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As one of the nearest and most important groups of resource guardians, local teenagers are an important force for current and especially future world heritage protection. Nurturing their awareness of world heritage protection is an important way to achieve the sustainable development of world heritage sites (WHS). The purpose of this paper is to present a case study of local teenagers of Tai’an city in China with the aim of examining how they have developed consciousness of their role as “world heritage guardians.”

Semi-structured interviews are used to examine the awareness of 15 local teenagers on world heritage protection. The interview results are then qualitatively analyzed by using a systematic coding process.

The analysis shows that the formation of the guardianship role is realized through three dimensions: recognition, emotion, and action, which are subjected to role awakening, role identifying, and role strengthening stage by stage. The personal experiences of the interviewees, as well as the impact from surrounding groups both serve to influence the formation of their role as “world heritage guardians.”

The framework constructed in this study on the development of teenagers’ role consciousness cannot only apply to world heritage and environmental contexts in terms of awareness of the need for conservation, but be used toward behavioral studies of other age groups.

The findings can be used as a practical guide for school teachers, government officials, and heritage managers to better carry out educational programs in terms of heritage tourism and sustainable development of WHS for teenagers.

The teenagers’ population is underrepresented in the literature in terms of community involvement with world heritage. This study is meaningful because the focus is on improving local teenagers’ awareness of the value and importance of world heritage and their preservation.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

The purpose of this paper is to explore the properties of platform service innovation and its relationship to value co-creation activities and the network effect. This is done over the course of a platform’s evolution through three stages: emergence, expansion and maturity.

Based on grounded theory, this study adopts a multiple case study research design. An in-depth analysis of the case data is done using ATLAS.TI software.

At the emergence stage, platform service innovations focus on building infrastructure. Platform owners stimulate the network effect directly via platform service innovations, rather than indirectly via value co-creation activities. At the expansion stage, the platform service innovations focus on building relationships among platform owner and different sides of participants. Platform owners stimulate the network effect indirectly, via value co-creation activities, rather than directly via platform service innovations. At the maturity stage, platform service innovations focus on building an environment for the platform ecosystem. Platform owners stimulate the network effect indirectly, via value co-creation activities rather than directly.

This research contributes to the service innovation literature by exploring the properties of platform service innovation and its relationship to value co-creation activities and the network effect from a longitudinal perspective. The principal managerial implication is that platform managers need to consider the developmental stage of the platform, as a mismatching of stage of development (emergence/expansion/maturity) and focus (an orientation toward building infrastructure, relationships or environment) may lead to a failure to stimulate or enhance the network effect.

Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.

In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.

The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.

An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.

The purpose of this study is to analyse and discuss the influencing factors of user experience in university mobile libraries and the improvement path of user experience in the context of mobile learning.

The study adopted the grounded theory research method, and the sample included 28 students from five universities, with mobile libraries as the research objects and semi-structured interview as data acquisition method. A step-by-step coding analysis of the original interview materials was conducted, which comprehensively identified the main concerns and problems encountered by users of the university mobile library apps especially in the mobile learning behaviour mode, and then a theoretical model of the influencing factors of the app user experience of the university mobile library was constructed.

A theoretical model of influencing factors was constructed, which determined that system quality, interaction quality, content quality, interface quality and function quality were the key elements of mobile library user experiences. Furthermore, based on the research results and user feedback obtained in the research process, the content and key points relating to the user experience can be elaborated in detail. In addition, this study was able to determine users' perspectives and their behavioural characteristics when engaging in mobile learning.

This study establishes a theoretical model of the factors influencing of the user experience of university mobile libraries based on mobile learning, which could provide a valuable reference for the design of other programs and strategies to promote user learning experiences of mobile library app in colleges and universities.

The purpose of this paper is to systematically review the platform literature and synthesize the various topics of research into a common framework to reveal the relations between platform-based service innovation, system design and other platform-related factors.

A quantitative descriptive analysis led to an overview of the distribution of research focuses of the 187 sample articles identified by a well-established search strategy. A qualitative in-depth review was then used to clarify the detailed research topics and generate an overall conceptual model to link them, with a focus on platform-based service innovation and system design.

In total, 11 research topics of three research perspectives were identified and linked by a framework that accounts for the relationships between platform-based service innovation and system design and their influences on platform evolution. A small panel of industry experts validated the accuracy and utility of the proposed framework.

This paper provides an integrated framework for separately developed research perspectives and the topics investigated in the platform literature. Through the proposed framework, this paper helps to improve the knowledge on platform study and management, and lays a foundation for exploring the research opportunities in platform-based service innovation and system design.

The purpose of this paper is to explore research opportunities for service innovation and system design in network platforms by investigating representative case examples in an emerging market, China.

The authors provide four case examples to illustrate the manner in which network platforms facilitate and lead to service innovation and effective system design. Then, the authors propose nine research directions that present significant research opportunities, and the authors make theoretical and methodological suggestions.

The results indicate that research opportunities exist mainly in three areas: ecosystem design and evolution, including role design, coherence and sustainability issues; leader–participant collaboration, including governance, co-opetition and innovation stimulation issues; and transformation of traditional industries, including process informatization, modularization and standardization issues.

This study extends the platform-related literature by showing new platform features represented by case evidence from emerging economies, and provides recommendations to guide and direct management practices and efforts in platform-based service innovation and system design.

With the increased speed and mileage of high-speed lines, the problem of rail wear is increasing. In actual operation, a large number of abnormal wear phenomena occur on both vehicles and rails during fixed line operation; therefore, the purpose of the study is to explored the rail wear for a variety of vehicles running in mixed operation.

This paper used the universal mechanism multibody dynamics software to establish the CRH2 high speed train (HST) and the CRH3 HST vehicle dynamic models, respectively. The mixed running of HSTs on the effect of rail wear evolution law was analyzed. The rail wear of the two vehicles with different curve radii, different wheel diameters and different under-rail stiffness was compared and analyzed.

The result showed that the rail wear of CRH3 HST is greater than that of CRH2 HST. The rail wear in the tangent track under mixed operation conditions is 25.4% less than when CRH3 HST operated independently. When there is a 1-mm wheel diameter difference, the maximum rail wear of CRH2 HST and CRH3 HST increases by 263% and 44%, respectively. The amount of rail wear is proportional to the under-rail stiffness, and the position of the maximum wear is almost unchanged.

Most studies on the evolution law of rail wear are conducted for a single vehicle type and a single line. This study explored the mixed running of HSTs on the effect of rail wear evolution law.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0276/

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.