultsmentioning

confidence: 99%

Hydroxyethyl starch with high surface activity via ethylene carbonate functionalization

Zhong

Zhang

et al. 2019

J of Applied Polymer Sci

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Biosurfactants‐hydroxyethyl starches (HES‐EC) with the total degree of substitution (DSt) of 26, 37, and 46% are prepared via ethylene carbonate functionalization. GC–MS semi‐quantitative analysis results show that monosubstituents, especially, C‐2 monosubstituents are predominant in all substituents. A small amount of disubstituents with O(CH2CH2O)2H group per polyethylene chain exist in HES‐EC. The structural characteristics of HES‐EC demonstrate that hydroxyethyl groups distribute more uniformly on starch main chains as compared with corresponding HES‐EO. The even distribution of hydroxyethyl groups further promotes the formation of the hydrophilic–hydrophobic structure inside HES‐EC. HES‐EC exhibits lower surface tension and more notable emulsion ability than HES‐EO and traditional petroleum‐based surfactant NP‐10. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48807.

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“…2 The computing models are usually called P system, which is a new distributed and parallel computing model. As we know, the traditional All-SAT algorithms have a exponential time complexity, such as DP 3 and DPLL, 4 annealing algorithm, 5 Wu's method. 6 Therefore, we need to find new methods.…”

Section: Introductionmentioning

confidence: 99%

A Uniform Algorithm of Solving All-SAT Using Membrane Systems

Luo

Xiong

Tan

et al. 2015

j comput theor nanosci

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This paper investigates the All-SAT problem via cell-like membrane system, which is a new distributed and parallel computing model. Up to now, All-SAT has been difficut to solve. As we know, the traditional All-SAT algorithms have a exponential time complexity. Therefore, we need to find new methods. In this paper, we present a uniform solution to the All-SAT problem to obtain an exponential working space in a linear time by using membrane division. First, we propose a method to solve All-SAT problem based on membrane computing model and give the detailed algorithm. Second, according to the algorithm, we introduce the definition of the membrane system and its specific membrane model. Then we construct the evolution rules of the model. Furthermore, we put forward specific implementation process. Our membrane model has fewer nested membranes and simpler rules, therefore it has high efficiency. The effectiveness and efficiency of the proposed system is demonstrated by an instance. Comparing with most of the algorithms using membrane systems, the proposed algorithm can provide all solutions of SAT.

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Closure to “Characterization of Model Uncertainty for Cantilever Deflections in Undrained Clay” by D. M. Zhang, K. K. Phoon, H. W. Huang, and Q. F. Hu

Cited by 153 publications

References 10 publications

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning

Hydroxyethyl starch with high surface activity via ethylene carbonate functionalization

A Uniform Algorithm of Solving All-SAT Using Membrane Systems

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