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Background: The number of artificial intelligence (AI) literacy studies in K‐12 education has recently increased, with most research focusing on primary and secondary education contexts. Little research focuses on AI literacy programs in early childhood education. Objectives: The aim of this mixed‐methods study is to examine the feasibility of an AI literacy program called "AI4KG" and explore how it might affect kindergarteners' perceptions of robots and attitudes towards engineering and science. Methods: A total of 26 child–parent dyads recruited from a Hong Kong kindergarten were involved in this study, consisting of 26 children (Mage = 4 years, SD = 0.28) and their parents. Quantitative and qualitative data were collected through surveys and interviews designed to explore children's perceptions of robots and attitudes towards engineering and science, and parents' perceptions of the AI4KG intervention. Results: It is found that children have increased their perceptions of robots after the AI literacy program, but the AI4KG curriculum had no significant effects on kindergarten children's engineering and science attitudes. Most parents (22 out of 26) agreed that their children's AI knowledge, AI skills, and AI attitudes have been enhanced after learning through the AI4KG curriculum. Conclusions: This study suggests that the AI4KG curriculum is potentially effective in promoting early AI literacy and favourable attitudes towards the technology, but further research is needed to develop age‐appropriate measures and assess its long‐term impact on children's education and career paths. Lay Description: What is currently known about this topic?: AI literacy is becoming important as AI transforms society, but few studies have explored AI literacy in early childhood education.Prior research found AI activities can positively influence older children's views of robots and engineering. What does this paper add?: It evaluates an 8‐week AI literacy curriculum called AI4KG for kindergarteners in Hong Kong.The curriculum used tools like Teachable Machine and taught concepts through play and storytelling.It improved children's perceptions of robots but did not significantly impact engineering/science attitudes.Parents perceived improvements in children's AI knowledge, skills, and attitudes after the curriculum. Implications for practice/policy: The curriculum provides an example for developing age‐appropriate AI literacy activities.More rigorous research on impacts over time is needed, but results suggest potential benefits of AI literacy in early childhood.Policymakers could consider incorporating AI literacy into kindergarten curricula to prepare children for the future. [ABSTRACT FROM AUTHOR]

For the reinforcement of soft marine deposit, relying on the main reclamation works of the Hong Kong Airport three runway system project (hereinafter referred to as the Hong Kong three runway project), the on-site trial process for high pressure jet grouting piles was carried out, and the advanced multi-functional high pressure jet grouting rig from Italy was adopted, which has high degree of construction automation, good quality of jet grouting pile and fast work efficiency. This paper focuses on the influence of cement dosage, curing age, etc. on the strength on-site cement soil, as well as the strength proportion of indoor cement soil at different ages. It provides a good engineering and technical reference for the popularization and application of high-pressure jet grouting pile reinforcement method in coastal soft soil foundation in Hong Kong. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

During the construction of deep cement mixing（ DCM） vessel, the original soil is mainly mixed by rotating and cutting the mixing blades of the processor to ensure the pile quality. Due to the different structures of the mixing blades, it has a great impact on the work efficiency of the DCM pile, thus affecting the overall progress of the project. Taking the offshore DCM pile construction of Hong Kong Airport project as an example, this paper analyzes the working efficiency of the vessel under different mixing blade designs, so as to provide basic construction data reference for the implementation of engineering projects in the future. [ABSTRACT FROM AUTHOR]