受限空间油气爆燃火焰形态

通过受限空间油气爆燃可视化实验发现，在不同初始油气体积分数下，爆燃火焰呈现出不同的表观特征，据此提出了受限空间油气爆燃的3种火焰形态，即光滑球形火焰、褶皱球形火焰和卷曲絮状火焰。分析了3种火焰形态的形成机理，并通过实验观测与理论分析，给出了区分3种火焰形态的临界条件。结合实验中采集到的关键参数，总结了不同的火焰形态下受限空间油气爆燃的反应产物、最大压力、升压速率、反应时间、火焰强度等关键参数的特征与变化规律。     

Molecular signal suppression by in situ microextraction in nuclear magnetic resonance spectroscopy

The detailed characterization of complex mixtures by NMR is often hampered by the presence of signals from uninformative compounds, the resonances of which overlap with those of the molecules of interest. We provide here a proof of principle for an approach to NMR signal suppression in complex samples using Molecularly Imprinted Polymers (MIPS). Addition of a few milligrams of polymer to a solution traps the target molecule in typical micromolar to millimolar concentration, thus achieving in situ signal suppression, without altering any other spectral features. This method minimized any manipulation or perturbation of the spectrum and was applied to a complex mixture of known compounds and to a plant extract, in both cases spiked with a compound (bisphenol A), which was subsequently removed by selective binding to a complementary MIP. What is described in this report is comparable with microextraction and may in due course be applied to a large number of analytical challenges. Copyright © 2014 John Wiley & Sons, Ltd.     

Evaluation of RBFs for volume data interpolation in CFD

A greedy method for choosing an optimum reduced set of control points is integrated with RBF interpolation and evaluated for the purpose of interpolating large‐volume data sets in CFD. Given a function defined at a set of points, the greedy method selects a small subset of these points that is sufficient to keep the interpolation error at all the remaining points below a chosen bound. This is equivalent to a type of data compression and would have useful storage, post‐processing, and computational applications in CFD. To test the method in terms of both the point selection scheme and the suitability of reduced control point volume interpolation, a trial application of the interpolation to velocity fields in CFD volume meshes is considered. To optimise the point selection process, and attempt to be able to capture multiple length scales, a variable support radius formulation has also been included. Structured and unstructured mesh cases are considered for aerofoils, a wing case and a wing‐body case. For smooth volume functions, the method is shown to work well, producing accurate velocity interpolations using a very small number of the cells in the mesh. For general complex fields including large gradients, the method is still shown to be effective, although large gradients require more interpolation points to be used.Copyright © 2013 John Wiley & Sons, Ltd.     

Vesicle formation in aqueous mixture of the cetyltrimetylammonium bromide and an anionic chitosan derivative

The present work aimed at research the physic-chemical properties of the interaction of N-decyl-O-chitosan sulfate (an amphiphilic chitosan derivative, C₁₀-OCHS) with cetyltrimetylammonium bromide (CTAB) by the steady-state fluorescent, static/dynamic surface tension, turbidity and transmission electron microscopy (TEM). The results showed that the complex of C₁₀-OCHS/CTAB had high surface activity and lower critical aggregation concentration. Besides, the C₁₀-OCHS/CTAB could self-assemble into various aggregates like irregular spherical aggregates, vesicles or polydisperse aggregates from lower to higher concentrations of CTAB with a mixed C₁₀-OCHS concentration of 200?mg/L.     

Hierarchical Model-Based Clustering for Large Datasets

In recent years, hierarchical model-based clustering has provided promising results in a variety of applications. However, its use with large datasets has been hindered by a time and memory complexity that are at least quadratic in the number of observations. To overcome this difficulty, this article proposes to start the hierarchical agglomeration from an efficient classification of the data in many classes rather than from the usual set of singleton clusters. This initial partition is derived from a subgraph of the minimum spanning tree associated with the data. To this end, we develop graphical tools that assess the presence of clusters in the data and uncover observations difficult to classify. We use this approach to analyze two large, real datasets: a multiband MRI image of the human brain and data on global precipitation climatology. We use the real datasets to discuss ways of integrating the spatial information in the clustering analysis. We focus on two-stage methods, in which a second stage of processing using established methods is applied to the output from the algorithm presented in this article, viewed as a first stage.     

Composite Likelihood Inference in a Discrete Latent Variable Model for Two-Way “Clustering-by-Segmentation” Problems

We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g., exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g., consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. Therefore, we introduce a composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data.     

Glutamine: fructose-6-phosphate amidotransferase (GFAT): homology modelling and designing of new inhibitors using pharmacophore and docking based hierarchical virtual screening protocol

Glutamine: fructose-6-phosphate amidotransferase (GFAT), also termed GFPT1 and GFAT1, catalyzes the first committed step of the hexosamine biosynthesis pathway in mammals and consequently plays an important role in type 2 diabetes. In the present study, a combination of pharmacophore modelling, homology modelling, and molecular docking analysis was performed to design new glutamine competitive inhibitors of human GFAT, and to investigate important interaction details of inhibitor molecules. A pharmacophore model of GFAT inhibitors was developed, subsequently validated, and utilized for the screening by the PHASE database to identify new molecules. Afterwards, homology modelling was performed to construct the glutamine-binding site of the GFAT protein. The modelled active site was utilized to dock the studied molecules to investigate important receptor-ligand interactions and to scrutinize database-screened molecules on the basis of essential interactions. This systematic in silico protocol helped us to identify new molecules that would be explored for the treatment of type 2 diabetes and its complications.     

Viscosity Mixing Rules for Binary Systems Containing One Ionic Liquid

In this work the applicability of four of the most commonly used viscosity mixing rules to [ionic liquid (IL)+molecular solvent (MS)] systems is assessed. More than one hundred (IL+MS) binary mixtures were selected from the literature to test the viscosity mixing rules proposed by 1) Hind (Hi), 2) Grunberg and Nissan (G–N), 3) Herric (He) and 4) Katti and Chaudhri (K–C). The analyses were performed by estimating the average (absolute or relative) deviations, AADs and ARDs, between the available experimental data and the predicted ideal mixture viscosity values obtained by means of each rule. The interaction terms corresponding to the adjustable parameters inherent to each rule were also calculated and their trends discussed.     

The solubility of bosentan in aqueous-2-propanol mixtures at several temperatures,measurement and data correlation

ABSTRACT

The solubilities of bosentan (BST) in binary aqueous mixtures of 2-propanol at temperatures ranging from 293.15 to 313.15 K were determined using a shake-flask method. The produced data were modelled with the Jouyban-Acree-van’t Hoff model and difference between the predicted data and experimental ones were illustrated by percent average relative deviations (%ARD). Moreover, the thermodynamic functions of dissolution for BST in the aqueous 2-propanol solutions were computed which suggest that the dissolution process is endothermic and not spontaneous.     

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

The nonlinear, nonnegative single‐mixture blind source separation problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined blind source separation problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi‐mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi‐mixture into a high‐dimensional reproducible kernel Hilbert space. Under sparse probabilistic conditions that were previously imposed on sources, the single‐mixture nonlinear problem is converted into an equivalent linear, multiple‐mixture problem that consists of the original sources and their higher‐order monomials. These monomials are suppressed by robust principal component analysis and hard, soft, and trimmed thresholding. Sparseness‐constrained nonnegative matrix factorizations in reproducible kernel Hilbert space yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of eight dependent components from one nonlinear mixture. The method is further demonstrated on three nonlinear chemical reactions of peptide synthesis in which 25, 19, and 28 dependent analytes are extracted from one nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry‐based non‐targeted metabolic profiling, such as biomarker identification studies. Copyright © 2015 John Wiley & Sons, Ltd.