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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

Battery Electric Aircraft (BEA) technology is gaining attention due to the potential to reduce carbon emissions and noise pollution, contributing to global environmental sustainability. Grounded in the Theory of Planned Behavior (TPB), this paper explores the determinants of attitude, social norm, behavioural control, and perceived risks related to the intention of Macau residents to use electric aeroplanes within the Greater Bay Area (GBA). This research uses a quantitative approach. Data is collected through structured surveys distributed to potential adopters. To assess the relationships between the determinants in our model, Structural Equation Modeling (SEM) is employed. The findings reveal that a favourable attitude and perceived behavioural control positively influence individuals’ intentions to adopt electric aeroplanes. However, perceived risks strongly impact adoption intentions, suggesting that addressing safety and reliability concerns is essential for promoting the technology within the region. The implications of this research extend beyond academic interests, as Macau’s unique position within the GBA offers the opportunity for electric aeroplanes’ adoption. Further, the reduced carbon emissions and noise pollution align with the city’s objectives and create a harmonious balance between economic prosperity and environmental preservation for future generations. This study offers important insights for integrating advanced computing technologies into BEA systems to enhance electric aeroplanes’ operational efficiency and safety to support their adoption. It also provides a path for policymakers and industry stakeholders toward sustainable economic development and integration of Macau within the GBA.

<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.