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Abstract: The accuracy of the point-kernel method, which is a widely used practical tool for γ-ray shielding calculations, strongly depends on the quality and accuracy of buildup factors used in the calculations. Although, buildup factors for single-layer shields comprised of a single material are well known, calculation of buildup factors for stratified shields, each layer comprised of different material or a combination of materials, represent a complex physical problem. Recently, a new compact mathematical model for multi-layer shield buildup factor representation has been suggested for embedding into point-kernel codes thus replacing traditionally generated complex mathematical expressions. The new regression model is based on support vector machines learning technique, which is an extension of Statistical Learning Theory. The paper gives complete description of the novel methodology with results pertaining to realistic engineering multi-layer shielding geometries. The results based on support vector regression machine learning confirm that this approach provides a framework for general, accurate and computationally acceptable multi-layer buildup factor model. [Copyright &y& Elsevier]

Abstract: A detailed numerical procedure for the calculation of the isentropic exponent of a natural gas is derived. The procedure follows the Redlich–Kwong solution using the AGA8/1992 extended virial-type characterization equation (AGA8-92DC equation) for the determination of the compression factor. The AIChE generic ideal heat capacity equations are used to derive the molar heat capacities of the natural gas mixture. The compression factor and its derivatives are calculated on the basis of ISO-12213-2. Two calculation examples are given and the results are graphically illustrated. The calculation procedure is implemented in a digital computer using an object-oriented programming approach. The effect of the isentropic exponent on the accuracy of orifice-based flow-rate measurements is demonstrated and discussed. [Copyright &y& Elsevier]

Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data. [ABSTRACT FROM AUTHOR]

Abstract: 18-crown-6 ethers are known to exert their biological activity by transporting K+ ions across cell membranes. Using non-linear Support Vector Machines regression, we searched for structural features that influence antiproliferative activity in a diverse set of 19 known oxa-, monoaza- and diaza-18-crown-6 ethers. Here, we show that the logP of the molecule is the most important molecular descriptor, among ∼1300 tested descriptors, in determining biological potency (R 2 cv = 0.704). The optimal logP was at 5.5 (Ghose-Crippen ALOGP estimate) while both higher and lower values were detrimental to biological potency. After controlling for logP, we found that the antiproliferative activity of the molecule was generally not affected by side chain length, molecular symmetry, or presence of side chain amide links. To validate this QSAR model, we synthesized six novel, highly lipophilic diaza-18-crown-6 derivatives with adamantane moieties attached to the side arms. These compounds have near-optimal logP values and consequently exhibit strong growth inhibition in various human cancer cell lines and a bacterial system. The bioactivities of different diaza-18-crown-6 analogs in Bacillus subtilis and cancer cells were correlated, suggesting conserved molecular features may be mediating the cytotoxic response. We conclude that relying primarily on the logP is a sensible strategy in preparing future 18-crown-6 analogs with optimized biological activity. [Copyright &y& Elsevier]