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Small fatigue crack growth in high strength aluminium alloys
This work presents an investigation of small fatigue crack growth behaviour in high strength aluminium alloys. Variation in slip distribution in Al-Zn-Mg-Cu alloys caused by the addition of different volume fractions of dispersoids, was not found to noticeably affect small fatigue crack growth rates. Small fatigue cracks grew more rapidly than long fatigue cracks under the same nominal AK and exhibited the familiar growth pattern of deceleration and acceleration.
The selected area electron channelling pattern (SAECP) method was used to measure the plastic zone size (PZS) and shape of small fatigue cracks. A novel experiment enabling PZS measurements on growing small fatigue cracks was developed to investigate the development of plastic zone size and shape with crack growth and the relation between PZS and small crack growth rates. It was revealed that small crack growth was accompanied by relatively large plastic zone sizes, with the ratio of plastic zone size to half crack length ranging from 0.8 to 0.2 and that small crack growth rates were proportional to their plastic zone sizes.
It was observed that small fatigue cracks began to decelerate when their relatively large plastic zones, not their crack tips, were blocked by grain boundaries. Both plastic zone size and shape were found to be dependent on crack length and growth morphology. Naturally initiated fatigue cracks were predominantly crystallographic in nature and were accompanied by a relatively long, slender plastic zone shape. However, during subsequent growth over 1-2 grain diameters this shape evolved, firstly to a semicircular shape, and finally to the lobed configuration typically found associated with long cracks.
The plastic deformation associated with small cracks was characterized by the introduction of a variable friction stress into the ECS dislocation model and the calculated PZS was used to correlate small crack growth. Crack deceleration was described by blocked PZS and the recovery of the growth was expressed by the necessity of a stress concentration within the plastic zone to overcome the grain boundary barrier to operate a dislocation source in the next grain. The prediction was compared with the experimental results and showed good agreement
Identification-method research for open-source software ecosystems
In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework
Healthy or Not: A Way to Predict Ecosystem Health in GitHub
With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development
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