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The full jet production cycle observed during fast state transitions in the black hole X-ray binary MAXI J1348630
No full textInternational audienceBlack hole X-ray binaries (BH XRBs) launch powerful relativistic jets during bright outburst phases. The properties of these outflows change dramatically between different spectral/accretion states. Collimated, compact jets are observed during the hard state and are quenched during the soft state, while discrete ejecta are mainly launched during the hard-to-soft state transition. Currently, we still do not understand what triggers the formation and destruction of compact jets or the launch of discrete ejecta. In this context, finding a unique link between the jet evolution and the properties of the X-ray emission, such as its fast variability, would imply a major progress in our understanding of the fundamental mechanisms that drive relativistic outflows in BH XRBs. Here we show that a brief but strong radio re-brightening during a predominantly soft state of the BH XRB MAXI J1348630 was contemporaneous with a significant increase in the X-ray rms variability observed with NICER in 2019. During this phase, the variability displayed significant changes and, at the same time, MAXI J1348630 launched two pairs of relativistic discrete ejecta that we detected with the MeerKAT and ATCA radio-interferometers. We propose that short-lived compact jets were reactivated during this excursion to the hard-intermediate state and were switched off before the ejecta launch, a phenomenology that has been very rarely observed in these systems. Interestingly, with the caveat of gaps in our radio and X-ray coverage, we suggest a tentative correspondence between the launch of ejecta and the drop in X-ray rms variability in this source, while other typical X-ray signatures associated with discrete ejections are not detected. We discuss how these results provide us with insights into the complex and dynamic coupling between the jets and hot corona in BH XRBs
An efficient column-and-constraint generation algorithm for solving the adaptive robust elective surgery problem under uncertainties in surgery duration, length of stay, and emergency arrivals
No full textInternational audienceThis paper addresses the robust elective surgery problem in the context of Operating Room (OR) planning, incorporating downstream resource constraints related to the Intensive Care Unit (ICU) and uncertainties in surgery duration, length of stay in the ICU, and emergency arrivals. We propose a novel two-stage robust optimization approach, using the “here-and-now” and “wait-and-see” decision-making principles. In the first stage, decisions on the master surgical schedule and surgical case assignment problem are made, allocating patients, surgeons, and specialties to OR sessions under the block scheduling strategy, using symmetry breaking inequalities. The second stage addresses multiple uncertainties and aims to minimize the costs in the worst-case scenarios, considering session overtime, emergency costs, and the costs of denied ICU beds. Polyhedral uncertainty sets and structural properties enable the use of the Column-and-Constraint Generation algorithm for efficient problem resolution. Computational experiments using real data from a medium-sized French hospital demonstrate the approach’s superior resource utilization and computational efficiency over the cutting-plane method. Value at Risk, Conditional Value at Risk, and Monte Carlo simulation are used to assess robustness, aiding in parameter adjustments for risk prevention and cancellations. This research contributes to enhancing decision-making in elective surgery planning under multiple uncertainties, offering practical insights for improving hospital productivity
Combining LIDAR, all-sky camera, and ECMWF-ERA5 reanalysis to investigate contrail formation and evolution over Clermont-Ferrand, France on June 2, 2023
No full textInternational audienceContrails formed by aircraft in the upper troposphere contribute to anthropogenic climate forcing. However, the conditions driving their formation and persistence remain incompletely understood. This study combines a ACTRIS/EARLINET ground-based LIDAR, all-sky camera imagery, ADS-B aircraft tracking, and ECMWF-ERA5 reanalysis to analyse contrails formation and evolution over Clermont-Ferrand, France, on June 2, 2023. Twelve contrails are documented throughout the day, including five persistent and seven non-persistent contrails. Persistent contrails formed at 10.36 km altitude or lower are observed under ice-supersaturated conditions (relative humidity with respect to ice, RHi > 105 %) and at temperatures between 217 and 223 K. Non-persistent contrails produced by higher altitudes aircrafts, are associated with lower RHi mostly below 100 % and colder temperatures (214-217 K). The horizontal persistent contrail widths range from 0.53 ± 0.10 to 1.60 ± 0.44 km (all-sky camera estimation) and 0.35 ± 0.14 to 1.90 ± 0.32 km (LIDAR estimation), and vertical extents varied from 340 ± 10 to 440 ± 20 m. The optical properties of these contrails have also been estimated by LIDAR. Aerosol backscatter coefficient vary from 0.02 to 0.05 km-1 sr-1, scattering ratios from 8 to 20, volume linear depolarization from 0.13 to 0.24 and particle linear depolarization from 0.17 to 0.45. The maximum contrail observation duration by camera is 180 min. The study highlights the potential of ground-based remote sensing for contrail monitoring
Climate finance for sustainable development: substantive agendas and future directions
No full textFNEGE 4International audienceThis study analyzes 440 online news articles and 62 videos using Gioia's approach to present a multi-scalar examination of the climate finance (CF) ecosystem. Analysis revealed six aggregate dimensions: funding-related gaps, strategic misalignments, re-orienting the governance ecosystem, addressing systemic issues in the funding ecosystem, scaling up existing funding sources, and unlocking newer funding sources, representing key themes of CF conversation in popular media. These dimensions comprise 16 second-order themes and 90 first-order concepts. Each dimension and its underlying codes offer a detailed exposition that reflects the complex nuances of CF shaped by the intersection of international politics, sovereign priorities, regional power dynamics, and intricate financial governance. Based on these findings, the study offers (i) inputs for strategic policy and regulatory decision-making at both transnational and national levels, and (ii) actionable recommendations on how different stakeholders can mobilize investments to foster a resilient response to climate change while promoting sustainable development
Three-flavor neutrino oscillations using the Phase Space Approach
No full textInternational audienceThe Phase-Space Approximation (PSA) approach, originally applied in [Phys. Rev. D 106, 123006 (2022)] to describe neutrino oscillations from a stellar object in the two-flavor limit, is extended here to describe the more realistic case where neutrinos can oscillate between three different flavors. The approach is successfully validated against the exact solutions up to eight neutrinos. In all cases where the exact solution is feasible, the PSA provides excellent reproduction of the neutrino oscillation dynamics. By replacing the full problem with a set of simple mean-field equations, the PSA offers a versatile, predictive, and easily parallelizable approach for tackling three-flavor problems. This enables the simulation of large-scale neutrino oscillations, as illustrated here with simulations involving up to 300 neutrinos. Additionally, the method provides insight into the system's equilibration properties
(Super)Gravity from Positivity
No full textInternational audienceWe investigate whether the effective theory for isolated, massive, and weakly interacting spin- particles is compatible with causality and unitarity-i.e., the positivity of scattering amplitudes. We find no solution to positivity constraints, except when gravitons are also present and couple in a (nearly) supersymmetric way. Gravity is thus bootstrapped from -matrix consistency conditions for the longitudinal and transverse polarizations of massive spin- states. For two such particles forming a -charged state, a (gravi)photon gauging the symmetry is also required, with couplings characteristic of supergravity and consistent with both the no global symmetry and weak gravity conjectures. We further explore the EFT-hedron associated with the longitudinal polarizations, the Goldstinos, through novel - symmetric dispersion relations. We identify the extremal UV models that lie at the corners of the allowed parameter space, recovering familiar models of supersymmetry breaking and uncovering new ones
Additive manufacturing for improving supply chain resilience under the ripple effect
No full textInternational audienceAdditive manufacturing (AM) is a revolutionary technology gaining substantial interest from academia and industry. Facing supply chain (SC) disruption risks, AM helps SC partners restore capacities through in-house and on-demand production. Compared to common resilience strategies, e.g. using backup suppliers and outsourcing, AM can reduce structural SC redundancy and enhance responsiveness to market demands. However, AM's impacts on SCs under the ripple effect remain insufficiently explored. This work investigates a SC resilience improvement problem by combining a novel AM strategy with inventory redundancy. For the problem, a dynamic Bayesian network is applied to portray the ripple effect, and a problem-specific Markov decision process is proposed to quantify the impacts of resilience strategies. A new mixed-integer non-linear non-convex optimisation model is established to minimise the disruption risk, and a Q-learning-based genetic algorithm is designed for solving large-scale problems. Key managerial insights from the case study include: (i) the proposed approach assists SC managers in prioritising key partners and implementing differentiated strategies based on partners' positions within the SC under limited budgets; and (ii) the temporal factor is critical, necessitating AM machine rentals for immediate post-disruption response and early AM machine purchases to ensure long-term resilience
VENDETA: VErsatile Neutron DETector Array, a new high-resolution neutron time-of-flight measurement array
No full textInternational audienceThe VErsatile Neutron DETector Array (VENDETA) is a high-resolution time-of-flight array for neutron detection. VENDETA’s liquid scintillator detectors offer a high intrinsic efficiency for neutron detection from 100keV to 20MeV, as well as neutron-γ discrimination capabilities down to 10keVee. VENDETA was specifically designed for versatility and is relevant for a wide range of physics measurements, from prompt fission neutron spectra measurements to neutron spectroscopy studies, such as elastic and inelastic neutron scattering measurements. VENDETA was first deployed at the Los Alamos Neutron Science Center. This article will provide a detailed overview of its characteristics
An approximate dynamic programming approach for multi-stage stochastic lot-sizing under a Decision-Hazard-Decision information structure
No full textInternational audienceThis work studies a combinatorial optimization problem encountered in industrial production planning: the single-item multi-resource lot-sizing problem with inventory bounds and lost sales. The demand to be satisfied by the production plan is subject to uncertainty and only probabilistically known. We consider a multi-stage decision process with a Decision-Hazard-Decision information structure in which decisions are made at each stage both before and after the uncertainty is revealed. Such a setting has not yet been studied for stochastic lot-sizing problems, and the resulting problem is modeled as a multi-stage stochastic integer program. We propose a solution approach based on an approximate stochastic dynamic programming algorithm. It relies on a decomposition of the problem into single-stage sub-problems and on the estimation at each stage of the expected future costs. Due to the Decision-Hazard-Decision information structure, each nested single-stage sub-problem is itself a two-stage stochastic integer program. We therefore introduce a Benders decomposition scheme to reduce the computational effort required to solve each nested sub-problem, and present a specialpurpose polynomial-time algorithm to efficiently solve the single-scenario second-stage sub-problems involved in the Benders decomposition. The results of extensive simulation experiments carried out on large-size randomly generated instances are reported. They demonstrate the practical benefit, in terms of the actual production cost, of using the proposed approach as compared to a naive deterministic optimization approach based on the expected demand
Evaluating innovation output of companies backed by corporate, independent and syndicated venture capital
No full textFNEGE 3, ABS 3International audienceThis paper examines how Corporate Venture Capital (CVC), Independent Venture Capital (IVC) and Venture Capital Syndicate (VCS) promote innovation among startups. Drawing on a dataset of 4406 venture-backed deals in North America, spanning 1998–2019, it explores how the configurations of investors and their contextual factors influence innovation output. The findings show that syndicated and CVC-backed ventures outperform IVC-backed ventures. Syndicates with a larger membership are positively associated with innovation outcomes based on resource pooling and knowledge sharing; contextual factors, such as location and technology fit, environmental munificence and absorptive capacity have a positive moderating effect on the relationship between VC type and innovation outcomes. This research adds to both academic knowledge and practical implications, offering entrepreneurs, investors and policymakers' actionable insights about how to facilitate innovation, improve venture funding and enhance innovation management to ultimately strengthen the innovation ecosystem