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Standards and Guidelines for Algae LCAs
No full textLife cycle assessment (LCA) is a crucial tool to evaluate the environmental impacts of products and systems, including algae-based products and algae production and processing facilities. To conduct meaningful LCA studies, practitioners must navigate a complex framework of standards, guidelines, and initiatives. This information paper provides a concise overview of these resources, clarifying their roles and interrelationships. Standards, such as the International Organization for Standardization (ISO) 14040 and 14044, alongside the European Standard EN 17983, establish the foundational principles and requirements for algae LCAs, ensuring consistency and comparability across studies. Complementing these, guidelines such as the Product Environmental Footprint (PEF) introduced by the European Commission, the International Reference Life Cycle Data System (ILCD) Handbook, and Publicly Available Specification (PAS) 2050 deliver practical instructions to implement these standards effectively. In addition, projects and initiatives such as ALIGNED (“Aligning life cycle assessment methods and bio-based sectors for improved environmental performance”) and LCA4BIO (“Harmonised life cycle assessment methods for sustainable and circular bio-based systems”) promote methodological innovation and sector-specific alignment (or harmonisation) by addressing emerging challenges and enhancing collaboration. Understanding the complementary functions of these standards, guidelines, and initiatives enables practitioners to produce scientifically robust and transparent algae LCAs aligned with best international practices. This information paper serves as a foundational resource for selecting and applying the most appropriate frameworks and tools in algae LCA studies
How To Keep Track On Impact Investing Promises: Exploring The Potentials Of New Governance Schemes
No full textInternational audienceImpact investing is gaining momentum but still calls for an examination of how such a promise regarding the allocation of capital toward social and environmental goals can be kept on track. This paper explores the role and different levers of governance, particularly formal structures, in the advancement of impact investing. Through a focus group convened by the French Sustainable Investment Forum, we build on an innovative framework, the Société à Mission, to uncover investor expectations regarding the need to transform governance schemes for impact. Our review shows that traditionally, governance has been utilized to address agency biases and mitigate the risk of opportunism among actors, thereby reinforcing transparency and alignment. However, we argue that some of the practical challenges regarding evaluation, integration, and institutionalization stem from the specific nature of impact, which calls for extending governance concerns to include cognitive and methodological matters, at every stage of the investment chain
Thermal and mechanical behaviors of optical silica glass fiber during the drawing process
No full textInternational audienceThe process of fiber drawing in a vertical furnace is described using the lubrication approximation coupled to heat transfer. The radiative heat transfer is detailed by studying the emitted and absorbed fluxes experimented by the fiber. The emissivity is carefully determined with high spectral resolution. Using a two-band gray absorption coefficients, the Planck average emissivity is overestimated by 24 % in comparison with a high spectral resolution of absorption coefficient. The profile of the heating area is obtained from the experimental data. The steady state lubrication model is solved numerically using a finite difference method. A numerical prediction obtained under certain operating conditions allows a comparison of the fiber radius profiles obtained experimentally and numerically. The convective heat transfer after the neck-down region is needed to control the cooling and the fiber shape. Drawing forces obtained experimentally are compared to numerical results. When the operating temperature is equal to 1950 • C, the agreement is satisfying. The effects of variations in drawing velocity and furnace wall temperature on the relevant operating conditions for adjusting the cooling rate, drawing force, and other variables are numerically studied.</div
Design of an eco-industrial park for hydrogen and Fischer–Tropsch fuel deployment under the European policy framework
No full textInternational audienceOptimizing the hydrogen economy within industrial hubs represents a key aspect contributing to global decarbonization, yet current design approaches often overlook macroeconomic constraints, synergy opportunities and actor cooperation dynamics. This study addresses these gaps by proposing a multi-period, multi-resource integration framework for evaluating the feasibility of Fischer–Tropsch fuel production within an Eco-Industrial Park (EIP). A Mixed Integer Linear Programming (MILP) model is developed to minimize the annualized net present value of the system, considering both cooperative and non-cooperative governance schemes. The model integrates macroeconomic parameters such as EU ETS market prices, carbon dioxide sequestration costs, and RFNBO regulations, factors rarely accounted for in hydrogen-related EIP design. Results show that electricity is the dominant feedstock cost driver, contributing five times more than carbon dioxide to synthetic fuel production costs. Under favorable conditions, synthetic fuels must be priced at least three times higher than conventional fuels to justify investment, while under unfavorable conditions, this threshold rises to nine times. Cooperative governance enables economic gains through synthetic fuel sales, heat valorization and hydrogen production scaling, whereas non-cooperative governance leads to higher synthetic fuel prices due to resource bidding dynamics. These bidding dynamics, especially regarding carbon dioxide, are deeply influenced by policies, hence, the study concludes that policy design, as well as actor coordination, are critical to the viability of hydrogen-based EIPs in Europe
Review Ulrike Felt (2025) Contesting the Chronopolitics of Research
No full textInternational audienc
Meta and Multi-Task Learning for Action Recognition: A Survey
No full textInternational audienceHuman movement recognition currently plays an important role in fields where human-machine interaction is included, such as virtual reality and rehabilitation. In these cases, the accurate recognition of human actions has become essential, and is currently making significant steps due to the wide use of Machine Learning algorithms. However, the standard methods in that category currently face several challenges that concern their ability to generalize and adapt to new, unseen data, as well as the need for large amounts of data for training. This survey examines how the methods of Multi-Task Learning and Meta Learning have been deployed as strategies to tackle the above challenges. It studies how the two approaches are applied to human action recognition, focusing on the utilized methodologies, data modalities, and architectural approaches, highlighting the way that the first method promotes task-related knowledge sharing, while the second enables adaptation to scarce data. Through the mapping of studies from 2014 to 2025, this survey identifies trends, methodological gaps, and future insights for the development of more adaptive and context-aware recognition systems.</div
Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships
No full textInternational audienceTraditional structure-based pre-screen compound selection relies on the assumption that chemical similarity implies similar biological activity. This paradigm narrows the exploration of chemical space and often fails to account for functional convergence, where structurally diverse compounds act through distinct targets to produce similar phenotypic effects. As a result, compounds with therapeutic potential may be overlooked. To overcome this constraint, we introduce a training-free, transfer learning-based method for large scale compound preselection that leverages deep phenotypic profiling of human cells. Notably, this enables robust pairwise comparison of phenotypic signatures across any source of the entire JUMP-CP, the largest publicly available cell painting dataset (112,480 compounds), preserving biological signals while mitigating batch effects. Validated across 65 high-throughput assays—including in vitro and in cellulo systems—our method provides efficient pre-screen enrichment of biologically active compounds, bypassing the blind spots of structure-centric approaches. Interestingly, because it is large scale, it also allows for a comprehensive analysis of structure–phenotypic activity relationships, revealing potentially thousands of compound activity cliffs, where minimal chemical changes in structure may result in profound phenotypic shifts. We show that these cliffs capture subtle, atom-level determinants of bioactivity that cannot be accessed by structure-based models. Furthermore, we demonstrate that structurally diverse compounds targeting different genes in the same biological pathway can induce either convergent or opposite phenotypes—a phenomenon validated across 30 pathways, hundreds of genes, and thousands of compounds. Finally, to support the broader community, we propose Phenoseeker, a web-based tool enabling instant retrieval of JUMP-CP compounds with similar phenotypic profiles. Together, these findings position phenotypic profiling not merely as a complementary tool, but as a transformative and scalable framework for navigating chemical space through a biological lens. By capturing rich morphological signatures that reflect functional outcomes—regardless of structural similarity—this approach enables the discovery of bioactive compounds, novel mechanisms of action, and unexpected target-pathway relationships. Applied at the scale of the JUMP-CP dataset, phenotypic profiling emerges as a powerful strategy for prioritizing compounds, illuminating activity cliffs, and accelerating the identification of therapeutically relevant candidates across diverse biological contexts
Cosmological and High Energy Physics implications from gravitational-wave background searches in LIGO-Virgo-KAGRA's O1-O4a runs
No full textInternational audienceWe search for gravitational-wave background signals produced by various early Universe processes in the Advanced LIGO O4a dataset, combined with the data from the earlier O1, O2, and O3 (LIGO-Virgo) runs. The absence of detectable signals enables powerful constraints on fundamental physics. We derive gravitational-wave background energy density upper limits from the O1-O4a data to constrain parameters associated with various possible processes in the early Universe: first-order phase transitions, cosmic strings, domain walls, stiff equation of state, axion inflation, second-order scalar perturbations, primordial black hole binaries, and parity violation. In our analyses, the presence of an astrophysical background produced by compact (black hole and neutron star) binary coalescences throughout the Universe is also considered. We address the implications for various cosmological and high energy physics models based on the obtained parameter constraints. We conclude that LIGO-Virgo data already yield significant constraints on numerous early Universe scenarios
Modèles efficaces de systèmes quantiques ouverts bipartis
No full textThis thesis develops model reduction methods for open quantum system dynamics, yielding efficient models for bipartite systems, with direct application to circuit QED and broader applicability beyond this platform. First, we consider dissipative dynamics characterized by timescale separation, which translates to a gap on the real axis of the spectrum of the Liouvillian generating it. This allows to compute the reduced evolution for a target subsystem by adiabatically eliminating fast-decaying degrees of freedom. A Sylvester equation integral representation yields closed forms for the reduced generators and explicit reconstruction maps to arbitrary order without a preliminary interaction-picture averaging step, providing a linear center manifold description that separates long-lived from fast-decaying modes by the real parts of the spectrum. The reduced dynamics takes place on the center manifold, that is, the spectral subspace associated with eigenvalues whose real parts are small (long-lived modes), while placing no restriction on imaginary parts, thus allowing for fast unitary dynamics on the target subsystem. We further demonstrate the equivalence of such a geometric approach to adiabatic elimination with the time-convolutionless master equation framework, by showing that the maps defining the reduced model satisfy the same invariance equation in both frameworks. The time-convolutionless formulation of adiabatic elimination extends to cases where the target system undergoes fast unitary dynamics and retains explicit time dependence in the reduced model, hence capturing initial transients that are neglected in time-independent geometric reductions. Second, we apply a unitary chain mapping of microscopic system–environment Hamiltonians to a nearest-neighbor form, enabling efficient time evolution exploiting tensor network time evolution algorithms based on the time dependent variational principle. Accuracy of the tensor network representation, and thus the size of the reduced model, is controlled by entanglement entropy growth, which we quantify numerically. While the methods are general and apply beyond the specific platform considered, we focus on dispersive readout in circuit QED (a qubit dipolarly coupled to a driven, lossy cavity). We quantify the dependence of the relaxation time of the qubit on intracavity photon number (and thus on the drive amplitude) and on the spectral structure of the environment, and compute the emission spectrum directly from environmental observables. Simulations including a Purcell notch filter predict a decrease of with increasing drive power, a qualitatively different behavior with respect to Born–Markov–secular GKSL predictions but consistent with experimental trends, indicating that explicit filter modeling is required in this case. Together, the results specify when reduced Liouvillian models under a dissipative gap are reliable and when microscopic, non-perturbative simulations are required.Cette thèse développe des méthodes de réduction de modèles pour la dynamique des systèmes quantiques ouverts, produisant des modèles efficaces pour les systèmes bipartites, avec une application directe à l'électrodynamique quantique des circuits (circuit QED) et une applicabilité plus large au-delà de cette plateforme. Premièrement, nous considérons la dynamique dissipative caractérisée par une séparation d'échelles de temps, qui se traduit par un écart sur l'axe réel du spectre du Liouvillien la générant. Cela permet de calculer l'évolution réduite d'un sous-système cible en éliminant adiabatiquement les degrés de liberté à décroissance rapide. Une représentation intégrale de l'équation de Sylvester produit des formes closes pour les générateurs réduits et des applications de reconstruction explicites à un ordre arbitraire sans étape préalable de moyenne en représentation d'interaction. Cette approche fournit une description par variété centrale linéaire qui sépare les modes à longue durée de vie des modes à décroissance rapide selon les parties réelles du spectre. La dynamique réduite se déroule sur la variété centrale — c'est-à-dire le sous-espace spectral associé aux valeurs propres dont les parties réelles sont petites (modes à longue durée de vie) — tout en n'imposant aucune restriction sur les parties imaginaires, permettant ainsi une dynamique unitaire rapide sur le sous-système cible. Nous démontrons de plus l'équivalence de cette approche géométrique avec le cadre de l'équation maîtresse sans convolution temporelle (TCL, time-convolutionless), en montrant que les applications définissant le modèle réduit satisfont la même équation d'invariance dans les deux formalismes. La formulation TCL de l'élimination adiabatique s'étend aux cas où le système cible subit une dynamique unitaire rapide et conserve une dépendance temporelle explicite dans le modèle réduit, capturant ainsi les transitoires initiaux qui sont négligés dans les réductions géométriques indépendantes du temps. Deuxièmement, nous appliquons une transformation unitaire de type chaîne (chain mapping) des Hamiltoniens microscopiques système–environnement vers une forme à plus proches voisins. Cette structure permet une évolution temporelle efficace exploitant des algorithmes de réseaux de tenseurs basés sur le principe variationnel dépendant du temps (TDVP). La précision de la représentation en réseaux de tenseurs, et donc la taille du modèle réduit, est contrôlée par la croissance de l'entropie d'intrication, que nous quantifions numériquement. Bien que les méthodes soient générales, nous nous concentrons sur la lecture dispersive en circuit QED (un qubit couplé dipolairement à une cavité forcée et dissipative). Nous quantifions la dépendance du temps de relaxation T1 du qubit par rapport au nombre de photons intracavité nˉ (et donc par rapport à l'amplitude du forçage) ainsi qu'à la structure spectrale de l'environnement, et calculons le spectre d'émission directement à partir des observables du bain. Des simulations incluant un filtre coupe-bande Purcell prédisent une diminution de T1 avec l'augmentation de la puissance du forçage, un comportement qualitativement différent des prédictions de Born–Markov–secular (GKSL) mais cohérent avec les tendances expérimentales, indiquant qu'une modélisation explicite du filtre est requise dans ce cas. Ensemble, ces résultats précisent quand les modèles de Liouvillien réduits sous un écart dissipatif sont fiables et quand des simulations microscopiques non-perturbatives sont nécessaires