Chunping Zhou, Zheng Wei, Huajin Lei, Fangyun Ma and Wei Li
Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models…
Abstract
Purpose
Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.
Design/methodology/approach
In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.
Findings
The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.
Originality/value
This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.
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Guang-Zhi Zeng, Zhi-Wei Li, Sha Huang and Zheng-Wei Chen
Based on the aerodynamic loads and dynamic performances of trains, this study aims to investigate the effect of crosswinds and raindrops on intercity trains operating on viaducts…
Abstract
Purpose
Based on the aerodynamic loads and dynamic performances of trains, this study aims to investigate the effect of crosswinds and raindrops on intercity trains operating on viaducts to ensure the safe operation of intercity railways in metropolitan areas.
Design/methodology/approach
An approach coupled with the Euler multiphase model as well as the standard k-ɛ turbulence model is used to investigate the coupled flow feature surrounding trains and viaducts, including airflow and raindrops, and the numerical results are validated with those of the wind tunnel test. Additionally, the train’s dynamic response and the operating safety region in different crosswind speeds and rainfall is investigated based on train’s aerodynamic loads and the train wheel–rail dynamics simulation.
Findings
The aerodynamic loads of trains at varying running speeds exhibit an increasing trend as the increase of wind speed and rainfall intensity. The motion of raindrop particles demonstrates a significant similarity with the airflow in wind and rain environments, as a result of the dominance of airflow and the supplementary impacts of droplets. As the train’s operating speed ranged between 120 and 200 km/h and within a rainfall range of 20–100 mm/h, the safe operating region of trains decreased by 0.56%–7.03%, compared with the no-rain condition (0 mm/h).
Originality/value
The impact of crosswind speeds and rainfall on the train’s aerodynamic safety is studied, including the flow feature of crosswind and different particle-sized raindrops around the train and viaduct, aerodynamic loads coefficients suffered by the intercity train as well as the operating safety region of intercity trains on the viaduct.
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Ziwei Ma, Tonghui Wang, Zheng Wei and Xiaonan Zhu
The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples…
Abstract
Purpose
The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples from multivariate skew normal (SN) distributions.
Design/methodology/approach
Based on generalized Hotelling's T2 statistics, confidence regions are constructed for the difference between location parameters in two independent multivariate SN distributions. Simulation studies show that the confidence regions based on the closed SN model outperform the classical multivariate normal model if the vectors of skewness parameters are not zero. A real data analysis is given for illustrating the effectiveness of our proposed methods.
Findings
This study’s approach is the first one in literature for the inferences in difference of location parameters under multivariate SN settings. Real data analysis shows the preference of this new approach than the classical method.
Research limitations/implications
For the real data applications, the authors need to remove outliers first before applying this approach.
Practical implications
This study’s approach may apply many multivariate skewed data using SN fittings instead of classical normal fittings.
Originality/value
This paper is the research paper and the authors’ new approach has many applications for analyzing the multivariate skewed data.
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Wiyata Wiyata, Edy Yulianto, Muhammad Aliff Asyraff, Nur Adilah Md Zain and Mohd Hafiz Hanafiah
This study aims to examine theme park visitor interactions and their effects on experiences. It specifically aims to investigate perceived similarity’s impact on immersion and…
Abstract
Purpose
This study aims to examine theme park visitor interactions and their effects on experiences. It specifically aims to investigate perceived similarity’s impact on immersion and memorable experiences and how these experiences influence revisit intention and recommendations while exploring the mediating role of immersion within theme park settings.
Design/methodology/approach
A cross-sectional survey design was used, collecting data through purposive sampling from 494 respondents through a face-to-face survey at Jatim Theme Park, Indonesia.
Findings
The analysis confirms visitors’ perceived similarity significantly influences their sense of immersion and, in return, contributes to creating memorable experiences. In return, their memorable theme park experiences significantly impact their revisit intentions and willingness to recommend. In addition, this study also found that sense of immersion substantially mediates the relationship between perceived similarity and memorable experiences.
Research limitations/implications
This research bridges gaps in the existing literature by integrating social interaction factors with experiential outcomes, contributing to current theoretical advancement and practical applications in theme park research.
Practical implications
This study contributes to the advanced understanding of social identity theory, similarity-attraction theory and flow theory within theme park management settings, providing valuable practical insights for theme park managers.
Originality/value
This study’s originality lies in its integrated analysis of the interplay between perceived similarity among theme park visitors and their immersive experiences and how these elements collectively enhance memorable tourism experiences. By highlighting the mediating role of immersion, it offers novel insights into the mechanisms that drive visitor engagement and behavioral intentions, thus providing a better understanding of visitor dynamics in theme parks.
目的
本研究考察了主题公园游客互动及其对体验的影响。具体来说, 旨在调查感知相似性对沉浸感和难忘体验的影响, 以及这些体验如何影响游客的重游意图和推荐意愿, 同时探索沉浸感在主题公园环境中的中介作用。
方法
采用横断面调查设计, 通过在印度尼西亚贾丁主题公园进行面对面调查, 从494名受访者处收集数据, 采用目的性抽样方法。
发现
结构方程模型(SEM)分析确认, 游客的感知相似性显著影响他们的沉浸感, 进而有助于创造难忘的体验。反过来, 这些难忘的主题公园体验显著影响了他们的重游意图和推荐意愿。此外, 本研究还发现, 沉浸感在感知相似性与难忘体验之间的关系中起到了显著的中介作用。
研究意义
本研究通过将社会互动因素与体验结果结合, 填补了现有文献中的空白, 为主题公园研究的理论发展和实践应用做出了贡献。
实践意义
本研究有助于深入理解社会身份理论、相似吸引理论和流动理论在主题公园管理中的应用, 为主题公园经理提供了宝贵的实践洞见。
原创性
本研究的原创性在于对主题公园游客之间的感知相似性与他们的沉浸体验之间相互作用的综合分析, 以及这些因素如何共同增强难忘的旅游体验。通过突显沉浸感的中介作用, 本研究提供了关于驱动游客参与和行为意图的机制的新见解, 从而更好地理解了主题公园中的游客动态。
Objetivo
Este estudio analiza las interacciones de los visitantes de parques temáticos y sus efectos en las experiencias. En concreto, pretende investigar el impacto de la similitud percibida en la inmersión y las experiencias memorables y cómo estas experiencias influyen en la intención de volver a visitar el lugar y en las recomendaciones, al tiempo que explora el papel mediador de la inmersión en el entorno de los parques temáticos.
Diseño/metodología/enfoque
Se empleó un diseño de encuesta transversal, recogiendo datos mediante muestreo por conveniencia de 494 encuestados a través de una encuesta personal en el Parque Temático de Jatim, Indonesia.
Resultados
El análisis confirma que la similitud percibida por los visitantes influye significativamente en su sensación de inmersión y, a su vez, contribuye a crear experiencias memorables. Asimismo, las experiencias memorables en los parques temáticos influyen significativamente en su intención de volver a visitarlos y en su disposición a recomendarlos. Además, este estudio también evidencia que el sentido de inmersión media significativamente en la relación entre la similitud percibida y las experiencias memorables.
Limitaciones/implicaciones de la investigación
Esta investigación cubre lagunas en la literatura existente al integrar factores de interacción social con resultados experienciales, contribuyendo al avance teórico actual y a las aplicaciones prácticas en la investigación de parques temáticos.
Implicaciones prácticas
Este estudio contribuye a la comprensión avanzada de la teoría de la identidad social, la teoría de la similitud-atracción y la teoría del flujo en entornos de gestión de parques temáticos, proporcionando valiosas perspectivas prácticas para los gestores de parques temáticos.
Originalidad/valor
La originalidad de este estudio radica en su análisis integrado de la interacción entre la similitud percibida entre los visitantes de parques temáticos y sus experiencias de inmersión y cómo estos elementos potencian colectivamente las experiencias turísticas memorables. El papel mediador de la inmersión ofrece nuevas perspectivas sobre los mecanismos que impulsan el compromiso y las intenciones de comportamiento de los visitantes, proporcionando así una mejor comprensión de la dinámica de los visitantes en los parques temáticos.
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This paper aims to examine the relationships between the group affiliates’ dual legitimacy (membership legitimacy and societal legitimacy) and dual resource acquisition…
Abstract
Purpose
This paper aims to examine the relationships between the group affiliates’ dual legitimacy (membership legitimacy and societal legitimacy) and dual resource acquisition (intra-group and out-group), and the moderating roles of environmental uncertainty and munificence in the emerging economies.
Design/methodology/approach
This paper adopts hierarchical regression analysis to test the hypotheses based on the unique data of 251 group affiliated firms in China and applies the alternative measurements and alternative methodology of structural equation modeling into robustness check to confirm the results.
Findings
The results show as follows: the group affiliates can benefit from membership legitimacy for intra-group resource acquisition and out-group resource acquisition through the mediations of societal legitimacy and intra-group resource acquisition. However, in the linkage between affiliates’ membership legitimacy and intra-group resource acquisition and the linkage between societal legitimacy and out-group resource acquisition, environmental uncertainty plays the positive moderating roles while environmental munificence plays the negative moderating roles. Under the condition of high environmental uncertainty and low environmental munificence, the linkage between membership legitimacy and intra-group resource acquisition, and the linkage between societal legitimacy and out-group resource acquisition reach the strongest level.
Research limitations/implications
The findings highlight the importance of dual legitimacy building for group affiliates to acquire resources both inside and outside the business group when they operate in emerging economies characterized by high environmental uncertainty and low environmental munificence. However, it does not explore the contextual factors (e.g. institutional distance) affecting the relationship between the affiliate’s membership legitimacy and societal legitimacy. Then more group-level factors are expected to be included and explored with multi-level models in the future studies.
Originality/value
The findings reveal the mechanism of how group affiliates benefiting differently from dual legitimacy to acquire resources in the emerging economies, which also provide a new interpretation for the questions of who benefiting more from the group affiliation, how and why (Carney et al., 2009). This research also explores the moderating roles of task environmental characteristics (environmental uncertainty and environmental munificence) on the affiliate's dual legitimacy and dual resource acquisition, which helps understand why legitimacy building is more important in terms of resource acquisition in the emerging economy characterized by uncertainty and non-munificence.
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Meijiao Zhao, Yidi Wang and Wei Zheng
Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering…
Abstract
Purpose
Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.
Design/methodology/approach
To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).
Findings
The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.
Originality/value
The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.
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Guang-Zhi Zeng, Zheng-Wei Chen, Yi-Qing Ni and En-Ze Rui
Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of…
Abstract
Purpose
Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.
Design/methodology/approach
PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.
Findings
The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.
Originality/value
In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.
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Abstract
Purpose
This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.
Design/methodology/approach
A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.
Findings
The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.
Originality/value
Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.
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En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…
Abstract
Purpose
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.
Design/methodology/approach
A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.
Findings
Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.
Originality/value
In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.
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Simin An, Bo Li, Minxue Wang and Wei Zheng
This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply…
Abstract
Purpose
This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply chain.
Design/methodology/approach
To characterize the impact of blockchain on credit period and enterprise credit level, the study formulates a newsvendor model to analyze a supply chain in which a financially constrained supplier sells products to a financially sound manufacturer, subject to uncertain demand. The study investigates three repayment methods: the benchmark case without blockchain and two blockchain-enabled cases with the hybrid repayment mode and single repayment mode (SRM), respectively. The study derives and compares the equilibria under each repayment method to characterize their impact.
Findings
When the bank interest rate is low and the carbon cap is also low, choosing to implement blockchain technology leads to higher profitability for the manufacturer than not utilizing it. Within the framework of blockchain technology, when comparing the two repayment models, the manufacturer exhibits a preference for SRM. Furthermore, under specific conditions of the bank interest rate, blockchain technology can effectively facilitate consensus among supply chain members in terms of channel selection.
Practical implications
The results derived in this paper provide novel managerial implications to the capital-constrained members in terms of pricing decisions and order quantity under demand uncertainty considering blockchain technology, which transfers the creditor's rights to the bank and shortens the collection time. In addition, blockchain technology enables efficient and intelligent collaborative development of supply chains, which can reduce carbon emissions during the transportation of goods.
Originality/value
Few studies incorporate blockchain technology into supply chain finance, and this paper considers the credit period and capital's time value for a capital-constrained supplier facing the adoption of blockchain technology within a stochastic demand environment.