Intensive care unit patients are heavily monitored, and several clinically-relevant parameters are routinely extracted from high resolution signals. Objective : The goal of the 2016 PhysioNet/CinC Challenge was to encourage the creation of an intelligent system that fused information from different phonocardiographic signals to create a robust set of normal/abnormal signal detections. Approach : Deep convolutional neural networks and mel-frequency spectral coefficients were used for recognition of normal–abnormal phonocardiographic signals of the human heart. This technique was developed using the PhysioNet.org Heart Sound database and was submitted for scoring on the challenge test set. Main results : The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.

Numerical simulation of the acoustic agglomeration of micron-sized aerosol particles by a discrete element method (DEM) is demonstrated. The conventional DEM technique used in granular dynamics is modified for simulation of the acoustically induced attractive motion of particles in an incompressible fluid. The problem-specific orthokinetic collision, acoustic wake, and mutual radiation pressure effects yielding binary attraction and sticking of the particles are considered within the DEM approach. The acoustically induced agglomeration of two aerosol particles and 3D particles’ system is illustrated by numerical results. Numerical values of the agglomeration time of two particles obtained for a wide range of acoustic frequencies are analyzed. Comparison of various hydrodynamic effects with available experimental data indicates an overestimated contribution of the mutual radiation pressure model. The performance of the DEM technique and specific features concerning long-range interactions between particles are demonstrated by simulating 3D particles’ systems. The obtained numerical results illustrating the variation of number concentration with time are compared to available experimental data of coal-fired fly ash particles’ agglomeration; a relatively good agreement with the acoustic wake mechanism is observed.

The paper describes the development and performance of parallel algorithms for the discrete element method (DEM) software. Spatial domain decomposition strategy and message passing inter-processor communication have been implemented in the DEMMAT code for simulation of visco-elastic frictional granular media. The novel algorithm combining link-cells for contact detection, the static domain decomposition for parallelization and MPI data transfer for processors exchanging particles has been developed for distributed memory PC clusters. The parallel software DEMMAT_PAR has been applied to model compacting of spherical particles in the rectangular box. Two benchmark problems with different numbers of particles have been solved in order to measure parallel efficiency of the code. The inter-processor communication has been examined in order to improve domain decomposition topology and to achieve better load balancing. The speed-up equal to 11 has been obtained on 16 processors. The parallel performance study has been performed on the PC cluster VILKAS of Vilnius Gediminas Technical University, Lithuania.