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A database consisting of 1870 data sets on catalyst compositions and their performances in the oxidative coupling of methane was compiled. For this goal, about 1000 full-text references from the last 30 years have been analyzed and about 420 of them, which contained all the necessary information, were selected for the data extraction. The accumulated data were subject to statistical analysis: analysis of variance, correlation analysis, and decision tree. On the basis of the results, 18 catalytic key elements were selected from originally 68 elements. All oxides of the selected elements, which positively affect the selectivity to C2 products, show strong basicity. Analysis of binary and ternary interactions between the selected key elements shows that high-performance catalysts are mainly based on Mg and La oxides. Alkali (Cs, Na) and alkalineearth (Sr, Ba) metals used as dopants increase the selectivity of the host oxides, whereas dopants such as Mn, W, and the Cl anion have positive effects on the catalyst activity. The maximal C2 selectivities for the proposed catalyst compositions range from 72 to 82%, and the respective C2 yields range from 16 to 26%

New active and selective catalyst compositions for the hydrogenation of CO2 to mainly fuel‐type higher hydrocarbons were developed by application of an evolutionary strategy. It was shown that Fe and K supported on TiO2 and modified by Cu plus other modifiers resulted in highest selectivity for C5–C15 hydrocarbons at high degrees of CO2 conversion. Co containing catalysts were less suited since they produced methane and light hydrocarbons with high selectivities. A detailed study of reaction conditions showed that selected catalyst compositions were able to reach high CO2 conversion with still low selectivities to methane at higher reaction temperatures and a higher H2/CO2 ratio.

Based on available 1870 literature data for the oxidative coupling of methane (OCM), various statistical models were applied i) to design three-component catalysts consisting of one host metal oxide (La_{2}O_{3} or MgO) and two oxide (Li, Na, Cs, Sr, Ba, La, or Mn) dopants and ii) to predict their OCM performance. To validate this approach for catalyst design, selected materials were prepared and experimentally tested for their activity and selectivity in the target reaction. The effects of kinds of host oxides, dopants and their interplay on the OCM performance of differently composed catalysts were statistically evaluated

Copulas are distribution functions with standard uniform univariate marginals. Copulas are widely used for studying dependence among continuously distributed random variables, with applications in finance and quantitative risk management; see, e.g., the pricing of collateralized debt obligations (Hofert and Scherer, Quantitative Finance, 11(5), 775-787, 2011). The ability to model complex dependence structures among variables has recently become increasingly popular in the realm of statistics, one example being data mining (e.g., cluster analysis, evolutionary algorithms or classification). The present work considers an estimator for both the structure and the parameters of hierarchical Archimedean copulas. Such copulas have recently become popular alternatives to the widely used Gaussian copulas. The proposed estimator is based on a pairwise inversion of Kendall's tau estimator recently considered in the literature but can be based on other estimators as well, such as likelihood-based. A simple algorithm implementing the proposed estimator is provided. Its performance is investigated in several experiments including a comparison to other available estimators. The results show that the proposed estimator can be a suitable J Intell Inf Syst alternative in the terms of goodness-of-fit and computational efficiency. Additionally, an application of the estimator to copula-based Bayesian classification is presented. A set of new Archimedean and hierarchical Archimedean copula-based Bayesian classifiers is compared with other commonly known classifiers in terms of accuracy on several well-known datasets. The results show that the hierarchical Archimedean copula-based Bayesian classifiers are, despite their limited applicability for high-dimensional data due to expensive time consumption, similar to highly-accurate classifiers like support vector machines or ensemble methods on low-dimensional data in terms of accuracy while keeping the produced models rather comprehensible.

Research on structure determination and parameter estimation of hierarchical Archimedean copulas (HACs) has so far mostly focused on the case in which all appearing Archimedean copulas belong to the same Archimedean family. The present work addresses this issue and proposes a new approach for estimating HACs that involve different Archimedean families. It is based on employing goodness-of-fit test statistics directly into HAC estimation. The approach is summarized in a simple algorithm, its theoretical justification is given and its applicability is illustrated by several experiments, which include estimation of HACs involving up to five different Archimedean families.

a r t i c l e i n f o a b s t r a c tThe paper presents the history and present state of the GUHA method, its theoretical foundations and its relation and meaning for data mining. A survey of development of the GUHA method"GUHA" is the acronym for General Unary Hypotheses Automaton. The idea of the method is: given data, let the computer generate all (or as much as possible) interesting hypotheses of a given logical form that are supported by the data. This idea was elaborated by M. Chytil and P. Hájek in mid-sixties of the last century, the first paper in English being [16]. The approach was as follows: Data to be processed form a rectangular matrix of zeros and ones, rows corresponding to objects and columns to attributes (properties). Let P 1 , . . . , P n be names of the attributes. For each attribute P i , ¬P i is the name of its negation. An elementary conjunction of length k (1 k n) is a conjunction of k literals in which each predicate occurs at most once, e.g. ¬P 3 , P 1 & ¬P 3 & P 7 ; similarly an elementary disjunction (e.g. P 1 ∨ ¬P 3 ∨ P 7 ). An object satisfies an elementary conjunction if it satisfies all its members; it satisfies an elementary disjunction if it satisfies at least one of its members.Let 0 p 1. A formula A ⇒ p S where A is an elementary conjunction (antecedent) and S is an elementary disjunction (succedent) is true in the data if at least 100p percent of objects satisfying A satisfies S, i.e. a/r p where r is the number of objects satisfying A and a is the number of objects satisfying both A and S. The antecedent A is t-good (where t is a natural number) if at least t objects satisfy it. The version of GUHA described in [16] systematically generates "strongest" true formulas A ⇒ p S with a t-good antecedent, notation: A ⇒ p,t S. (Details omitted; "strongest" refers to a notion of a logical rule of immediate consequence among formulas of our form.) See also [2].The reader easily recognizes similarity with the notion of an "associational rule with support and confidence" introduced by Agrawal [1] about 25 years later: his A and S are elementary conjunctions containing no negation, p is the confidence and support is t/m, where m is the number of all objects in the data. 35 The formulas found by GUHA (i.e. by a computer program implementing it) have the form "almost all objects satisfying the antecedent satisfy the succedent (and the number of objects satisfying the antecedent is not too small)." It is stressed that the found results are formulas true in the data and they are hypotheses from the point of view of a universe from which the data are a sample. The slogan has been "GUHA offers everything interesting" (all hypotheses of the given form true in the data). The first implementation (by I. Havel) worked on a computer MINSK22.In 1968 Hájek (in a paper in Czech) suggested a different version based on the statistical Fisher test. Given A and S (now two elementary conjunctions with no predicates in common), let a, b, c, d be the numbers of objects satisfying A & B,A & ¬B, ¬A & B and ¬...

For converting methane and ammonia to hydrocyanic acid, catalysts were prepared and tested in a 48-parallel channel fixed-bed reactor unit operating at temperatures up to 1373 K. The catalysts were synthesized with a robot applying a genetic algorithm as the design tool. New and improved catalyst compositions were discovered by using a total of seven generations each consisting of 92 potential catalysts. Thereby, the catalyst support turned out as an important input variable. Furthermore, platinum, which is well known as a catalytic material was confirmed. Moreover, improvements in HCN yield were achieved by addition of promoters like Ir, Au, Ni, Mo, Zn and Re. Multi-way analysis of variance and regression trees were applied to establish correlations between HCN yield and catalyst composition (support and metal additives). The obtained results are considered as the base for future even more efficient screening experiments. #

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