Comparing the Performance of Organic Solvent Nanofiltration Membranes in Non-Polar Solvents

Organic solvent nanofiltration (OSN) is gradually expanding from academic research to industrial implementation. The need for membranes with low and sharp molecular weight cutoffs that are able to operate under aggressive OSN conditions is increasing. However, the lack of comparable and uniform performance data frustrates the screening and membrane selection for processes. Here, a collaboration is presented between several academic and industrial partners analyzing the separation performance of 10 different membranes using three model process mixtures. Membrane materials range from classic polymeric and thin film composites (TFCs) to hybrid ceramic types. The model solutions were chosen to mimic cases relevant to today's industrial use: relatively low molar mass solutes (330–550 Da) in n-heptane, toluene, and anisole.     

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.     

Invariant representation of facial expressions for blended expression recognition on unknown subjects

Facial expressions analysis plays an important part in emotion detection. However, having an automatic and non-intrusive system to detect blended facial expression is still a challenging problem, especially when the subject is unknown to the system. Here, we propose a method that adapts to the morphology of the subject and that is based on a new invariant representation of facial expressions. In our system, one expression is defined by its relative position to 8 other expressions. As the mode of representation is relative, we show that the resulting expression space is person-independent. The 8 expressions are synthesized for each unknown subject from plausible distortions. Recognition tasks are performed in this space with a basic algorithm. The experiments have been performed on 22 different blended expressions and on either known or unknown subjects. The recognition results on known subjects demonstrate that the representation is robust to the type of data (shape and/or texture information) and to the dimensionality of the expression space. The recognition results on 22 expressions of unknown subjects show that a dimensionality of the expression space of 4 is enough to outperform traditional methods based on active appearance models and accurately describe an expression.     

Synthesis methods of array antenna excitation laws

Standard numerical optimization methods become very computational time consuming when dealing with synthesis of array antenna excitation laws, especially when number of elements exceeds a few tenths. Array antenna designer needs a fast and efficient synthesis tool, since the design phase implies several reoptimizations within the definition phase, in order to satisfy tradeoffs on antagonistic requirements : performances, size, cost,… We propose the use of an efficient, from the computational time point of view, and secured, from the convergence point of view, synthesis method illustrated by two examples. The optimized solution satisfies a sta-tionarity condition, which leads to steady solutions in frequency but also with regard to errors (manufacturing, …). Based on analytical calculus performed before any numerical resolution and together with the use of fast Fourier transform algorithms, this method leads to reduced optimization times (in the order of a minute) for arrays of some tenths up to some hundred feeds, computations being performed on a pc. Two theoretical applications are presented and for each of them a computed example is also shown; moreover possible extensions are considered.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.     

A prismoid framework for languages with resources

Inspired by the Multiplicative Exponential fragment of Linear Logic, we define a framework called the prismoid of resources where each vertex is a language which refines the λ-calculus by using a different choice to make explicit or implicit (meta-level) the definition of the contraction, weakening, and substitution operations. For all the calculi in the prismoid we show simulation of β-reduction, confluence, preservation of β-strong normalisation and strong normalisation for typed terms. Full composition also holds for all the calculi of the prismoid handling explicit substitutions. The whole development of the prismoid is done by making the set of resources a parameter of the formalism, so that all the properties for each vertex are obtained as a particular case of the general abstract proofs.     

Use of neural networks for predictions using time series: Illustration with the El Niño Southern oscillation phenomenon

This paper introduces the use of sets of multiple networks (bundled networks) to manage the variability due to different initialization parameters. This method makes it statistically impossible for the networks to be trapped in the same local minimum, and therefore allows better control of the confidence of the prediction eventually given. The spread of the forecasts given by these different networks can be used for prediction reliability purposes. An illustration of this usage is given with the El Niño phenomenon.     

Proteomic analysis of secretory proteins and vesicles in vascular research

The release of proteins and membrane vesicles in the bloodstream regulates diverse vascular processes, both physiological, such as angiogenesis and haemostasis, and pathological, such as atherosclerosis and atherothrombosis. Proteomics, beside its canonical application for the expression profiling in cells and organs, can be applied to the study of secreted proteins and microvesicles, which play a significant role in the homeostasis of the vasculature, and the development of the atherosclerotic disease.