12,380 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
sj-docx-1-pie-10.1177_09544089221133180 - Supplemental material for Study on the leakage in water-injected twin-screw steam compressor
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089221133180 for Study on the leakage in water-injected
twin-screw steam compressor by Yafen Tian, Hao Yuan, Yanting Geng and Zhaorui Zhao in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Raw data of Zhao et al., 2022, Geoderma
No full textRaw data associated with Zhao et al., 2022, Geoderma. Any use of the data set should be approved by the corresponding author Kai Yue at "[email protected]".</p
Chao Yuen Ren (1892–1982)
No full textY. R. Chao is easily the most famous linguist to have come out of China. Born before the end of the last dynasty in China, he received a traditional Confucian education, but was also one of the first Chinese people to be sent to the West for training in modern Western science (under the Boxer Indemnity Fund). The remarkable breadth and scope of his studies included physics, mathematics, linguistics, musical and literary composition, and translation, and he was a pioneer in many of these fields
A C(sp<sup>2</sup> )−H Dehydrogenation of Heteroarenes and Arenes by a Functionalized Aluminum Hydride
No full textExtensive high-resolution photoassociation spectra and perturbation analysis of 2(0‒) long-range state of ultracold RbCs molecules
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Previous issue date: 2019-06-18We report high-resolution photoassociation (PA) spectra of RbCs in the long-range state.
Transitions to more than fifty vibrational levels were recorded with the largest binding energy being
507.5 cm. By fitting the experimental transition frequencies to the improved LeRoy-Bernstein
formula, the coefficient for the potential energy curve of the state was determined to
be -150997 a.u.. Perturbation-induced energy level shift and state mixing of the long-range
and states have been analyzed using an effective Hamiltonian that may be applied to
mixing between other excited states of RbCs, as well as other heteronuclear diatomic molecules.
Experimentally observed PA transitions to the vibrational level of the state and a
vibrational perturbing level in the state have been fit using the effective Hamiltonian, which
provides the accurate value of the perturbation coefficient . The experimentally determined
rovibronic structure and the deperturbation analysis provide critical information for the search of
new schemes for efficient production of ultracold RbCs molecules in the ground state
Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation
No full textCitation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods
Modeling nitrogen nutrient loss and ammonia emissions from animal farms
No full textAuthor institution (Zhao): Department of Food, Agricultural, and Biological Engineering, The Ohio State Universit
Author Correction: Antiviral fibrils of self-assembled peptides with tunable compositions
No full textCorrection to: Nature Communicationshttps://doi.org/10.1038/s41467-024-45193-3, published online 07 February 2024 This Article contains an error in the spelling of the author Boyang Zhao, which was incorrectly given as Boyan Zhao. The error has been corrected in the PDF and HTML versions of the Article
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