Digital twins are developing rapidly, it involves smart manufacturing, aerospace and other fields. The rise of prefabricated buildings has significantly influenced the construction industry. This paper conducts a literature utilization of prefabricated buildings and their intelligent development status utilizing the resources from the Web of Science and China National Knowledge Network databases. The discussion focuses on digital twin technology, exploring how digitization of building information and its translation into a virtual model ensure a smooth construction phase. Analyze the incidence of prefabricated buildings on the exploitation and building life cycle of green buildings after the adhibition of digital twin capability; This paper elucidates the utilization of digital twin technology in prefabricated buildings prior to the commencement of construction costs, steel structure lifting, seismic capacity, foundation pit excavation, construction process and other risks analysis, and gives a suitable solution. The operation and maintenance management mode of the prefabricated building after construction is analyzed, and the optimization scheme is summarized[1]. The current status of prefabricated building development, particularly in the context of digital twin technology for construction intelligence and risk intelligence, is concisely outlined. The core developments and future trends in the architectural realm for prefabricated buildings are synthesized, with the objective of providing a fundamental guide for the investigation of prefabricated buildings utilizing digital twin technology. This paper uses a methodological review to sort out relevant literature to get the specific application of digital twin technology in prefabricated buildings, including the establishment of virtual models, solid models, and embodiments of virtual-real interaction. The article adopts the method of integrated review on the status quo of a certain research problem such as the prefabricated building's prefabricated component production, risk analysis, operation and maintenance management, and intelligent development.

Spiders have unique biological characteristics and excellent maneuverability, making them an ideal model for bionic robot design. However, traditional bionic spider robot designs usually have multiple degrees of freedom and confront many challenges. These challenges include complex control requirements, higher energy consumption, larger size and weight, higher risk of failure, and higher cost. This study proposes a leg design with two degrees of freedom to reduce its control and manufacturing costs. It can better control leg movement and improve leg force through a multi-link mechanism and a dual-motor system. In addition, the triangular gait and hexagonal body structure align the weight of the body with the support point, thereby enhancing stability. This study offers a comprehensive and organized approach to bio-inspired robot design, providing a valuable reference for future bionic robot development.

Biomass carbon materials with high theoretical specific capacity, low cost and pollution-free, play an essential role in secondary batteries as anode materials or modified substrates. This review classifies biomass based on its resource and composition and introduces the application of biomass raw fibers, biomass extracts, biomass synthetic fibers, and other materials in lithium-sulfur, potassium-ion, sodium-ion and lithium-ion batteries. It provides a systematic review of the processing methods of different forms of biomass materials. Additionally, the perspectives are also proposed for next-generation biomass carbon materials-based ion batteries.

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis.