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Este livro é resultado do I International Meeting da Law and Development Research Network (LDRN) ou Rede de Pesquisa Direito e Desenvolvimento. As temáticas do encontro internacional foram os objetivos do desenvolvimento sustentável, o desenvolvimento e a inclusão socioeconômicos. O evento foi organizado pelo grupo de pesquisa Direito e Sociedade Econômica (DISE), que completa uma década, orientado à pesquisa e à solução de problemas socioeconômicos sob a ótica jurídica. Está vinculado ao Programa de Pós-Graduação em Direito da Universidade do Extremo Sul Catarinense (PPGD/UNESC), localizado em Criciúma, Santa Catarina, Brasil. O evento contou com o apoio institucional da Universidade São José (USJ, Macau-China), da Universidade Eduardo Mondlane (UEM, Moçambique) e da UNESC. Participaram do comitê científico Prof. Dr. Almeida Zacarias Machava (UEM); Prof. Dr. Ângelo Patrício Rafael (USJ); Prof. Dra. Camila Villard Duran, ESSCA School of Management, França; Prof. Dr. Fernando de Magalhães Furlan, UNICEPLAC, Brasil; e Prof. Dra. Rúbia Carneiro Neves, Universidade Federal de Minas Gerais, Brasil. A coordenação-geral coube ao Prof. Dr. Yduan de Oliveira May, UNESC, coordenador do DISE e da LDRN. Cumprimenta-se o Prof. Dr. Ansoumane Douty Diakité (USJ) pelo prefácio, no qual discorre com generosidade suas impressões das atividades da LDRN e a ordenação temática deste livro.

The study aimed to analyze teachers' perceptions of co-teaching within an inclusive educational environment, focusing on the challenges they face, the tools and resources available, and how these factors contribute to promoting inclusion. Conducted with 16 primary and secondary school teachers from an inclusive school in Macau, the research utilized in-depth semi- structured interviews to gather qualitative data on their experiences and perceptions. Overall, the findings highlight co-teaching as a powerful strategy in educational settings, particularly for enhancing students’ learning outcomes. Teachers reported positive views on co-teaching, recognizing its potential to promote collaboration and support diverse learners effectively. Nevertheless, cultural factors significantly influenced teachers’ attitudes toward inclusion. The study revealed that teachers varied cultural and professional backgrounds lead to different perspectives on inclusive practices. This complexity highlights the need for culturally responsive approaches in teacher training and professional development. Despite the positive perceptions of co-teaching, teachers encountered several challenges, namely inconsistent school practices, lack of institutional support from school administration and leadership and inadequate training opportunities. This study suggests that addressing these challenges is crucial for the successful implementation of co-teaching approaches, namely policy adjustments by stakeholders to support co-teaching initiatives, effective allocation of resources for teachers, professional development for school leaders to enhance their ability to foster a collaborative environment, and ongoing training focused on co-teaching methodologies for teachers.

This research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k-Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1-score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.

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.

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.

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.

This work provides a comprehensive systematic review of optimization techniques using artificial intelligence (AI) for energy storage systems within renewable energy setups. The primary goals are to evaluate the latest technologies employed in forecasting models for renewable energy generation, load forecasting, and energy storage systems, alongside their construction parameters and optimization methods. The review highlights the progress achieved, identifies current challenges, and explores future research directions. Despite the extensive application of machine learning (ML) and deep learning (DL) in renewable energy generation, consumption patterns, and storage optimization, few studies integrate these three aspects simultaneously, underscoring the significance of this work. The review encompasses studies from Web of Science, Scopus, and Science Direct up to December 2023, including works scheduled for publication in 2024. Each study related to renewable energy storage was individually analyzed to assess its objectives, methodology, and results. The findings reveal useful insights for developing AI models aimed at optimizing storage systems. However, critical areas need further exploration, such as real-time forecasting, long-term storage predictions, hybrid neural networks for demand-based generation forecasting, and the evaluation of various storage scales and battery technologies. The review also notes a significant gap in research on large-scale storage systems in Brazil and Latin America. In conclusion, the study emphasizes the need for continued research and the development of new algorithms to address existing limitations in the field.

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.