Stability of ruthenium catalysts supported on TiO2 or ZrO2 in catalytic wet air oxidation

The stability of ruthenium catalysts supported on TiO₂ and ZrO₂ were studied in the wet air oxidation of aqueous solution of succinic and p-hydroxybenzoic acids taken as model effluent and on real effluents from the paper-pulp industry. Catalyst recycling experiments were conducted in batch reactor and long-term stability tests were conducted in trickle-bed reactor. In all experiments, ruthenium and support materials were perfectly stable to leaching, sintering and fouling. Ruthenium catalysts experienced a weak deactivation as they were exposed to air, e.g., in recycling experiments however the loss of activity occurred only after the first exposure and was completely reversible upon catalyst reduction. The deactivation was attributed to an over-oxidation of the catalyst surface particularly noticeable in the case of very small Ru-clusters (1 nm).     

Molecular analysis of intact preen waxes of Calidris canutus (Aves: Scolopacidae) by gas chromatography/mass spectrometry

The intact preen wax esters of the red knot Calidris canutus were studied with gas chromatography/mass spectrometry (GC/MS) and GC/MS/MS. In this latter technique, transitions from the molecular ion to fragment ions representing the fatty acid moiety of the wax esters were measured, providing additional resolution to the analysis of wax esters. The C₂₁−C₃₂ wax esters are composed of complex mixtures of hundreds of individual isomers. The odd carbon-numbered wax esters are predominantly composed of even carbon-numbered n-alcohols (C₁₄, C₁₆, and C₁₈) esterified predominantly with odd carbon-numbered 2-methyl fatty acids (C₇, C₉, C₁₁, and C₁₃), resulting in relatively simple distributions. The even carbon-numbered wax esters show a far more complex distribution due to a number of factors: (i) Their n-alcohol moieties are not dominated by even carbon-numbered n-alcohol moieties are not dominated by even carbon-numbered n-alcohols esterified with odd carbon-numbered 2-methyl fatty acids, but odd and even carbon-numbered n-alcohols participate in approximately equal amounts; (ii) odd carbon-numbered methyl-branched alcohols participate abundantly in these wax ester clusters; and (iii) with increasing molecular weight, various isomers of the 2,6-, 2,8-, and 2,10-dimethyl branched fatty acids also participate in the even carbon-numbered wax esters. The data demonstrate that there is a clear biosynthetic control on the wax ester composition although the reasons for the complex chemistry of the waxes are not yet understood.     

New phospholipid fatty acids from the marine spongeCinachyrella alloclada uliczka

The fatty acid composition of phospholipids from the Senegalese spongeCinachyrella alloclada was examined. Two new fatty acids not hitherto found in nature, namely 10,13-octadecadienoic acid and 16-tricosenoic acid, were identified. 8-Hexadecenoic, 13-nonadecenoic and 5,9,13-trimethyltretradecanoic fatty acids were also found for the first time in sponges. The latter compound (1.4% of the total fatty acid mixture), an isoprenoid fatty acid, accompanies the major fatty acid 4,8,12-trimethyltridecanoic acid (19.7%). The monomethyl branched fatty acids (22%) identified include 23-methylpentacosanoic acid (anteiso-26∶0), not previously observed in sponged. The major long-chain fatty acids encountered were the known 17-tetracosenoic 19-heptacosadienoic and 5,9,23-tricontatrienoic acid. Some sixty fatty acids were identified as methyl esters andN-acyl pyrrolidides by gas chromatography and gas chromatography/mass spectrometry.     

Characterization of bimetallic NaY-supported Pt-Pd catalyst by EXAFS,TEM and TPR

NaY-supported bimetallic Pt-Pd catalysts have been studied by TEM, TPR and EXAFS. Small, randomly mixed particles are formed in the zeolite cages. When the Pt content is increased, a Pt core structure appears and the dispersion increases. Particle diameters are about 1–2 nm for bimetallic samples.     

Discovering social structures of local influence by using tweetStimuli

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communities.     

Interaction between dietary protein and fat in triglyceride metabolism in the rat: Effects of soy protein and menhaden oil

The objective of the present study was to determine the mechanisms by which dietary proteins interact with dietary lipids in the regulation of triglyceridemia in rats. Male Sprague-Dawley rats (n=56) were subjected to 28-d experimental diets containing different combinations of proteins (20% w/w) and lipid sources (14% w/w): (i) casein-menhaden oil, (ii) casein-beef tallow, (iii) soy protein-menhaden oil, and (iv) soy protein-beef tallow. Significant protein-lipid interactions were observed on triglyceridemia and hepatic cholesterol in fasted rats. The combination of casein and beef tallow was associated with high plasma TG and hepatic cholesterol concentrations, which were reduced by substitution either of soy for casein or of menhaden oil for beef tallow. Therefore, triglyceridemia and liver cholesterol remained low with soy protein feeding, independently of the lipid source, as well as with menhaden oil feeding, regardless of the protein source. The menhaden oil diets reduced plasma cholesterol, hepatic TG, and TG secretion compared with beef tallow diets independently of the dietary protein source. Modifying the source of dietary proteins and lipids had no effect on post-heparin plasma lipoprotein lipase activity. These results demonstrate that soy protein can lower rat triglyceridemia relative to casein when associated with beef tallow consumption, whereas menhaden oil can attenuate hypertriglyceridemia when rats are fed casein. The data further suggest that part of the hypotriglyceridemic effect of soy protein in the rat may be mediated by reduced hepatic lipid synthesis, as is the case for menhaden oil.     

NH_3 Plasma Surface Treatments of Engineering Fluoropolymers: A Way to Enhance Adhesion of Ni or Cu Thin Films Deposited by Electroless Plating

FLUOROPOLYMERS(FPs)are thermoplasticmaterials which exhibit a number of unique chemicaland physical characteristics that no other man-madeplastic product gets together.For example,FPs showvery good resistance to almost all chemicals,excellentwater vapor barrier properties,high thermal stability,outstanding electrical insulation properties(lowdielectric constant and dissipation factors),extremelylow frictional coefficient giving them high auto-lubrication properties,high resistance to rad…     

Dynamic Model Adaptation for Multiscale Simulation of Hyperbolic Systems with Relaxation

In numerous industrial CFD applications, it is usual to use two (or more) different codes to solve a physical phenomenon: where the flow is a priori assumed to have a simple behavior, a code based on a coarse model is applied, while a code based on a fine model is used elsewhere. This leads to a complex coupling problem with fixed interfaces. The aim of the present work is to provide a numerical indicator to optimize to position of these coupling interfaces. In other words, thanks to this numerical indicator, one could verify if the use of the coarser model and of the resulting coupling does not introduce spurious effects. In order to validate this indicator, we use it in a dynamical multiscale method with moving coupling interfaces. The principle of this method is to use as much as possible a coarse model instead of the fine model in the computational domain, in order to obtain an accuracy which is comparable with the one provided by the fine model. We focus here on general hyperbolic systems with stiff relaxation source terms together with the corresponding hyperbolic equilibrium systems. Using a numerical Chapman–Enskog expansion and the distance to the equilibrium manifold, we construct the numerical indicator. Based on several works on the coupling of different hyperbolic models, an original numerical method of dynamic model adaptation is proposed. We prove that this multiscale method preserves invariant domains and that the entropy of the numerical solution decreases with respect to time. The reliability of the adaptation procedure is assessed on various 1D and 2D test cases coming from two-phase flow modeling.     

A memetic algorithm to solve an unrelated parallel machine scheduling problem with auxiliary resources in semiconductor manufacturing

In this paper, we propose a metaheuristic for solving an original scheduling problem with auxiliary resources in a photolithography workshop of a semiconductor plant. The photolithography workshop is often a bottleneck, and improving scheduling decisions in this workshop can help to improve indicators of the whole plant. Two optimization criteria are separately considered: the weighted flow time (to minimize) and the number of products that are processed (to maximize). After stating the problem and giving some properties on the solution space, we show how these properties help us to efficiently solve the problem with the proposed memetic algorithm, which has been implemented and tested on large generated instances. Numerical experiments show that good solutions are obtained within a reasonable computational time.     

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the \( UCE \) and \( OCE \) curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.