The Ninth Visual Object Tracking VOT2021 Challenge Results

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A sand particle characterization method for water-bearing high-production gas wells based on a multifrequency collision response

Excessive erosion caused by the continuous collision of sand-carrying annular flow with the gas well wellbore can lead to serious production accidents. This study combined the multifrequency response characteristics of sand particle-wall collision with a deep learning algorithm to improve the recognition accuracy of sand particle information in annular flow. The findings showed that sand-wall collision strength was closely related to the velocity, size, and number of sand particles and that the shielding effect generated by the collision behavior between multiple particles had a protective effect on the elbow. In addition, sand-wall collision strength increased with increases in gas velocity and particle size and decreased with an increase in liquid velocity. The shear effect, the secondary flow effect, and the liquid film buffering effect were shown to be key factors affecting the transportation behavior and spatial distribution of sand particles in annular flow. Furthermore, the fast Fourier transform (FFT) and short-time Fourier transform (STFT) analysis results showed that the multifrequency collision response characteristics of sand carrying annular flow were complex and that the main frequency response of sand-wall collision was concentrated in the high frequency range of 50–80 kHz. Moreover, the recognition accuracy results of convolutional neural network (CNN) models for particle size, gas velocity, and liquid velocity were 93.8%, 91.7%, and 91%, respectively, which were significantly higher than the results for the long short-term memory (LSTM) model. The combination of multifrequency collision response and deep learning effectively characterized sand particle feature information in strong gas-liquid turbulence, providing a reference for the accurate monitoring of sand particle information in high-yield water-bearing gas wells

A Novel SnO₂/ZnFe₂O₄ Magnetic Photocatalyst with Excellent Photocatalytic Performance in Rhodamine B Removal

In this study, we prepared the SnO2/ZnFe2O4 (SZ) composite magnetic photocatalyst via a two-step hydrothermal method. Structural and performance analyses revealed that SZ-5 with a ZnFe2O4 mass ratio of 5% (SZ-5) exhibited optimal photocatalytic activity, achieving a 72.6% degradation rate of Rhodamine B (RhB) solution within 120 min. SZ-5 consisted of irregular nano blocks of SnO2 combined with spherical nanoparticles of ZnFe2O4, with a saturated magnetization intensity of 1.27 emu/g. Moreover, the specific surface area of SnO2 loaded with ZnFe2O4 increased, resulting in a decreased forbidden bandwidth and expanded light absorption range. The construction of a Z-type heterojunction structure between SnO2 and ZnFe2O4 facilitated the migration of photogenerated charges, reduced the recombination rate of electron-hole pairs, and enhanced electrical conductivity. During the photocatalytic reaction, RhB was degraded by·OH, O2−, and h+, in which O2− played a major role

Hybrid mixed-dimensional perovskite/metal-oxide heterojunction for all-in-one opto-electric artificial synapse and retinal-neuromorphic system

Human central nervous system and the peripheral nervous system have played significant roles in mediating the interactions with the outside world. Inspired by the human nervous systems, artificial sensory and neuromorphic innovations have been developed to mimic nervous functions. A hybrid mixed-dimensional perovskite/metal-oxide heterojunction has been demonstrated in this work for three-terminal all-in-one opto-electric artificial synapse to integrate opto-electric synaptic emulations and optical perception functions. Based on the well-designed layer configuration, an all-in-one device, consisting of the ion-electrolyte layer, ion-permeable metal-oxide semiconductor channel layer, the mixed-dimensional perovskite optical perception layer, and the amorphous ZnO passivation layer, has been demonstrated with superior electrical performance. Utilizing an ion-electrolyte and ion-permeable metal-oxide semiconductor structure, the synaptic emulation modulated by electrical gate-stimulus could be effectively achieved. The optical perception and synaptic plasticity modulated by the optical stimulus have been integrated into the all-in-one device. Furthermore, the electrolyte gated device enables artificial visual adaptation with adaptive behavior of environmental lightness under dim and bright conditions. Addition, artificial visual persistence has been emulated by the device utilizing the optical synaptic behavior. Based on those properties, a cascaded near-sensor face recognition access control retinal-neuromorphic computing system has been developed based on the all-in-one device. The retinal-neuromorphic system based on all-in-one devices could recognize the face of a requester with an accuracy rate over 90 %, and ignore the passers with the lingering phenomenal trace as the result of the artificial visible persistence