Density Functional Study on Reaction Activity of Steam Decomposition Products Adsorbed on Carbonaceous Surface

A systematic theoretical study on the adsorption of steam and its thermal decomposition products on carbon both zigzag and armchair surface was performed to provide molecular-level understanding of the reaction activity of all these reactants in biomass steam gasification process. All the calculations were carried out using density functional theory (DFT) at the B3LYP/6-31+g(d,p) level. The structures of carbonaceous surfaces, all reactants and surface complexes were optimized and characterized. Based on the value of adsorption heat been obtained from the calculation, the activity of all reactants can be ordered as: O > O2 >H2 >H >OH >H2O for both zigzag and armchair surface, and the adsorption style is physisorption to water molecule and chemisorption to the other dissociated components.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

NP-hardness of MAP in Ternary Tree Bayesian Networks

This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.

The photocatalytic decomposition of microcystin-LR using selected titanium dioxide materials

Microcystins (cyclic heptapeptides) produced by a number of freshwater cyanobacteria are a potential cause for concern in potable water supplies due to their acute and chronic toxicity. TiO₂ photocatalysis is a promising technology for removal of these toxins from drinking water. It is, however, necessary to have a sufficient knowledge of how the catalyst materials cause the degradation of the toxins through the photocatalytic process. The present study reports microcystin degradation products of the photocatalytic oxidation by using a number of commercial TiO₂ powder (P25, PC50, PC500 and UV100) and granular (KO1, KO3, TiCat-C, TiCat-S) materials, so aiding the mechanistic understanding of this process. Liquid chromatography-mass spectrometry analysis demonstrated that the major destruction pathway of microcystin for all the catalysts tested followed almost the same pathway, indicating the physical properties of the catalysts had little effects on the degradation pathway of microcystin-LR. 

Mechanistic studies of the photocatalytic oxidation of microcystin-LR: An investigation of byproducts of the decomposition process

Microcystins (cyclic heptapeptides) are produced by a number of freshwater cyanobacteria and cause concern in potable water supplies due to their acute and chronic toxicity. The present study reports the structural characterization of the degradation products of the photocatalytic oxidation of microcystin-LR, so aiding the mechanistic understanding of this process. TiO₂ photocatalysis is a promising technology for removal of these toxins from drinking water. However, before it can be adopted in any practical application it is necessary to have a sufficient knowledge of degradation byproducts and their potential toxicity. Liquid chromatography-mass spectrometry analysis demonstrated that the major destruction pathway of microcystin appears to be initiated via three mechanisms: UV irradiation, hydroxyl radical attack, and oxidation. UV irradiation caused geometrical isomerization of microcystin converting the (4E), (6E) of the Adda configuration to (4E), 6(Z) or 4(Z), 6(E). Hydroxyl radical attack on the conjugated diene structure of Adda moiety produced dihyroxylated products. Further oxidation cleaved the hydroxylated 4-5 and/or 6-7 bond of Adda to form aldehyde or ketone peptide residues, which then were oxidized into the corresponding carboxylic acids. Photocatalysis also hydrolyzed the peptide bond on the ring structure of microcystin to form linear structures although this appeared to be a minor pathway.

Does chemical composition of individual Scots pine trees determine the biodiversity of their associated ground vegetation?

Despite plant secondary metabolites being major determinants of species interactions and ecosystem processes, their role in the maintenance of biodiversity has received little attention. In order to investigate the relationship between chemical and biological diversity in a natural ecosystem, we considered the impact of chemical diversity in individual Scots pine trees (Pinus sylvestris) on species richness of associated ground vegetation. Scots pine trees show substantial genetically determined constitutive variation between individuals in concentrations of a group of secondary metabolites, the monoterpenes. When the monoterpenes of particular trees were assessed individually, there was no relationship with species richness of associated ground flora. However, the chemical diversity of monoterpenes of individual trees was significantly positively associated with the species richness of the ground vegetation beneath each tree, mainly the result of an effect among the non-woody vascular plants. This correlation suggests that the chemical diversity of the ecosystem dominant species has an important role in shaping the biodiversity of the associated plant community. The extent and significance of this effect, and its underlying processes require further investigation.

Density Functional Theory Studies of Ethanol Decomposition on Rh(211)

Density functional theory calculations were carried out to examine the mechanism of ethanol decomposition on the Rh(211) surface. We found that there are two possible decomposition pathways: (1) CH(3)CH(2)OH -> CH(3)CHOH -> CH(3)COH -> CH(3)CO -> CH(3) + CO -> CH(2) + CO -> CH + CO -> C + CO and (2) CH(3)CH(2)OH -> CH(3)CHOH -> CH(3)COH -> CH(2)COH -> CHCOH -> CHCO -> CH + CO -> C + CO. Both pathways have a common intermediate of CH(3)COH, and the key step is the formation of CH(3)CHOH species. According to our calculations, the mechanism of ethanol decomposition on Rh(211) is totally different from that on Rh(111): the reaction proceeds via CH(3)COH rather than an oxametallacycle species (-CH(2)CH(2)O- for Rh( 111)), which implies that the decomposition process is structure sensitive. Further analyses on electronic structures revealed that the preference of the initial C(alpha)-H path is mainly due to the significant reduction of d-electron energy in the presence of the transition state (TS) complex, which may stabilize the TS-surface system. The present work first provides a clear picture for ethanol decomposition on stepped Rh(211), which is an important first step to completely understand the more complicated reactions, like ethanol steam reforming and electrooxidation.