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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.

In the hype of multi-/inter-disciplinarity, is the voi-ce or voices of theistic religions and the attendant philosophical moral awareness (etymologically bet-ter rendered as conscientização in Portuguese) still meant to be heard? Can classical tales of saints and sinners remain part of the canon of public literacy? How existential is the threat of “organised religions” or otherwise established ecclesiastical structures posed to society when they are accused of attempting to fight proxy crusades against humanitarian enlightenment under the guise of religious literature? Are tenets pro-pounded by scholars like Gavin D’Costa in Theology and the Public Square (2005) to be politely bracketed when discussing perennial values? Values that respon-sible media strive to propagate, particularly the value of human dignity eulogised by the life exemplars of great figures in times of existential crises of whatever magnitude. With these questions in mind, this article will hearken back to the stories of two “grandees” in the Roman Catholic tradition who left their marks on the pages of the development of modern English and Chinese literacy. Newman’s Apologia pro vita sua(1865) is just but one of the tactical devises for his defense of creedal integrity, while Ma Xiangbo engaged in catholicising the Chinese national ethos through educational literacy for close to half a century. We shall phenomenologically draw inspirations from their parallel vision and experience on what lends power to the medium of words and deeds in shaping informed public conscience in regard to the core values of truth, good, and beauty.

Critical thinking disposition (CTD) is increasingly recognized as an important trait in education, reflecting the inclination and habits necessary for addressing complex challenges in today's world. This study assessed the CTD of students enrolled in a tourism and gaming management programme, focusing on two key dimensions: Analyticity and Open-Mindedness. This study was conducted at a university in Macao and involved 65 participants. The students were presented with an article relevant to their major, written in Traditional Chinese, and were asked to provide their opinions on each statement in the article. A rubric was designed to analyze their responses and assess their Analyticity and Open-Mindedness within the CTD framework. The results demonstrated high reliability (Cronbach's α = 0.91) and revealed an association between Analyticity and Open-Mindedness. Using Python programming, the study analyzed the frequency of parts of speech (POS) in students' responses, introducing a novel approach for evaluating CTD in Traditional Chinese. Regression modeling showed that parallel and adversative conjunctions significantly predicted Analyticity, while the frequency of conjunction use varied across Open-Mindedness classifications. These findings highlighted an innovative and objective method for assessing CTD through text analysis, offering promising applications for educational research in Traditional Chinese-speaking contexts.

In this paper, we demonstrate that the effects of Dark-Matter, partially in the way proposed by the Modified Newtonian Dynamics (MOND), emerge naturally from the standard theory of General Relativity (without any modification) under a new proposed vacuum solution. There is a family of metric solutions able to reproduce the galaxy rotation curves and the relevant scales where the Dark-Matter effects are supposed to appear in a galaxy. This family of solutions deviate from the standard spherically symmetric solution. The proposed formulation, being relativistic by nature, opens the scenario where we can test the relativistic effects attributed to Dark-Matter and having relevance in cosmology. Among such effects, we have gravitational lensing, effects on the CMB scenario and effects on the formation of galaxies.

The pandemic exposed weaknesses in the global trade system, making it clear that climate actions are the priority in the recovery. International organizations are urging countries to seize this opportunity and integrate climate-friendly trade and investment rules to promote sustainable development. Trade is recognized as a powerful tool for tackling climate change, offering economies ways to both reduce emissions and adapt to environmental changes. In this paper, we investigate the digital and sustainable trade facilitation measures implemented in ASEAN countries, namely Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. We use a well-established trade model, the gravity model, to assess the impacts of trade facilitation efforts, particularly those that leverage digital technologies and promote sustainability. The data for this analysis comes from the UN Global Survey on digital and sustainable trade facilitation in 2017, 2019, and 2021. The results show that trade facilitation measures are crucial to increasing trade among the ASEAN countries. Measures of transparency of trade procedures, trade formality alleviation, and cross-border paperless trade have significant positive impacts on bilateral trade between ASEAN countries.

本書由36 位不同界別的領袖、專家和學者，分享與人口老齡化相關的精闢觀察與洞見，探索創新永續的生活和經濟模式，包括相關的政策、黃金時代經濟的發展、中國安老服務的新視野、醫康養老新發展、智齡科技的應用、永續人才和社區發展等議題，為業界提供參考，亦為45 歲以上的黃金一代應對未來退休生活提供啟發。 「我們的生活越來越受創新技術的影響，我們的社會也更加重視綠色生活和可持續發展。科技和綠色生活方式必須融入智齡產品和服務中。」 —— 陳茂波 香港特別行政區財政司司長 「我們的共同目標是在老齡化世界中不讓任何人掉隊。」 —— 威廉•史密斯博士 聯合國紐約總部老年事務非政府組織委員會主席 「我們深信人口老化為全球帶來嶄新的機遇。中、老年人是唯一正在不斷增長的人力資源，也是創新產品和服務的龐大消費群體。」 —— 容蔡美碧 黃金時代基金會創會主席

By using both the weak-value formulation as well as the standard probabilistic approach, we analyze Hardy’s experiment introducing a complex and dimensionless parameter (ϵ), which eliminates the assumption of complete annihilation when both the electron and the positron departing from a common origin cross the intersection point P. We then find that the paradox does not exist for all the possible values taken by the parameter. The apparent paradox only appears when ϵ=1, which is just a singular value. In this paper we demonstrate that this particular value is forbidden inside the scenario proposed by the experiment.

The aviation sector is transforming as electrification emerges as a promising technology. Adopting battery-electric aircraft (BEA) - aircraft that solely rely on rechargeable onboard batteries - is a sustainable alternative to conventional aviation that could change short-haul regional travel habits for business and leisure travellers. This study examines the factors influencing individuals’ public acceptance in China's Greater Bay Area (GBA) context. Given the limited research, a qualitative methodology grounded in the Theory of Planned Behaviour (TPB) examines the underlying factors influencing behavioural intentions (attitudes, subjective norms, perceived behavioural control, and perceived risks). The findings indicate that participants recognise the technology's environmental benefits and potential to enhance regional connectivity; however, they still have concerns about safety, infrastructure, and operations. The respondents’ perceived ease of access, information available, and endorsements from reputable sources also have essential roles in influencing broader acceptance. Addressing these factors with appropriate communication efforts is vital for promoting trust and accelerating technology acceptance and use. Although exploratory, this study offers insights to develop strategies for infrastructure readiness, build public confidence, and endorse sustainable aviation. The research is conducted within the GBA context. Still, the findings also apply to regions with fragmented geographies or developing transportation networks, thus contributing to global environmental sustainability and advancing regional integration goals.

Critical thinking disposition (CTD) is increasingly recognized as an important trait in education, reflecting the inclination and habits necessary for addressing complex challenges in today's world. This study assessed the CTD of students enrolled in a tourism and gaming management programme, focusing on two key dimensions: Analyticity and Open-Mindedness. This study was conducted at a university in Macao and involved 65 participants. The students were presented with an article relevant to their major, written in Traditional Chinese, and were asked to provide their opinions on each statement in the article. A rubric was designed to analyze their responses and assess their Analyticity and Open-Mindedness within the CTD framework. The results demonstrated high reliability (Cronbach's α = 0.91) and revealed an association between Analyticity and Open-Mindedness. Using Python programming, the study analyzed the frequency of parts of speech (POS) in students' responses, introducing a novel approach for evaluating CTD in Traditional Chinese. Regression modeling showed that parallel and adversative conjunctions significantly predicted Analyticity, while the frequency of conjunction use varied across Open-Mindedness classifications. These findings highlighted an innovative and objective method for assessing CTD through text analysis, offering promising applications for educational research in Traditional Chinese-speaking contexts.

We demonstrate that the flavor oscillation when a neutrino travels through spacetime, is equivalent to permanent changes on the vacuum state condition perceived by the same particle. This can be visualized via the Quantum Yang Baxter equations (QYBE). From this perspective, the neutrino never breaks the symmetry of the ground state because it never selects an specific vacuum condition. Then naturally the Higgs mechanism cannot be the generator of the neutrino masses. The constraints emerging from this model predict a normal mass hierarchy and some specific values for the mass eigenvalues once we fix the mixing angles. Interestingly, the model suggests that the sum of the mix angles is equal to $\pi/2$.

The origins of neutrino masses is one of the biggest mysteries in modern physics since they are beyond the realm of the Standard Model. As massive particles, neutrinos undergo flavor oscillations throughout their propagation. In this paper we show that when a neutrino oscillates from a flavor state {\alpha} to a flavor state \b{eta}, it follows three possible paths consistent with the Quantum Yang- Baxter Equations. These trajectories define the transition probabilities of the oscillations. Moreover, we define a probability matrix for flavor transitions consistent with the Quantum Yang-Baxter Equations, and estimate the values of the three neutrino mass eigenvalues within the framework of the triangular formulation.

In this paper, we investigate black hole evaporation from the path integral perspective. We demonstrate that besides the standard thermodynamic modes, there are non-thermodynamic modes of black hole evaporation which contain remnants. The pure thermodynamic process is recovered when the Gauss-Bonnet action is involved. This scenario opens a new window for analyzing the process of black-hole evaporation.

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.

We derive the vacuum energy from the zero-point quantum fluctuations after imposing a natural constraint emerging from the rotational symmetry inside the de-Sitter metric. The constraint imposes a maximum azimuthal angle for each frequency mode emerging from the vacuum. In this way, the shorter the wavelength of the mode, the larger will be its suppression. The same result is derived subsequently by using the Friedmann–Lemaitre–Robertson–Walker (FLRW) metric. We then make a physical interpretation of the physical effects from the perspective of pair creations over the vacuum, where the mentioned constraint emerges, limiting then the maximum angle which each pair generated from the vacuum can rotate with respect to each other during their short existence.

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.

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.

There are many systematic reviews on predicting stock. However, each reveals a different portion of the hybrid AI analysis and stock prediction puzzle. The principal objective of this research was to systematically review the existing systematic reviews on Artificial Intelligence (AI) models applied to stock market prediction to provide valuable inputs for the development of strategies in stock market investments. Keywords that would fall under the broad headings of AI and stock prediction were looked up in Scopus and Web of Science databases. We screened 69 titles and read 43 systematic reviews, including more than 379 studies, before retaining 10 for the final dataset. This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction. In addition, the time series of historical closing stock prices are the most commonly used data source, and accuracy is the most employed performance metric of the predictive models. We also identified several research gaps and directions for future studies. Specifically, we indicate that future research could benefit from exploring different data sources and combinations, while we also suggest comparing different AI methods and techniques, as each may have specific advantages and applicable scenarios. Lastly, we recommend better evaluating different prediction indicators and standards to reflect prediction models’ actual value and impact.

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

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.