Preface: The 2024 4th International Conference on Social Sciences and Intelligence Management ^†

This volume comprises the proceedings of the 2024 4th International Conference on Social Sciences and Intelligence Management (SSIM 2024), held in Taichung, Taiwan, from the 20th to the 22nd of December 2024, and the 2024 Cross Strait Conference on Social Sciences and Intelligence Management (CRSSIM 2024), held in Shanghai, China, from the 13th to the 15th of December 2024.

The 2024 4th International Conference on Social Sciences and Intelligence Management (SSIM 2024) was a collaborative effort between Chaoyang University of Technology, the IEEE Tainan Section Sensors Council (IEEE TSSC), and the International Institute of Knowledge Innovation and Invention (IIKII). The 2024 Cross Strait Conference on Social Sciences and Intelligence Management (CRSSIM 2024) represents a collaborative endeavor among Shanghai Zhongqiao Vocational and Technical University, the IEEE Tainan Section Sensors Council (IEEE TSSC), and the International Institute of Knowledge Innovation and Invention (IIKII).

In today’s rapidly evolving social landscape, science and technology are profoundly reshaping human perceptions and the ability to tackle new challenges. This technological revolution has deeply impacted the humanities, social sciences, and intelligence management. SSIM 2024 and CRSSIM 2024 uniquely intertwine social sciences with smart management, charting a course for changing society and highlighting emerging issues and challenges for further exploration and dialog. The conferences gathered leading experts, scholars, researchers, and doctoral students from relevant fields worldwide, fostering robust interactions and disseminating high-quality research. Figure 1 shows the group photo from the conference opening ceremony of SSIM 2024.

SSIM 2024 featured two insightful keynote speeches that were directly relevant to the conference themes. The first keynote, “Examination of Healthcare Waiting Times and Policy Insight,” was delivered by Associate Professor Dr. Qian Qu from the Singapore University of Social Sciences (Figure 2). Professor Qu presented healthcare waiting times as a crucial indicator of operational system congestion, influenced by demand–supply dynamics and policymakers’ considerations for balancing efficiency and equity. Her examination of patient waiting times in public health systems under various regulatory settings and financing models revealed a strong association between operational efficiency and policies promoting private health services. This association varied in strength depending on the policy, a difference explained by the relative effects of such policies on demand and supply. Professor Qu also explored different subsidy schemes widely used to reduce public healthcare waiting times. Theoretical and simulation results demonstrated that unconditional patient subsidy schemes outperform others in reducing total patient costs and achieving optimal serviceability. The results underscore the power of patient self-selection among available options for known care path delays and provide empirical evidence highlighting the importance of offering patients choices for prolonged waiting times in the public health system.

The second keynote speech, “Achieving Personalized Learning with AI,” was given by Distinguished Professor Dr. Ting-Chia Hsu from the Department of Technology Application and Human Resource Development, National Taiwan Normal University (Figure 3). Professor Hsu offered a comprehensive exploration of integrating artificial intelligence (AI) into education, emphasizing personalized learning, teacher development, and AI literacy. Key highlights included the progression of AI literacy development for both teachers and students, aiming to deepen understanding and foster innovative teaching methodologies. The presentation underscored AI-assisted teaching strategies, updating traditional pedagogical frameworks, such as TPACK, to accommodate AI tools. Additionally, adaptive e-learning applications were discussed, focusing on personalized learning paths, content, and assessments, with examples including adaptive video caption filtering and peer assessment tools. The integration of AI in language learning to enhance vocabulary acquisition and reduce learning anxiety was also showcased. Future research directions highlighted the ethical, societal, and interdisciplinary aspects of AI literacy, stressing the need for empirical studies, innovative tools, and professional development to effectively embed AI into educational environments. The presentation concluded by addressing generative AI’s transformative role in shifting traditional education toward a more interactive and learner-centered model.

SSIM 2024 adopted a hybrid form of on-site and online presentations. The conference received 195 submissions, with 141 papers ultimately selected and registered from 9 countries and regions, including China, Indonesia, Japan, Korea, Latvia, Poland, Taiwan, Thailand, and the Philippines. These papers cover diverse topics and were organized into Regular sessions. Figure 4 and Figure 5 display photos of on-site and online oral presentation sessions in SSIM 2024.

Figure 6 presents a group photograph from the CRSSIM 2024 conference’s opening ceremony.

CRSSIM 2024 featured two insightful keynote speeches directly pertinent to the conference themes. The first keynote address, “Dynamic Order Forecast Sharing with Dual Shipping Options under Quantity Flexibility Contract,” was delivered by Professor Dr. Jia Zhen Huo from Tongji University, China (Figure 7). Professor Huo’s presentation focuses on optimizing dynamic order forecast sharing from a manufacturer to a supplier under a Quantity Flexibility (QF) contract, incorporating dual shipping options. It underscores the importance of order forecast sharing in reducing lead times and enhancing resource organization. The QF contract is introduced to mitigate the bullwhip effect and improve forecast accuracy, concurrently considering dual shipping options to bolster responsiveness and reduce associated costs. The problem is addressed using a rolling-horizon framework and the Martingale Model of Forecast Evolution (MMFE) to quantify the demand uncertainty without presuming a specific demand probability distribution. A stochastic dual dynamic programming (SDDP) algorithm is employed to resolve the resultant multi-stage stochastic linear programming (MSLP) problem. The methodology encompasses forward and backward passes to approximate the value function within Bellman equations. The experimental results demonstrate the efficacy of SDDP when compared with benchmark policies, exhibiting commendable solution quality and convergence. A case study utilizing Intel’s authentic customer demand data further validates the approach, indicating substantial cost savings across diverse settings and forecasting methodologies. Professor Huo’s research contributes to the construction of a novel decision framework that integrates forecast evolution with QF contracts, leveraging dual shipping options for prospective flexibility and furnishing managerial insights for designing efficacious QF contracts.

The second keynote speech, “The real-world assets tokenization”, was delivered by Dr. Vincent Ming, CEO and Partner of Singapore Azura Investment Partners Pte. Ltd. (Figure 8). Dr. Ming identified challenges associated with real-world asset tokenization, outlined as follows: (1) Complexity: Asset tokenization presents inherent complexity, potentially creating steep learning curves or significant entry barriers for numerous market participants. (2) Price volatility: Tokenized assets, particularly those actively traded on secondary markets, are susceptible to substantial price fluctuations, which could lead to investor losses. (3) Valuation difficulties: Accurately valuing a tokenized asset proves challenging, especially when dealing with unique or rare items, such as collectibles. (4) Tax complexity: The tax regulations governing tokenized assets often lack clarity, with requirements differing across jurisdictions. The sale or trading of these assets can impose considerable tax reporting burdens. (5) Custodial risks: The secure holding and management of tokenized assets necessitate robust storage solutions. There is a risk of tokens being lost, stolen, or otherwise mismanaged. (6) Incompatibility with traditional systems: Integrating tokenized assets with established financial and legal frameworks poses significant difficulties. (7) Other technical challenges: Blockchain technology, smart contracts, and other Web3 systems may be vulnerable to security risks and technological failures.

All presentations at CRSSIM 2024 were conducted on-site. Figure 9 and Figure 10 display photographs of the on-site presentation sessions during CRSSIM 2024.

SSIM 2024 and CRSSIM 2024 showcased a wealth of substantial findings shared by enthusiastic participants. The conference proceedings are expected to foster interdisciplinary collaboration among science and engineering academicians, enhance academic and industrial collaboration, and strengthen international networking.