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This study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.

The authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.

The simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.

Two simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

This study aims to investigate the effect of overconfident chief executive officers (CEOs) on corporate tax avoidance and whether this relationship is affected by institutional and family ownership.

Using a sample of French-listed firms from 2009 to 2021, the authors find that firms managed by overconfident CEOs engage in more tax avoidance practice.

The authors further find that institutions and families are likely to discourage tax avoidance practices, paying close attention to their long-term horizons and reputational concerns. Overall, the authors' findings shed light on the monitoring role of institutional and family shareholders in restraining the effect of CEO behavioral bias on companies' tax avoidance.

To the authors' knowledge, no study has investigated the impact of managerial overconfidence on the tax behavior of French firms. The authors also extend the growing literature regarding managerial effects by providing new evidence that French firms held by concentrated institutional and family ownership curtail CEO overconfidence behavior toward corporate tax avoidance practices.

The increasing popularity of immersive activities (e.g. immersive performing art (IPA) at tourism destinations calls for the need to understand customers’ immersive experiences and its impact on their attitudes and behavioral intentions. This study aims to conceptualize customers’ interactive experience in IPA and systematically examine how interaction in IPA experience shapes customers’ word-of-mouth (WOM) intention and thus generates business values for destination managers.

A two-step mixed-methods approach was used. The qualitative study was adopted to identify the salient dimensions of interaction in IPA experience and develop a context-specific measurement scale. Structural equation modeling approach was applied to test the theoretical model using partial least squares 3.3.0.

The results indicated that the three dimensions of interaction in IPA experience (i.e. parasocial interaction, narrative interaction and environmental interaction) are positively related to customers’ experiences of narrative transportation and self-expansion related to the IPA, which further influence their WOM intention.

This study considers IPA as an emerging form of immersive tourism activities and conceptualizes its interactive nature, and contributes to the understanding of how customers’ interactive experience helps them construct meanings through narrative transportation, and offers valuable guidance for IPA designers and destination management organizations.

This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining.

The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales.

When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits.

This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

Evaluating the implementation of environmental management plans (EMPs) in highway construction projects is essential to avoid climate change. Public evaluations can help ensure that the EMP is implemented correctly and efficiently. To allow public evaluation of EMP implementations, this study aims to investigate performance indicators (PIs) for assessing EMP implementation in highway construction projects. To that end, the study objectives are to compare the critical PIs between environment auditors (EAs) and environment officers (EOs) and among the main project stakeholders (i.e. clients, contractors and consultants), create components for the critical PIs and assess the efficiency of the components.

The paper identified 39 PIs from interviews with environmental professionals and a systematic literature review. Then a questionnaire survey was developed based on the PIs and sent to EAs and EOs. The data were analyzed via mean score ranking, normalization, agreement analysis, factor analysis and fuzzy synthetic evaluation (FSE).

The analyses revealed 21 critical PIs for assessing EMP implementation in highway construction projects. Also, the critical PIs can be grouped into four components: ecological, pollution, public safety and ecological. Finally, the overall importance of the critical PIs from the FSE is between important and very important.

To the best of the authors’ knowledge, this paper is the first-of-its-kind study on the critical PIs for assessing EMP implementation in highway construction projects.