Zebrafish hoxd4a Acts Upstream of meis1.1 to Direct Vasculogenesis, Angiogenesis and Hematopoiesis

10.1371/journal.pone.0058857PLoS ONE83

Performance de la modélisation hybride sur un processus de défaillance dans les systèmes industriels

Une approche hybride est proposée pour établir le diagnostic d'un moteur électrique. L'approche se caractérise par un assemblage entre la modélisation physique du système réel et la mise en place d'un algorithme d'apprentissage automatique, en vue d'améliorer les performances de diagnostic. Mots-clefs-Modèle hybride, apprentissage automatique, modèle basé sur la connaissance, processus de défaillance, calcul haute performanc

Performance de la modélisation hybride sur un processus de défaillance dans les systèmes industriels

A non-localised failure on a component can cause irreparable damage but it can also lead to the complete shutdown of the industrial system if it is not detected in time. Indeed, the first step in a failure process is the detection of the fault. Locating the fault is the second step in the process to know at which level of the system to intervene. Numerous methods for diagnosing industrial systems have already proved their worth. They are mainly based on physics-based behaviour laws. However, these behavioural models are generic and present difficulties of adaptation when applied to particular job profiles. Moreover, when dealing with complex systems, the implementation of behavioural laws the coupling of multiple components, interacting with each other with each other, is a laborious and time-consuming task. time-consuming task. The development of industrial systems instrumentation also encourages the use of the potential of real-time data collected on the systems. The problem in studying the data is the transparency of the models created solely from this data. The weight of the interactions present between the system's variables is not always identifiable. This means that the models developed from the data will not be easily transposable from one system to another, guaranteeing the same performance. To improve this adaptability, the idea is to draw on the knowledge of the system in question and to integrated into the modelling. For this purpose, models based on and those based on data learning will be coupled in order to data learning will be coupled in order to study the overall performance of this type of modelling. In the literature, this In the literature, this coupling is called hybrid modelling. To understand the construction process of such a model, the To understand the construction process of such a model, the study proposes to focus on the modelling of a DC electric motor. This application, which is widely studied in the literature, allows us to to exploit existing physical models of the system. The objective of this paper is therefore to study the The objective of this paper is therefore to study the performance of hybrid modelling to diagnose The objective of this paper is therefore to study the performance of hybrid modelling to diagnose the failures of a DC electric motor. To this end, the paper will describe the construction of the data-based model and the theoretical model. model and the theoretical model by discussing the capabilities and the capabilities and limitations of each model. The implementation of a The implementation of a hybrid approach will then be detailed. Finally, the performance of the implemented models will be presented

Measurement of the forward Z boson production cross-section in pp collisions at s=13\sqrt{s} = 13 TeV

A measurement of the production cross-section of Z bosons in pp collisions at $\sqrt{s} = 13$ TeV is presented using dimuon and dielectron final states in LHCb data. The cross-section is measured for leptons with pseudorapidities in the range $2.0 \eta 4.5$, transverse momenta $p_\text{T} 20$ GeV and dilepton invariant mass in the range $60 m(\ell\ell) 120$ GeV. The integrated cross-section from averaging the two final states is \begin{equation*}\sigma_{\text{Z}}^{\ell\ell} = 194.3 \pm 0.9 \pm 3.3 \pm 7.6\text{ pb,}\end{equation*} where the first uncertainty is statistical, the second is due to systematic effects, and the third is due to the luminosity determination. In addition, differential cross-sections are measured as functions of the Z boson rapidity, transverse momentum and the angular variable $\phi^*_\eta$

Les droits disciplinaires des fonctions publiques : « unification », « harmonisation » ou « distanciation ». A propos de la loi du 26 avril 2016 relative à la déontologie et aux droits et obligations des fonctionnaires

The production of tt‾ , W+bb‾ and W+cc‾ is studied in the forward region of proton–proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98±0.02 fb−1 . The W bosons are reconstructed in the decays W→ℓν , where ℓ denotes muon or electron, while the b and c quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions.The production of $t\overline{t}$, $W+b\overline{b}$ and $W+c\overline{c}$ is studied in the forward region of proton-proton collisions collected at a centre-of-mass energy of 8 TeV by the LHCb experiment, corresponding to an integrated luminosity of 1.98 $\pm$ 0.02 \mbox{fb}^{-1}. The $W$ bosons are reconstructed in the decays $W\rightarrow\ell\nu$, where $\ell$ denotes muon or electron, while the $b$ and $c$ quarks are reconstructed as jets. All measured cross-sections are in agreement with next-to-leading-order Standard Model predictions

Next generation Arctic vegetation maps:Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m−2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m−2 (mean ≈ 350 g m−2), while predicted values ranged from 0 to ∼4000 g m−2 (mean ≈ 275 g m−2), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m−2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling

Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHCb spectrometer. The LHCb Upgrade I will begin operation in 2020. Consolidation will occur, and modest enhancements of the Upgrade I detector will be installed, in Long Shutdown 3 of the LHC (2025) and these are discussed here. The main Upgrade II detector will be installed in long shutdown 4 of the LHC (2030) and will build on the strengths of the current LHCb experiment and the Upgrade I. It will operate at a luminosity up to 2×1034 cm−2s−1, ten times that of the Upgrade I detector. New detector components will improve the intrinsic performance of the experiment in certain key areas. An Expression Of Interest proposing Upgrade II was submitted in February 2017. The physics case for the Upgrade II is presented here in more depth. CP-violating phases will be measured with precisions unattainable at any other envisaged facility. The experiment will probe b → sl+l−and b → dl+l− transitions in both muon and electron decays in modes not accessible at Upgrade I. Minimal flavour violation will be tested with a precision measurement of the ratio of B(B0 → μ+μ−)/B(Bs → μ+μ−). Probing charm CP violation at the 10−5 level may result in its long sought discovery. Major advances in hadron spectroscopy will be possible, which will be powerful probes of low energy QCD. Upgrade II potentially will have the highest sensitivity of all the LHC experiments on the Higgs to charm-quark couplings. Generically, the new physics mass scale probed, for fixed couplings, will almost double compared with the pre-HL-LHC era; this extended reach for flavour physics is similar to that which would be achieved by the HE-LHC proposal for the energy frontier

LHCb upgrade software and computing : technical design report

This document reports the Research and Development activities that are carried out in the software and computing domains in view of the upgrade of the LHCb experiment. The implementation of a full software trigger implies major changes in the core software framework, in the event data model, and in the reconstruction algorithms. The increase of the data volumes for both real and simulated datasets requires a corresponding scaling of the distributed computing infrastructure. An implementation plan in both domains is presented, together with a risk assessment analysis