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Article
Publication date: 14 February 2025

Xuemei Li, Yuyu Sun, Yansong Shi, Yufeng Zhao and Shiwei Zhou

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote…

3

Abstract

Purpose

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

Design/methodology/approach

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Findings

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

Originality/value

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Available. Open Access. Open Access
Article
Publication date: 31 May 2024

Zirui Zeng, Junwen Xu, Shiwei Zhou, Yufeng Zhao and Yansong Shi

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of…

489

Abstract

Purpose

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

Design/methodology/approach

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

Findings

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

Practical implications

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

Originality/value

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

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Article
Publication date: 4 November 2019

Shuangshuang Li, Xintian Liu, Xiaolan Wang and Yansong Wang

During the running of automobile, the stabilizer bar is frequently subjected to the impact of complex random loads, which is prone to fatigue failure and accident. In regard to…

241

Abstract

Purpose

During the running of automobile, the stabilizer bar is frequently subjected to the impact of complex random loads, which is prone to fatigue failure and accident. In regard to this, the purpose of this paper is to study and discuss fatigue life of automobile stabilizer bar.

Design/methodology/approach

Durability bench test shows that failure is located at the joint of sleeve and stabilizer bar body. Based on the collection and compilation of micro-strain load spectrum of the stabilizer bar, the strain-life model is studied considering the influence of average stress and maximum stress at failure area. Seven-grade strain-life curves of the stabilizer bar are established. According to the principle of linear damage accumulation, the relationship between fatigue life and damage is discussed, then the fatigue life of stabilizer bar is predicted. Fatigue life evaluation is carried out from three aspects: reliability analysis, static analysis and fatigue life simulation.

Findings

The results show that the reliability of the test sample is 99.9 percent when the confidence is 90 percent and the durability is 1,073 load spectrum cycles; the ratios of predicted and simulated life to design life are 2.77 and 2.30, respectively.

Originality/value

Based on the road load characteristics of automobile stabilizer bar, the method of fatigue life prediction and evaluation is discussed, which provides a basis for the design and development of automobile chassis components.

Details

International Journal of Structural Integrity, vol. 11 no. 2
Type: Research Article
ISSN: 1757-9864

Keywords

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Article
Publication date: 17 January 2022

Xintian Liu, Que Wu, Shengchao Su and Yansong Wang

The properties of materials under impact load are introduced in terms of metal, nonmetallic materials and composite materials. And the application of impact load research in…

682

Abstract

Purpose

The properties of materials under impact load are introduced in terms of metal, nonmetallic materials and composite materials. And the application of impact load research in biological fields is also mentioned. The current hot research topics and achievements in this field are summarized. In addition, some problems in theoretical modeling and testing of the mechanical properties of materials are discussed.

Design/methodology/approach

The situation of materials under impact load is of great significance to show the mechanical performance. The performance of various materials under impact load is different, and there are many research methods. It is affected by some kinds of factors, such as the temperature, the gap and the speed of load.

Findings

The research on mechanical properties of materials under impact load has the characteristics as fellow. It is difficult to build the theoretical model, verify by experiment and analyze the data accumulation.

Originality/value

This review provides a reference for further study of material properties.

Details

International Journal of Structural Integrity, vol. 13 no. 2
Type: Research Article
ISSN: 1757-9864

Keywords

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Article
Publication date: 6 December 2020

Zhiqiang Liang, Xintian Liu, Wang Yansong and Xiaolan Wang

This study aims to accurately evaluate the influence of various error intervals on the performance of the wiper.

124

Abstract

Purpose

This study aims to accurately evaluate the influence of various error intervals on the performance of the wiper.

Design/methodology/approach

The wiper structural system is decomposed into classical four-link planar for kinematics analysis, and it was modeled respectively by using interval method, universal grey number theory and enumeration approach depending on the nature of uncertainty.

Findings

The universal grey number theory is a viable methodology for the accurate analysis of uncertain structural system.

Originality/value

(1) The model of uncertain wiper structural system is established. (2) Universal grey number theory and new parameters are adopted to analyze the presence of uncertain wiper structural system. (3) Comparative analysis of response quantities is obtained by interval method, universal grey number theory and enumeration method.

Details

Assembly Automation, vol. 41 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

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