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All-electric aircraft (AEA) have garnered significant attention due to their potential to achieve zero carbon emissions and reduce noise pollution, contributing to global environmental sustainability. This study examines consumer behavioural intentions toward AEA in Guangdong, China, using the Theory of Planned Behavior (TPB) as the theoretical framework. Data were collected through structured questionnaires distributed to 100 potential adopters, assessing their awareness, influencing factors, and willingness to adopt the technology. Structural Equation Modeling (SEM) was employed to analyse the responses. The findings indicate that consumers’ attitudes, subjective norms, and environmental concerns positively influence their adoption of AEAs. Interestingly, perceived behavioural control and perceived risk were found to have no significant effect. These insights offer practical implications for accelerating AEA adoption in Guangdong's aviation sector. For airlines, the results highlight the importance of emphasising environmental benefits and social endorsements in marketing campaigns. Manufacturers can strengthen safety perceptions to align with consumer expectations, while policymakers may consider infrastructure investments to mitigate adoption barriers. Collectively, these measures could foster broader acceptance of AEAs, supporting regional decarbonisation goals in the regional air transport sector.

While the initial adoption of technology is widely studied, the factors driving its long-term retention remain a critical gap. This research shifts the focus from adoption to sustained use by applying the Model for Sustained Technology Use (MSTU) to investigate generative AI engagement among Vietnamese university students. A cross-sectional survey of 100 students measured the key constructs of Habit, Satisfaction, and Perceived Usefulness, as well as their impact on Sustained Technology Use. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that Habit is the strongest direct predictor of sustained use, surpassing the influence of Perceived Usefulness (PU). While PU drives initial adoption, its effect diminishes over time, whereas Satisfaction (ST) plays a crucial mediating role in long-term engagement. These results challenge the prevailing assumption that perceived usefulness alone is sufficient to ensure long-term success. The study offers key implications for researchers and practitioners, emphasizing the importance of designing AI educational tools that seamlessly integrate into and adapt to user workflows to promote habitual use. For educators and developers, this means prioritizing features that create engaging, positive, automatic user experiences to ensure generative AI remains a retained educational resource, not a momentary novelty.

<jats:p>This study aims to understand how companies address and integrate sustainability challenges in packaging design, as well as the motivations and processes that influence managers’ decisions when adopting sustainable practices. Semi-structured interviews were conducted with managers from five major Portuguese companies to gather qualitative data on the motivations and processes related to sustainable packaging strategies and actions. The list of questions was developed based on the literature review, from which the dimensions to be analyzed were identified. The results indicate that several factors influence companies’ decisions regarding sustainability in packaging. Despite some factors being beyond the control of companies, the interviews reveal that companies possess the necessary knowledge and are committed to adopting more sustainable packaging.</jats:p>

Background: Maternal infections are linked to neurodevelopmental impairments, highlighting the need to investigate SARS-CoV-2-induced immune activation. Objective: This study aimed to evaluate the impact of maternal infection on neurodevelopment and investigate whether cytokine and chemokine profiles predict delays at 24 months. Methods: Conducted in Brazil (January 2021–March 2022), this follow-up study included 18 SARS-CoV-2 positive pregnant women at 35–37 weeks’ gestation, 15 umbilical cord blood samples, and blood samples from 15 children at 6 months and 14 at 24 months. Developmental delay was defined using the Bayley Scales of Infant and Toddler Development, Third Edition, with scores below 90 in cognitive, communication, or motor domains. Results: At 6 months, 33.3% of infants exhibited cognitive delays, 20% communication delays, and 40% motor delays, increasing to 35.71%, 64.29%, and 57.14% at 24 months, respectively. Elevated interferon-gamma and tumor necrosis factor-alpha in cord blood correlated with cognitive delays, while interleukin (IL)-6, IL-8, IL-17, and IL-1β were associated with motor delays. Increased C-X-C motif chemokine ligand 10 and other cytokines were associated with communication delays. Conclusion: Maternal SARS-CoV-2 may impact infant neurodevelopment, as early cytokine elevations correlate with delays, highlighting the importance of early monitoring and interventions to reduce long-term effects. Impact: Prenatal SARS-COV-2 infection in pregnant women is linked to developmental delays in toddlers, with cytokine and chemokine changes associated with neurodevelopmental outcomes at 24 months. This study shows the long-term impact of maternal SARS-COV-2 infection on child development, highlighting inflammatory markers like IFN-γ, TNFα, IL-6, IL-8, IL-17, IL-1β, and CXCL10. Identifying specific cytokines correlating with cognitive, communication, and motor delays suggests potential biomarkers for early intervention. Conducted in Fortaleza, Brazil, the study emphasizes understanding local epidemiological impacts on child development, especially in regions with high infection rates. (Figure presented.)

The ongoing Russia–Ukraine conflict has had significant repercussions for businesses, with many scaling back operations in Russia due to international sanctions. However, some companies continue operating there while making superficial gestures to appear supportive of the oppressed side (a practice known as ‘warwashing’). These actions conflict with profit motives and contribute to consumer skepticism and potential boycotts. This study examines how Portuguese and Danish consumers respond to warwashing, aiming to assess if cultural differences influence reactions. A quantitative survey, including nine questions based on literature and key differences between the two countries, was conducted using a deductive approach. Results were analyzed via JMP statistical software, with paired t-tests applied. Findings reveal a significant difference in reactions between Portuguese and Danish consumers, with Danish consumers showing a heightened response, engaging more frequently in impactful actions. This aligns with Hofstede’s cultural model, which portrays Danes as more open to change and expecting transparency. Boycott theory is also supported, suggesting that Danes are more inclined to boycott products and services, while Portuguese consumers show less faith in the effectiveness of such actions. This cross-country comparison reaffirms Hofstede’s Cultural Value Dimensions, providing insight into real-world cultural differences. Additionally, the study highlights the concept of collective action, where individuals avoid certain products or services as a form of protest, revealing variations in the prevalence of this behaviour across different societies.

Purpose – This work investigates how different strategies for providing cues about the non-human identity of a sales agent influence consumers’ perceptions and purchase-related outcomes, and how a social interaction style shapes these responses. Additionally, the authors explore the role of consumers’speciesism against non-human entities in eliciting unfavourable responses to the disclosure of the agent’s artificial nature. Design/methodology/approach – Three experimental studies were conducted using real chatbot interactions. Study 1 investigates how non-human identity cues impact consumer trust and, subsequently, attitude towards the firm and intention to purchase the product offered. Study 2 tests these effects across different levels of social presence. Study 3 examines consumer responses to different non-human identity disclosure strategies, considering speciesism’s moderating role. Findings – Study 1 proves that disclosing (vs not disclosing) the artificial nature of a sales agent leads to a decline in trust towards the firm, which in turn negatively influences both attitude towards the firm and purchase intention. This finding reveals discrimination against disclosed (vs non-disclosed) artificial sales agents despite identical, flawless performance. However, Study 2 proves that the negative effects vanish when perceived social presence is high. Study 3 underlines that high speciesism leads to a trust decline if non-human identity cues are presented during the interaction but not if presented earlier in the journey before the interaction. Research limitations/implications – The study highlights the negative effects of disclosure on important, firm-related outcomes. These insights advance current literature by showing that disclosing cues about the non-human nature of a sales agent can undermine psychological and behavioural responses–even when the disclosed agent performs just as effectively as its undisclosed counterpart. This result is noteworthy, as most prior research has linked aversive reactions to artificial agents with situations in which algorithms underperform, whereas this study examines agents that function flawlessly. Furthermore, the study reveals that these adverse effects are driven by speciesism–prejudices against non-human entities–offering a novel explanation for consumers’ negative responses. Practical implications – The findings stress that transparency about the artificial nature of sales agents is penalised by customers and comes at a high cost for business-relevant outcomes. However, by transforming an artificial agent into a social actor through subtle design modifications, firms can overcome the unfavourable prejudice against artificial agents. By creating a social appearance, firms can harness the potential of automated sales services–even when disclosure of the agent’s artificial identity is required. As firms may soon be obliged to disclose the artificial identity of their sales agents, the critical question shifts from whether to disclose to how to disclose in order to mitigate negative consequences. Finally, we offer guidance on targeting the right consumers with artificial agents–specifically, those with lower levels of speciesism-related prejudices. Originality/value – This work addresses pressing issues for managers concerned with the implementation of artificial sales agents. Results extend knowledge on speciesism towards digital agents, inform which consumers are particularly prone to respond negatively to such agents, and present levers for designing chat-based social interactions that prevent non-human-related prejudices that could undermine the effectiveness of conversational technologies.

<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>

Counterfeiting in luxury fashion presents unique opportunities for brands. While it negatively impacts them by diluting exclusivity, it also affects consumer psychology in unexpectedly positive ways. Authentic consumers may feel pride in being copied, enhancing their perceived status by owning something only a few can possess. Additionally, counterfeits act as free marketing tools, increasing brand awareness on a scale not otherwise accessible, especially for niche or inconspicuous luxury brands. Moreover, counterfeiting offers an opportunity for differentiation through sustainability. Counterfeiters, focused on low-cost production, often cannot commit to sustainable and ethical standards. Luxury brands can leverage this by emphasizing their commitment to sustainability, distancing themselves from counterfeits. Strategically, this allows brands to attract consumers who previously purchased counterfeits, converting them into loyal customers of authentic products. This chapter explores how luxury fashion brands can leverage these dual dynamics to strengthen their market position.

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 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.

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.

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.

<jats:p>Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. Key performance metrics, including test accuracy, training time, and weight file size, were analyzed to assess the learning efficiency, generalization capabilities, and architectural innovations of each model. EfficientNet V2 and ResNet50 emerged as top performers, achieving high accuracy and stable convergence using compound scaling and residual connections, enabling them to capture complex emotional features with minimal overfitting. DenseNet, GoogLeNet V1, and RepVGG also demonstrated strong performance, leveraging dense connectivity, inception modules, and re-parameterization techniques, though they exhibited slower initial convergence. In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. The results highlight the critical trade-offs between computational efficiency and predictive accuracy, emphasizing the importance of selecting appropriate architecture based on application-specific requirements. This research contributes to ongoing advancements in deep learning, particularly in domains such as facial expression recognition, where capturing subtle and complex patterns is essential for high-performance outcomes.</jats:p>

<jats:p>Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focus on prediction and pattern recognition. In contrast, causal machine learning goes a step further by revealing causal relationships between different variables. We explore a novel concept called Double Machine Learning that embraces causal machine learning in this research. The core goal is to select independent variables from a gesture identification problem that are causally related to final gesture results. This selection allows us to classify and analyze gestures more efficiently, thereby improving models’ performance and interpretability. Compared to commonly used feature selection methods such as Variance Threshold, Select From Model, Principal Component Analysis, Least Absolute Shrinkage and Selection Operator, Artificial Neural Network, and TabNet, Double Machine Learning methods focus more on causal relationships between variables rather than correlations. Our research shows that variables selected using the Double Machine Learning method perform well under different classification models, with final results significantly better than those of traditional methods. This novel Double Machine Learning-based approach offers researchers a valuable perspective for feature selection and model construction. It enhances the model’s ability to uncover causal relationships within complex data. Variables with causal significance can be more informative than those with only correlative significance, thus improving overall prediction performance and reliability.</jats:p>