振动力场作用下聚合物挤出制品结构与性能的质量控制与优化

利用实验数据，分析与研究振动场振动参数对聚合物挤出制品质量的影响，对聚合物挤出制品的熔体流动速率，微晶结构，拉伸强度等主要性能质量指标，建立以振动频率为主要控制变量的神经网络模型，并引入信息分配模型，探讨了一个网络输入节点下神经网络学习样本的特征提取与优化。实验结果表明，经过信息预处理的学习样本，可以使网络有更好的收敛效果。     

Internal Reduction of Chromium-Doped α-Alumina

(Al,Cr)₂O₃ single crystals and polycrystals were internally reduced at 1873 K in an Al/Al₂O₃ buffer for periods of time ranging from 1 to 100 h. The growth kinetics of the reduction scale were measured. The microstructure of the reduction scale was investigated by SEM and TEM. As a result of the reduction, two types of discrete chromium precipitates developed inside the alumina matrix (inside the single crystal or the polycrystalline grains), each one being characterized by a particular morphology (needle or spheroid) and a low-energy orientation relationship with respect to the alumina matrix. In addition, larger precipitates without special orientation relationship developed along the grain boundaries and at the triple junctions of the polycrystais. In the first part of this paper, the precipitate morphology and size are described in terms of the crystallography of the interface between the two crystal structures in relation to the reduction mechanism. In the second part, the global reduction scale growth is analyzed in terms of point defect fluxes across the reduction scale.     

The Multifunctional Sactipeptide Ruminococcin C1 Displays Potent Antibacterial Activity In Vivo as Well as Other Beneficial Properties for Human Health

The world is on the verge of a major antibiotic crisis as the emergence of resistant bacteria is increasing, and very few novel molecules have been discovered since the 1960s. In this context, scientists have been exploring alternatives to conventional antibiotics, such as ribosomally synthesized and post-translationally modified peptides (RiPPs). Interestingly, the highly potent in vitro antibacterial activity and safety of ruminococcin C1, a recently discovered RiPP belonging to the sactipeptide subclass, has been demonstrated. The present results show that ruminococcin C1 is efficient at curing infection and at protecting challenged mice from Clostridium perfringens with a lower dose than the conventional antibiotic vancomycin. Moreover, antimicrobial peptide (AMP) is also effective against this pathogen in the complex microbial community of the gut environment, with a selective impact on a few bacterial genera, while maintaining a global homeostasis of the microbiome. In addition, ruminococcin C1 exhibits other biological activities that could be beneficial for human health, as well as other fields of applications. Overall, this study, by using an in vivo infection approach, confirms the antimicrobial clinical potential and highlights the multiple functional properties of ruminococcin C1, thus extending its therapeutic interest.     

Catalytic hydrogenation of nitrile rubber using palladium and ruthenium complexes

The catalytic hydrogenation of acrylonitrile‐butadiene copolymer (nitrile rubber, NBR) using Pd(OAc)₂ or RuCl₂(PPh₃)₃ catalysts has been investigated in order to produce a totally saturated nitrile rubber. The hydrogenation of NBR is effective with both catalysts and achieved total conversion under the appropriate reaction conditions. In the case of palladium the effects of reaction parameters such as reaction temperature, pressure, time, catalyst concentration, and NBR concentration have been investigated. Even though both ruthenium‐ and palladium‐based catalysts are effective in the production of HNBR, the former requires harsh reaction conditions and has the drawback of gel formation under high conversion, motivating the migration to RuCl₂ (PPh₃)₃ as an alternative catalyst. The degree of hydrogenation was determined by IR and NMR spectroscopy. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Ice sheet survey over Antarctica using satellite altimetry: ERS-2, Envisat,SARAL/AltiKa,the key importance of continuous observations along the same repeat orbit

From September 2002 to October 2010, the Envisat radar altimeter surveyed Greenland and Antarctica ice sheets on a 35 day repeat orbit, providing a unique data set for ice sheet mass balance studies. Up to 85 repeat cycles are available and the whole Envisat data set may be along-track processed in order to provide height variability and trend with a good spatial resolution for the objectives of ice sheet survey.

Soon, a joint Centre National d’Etudes Spatiales/Indian Space Research Organisation mission, SARAL (Satellite with Argos and AltiKa), with the AltiKa payload on board, will be launched on exactly the same orbit (less than 1 km of the nomimal orbit in the across-track direction). This will allow an extension of previous European Remote Sensing (ERS) satellite, ERS-1 and ERS-2, and Envisat missions of the European Space Agency (ESA), in particular from the point of view of ice altimetry. However, AltiKa operates in the Ka band (36.8 GHz), a higher frequency than the classical Ku band (13.6 GHz), leading to important modifications and potential improvements in the interactions between radar wave and snow-pack.

In this paper, a synthesis is presented of all available information relevant to ice altimetry scientific purposes as derived from the Envisat mission: mean and temporal derivatives of the height ? but also of the backscatter and of the two waveform parameters ? snow-pack change corrections, across-track surface slope at 1 km scale, etc. The spatial and temporal variability of ice sheet surface elevation is investigated with the help of the high-resolution Envisat along-track observations. We show that at least 50 repeat cycles are needed to reach the required accuracy for the elevation trend. It is thus advocated as potentially highly beneficial for SARAL/AltiKa as a follow-on mission. Moreover, the novel characteristics of SARAL/AltiKa are promising in improving our understanding of snow penetration impact.     

Selecting linear algebra kernel composition using response time prediction

Numerical linear algebra libraries provide many kernels that can be composed to perform complex computations. For a given computation, there is typically a large number of functionally equivalent kernel compositions. Some of these compositions achieve better response times than others for particular data and when executed on a particular computer architecture. Previous research provides methods to enumerate (a subset of) these kernel compositions. In this work, we study the problem of determining the composition that yields the lowest response time. Our approach is based on a response time prediction for each candidate combination. While this prediction could in principle be obtained using analytical and/or empirical performance models, developing accurate such models is known to be challenging. Instead, we define a feature space that captures salient properties of kernel combinations and predict response time using supervised machine learning. We experiment with a standard set of machine learning algorithms and identify an effective algorithm for our kernel composition selection problem. Using this algorithm, our approach widely outperforms the strategy that would consist in always using the simplest kernel composition and is often close to the fastest kernel compositions among those evaluated. We quantify the potential benefit of our approach if it were to be implemented as part of an interactive computational tool. We find that although the potential benefit is substantial, a limiting factor is the kernel composition enumeration overhead. Copyright © 2014 John Wiley & Sons, Ltd.     

Anticipating Cutoff Diameters in Deterministic Lateral Displacement (DLD) Microfluidic Devices for an Optimized Particle Separation

Deterministic lateral displacement (DLD) devices enable to separate nanometer to micrometer‐sized particles around a cutoff diameter, during their transport through a microfluidic channel with slanted rows of pillars. In order to design appropriate DLD geometries for specific separation sizes, robust models are required to anticipate the value of the cutoff diameter. So far, the proposed models result in a single cutoff diameter for a given DLD geometry. This paper shows that the cutoff diameter actually varies along the DLD channel, especially in narrow pillar arrays. Experimental and numerical results reveal that the variation of the cutoff diameter is induced by boundary effects at the channel side walls, called the wall effect. The wall effect generates unexpected particle trajectories that may compromise the separation efficiency. In order to anticipate the wall effect when designing DLD devices, a predictive model is proposed in this work and has been validated experimentally. In addition to the usual geometrical parameters, a new parameter, the number of pillars in the channel cross dimension, is considered in this model to investigate its influence on the particle trajectories.     

Ecological assessment of Phragmites australis wetlands using multi-season SPOT-5 scenes

Ecologists and conservationists need accurate and replicable tools for monitoring wetland conditions in order to develop and implement adaptive management strategies efficiently. The Rhone Delta (Camargue) in southern France encloses 9200 ha of fragmented reed marshes actively managed for reed harvesting, waterfowl hunting or cattle grazing, and holding significant numbers of vulnerable European birds. We used multi-season SPOT-5 data in conjunction with ground survey to assess the predictive power of satellite imagery in modelling indicators of reed structure (height, diameter, density and cover of green/dry stems) relevant to ecosystem management and bird ecology. All indicators could be predicted accurately with a combination of bands (SWIR, NIR) and indices (SAVI, OSAVI, NDWI, DVI, DVW, MSI) issued from scenes of March, June, July, September or December and subtraction between these. All models were robust when validated with an independent set of satellite and field data. The high spatial resolution of SPOT-5 scenes (pixel of 10 × 10 m) permits the monitoring of detailed attributes characterizing the reed ecosystem across a large spatial extent, providing a scientifically-based, replicable tool for managers, stakeholders and decision-makers to follow wetland conditions in the short and long-term. Combined with models on the ecological requirements of vulnerable bird species, these tools can provide maps of potential species ranges at spatial extents that are relevant to ecosystem functioning and bird populations.     

Tracking with occlusions via graph cuts

This work presents a new method for tracking and segmenting along time-interacting objects within an image sequence. One major contribution of the paper is the formalization of the notion of visible and occluded parts. For each object, we aim at tracking these two parts. Assuming that the velocity of each object is driven by a dynamical law, predictions can be used to guide the successive estimations. Separating these predicted areas into good and bad parts with respect to the final segmentation and representing the objects with their visible and occluded parts permit handling partial and complete occlusions. To achieve this tracking, a label is assigned to each object and an energy function representing the multilabel problem is minimized via a graph cuts optimization. This energy contains terms based on image intensities which enable segmenting and regularizing the visible parts of the objects. It also includes terms dedicated to the management of the occluded and disappearing areas, which are defined on the areas of prediction of the objects. The results on several challenging sequences prove the strength of the proposed approach.