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This dissertation studies the regulatory framework of aviation reform in the Macau Special Administrative Region, focus on the balance between protecting local businesses and promoting market liberalization. The study explores how Macau, as a highly autonomous local administrative region in China with a relatively small economy, can effectively reform its aviation industry while safeguarding local interests in the face of open market competition. The importance of this study lies in its analysis of Macau's aviation sector reforms. As Macau seeks to diversify its economy and strengthen its position as a member of the Greater Bay Area initiative, the aviation industry presents both opportunities and challenges. This research contributes to the broader discussion of how small jurisdictions with special political status can participate in regional economic integration while maintaining their distinct advantages and protecting local interests. The research uses Doctrinal Legal Research and Comparative Legal Research methodologies, utilizing documentary analysis and comparative study methods. This research examines the legal framework of the aviation industry in Macau and the challenges it is currently facing. A comprehensive review of international aviation laws, bilateral air service agreements, and domestic regulations provides the foundation for analyze. The study also conducts a comparative analyze with Hong Kong's aviation framework, offering alternative regulatory approaches for a similar Special Administrative Region of China. Several interesting findings emerged from this research. First, the study identified a fundamental tension between Macau's stated open skies policy and its practical implementation, particularly in limitations of its current concession-based system and the underutilization of international traffic rights. Second, the analysis highlights how the current concession-based system has created a monopolistic market structure that hinders competition and innovation, exacerbated by outdated legislation. Third, the comparative analysis with Hong Kong highlights the importance of a clear legal framework and rules in balancing market liberalization with local industry protection, areas where Macau's current regulations show deficiencies. The concluding thought based on these findings is that Macau's aviation industry reform requires a careful approach that gradually introduces competitive elements while maintaining protections for local enterprises. The study suggests that successful reform will depend on establishing a comprehensive legal framework, implementing a flexible multi-tiered licensing system, enhancing regulatory oversight, and developing mechanisms for regional integration. These reforms must be tailored to Macau's unique circumstances, considering its small market size, political status, and strategic position within the Greater Bay Area.

This article explores the intersection between traditional textile craftsmanship and digital innovation through the Hands series, a project that integrates tangible and virtual artefacts. Grounded in post-digital aesthetics, Hands examines the rematerialisation of textile heritage by combining traditional techniques with immersive technologies such as augmented reality and digital modelling. The project questions the physical and digital dichotomy, proposing new ways of experiencing textile art beyond its material constraints. By incorporating multisensory elements and interactivity, Hands redefines the engagement between spectators and artefacts, expanding the narrative potential of textile traditions in contemporary artistic practice. This study critically analyses how post-digital textile aesthetics can serve as a bridge between preservation and innovation, fostering an enriched sensory experience. The discussion highlights the challenges and opportunities of integrating emerging technologies into artistic processes, reinforcing the relevance of sensory engagement in digital art contexts.

<jats:p xml:lang="en">Over the last few years, brands have increasingly looked to influencer marketing to promote their products. More recently, a new approach has emerged, leveraging artificial intelligence to create virtual influencers. Despite the growing importance of virtual brand ambassadors, academic research on virtual influencers remains fragmented, with limited discussion regarding the ideal characteristics of such agents. This paper addresses this gap in the literature and identifies the conditions necessary for virtual influencers to deliver positive outcomes. Based on existing literature, we identify eight essential attributes that significantly influence the effectiveness of virtual influencers. We also propose an agenda for future research and present a conceptual model to elucidate virtual influencer dynamics. This research enhances our understanding of virtual influencers’ role and impact in contemporary brand promotion, providing valuable insights for scholars and practitioners.</jats:p>

This current study assessed the toxicity of selected heavy metals in paddy and sediments of non-major production sites in Southern Peninsular Malaysia, complemented by bibliometric analysis of research trends and health implications of rice contamination. Paddy (grains, stems, roots) and soil samples were collected from seven selected sites in the Southern parts of Peninsular Malaysia and analyzed for their heavy metals content. The health risk assessments were conducted based on estimated daily intake, and the Web of Science database was used for bibliometric analysis. The results indicated elevated levels of manganese, Mn (0.4 ± 0.07), especially in the roots, compared to other heavy metals. Generally, the heavy metal levels in paddy grains were below FAO/WHO’s tolerable daily intake levels, indicating minimal non-carcinogenic risks to both adults and children. The bibliometric analysis indicated a significant increase in related publications, reflecting growing academic interest. This study highlights the potential of non-major sites to produce rice with lower contamination levels, provides insights into research trends, and identifies future investigation areas, especially for major production sites and post-COVID-19 periods. Therefore, this study offers a robust scientific context, identifies research gaps, benchmarks findings, and guides future research directions, ensuring an in-depth perception on heavy metal contamination and its health risks. © This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

As a special administrative region of China, Macau, despite its small size and limited resources, has been actively promoting low-carbon development to address the global climate change challenge. In recent years, Macao has provided important support for achieving the goal of carbon peaking and carbon neutrality by formulating a series of policies, such as energy management, promotion of green buildings and optimization of waste treatment. However, despite the clarity of the policy framework, its actual implementation and effectiveness still need to be systematically reviewed. This study uses a combination of qualitative and quantitative research methods, interviews and questionnaires to gain an in-depth understanding of the social acceptance and public participation of policies, and quantitative data to analyze the implementation effects of policies, with a view to revealing the advantages and disadvantages of low-carbon policies in Macao. The research results will systematically evaluate the effectiveness of low-carbon policies in Macao from multiple levels. On the one hand, quantitative data will provide a clear empirical basis for the progress of low-carbon policies; On the other hand, qualitative research will reveal key social and technological barriers to policy implementation, helping to understand the impact of public attitudes and behaviour on policy effectiveness. These results can not only provide reference for Macao's future low-carbon policy adjustment, but also provide experience for other similar cities to cope with similar challenges in low-carbon development. The importance of this study lies in its dual contribution to academia and practice. At the academic level, this study has enriched the theoretical research on low-carbon city development, especially the special challenges of carbon emission reduction in resource-limited cities. At the practical level, the results of this study can directly provide policy makers with specific suggestions for improving low-carbon policies, such as resource allocation, technology introduction and public awareness. Through these analyses, the study hopes to provide a practical roadmap for Macao to achieve its goal of carbon neutrality, as well as a valuable reference for the low-carbon development of similar cities around the world.

In the wave of digital transformation, Chinese banks have prioritized digital banking services as key strategic goals, aiming to revolutionize the mobile banking experience. This study aims to assess the factors influencing the willingness to use the various financial and contextual services offered through digital banking. Specifically, it is proposed a model based on users' perceptions of mobile banking scenarios and examines how the development of digital banking services influences users' willingness to use them. The study involved qualitative in-depth interviews with 12 mobile banking users, with the interview content analyzed using Nvivo qualitative analysis software. The data analysis identified 9 core coding categories: Financial Professionalism, Security, Marketing Stimulation, Innovative Products, Use Experience, Strong Relationship, Trust, Perceived Usefulness, and Willingness to Use. These categories were further refined to construct a theoretical model of user willingness in digital banking services, drawing from the optimized Technology Acceptance Model (TAM). The findings provide valuable insights for the banking industry in Macau, aiding in understanding customer needs and supporting the positive development of mobile finance and contextual digital banking services in the region.

Purpose Retail omnichannel implementation faces barriers hindering accurate and efficient integration across marketing channels. Our desk examination identified a need for a broader perspective in investigating these barriers, moving away from a dominant, narrow approach. This research aims to develop a comprehensive set of items to measure retail omnichannel obstacles, refine the scale and assess its reliability and validity for a robust measurement tool. Design/methodology/approach Our approach combines quantitative and qualitative methods, using data from primary and secondary sources to create and validate the omnichannel obstacles scale. Findings This study emphasises the inclusive nature of retail functional areas, departing from prior literature that examined them in isolation. Instead of focussing on separate domains where retail omnichannel obstacles may arise, we adopt a holistic perspective by integrating previously disconnected elements. Originality/value We assert that challenges in retail omnichannel operations encompass three distinct dimensions: operational efficiency, channel inefficiency, and strategy and organisational culture within retailing. In our final validated measurement model, we consolidate the channel inefficiency dimension and refine the omnichannel obstacles scale to emphasise two areas of consideration.

Purpose Retail omnichannel implementation faces barriers hindering accurate and efficient integration across marketing channels. Our desk examination identified a need for a broader perspective in investigating these barriers, moving away from a dominant, narrow approach. This research aims to develop a comprehensive set of items to measure retail omnichannel obstacles, refine the scale and assess its reliability and validity for a robust measurement tool. Design/methodology/approach Our approach combines quantitative and qualitative methods, using data from primary and secondary sources to create and validate the omnichannel obstacles scale. Findings This study emphasises the inclusive nature of retail functional areas, departing from prior literature that examined them in isolation. Instead of focussing on separate domains where retail omnichannel obstacles may arise, we adopt a holistic perspective by integrating previously disconnected elements. Originality/value We assert that challenges in retail omnichannel operations encompass three distinct dimensions: operational efficiency, channel inefficiency, and strategy and organisational culture within retailing. In our final validated measurement model, we consolidate the channel inefficiency dimension and refine the omnichannel obstacles scale to emphasise two areas of consideration.

The peacock blenny Salaria pavo is notorious for its extreme male sexual polymorphism, with large males defending nests and younger reproductive males mimicking the appearance and behavior of females to parasitically fertilize eggs. The lack of a reference genome has, to date, limited the understanding of the genetic basis of the species phenotypic plasticity. Here, we present the first reference genome assembly of the peacock blenny using PacBio HiFi long-reads and Hi-C sequencing data. The final assembly of the S. pavo genome spanned 735.90 Mbp, with a contig N50 of 3.69 Mbp and a scaffold N50 of 31.87 Mbp. A total of 98.77% of the assembly was anchored to 24 chromosomes. In total, 24,008 protein-coding genes were annotated, and 99.0% of BUSCO genes were fully represented. Comparative analyses with closely related species showed that 86.2% of these genes were assigned to orthogroups. This high-quality genome of S. pavo will be a valuable resource for future research on this species’ reproductive plasticity and evolutionary history.

Purpose: This study explores the emotional impact of post-purchase guilt on younger consumers in the Chinese luxury retail market, with a specific focus on the role of Cause-related Marketing (CrM) in mitigating negative emotions across luxury and non-luxury product categories.Design/Methodology/Approach: A quantitative experimental design was utilized, involving 326 respondents exposed to different advertising scenarios. The study tested the impact of CrM on post-purchase guilt in both luxury (high-priced) and non-luxury (moderately priced) product conditions, using a 2 × 2 factorial design. The data were analyzed using ANCOVA to assess the effects of CrM campaigns across conditions.Findings: The results demonstrate that CrM effectively reduces post-purchase guilt across both luxury and non-luxury product categories, providing a moral justification for purchases by linking them to a positive social cause. However, contrary to expectations, the impact of CrM was not significantly stronger in the luxury context compared to non-luxury. This suggests that CrM's influence on post-purchase guilt operates uniformly, regardless of product type.Originality: This research enhances understanding Millennial and Gen Z consumer behavior in the Chinese luxury market. The findings offer actionable insights for luxury brands, highlighting the effectiveness of CrM in addressing guilt-related concerns, thereby informing marketing strategies aimed at younger generations.Keywords: post-purchase guilt, Millennials, Gen Z, Chinese luxury retail industry, cause-related marketing.Acknowledgments: The first author would like to thank CEGE – Research Centre in Management and Economics, funded by The Multiannual Funding Programme of R&D Centres of FCT – Fundação para a Ciência e Tecnologia under the project UIDB/00731/2020. The fourth author would like to thank COMEGI funded by FCT – Fundação para a Ciência e Tecnologia under the project UIDB/04005/2020.DOI: https://doi.org/10.58869/EJABM10(3)/06

<jats:p> This work compares the performance of different algorithms — quantum Fourier transform, Gaussian–Newton method, hyperfast, metropolis-adjusted Langevin algorithm, and nonparametric classification and regression trees — for the classification of fetal health states from FHR signals. In the conducted research, the effectiveness of each algorithm was measured using confusion matrices, which gave information about class precision, recall, and total accuracy in three classes: Normal, Suspect, and Pathological. The QFT algorithm gives an overall accuracy of 90%, where it is highly reliable in recognizing Normal (94% F1-score) and Pathological states (91% F1-score), but performs poorly regarding the Suspect cases, at 58% F1-score. On the other hand, using the GNM method gives an accuracy of 88%, whereby it performed well on Normal cases, at 93% F1-score, and poor performance with Suspect, at 50% F1-score, and Pathological classifications, at 82% F1-score. The hyperfast algorithm yielded an accuracy of 89%, thus performing well on Normal classifications with an F1-score of 93%, but less well on the Suspect states with an F1-score of 56%. The MALA algorithm outperformed all other algorithms tested in this study, giving an overall accuracy of 91% and adequately classifying Normal, Suspect, and Pathological states with corresponding F1-scores of 94%, 63%, and 90%, respectively; therefore, the algorithm is quite robust and reliable for fetal health monitoring. The NCART algorithm achieved an accuracy of 89%, thus showing great capability for classification in Normal cases with 94% F1-score and in Pathological cases with 88% F1-score; this is moderate for Suspect cases with 53% F1-score. Overall, while all algorithms exhibit potential for fetal health classification, MALA stands out as the most effective, offering reliable classification across all health states. These findings highlight the need for further refinement, particularly in enhancing the detection of Suspect conditions, to ensure comprehensive and accurate fetal health monitoring. </jats:p>

Having navigated up to two years of online course delivery worldwide as a result of the Covid-19 pandemic. Education systems are now in a better position to leverage the benefits of technology in facilitating the language acquisition process more effectively. Regardless of the student population served, there is no longer a concern as to whether students have access to facilities necessary for online delivery as the smartphone has become a standard household necessity. This conceptual literature review introduces a potential model for second language instruction utilizing a flipped classroom approach based on evidence gained through empirical research interpreted through the lens of present reality. Technology enables learners to explore the form and structure of language through the use of online autonomous learning units with no limitation of time or accessibility, while scheduled classroom engagement allows opportunity for authentic language practice and refinement. The implications of this study add value to second and foreign language instruction, providing language teachers with a pragmatic approach to enhance their instructional delivery. © 2025 selection and editorial matter, Leung Sze Ming and Chan Sin-wai; individual chapters, the contributors.

<jats:title>Abstract</jats:title> <jats:p>Under the agreement signed with Portugal, which defined the terms of the handover to China, Macau became a Special Administrative Region on 20 December 1999. China undertook to maintain the way of life, the rights and freedoms of the residents and the essence of the laws previously in force, and guarantee the inapplicability of the socialist system. Events in Hong Kong since 2019 and the concerns of the Central Government have led to changes in the national security law and electoral laws which, among other things, have imposed political screening on candidates for the Legislative Assembly and Chief Executive, which can lead to their exclusion without appeal, while criminalising calls for blank votes, null votes, and abstentions. This article answers the question of whether these changes are compatible with the guarantees provided, the Luso-Chinese Joint Declaration and Macau’s Basic Law.</jats:p>

Accurate classification of brain tumors from MRI is critical for effective diagnosis and treatment. In this study, we introduce Trans-EffNet, a hybrid model combining pre-trained EfficientNet architectures with a transformer encoder to enhance brain tumor classification accuracy. By leveraging EfficientNet's deep CNN capabilities for localized feature extraction and the transformer encoder for capturing global contextual relationships, our model improves the identification of intricate tumor characteristics. Fine-tuned with ImageNet-derived weights and utilizing extensive data augmentation, Trans-EffNet was validated on both multi-class and binary datasets. Trans-EffNetB1 achieved 99.49 % accuracy on the multi-class dataset, while Trans-EffNetB2 recorded 99.83 % accuracy on the binary dataset, with perfect precision, recall, and F1-Score. These results underscore Trans-EffNet's robustness and potential as a significant advancement in brain tumor detection and classification.

This proposed study looks into the popular TikTok app and its impact on the identity formation and expression of young people. It investigates how TikTok enables young individuals to create, share, and consume diverse and authentic content that reflects their interests, values, and experiences. People on the app can work together, take part in trends, and interact with others. Also, the paper dives into how TikTok gives people a place to chase their dreams and find out what other possibilities their lives could hold. It indicates how TikTok can have some good and bad effects on young generations in terms of culture and personal development. These youngsters could be the ones leading in the future. However, we also need to think about the possible dangers TikTok could pose to young people. This includes the chance that they come across something damaging, feel like they have to live up to hard-to-reach standards, have their private information leaked, or fall victim to someone with bad intentions. Because of these risks, it's very important to teach youngsters how to use TikTok in a way that's safe and responsible. The dissertation highlights the need for responsible usage, awareness of potential challenges, and the development of strategies to support the safe and healthy engagement of young people with TikTok and similar platforms.

<jats:p>The use of artificial intelligence (AI) tools in writing and proofreading is beginning to develop. Studies show that AI tools can positively influence students' writing and proofreading skills. This study presents the perceptions of vocational education students regarding the assessments and suggestions for improvement provided by the AI assistant Curipod and followed by students in the proofreading phase. It centres on a case study, with data collected using a survey with open and closed questions, participant observation, and an interview. The students positively perceived the feedback they received from the AI assistant on their initial text and consider that it helped them to revise and improve the final versions of the texts written on paper and digitally. The students are interested in using tools like these in writing revision activities, as they see the potential they have for the classroom and autonomous learning.</jats:p>

Objetivo: Explorar a aplicação de inteligência artificial (IA) na predição da idade óssea a partir de imagens de raios-X. Método: Utilizou-se a Metodologia Interdisciplinar para o Desenvolvimento de Tecnologias em Saúde (MIDTS) para desenvolver uma ferramenta de predição. O treinamento foi realizado com redes neurais convolucionais (CNNs) usando um conjunto de dados de 14.036 imagens de raios-X. Resultados: A ferramenta alcançou um coeficiente de determinação (R²) de 0,94807 e um Erro Médio Absoluto (MAE) de 6,97, destacando sua precisão e potencial de aplicação clínica. Conclusão: O projeto demonstrou grande potencial para aprimorar a predição da idade óssea, com possibilidades de evolução conforme a base de dados aumenta e a IA se torna mais sofisticada.

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer’s disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper. <jats:italic>Approach.</jats:italic> The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification. <jats:italic>Main results.</jats:italic> Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model. <jats:italic>Significance.</jats:italic> This study demonstrate<jats:bold>s</jats:bold> the proposed model’s ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.</jats:p>