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The paradox of art: a reflection on the existence of virtuality in a city-mind-scape
No full textInternational audienceWe propose to approach the complex notion of power through paradox and resistance, coupled with art-based approaches. Our engagement helps examine how expressions of power can be put into question and undermined by art-infused flâneurie. Art can serve as a paradoxical bridge between power and resistance, inhabiting the liminal space between the possible, the imagined, and the enforced. We undertake an explorative quest, using archetypes to bridge some gaps between understandings. This text contributes to paradox theory by proposing how art can throw light on the dynamics between power and resistance by revealing aspects of unseen layers of this tension
BdNRT2A and BdNRT3.2 Are the Major Components of the High‐Affinity Nitrate Transport System in <i>Brachypodium distachyon</i>
No full textInternational audienceAn efficient nitrate uptake system contributes to the improvement of crop nitrogen use efficiency under low nitrogen availability. The High Affinity nitrate Transport System (HATS) in plants is active in low range of external nitrate and is mediated by a two-component system (high affinity transporters NRT2 associated to a partner protein NRT3 (NAR2)). In Brachypodium, the model plant for C3 cereals, we investigated the role of BdNRT2A and BdNRT3.2 through various experimental approaches. Expression profile of BdNRT2.A and BdNRT3.2 genes in response to nitrate availability fits perfectly with the characteristics of the HATS components. 15 Nitrate influx measurements decreased in bdnrt2a mutants (one NaN 3 induced mutant with a truncated NRT2A protein and two amiRNA mutants). In addition, the N limited phenotype of the mutant with a truncated NRT2A protein confirmed that BdNRT2A is a major contributor of the HATS in Brachypodium. An effective nitrate transport in the heterologous expression system Xenopus oocytes required the coexpression of BdNRT2A and BdNRT3.2 that characterizes twocomponent system of the HATS. Functional interaction between BdNRT2A-GFP and BdNRT3.2-RFP fusion proteins was observed at the plasma membrane in Arabidopsis protoplasts in transient expression experiments with BdNRT3.2 being necessary for the plasma membrane localization of BdNRT2A. The role of a conserved Ser residue in BdNRT2A (S461) specific to monocotyledons was evaluated in the BdNRT2A and BdNRT3.2 interaction leading to plasma membrane targeting. Assuming that S461 could be regulated by phosphorylation, a directed mutagenesis was performed to mimic a nonphosphorylated (S461A) or a constitutively phosphorylated (S461D), However, the mimicking the phosphorylation status of S461 by mutagenesis did not modify the BdNRT2A and BdNRT3.2 interaction, suggesting a more complex regulating mechanism. In conclusion, our data show that BdNRT2A and BdNRT3.2 are the main components of the nitrate HATS activity in Brachypodium (Bd21-3) and allow an optimal growth in low N conditions
Rigid Supramolecular Aramid Nanotubes as Catalyst Supports
No full textInternational audienceAbstract Solution‐phase heterogeneous catalysts benefit from nanoscale dimensions, which maximize specific surface area and enhance catalytic activity. However, the ease of recovering such nanocatalysts depends on the design of the support materials, which are often particle‐like. Rigid 1D nanomaterials are proposed as supports that can enhance separability while offering high volumetric specific surface area for greater catalyst loading and activity. Here, aramid amphiphiles (AAs) are designed to spontaneously self‐assemble in water into high‐aspect‐ratio supramolecular nanotubes with tunable surface chemistry. These AA nanotubes exhibit high persistence lengths ( P = 750 ± 340 µm) and mechanical stiffnesses (3 N/m). Incorporating surface thiol groups enables immobilization of catalytic gold nanoparticles. The resulting AA nanotube‐gold nanoparticle complexes exhibit high catalytic activity, efficient recoverability via simple microfiltration, and sustained reusability over ten reaction cycles. This study demonstrates the utility of molecular self‐assembled 1D nanomaterials as versatile scaffolds for the reuse and recovery of nanoscale catalysts
Biofuel technologies and global decarbonization by 2050: the role of technological learning, spillovers effects and feedstock constraints
No full textInternational audienceBy 2023, bioenergy represented an important energy resource, accounting for approximately 10.81 % of global final energy consumption, or about 42 EJ/y. Of this value, around 35 EJ/y came from modern bioenergy, primarily modern solid biomass, while liquid biofuels contributed only 3.8 EJ/y. To support their net zero ambitions by 2050, several governments have set targets to increase the production of advanced biofuels, which utilize non-agricultural feedstocks, hence mitigating the environmental impacts of conventional biofuels. However, the deployment of these technologies faces several challenges due to high production costs and limited feedstock availability, creating uncertainty around the viability of these targets. This research develops a global integrated assessment model that incorporates learning curves and technological spillover effects for biofuel technologies calibrated using patent data. The model evaluates mid-century biofuel production across three scenarios, each varying in land resource availability and feedstock constraints, to provide insights about the effects of climate policy induced technological change. The scenarios are defined based on current literature exploring crop yields, land availability and food security, as well as residual feedstocks for advanced biofuels. Results show that climate induced damages can reduce conventional biofuels output almost by 6.004 EJ throughout the entire simulation period. Alternatively, it is found that across all scenarios advanced biofuels production remains below the technical potential found in most literature, and that when spillover effects are accounted for, a reduction of 7.13–13.7 % in the costs of advanced biofuels energy supply is achieved by 2050 across all scenarios
Automated Flash Audit for Web Cybersecurity: Design of a Proactive Vulnerability Scanner
No full textInternational audienceWeb application security is now a major strategic challenge for companies, whether large corporations or very small/small and medium-sized enterprises (VSEs/SMEs). Threats and attacks are diversifying and multiplying, including data breaches, ransomware, defacements, and intellectual property theft. These cyberattacks have severe repercussions for VSEs and SMEs, which are often not sufficiently protected. Even if reliable solutions such as penetration testing and vulnerability scanning tools exist they are generally expensive, not easy to use and not suitable for non-specialist users. Moreover, European legislation (such as GDPR, NIS2 Directive, Cyber Resilience Act and the Digital Operational Resilience Act) imposes increasing cybersecurity obligations and requirements to companies that must adopt security solutions that are compliant with the legislation. To offer a response to VSEs and SMEs companies (i.e. a solution that is affordable and can be used easily), we propose a platform, called WeakSpotter. This latter was developed to enable these companies to understand cybersecurity challenges and thus correct the vulnerabilities they encounter on their websites
Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods
No full textInternational audienceThe 6G mobile network is the next evolutionary step after 5G, with a prediction of an explosive surge in mobile traffic. It provides ultra-low latency, higher data rates, high device density, and ubiquitous coverage, positively impacting services in various areas. Energy saving is a major concern for new systems in the telecommunications sector because all players are expected to reduce their carbon footprints to contribute to mitigating climate change. Network slicing is a fundamental enabler for 6G/5G mobile networks and various other new systems, such as the Internet of Things (IoT), Internet of Vehicles (IoV), and Industrial IoT (IIoT). However, energy-saving methods embedded in network slicing architectures are still a research gap. This paper discusses how to embed energy-saving methods in network-slicing architectures that are a fundamental enabler for nearly all new innovative systems being deployed worldwide. This paper's main contribution is a proposal to save energy in network slicing. That is achieved by deploying ML-native agents in NS architectures to dynamically orchestrate and optimize resources based on user demands. The SFI2 network slicing reference architecture is the concrete use case scenario in which contrastive learning improves energy saving for resource allocation
Nonparametric estimation of the stationary density for Hawkes-diffusion systems with known and unknown intensity
No full textInternational audienceWe investigate the nonparametric estimation problem of the density π, representing the stationary distribution of a two-dimensional system (Z t ) t∈[0,T ] = (X t , λ t ) t∈[0,T ] . In this system, X is a Hawkes-diffusion process, and λ denotes the stochastic intensity of the Hawkes process driving the jumps of X. Based on the continuous observation of a path of (X t ) over [0, T ], and initially assuming that λ is known, we establish the convergence rate of a kernel estimator π (x * , y * ) of π (x * , y * ) as T → ∞. Interestingly, this rate depends on the value of y * influenced by the baseline parameter of the Hawkes intensity process. From the rate of convergence of π (x * , y * ), we derive the rate of convergence for an estimator of the invariant density λ. Subsequently, we extend the study to the case where λ is unknown, plugging an estimator of λ in the kernel estimator and deducing new rates of convergence for the obtained estimator. The proofs establishing these convergence rates rely on probabilistic results that may hold independent interest. We introduce a Girsanov change of measure to transform the Hawkes process with intensity λ into a Poisson process with constant intensity. To achieve this, we extend a bound for the exponential moments for the Hawkes process, originally established in the stationary case, to the non-stationary case. Lastly, we conduct a numerical study to illustrate the obtained rates of convergence of our estimators
Poverty traps under endogenous discounting
No full textWe consider a general Ramsey model with endogenous discounting, depending on current consumption or future capital, study the monotonicity properties of the optimal path, and provide a new narrative for the existence of a poverty trap, alternative to the literature on convex-concave production functions. We prove the continuity and differentiability properties of the value function, as well as the monotonicity of the policy correspondence, which in turn entails the strict monotonicity of the optimal path. Importantly, the existence of a poverty trap relies on the existence of a critical level of capital such that, if the initial condition is lower, the optimal path converges to the origin, while, if it is higher, this path converges to a positive steady state. Since it is impossible to compute this critical level under endogenous discounting when the discount factor is a general function of current consumption or future capital, in both these cases, we complement the theoretical analysis with robust corresponding examples and, showing that a poverty trap exists for a nonzero-measure set of parameter values, we demonstrate that the poverty trap is a pervasive feature under endogenous discounting.</div
Parameter interactions in cumulative prospect theory in relation to probability weighting
No full textInternational audienceTversky and Kahneman’s cumulative prospect theory assumes symmetric probability cumulation with regard to the reference point in decision weights. Theoretically, this model should be fixed despite a change in the direction of probability cumulation. We investigate this phenomenon by proposing an alternative model with one-direction probability cumulation. By doing so, we create a reference model that allows us to verify the parameter interactions in cumulative prospect theory specifications. We apply the simultaneous parametric fitting of utility and weighting functions using binary choice data from our own incentivized choice experiment (N = 70). We consider two parametric forms of probability weighting functions, namely, the one-parameter Tversky–Kahneman and two-parameter Prelec functions. We find that the Prelec function is sufficiently flexible to make these two models equivalent, thereby preserving the stability of the utility parameters. We also observe parameter interactions in the other specifications, especially with the Tversky–Kahneman weighting function