Brassica campestris var. Span: I. Fractionation of Rapeseed oil by molecular distillation and adsorption chromatography

Procedures for the large scale isolation of pure triglycerides and fractions rich in nontriglyceride components from Span rapeseed oil are described. Fractionation ofBrassica campestris var. Span rapeseed oil by molecular distillation yielded 4 triglyceride fractions, all of which contained traces of sterol esters. An additional triglyceride fraction rich in free and esterified sterols and other volatile components was obtained from the oil. Separation by adsorption chromatography of Span rapeseed oil yielded three fractions: A) a pure triglyceride fraction; B) a triglyceride fraction rich in sterol esters; and C) another fraction containing free sterols and other polar components. Contribution no. 559 Animal Research Institute.     

Theory of asymmetric organocatalysis of Aldol and related reactions: rationalizations and predictions

Computational studies have led to models to understand some classic and contemporary asymmetric reactions involving organocatalysts. The Hajos-Parrish-Eder-Sauer-Wiechert reaction and intermolecular aldol reactions as well as Mannich reactions and oxyaminations catalyzed by proline and other amino acids, and Diels-Alder reactions catalyzed by MacMillan's chiral amine organocatalysts have been studied with density functional theory. Quantitative predictions for several new catalysts and reactions are provided.     

Flux pinning effect of cubic equiaxed morphology and its Ti stabilizing in Nb<Subscript>3</Subscript>Sn superconductors

Two sets of internal-Sn Nb3Sn superconducting strands were fabricated through RRP method, one with 2 wt% of Ti alloyed in Sn core and the other just pure Sn. Four reaction temperatures of 650℃, 675℃, 700℃ and 725℃ and 128 h duration were applied for A15 phase formation heat treatment after Cu-Sn alloying procedure of 210℃/50 h + 340℃/25 h. For the heat-treated coil samples, transport non-Cu JC was examined through standard 4-probe technique and phase microstructure was observed by means of Field Emission Sc...     

Nonstationary filtering of transmission tomograms in high photon counting noise

The algorithm presented here for nonstationary constrained least-squares filtering deals with the transmission photon counting noise problem in the presence of limited dosages and highly nonhomogeneous fields. This technique consists of a design for a set of nonstationary filters, tuned to local noise autocorrelation functions in the reconstructed image. Estimates of noise properties can be taken from object detection information. The approach yields improvement in the tradeoff between noise levels and resolution of the image by exchanging artifacts for less colored noise, which is more easily overcome by the viewer.     

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

Siamese multi-layer perceptrons for dimensionality reduction and face identification

This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training with side information achieves comparable classification performance with the classical MLP training on fully labeled data. Besides, while the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence we also apply the siamese MLP for visualization of the dimensionality reduction to the 2-d and 3-d spaces.     

Exact Sampling of a Linear Interval Predictor

Recent advances in the design of interval observers have made it possible to ensure the non‐divergence of the computed state bounds from the stability of LTI systems under bounded inputs, with no need for additional monotony assumptions. Time‐varying changes of coordinates can be used to that purpose. Most of the related works result in either continuous‐time or discrete‐time interval dynamics. This paper proposes a constructive algorithm to compute the exact sampled response of a linear interval predictor under bounded inputs, gives a stability equivalence result and discusses the design of interval observers. The exact sampling requires held input bounds but the uncertain input itself needs not to be held. A numerical example exhibiting an oscillatory behavior illustrates the main results.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

SplitLab: A shear-wave splitting environment in Matlab

We present a graphical user interface to facilitate the processing of teleseismic shear-wave splitting observations. In contrast to a fully automated technique, we present a manual, per-event approach that maintains user control during the sequence of processing. The SplitLab environment is intended to undertake the repetitive processing steps while enabling the user to focus on quality control and eventually the interpretation of the results. Pre-processing modules of SplitLab create a database of events and link the corresponding seismogram files. The seismogram viewer tool uses this database to perform the measurement interactively. Post-processing of the combined results of such a project includes a viewer and export option. Our emphasis lies in the application to teleseismic shear-wave splitting analysis, but our code can be extended easily for other purposes. SplitLab can be downloaded at http://www.gm.univ-montp2.fr/splitting/.     

A review of smart homes- present state and future challenges

In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.