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This manuscript presents airborne jet propulsion by audio sounds and ultrasounds through orifices formed by bulk-micromachining of a silicon wafer. The propeller is integrated with a small, printed circuit board (PCB) with a DC/DC converter, an oscillator, and a power amplifier, all powered by a 100F lithium-ion capacitor to make the propeller operable wirelessly. The peak propulsion force of the wireless propeller is measured to be 63.1 mg (or 618 mN) while the packaged wireless propeller’s weight is 10.6 g, including the drive electronics and adapter) when driven by 2.5kHz sinusoidal voltage with 21.4Vpp. A wired propeller (with 563 mg weight without adapter) is shown to high jump, long jump, wobbly fly, and propel objects. Also, the propeller is shown to work when driven by ultrasounds with a maximum propulsion force of 8.4 mg (82 mN) when driven by 20kHz, 20Vpp sinusoidal signal. Varying the frequency gradient of the applied sinusoidal pulses is shown to move the propeller to the left or right on demand to reach a specific location. 

Fronts and near-inertial waves (NIWs) are energetic motions in the upper ocean that have been shown to interact and provide a route for kinetic energy (KE) dissipation of balanced oceanic flows. In this paper, we study these KE exchanges using an idealized model consisting of a two-dimensional geostrophically balanced front undergoing strain-induced semigeostrophic frontogenesis and internal wave (IW) vertical modes. The front–IW KE exchanges are quantified separately during two frontogenetic stages: an exponential sharpening stage that is characterized by a low Rossby number and is driven by the imposed strain (i.e., mesoscale frontogenesis), followed by a superexponential sharpening stage that is characterized by anRossby number and is driven by the convergence of the secondary circulation (i.e., submesoscale frontogenesis). It is demonstrated that high-frequency IWs quickly escape the frontal zone and are very efficient at extracting KE from the imposed geostrophic strain field through the deformation shear production (DSP). Part of the extracted KE is then converted to wave potential energy. On the contrary, NIWs remain locked to the frontal zone and readily exchange energy with the ageostrophic frontal circulation. During the exponential stage, NIWs extract KE from the geostrophic strain through DSP and transfer it to the frontal secondary circulation via the ageostrophic shear production (AGSP) mechanism. During the superexponential stage, a newly identified mechanism, convergence production (CP), plays an important role in the NIW KE budget. The CP transfers KE from the convergent ageostrophic secondary circulation to the NIWs and largely cancels out the KE loss due to the AGSP. This CP may explain previous findings of KE transfer enhancement from balanced motions to IWs in frontal regions of realistic ocean models. We provide analytical estimates for the aforementioned energy exchange mechanisms that match well the numerical results. This highlights that the strength of the exchanges strongly depends on the frontal Rossby and Richardson numbers.

Fronts with large horizontal density and velocity gradients are ubiquitous in the upper ocean. They are generated by a process known as frontogenesis, which is often initialized by straining motions of mesoscale balanced circulations. Here we examine the energy exchanges between fronts and internal waves in an idealized configuration, aiming to elucidate the mechanisms that can drain energy from oceanic balanced circulations. We identify a new mechanism for energy transfers from the frontal circulation to near-inertial internal waves called convergence production. This mechanism is especially effective during the later stages of frontogenesis when the convergent ageostrophic secondary circulation that develops is strong.

 

Three-dimensional (3D) printing is implemented for surface modification of titanium alloy substrates with multilayered biofunctional polymeric coatings. Poly(lactic-co- glycolic) acid (PLGA) and polycaprolactone (PCL) polymers were embedded with amorphous calcium phosphate (ACP) and vancomycin (VA) therapeutic agents to promote osseointegration and antibacterial activity, respectively. PCL coatings revealed a uniform deposition pattern of the ACP-laden formulation and enhanced cell adhesion on the titanium alloy substrates as compared to the PLGA coatings. Scanning electron microscopy and Fourier-transform infrared spectroscopy confirmed a nanocomposite structure of ACP particles showing strong binding with the polymers. Cell viability data showed comparable MC3T3 osteoblast proliferation on polymeric coatings as equivalent to positive controls. In vitro live/dead assessment indicated higher cell attachments for 10 layers (burst release of ACP) as compared to 20 layers (steady release) for PCL coatings. The PCL coatings loaded with the antibacterial drug VA displayed a tunable release kinetics profile based on the multilayered design and drug content of the coatings. Moreover, the concentration of active VA released from the coatings was above the minimum inhibitory concentration and minimum bactericidal concentration, demonstrating its effectiveness against Staphylococcus aureus bacterial strain. This research provides a basis for developing antibacterial biocompatible coatings to promote osseointegration of orthopedic implants. 

Oceanic mixing, mostly driven by the breaking of internal waves at small scales in the ocean interior, is of major importance for ocean circulation and the ocean response to future climate scenarios. Understanding how internal waves transfer their energy to smaller scales from their generation to their dissipation is therefore an important step for improving the representation of ocean mixing in climate models. In this study, the processes leading to cross-scale energy fluxes in the internal wave field are quantified using an original decomposition approach in a realistic numerical simulation of the California Current. We quantify the relative contribution of eddy–internal wave interactions and wave–wave interactions to these fluxes and show that eddy–internal wave interactions are more efficient than wave–wave interactions in the formation of the internal wave continuum spectrum. Carrying out twin numerical simulations, where we successively activate or deactivate one of the main internal wave forcing, we also show that eddy–near-inertial internal wave interactions are more efficient in the cross-scale energy transfer than eddy–tidal internal wave interactions. This results in the dissipation being dominated by the near-inertial internal waves over tidal internal waves. A companion study focuses on the role of stimulated cascade on the energy and enstrophy fluxes.

 

This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the scene context and detect the OOC object with a bounding box. In this work, we consider commonly explored contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects. 

Heritable, facultative symbionts are common in arthropods, often functioning in host defence. Despite moderately reduced genomes, facultative symbionts retain evolutionary potential through mobile genetic elements (MGEs). MGEs form the primary basis of strain‐level variation in genome content and architecture, and often correlate with variability in symbiont‐mediated phenotypes. In pea aphids (Acyrthosiphon pisum), strain‐level variation in the type of toxin‐encoding bacteriophages (APSEs) carried by the bacteriumHamiltonella defensacorrelates with strength of defence against parasitoids. However, co‐inheritance creates difficulties for partitioning their relative contributions to aphid defence. Here we identified isolates ofH. defensathat were nearly identical except for APSE type. When holdingH. defensagenotype constant, protection levels corresponded to APSE virulence module type. Results further indicated that APSEs move repeatedly within someH. defensaclades providing a mechanism for rapid evolution in anti‐parasitoid defences. Strain variation inH. defensaalso correlates with the presence of a second symbiontFukatsuia symbiotica. Predictions that nutritional interactions structured this coinfection were not supported by comparative genomics, but bacteriocin‐containing plasmids unique to co‐infecting strains may contribute to their common pairing. In conclusion, strain diversity, and joint capacities for horizontal transfer of MGEs and symbionts, are emergent players in the rapid evolution of arthropods.

 

This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the scene context and detect the OOC object with a bounding box. In this work, we consider commonly explored contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects.