12,388 research outputs found
Supplementary_File_(1) - Reliability and validity of the Chinese version of the Breathlessness Beliefs Questionnaire
No full text
Supplementary_File_(1) for Reliability and validity of the Chinese version of the Breathlessness Beliefs Questionnaire by Qing Wu, AiMin Guo, YanWei Zhao, SiJia Li, and Hui Huang in Chronic Respiratory Disease
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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
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
Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction
No full textThe multiobjective vehicle routing problem considering customer satisfaction MVRPCS involves
the distribution of orders from several depots to a set of customers over a time window. This
paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm MOQEA
for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The
degree of customer satisfaction is represented by proposing an improved fuzzy due-time window,
and the optimization problem is modeled as a mixed integer linear program. In the MOQEA,
nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid
is designed to achieve the diversity of solution sets; that is, the number of grids in each generation
is not fixed but is automatically adjusted based on the distribution of the current generation of
nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical
benchmark problems. Results of numerical simulation and comparison show that the established
model is valid and the MOQEA is effective for MVRPCS
Designs with a simple automorphism group
No full textThe classification of the 2-designs with lambda = 2 admitting a flag-transitive automorphism groups with socle PSL(2, (2 , q ) is completed by settling the two open cases in [2]. The result is achieved by using conics and hyperovals of PG (2 , q )
sj-doc-1-acr-10.1177_02841851221127563 - Supplemental material for The additional value of high-resolution vessel wall imaging in screening suitable chronic internal carotid artery occlusion candidates for endovascular recanalization: comparison with digital subtraction angiography
No full textSupplemental material, sj-doc-1-acr-10.1177_02841851221127563 for The additional value of high-resolution vessel wall imaging in screening suitable chronic internal carotid artery occlusion candidates for endovascular recanalization: comparison with digital subtraction angiography by Yanwei Hou, Lei Ren, Chen Cao, Heliang Zhang, Wei Zhao, Jinxia Zhu, Zaiyu Guo and Shuang Xia in Acta Radiologica</p
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
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
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