Optimal sensing for fish school identification

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information

Sedimentary processes in the Thau Lagoon (France): From seasonal to century time scales

As a part of the MICROBENT programme, an investigation of the sedimentation framework was carried out at the water-sediment interface in the Thau Lagoon (French Mediterranean coast). Two main sites, C4 in the middle of the lagoon and C5 near oyster farms, were visited six times between December 2001 and May 2003. Interface sediments were studied using classical sedimentology parameters (radiography RX, grain size distribution) and analysis of selected radionuclides (234Th, 7Be, 210Pb, 226Ra). On a century time scale, excess 210Pb (210Pbxs) presents classical profiles with an upper mixed layer, followed by an exponential decrease of activities to undetectable levels below 20 – 30 cm. At the central site, C4, cores seem to register episodic changes in mean grain size, presenting recurrently peaks. The upper 10 cm of 210Pbxs profiles at site C5 exhibit a mixed layer associated with coarser sediments: this could be related to biological activity. Sedimentation rates derived from 210Pbxs varied from 0.15 cm y−1 at the edge of the basin, to 0.25 cm y−1 at the central site. On a seasonal time scale, 234Th and 7Be both show significant variations in activities and in penetration within the sediment. Bioturbation rates derived from both radionuclides agree well and range between 1–10 cm2 y−1 at site C4 and 1–31 cm2 y−1 at site C5. 234Th and 7Be fluxes at the water-sediment interface show too seasonal variations, more pronounced for site C5. This latter site presents especially a higher variability that is well marked with season, probably in relation with its position near oyster farms

Drag Reduction in Flows Past 2D and 3D Circular Cylinders Through Deep Reinforcement Learning

We investigate drag reduction mechanisms in flows past two- and three-dimensional cylinders controlled by surface actuators using deep reinforcement learning. We investigate 2D and 3D flows at Reynolds numbers up to 8,000 and 4,000, respectively. The learning agents are trained in planar flows at various Reynolds numbers, with constraints on the available actuation energy. The discovered actuation policies exhibit intriguing generalization capabilities, enabling open-loop control even for Reynolds numbers beyond their training range. Remarkably, the discovered two-dimensional controls, inducing delayed separation, are transferable to three-dimensional cylinder flows. We examine the trade-offs between drag reduction and energy input while discussing the associated mechanisms. The present work paves the way for control of unsteady separated flows via interpretable control strategies discovered through deep reinforcement learning

Edge minimization in de Bruijn graphs

This paper introduces the de Bruijn graph edge minimization problem, which is related to the compression of de Bruijn graphs: find the order-k de Bruijn graph with minimum edge count among all orders. We describe an efficient algorithm that solves this problem. Since the edge minimization problem is connected to the BWT compression technique called "tunneling", the paper also describes a way to minimize the length of a tunneled BWT in such a way that useful properties for sequence analysis are preserved. Although being a restriction, this is significant progress towards a solution to the open problem of finding optimal disjoint blocks that minimize space, as stated in Alanko et al. (DCC 2019).Comment: Accepted for Data Compression Conference 202

Pôles nationaux de ressources : évaluation qualitative, analyse et propositions (Les)

Premier bilan des pôles nationaux de ressources (documentation, information et conseils aux professionnels), issus d\u27un partenariat entre les ministères de l\u27Education et de la Culture pour le développement de l\u27éducation artistique et culturelle

Atomic Dipole Traps with Amplified Spontaneous Emission: A Proposal

We propose what we believe to be a novel type of optical source for ultra-cold atomic Far Off-Resonance optical-dipole Traps (FORTs). The source is based on an Erbium Amplified Spontaneous Emission (ASE) source that seeds a high power Erbium Doped Fiber Amplifier (EDFA). The main interest of this source is its very low coherence length, thus allowing an incoherent superposition of several trapping beams without any optical interference. The behavior of the superimposed beams is then a scalar sum greatly simplifying complex configurations. As an illustration, we report an estimation of the intensity noise of this source and an estimation of the atomic excess heating rate for an evaporative cooling experiment application. They are both found to be suitable for cold atoms experiments

Handheld Device for Selective Benzene Sensing over Toluene and Xylene

More than 1 million workers are exposed routinely to carcinogenic benzene, contained in various consumer products (e.g., gasoline, rubbers, and dyes) and released from combustion of organics (e.g., tobacco). Despite strict limits (e.g., 50 parts per billion (ppb) in the European Union), routine monitoring of benzene is rarely done since low-cost sensors lack accuracy. This work presents a compact, battery-driven device that detects benzene in gas mixtures with unprecedented selectivity (>200) over inorganics, ketones, aldehydes, alcohols, and even challenging toluene and xylene. This can be attributed to strong Lewis acid sites on a packed bed of catalytic WO3 nanoparticles that prescreen a chemoresistive Pd/SnO2 sensor. That way, benzene is detected down to 13 ppb with superior robustness to relative humidity (RH, 10–80%), fulfilling the strictest legal limits. As proof of concept, benzene is quantified in indoor air in good agreement (R2 ≥ 0.94) with mass spectrometry. This device is readily applicable for personal exposure assessment and can assist the implementation of low-emission zones for sustainable environments