One-step catalytic cyclohexane oxidation to adipic acid using molecular oxygen

Using combination of Mn-Co transition metal species with N-hydroxyphthalimide as a catalyst for one-step oxidation of cyclohexane with molecular oxygen in acetic acid at 353 K can give more than 95% selectivity towards oxygenated products with adipic acid as a major product at a high conversion (ca. 78%). A turnover number of 74 for this partial oxidation are also recorded.

One-pot synthesis and characterization of soluble poly(aryl ether-ketone)s having pendant carboxyl groups

Aromatic poly(ether-ketone)s having pendant carboxyl groups have been obtained by direct, one-pot, Friedel-Crafts copolycondensation of 4,4'-diphenoxybenzophenone with a mixture of terephthaloyl chloride (TC) and trimellitic anhydride acid chloride (TAAC), over a wide range of TAAC/TC molar ratios, in the presence of anhydrous aluminum chloride. The syntheses were performed as precipitation-polycondensations, and the polymers were obtained in particulate form. Besides globular particles of polymer, small quantities of elongated, needlelike particles were observed when the mole ratio TAAC/TC was less than 1. Use of X-ray microdiffraction with synchrotron radiation has revealed that the needlelike material consists of a cyclic compound containing 10 phenylene units, i.e., the crystals are of a [2 + 2] macrocyclic dimer. The polymers obtained are soluble in strong acids and in mixtures of methanesulfonic acid or trifluoroacetic acid with chlorinated hydrocarbons. The molecular structures of the polymers were confirmed by H-1 and C-13 NMR spectroscopy. Reaction of TAAC with 4,4'-diphenoxybenzophenone produced mainly meta-orientation of the resulting ketone linkages. The size of the polymer particles, their molecular weights, and the melting behavior of the products obtained depend on the TAAC/TC ratio used. Ortho-keto acid residues, formed during reaction of anhydride groups of TAAC with 4,4'-diphenoxybenzophenone, exhibit ring-chain tautomerism. A carboxyl-containing aromatic polyketone derived from p-terphenyl, and thus having with no ether linkages in the main chain, was prepared by analogous chemistry, and functional derivatives of carboxy-substituted polyketones were also obtained and characterized.

Identification of Bartonella species in rodents, shrews and cats in Denmark: detection of two B-henselae variants, one in cats and the other in the long-tailed field mouse

Small mammals and stray cats were trapped in two areas of North Zealand, Denmark, and their blood cultured for hemotrophic bacteria. Bacterial isolates were recovered in pure culture and subjected to 16S rDNA gene sequencing. Bartonella species were isolated from five mammalian species: B. grahamii from Microtus agrestis (field vole) and Apodemus flavicollis (yellow-necked field mouse); B. taylorii from M. agrestis, A. flavicollis and A. sylvaticus (long-tailed field mouse); B. tribocorum from A. flavicollis; R vinsonii subsp. vinsonii from M. agrestis and A. sylvaticus; and B. birtlesii from Sorex vulgaris (common shrew). In addition, two variant types of B. henselae were identified: variant I was recovered from three specimens of A. sylvaticus, and B. henselae variant 11 from I I cats; in each case this was the only B. henselae variant found. No Bartonella species was isolated from Clethrionomys glareolus (bank vole) or Micromys minutus (harvest mouse). These results suggest that B. henselae occurs in two animal reservoirs in this region, one of variant I in A. sylvaticus, which may be transmitted between mice by the tick Ixodes ricinus, and another of variant 11 in cats, which may be transmitted by the cat flea (Ctenocephalides felis). To our knowledge, this is the first report of the occurrence of B. henselae and B. tribocorum in Apodemus mice.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Parallel implementation and one year experiments with the Danish Eulerian Model

Large scale air pollution models are powerful tools, designed to meet the increasing demand in different environmental studies. The atmosphere is the most dynamic component of the environment, where the pollutants can be moved quickly on far distnce. Therefore the air pollution modeling must be done in a large computational domain. Moreover, all relevant physical, chemical and photochemical processes must be taken into account. In such complex models operator splitting is very often applied in order to achieve sufficient accuracy as well as efficiency of the numerical solution. The Danish Eulerian Model (DEM) is one of the most advanced such models. Its space domain (4800 × 4800 km) covers Europe, most of the Mediterian and neighboring parts of Asia and the Atlantic Ocean. Efficient parallelization is crucial for the performance and practical capabilities of this huge computational model. Different splitting schemes, based on the main processes mentioned above, have been implemented and tested with respect to accuracy and performance in the new version of DEM. Some numerical results of these experiments are presented in this paper.