Duncai Lei, Xiannian Kong, Siyu Chen, Jinyuan Tang and Zehua Hu
The purpose of this paper is to investigate the dynamic responses of a spur gear pair with unloaded static transmission error (STE) excitation numerically and experimentally and…
Abstract
Purpose
The purpose of this paper is to investigate the dynamic responses of a spur gear pair with unloaded static transmission error (STE) excitation numerically and experimentally and the influences of the system factors including mesh stiffness, error excitation and torque on the dynamic transmission error (DTE).
Design/methodology/approach
A simple lumped parameters dynamic model of a gear pair considering time-varying mesh stiffness, backlash and unloaded STE excitation is developed. The STE is calculated from the measured tooth profile deviation under the unloaded condition. A four-square gear test rig is designed to measure and analyze the DTE and vibration responses of the gear pair. The dynamic responses of the gear transmission are studied numerically and experimentally.
Findings
The predicted numerical DTE matches well with the experimental results. When the real unloaded STE excitation without any approximation is used, the dynamic response is dominated by the mesh frequency and its high order harmonic components, which may not be result caused by the assembling error. The sub-harmonic and super-harmonic resonant behaviors are excited because of the high order harmonic components of STE. It will not certainly prevent the separations of mesh teeth when the gear pair is under the condition of high speed and heavy load.
Originality/value
This study helps to improve the modeling method of the dynamic analysis of spur gear transmission and provide some reference for the understanding of the influence of mesh stiffness, STE excitation and system torque on the vibration behaviors.
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Xishuang Jing, Duanping Lv, Fubao Xie, Chengyang Zhang, Siyu Chen and Ben Mou
3D printing technology has the characteristics of fast forming and low cost and can manufacture parts with complex structures. At present, it has been widely used in various…
Abstract
Purpose
3D printing technology has the characteristics of fast forming and low cost and can manufacture parts with complex structures. At present, it has been widely used in various manufacturing fields. However, traditional 3-axis printing has limitations of the support structure and step effect due to its low degree of freedom. The purpose of this paper is to propose a robotic 3D printing system that can realize support-free printing of parts with complex structures.
Design/methodology/approach
A robotic 3D printing system consisting of a 6-degrees of freedom robotic manipulator with a material extrusion system is proposed for multi-axis additive manufacturing applications. And the authors propose an approximation method for the extrusion value E based on the accumulated arc length of the already printed points, which is used to realize the synchronous movement between multiple systems. Compared with the traditional 3-axis printing system, the proposed robotic 3D printing system can provide greater flexibility when printing complex structures and even realize curved layer printing.
Findings
Two printing experiments show that compared with traditional 3D printing, a multi-axis 3D printing system saves 47% and 79% of materials, respectively, and the mechanical properties of curved layer printing using a multi-axis 3D printing system are also better than that of 3-axis printing.
Originality/value
This paper shows a simple and effective method to realize the synchronous movement between multiple systems so as to develop a robotic 3D printing system that can realize support-free printing and verifies the feasibility of the system through experiments.
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Sheng Yao, Siyu Wei and Lining Chen
Existing studies have shown that all kinds of audit risks greatly affect audit pricing for accounting firms. However, it is still unclear whether environmental risks caused by…
Abstract
Purpose
Existing studies have shown that all kinds of audit risks greatly affect audit pricing for accounting firms. However, it is still unclear whether environmental risks caused by environmental violations lead to a high audit fee. This study aims to investigate whether accounting firms raise audit fees after client firms have violated environmental regulations or have been punished for such violations.
Design/methodology/approach
This study selects listed firms with environmental violations between 1994 and 2018 as the treatment sample and match the treatment group with a control group of firms from the same industry, of similar asset size and with no environmental violations for the same time period. Then, this study constructs a difference-in-difference (DID) model to explore the impact of firm environmental violations (or punishment for environmental violations) on the audit pricing.
Findings
This study finds that accounting firms tend to raise audit fees after client firms have violated environmental regulations or have been punished for such violations, and this increasing effect is different due to environmental regulation intensity, regional span and internal control defects. Further evidences show that environmental violations influence audit fees through financial restatement, whereas environmental punishments impact audit fees through earnings management and risk-taking.
Originality/value
This study enriches the literature on determining factors of audit fees and economic consequences of environmental violations and provides empirical supports to understand the pricing behavior of accounting firms.
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Yanlin Sun, Siyu Liu and Shoudong Chen
This paper aims to identify the direct impact of fund style drift on the risk of stock price collapse and the intermediary mechanism of financial risk, so as to better protect the…
Abstract
Purpose
This paper aims to identify the direct impact of fund style drift on the risk of stock price collapse and the intermediary mechanism of financial risk, so as to better protect the interests of minority investors.
Design/methodology/approach
This paper takes all the non-financial companies on the Chinese Growth Enterprise Market from 2011 to 2020 as study object and selects securities investment funds of their top ten circulation stocks to study the relationship between fund style drift and stock price crash risk.
Findings
Fund style drift is likely to add stock price crash risk. Financial risk is positively correlated with stock price crash risk. Fund style drift affects stock price crash risk via the mediating effect of financial risk, and fund style drift and financial risk have a marked impact on the stock price crash risk of non-state enterprises, yet a non-significant impact on that of state-owned enterprises.
Originality/value
This paper links fund style drift with stock price crash risk in an exploratory manner and enriches the study perspectives of relationship between institutional investors’ behaviors and stock price crash risk, thus enjoying certain academic value. On the one hand, it furnishes a new approach to the academic frontier issue concerning financial risk and stock price crash risk, and proves that financial risk is positively correlated with stock price crash risk. On the other hand, it regards financial risk as a mediating variable of fund style drift for stock price crash risk and further explores different influencing mechanism of institutional investors’ behaviors.
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Chaochao Guo, Youchao Sun, Siyu Su and Chong Peng
The purpose of this paper is to assess the risk of controlled flight into terrain (CFIT) for airlines and to develop a practical method for evaluating and predicting CFIT risk to…
Abstract
Purpose
The purpose of this paper is to assess the risk of controlled flight into terrain (CFIT) for airlines and to develop a practical method for evaluating and predicting CFIT risk to ensure safe and efficient airline operations.
Design/methodology/approach
In accordance with the monitoring project specification issued by the Flight Standards Department of the Civil Aviation Administration of China (CAAC), a preliminary draft of evaluation indicators for CFIT risk was developed based on the literature review and semi-structured interviews. Fifteen aviation experts were then selected and invited to participate in a Delphi method to revise the draft. Analytic hierarchy process (AHP) and entropy weight method were used to determine the combined weight of the indicators. The variable fuzzy set model and quick access recorder (QAR) data were applied to evaluate the CFIT risk of an airline from 2007 to 2018, and the classification results were compared with actual operational data.
Findings
The research findings reveal that the six most significant monitoring items affecting CFIT risk are incorrect configuration settings during landing, loss of altitude during climbing, ground proximity warning, G/S deviation, flap extension delay during landing and incorrect takeoff configuration. The CFIT risk of airlines has shown an increasing trend since 2015. The values in 2010, 2017 and 2018 were greater than 2 and less than 2.5, indicating that the CFIT risk is at Level 2, close to Level 3, and the risk is low but approaching medium.
Practical implications
Using the combination weight determined by AHP and entropy weight method to rank the weight of 15 monitoring items, airlines can take necessary measures (simulator training, knowledge training) to reduce the occurrence of monitoring items with high weight to reduce CFIT risk. This risk assessment method can quantitatively evaluate the CFIT risk of airlines and provide theoretical guidance and technical support for airlines to formulate safety management measures and flight training programs, enabling the interconnection between QAR data and flight quality.
Originality/value
The proposed method in this study differs from traditional approaches by offering a quantitative assessment of CFIT risk for airlines and enabling the interconnection between QAR data and flight quality.
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Siyu Li, Xiling Cui, Baofeng Huo and Xiande Zhao
The purpose of this paper is to explore the effects that customer structured and unstructured information sharing (IS) can have on customer operational and strategic coordination…
Abstract
Purpose
The purpose of this paper is to explore the effects that customer structured and unstructured information sharing (IS) can have on customer operational and strategic coordination and on supply chain performance (SCP). In addition, the study examines how customer IS influences customer coordination under various levels of demand uncertainty (DU).
Design/methodology/approach
The conceptual model for this study is designed on the basis of information-processing theory (IPT). Using data collected from 622 manufacturers in mainland China and Taiwan, the theoretical model is tested using the structural equation modeling method.
Findings
The authors find that both customer structured IS and unstructured IS are positively associated with customer strategic coordination. Customer structured IS increases customer operational coordination, but customer unstructured IS does not. DU positively moderates the relations between customer unstructured IS and strategic coordination, and between customer structured IS and operational coordination. Also, DU negatively moderates the relationship between customer structured IS and strategic coordination. Customer strategic coordination is positively related to SCP and to operational coordination. Customer operational coordination has no significant impact on SCP.
Originality/value
This study deepens our understanding of customer IS by distinguishing between customer structured and unstructured IS. The study also provides a greater understanding of customer coordination by making a distinction between the customer strategic and the operational coordination. The findings extend the empirical application of IPT. In addition, this study’s findings direct SC managers to apply varied customer IS practices that can enhance specific kinds of customer coordination activities, thereby enabling improved SCP.
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Juanyan Miao, Yiwen Li, Siyu Zhang, Honglei Zhao, Wenfeng Zou, Chenhe Chang and Yunlong Chang
The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for…
Abstract
Purpose
The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.
Design/methodology/approach
External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.
Findings
In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.
Originality/value
However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.
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Siyu Zhang, Ze Lin and Wii-Joo Yhang
This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN)…
Abstract
Purpose
This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.
Design/methodology/approach
This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.
Findings
This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.
Originality/value
This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.
研究目的
本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。
研究方法
本研究通过利用长短期记忆(LSTM)网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下:后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。
研究发现
本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。
研究创新
本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。
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Siyu Gong, Guanghua Sheng, Peter Peverelli and Jialin Dai
This study aims to develop a comprehensive conceptual framework to investigate how green brand positioning strategies positively impact consumer response. It focusses on…
Abstract
Purpose
This study aims to develop a comprehensive conceptual framework to investigate how green brand positioning strategies positively impact consumer response. It focusses on uncovering the causal mechanism in which such effect is mediated by brand stereotypes. Additionally, it outlines the moderating role of construal level in this formation process.
Design/methodology/approach
Three experimental studies were conducted to examine the hypotheses. Study 1 tests the positive influence of green brand positioning on consumer response. Study 2 tests the dual mediating effect of warmth and competence in the relationship between green brand positioning and consumer response. Study 3 further examines the moderating role of construal level in the effects of green brand positioning on brand stereotypes.
Findings
The findings reveal that green emotional positioning strategies are predominantly stereotyped as warm while green functional positioning strategies are predominantly stereotyped as competent. Both warm and competent mediate the effects of green brand positioning on consumer response. Furthermore, a congruency between green emotional positioning and high-level construal, as well as the match between green functional positioning and low-level construal, leads to more warmth and competence perception.
Originality/value
This study contributes to green brand management literature by proposing a brand stereotype-based mechanism to explain how green brand positioning strategies trigger consumers’ stereotyping process, leading to positive consumer response. This study also identifies the construal level as a moderating variable that impacts consumers’ warmth and competence perceptions towards two kinds of green brand positioning strategies. Managerially, the findings of this study provide managerial ideas for developing green branding strategies.
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Chunxia Qi, Mun Yee Lai, Lizhe Liu, Siyu Zuo, Haili Liang and Ruisi Li
This study explored how teachers change, what teachers learn and how they learn during the implementation of project-based learning through lesson study.
Abstract
Purpose
This study explored how teachers change, what teachers learn and how they learn during the implementation of project-based learning through lesson study.
Design/methodology/approach
In this study, three university researchers, one doctoral student and six mathematics school teachers formed a lesson study team. Using a qualitative research method, this study employed a locally integrating networking strategy to combine the modified Interconnected Model of Teacher Professional Growth (IMTPG) and Bannister's framework to describe the teachers' knowledge change when participating in a lesson study on project-based learning.
Findings
The research revealed that the school teachers' knowledge about authenticity and assessment in the context of project-based learning was changed after the lesson study and how the changes were triggered.
Originality/value
The study demonstrates how the networking of two different theories—modified IMTPG and Bannister's framework—contributes to a better understanding of the process of teachers' collective practice, as well as the knowledge change in PjBL. This networking was done by combining the two theories, which were superimposed at the domain of practice.