Utilization of Boiler Slag from Pulverized-Coal-Combustion Power Plants in China for Manufacturing Acoustic Materials

número del art. 5705The potential utilization of boiler slag generated in large amounts from pulverized-coalcombustion (PCC) power plants has recently drawn much attention due to the serious problems caused to ecosystems. In order to make maximal use of the boiler slag and reduce the environmental risk it poses, this study focused on manufacturing acoustic materials using boiler slag from Chinese PCC power plants. Three promising acoustic materials were successfully manufactured from up to 80% boiler slag with different grain sizes, with the addition of 20% Portland cement. The density and compressive strength of the products were inversely proportional and the sound absorption coefficient was positively proportional to the grain size of the boiler slag. The best sound absorption coefficient was obtained in products made from the coarsest fraction of the boiler slag (MS-C). Nonetheless, all the boiler-slag-based acoustic products still demonstrated compressive strength and densities comparable to those of other acoustic materials made of Spanish bottom ash or other conventional/recycled materials. The acoustic products made from the coarsest fraction (MS-C) and medium fraction (MS-M) of the boiler slag presented good noise absorption characteristics, like those of the commercial coarse porous cement that is traditionally used as an acoustic product. Furthermore, the acoustic products were characterized by very low leach ability of potentially hazardous elements. Consequently, the manufacture of acoustic materials is a very promising application for boiler slag. On the one hand, it consumes huge amounts of boiler slag that is generated in large amounts in China. On the other hand, the acoustic products can be used extensively to produce road acoustic barriers with a high sound absorption efficiency, no significant physical or mechanical limitations and no environmental implicationFoundation of China 41972179Foundation of China 41972180National Key R&D Program of China 2018YFF0215400Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) CUGCJ181

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press