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AI-driven optimization of Congo red photo degradation using the spinel CdCr2O4 photocatalyst: From sol-gel synthesis to DT_LSBOOST predictive modeling coupled with the dragonfly algorithm
No full textInternational audienceIn this study, a nanostructure CdCr2O4 spinel photocatalyst was successfully synthesized via a low-cost sol-gel combustion route and thoroughly characterized by XRD, TGA-DTA, SEM-EDS, FTIR, and UV-Vis spectroscopy. The catalyst exhibited a well-defined spinel structure, high crystallinity, and nanometric grain size (similar to 29 nm), with strong visible-light absorption (band gap approximate to 1.97 eV). Photocatalytic performance was evaluated using Congo red (CR) as a model pollutant under visible LED light. Optimal degradation conditions (pH 6, [CR] (0) = 10 mg/L, 1 g/L catalyst, 150 min) led to an outstanding removal efficiency of 98.45 %, with a kinetic constant of 2.11 x 10(-2) min(-1). Mechanistic studies revealed that hydroxyl (center dot OH) and superoxide (center dot O-2(-)) radicals played dominant roles in the degradation process. To model and optimize the system, a hybrid machine learning approach combining Decision Tree with Least Squares Boosting (DT_LSBOOST), optimized using the Dragonfly algorithm, was implemented. The model demonstrated excellent prediction accuracy (R = 0.9998, RMSE = 0.66) and successfully identified optimal operating conditions with <1 % deviation from experimental results. Stability and reusability tests confirmed the photocatalyst retained >90 % efficiency after five successive cycles, with no significant structural degradation. Compared to state-of-the-art materials, CdCr2O4 proved highly competitive in visible-light-driven photocatalysis, establishing its suitability for advanced wastewater treatment applications
Dancing marbles in a soap film
No full textInternational audienceTwo millimeter-sized particles deposited in a large horizontal soap film are attracted towards each other. Due to the very low friction at the surface of the soap film, the particles can exhibit a complex trajectory, and appear to dance together for about ten seconds before colliding. We give here a short overview of the topic and its perspectives
Differences in whole-body angular momentum between treadmill and overground walking
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The effect of increasing sward species diversity on enteric methane emissions from Holstein-Friesian and Holstein-Friesian × Jersey crossbred dairy cows in a rotational grazing system
No full textInternational audienceAn experiment was conducted to investigate the effect of sward system and dairy cow breed on enteric methane emissions from spring-calving grazing dairy cows using GreenFeed technology (C-lock Inc.). The study comprised 3 sward systems: a perennial ryegrass (Lolium perenne L.) monoculture receiving 250 kg N/ha per year (PRG), a perennial ryegrass white clover (Trifolium repens L.) sward receiving 125 kg N/ha per year (PRGWC), and a multispecies sward sown with grasses, legumes and herbs receiving 125 kg N/ha/yr (MSS). Each sward system had its own herd of dairy cows on a total area of 18.7 ha divided into 20 paddocks. Each herd comprised Holstein-Friesian (HF) purebred and HF × Jersey crossbred (JFX) animals which were divided equally across each sward system. Milk production and methane emissions were measured from mid-May to mid-October, and DMI and rumen characteristics were measured in late-July and early-October (165 and 228 DIM, respectively). Milk solids (milk fat + protein; MSo) was greatest for cows grazing MSS due to an associated increase in DMI. The greatest average daily methane output across the study was observed for cows grazing PRGWC (311 g/d) while those grazing PRG and MSS were similar (294 and 297 g/d, respectively). Methane intensity (g methane/kg MSo) was reduced for the MSS cows compared with both the PRG and PRGWC cows (208, 217, and 219 g/kg MSo respectively). The lowest methane yield (methane per kg of DMI) was also observed for the MSS cows (15.5 g/kg), whereas there was no difference between the PRG and PRGWC cows (17.6 and 17.7 g/kg, respectively). Greater VFA concentrations were observed for both the MSS and PRG cows compared with the PRGWC cows, whereas the MSS cows had a greater butyrate proportion compared with the cows grazing both other sward systems. Breed also had a significant effect on both milk and methane production characteristics with JFX animals having increased fat and protein concentration and increased MSo production compared with HF. The JFX cows also had reduced BW. Breed had no effect on DMI or methane yield, but JFX also had improved feed efficiency, both per kilogram of DMI and per kilogram BW. The HF cows had an increased methane intensity (223 g/kg MSo) compared with JFX (207 g/kg MSo). Rumen ammonia concentration and acetate-to-propionate ratio were increased for JFX, whereas VFA propionate proportion was decreased, and butyrate proportions were increased compared with HF. The results of this study highlight the potential for more diverse swards and Holstein-Friesian × Jersey crossbreeding to reduce enteric methane emission intensity within pasture-based dairy systems
Quantum Advantage via Solving Multivariate Polynomials
No full textInternational audienceIn this work, we propose a new way to (non-interactively, verifiably) demonstrate quantum advantage by solving the average-case NP search problem of finding a solution to a system of (underdetermined) constant degree multivariate equations over the finite field F 2 drawn from a specified distribution. In particular, for any d ≥ 2, we design a distribution of degree up to d polynomials {p i (x 1 , . . . , x n )} i∈ [m] for m < n over F 2 for which we show that there is a expected polynomial-time quantum algorithm that provably simultaneously solves {p i (x 1 , . . . , x n ) = y i } i∈[m] for a random vector (y 1 , . . . , y m ). On the other hand, while solutions exist with high probability, we conjecture that for constant d > 2, it is classically hard to find one based on a thorough review of existing classical cryptanalysis. Our work thus posits that degree three functions are enough to instantiate the random oracle to obtain non-relativized quantum advantage.Our approach begins with the breakthrough Yamakawa-Zhandry (FOCS 2022) quantum algorithmic framework. In our work, we demonstrate that this quantum algorithmic framework extends to the setting of multivariate polynomial systems.Our key technical contribution is a new analysis on the Fourier spectra of distributions induced by a general family of distributions over F 2 multivariate polynomials-those that satisfy 2-wise independence and shift-invariance. This family of distributions includes the distribution of uniform random degree at most d polynomials for any constant d ≥ 2. Our analysis opens up potentially new directions for quantum cryptanalysis of other multivariate systems.</div
Validating and refining a psychoacoustic test to diagnose hyperacusis
No full textInternational audienceDecreased sound tolerance refers to conditions like hyperacusis and misophonia, in which everyday sounds may provoke discomfort or distress. Hyperacusis is characterized by exaggerated loudness perception and aversive reactions to moderate to loud sounds, often leading to significant impairment. Despite its estimated prevalence of 10–15 %, no objective clinical test exists. Current assessment relies on interviews, questionnaires, and loudness discomfort levels (LDLs), which lack reliability and ecological validity. Recent work has shown that pleasantness ratings of natural sounds can differentiate individuals with hyperacusis from controls. A subset of seven natural sounds, termed Core Discriminant Sounds (CDShyper), was identified post-hoc as particularly effective, yielding 81 % sensitivity and 88 % specificity in prior lab-based testing. A similar approach for misophonia identified ten distinct trigger sounds (CDSmiso). This study aimed to validate a tablet-based version of the CDS test to diagnose hyperacusis prospectively. Forty-nine participants (20 with hyperacusis, 29 controls) completed the test that presented randomly CDShyper and CDSmiso sounds at 60, 70, and 80 dBA. Participants rated each sound presentation on visual analog scales for pleasantness and loudness. Hyperacusis was defined by clinical complaints, Hyperacusis Questionnaire scores (≥22), and LDLs (≤77 dB HL). Results showed that individuals with hyperacusis rated CDShyper sounds as significantly less pleasant (means score of 39 vs 25 for hyperacusis vs controls, p = .002, η² = 0.185) and louder (means score of 71 vs 63 for hyperacusis vs controls, p = .024, η² = 0.104) than controls. No group differences emerged for misophonia trigger sounds (CDSmiso sounds). The sensitivity and specificity for the combined CDShyper scores of pleasantness and loudness at detecting hyperacusis were 90 % and 69 %, respectively. These findings validate the tablet-based test as an efficient, ecologically valid tool for diagnosing hyperacusis
Ferroelectric KNbO3 nanoplatelets for thermally driven pyrocatalytic hydrogen evolution and dye degradation
No full textInternational audienceDay and night shift-induced thermal cycling offers a promising route toward free energy for green hydrogen production and dye degradation. Pyroelectric materials make this possible by converting temperature fluctuations into electrical charges that drive water splitting catalytic reactions and produce hydrogen fuel. Herein, we demonstrate an efficient pyrocatalytic hydrogen evolution reaction and Rhodamine B (RhB) degradation using ferroelectric potassium niobate (KNbO3) perovskite nanoplatelets (KN-np) with an orthorhombic phase. Under thermal cycling between 20 and 50 degrees C, KN-np produced a high hydrogen yield of 680 mu mol & sdot;g-1 over 30 thermal cycles, with an average hydrogen generation rate of approximately 22.67 mu mol g-1 per thermal cycle. Besides, KN-np pyrocatalytic activity enabled efficient degradation of the RhB dye up to 84 % after only 16 cycles with a high kinetic rate constant of 0.11 per thermal cycle. Our findings show that the excellent pyroelectric properties of KN-np are at the origin of the catalytic activity enhancement. This work lays the foundation for the future design of pyroelectric materials for clean energy production and environmental remediation
Interaction of bioactive glasses with fibrinogen protein. Effect of porosity and surface modification
No full textInternational audienceBioactive glasses are of great interest because of their osteostimulative effects and their intimate bond with bone tissue. Their porosity structuring and surface modification also offer opportunities for controlled adsorption, and further controlled release, of active molecules. These new textural properties and chemical surface modifications undoubtedly have an impact on their interaction with blood proteins. In the present work, we investigate the influence of two parameters, together or separately, mesostructuring porosity and surface modification by silanization, on the interactions of a sol-gel derived bioactive glass (92S6) with the blood protein fibrinogen. Results indicate first that the ordered porosity significantly favors the fibrinogen adsorption at the glass surface. On the other hand, surface silanization induces systematically a delayed adsorption of fibrinogen for at least 8 h. After this period of inhibition of protein adsorption, the protein is better adsorbed on silanized glasses surfaces comparatively to unsilanized surfaces. This effect is particularly noticeable when the glass has no mesostructured porosity. In the case of mesostructured porosity, the surface silanization has a minor impact on the adsorbed quantity of protein after 48 h, the protein adsorption being more influenced by the mesostructured porosity. The study also highlights that a modification of the protein conformation occurs when its approaches the glass surface. This modification is not related to ions release in the medium but really related to surface contact. Additionally, the study shows that the strong fibrinogen adsorption is not associated to the creation of covalent bond between fibrinogen and glass surface but due to a reversible electrostatic interaction.The mesostructured porosity and surface silanization offer new opportunities for tunable properties of the 92S6 bioactive glass
Robust a posteriori estimation of probit-lognormal seismic fragility curves via sequential design of experiments and constrained reference prior
No full textInternational audienceA seismic fragility curve expresses the probability of failure of a structure conditional to an intensity measure (IM) derived from seismic signals. When only limited data is available, the practitioner often refers to the probit-lognormal model coupled with maximum likelihood estimation (MLE) to obtain estimates of these curves. This means that only a binary indicator of the state (BIS) of the structure is known, namely a failure or non-failure state indicator, when it is subjected to a seismic signal with an intensity measure IM. In this context, the objective of this work is to propose a method for optimally estimating such curves by obtaining the most precise estimate possible with the minimum of data. The novelty of our work is twofold. First, we present and show how to mitigate the likelihood degeneracy problem which is ubiquitous with small data sets and hampers frequentist approaches such as MLE. Second, we propose a novel strategy for sequential design of experiments (DoE) that selects seismic signals from a large database of synthetic or real signals via their IM values, to be applied to structures to evaluate the corresponding BISs. This strategy relies on a criterion based on information theory in a Bayesian framework. It therefore aims to sequentially designate the IM value such that the pair (IM, BIS) has on average, with respect to the BIS of the structure, the greatest impact on the posterior distribution of the fragility curve. The methodology is applied to a case study from the nuclear industry. The results demonstrate its ability to efficiently and robustly estimate the fragility curve, and to avoid degeneracy even with a limited amount of data, i.e., less than 100. Furthermore, we demonstrate that the estimates quickly reach the model bias induced by the probit-lognormal modeling. Eventually, two criteria are suggested to help the user stop the DoE algorithm