Décodage Syndromique à Complexité Réduite Pour Codes Convolutifs

The decoding of convolutional codes in the maximum likelihood sense is carried out in a traditional way with the Viterbi algorithm (Va). We proposed a soft and hard input decoder where theVa, associated with an relevant metric, is applied to identify the error vector rather than the information message. In this paper, we show that, with this type of decoding, the exhaustive computation of a majority ofAcs (Add Compare Select) is unnecessary. Moreover, we show that optimal performance is achieved in the case of a hard input decoder, and that performance closed to the optimum is achieved in the case of a soft input decoder, while offering of a reduction of the complexity which is all the more important than the Ec/No ratio is high (e. g. for ratio Ec/No greater than 3 dB, more than 80 % of theAcs can be avoided). We also propose an algorithm allowing rejecting a frame without having to carry out any iteration of theVa.     

Bias-corrected and robust estimation of the bivariate stable tail dependence function

The stable tail dependence function gives a full characterisation of the extremal dependence between two or more random variables. In this paper, we propose an estimator for this function which is robust against outliers in the sample. The estimator is derived from a bivariate second-order tail model together with a proper transformation of the bivariate observations, and its asymptotic properties are studied under some suitable regularity conditions. Our estimation procedure depends on two parameters: \(\alpha \), which controls the trade-off between efficiency and robustness of the estimator, and a second-order parameter \(\tau \), which can be replaced by a fixed value or by an estimate. In case where \(\tau \) has been replaced by the true value or by an external consistent estimator, our robust estimator is asymptotically unbiased, whereas in case where \(\tau \) is mis-specified, one loses this property, but still our estimator performs quite well with respect to bias. The finite sample performance of our robust and bias-corrected estimator of the stable tail dependence function is examined on a simulation study involving uncontaminated and contaminated samples. In particular, its behavior is illustrated for different values of the pair \((\alpha , \tau )\) and is compared with alternative estimators from the extreme value literature.     

Impact of substrates and cell immobilization on siderophore activity by Pseudomonads in a Fe and/or Cr, Hg, Pb containing-medium

To increase the amount of bioavailable metals in phytoextraction purposes, soil bioaugmentation with Pseudomonads, as siderophore producers with high metal complexation levels, could be relevant. Unfortunately, siderophore synthesis may be inhibited by soluble iron in soil and bacteria can suffer at the same time from the toxicity of some other metals, predation and oligotrophy. To overcome these drawbacks, we attempted to co-locate a carbon substrate and Pseudomonas aeruginosa or P. fluorescens in Ca-alginate beads. First, free-cell cultures showed that glycerol, fructose, mannitol and skim milk enhanced the siderophore activity which was the highest in the medium with neither Fe or TM (toxic metal) (Cr, Hg and Pb) and the lowest in the Fe-containing medium without TM. The negative effect of iron was partly offset when TM was added to the medium. In a second part, co-location of microorganisms and substrates was only feasible with skim milk. By comparison with free cells, siderophore activity by immobilized cells was higher in culture media containing Fe with or without TM (up to a ratio of 9), and varied in a narrow margin, according to the medium composition.     

Bias-reduced estimators of the Weibull tail-coefficient

In this paper, we consider the problem of the estimation of the Weibull tail-coefficient θ. In particular, we propose a regression model, from which we derive a bias-reduced estimator of θ. This estimator is based on a least-squares approach. The asymptotic normality of this estimator is established. We also introduce an adaptive selection procedure to determine the number of upper order statistics to be used in the estimator. A simulation study as well as an application to a real data set are provided in order to prove the efficiency of the above mentioned methods.     

Recent Advances in Research on Polyphenols: Effects on Microbiota,Metabolism, and Health

Polyphenols have attracted huge interest among researchers of various disciplines because of their numerous biological activities, such as antioxidative, antiinflammatory, antiapoptotic, cancer chemopreventive, anticarcinogenic, and antimicrobial properties, and their promising applications in many fields, mainly in the medical, cosmetics, dietary supplement and food industries. In this review, the latest scientific findings in the research on polyphenols interaction with the microbiome and mitochondria, their metabolism and health beneficial effects, their involvement in cognitive diseases and obesity development, as well as some innovations in their analysis, extraction methods, development of cosmetic formulations and functional food are summarized based on the papers presented at the 13th World Congress on Polyphenol Applications. Future implications of polyphenols in disease prevention and their strategic use as prophylactic measures are specifically addressed. Polyphenols may play a key role in our tomorrow´s food and nutrition to prevent many diseases.     

Innovative method for prioritizing emerging disinfection by-products (DBPs) in drinking water on the basis of their potential impact on public health

Providing microbiologically safe drinking water is a major public health issue. However, chemical disinfection can produce unintended health hazards involving disinfection by-products (DBPs). In an attempt to clarify the potential public health concerns associated with emerging disinfection by-products (EDBPs), this study was intended to help to identify those suspected of posing potential related health effects. In view of the ever-growing list of EDBPs in drinking water and the lack of consensus about them, we have developed an innovative prioritization method that would allow us to address this issue. We first set up an exhaustive database including all the current published data relating to EDBPs in drinking water (toxicity, occurrence, epidemiology and international or local guidelines/regulations).We then developed a ranking method intended to prioritize the EDBPs. This method, which was based on a calculation matrix with different coefficients, was applied to the data regarding their potential contribution to the health risk assessment process. This procedure allowed us to identify and rank three different groups of EDBPs: Group I, consisting of the most critical EDBPs with regard to their potential health effects, has moderate occurrence but the highest toxicity. Group II has moderate to elevated occurrence and is associated with relevant toxicity, and Group III has very low occurrence and unknown or little toxicity. The EDBPs identified as posing the greatest potential risk using this method were as follows: NDMA and other nitrosamines, MX and other halofuranones, chlorate, formaldehyde and acetaldehyde, 2,4,6-trichlorophenol and pentachlorophenol, hydrazine, and two unregulated halomethanes, dichloromethane and tetrachloromethane. Our approach allowed us to define the EDBPs that it is most important to monitor in order to assess population exposure and related public health issues, and thus to improve drinking water treatment and distribution. It is also important to extend our knowledge about exposure to mixtures of emerging DBPs and possible related health effects.