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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
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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.
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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.
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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.
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Faculty of Business and Law
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- Alessandro Lampo (2)
- Alexandre Lobo (4)
- Angelo Rafael (1)
- Florence Lei (2)
- Ivan Arraut (6)
- Silva, Susana C. (5)
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Faculty of Health Sciences
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- Edward Kwan (1)
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Faculty of Religious Studies and Philosophy
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- Cyril Law (1)
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School of Education
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- Hao Wu (2)
- Rochelle Ge (2)
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