Shahriman Zainal Abidin

This paper employs structural equation modelling (SEM) to quantitatively analyse users as the research subjects in the current design of Chinese tea ware, which faces issues such as severe appearance homogenization and a lack of cultural connotation. Based on this, we integrate sensibility theory into tea ware design to arouse deep emotional resonance between users and the unique cultural attributes of tea ware. This integration aims to realize a new tea-drinking experience. Several issues related to the characteristics of tea ware contribute to establishing a hierarchical framework for tea wares. Taking the inheritance of tea-drinking culture, the reshaping of the tea-drinking experience, and the satisfaction of users' emotional needs as the starting point, quantitative methods have been employed to emphasize objective measurements and the statistical analysis of data collected through polls, questionnaires, or computational techniques (e.g., analysing pre-existing statistical data). The results highlight the influence of users' sensibility and emotional identity on design feedback and strategies throughout the process from selecting tea wares to tea drinking. The study further suggests that "to optimize the outcomes, cultural identity should be integrated with users' emotional identity, and factors influencing the design strategy should be deeply examined within the context of tea culture." However, limitations exist due to the reliance on quantitative methods.

The artistic expression of traditional Chinese auspicious patterns in modern design is unique and charming, and it is intersecting more and more with modern design, and there is a lack of scientific methods to analyse traditional Chinese patterns from the perspective of Western composition. The objective of this study is to explore the translation path of traditional Chinese auspicious pattern design and realize its protection and reuse. The literature analysis method is mainly used to analyse the theme and cultural connotation of the pattern, the pattern and colour are extracted by using the mapping software, the pattern and colour factor library are constructed, and the shape grammar is used to evolve the pattern design. As a result, a new pattern design scheme is generated by using the law of formal beauty, and the feasibility and effectiveness of shape grammar in the innovative design of traditional patterns are demonstrated.

This paper analyzes how TikTok China's live-streaming platform transforms the marketing of Jingdezhen ceramics. Using Structural Equation Modeling (SEM), the research examines how consumers' perceived value influences their purchase satisfaction and subsequent behavior during Jingdezhen ceramics live-streaming sessions. The model identifies and quantifies key influence pathways, revealing that perceived enjoyment during live-streaming is a crucial mediating variable affecting consumers' perceived value and purchase satisfaction of Jingdezhen ceramics. The study validates the significant impact of integrating Jingdezhen ceramics' traditional cultural characteristics with live-streaming presentation methods on consumer purchase decisions through rigorous SEM analysis. The statistical modeling reveals causal relationships among ceramic value perception, live-streaming interaction experience, and purchase outcomes. While the research is limited to Jingdezhen ceramic products, this SEM-based approach provides empirical evidence for optimizing traditional ceramic product marketing on digital platforms while offering theoretical guidance and practical implications for Jingdezhen ceramics' inheritance and innovation in the digital era.

This paper explores integrating micro materials into Chinese line drawing to enhance decorative qualities and address modern challenges like cultural disinterest and material degradation. Using a mixed-methods with data from 613 respondents and 20 professionals, the paper identifies key decorative elements and assesses micro materials' impact on design and conservation. Findings show that understanding micro materials and artistic preferences significantly influences integration challenges and success perceptions. While micro materials offer creative and conservation benefits, their adoption must balance innovation with tradition. Recommendations include artist experimentation, respect for cultural authenticity, and institutional support.

Their cultural and artistic significance, faces unique challenges due to their delicate nature and intricate details. Traditional conservation methods often fall short in addressing the complexities of these artworks, necessitating innovative solutions to maintain their aesthetic and structural integrity. This study explores the integration of AI-generated content (AIGC) and micro materials in conserving Chinese line drawings. The objectives are to enhance aesthetic restoration, structural reinforcement, and long-term preservation while respecting the cultural significance of these artworks. The research employs a quantitative methodology, utilizing structured assessments and statistical analysis to evaluate the impact of AIGC and micro materials on conservation practices. Participants provide diverse perspectives, including art conservationists, historians, and digital artists. Ethical considerations ensure informed consent and adherence to cultural preservation standards. The study reveals that AIGC significantly enhances the precision of aesthetic restoration by simulating realistic restoration scenarios and providing personalized feedback. Micro materials, particularly nanomaterials, offer structural support without altering the artwork's appearance. The collaboration between AIGC and human expertise ensures culturally sensitive and technically robust restorations. Integrating AIGC and micro materials revolutionizes the conservation of Chinese line drawings, combining advanced digital simulations with precise structural reinforcements. This approach ensures the enduring preservation of these cultural artifacts, balancing artistic integrity and structural stability. Recommendations include targeted training programs and ongoing technical support to facilitate the effective integration of these technologies into traditional conservation practices.

With the rapid development of e-commerce and the increasing aging population, more elderly people are engaging in online shopping. However, challenges they face during this process are becoming more apparent. This article proposes a recommendation system based on click-through rate (CTR) prediction, aiming to enhance the online shopping experience for elderly users. By analyzing user characteristics, product features, and their interactions, we constructed a model combining bidirectional long short-term memory (Bi-LSTM) and multi-head self-attention mechanism to predict the item click behavior of elderly users in the recommendation section. Experimental results demonstrated that the model excels in CTR prediction, effectively improving the relevance of recommended content. Compared to the baseline model long short-term memory (LSTM), the GATI-RS framework improved CTR prediction accuracy by 40%, and its loss function rapidly decreased and remained stable during training. Additionally, the GATI-RS framework showed significant performance improvement when considering only elderly users, with accuracy surpassing the baseline model by 42%. These results indicate that the GATI-RS framework, through optimized algorithms, significantly enhances the model’s global information integration and complex pattern recognition capabilities, providing strong support for developing recommendation systems for elderly online shoppers. This research not only offers new insights for e-commerce platforms to optimize services but also contributes to improving the quality of life and well-being of the elderly.

This paper investigates the interrelationship among tourists' cultural value perception, satisfaction, and behavioral intentions in the context of Jingdezhen's ceramic cultural tourism. Through the application of structural equation modeling (SEM), this study empirically examines the critical factors influencing cultural value perception and their subsequent effects on tourist satisfaction and post-visit behavior from the visitors' perspective. In conclusion, the results demonstrate that five distinctive dimensions - ceramic cultural space, ceramic cultural history, ceramic art life, ceramic art experience, and ceramic art products-significantly enhance tourists' perception of Jingdezhen's ceramic culture.

The global tourism industry is expanding rapidly, making effective management of hotel booking cancellations crucial for improving service and efficiency. Existing models, based on static data assumptions and fixed parameters, fail to capture dynamic changes and temporal trends. Real-world cancellation decisions are influenced by factors such as seasonal variations, market demand fluctuations, holidays, and special events, which cause significant changes in cancellation rates. Traditional models struggle to adjust dynamically to these changes. This article proposes a novel approach using deep reinforcement learning techniques for predicting hotel booking cancellations over time. We introduce a framework that combines dynamic temporal reinforcement learning with policy-enhanced LSTM, capturing temporal dynamics and leveraging multi-source information to improve prediction accuracy and stability. Our results show that the proposed model significantly outperforms traditional methods, achieving over 95.9% prediction accuracy, a model stability of 0.98, an F1 Score approaching 1, and a mutual information score of approximately 0.93. These results validate the model’s effectiveness and generalization across diverse data sources. This study provides an innovative and efficient solution for managing hotel booking cancellations, demonstrating the potential of deep reinforcement learning in handling complex prediction tasks.