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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.

In this research, we explore the adverse impact of foreign ownership on operational security, a critical operational implication of the liability of foreignness (LOF).

The empirical analysis is based on a multi-country dataset from the World Bank Enterprises Survey, which contains detailed firm-level information from over 8,902 firms in 82 emerging market countries. We perform a series of robustness checks to further confirm our findings.

We find that a high ratio of foreign ownership is associated with an increased likelihood of security breaches and higher security costs. Our results also indicate that high levels of host countries’ institutional quality and firms’ local embeddedness can mitigate such vulnerability in operational security.

This study is one of the first to uncover the critical operational implication of the LOF, indicating that a high ratio of foreign ownership exposes firms to operational security challenges.

Organizations are partly responsible for the pollution in the world and are expected to contribute towards curbing climate change. Despite the growing importance of the environmental aspect of corporate social responsibility (CSR), i.e. corporate environmental responsibility (CER), current literature focuses more on its antecedents and outcomes rather than drilling deeper into the essential elements of the concept. This has resulted in conceptual confusion as researchers use different aspects to define, understand and measure CER. Hence, this study aims to identify the critical dimensions of CER from a practitioner’s point of view.

Twenty-eight semi-structured interviews were conducted with senior sustainability professionals across top Bombay Stock Exchange-indexed organizations in India. Manual content analysis and the Gioia method were used to arrive at the findings.

The critical components of CER are as follows: encompassing environmental responsibility mindset; optimized resource consumption; neutral water, energy and air status; multi-level environmental responsibility approach and targets; compliance, disclosure, reporting and policy formation; and green supply chain.

Our research introduces a comprehensive framework of dimensions to study, measure and represent CER, addressing a critical gap in the current literature. The authors identify and propose novel dimensions, such as the CER mindset and a multi-level approach, which are essential for a holistic understanding of CER. These dimensions, presently absent in academic definitions, render existing research based on those definitions incomplete. Integrating these new dimensions will significantly enhance the rigor and relevance of CER studies, offering a more robust foundation for future research and practical application.

In today’s business landscape, drawing upon the critical role of environmental sustainability, this study investigates the intricate relationship between green human resource management practices (GHRMP), big data analytics capability (BDAC), green competitive advantage (GCA) and environmental performance (EP), further moderated by managerial environmental concern (MEC).

This study employs a quantitative approach using the latest version of SmartPLS 4 version 4.0.9.6 on a data sample of 467 participants representing a diverse range of manufacturing SMEs. Data were collected from managers and directors using a structured questionnaire and analyzed using structural equation modeling (SEM). This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a comprehensive understanding of how these practices enhance SME`s sustainability.

The findings provide valuable insights into the manufacturing sector, aiming to enhance SMEs' green competitive advantage. Implementing GHRMP fosters environmental awareness within the workforce, and building BDAC allows for effectively translating that GHRMP into actionable insights, maximizing the potential for achieving GCA. Furthermore, recognizing MEC’s moderating role strengthens positive environmental outcomes associated with GCA. The findings confirm that GHRMP and BDAC are valuable resources and key drivers contributing to competitive advantage in sustainability of enterprises.

For SMEs, our findings suggest that strategically integrating GHRMP with BDAC not only boosts environmental stewardship but also improves operational efficiency and market positioning. This research outlines actionable steps for SMEs aiming to achieve sustainability targets while enhancing profitability. This research provides actionable insights for SMEs in strategic decision-making and policy formulation, aiding SMEs in navigating the complexities of sustainable development effectively.

This study contributes to the existing knowledge by integrating GHRMP and BDAC within the GCA framework, providing a robust theoretical explanation of how HRM practices and BDAC help SMEs gain green competitiveness. The implication of this study reveals that SMEs implementing and integrating green HRM practices with advanced data analytics are more likely to gain competitive advantage. This study draws theoretical support from the resource-based view (RBV) theory, positing that a firm’s sustainable competitive advantage stems from its unique and valuable resources and capabilities that are difficult for competitors to imitate or substitute.

There have been numerous publications on human monkeypox since it was reported. With the help of bibliometric analysis, this study examined research hotspots and future trends related to human monkeypox. Science mapping was used in this study to identify influential monkeypox researchers, institutions, articles, keywords, thematic structures, and clusters of articles.

Based on a validated search query, bibliometric analysis of data collected from Web of Science from 1989 to September 2022 was conducted. Using the “Title-Keyword-Abstract” search option, the search query consisted of keywords “Monkeypox” OR “Monkeypox virus” OR “monkeypox” OR “monkey pox” OR “MPXV.” With the state-of-the-art tools Bibliometrix package of R Studio and VOSviewer, performance analysis and science mapping, as a part of standard bibliometric research of monkeypox research were conducted.

Researchers published 708 monkeypox papers from 1989 to September 2022, with American researchers publishing 460 papers. Further, USA had the highest international cooperation in terms of collaborative research output. Centers for Disease Control and Prevention (CDC) is a global leader in monkeypox research since it is the most prolific and collaborative organization. There have been the most published papers on monkeypox in the Journal of Virology. Damon Inger K is also the most prolific and influential researcher in monkeypox research, with the highest number of publications and citations. In total, 1,679 keywords were identified in the study. From the cluster analysis four themes were identified in monkeypox research. They are (1) clinical features, (2) monkeypox virus epidemiology, (3) monkeypox virus vaccine defense, and (4) monkeypox virus-related treatment measures.

Analysis of collaboration, findings, networks of research, and visualization separates this study from traditional metrics analysis. Currently, there are no similar studies with similar objectives based on the authors' knowledge.

The primary objective of the study is to enhance the accuracy and efficiency of assessing the proliferation index in cancer cells, specifically focusing on the role of Ki-67. The purpose is to address the limitations of traditional visual assessments conducted by pathologists by integrating AI technologies, particularly deep learning. By accurately computing the percentage of Ki-67-labeled cells, the research aims to streamline the diagnostic process, reduce subjectivity and contribute to the advancement of diagnostic precision in pathological anatomy.

The research employs a methodological approach that integrates Ki-67, a non-histone nuclear protein, as a vital biomarker for assessing the proliferative status of cancer cells. Given the challenges associated with traditional visual assessments by pathologists, including inter- and intra-observer variability and time-consuming efforts, the study adopts a novel methodology leveraging artificial intelligence (AI) solutions. Deep learning is applied to precisely calculate the percentage of Ki-67-labeled cells. The process involves pathologists delineating the tumor area at x40 magnification, enabling the segmentation of various cell types (positive, negative and tumor-infiltrating lymphocytes). The subsequent percentage calculation enhances efficiency and minimizes subjectivity in the diagnostic process.

Despite inherent errors, the research findings indicate that the model surpasses existing benchmarks, showcasing superior accuracy in terms of average error measurement. The comparison with diverse datasets and benchmarking against pathologists’ diagnoses contributes empirical evidence to support the effectiveness of the AI-based model in accurately computing the percentage of Ki-67-labeled cells. These findings signify a noteworthy advancement in diagnostic methodologies and reinforce the potential of AI technologies in improving the precision of cancer diagnostics within the realm of pathological anatomy.

The research contributes to the field by introducing an innovative approach that combines Ki-67 as a biomarker and AI technologies for improved diagnostic precision. The originality lies in the utilization of deep learning to calculate the percentage of labeled cells, mitigating the challenges associated with manual assessments. The validation of the model against diverse datasets and benchmarking against pathologists’ diagnoses demonstrates its superior accuracy, highlighting the value of integrating AI in pathological anatomy for enhanced diagnostic outcomes. The study represents a significant stride in original research, offering novel insights and methodologies in the pursuit of more precise cancer diagnostics.

The purpose of this study is to examine the mechanisms of absorptive capacity (ACAP) in the specific context of digital product development teams. More precisely, it explores the process of internalizing and utilizing external knowledge from sources outside the team to promote the development of innovative ambidexterity.

The study employs the ACAP framework and directs specific attention to the concept of knowledge assimilation, encompassing comprehension, documentation and dissemination. Seventy-five employees of one of Indonesia’s largest commercial banks were surveyed about two initiatives they participated in. To this end, 12 research hypotheses are formulated, tested and analyzed using structural equation modeling.

Knowledge comprehension, documentation and dissemination are found to mediate between knowledge acquisition and knowledge exploitation. At the same time, the relationship between knowledge acquisition and knowledge transformation is mediated by knowledge comprehension and dissemination, but not documentation. The authors also found that knowledge transformation positively mediates between knowledge comprehension and dissemination on the one hand and ambidexterity on the other hand. Importantly, knowledge exploitation negatively mediates between knowledge comprehension and documentation on the one hand and ambidexterity on the other hand.

This study contributes to the existing body of knowledge by offering nuanced insights into the interplay of knowledge assimilation processes within ACAP, particularly in the context of digital product development. The identification of mediating factors and their impacts on ambidexterity provides valuable implications for both theory and practice in this domain.

The issue of excessive carbon emissions continues to be a critical global challenge. As a prominent mode of transportation for long-distance travel, aircraft is widely acknowledged as a significant source of these emissions. Carbon offset initiatives function as ecological measures, helping to reduce the negative environmental impact. This study aims to explore how benefit appeals (BA) within the aviation industry impact tourists’ carbon offset payment intentions (COPIt).

In Studies 1 and 2, scenario-based experiments were conducted to explore how goal framing (GF) and (BA) interact to influence COPIt. Study 2 further investigated the mediating roles of moral responsibility and trust in airlines within this interaction. Study 3 used real-world surveys to examine the moderating influence of moral elevation, thereby supporting the interactive effects and mediation mechanisms identified in the earlier studies.

Across three studies, the authors consistently identified pivotal factors shaping COPIt in the context of air travel. Study 1 revealed that the combination of BA and GF significantly impacts COPIt, with egoistic appeals linked to loss framing and altruistic appeals connected to gain framing being particularly effective in encouraging COPIt. Study 2 extended these insights by showing that moral responsibility and trust in airlines serve as mediators between BA, GF and COPIt. In Study 3, moral elevation was found to moderate the influence of BA and GF on both moral responsibility and COPIt, deepening the understanding of these dynamics.

This study expands the range of factors affecting COPIt and delves into the underlying mechanisms through which BA and GF shape COPIt. Additionally, it advances current understanding by revealing the intricate processes influenced by moral elevation. The findings not only contribute to the existing knowledge on COPIt determinants but also offer practical guidance for the aviation industry and related sectors in promoting tourists’ participation in carbon offset programs.

本研究探讨航空业中不同利益诉求（利他 vs. 利己）和目标框架（收益 vs. 损失）如何影响游客的碳补偿支付意向, 以应对航空业作为主要碳排放源的环境挑战。

通过三项实验分析利益诉求与目标框架对碳补偿支付意向的影响, 前两项研究探讨利益诉求与目标框架的交互作用以及道德责任感和信任的中介作用, 第三项在机场调研中考察道德提升感的调节作用。

结果显示, 利他诉求与收益框架、利己诉求与损失框架的匹配显著提高游客的支付意向, 道德责任感和信任为中介, 且道德提升感调节了这一影响。

本研究丰富了碳补偿支付意向的理论, 揭示利益诉求与目标框架的交互作用机制, 并为航空业提供了有效推动碳补偿项目的实践建议。

Este estudio analiza cómo los intereses (altruismo vs. egoísmo) y el marco objetivo (ganancia vs. pérdida) influyen en la intención de pago de compensación de carbono de los turistas en la industria aérea.

Se emplearon métodos experimentales en dos estudios iniciales para explorar la interacción entre intereses y marco objetivo, y el papel mediador de la responsabilidad moral y la confianza en la aerolínea. Un tercer estudio en un aeropuerto evaluó el efecto moderador de la elevación moral.

Los intereses y el marco objetivo influyen conjuntamente en la intención de pago de compensación de carbono. La combinación de altruismo con un marco de ganancia y egoísmo con un marco de pérdida aumenta esta intención. La responsabilidad moral y la confianza en la aerolínea median estos efectos, mientras que la elevación moral modera su impacto.

Este estudio amplía la investigación sobre la compensación de carbono, revelando cómo la interacción entre intereses y marco objetivo afecta la intención de pago, ofreciendo recomendaciones prácticas para la industria aérea.

This paper examines the effect of intellectual capital (IC) and market capitalization (MC) on corporate investment decisions (ID) through the mediating and moderating effects of knowledge/information sharing (KS) and the Covid-19 pandemic.

With the use of SPSS 26 and SmartPLS version 3.0, the partial least square structural equation modelling (PLS-SEM) technique is employed with 1,036 observations to examine the effects.

Our findings show that IC and social interactions (SI) have a positive effect on KS. KS affects positively both MC and ID. KS has the mediating and moderating effects while the Covid-19 has the moderating impact on ID.

This research suggests that company leaders should understand the important role of IC and MC in enhancing ID through KS. They should pay attention to MC to enhance their investment and SI among employees, partners, consumers and authorities should be encouraged.

This research contributes to the existing literature by employing the perceptual scale to examine the effect of IC and MC, the mediating and moderating effects of KS, and analyze the moderating role of the Covid-19 on ID. It also expands the current models by including the Covid-19 and MC to clarify the ID determinants. New measurements of MC and the Covid-19 constructed are also another contribution.

This study aims to examine the impact of CEO age on corporate financialization by considering the moderating effects of CEO gender, identity and tenure in this relationship.

The analyses use ordinary least squares across 213 nonfinancial firms listed in Bursa Malaysia throughout 2015–2021. The author addresses potential endogeneity through propensity score matching and the generalized method of moments. The results are also robust to alternative measures of corporate financialization and CEO age.

The results show that firms with young CEOs are more likely to avoid taking short-term financial investments and, as a result, inhibit corporate financialization. Furthermore, the findings indicate that firms with female CEOs and those with family members as CEOs are less likely to invest in financial assets. The results also show that corporate financialization is weakened in the early stages of CEO tenure and strengthened in the late stages.

The empirical results have useful policy implications. For researchers, this study finds prominent differences in corporate financialization related to each stage of a person’s career. The study findings can be used by policymakers to guide programs that attempt to undertake the necessary measures to optimize corporate governance standards and restrict managers’ shortsighted conduct. In the long run, these kinds of projects could improve the way surplus financial reserves are used and raise economic output in general. The study also provides investors with insightful information about the possible relationship between CEO traits and company performance, especially with regard to measures for financial resource allocation.

This paper expands the existing research on corporate investment behavior and provides a new theoretical basis for the underlying factors of corporate financialization. It studies the influence of managerial traits on corporate financialization and deepens the understanding of CEO age and companies’ financialization levels.