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Article
Publication date: 6 July 2020

Mengsi Cai, Ge Huang, Yuejin Tan, Jiang Jiang, Zhongbao Zhou and Xin Lu

With the development of global food markets, the structural properties of supply chain networks have become key factors affecting the ability to evaluate and control infectious…

338

Abstract

Purpose

With the development of global food markets, the structural properties of supply chain networks have become key factors affecting the ability to evaluate and control infectious diseases and food contamination. The purpose of this paper is to describe and characterize the nationwide pork supply chain networks (PSCNs) in China and to demonstrate the potential of using social network analysis (SNA) methods for accessing outbreaks of diseases and contaminations.

Design/methodology/approach

A large-scale PSCN with 17,582 nodes and 49,554 edges is constructed, using the pork trade data collected by the National Important Products Traceability System (NIPTS) in China. A network analysis is applied to investigate the static and dynamic characteristics of the annual network and monthly networks. Then, the metric maximum spreading capacity (MSC) is proposed to quantify the spreading capacity of farms and estimate the potential maximum epidemic size. The structure of the network with the spatio-temporal pattern of the African swine fever (ASF) outbreak in China in 2018 was also analysed.

Findings

The results indicate that the out-degree distribution of farms approximately followed a power law. The pork supply market in China was active during April to July and December to January. The MSC is capable of estimating the potential maximum epidemic size of an outbreak, and the spreading of ASF was positively correlated with the effective distance from the origin city infected by ASF, rather than the geographical distance.

Originality/value

Empirical research on PSCNs in China is scarce due to the lack of comprehensive supply chain data. This study fills this gap by systematically examining the nationwide PSCN of China with large-scale reliable empirical data. The usage of MSC and effective distance can inform the implementation of risk-based control programmes for diseases and contaminations on PSCNs.

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Article
Publication date: 30 January 2023

Zhongbao Liu and Wenjuan Zhao

In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly…

169

Abstract

Purpose

In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.

Design/methodology/approach

In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.

Findings

The comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.

Originality/value

The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.

Details

Data Technologies and Applications, vol. 57 no. 4
Type: Research Article
ISSN: 2514-9288

Keywords

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Article
Publication date: 2 August 2022

Zhongbao Liu and Wenjuan Zhao

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective…

145

Abstract

Purpose

The research on structure function recognition mainly concentrates on identifying a specific part of academic literature and its applicability in the multidiscipline perspective. A specific part of academic literature, such as sentences, paragraphs and chapter contents are also called a level of academic literature in this paper. There are a few comparative research works on the relationship between models, disciplines and levels in the process of structure function recognition. In view of this, comparative research on structure function recognition based on deep learning has been conducted in this paper.

Design/methodology/approach

An experimental corpus, including the academic literature of traditional Chinese medicine, library and information science, computer science, environmental science and phytology, was constructed. Meanwhile, deep learning models such as convolutional neural networks (CNN), long and short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) were used. The comparative experiments of structure function recognition were conducted with the help of the deep learning models from the multilevel perspective.

Findings

The experimental results showed that (1) the BERT model performed best, with F1 values of 78.02, 89.41 and 94.88%, respectively at the level of sentence, paragraph and chapter content. (2) The deep learning models performed better on the academic literature of traditional Chinese medicine than on other disciplines in most cases, e.g. F1 values of CNN, LSTM and BERT, respectively arrived at 71.14, 69.96 and 78.02% at the level of sentence. (3) The deep learning models performed better at the level of chapter content than other levels, the maximum F1 values of CNN, LSTM and BERT at 91.92, 74.90 and 94.88%, respectively. Furthermore, the confusion matrix of recognition results on the academic literature was introduced to find out the reason for misrecognition.

Originality/value

This paper may inspire other research on structure function recognition, and provide a valuable reference for the analysis of influencing factors.

Details

Library Hi Tech, vol. 42 no. 3
Type: Research Article
ISSN: 0737-8831

Keywords

Available. Open Access. Open Access
Article
Publication date: 7 June 2021

Tamoor Khan, Jiangtao Qiu, Ameen Banjar, Riad Alharbey, Ahmed Omar Alzahrani and Rashid Mehmood

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.

2131

Abstract

Purpose

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.

Design/methodology/approach

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

Findings

The Johansen test findings have shown that the fruit crop growth, energy use, CO2 emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO2 emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO2 will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO2 emissions to agricultural areas to fruit crops.

Originality/value

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

Details

International Journal of Climate Change Strategies and Management, vol. 13 no. 2
Type: Research Article
ISSN: 1756-8692

Keywords

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