Université Gustave Eiffel
  • Champs-sur-Marne, France
Recent publications
Flow and spontaneous imbibition phenomena in porous media are important for various industrial applications, including printing and medical lateral flow assays. Their quantitative characterization is important to better understand and select the appropriate raw materials. However, standard methods often require time-consuming tests, and/or expensive equipment. Different time scales must be considered, limiting the range of possible characterization tools. A novel experimental approach based on image analysis for characterizing spontaneous imbibition processes is presented. Hence, ultra-fast diffusion may be quantitatively characterized. Models are issued from the literature to consider physical phenomena at small (milliseconds) and medium range (seconds) of time scales. The obtained experimental data fit with theoretical results, providing valuable insights into the understanding of fluid flow behavior at different time scales. Furthermore, the identification of some physical properties for either the fluid, or the substrate, based on the theoretical models are possible, as the contact angle, which remains to be otherwise challenging. This study contributes to bridging the gap between spontaneous imbibition and capillary phenomena at different time scales, their modeling, and a characterization of material and/or fluid properties paving the way for enhanced understanding and control of fluid behavior in porous media. Different papers are considered to illustrate the method.
Lane reservation optimization is important in intelligent transportation systems. Most existing studies are carried out under deterministic road conditions by assuming constant road travel time. However, road conditions vary due to various factors, resulting in uncertain road travel time. This work addresses a new reliability-based lane reservation and route design problem by considering uncertain road travel time with its known mean and covariance matrix. It aims to decide which road segments in a network should implement reserved lanes and to design routes for special time-crucial transportation tasks. The objective is to maximize transportation service reliability (i.e., the probability of completing the special tasks on time). For this problem, a novel distributionally robust optimization model is first established. To solve it, this work proposes i) an adapted sample average approximation-based approach and ii) a two-stage hierarchical heuristic algorithm based on second-order cone programming. Experimental results on an illustrative example and a real-world case demonstrate that the latter is more effective and efficient than the former. In addition, we conduct a series of parameter sensitivity analysis experiments to reveal the factors affecting lane reservation and provide optimal solutions given different parameter settings.
This study explores the variation in mechanical properties of additively manufactured composite structures for robotic applications with different infill densities and layer heights using fused deposition modelling (FDM). Glass fibre-reinforced polyamide (GFRP), and carbon fibre-reinforced polyamide (CFRP) filaments are used, and the specimens are printed with 20%, 40%, 60% and 100% infill density lattice structures for tensile and three-point bending tests. These printed samples are examined in the microscope to gain more understanding of the microstructure of the printed composites. To characterise the mechanical properties, a set of tensile and three-point bend tests are conducted on the manufactured composite samples. Test results indicate the variations in tensile strength and Young's modulus of specimens based on the printing parameters and reveal the tensile and bending behaviour of those printed composite structures against varying infill ratios and reinforcing fibres. The experimental findings are also compared to analytical and empirical modelling approaches. Finally, based on the results, the applications of the additively manufactured structure to the robotic components are presented.
In this paper, we present recent results about the developement of a semiclassical approach in the setting of nilpotent Lie groups and nilmanifolds. We focus on two-step nilmanifolds and exhibit some properties of the weak limits of sequence of densities associated with eigenfunctions of a sub-Laplacian. We emphasize the influence of the geometry on these properties.
The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed.
We show that closed starshaped hypersurfaces of space forms with almost constant mean curvature or almost constant higher order mean curvature are close to geodesic spheres.
Background Several initiatives have been implemented to develop, manage, and assess patient safety (PS) competencies, which are considered as a serious public health issue across the world. The Health Professional Education in Patient Safety Survey (H-PEPSS) is widely used as a psychometric scale for evaluating perceived PS competencies but has not been validated in French. The purpose of the study was to investigate the main psychometric properties of the French version of the H-PEPSS. Methods A total of 449 students enrolled in nursing and physiotherapy schools in France and French-speaking Switzerland completed a self-administered questionnaire. The 38 items of the H-PEPSS were translated into French following a committee approach. The scale’s construct validity was assessed using confirmatory factor analysis. Reliability of the six factors of the H-PEPSS was evaluated using Cronbach α and McDonald’s ω. Measurement invariance across countries and academic majors as well as discriminant validity were also investigated. Results After we removed one item, the H-PEPSS 6-factor model demonstrated adequate goodness-of-fit statistics (χ²[194] = 316.633, χ²/df = 1.632, p < 0.001, CFI = 0.934, TLI = 0.922, RMSEA = 0.041 [0.033, 0.049], SRMR = 0.044). The total score can be also used as an overall measure of PS competence (χ²[203] = 342.251, χ²/df = 1.686, p < 0.001, CFI = 0.925, TLI = 0.915, RMSEA = 0.043 [0.035, 0.051], SRMR = 0.047). One item was removed because of its high multicollinearity with other items. The reliability was deemed satisfactory (Cronbach α ≥ 0.60), except for the “Understanding human and environmental factors” subscale. Consistently, this subscale was often reported with the lowest reliability in previous studies. We confirmed scalar invariance between countries and partial scalar invariance between majors (ΔCFI ≤ 0.01). The heterotrait-monotrait ratio of correlations ranged from 0.63 to 0.91. In our results, country, academic year, and academic satisfaction were frequently the main predictors of self-reported PS competencies. Conclusion Perceived PS competencies can be assessed and fairly compared across France and Switzerland and across nursing and physiotherapy students. We discuss the relevance of the introduction of the H-PEPSS in the training pathway of health professions degree courses and the fallout in clinical contexts.
We study the problem of predicting hierarchical image segmentations using supervised deep learning. While deep learning methods are now widely used as contour detectors, the lack of image datasets with hierarchical annotations has prevented researchers from explicitly training models to predict hierarchical contours. Image segmentation has been widely studied, but it is limited by only proposing a segmentation at a single scale. Hierarchical image segmentation solves this problem by proposing segmentation at multiple scales, capturing objects and structures at different levels of detail. However, this area of research appears to be less explored and therefore no hierarchical image segmentation dataset exists. In this paper, we provide a hierarchical adaptation of the Pascal-Part dataset [2], and use it to train a neural network for hierarchical image segmentation prediction. We demonstrate the efficiency of the proposed method through three benchmarks: the precision-recall and F-score benchmarks for boundary location, the level recovery fraction for assessing hierarchy quality, and the false discovery fraction. We show that our method successfully learns hierarchical boundaries in the correct order, and achieves better performance than the state-of-the-art model trained on single-scale segmentations.
In this article, we propose an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree to compute a seeded watershed cut. We show that this algorithm fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in time linear with respect to the number of affected pixels. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods achieving significant speedup from 15 to 90, thus improving the user experience on large images.
Clefts of the lip and palate (CLP) are facial deformities that require multiple surgical procedures during childhood. One of these steps consists of filling the alveolar space with bone graft, traditionally removed from the iliac crest. However, this procedure could be invasive in children. Here, we aimed to evaluate the outcomes of GlassBONE™ graft, a bioactive glass used as a bone substitute, as an alternative to the deleterious autologous bone graft in children. Retrospective monocentric study with 17 children aged 7.5 ± 2.2 yo [3.8–13.3 yo] carrying CLP. This technique has been established at La Timone Children hospital (Assistance Publique - Hôpitaux de Marseille) since 2011. Clinical (scar, graft rejection and periodontal status) and radiological (both panoramic radiographs and cone beam-CT) follow-up was conducted one year after the graft. The primary outcome was the reduction of the cleft volume, and secondary was the eruption of the adjacent tooth through the graft. GlassBONE™ permitted a significant reduction in the cleft volume by 42.4 ± 27.7% [0.6–81.1%] (p < 0.0001), corresponding to a filling of 57.6 ± 27.7% of the alveolar cleft. GlassBONE™ is well tolerated, ensuring satifactory clinical results (improvement in both scar and periodontal coverage), as well as the physiological evolution of the germs through the biomaterial. GlassBONE™ appears particularly suitable for small volumes, and we were able to determine a minimum volume of approximtely 0.259 + / − 0.155 cc required for a successful bone fusion. The bioactive glass GlassBONE™ could be safely used in children with small CLP cases, providing satisfactory clinical and radiological results.
Recently, crowdsourcing has been proposed as a tool for fighting misinformation online. Will internet users listen to crowdsourced fact-checking, and how? In this experiment we test how participants follow others’ opinions to evaluate the validity of a science-themed Facebook post and examine which factors mediate the use of this information. Participants observed a post presenting either scientific information or misinformation, along with a graphical summary of previous participants’ judgements. Even though most participants reported not having used information from previous raters, their responses were influenced by previous assessments. This happened regardless of whether prior judgements were accurate or misleading. Presenting crowdsourced fact-checking however did not translate into the blind copying of the majority response. Rather, participants tended to use this social information as a cue to guide their response, while also relying on individual evaluation and research for extra information. These results highlight the role of individual reasoning when evaluating online information, while pointing to the potential benefit of crowd-sourcing-based solutions in making online users more resilient to misinformation.
Microplastic (MP) pollution is an emerging problem in many areas around the world and in coastal areas of Vietnam, requiring more studies dedicated to the accumulation of this pollutant in the food chain as well as its potential risk to human health. This study investigated MP levels in tissues of five common bivalve species collected from aquaculture areas along the coast of Vietnam. MPs were found in all bivalve samples, with average values of 10.84 ± 2.61 items/individual or 2.40 ± 1.34 items/g wet weight. Impacts of feeding habits of bivalves showed influences on MP abundance in the samples. Fibers were the dominant shape of MPs recorded, most of which accumulated in the gills and digestive glands of all bivalve samples, with the majority falling within the size range of 300—2000 µm. MPs found in all studied species had relatively similar chemical compositions, mainly composed of polypropylene (PP) and polyethylene (PE). In this study, a diverse diet consisting of different bivalve species and detailed data on the consumption rate of these species were used to assess the human health risk of MPs dedicated to the coastal communities of Vietnam. The results suggested a significant part of MP uptake by human could be via bivalve consumption, in which removing viscera and proper depuration should be applied prior to eating, thereby reducing the risk.
Ubiquitous moisture is attractive for developing sustainable mobile power sources. However, how to endow moisture‐electric generators (MEGs) with fine design, mass customization, high power output, and foldability is still a largely unsolved problem. In this work, based on the Patterned Coating method, use is made of modulated carbon nanotube, nano‐Al 2 O 3, and liquid metal inks to design and fabricate MEGs with properties required for their applications. A single MEG of the ionic diode type thus fabricated can generate an open‐circuit voltage ( V OC ) of 1.03 V and a short‐circuit current density ( J SC ) of 47.77 µA cm ⁻² . Through large‐area integrated fabrication, an array containing 160 MEGs can generate a V OC of 154.1 V, and an array containing 100 MEGs can produce a short‐circuit current of 1.95 mA. In addition, the excellent wearability of MEG is verified by analyzing its electrical output performance after subjecting it to 800 cycles of bending deformations. The simple and cost‐saving fabrication technology issued from the work opens new perspectives for MEG applications and paves a path to their industrialization. For example, the customizable ionic diode arrays can meet the on‐demand power supply requirements of wireless sensor network nodes in the Internet of Things.
State-of-the-art generative artificial intelligence (AI) can now match humans in creativity tests and is at the cusp of augmenting the creativity of every knowledge worker on Earth. We argue that enriching generative AI applications with insights from the psychological sciences may revolutionize our understanding of creativity and lead to increasing synergies in human–AI hybrid intelligent interfaces.
The purpose of this study is to clarify the bond behavior between rebar and concrete during DEF expansion and pullout testing. The details of the expansion test and the influence of reinforcing bar on DEF expansion have been precisely described in Part I. In Part II, the data related to the bond test is described. The change in bond behavior due to DEF expansion is investigated via the one-end pullout test and the influence of DEF expansion on the bond behavior is discussed. The local bond behavior (slip and bond stress) during the pullout test of the specimens without stirrups is observed to be dramatically changed by DEF expansion. Regarding the specimens with stirrups, failure did not occur during the pullout test and the local bond behavior slightly changed as in the case without stirrups. From the experimental results, a conceptual diagram is proposed to explain the bond behavior during DEF expansion and the pullout test based on the general conceptual understanding of the bond. It can be considered that the direction of local slip and local bond stress during the pullout test is opposite to that during the expansion process. This results in the observed complex local bond behavior during DEF expansion and the pullout test and the effect of stirrups on DEF expansion.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
2,270 members
Mahdi Zargayouna
  • Génie des Réseaux de Transports Terrestres et Informatique Avancée (GRETTIA)
Nathalie Fabry
  • Dispositifs d'Information et de Communication à l'Ère Numérique (Dicen)
Alexandra Fort
  • Laboratoire Ergonomie et Sciences Cognitives pour les Transports (LESCOT)
Champs-sur-Marne, France