An Advanced Engineering Framework experimented on a R&AE Electric Vehicle case

International audienceThis article describes modeling activity experimented on an Advanced engineering case of Zero Emission Vehicles at Renault. A key advantage of our approach is that system architecture and requirements management at all the stages of the system life cycle are managed in a unique data model and unique database. It reviews conceptualization and production process of a complex system. It presents a spectrum of activity modeling techniques, ranging from a widely used systems engineering diagram, to continuous flow modeling. The techniques include use case definition, requirements elicitation, system architecture definition and finally Electric and Electronic architecture. The article also describes refinements of modeling activity using arKItect© tool

Digital technologies and privacy: State of the art and research directions

Digital technologies have transformed every aspect of marketing and have brought consumer privacy front and center of research and discourse over the last two decades. Whereas companies and consumers have arguably benefited through the availability and use of data made possible by digitalization, consumer privacy-related concerns raise compelling questions that researchers, companies, and policymakers are addressing. In this Review Paper, we review privacy related to digital technologies in marketing, highlighting the constantly evolving nature of the field. We provide an overview of the rich contributions made by the articles in the special issue on digital technologies and privacy, and the original insights they provide for researchers and practitioners in four domains – communication, retailing, pricing, and product personalization. We identify and outline future research directions in each of these four domains to expand our understanding of privacy at the intersection of psychology and marketing by motivating new scholarly research and providing actionable insights to managers and policymakers

Expression of Democracy: Local Elections in Petorca, Chile

The municipal elections of Chile were held on April 2,1967. On April 3, in Santiago, spokesmen from the national committees of the five major parties --the Christian Democrats, the Radicals, the Communists, the Nationalists, and the Socialists--all proclaimed that the results showed that their political aggregation had been victorious on the previous day. The debate concerning who had won the election raged for several weeks in the press, in Congress and in spirited social conversation. The Christian Democrats argued that although their percentage of the national vote dropped from forty-two per cent to thirty-five per cent, they had increased their strength in the municipal councils by over two hundred representatives to six hundred and forty-nine councilmen, a new record for any single political party in all of Chile\u27s history. The leftist coalition of FRAP (Communist-Socialist) boasted that they reflected the coming wave in Chilean politics by gathering nearly twenty-eight per cent of the total vote, an increase of six per cent from 1965. The Radicals announced with relief that they had retained second place in party percentages (sixteen per cent), and that their vote represented a vehement renunciation of the whole Christian Democratic movement. The National Party, perhaps the most surprised by its strong showing (fourteen per cent), predicted that the Right was not a dead letter in Chile, and that a new awakening was imminent

Network classifiers based on social learning

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their performance even during this phase. By doing so, the proposed architecture is able to improve prediction performance over time with unlabeled data. Inspired by social learning algorithms, which require prior knowledge of the observations distribution, we propose a Social Machine Learning (SML) paradigm that is able to exploit the imperfect models generated during the learning phase. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers. Simulations with an ensemble of feedforward neural networks are provided to illustrate the theoretical results

A study of common aero-allergen in Mewar region, Udaipur, Rajasthan, India

Background: Aero-allergens are important causative factor in pathogenesis of allergic respiratory diseases (Asthma, Allergic Rhinitis). Present study aimed to identify the common aeroallergens in Mewar region, Udaipur, Rajasthan, India.Methods: Intradermal allergic testing done on 1050 respiratory allergic patients in last 15 yrs (2002 to 2016) by kit containing 125 allergen extracts includes pollen, fungi, insects, dust, dander’s, fabrics, feathers and wood. In 1020 patients (after excluding 30 patients), marked positive skin reaction (3+/4+) to one or more aeroallergen noted.Results: Most common aero allergens found were pollens (62%), woods (58.5%), dander (52%), insects (45%), dust mite (44.2%) and fungi (38.4%). Among pollens most common allergens were Holoptelia integrifolia, Parthenium hysterophorn, Cynodon. Among fungi aspergillus and candida species were most common. Cockroach and fly were predominant insects.Conclusions: Role of allergen testing have important role in management of allergic respiratory diseases as allergen immunotherapy or desensitization is only disease modifying treatment

Cerebral pressure autoregulation and carbon dioxide reactivity during propofol-induced EEG suppression

We studied cerebral pressure autoregulation and carbon dioxide reactivity during propofol-induced electrical silence of the electroencephalogram (EEG) in 10 patients. Anaesthesia was induced with propofol 2.5 mg kg−1, fentanyl 3 μg kg−1 and vecuronium 0.1 mg kg−1, and a propofol infusion of 250-300 μg kg−1 min−1 was used to induce EEG silence. Cerebral pressure autoregulation was tested by increasing mean arterial pressure (MAP) by 24 (SEM 5) mm Hg from baseline with an infusion of phenylephrine and simultaneously recording middle cerebral artery blood flow velocity (vmca) using transcranial Doppler. Carbon dioxide reactivity was tested by varying Paco2 between 4.0 and 7.0 kPa and recording vmca simultaneously. Although absolute carbon dioxide reactivity was reduced, relative carbon dioxide reactivity was within normal limits for all patients studied (mean 8.5 (SEM 0.8) cm s−1 kPa−1 and 22 (2)% kPa−1, respectively). No significant change in vmca (34 (2) and 35 (2) cm s−1) was observed with the increase in MAP (77 (4) to 101 (4) mm Hg) during autoregulation testing. We conclude that cerebral carbon dioxide reactivity and pressure autoregulation remain intact during propofol-induced isoelectric EE

Dual ectopic thyroid gland

info:eu-repo/semantics/publishedVersio

Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements

Modern manufacturing systems can benefit from the use of digital tools to support both short- and long-term decisions. Meanwhile, such systems reached a high level of complexity and are frequently subject to modifications that can quickly make the digital tools obsolete. In this context, the ability to dynamically generate models of production systems is essential to guarantee their exploitation on the shop-floors as decision-support systems. The literature offers approaches for generating digital models based on real-time data streams. These models can represent a system more precisely at any point in time, as they are continuously updated based on the data. However, most approaches consider only isolated aspects of systems (e.g., reliability models) and focus on a specific modeling purpose (e.g., material flow identification). The research challenge is therefore to develop a novel framework that systematically enables the combination of models extracted through different process mining algorithms. To tackle this challenge, it is critical to define the requirements that enable the emergence of automated modeling and simulation tasks. In this paper, we therefore derive and define data requirements for the models that need to be extracted. We include aspects such as the structure of the manufacturing system and the behavior of its machines. The paper aims at guiding practitioners in designing coherent data structures to enable the coupling of model generation techniques within the digital support system of manufacturing companies