Study of the dynamic behavior of tablet movement in a rotating drum using discrete element modeling (DEM) method

Particle shape is a very important factor affecting the accuracy of discrete element modeling (DEM) simulations. Therefore, the purpose of this study is to determine its effect on the dynamic behavior of tablet movement in a rotating drum. First, the shape of typical standard round tablets was represented using the intersection of three convex spherical surfaces. Contact algorithms for Tablet-Flat Surface, Tablet-Curved Surface and Tablet-Tablet were developed based on the geometry of this representation of the tablets\u27 shape. Good agreement was obtained between the simulation results for the collision of two tablets and those obtained from experiments. In addition, simulation results for the collision of two tablets using multi-sphere representations showed that the dynamic behavior of the tablet was very different from any of the multi-sphere representations.;A study of the effect of liquid bridging between two tablets on the dynamic behavior of tablets indicated that the capillary force can be ignored compared to the viscous forces caused by the liquid bridge, especially for liquids with high viscosity. Additionally, there was a linear relationship between limiting separation distance and viscosity of the liquid. The order of magnitude of the limiting separation distance in the liquid force model is about 1 mm.;For the multi-particle DEM simulations using spheres in rotating drums, the simulations reached steady state after approximately 6 s of pan rotation. A study of the influence of particle size and the radius of the drum on the surface velocity of particles along the inclined surface showed that the surface velocity of particles was inversely proportional to the square root of the diameter of the particles, and the surface velocity of particles is proportional to the square of the radius of the drum. In addition, the fractional fill volume in the 29 cm drum should be larger than 0.25 to obtain a velocity profile consistent with larger equipment.;Comparison of the surface velocity profiles from multi-particle simulations using tablets with that from multi-particle simulations using spheres showed that the surface velocity of tablets was much larger than that for spherical particles. It was also found that simulation results for batches of 1500 tablets and spheres in a rotating drum showed that the computational time for tablet simulations was about 60 times longer than that for spherical particle simulations

Mass spectra of doubly heavy tetraquarks in diquark-antidiquark picture

Inspired by recent observation of the first doubly charmed tetraquark $T_{cc}$, we apply linear Regge relation and mass scaling to study low-lying mass spectra of the doubly heavy tetraquark in heavy-diquark-light-antidiquark picture. The measured data and other compatible estimates of ground-state masses of doubly heavy baryons are employed to evaluate masses of heavy diquark $M_{QQ}$ ($Q=c,b$) and the $D/D_{s}$ meson masses are used in mass scaling to determine the hyperfine mass splitting. Our mass computation indicates that all low-lying states of doubly heavy tetraquarks are unstable against strong decays to two heavy-light mesons, except for the ground states of nonstrange $bb$ tetraquarks.Comment: Created by Revtex 4, with 10 pages and 0 figures. Accepted by Common. Theor. Phy

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

UAS Detection and Negation

Unauthorized operation of a UAV may present privacy or security risks. A software-defined radio (SDR) or other receiver can be used to monitor a specified range of frequencies to provide detection of wireless communication signals suspected of relating to UAV operation. A protocol detector corresponding to a trained classifier can be applied to data packets demodulated by the SDR. A transmitter can then be triggered to provide warnings by injecting warning data into a video channel in response to the detected protocol. Control of the UAV can be established by transmitting simulated control commands that overwhelm the signals received from the UAVs normal remote control. If transmission of warnings or simulated control signals fail to suppress unwanted UAV operation, other actions can be triggered such as jamming or dispatch of an interceptor such as a surveillance UAV