929 research outputs found
The utilization of paper-level classification system on the evaluation of journal impact
CAS Journal Ranking, a ranking system of journals based on the bibliometric
indicator of citation impact, has been widely used in meso and macro-scale
research evaluation in China since its first release in 2004. The ranking's
coverage is journals which contained in the Clarivate's Journal Citation
Reports (JCR). This paper will mainly introduce the upgraded version of the
2019 CAS journal ranking. Aiming at limitations around the indicator and
classification system utilized in earlier editions, also the problem of
journals' interdisciplinarity or multidisciplinarity, we will discuss the
improvements in the 2019 upgraded version of CAS journal ranking (1) the CWTS
paper-level classification system, a more fine-grained system, has been
utilized, (2) a new indicator, Field Normalized Citation Success Index (FNCSI),
which ia robust against not only extremely highly cited publications, but also
the wrongly assigned document type, has been used, and (3) the calculation of
the indicator is from a paper-level. In addition, this paper will present a
small part of ranking results and an interpretation of the robustness of the
new FNCSI indicator. By exploring more sophisticated methods and indicators,
like the CWTS paper-level classification system and the new FNCSI indicator,
CAS Journal Ranking will continue its original purpose for responsible research
evaluation
Exploring the Potential of Large Language Models in Computational Argumentation
Computational argumentation has become an essential tool in various fields,
including artificial intelligence, law, and public policy. It is an emerging
research field in natural language processing (NLP) that attracts increasing
attention. Research on computational argumentation mainly involves two types of
tasks: argument mining and argument generation. As large language models (LLMs)
have demonstrated strong abilities in understanding context and generating
natural language, it is worthwhile to evaluate the performance of LLMs on
various computational argumentation tasks. This work aims to embark on an
assessment of LLMs, such as ChatGPT, Flan models and LLaMA2 models, under
zero-shot and few-shot settings within the realm of computational
argumentation. We organize existing tasks into 6 main classes and standardise
the format of 14 open-sourced datasets. In addition, we present a new benchmark
dataset on counter speech generation, that aims to holistically evaluate the
end-to-end performance of LLMs on argument mining and argument generation.
Extensive experiments show that LLMs exhibit commendable performance across
most of these datasets, demonstrating their capabilities in the field of
argumentation. We also highlight the limitations in evaluating computational
argumentation and provide suggestions for future research directions in this
field
Investigating the Impact of Shading Effect on the Characteristics of a Large-Scale Grid-Connected PV Power Plant in Northwest China
Northwest China is an ideal region for large-scale grid-connected PV system installation due to its abundant solar radiation and vast areas. For grid-connected PV systems in this region, one of the key issues is how to reduce the shading effect as much as possible to maximize their power generation. In this paper, a shading simulation model for PV modules is established and its reliability is verified under the standard testing condition (STC) in laboratory. Based on the investigation result of a 20āMWp grid-connected PV plant in northwest China, the typical shading phenomena are classified and analyzed individually, such as power distribution buildings shading and wire poles shading, plants and birds droppings shading, and front-row PV arrays shading. A series of experiments is also conducted on-site to evaluate and compare the impacts of different typical shading forms. Finally, some feasible solutions are proposed to avoid or reduce the shading effect of PV system during operation in such region
Multi-stage collaborative efficiency measurement of scitech finance: network-DEA analysis and spatial impact research
Sci-tech and finance plays an increasingly important role and
have become an important driving force in economic development. In China, the problem of insufficient financial support for
sci-tech innovation is important to enterprises. According to the
internal relationship between different stages of Sci-tech and the
finance system, this paper is aimed at exploring the efficiency
measurement method between sci-tech and finance systems.
Firstly the multi-stage collaborative structure of sci-tech finance is
built, where the system of sci-tech is divided into three stages
including the R&D stage, transformation stage of sci-tech achievements and industrialization stage, and the financing channel is
the input of the finance system into the sci-tech system at different stages. The measurement method of the multi-stage collaborative efficiency between sci-tech and finance systems is put
forward by the framework of network DEA. Then, taking China as
an example, we collect the information of 30 provinces and cities
from 2009 to 2016 and measure the efficiency of each system
and the collaborative efficiency of the both. The efficiencyās spatial correlation is tested by means of Moran index. Finally, the
influencing factors of the collaborative efficiency are analyzed
based on the spatial econometric regression model, which considers the financing channels and human capital. To sum up, there
are significant differences in the sci-tech finance collaborative efficiency among regions in China. Among them, the collaborative
efficiency of Beijing, Shanghai and Jiangsu ranks in the top three.
Comparing the different stages of the sci-tech system, the commercialization stage is a weak link in many regions of China.
Human capital and financing channels of sci-tech finance have
different degrees of positive impact on the sci-tech finance collaborative efficiency. Among them, human capital plays a greater
role in promoting the sci-tech finance collaborative development
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