Gibbsputnam1420

Research on soft robots and swimming robots have been widely reported and demonstrated. However, none of these soft swimming robots can swim flexibly and efficiently using legs just like frog.This paper demonstrates a self-contained, untethered swimming robotic frog actuated by twelve pneumatic soft actuators, which can swim in the water for dozens of minutes by mimicking the paddling gait of the natural frog. We designed two kinds of pneumatic soft actuators as the joints on the robotic frog's legs, which allows the legs to be lighter and compact. It is found that such soft actuators have great potential in developing amphibious bionic robots, because they are fast response, inherently water-tight and simple in structural design. The kinematic analysis in swimming locomotion was conducted for the prototype robotic frog. And the locomotion trajectory of each leg is planned based on the analysis in paddling gait of frogs. Combined with the deformation model of the soft actuators, the robotic frog's legs are controlled by coordinating the air pressure of each joint actuators. The robotic frog's body is compact and the total mass is 1.29kg. Different paddling gaits were tested to investigate swimming performance. The results show that the robotic frog has agile swimming ability and high environmental adaptability. The robotic frog can swim forward more than 0.6m (3.4 times the body length) in one paddling gait cycle(6s), whose average swimming velocity is about 0.1m/s . And the minimum turning radius is about 0.15m(less than 1 time the body length). © 2020 IOP Publishing Ltd.Using Su-Schrieffer-Heeger Hamiltonian and exploiting Green's function method in the framework of Landauer-Büttiker formalism, the topological and spin dependent electron transport properties of a trans polyacetylene molecule are studied. It is found that molecules with intracell single carbon-carbon bonding and even monomers in their chains have two edge states and possess topological properties though their Hamiltonians do not respect chiral symmetry. A perpendicular exchange magnetic field and two perpendicular and transverse electric fields are used to induce and manipulate the quantum spin dependent transport properties. The exchange field induces spin polarization in different electron energy regions which are expanded by stronger exchange fields. Therefore this proposed device works as a perfect spin filter. The spin polarization can be manipulated by applying the perpendicular electric field and remains robust against the transverse electric field variations. © 2020 IOP Publishing Ltd.Hot electron photodetection (HEPD) excited by surface plasmon can circumvent bandgap limitations, opening pathways for additional energy harvesting. However, the costly and time-consuming lithography has long been a barrier for large-area and mass production of HEPD. In this paper, we proposed a planar and electron beam lithography-free hot electron photodetector based on the Fabry-Pérot resonance composed of Au/MoS2/Au cavity. The hot electron photodetector has a nanoscale thickness, high spectral tenability, and multicolour photoresponse in the near-infrared region due to the increased round-trip phase shift by using high refractive index MoS2. We predict that the photoresponsivity can achieve up to 23.6 mA/W when double cavities are integrated with the Fabry-Pérot cavity. The proposed hot electron photodetector that has a nanoscale thickness and planar stacking is a perfect candidate for large-area and mass production of HEPD. © 2020 IOP Publishing Ltd.The band structure of the quasi-one-dimensional transition metal trichalcogenide ZrS3(001) was investigated using nanospot angle resolved photoemission spectroscopy (nanoARPES) and shown to have many similarities with the band structure of TiS3(001). We find that ZrS3, like TiS3, is strongly n-type with the top of the valence band ~1.9 eV below the Fermi level, at the center of the surface Brillouin zone. The nanoARPES spectra indicate that the top of the valence band of the ZrS3(001) is located at Γ. The band structure of both TiS3and ZrS3exhibit strong in-plane anisotropy, which results in a larger hole effective mass along the quasi-one-dimensional chains than perpendicular to them. © 2020 IOP Publishing Ltd.Efficient optical sensing is desirable for wide applications. For the sensors, the spectral factors of the sensitivity (S) and the figure of merit (FoM) and the intensity change related figure of merit (FOM*) are all the key points for sensing measurement. In this work, we propose and demonstrate a novel high-performance plasmonic sensor platform via using a resonant cavity array grating under the oblique excitation. check details Ultra-sharp absorption mode with the bandwidth down to 1.3 nm is achieved when the oblique angle is 7.5o. During the sensing of the Na+ (Cl-) ions in the solution, the spectral S and FoM factors reach 568 nm/RIU (refractive index unit) and 436, respectively. The minimum detection limit is as low as 3.521 × 10-6 RIU. The FOM* factor is simultaneously up to 907. Moreover, the spectral intensity change is up to 57% when only 1% concentration change is introduced for the solution. The detection limit of the ions' concentration can be as low as 0.002%. The sensor has great potential applications due to its ultrahigh S, FoM and FOM*. © 2020 IOP Publishing Ltd.PURPOSE To reduce the variability of radiomics features caused by computed tomography (CT) imaging protocols through using a generative adversarial network (GAN) method. MATERIAL AND METHODS In this study, we defined a set of images acquired with a certain imaging protocol as a domain, and a total of 4 domains (A, B, C, and T [target]) from 3 different scanners were included. In dataset#1, 60 patinets for each domain were collected. Datasets#2 and #3 included 40 slices of spleen for each of the domains. In dataset#4, the slices of 3 colorectal cancer groups (n = 28, 38, and 32) were separately retrieved from 3 different scanners, and each group contained short-term and long-term survivors. 77 features were extracted for evaluation by comparing features distributions. First, we trained the GAN model on dataset#1 to learn how to normalize images from domains A, B, and C to T. Next, by comparing feature distributions between normalized images of the different domains, we identified the appropriate model and assessed it , in dataset #2 and dataset#3, respectively.