The development of robotics courses for young children under vector space model.

Robotics education is important in training children's thinking, practical, and innovation abilities. It is significant to stimulate children's interest in learning and improve their learning quality. The existing research has not paid attention to the application of robotics education in children. It is necessary to stimulate children's interest in learning. This paper will take senior kindergarten students as the research object. It analyzes the application of the Vector Space Model (VSM) in robotics course development. The research and development of children's robotics courses incorporating Artificial Intelligence technology are based on the survey results of robotics courses offered by 38 kindergartens in Baoji City. An automatic document classification system based on VSM is designed to assist in compiling robotics teaching textbooks. Finally, the system performance is tested. The results show that about 24% of kindergartens offer robotics courses, and 76% do not. Besides, 70.14% of teachers support the establishment of children's robotics courses. The classification effect of the VSM system is better than that of Chinese documents. This system performs better than the automatic document classification system based on Term Frequency-Inverse Document Frequency. Its classification accuracy, recall, and F1 value are all above 85%. The development of the robotics course provides a better teaching environment for teaching young children about AI and robots. The robotics education discussed in this paper is a hot spot in the current curriculum reform and is of great significance to the development and innovation in early childhood education

Joint optimization of energy trading and consensus mechanism in blockchain-empowered smart grids: a reinforcement learning approach

Abstract Under the trend of green development, the traditional fossil fuel and centralized energy management models are no longer applicable, and distributed energy systems that can efficiently utilize clean energy have become the key to research in the energy field nowadays. However, there are still many problems in distributed energy trading systems, such as user privacy protection and mutual trust in trading, how to ensure the high quality and reliability of energy services, and how to motivate energy suppliers to participate in trading. To solve these problems, this paper proposes a blockchain-based smart grid system that enables efficient energy trading and consensus optimization, enabling electricity consumers to obtain high-quality, reliable energy services and electricity suppliers to receive rich rewards, and motivating all parties to actively participate in trading to maintain the balance of the system. We propose a reputation value assessment algorithm to evaluate the reputation of electricity suppliers to ensure that electricity consumers receive quality energy services. To minimize the cost, maximize the benefit for the electricity suppliers and optimize the system, we present an algorithm based on reinforcement learning DDPG to determine the power supplier, power generation capacity, and consensus mechanism between nodes to obtain power trading rights in each round. Simulation results show that the proposed energy trading scheme has good performance in terms of rewards

Zero‐waste emission design of sustainable and programmable actuators

Abstract Moisture‐responsive actuators are widely used as energy‐harvesting devices due to their excellent ability to spontaneously and continuously convert external energy into kinetic energy. However, it remains a challenge to sustainably synthesize moisture‐driven actuators. Here, we present a sustainable zero‐waste emission methodology to prepare soft actuators using carbon nano‐powders and biodegradable polymers through a water evaporation method. Due to the water solubility and recyclability of the matrixes employed here, the entire synthetic process achieves zero‐waste emission. Our composite films featured strong figures of merit and capabilities with a 250° maximum bending angle under 90% relative humidity. Programmable motions and intelligent bionic applications, including walkers, smart switches, robotic arms, flexible excavators, and hand‐shaped actuators, were further achieved by modulating the geometry of the actuators. This sustainable method for actuators’ fabrication has great potential in large‐scale productions and applications due to its advantages of zero‐waste emission manufacturing, excellent recyclability, inherent adaptive integration, and low cost

Zero-waste emission design of sustainable and programmable actuators

Funder: QMUL‐CSC (China Scholarship Council)Moisture‐responsive actuators are widely used as energy‐harvesting devices due to their excellent ability to spontaneously and continuously convert external energy into kinetic energy. However, it remains a challenge to sustainably synthesize moisture‐driven actuators. Here, we present a sustainable zero‐waste emission methodology to prepare soft actuators using carbon nano‐powders and biodegradable polymers through a water evaporation method. Due to the water solubility and recyclability of the matrixes employed here, the entire synthetic process achieves zero‐waste emission. Our composite films featured strong figures of merit and capabilities with a 250° maximum bending angle under 90% relative humidity. Programmable motions and intelligent bionic applications, including walkers, smart switches, robotic arms, flexible excavators, and hand‐shaped actuators, were further achieved by modulating the geometry of the actuators. This sustainable method for actuators’ fabrication has great potential in large‐scale productions and applications due to its advantages of zero‐waste emission manufacturing, excellent recyclability, inherent adaptive integration, and low cost