Qingxin Xie, Fujin Yi and Xu Tian
This paper aims to investigate the changes in living standard among families with different socio-economic status in China with the use of Engel's Coefficient. The authors develop…
Abstract
Purpose
This paper aims to investigate the changes in living standard among families with different socio-economic status in China with the use of Engel's Coefficient. The authors develop a decomposition methodology to figure out the driving forces behind changes in Engel's Coefficient, and investigate how dramatic economic growth, volatile food price and rapid nutrition transition affect living standard among different families.
Design/methodology/approach
The authors propose a statistical method to decompose the changes in living standard measured by Engel's Coefficient into structure effect, price effect, quantity effect and income effect. Using the China Health and Nutrition Survey data between 2000 and 2011, the authors estimate these four effects by employing a decomposition method.
Findings
Results show that Engel's Coefficient in China decreased by 8.7 percentage points (hereafter “pp”) during 2000–2011, where structure effect leads to 0.2 pp increase, price effect results in 17.7 pp increase, quantity effect brings about 12.4 pp decline and income effect contributes to 14.2 pp decline. Results indicate that rising food prices are the main obstacle to improve households' living standard. Typically, poor and rural families' living standard is more vulnerable to the rise in food prices, and they benefit less from income growth.
Originality/value
This study proposes a decomposition method to investigate the determinants of change in Engel's Coefficient, which provides a deeper understanding of how economic growth, food price change and nutrition transition affect people's living standard in different socio-economic groups in developing countries. This study also provides valuable insights on how to achieve common prosperity from the perspective of consumption upgrading.
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Kang Zhang, Xiaoxiao Zhu, Shimin Zhang, Qingxin Ding and Zichen He
Pipeline maintenance technology using smart isolation tool is becoming more widely used in the global scope. This paper aims to investigate the effects of parameters on the…
Abstract
Purpose
Pipeline maintenance technology using smart isolation tool is becoming more widely used in the global scope. This paper aims to investigate the effects of parameters on the frictional resistance between the slip and pipeline and the frictional characteristics under different lubrication films.
Design/methodology/approach
An experimental platform consisting of slip, pipeline and data acquisition system was developed, wherein the slip slips on the pipeline under different normal forces and velocities. In addition, three lubrication conditions, namely, dry wall, oil liquid and black powder on the wall, were investigated to study the effects of lubrications on the frictional coefficient and characteristics.
Findings
Research results indicate that the frictional force and coefficient were sensitive to normal force. The crude oil affected the frictional coefficient within a certain range of normal force, and the black powder enhanced the surface roughness in the natural gas pipeline. However, velocity had no effect on them. In addition, different contact behaviors could be observed from the frictional coefficient curves.
Originality/value
In this paper, the effects of normal force and velocity on frictional resistance of sliding slip during decelerating process in pipeline were investigated, and the effects of lubrication films on frictional characteristics were also revealed. The research results are of great value to improve the prediction accuracy of smart isolation tool, and also provide a guiding significance for the development of maintenance operation in pipelines.
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Xinquan Cheng, Yuanhong Chen, Pingfan Wang, YanXi Zhou, Xiaojing Wei, Wenjiang Luo and Qingxin Duan
This study aims to introduce an innovative framework for mining tourism reviews that not only excels in sentiment analysis accuracy but also prioritizes user-friendly design for…
Abstract
Purpose
This study aims to introduce an innovative framework for mining tourism reviews that not only excels in sentiment analysis accuracy but also prioritizes user-friendly design for enhanced usability.
Design/methodology/approach
Online reviews of China’s Five Sacred Mountains were analyzed using an integrated methodology. Sentiment analysis was performed using ChatGPT, bidirectional encoder representations from transformers (BERT) and convolutional neural networks, with ChatGPT demonstrating superior performance. Latent Dirichlet allocation extracted key attributes. Models including importance–performance analysis (IPA), asymmetric impact-performance analysis (AIPA) and importance–performance competitor analysis (IPCA) then synthesized findings.
Findings
The results demonstrate that ChatGPT outperforms both machine learning and lexicon-based models in sentiment recognition, exhibiting performance comparable to that of the BERT model. In the case study, integrating sentiment analysis outcomes with IPA reveals deficiencies in both topics and attributes. Moreover, the synergistic combination of IPA, AIPA and IPCA furnishes actionable recommendations for resource management and enables nuanced monitoring of sustainability attributes.
Practical implications
Leveraging this framework in conjunction with the ChatGPT platform for application development can bring practical convenience to the tourism industry. It supports sentiment analysis, topic categorization and opinion mining. Equipped with monitoring capabilities, it provides valuable insights for sustainable improvement, aiding managers in formulating effective marketing strategies.
Originality/value
This research develops a novel multimodel framework integrating various ML/DL techniques and business models in a synergistic way. It provides an innovative and highly accurate yet simple approach to tourism review mining and enhances accessibility of advanced artificial intelligence for sustainable tourism monitoring, addressing limitations of prior methods.
研究目的
本研究旨在引入一种创新的框架, 用于挖掘旅游评论, 不仅在情感分析准确性方面表现出色, 而且还优先考虑用户友好设计, 以提升可用性。
研究方法
本研究使用综合方法分析了中国五岳的在线评论, 使用ChatGPT进行情感分析。LDA提取了关键属性。然后, 包括IPA、AIPA和IPCA在内的模型综合了研究结果。
研究发现
结果表明, ChatGPT在情感识别方面优于机器学习和基于词典的模型, 表现与BERT模型相当。在案例研究中, 将情感分析结果与IPA结合起来揭示了主题和属性的不足。此外, IPA、AIPA和IPCA的协同组合为资源管理提供了可行的建议, 并实现了对可持续属性的细致监控
实践意义
结合ChatGPT平台在应用开发中利用该框架可以为旅游业带来实际便利。它支持情感分析、主题分类和意见挖掘。配备了监控功能, 为可持续改进提供了宝贵的见解, 帮助管理者制定有效的营销策略。
研究创新
本研究开发了一种新颖的多模型框架, 将各种ML/DL技术和商业模型以协同方式整合在一起。它提供了一种创新而高度准确但简单的方法, 用于旅游评论挖掘, 并提升了高级AI的可访问性, 以实现可持续旅游监测。