矿用钻孔深度测量仪的设计

设计了一种以STM32F103微控制器为控制核心,基于24位高精度模数转换器的钻孔深度测量仪器。利用弹性波回波测距原理,通过测量出钻孔中钻杆的长度,实现钻孔深度的快速、无损、准确的测量,主要用于井下瓦斯抽放钻孔、探放水钻孔等煤矿通防工作的施工验收。仪器小巧轻便,操作简单,单人即可完成测量工作,节约下井的人力物力,提高生产效率,最长测量长度可达250米,误差不超过1米。     

Microstructures and mechanical properties of B4C-TiB2-SiC composites fabricated by ball milling and hot pressing

B₄C-TiB₂-SiC composites were fabricated via hot pressing using ball milled B₄C, TiB₂, and SiC powder mixtures as the starting materials. The impact of ball milling on the densification behaviors, mechanical properties, and microstructures of the ceramic composites were investigated. The results showed that the refinement of the powder mixtures and the removal of the oxide impurities played an important role in the improvement of densification and properties. Moreover, the formation of the liquid phases during the sintering was deemed beneficial for densification. The typical values of relative density, hardness, bending strength, and fracture toughness of the composites reached 99.20%, 32.84?GPa, 858?MPa and 8.21?MPa?m^1/2, respectively. Crack deflection, crack bridging, crack branching, and microcracking were considered to be the potential toughening mechanisms in the composites. Furthermore, numerous nano-sized intergranular/intragranular phases and twin structures were observed in the B₄C-TiB₂-SiC composite.     

Semantic word shifts in a scientific domain

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.     

Do funded papers attract more usage?

Research funding has been seen as one of the most important resource in the reward system of science. And usage of publications denotes an interesting perspective of user behavior in scientific communication. This study aims to address the relationship between funding and Usage Count, which is a new metrics item established on the platform of Web of Science. Full records of 300,010 articles published in 2013 were downloaded in October 2015, and divided into six disciplines, including information science library science, education educational research, economics, computer science, materials science, and chemistry. Seven indicators were proposed to measure the impact, including Funding rate, Citation per paper, Usage rate, Usage per paper, Citation difference, Usage difference, and Conversion rate. It concluded funding has impact on usage and citation, and funded papers attract more usage, but varying in different disciplines. Usage Count can be used in the extension of citation metrics but with limits. This study originally engages with usage metrics and detected that there is positive correlation between usage and funding.     

A multi-criteria context-sensitive approach for social image collection summarization

Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features. The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identify attractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.