Jean Louis Duval

Bio: Jean Louis Duval is an academic researcher from ESI Group. The author has contributed to research in topics: Parametric statistics & Model order reduction. The author has an hindex of 7, co-authored 59 publications receiving 335 citations.

Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data

Abstract: Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.

Hybrid constitutive modeling: data-driven learning of corrections to plasticity models

Abstract: In recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.

A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition

Abstract: Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.

Plasmid-mediated pristinamycin resistance. PAC IIA: a new enzyme which modifies pristinamycin IIA.

Abstract: A wild strain of Staphylococcus aureus which inactivates a wide variety of antibiotics has been found to inactivate pristinamycin IIA, an antistaphylococcal antibiotic. This phenomenon has been demonstrated to be plasmid mediated. The plasmid directs the biosynthesis of an acetyltransferase which is able to O-acetylate the drug. We propose to call the new enzyme PAC (IIA): Pristinamycin acetyltransferase.

From ROM of Electrochemistry to AI-Based Battery Digital and Hybrid Twin

Abstract: Lithium-ion batteries are widely used in the automobile industry (electric vehicles and hybrid electric vehicles) due to their high energy and power density. However, this raises new safety and reliability challenges which require development of novel sophisticated Battery Management Systems (BMS). A BMS ensures the safe and reliable operation of a battery pack and to realize it a model must be solved. However, current BMSs are not adapted to the specifications of the automotive industry, as they are unable to give accurate results at real-time rates and during a wide operation range. For this reason, the main focus of this work is to develop a Hybrid Twin, as introduced in Chinesta et al. (Arch Comput Methods Eng (in press), 2018. https://doi.org/10.1007/s11831-018-9301-4 ), so as to meet the requirements of the new generation of BMS. To achieve this, three reduced order model techniques are applied to the most commonly used physics-based models, each one for a different range of application. First, a POD model is used to greatly reduce the simulation time and the computational effort for the pseudo-2D model, while maintaining its accuracy. In this way, cell design, optimization of parameters, and simulation of battery packs can be done while saving time and computational resources. In addition, its real-time performance has been studied. Next, a regression model is constructed from data by using the sparse-Proper Generalized Decomposition (s-PGD). It is shown that it achieves real-time performance for the whole electric vehicle (EV) system with a battery pack. In addition, this regression model can be used in a BMS without issues because of the simple algebraic expression obtained. A simulation of the EV with the proposed approach is demonstrated using the system simulation tool SimulationX (ESI ITI GmbH. Dresden, Germany). Furthermore, the Digital Twin created using the s-PGD does not only allow for real-time simulations, but it can also adapt its predictions taking into consideration the real driving conditions and the real driving cycle to change the planning in real-time. Finally, a data-driven model based on the employment of Dynamic Mode Decomposition techniques is developed to extract an on-line model that corrects the gap between prediction and measurement, thus constructing the first (to our knowledge) hybrid twin of a Li-ion battery able to self-correct from data. In addition, thanks to this model, the above gap is corrected during the driving process, taking into consideration real-time restrictions.

Nomenclature for macrolide and macrolide-lincosamide-streptogramin B resistance determinants.

Abstract: Macrolides are composed of 14 (erythromycin and clarithromycin)-, 15 (azithromycin)-, or 16 (josamycin, spiramycin, and tylosin)-membered lactones to which are attached amino and/or neutral sugars via glycosidic bonds. Erythromycin was introduced in 1952 as the first macrolide antibiotic. Unfortunately, within a year, erythromycin-resistant (Emr) staphylococci from the United States, Europe, and Japan were described (101). Erythromycin is produced by Saccharopolyspora erythraea, while the newer macrolides are semisynthetic molecules with substitutions on the lactone. The newer derivatives, such as clarithromycin and azithromycin, have improved intracellular and tissue penetration, are more stable, are better absorbed, have a lower incidence of gastrointestinal side effects, and are less likely to interact with other drugs. They are useable against a wider range of infectious bacteria, such as Legionella, Chlamydia, Haemophilus, and some Mycobacterium species (not M. tuberculosis), and their pharmacokinetics provide for less frequent dosing than erythromycin (21, 47, 96, 97). As a result, the usage of the newer macrolides has increased dramatically over the last few years, which has led to increased exposure of bacterial populations to macrolides (101–103, 107). Macrolides inhibit protein synthesis by stimulating dissociation of the peptidyl-tRNA molecule from the ribosomes during elongation (101, 103). This results in chain termination and a reversible stoppage of protein synthesis. The first mechanism of macrolide resistance described was due to posttranscriptional modification of the 23S rRNA by the adenine-N6 methyltransferase (101–103). These enzymes add one or two methyl groups to a single adenine (A2058 in Escherichia coli) in the 23S rRNA moiety. Over the last 30 years, a number of adenine-N6-methyltransferases from different species, genera, and isolates have been described. In general, genes encoding these methylases have been designated erm (erythromycin ribosome methylation), although there are exceptions, especially in the antibiotic-producing organisms (see Tables ​Tables11 and ​and3)3) (103). As the number of erm genes described has grown, the nomenclature for these genes has varied and has been inconsistent (Table ​(Table1).1). In some cases, unrelated genes have been given the same letter designation, while in other cases, highly related genes (>90% identity) have been given different names. TABLE 1 rRNA methylase genes involved in MLSB resistance TABLE 3 Location of antibiotic resistance genesa The binding site in the 50S ribosomal subunit for erythromycin overlaps the binding site of the newer macrolides, as well as the structurally unrelated lincosamides and streptogramin B antibiotics. The modification by methylase(s) reduces the binding of all three classes of antibiotics, which results in resistance against macrolides, lincosamides, and streptogramin B antibiotics (MLSB). The rRNA methylases are the best studied among macrolide resistance mechanisms (47, 101–103). However, a variety of other mechanisms have been described which also confer resistance (Table ​(Table2).2). Many of these alternative mechanisms of resistance confer resistance to only one or two of the antibiotic classes of the MLSB complex. TABLE 2 Efflux and inactivating genes In this review, we suggest a new nomenclature for naming MLS genes and propose to use the rules developed for identifying and naming new tetracycline resistance genes (51, 52). This system, with a few recent modifications, was originally designed because of the ability of two genes to be distinguished uniquely by DNA-DNA probe methodology (51). It was generally found that two genes with <80% amino acid sequence identity provided enough variability in nucleotide sequence to permit distinct probes to be designed. Although many investigators are likely to sequence new genes, the use of probe technology allows rapid identification of isolates containing potentially new genes, as well as a reliable way to screen populations and determine the frequency of any one resistant determinant. Therefore, we continued this paradigm by assigning two genes of ≥80% amino acid identity to the same class and same letter designation, while two genes that show ≤79% amino acid identity are given a different letter designation. Table ​Table11 shows the results of the classification, with some classes having members with little variability, while others, like classes A and O, show a greater range of homology at both the DNA and amino acid levels. As new gene sequences emerge, ideally they will need to be compared by oligonucleotide probe hybridization and/or sequence analysis against the bank of known genes before a new designation is assigned. If multiple genes are available in any one class, especially when there is a range as in class A, then all representative members of the class should be examined, not just one. To confirm that the proposed name or number for the newly discovered resistance determinant has not been used by another investigator, please contact M. C. Roberts for this information. A similar request has been made for new tet genes (52).

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Abstract: Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

Streptococcus pneumoniae and Streptococcus pyogenes resistant to macrolides but sensitive to clindamycin: a common resistance pattern mediated by an efflux system.

Abstract: Macrolide-resistant Streptococcus pyogenes isolates from Finland, Australia, and the United Kingdom and, more recently, Streptococcus pneumoniae and S. pyogenes strains from the United States were shown to have an unusual resistance pattern to macrolides, lincosamides, and streptogramin B antibiotics. This pattern, referred to as M resistance, consists of susceptibility to clindamycin and streptogramin B antibiotics but resistance to 14- and 15-membered macrolides. An evaluation of the macrolide-lincosamide-streptogramin B resistance phenotypes among our streptococcal strains collected from 1993 to 1995 suggested that this unusual resistance pattern is not rare. Eighty-five percent (n = 66) of the S. pneumoniae and 75% (n = 28) of the S. pyogenes strains in our collection had an M phenotype. The mechanism of M resistance was not mediated by target modification, as isolated ribosomes from a pneumococcal strain bearing the M phenotype were fully sensitive to erythromycin. Further, the presence of an erm methylase was excluded with primers specific for an erm consensus sequence. However, results of studies that determined the uptake and incorporation of radiolabeled erythromycin into cells were consistent with the presence of a macrolide efflux determinant. The putative efflux determinant in streptococci seems to be distinct from the multicomponent macrolide efflux system in coagulase-negative staphylococci. The recognition of the prevalence of the M phenotype in streptococci has implications for sensitivity testing and may have an impact on the choice of antibiotic therapy in clinical practice.