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Fish locomotion emerges from diverse interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e. fluid–structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-controlled FSI may possess embodied traits. However, their implications in motor learning, neuromuscular control, gait generation, and swimming performance remain to be uncovered. Using robot models, we studied the embodied traits in fish-inspired swimming. We developed modular robots with various designs and used central pattern generators (CPGs) to control the torque acting on robot body. We used reinforcement learning to learn CPG parameters for maximizing the swimming speed. The results showed that motor frequency converged faster than other parameters, and the emergent swimming gaits were robust against disruptions applied to motor control. For all robots and frequencies tested, swimming speed was proportional to the mean undulation velocity of body and caudal-fin combined, yielding an invariant, undulation-based Strouhal number. The Strouhal number also revealed two fundamental classes of undulatory swimming in both biological and robotic fishes. The robot actuators were also demonstrated to function as motors, virtual springs and virtual masses. These results provide novel insights in understanding fish-inspired locomotion.

 

In animal and robot swimmers of body and caudal fin (BCF) form, hydrodynamic thrust is mainly produced by their caudal fins, the stiffness of which has profound effects on both thrust and efficiency of swimming. Caudal fin stiffness also affects the motor control and resulting swimming gaits that correspond to optimal swimming performance; however, their relationship remains scarcely explored. Here using magnetic, modular, undulatory robots (μBots), we tested the effects of caudal fin stiffness on both forward swimming and turning maneuver. We developed six caudal fins with stiffness of more than three orders of difference. For aμBot equipped with each caudal fin (andμBot absent of caudal fin), we applied reinforcement learning in experiments to optimize the motor control for maximizing forward swimming speed or final heading change. The motor control ofμBot was generated by a central pattern generator for forward swimming or by a series of parameterized square waves for turning maneuver. In forward swimming, the variations in caudal fin stiffness gave rise to three modes of optimized motor frequencies and swimming gaits including no caudal fin (4.6 Hz), stiffness <10⁻⁴Pa m⁴(∼10.6 Hz) and stiffness >10⁻⁴Pa m⁴(∼8.4 Hz). Swimming speed, however, varied independently with the modes of swimming gaits, and reached maximal at stiffness of 0.23 × 10⁻⁴Pa m⁴, with theμBot without caudal fin achieving the lowest speed. In turning maneuver, caudal fin stiffness had considerable effects on the amplitudes of both initial head steering and subsequent recoil, as well as the final heading change. It had relatively minor effect on the turning motor program except for theμBots without caudal fin. Optimized forward swimming and turning maneuver shared an identical caudal fin stiffness and similar patterns of peduncle and caudal fin motion, suggesting simplicity in the form and function relationship inμBot swimming.

 

A dual energy harvester based upon the magnetoelectric mechanism is reported. The harvester can generate ∼52.1 mW under simultaneously applied magnetic field and ultrasound in porcine tissue operating under safety limits.

 

BSTRACT:Piezoelectricmaterialsare used to fabricateacoustictransducersforbubblechambersin searchfor particlesof dark matter.It has been shownthat bubblesinitiatedby nuclearrecoilsemit acousticradiationdistinguishablefrom the phasetransitionscausedby alpha-decay�themain backgroundnoisein such searches.However,these piezoelectricmaterialsmust exhibitultralowradioactivityto minimizethe neutronbackgroundfor dark matterdetectionwhilepossessinghigh acousticsensitivity.Here,for the first time, we demonstrateradiopurehigh-performancepiezoelectricceramicsmeetingthe criteriafor acousticsensing.The screeningofradiopureprecursorsis performedto identifythose with low238U,232Th, and210Pbcontents.Usingthe radiopureprecursors,piezoelectricceramicswith varyingcompositionsare synthesized,and their electromechanicalacousticsensingperformanceis evaluated.Multiplesynthesismodificationssuch as dopingand texturingare utilizedtotailor the piezoelectriccoefficientsof the piezoelectricceramics,and the relationshipbetweenthe piezoelectriccoefficientsand acousticsensingperformanceof the ceramicsis investigated.Acoustictransducersfabricatedusing texturedPb(Mg1/3Nb2/3)O3−PbTiO3(PMN−PT)ceramicsare found to exhibitsuperioracousticsensitivitydue totheir high piezoelectrictransductioncoefficient(d33×g33). This study demonstratesa usefulfigure of merit (FOM)for acousticsensingin bubblechambers 

To fulfill the demands of more bandwidth in 5G and 6G communication technology, new dielectric substrates that can be co‐fired into packages and devices that have low dielectric loss and improved thermal conductivity are desired. The motivation for this study is to design composites with low dielectric loss (tan δ) and high thermal conductivity (κ), while still limiting the electrical conductivity, for microwave applications involving high power and high frequency. This work describes the fabrication of high‐density electroceramic composites with a model dielectric material for cold sintering, namely sodium molybdate (Na₂Mo₂O₇), and fillers with higher thermal conductivity such as hexagonal boron nitride. The physical properties of the composites were characterized as a function of filler vol.%, temperature, and frequency. Understanding the variation in measured properties is achieved through analyzing the respective transport mechanisms.

 

An artificial intrinsic RGB photoreceptor matrix can replicate the neuromorphic panchromatic vision capability of the eye.