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…
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.
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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…
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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Yin Kedong, Shiwei Zhou and Tongtong Xu
To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The…
Abstract
Purpose
To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness.
Design/methodology/approach
The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis.
Findings
Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity.
Originality/value
The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.
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Kedong Yin, Yun Cao, Shiwei Zhou and Xinman Lv
The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems…
Abstract
Purpose
The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.
Design/methodology/approach
Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.
Findings
Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.
Originality/value
The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.
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Xuemei Li, Shiwei Zhou, Kedong Yin and Huichao Liu
The purpose of this paper is to measure the high-quality development level of China's marine economy and analyze corresponding spatial and temporal distribution characteristic.
Abstract
Purpose
The purpose of this paper is to measure the high-quality development level of China's marine economy and analyze corresponding spatial and temporal distribution characteristic.
Design/methodology/approach
Design and optimize the index system of high-quality development level of marine economy and use entropy and TOPSIS method for comprehensive evaluation.
Findings
The research finds that from 2017 to 2019, the high-quality development tendency of China's marine economy is on the rise, but the overall level is still low. The level of each subsystem has different distribution characteristics in different provinces and cities. Guangdong, Shandong and Shanghai have a high comprehensive level. According to the comprehensive level of high-quality development of marine economy, 11 coastal provinces are divided into three types: leading, general and backward.
Research limitations/implications
This paper clarifies the temporal and spatial distribution law of high-quality development level of China's marine economy, providing basis for promoting comprehensive and coordinated improvement of coastal provinces and cities.
Originality/value
An indicator system for the high-quality development level of the marine economy has been established, including social development guarantee, marine economic foundation, marine science and technology drive and green marine sustainability.
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Pinsheng Duan, Jianliang Zhou and Shiwei Tao
The outbreak of the pandemic makes it more difficult to manage the safety or health of construction workers in infrastructure construction. Risk events in construction workers'…
Abstract
Purpose
The outbreak of the pandemic makes it more difficult to manage the safety or health of construction workers in infrastructure construction. Risk events in construction workers' material handling tasks are highly relevant to workers' work-related musculoskeletal disorders. However, there are still many problems to be resolved in recognizing risk events accurately. The purpose of this research is to propose an automatic and non-invasive recognition method for construction workers in material handling tasks during the pandemic based on smartphone and machine learning.
Design/methodology/approach
This research proposes a method to recognize and classify four different risk events by collecting specific acceleration and angular velocity patterns through built-in sensors of smartphones. The events were simulated with anterior handling and shoulder handling methods in the laboratory. After data segmentation and feature extraction, five different machine learning methods are used to recognize risk events and the classification performances are compared.
Findings
The classification result of the shoulder handling method was slightly better than the anterior handling method. By comparing the accuracy of five different classifiers, cross-validation results showed that the classification accuracy of the random forest algorithm was the highest (76.71% in anterior handling method and 80.13% in shoulder handling method) when the window size was 0.64 s.
Originality/value
Less attention has been paid to the risk events in workers' material handling tasks in previous studies, and most events are recorded by manual observation methods. This study provided a simple and objective way to judge the risk events in manual material handling tasks of construction workers based on smartphones, which can be used as a non-invasive way for managers to improve health and labor productivity during the pandemic.
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Shiwei Wang, Qingxuan Jia, Gang Chen and Dan Liu
This paper aims to present a complete relative pose error model for robot calibration, considering both the relative distance error and the relative rotation error of the robot…
Abstract
Purpose
This paper aims to present a complete relative pose error model for robot calibration, considering both the relative distance error and the relative rotation error of the robot end-effector, which can improve calibration accuracy.
Design/methodology/approach
In this paper, the relative distance error model and the relative rotation error model of robot calibration are derived by ignoring high-order nonlinear errors, and the two models form into a complete relative pose error model. Besides, mathematical expectation of the nonlinear errors is calculated, indicating that they have little influence on calibration accuracy.
Findings
Comparative experiments have indicated that the proposed complete relative pose error model does better in robot calibration than only the distance error model.
Originality/value
The main contribution of this paper lies in the derivation of the relative rotation error model, which helps to form a complete relative pose error model for calibration. The proposed method improves calibration accuracy, with avoiding identifying the transformation matrix between the measurement system frame and the robot base frame.
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Zijian Wang, Ximing Xiao, Shiwei Fu and Qinggong Shi
This study aims to uncover the mechanisms behind the marginalization of county-level public libraries.
Abstract
Purpose
This study aims to uncover the mechanisms behind the marginalization of county-level public libraries.
Design/methodology/approach
The research surveyed 25 counties in central China, including Hubei, Chongqing, Hunan, and Guizhou provinces. Semi-structured interviews were conducted with library directors and deputy directors, focusing on main and branch library construction, cultural inclusivity, library assessment, and digital services.
Findings
Contributing factors to library marginalization were identified as economic pressure, institutional domain, longstanding issues, organizational entity, and societal misconceptions. Building on this, the study introduces the HBAC model to explain county-level public library marginalization. Considering the actual social context of these libraries, the article proposes a “3 + 1” approach to mitigate their marginalization.
Originality/value
The research methodology, analysis process, theoretical model, and recommendations provided could shed light on academic research and practical exploration in the field of public libraries globally.
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Yezhong Fang, Xiaotian Ji, Xingquan Zhang, Jun Wang, Bin Chen, Shiwei Duan, Jinyu Tong, Guangwu Fang and Shanbao Pei
The purpose of this paper is to investigate the dynamic forming process of the micro dent fabricated by laser shock processing on 2024-T3 aluminum alloy. The effect of laser pluse…
Abstract
Purpose
The purpose of this paper is to investigate the dynamic forming process of the micro dent fabricated by laser shock processing on 2024-T3 aluminum alloy. The effect of laser pluse energy on the deformation of micro dent was also discussed in detail.
Design/methodology/approach
It uses finite element analysis method and the corresponding laser shocking experiment.
Findings
The results demonstrate that the dynamic formation process of micro dent lasts longer in comparison with the shock wave loading time, and the depths of micro dents increase with the increasing laser energy. In addition, laser shocking with higher energy can result in more obvious pileup occurred at the outer edge of micro dent.
Originality/value
Surface micro dents can serve as fluid reservoirs and traps of the wear debris, which can decrease the effects of the wear and friction in rolling and sliding interfaces. The investigations can not only be propitious to comprehensively understand the forming mechanism of laser-shocked dent, but also be beneficial to get sight into the residual stress field induced by laser shocking.
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Weilang Cai, Dongqi Hua, Sihao Li, Shiwei Xue and Zhao Xu
BIM technology has a huge potential for improving the renovation efficiency for as-built buildings. However, due to the absence of raw design drawings and the complex interior…
Abstract
Purpose
BIM technology has a huge potential for improving the renovation efficiency for as-built buildings. However, due to the absence of raw design drawings and the complex interior environment, it is difficult to implement 3D reconstruction of building interiors in interior renovation projects. Therefore, this study proposes a 3D reconstruction framework of building interiors, with an aim to generate building interiors building information modeling (BIM) models quickly and accurately based on scan-to-BIM and generative design.
Design/methodology/approach
The proposed framework begins by reconstructing interior structured elements based on the scan-to-BIM process including collecting accurate information of as-built buildings by laser scanning, obtaining point clouds of structured elements through deep learning and developing an efficient dynamo algorithm workflow for generating structured elements BIM model. For unstructured elements, intelligent layout design and efficient BIM generation are conducted by combining the BIM tools and generative design.
Findings
The successful implementation of the proposed framework in a conference room demonstrated the feasibility of the proposed framework. The semantic segmentation scheme based on deep learning also exhibited excellent recognition and high efficiency for interior structured elements. Furthermore, it is proved that the combination of scan-to-BIM and generative design has high application value in the 3D reconstruction of building interiors.
Originality/value
On one hand, a feasible framework is proposed to generate BIM model of building interiors, improve interoperability among different software tools, streamline the complexity of the scan-to-BIM process and meet the reconfiguration requirement of unstructured elements layout scheme in interior renovation projects. On the other hand, the use of BIM and various emerging technologies can drive digital transformation and further advance the industrialization process of interior renovation in as-built buildings.