Artificial intelligence and virtual reality technologies have significant potential in traditional architectural education. Historically used separately, their educational impacts are not fully understood. To advance sustainable architecture education, this study incorporates language and illustration tools of artificial intelligence, along with immersive painting and simulation capabilities of virtual reality, into the curriculum of Jiangnan traditional architecture. Through a randomized controlled trial, 60 students were divided into AI, VR, and control groups. Based on the establishment of an adaptive course learning assessment system, this study empirically compares the effects of artificial intelligence methods, virtual reality methods, and traditional teaching methods across four dimensions: architectural knowledge, architectural design, architectural computation, and architectural learning. Independent sample t-tests and one-way analysis of variance are used to validate the differences in the effectiveness of these technological applications. Findings reveal that artificial intelligence notably enhances design and learning outcomes, whereas virtual reality shows pronounced effects in bolstering knowledge acquisition and computational tasks. Artificial intelligence proves particularly suited to conceptualization and narrative-based design tasks, while virtual reality aligns closely with model creation and post-design refinement activities. These findings provide new perspectives for hybridizing artificial intelligence and virtual reality in sustainable architecture education, contributing to sustainable outcomes.

China stands out as one of the countries with the largest and fastest-growing elderly populations. Elderly requirements for the quality of high-speed railway stations environment are increasing. The relationship between the built environment of high-speed railway stations and elderly health has become an urgent problem to be solved. The challenge necessitates a systematic approach to age-friendly design. Based on the practical experience of the authors in an age-friendly design of an indoor environment at Nanjing South Railway Station, questionnaire surveys and user interviews were employed to profile four types of typical elderly passengers. Data collection methods were used to create behavior maps. (1) This study identifies the age-friendly needs of elderly passengers, pinpointing the key environment issues. (2) It proposes age-friendly design strategies for various functional areas of Nanjing South Railway Station. (3) It introduces an age-friendly module combination design system, which including basic type I, enhanced type II, and optimized type III, in order to meet the demands for six levels of high-speed railway stations in China. This study promotes the age-friendly transformation of environments at a high-speed railway station, offering theoretical foundations and methodological references for constructing an age-friendly environment from a healthy ageing perspective.

1. Exposure to natural environments has been widely associated with substantial health benefits for older adults, including reduced stress and anxiety and enhanced subjective well-being, thereby co...

Autonomous ground vehicles operating in structured and semi-structured environments—such as urban roads, parking lots, and logistics warehouses—require fast, reliable, and collision-free path planning on occupancy grid maps. Existing metaheuristic planners often suffer from premature convergence, insufficient population diversity, and poor feasibility maintenance, limiting their deployment in safety-critical vehicular navigation. This paper proposes a multi-strategy enhanced Crested Porcupine Optimizer (MSCPO) that systematically addresses these limitations through four coordinated enhancements: chaos-opposition initialization with feasibility repair to ensure high-quality and diverse initial routes; a diversity-coupled adaptive mechanism for dynamic strategy scheduling throughout the search; elite-guided differential Lévy perturbation to escape local optima and accelerate convergence; and a two-stage safety-aware objective with elite local refinement to sharpen final solution precision. Experiments on four representative grid maps with varying obstacle densities, conducted over 30 independent runs per algorithm, demonstrate that MSCPO consistently outperforms state-of-the-art metaheuristic planners and deterministic baselines in path length, smoothness, and convergence speed. Statistical analysis via Wilcoxon rank-sum and Friedman tests confirms the significance of the improvements. An ablation study quantifies the individual contribution of each enhancement module, confirming the practical effectiveness of MSCPO for autonomous vehicle navigation tasks.