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The quality of the unstructured mesh has a considerable impact on the stability and accuracy of aerodynamic simulation in computational fluid dynamics (CFD). Typically, engineers spend a significant portion of their time on mesh quality evaluation to ensure a valid, high-quality mesh. The extensive manual interaction and a priori knowledge required to undertake an accurate and timely evaluation process have become a bottleneck in the idealized efficient CFD workflow. This paper aims to introduce a neural network-based quality evaluation approach for unstructured meshes to enable higher efficiency and the level of automation.

The paper investigates the capability of deep neural networks for the quality evaluation of unstructured meshes. For training the network, we build a training dataset for mesh quality learning algorithms. The dataset contains a rich variety of unstructured aircraft meshes with different mesh sizes, densities, cell distribution, growth ratios and cell numbers to ensure its diversity and availability. We also design a neural network, AircraftNet, to learn the effect of mesh quality on the convergent properties of the numerical solutions. The proposed network directly manipulates raw point data in mesh source files rather than passing it to an intermediate data representation. During training, AircraftNet extracts non-linear quality features from high-dimensional data spaces and then automatically predicts the overall quality of the input unstructured mesh.

The paper provides a series of experimental results on GPUs. It shows that AircraftNet is able to effectively analyze the quality-related features like mesh density and distribution from the extracted features and achieve high prediction accuracy on the proposed dataset with even a small number of training runs.

Because of the limited training dataset, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The paper publishes a benchmarking dataset for mesh quality learning algorithms and designs a novel neural network approach for unstructured mesh quality evaluation.

There is considerable research examining the consequences and contingency factors of customer participation in the service encounter. In comparison, there is disproportionately less research examining the antecedents of customer participation. This paper aims to propose and test an appraisal-emotive framework of the effects of front-line employees’ in-role and extra-role behaviours on customer participation.

A survey on 583 customers of retail banks in China has been conducted to test the framework. Structural equation modelling and dominance analysis have been used for hypotheses testing.

Employees’ extra-role behaviour (i.e. organisational citizenship behaviour or OCB) has a stronger effect than their in-role behaviour (i.e. role-prescribed behaviour) in inducing customer participation. These effects are mediated by customer emotions. Specifically, the effect of employees’ in-role behaviour on customer participation was mediated by customers’ positive and negative emotions, whereas the effect of employees’ OCB was mediated by customers’ positive emotions but not by their negative emotions.

The findings reveal that strategic management of employee behaviours can influence customer participation. While organisations often provide training to enhance employees’ in-role behaviour to deliver service performance, they should also recognise and encourage employees’ OCB as a means of increasing customer participation. In particular, employees who display positive emotions tend to evoke positive emotions in customers, which increase customer participation in the service encounter.

To the authors’ knowledge, this is one of the few studies in marketing to examine the differential effects of employees’ in-role and extra-role behaviours on customer participation. Importantly, the findings show that employees’ OCB is not only more effective than employees’ in-role behaviour in influencing customer participation but also these two behaviours have varying effects on customer emotions. These findings are new and contribute to the literatures on customer participation, value co-creation and human resource management.

The present paper aims to reveal how different cultures, as reflected by cultural norms, traditions, and social expectations, influence food waste behaviour in different regions of the world.

A systematic multifaceted literature review was employed as a main research tool.

The focal role of education and awareness campaigns in reducing household food waste and promoting responsible food consumption behaviours is revealed. The importance of guilt, behavioural control, negative attitudes towards leftovers, and social norms are among the most important factors predicting intentions to reduce food waste. Cultural beliefs significantly shape food attitudes and waste. Tailoring sustainable practices to traditions helps to ensure food security. Embracing cultural diversity can lead to the development of effective and sustainable food consumption patterns across different parts of the world.

To the best of the authors’ knowledge, this is the first paper fully devoted to revealing how different cultural backgrounds shape food consumption habits and which marketing strategies aiming to nudge positive changes in responsible food consumption are preferred in different cultural contexts.

Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

Integrality of surface mesh is requisite for computational engineering. Nonwatertight meshes with holes can bring inconvenience to applications. Unlike simple modeling or visualization, the downstream industrial application scenarios put forward higher requirements for hole-filling, although many related algorithms have been developed. This study aims at the hole-filling issue in industrial application scenarios.

This algorithm overcomes some inherent weakness of general methods and generates a high-level resulting mesh. Initially, the primitive hole boundary is filled with a more appropriate triangulation which introduces fewer geometric errors. And in order for better performances on shape approximation of the background mesh, the algorithm also refines the initial triangulation with topology optimization. When obtaining the background mesh defining the geometry and size field, spheres on it are packed to determine the vertex configuration and then the resulting high-level mesh is generated.

Through emphasizing geometry recovery and mesh quality, the proposed algorithm works well in hole-filling in industrial application scenarios. Many experimental results demonstrate the reliability and the performance of the algorithm. And the processed meshes are capable of being used for industrial simulation computations directly.

This paper makes input meshes more adaptable for solving programs through local modifications on meshes and perfects the preprocessing technology of finite element analysis (FEA).

The purpose of this study is use a bibliometric analysis to explore the relational nature of knowledge creation in WFM in operations. Companies live under constant pressure to find the best ways to plan their workforce, and the workforce emangement (WFM) is one of the biggest challenges faced by managers. Relevant research on WFM in operations has been published in a several range of journals that vary in their scope and readership, and thus the academic contribution to the topic remains largely fragmented.

To address this gap, this review aims to map research on WFM in operations to understand where it comes from and where it is going and, therefore, provides opportunities for future work. This study combined two bibliometric approaches with manual document coding to examine the literature corpus of WFM in operations to draw a holistic picture of its different aspects.

Content and thematic analysis of the seminal studies resulted in the extraction of three key research themes: workforce cross-training, planning workforce mixed methods and individual workforce characteristics. The findings of this study further highlight the gaps in the WFM in operations literature and raise some research questions that warrant further academic investigation in the future.

Likewise, this study has important implications for practitioners who are likely to benefit from a holistic understanding of the different aspects of WFM in operations.