In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures（450℃, 500℃, and 550℃） and over a range of time periods（2.5, 5, 7.5, and 10 h）, and ...In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures（450℃, 500℃, and 550℃） and over a range of time periods（2.5, 5, 7.5, and 10 h）, and at a fixed gas N2：H2 ratio of 75vol%：25vol%. The morphology of samples was studied using optical microscopy and scanning electron microscopy, and the formed phase of each sample was determined by X-ray diffraction. The elemental depth profile was measured by energy dispersive X-ray spectroscopy, wavelength dispersive spectroscopy, and glow dispersive spectroscopy. The hardness profile of the samples was identified, and the microhardness profile from the surface to the sample center was recorded. The results show that ε-nitride is the dominant species after carrying out plasma nitriding in all strategies and that the plasma nitriding process improves the hardness up to more than three times. It is found that as the time and temperature of the process increase, the hardness and hardness depth of the diffusion zone considerably increase. Furthermore, artificial neural networks were used to predict the effects of operational parameters on the mechanical properties of plastic mold steel. The plasma temperature, running time of imposition, and target distance to the sample surface were all used as network inputs; Vickers hardness measurements were given as the output of the model. The model accurately reproduced the experimental outcomes under different operational conditions; therefore, it can be used in the effective simulation of the plasma nitriding process in AISI P20 steel.展开更多
With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a f...With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.展开更多
文摘In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures（450℃, 500℃, and 550℃） and over a range of time periods（2.5, 5, 7.5, and 10 h）, and at a fixed gas N2：H2 ratio of 75vol%：25vol%. The morphology of samples was studied using optical microscopy and scanning electron microscopy, and the formed phase of each sample was determined by X-ray diffraction. The elemental depth profile was measured by energy dispersive X-ray spectroscopy, wavelength dispersive spectroscopy, and glow dispersive spectroscopy. The hardness profile of the samples was identified, and the microhardness profile from the surface to the sample center was recorded. The results show that ε-nitride is the dominant species after carrying out plasma nitriding in all strategies and that the plasma nitriding process improves the hardness up to more than three times. It is found that as the time and temperature of the process increase, the hardness and hardness depth of the diffusion zone considerably increase. Furthermore, artificial neural networks were used to predict the effects of operational parameters on the mechanical properties of plastic mold steel. The plasma temperature, running time of imposition, and target distance to the sample surface were all used as network inputs; Vickers hardness measurements were given as the output of the model. The model accurately reproduced the experimental outcomes under different operational conditions; therefore, it can be used in the effective simulation of the plasma nitriding process in AISI P20 steel.
文摘With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.
