Study of Double-Edged Gun Drills with Different Edge Types in Drilling Compacted Graphite Iron

Deep-hole machining is an important part in the field of mechanical processing of diesel engine. Gun drill has been widely used in deep-hole machining because of its high dimensional accuracy, high efficiency and good straightness. Through experiments on drilling compacted graphite iron with two different edge types of double-edged gun drills, the spindle power, axial force and tool wear were analyzed and found out one edge type which is more suitable for processing compacted graphite iron. This paper presents a simulation of deep hole drilling to validate the analysis. The research results have important guiding significance for deep hole processing of compacted graphite iron.

Thermal Conductivity of Graphene on Nanostructure Size from Nonequilibrium Molecular Dynamics Simulations

Thermal transport of graphene occupies a unique place in thermal management of electronic devices, especially for nanosize devices with high-density integration and high dissipated power. The structure of graphene on nanometer scale changes its thermal conductance. Here, the thermal characters of graphene have been researched by nonequilibrium molecular dynamics simulation (NEMDS) at room temperature. Special attention is focused on the edge type (zigzag or armchair) and nanostructure size dependence of conductivity for heat. The consequences suggest that the thermal conductivity of zigzag edge has been higher than that of armchair, which is because of the higher phonon group velocities. Furthermore, thermal conductivity shows a rising tendency, when the model is calculated from length of 21.84 nm to 43.78 nm. The result indicates that the thermal property performs a strong dependence on nanostructure size which is less than phonon mean free path (775 nm). Our research highlights the significance of structure attribute relationships together with providing useful guideline in calculations for nanosize devices thermal management.

Heterogeneous networks integration for disease–gene prioritization with node kernels

Motivation The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online.