The industrial risk mapping is a topical problem in the field of risk management that attracts many researchers to develop risk matrices to ensure consultation between their actors. In this context, this paper aims to propose the principal component analysis (PCA) method as support for this consultation. Indeed, the use of PCA method is justified by its robustness for aggregate initial data associated with industrial risks as principal factors and ranking of this risk in terms of their criticalities in risk matrices. However, the aggregation of initial data on industrial risks by the main factors, in some cases, leads to inaccuracies which make it difficult to classify certain risks. This paper proposes two variants of PCA method to solve this inaccuracy and succeeds in classifying risks according to their respective criticalities, namely PCA-Improved (PCA-I) and PCA-I-Fuzzy (PCA-IF). The results come from the PCA application and its proposed variants (PCA-I and PCA-IF) on an example of accident scenarios ranking. We have established a scientific basis for the capitalization of mapping tool for consultation and decision support to industrial risk managers.

This article aims to prepare Ni-Al2O3 composite coatings from a conventional electroplating process. The coatings development parameters taken into account for this study of the corrosion resistance of these coatings are: Al2O3 concentration, bath temperature and current. The experimental tests are carried out by two lost mass and polarization techniques. The objective of the work is to determine the influence of elaboration parameters on the corrosion resistance of NiAl2O3 composite coatings. The study carried out in this article has show

Group B Streptococcus (GBS) can cause severe pneumonia, sepsis and meningitis in neonates and represents one of the most prevalent causes of invasive neonatal infections. Prenatal screening and prenatal antibiotic prophylaxis can prevent maternal transmission of Streptococcus agalactiae during delivery. The objective of this study was to determine the maternal risk of maternal carriage of group B Streptococcus over time, to offer reliable epidemiological data to the health staff working at maternity Meriem Bouatoura, Batna (Northeast Algeria) or even all maternity hospitalsin Algeria. In this prospective study, vaginalspecimens(lower third) were collected from 150 pregnant women. The samples were cultured on 5% sheep blood and Chromagar Orientation. The method of confirming the identification of GBS was he agglutination test using the PASTOREX® Strep Kit. The antibiotic susceptibility testing was performed using the Kirby Bauer method. A total of 150 patients were evaluated, on average in the 34th week of pregnancy. Fifteen patients were GBS positive, a percentage rate of 10%. In conclusion, regular vaginal culture screening in the third trimester of pregnancy is warranted, as well as evaluation of risk factors and need for antibiotic administration to infants at risk.

Biomedical domains are characterized by the presence of large amounts of high dimensional data and a lack of general theories to understand structural and functional components. Artificial Neural Networks (ANN) are computer-based expert systems that have proved to be useful in pattern recognition tasks. ANN can be used in different phases of the decisionmaking process, from classification to diagnostic procedures. In this work, we develop a method based on a hybrid ANN. This method uses three different feed forward type multilayer neural networks, with the ability to classify ECGs as normal or carrying atrioventricular block (AVB). A single output unit encodes the probability of AVB occurrences. A value between 0 and 0.05 being the desired output for a normal ECG, a value between 0.05 and 1 would infer an occurrence of an AVB. The results show that, this hybrid network has a good performance to detect AVBs. They show a sensibility of 98% and a specificity of 92%.