COMPARATIVE ANALYSIS OF THE METHODS OF PIANISTS-PERFORMERS TRAINING IN ART INSTITUTIONS OF HIGHER EDUCATION OF CHINA AND UKRAINE

The article provides a comparative analysis of the methods of training pianists-performers in art institutions of higher education in China and Ukraine. It is found that both Chinese and Ukrainian art institutions of higher education have in their arsenal a powerful list of piano teaching methods that can improve the quality and efficiency of the educational process. The author refers to the leading methods of training pianists-performers in China traditional and innovative ones. It is stated that in Ukraine in the process of learning to play the piano such methods are used as: basic; non-verbal methods; didactic; general scientific; specific musical.Key words: pianists-performers, training of pianists-performers, methods, art institutions of higher education, China, Ukraine

Reconstitution and Mechanistic Studies on the Staphylococcal agr Quorum Sensing Circuit

Quorum sensing (QS) plays a central role in virulence induction in the commensal pathogen, Staphylococcus aureus. This bacterium secretes an auto-inducer peptide (AIP), a small, cyclic peptide containing a thiolactone linkage as an indicator of its population density, and up-regulates virulence gene expression in response to high extracellular AIP levels. We have investigated two key biochemical events in S. aureus QS and revealed several underlying regulatory mechanisms. The first such event, formation of the high energy thiolactone in AIP, is unusual in that it occurs directly through proteolysis of the precursor peptide, AgrD, without free-energy input from ATP hydrolysis. We showed that this proteolysis is, in line with the thermodynamic prediction, unfavorable and strongly reversible in vitro. As a consequence, rapid degradation of the concomitantly released C-terminal fragment of AgrD is required to power efficient AIP production in vivo. This observation provides a novel connection between protein homeostasis and QS in S. aureus. The second study focused on the AIP-sensing receptor histidine kinase (HK), AgrC, whose auto-phosphorylation exhibits several remarkable properties in our reconstitution system based on nanometer-scale lipid-bilayer discs (nanodiscs). Activation of this receptor by its native activator, for instance, requires a membrane environment enriched of anionic lipids mimicking the electrostatic property of the S. aureus cell membrane. This strong dependence on lipid composition might explain why homologous QS systems exist only in low-GC Gram-positive bacteria, or Firmicutes, whose cell membranes are predominantly highly anionic. AgrC also binds to ATP at an exceptionally weak affinity, likely due to its distinct adenine-binding pocket conserved only in a small subfamily of HK receptors existing also exclusively in Firmicutes. The low affinity to the nucleotide cofactor likely enables AgrC to sense the energy condition of the bacterium and shut down the QS regardless of the population density when energy starvation drives down the cellular ATP level. Even more intriguing is the plasticity of AgrC auto-kinase activity when bound to different ligands. This behavior contrasts with the generally accepted two-state model of HKs. To understand the plasticity of AgrC, we systematically perturbed the conformation of the AgrC kinase domain using a fusion protein strategy. We demonstrated that the conformational state of a helical linker preceding the kinase domain exercises rheostat-like control over the kinase activity. Using full-length AgrC embedded in nanodiscs, we showed that binding of activator and inhibitor peptides results in twisting of the linker in different directions. These findings provide the first view on molecular motions triggered by ligand binding on a membrane bound receptor HK. The smooth input-response landscape of the AgrC kinase domain also sheds new light on the mechanism of HK evolution through domain shuffling

Experimental Research on Pre-chamber Jet Ignition in Rapid Compression Machine and Natural Gas Engine

Pre-chamber jet ignition is a promising technology for spark ignition engines. In this paper, classical pre-chamber jet ignition and a new pre-chamber jet ignition method named flame accelerated ignition are investigated. Utilizing the connecting nozzles to generate the jets is the classical form of pre-chamber jet ignition. Two combustion modes were found by the RCM experiments with optical method: double stage combustion and single stage combustion. Double stage combustion mode takes place in the condition with relatively small nozzle dimension, showing long ignition delay and extremely short combustion duration. The jets cannot ignite the mixture directly. Instead, ignition happens at a central position in the main chamber after a lag time followed by the rapid development of the flame with similar speed in each direction. However, the double stage combustion mode has poor combustion stability due to the high randomness of the ignition process inside the main chamber. With single stage combustion mode, the ignition delay and the combustion duration can be shortened simultaneously with satisfying combustion stability. The combustion processes inside the pre-chamber and the main chamber take place continuously. The flame jet develops from the nozzle, composed of thin fire near the nozzle and approximately conical fire in the tip. The speed of flame jet exceeds 15 times than that of conventional flame propagation. According to the concept of pre-chamber jet ignition and the phenomenon of flame acceleration in tunnel, a new ignition method named flame accelerated ignition (FAI) is proposed. The flame acceleration tunnel can be regarded as a pre-chamber, where flame acceleration happens. Then the combustion in the main-chamber can be induced by the flame jet rushed out of the tunnel. The RCM experiments indicated that the combustion could be evidently enhanced by FAI. The flame jet maintains nearly cylindrical with favorable speed characteristic. Higher indicated thermal efficiency is gained by applying FAI compared to conventional spark ignition in the natural gas engine. In addition, introducing residual gas cavity into the flame acceleration tunnel could expand the misfire limit and improve the defect of FAI mode in lean-burn conditions

WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network

Deep learning has driven a great progress in natural and biological image processing. However, in material science and engineering, there are often some flaws and indistinctions in material microscopic images induced from complex sample preparation, even due to the material itself, hindering the detection of target objects. In this work, we propose WPU-net that redesigns the architecture and weighted loss of U-Net, which forces the network to integrate information from adjacent slices and pays more attention to the topology in boundary detection task. Then, the WPU-net is applied into a typical material example, i.e., the grain boundary detection of polycrystalline material. Experiments demonstrate that the proposed method achieves promising performance and outperforms state-of-the-art methods. Besides, we propose a new method for object tracking between adjacent slices, which can effectively reconstruct 3D structure of the whole material. Finally, we present a material microscopic image dataset with the goal of advancing the state-of-the-art in image processing for material science.Comment: technical repor

TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs

Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance of GNNs is usually unsatisfactory due to the highly sparse and irregular graph-based operations. To this end, we propose, TC-GNN, the first GPU Tensor Core Unit (TCU) based GNN acceleration framework. The core idea is to reconcile the "Sparse" GNN computation with "Dense" TCU. Specifically, we conduct an in-depth analysis of the sparse operations in mainstream GNN computing frameworks. We introduce a novel sparse graph translation technique to facilitate TCU processing of sparse GNN workload. We also implement an effective CUDA core and TCU collaboration design to fully utilize GPU resources. We fully integrate TC-GNN with the Pytorch framework for ease of programming. Rigorous experiments show an average of 1.70X speedup over the state-of-the-art Deep Graph Library framework across various GNN models and dataset settings

Intrinsically core-shell plasmonic dielectric nanostructures with ultrahigh refractive index

Topological insulators are a new class of quantum material s with metallic (edge) surface states and insulating bulk states. They demonstrate a variety of novel electronic and optical properties, which make them highly promising electronic, spintronic, and optoelectronic materials. We report on a novel conic plasmonic nanostructure that is made of bulk-insulating topological insulators and has an intrinsic core-shell formation. The insulating (dielectric) core of the nanocone displays an ultrahigh refractive index of up to 5.5 in the near-infrared frequency range. On the metallic shell, plasmonic response and strong backward light scattering were observed in the visible frequency range. Through in- tegrating the nanocone arrays into a-Si thin film solar cel ls, up to 15% enhancement of light absorption was predicted in the ultraviolet and visible ranges. With these unique features, the intrinsically core-shell plasmonic nanostructure paves a new way for designing low-loss and high-performance visible to infrared optical devices