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As one of the most cutting-edge technologies in the digital age, facial enhancement technology (FET) has greatly enhanced consumer online shopping experience and brought new e-commerce opportunities for cosmetics retailers. The purpose of this paper is to extend the unified theory of acceptance and use of technology (UTAUT) model in the context of FET. In addition to the concepts from the original model, the new FET-UTAUT model features (low) body esteem, social media addiction and FET adoption.

A purposive sampling of FET users in China via an online questionnaire yields 473 respondents. To analyze the data, this research uses the structural equation modeling method via statistical package for the social sciences and analysis of a moment structures software. A two-step approach, exploratory factor analysis and confirmatory factor analysis, was used to test the hypotheses and generate the findings.

Performance expectancy, effort expectancy, social influence, facilitating conditions and (low) body esteem have positive relationships with FET adoption. FET adoption has a positive relationship with online purchase intention of branded color cosmetics, and the empirical evidence for the moderating role of social media addiction in the relationship between FET adoption and online purchase intention is inconclusive.

This research extends the traditional UTAUT model by proposing a novel FET-UTAUT model that incorporates additional key concepts such as body esteem, FET adoption and social media addiction. Managerial implications of this research are provided for FET designers and branded color cosmetic retailers.

Managers play a crucial role in organizations. They make decisions that directly influence organizational success and significantly impact employees’ mental health, development and performance. They are responsible for ensuring the financial well-being and long-term sustainability of organizations. However, their mental health is often overlooked, which can negatively affect employees and organizations. This study aims to address managers’ mental health at work, by examining specifically the direct and indirect effects of identity verification on their psychological distress and depression through self-esteem at work. The study also aims to examine the moderating as well as moderated mediation effects of identity salience.

A sample of 314 Canadian managers working in 56 different companies was studied, using multilevel analyses.

The findings showed that the verification of managers’ identity vis-à-vis recognition is positively associated with psychological distress and depression. Self-esteem completely mediates the association between low identity verification vis-à-vis work control and psychological distress, and also the association between low identity verification vis-à-vis work control and superior support and depression, while it partially mediates the association between low identity verification vis-à-vis recognition and depression.

This study can also help both managers and human resource management practitioners in understanding the role of workplaces in the identity verification process and developing relevant interventions to prevent mental health issues among managers at work.

This study proposed a relatively unexplored approach to the study of managers’ mental health at work. Its integration of identity theory contributes to expanding research on management and workplace mental health issues.

Drawing on information foraging theory and the SERVQUAL model, this study built a research model to investigate the roles of middle-aged and elderly short-video creators' online attributes in attracting short-video viewers to be their followers.

Taking Douyin (a famous short-video platform in China) as an example, this study used a sequential triangulation mixed-methods approach (quantitative → qualitative) to examine the proposed model by investigating both creators and viewers.

Viewers who clicked the “like” button for the middle-aged and elderly creators' videos are more likely to follow the creators. Viewers will believe that middle-aged and elderly creators who received more likes are more popular. Thus, middle-aged and elderly creators with more likes usually have more followers. Viewers usually believe that middle-aged and elderly creators who more frequently publish professional and high-quality videos have invested more effort and who have official verification also have a high level of authority and are recognized by the platform. Thus, middle-aged and elderly creators with more professional videos and verification usually have more followers. Moreover, verification, the number of videos and the professionalism of videos can enhance the transformation of viewers who liked middle-aged and elderly creators' videos into their followers, and thus strengthen the positive relationship between the number of likes and the number of followers; however, the number of bio words will have an opposite effect.

These findings have implications for platform managers, middle-aged and elderly creators and the brands aiming to develop a “silver economy” by attracting more followers.

This study researches short-video platforms by using a mixed-methods approach to develop an understanding of viewers' decision-making when following middle-aged and elderly creators based on information foraging theory and the SERVQUAL model from the perspectives of both short-video creators and viewers.

The 2020 pandemic disrupted traditional student mobility and forced a larger majority of transnational programmes to switch to a virtual or hybrid mode, including joint and double degree programmes. Therefore, this study aims to perceive the linkage between quality assurance (QA) and delivery modes of cross-border higher education (CBHE) in Asia before and during the pandemic.

Through an online survey and semi-structured interviews, the process by which top 200 ranked universities in the 2022 QS global ranking responded to QA and qualification issues of joint/dual degree programs in conjunction with delivery modes was explored.

The study has discovered that most respondents from universities, to some extent, tended to be positive about the effectiveness of hybrid delivery of the joint/dual degree programs, even if they still preferred the physical mode to alternatives. Either “divergence” or “responsiveness” QA modes were not applied appropriately in most joint/dual degree programs of the selected universities during the pandemic. Moreover, a fair, transparent and convergent quality and qualification system should be established to facilitate agility and responsiveness of CBHE.

The findings are of value for policymakers, QA agencies and universities to advocate the new QA model for CBHE as a systematic approach in response to changing higher education landscape in the post-pandemic era.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

The aim of this study was to appraise the delivery of information communication technology (ICT) projects and identify key determinants for stakeholder integration.

Given that empirically, little was known about stakeholder integration in the ICT sector and its influence or effect on project delivery; qualitative method was used. Forty-seven semi-structured interviews were carried out to derive senior project practitioners and policymakers' constructs of stakeholder integration and infrastructure performance improvement of ICT projects. The verification and validation of the proposed assessment tool were achieved through the use of focus group discussion.

As established in this research study, there is a need for project delivery teams to evaluate the level of stakeholder integration, the formulation of a project business case, the project processes and issues of compliance and regulation in ICT projects. What is evident in the findings of the study is that the management model adopted for the stakeholders in the Kenyan ICT sector ought to make communication the fulcrum of their engagement.

The inferences made herein are critical in contributing to knowledge regarding the ICT infrastructure project management terrain in developing countries. There is evidence in the study to conclude that the concept of stakeholder management and integration has implications for the sustainability of ICT projects. One of the issues that predominantly featured in the research was the input of stakeholder integration in terms of project sustainability.

This study aimed to develop a method to calculate the mattress indentation for further estimating spinal alignment.

A universal indentation calculation model is derived based on the system theory, and the deformation characteristics of each component are analyzed by the finite element (FE) model of a partial air-spring mattress under the initial air pressure of 0.01–0.025 MPa. Finally, the calculation error of the model is verified.

The results indicate that the indentation calculation model could describe the stain of a mattress given the load and the constitutive model of each element. In addition, the FE model of a partial air-spring mattress can be used for further simulation analysis with an error of 1.47–3.42 mm. Furthermore, the deformation of the series system is mainly contributed by the air spring and the components directly in contact with it, while the top component is mainly deflection deformation. In addition, the error of the calculation model is 2.17–5.59 mm on the condition of 0.01–0.025 MPa, satisfying the engineering application. Finally, the supine spinal alignment is successfully extracted from the mattress indentation.

The limitation of this study is that it needs to verify the practicality of the indentation calculation model for the Bonnier spiral spring mattress. The main feature of the Bonnier spring mattress is that all springs are connected, so the mattress deflection and neighborhood effect are more significant than those of the air-spring mattress. Therefore, the applicability of the model needs to be tested. Moreover, it is worth further research to reduce the deformation error of each component.

As part of the series of studies on the intelligent air-spring mattress, the indentation-based evaluation method of spinal alignment in sleep postures will be studied for hardness and intelligent regulation based on this study.

The results of this research are ultimately used for the intelligent adjustment of air-spring mattresses, which automatically adjusts the hardness according to the user's sleep postures and spinal alignment, thus maintaining optimal spinal biomechanics. The successful application of this result could improve the sleep health of the general public.

Based on the series system theory, an indentation calculation model for mattresses with arbitrary structure is proposed, overcoming the dependence of parameters on materials and their combinations when fitting the Burgers model. Further, the spinal alignment in supine posture is extracted from the indentation, laying a theoretical foundation for further recognition and adjustment of the spinal alignment of the intelligent mattress.

This paper aims to analyze what Samarco and Brazilian magazines speak and say about Mariana’s environmental crime. Discover their doxa in this subject. Interpret the speakings, sayings and doxas through the theories of the treadmills of production, crime and law.

It is a qualitative and documental research and a narrative analysis. Regarding the documents: 45 were from public authorities, 14 from Samarco Mineração S.A. and 73 from Brazilian magazines. Theoretically, the authors resorted to Bourdieusian sociology (speaking, saying and doxa) and the treadmills of production, crime and law theories.

Samarco: speaking – mission statements; saying – detailed information and economic and financial concerns; doxa – assistance discourse. Brazilian magazines: speaking – external agents; saying – agreements; doxa – attribution, aggravations, historical facts, impacts and protests.

The absence of discussions that addressed this fatality, with its respective consequences, from an agenda that exposed and denounced how it exacerbated race, class and gender inequalities.

Regarding Mariana’s environmental crime: Samarco Mineração S.A. speaks and says through the treadmill of production theory and supports its doxa through the treadmill of crime theory, and Brazilian magazines speak and say through the treadmill of law theory and support their doxa through the treadmill of crime theory.

To provoke reflections on the relationship between the mining companies and the communities where they settle to develop their productive activities.

Concerning environmental crime in perspective, submit it to a theoretical interpretation based on sociological references, approach it in a debate linked to environmental criminology, and describe it through narratives exposed by the guilty company and by Brazilian magazines with high circulation.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.