Virtual reality (VR) games have become a popular method to preserve and transmit intangible cultural heritage in recent years. However, empirical studies pertaining to motivations behind the continuance intention to play VR games featuring intangible cultural heritage have been limited. The objective of this study focuses on answering an essential question: what factors influence user’s continuance intention to play intangible cultural heritage VR games? Both Stimulus-Organism-Response Theory and Technology Acceptance Model (TAM) are considered to develop twelve hypotheses and build the research framework. A survey of 190 respondents was conducted, and the results were analyzed by using PLS-SEM. The results show that visual attractiveness, interactivity, and immersion are significant indicators in measuring users’ continuance intention to play. Additionally, perceived usefulness, perceived ease of use, and perceived enjoyment of VR games positively influence their continuance intention. This study enriches the research of intangible cultural heritage VR games. It also provides theoretical implications for scholars and design strategies for VR developers and designers.

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence.

The accelerating global population aging has brought increasing attention to the loneliness and emotional needs experienced by older adults due to shrinking social networks and the loss of relatives and friends, which significantly impair their quality of life and psychological well-being. In this context, companion robots powered by artificial intelligence are increasingly regarded as a scalable and sustainable form of emotional intervention that can address older people’s affective and social requirements. This study systematically reviews research trends in this field, analyzing the structure of emotional needs among older users and their acceptance mechanisms toward robot functionalities. First, a keyword co-occurrence analysis was conducted using VOSviewer on relevant literature published between 2000 and 2025 from the Web of Science database, revealing focal research topics and emerging trends. Subsequently, questionnaire surveys and in-depth interviews were carried out to identify emotional needs and functional preferences among elderly users. Findings indicate that the field is characterized by increasing interdisciplinary integration, with affective computing and naturalistic interaction becoming central concerns. Empirical results reveal significant differences in need structures across age groups: the oldest-old prioritize safety monitoring and daily assistance, whereas the young-old emphasize social interaction and developmental activities. Regarding emotional interaction, older adults generally prefer natural and non-intrusive expressive styles and exhibit reserved attitudes toward highly anthropomorphic designs. Key factors influencing acceptance include practicality, ease of use, privacy protection, and emotional warmth. The study concludes that effective companion robot design should be grounded in a nuanced understanding of the heterogeneous needs of the aging population, integrating functionality, interaction, and emotional value. Future development should emphasize adaptive and customizable capabilities, adopt natural yet restrained interaction strategies, and strengthen real-world cross-cultural and long-term evaluations.

In response to the challenges of low accuracy in traditional pepper blight identification under natural complex conditions, particularly in detecting subtle infections on early-stage leaves, stems, and fruits. This study proposes a multi-site pepper blight disease image recognition algorithm based on YOLOv8, named MSPB-YOLO. This algorithm effectively locates different infection sites on peppers. By incorporating the RVB-EMA module into the model, we can significantly reduce interference from shallow noise in high-resolution depth layers. Additionally, the introduction of the RepGFPN network structure enhances the model’s capability for multi-scale feature fusion, resulting in a marked improvement in multi-target detection accuracy. Furthermore, we optimized CIOU to DIOU by integrating the center distance of bounding boxes into the loss function; as a result, the model achieved an impressive mAP@0.5 score of 96.4%. This represents an enhancement of 2.2% over the original algorithm’s mAP@0.5. Overall, this model provides effective technical support for promoting intelligent management and disease prevention strategies for peppers.

The key problem to solving constrained multi-objective optimization problems (CMOPs) is how to achieve a balance between objectives and constraints. Unfortunately, most existing methods for CMOPs still cannot achieve the above balance. To this end, this paper proposes an adaptive constraint relaxation-based evolutionary algorithm (ACREA) for CMOPs. ACREA adaptively relaxes the constraints according to the iteration information of population, whose purpose is to induce infeasible solutions to transform into feasible ones and thus improve the ability to explore the unknown regions. Completely ignoring constraints can cause the population to waste significant resources searching for infeasible solutions, while excessively satisfying constraints can trap the population in local optima. Therefore, balancing constraints and objectives is a crucial approach to improving algorithm performance. By appropriately relaxing the constraints, it induces infeasible solutions to be transformed into feasible ones, thus obtaining more information from infeasible solutions. At the same time, it also establishes an archive for the storage and update of solutions. In the archive update process, a diversity-based ranking is proposed to improve the convergence speed of the algorithm. In the selection process of the mating pool, common density selection metrics are incorporated to enable the algorithm to obtain higher-quality solutions. The experimental results show that the proposed ACREA algorithm not only achieved the best Inverse Generation Distance (IGD) value in 54.6% of the 44 benchmark test problems and the best Hyper Volume (HV) value in 50% of them, but also obtained the best results in seven out of nine real-world problems. Clearly, CP-TSEA outperforms its competitors.