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Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot's state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot's perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods. [ABSTRACT FROM AUTHOR]

Magnetic nanomaterials, distinguished by their unique magnetic phenomena, particularly their magnetically actuated capabilities, have found widespread application in the field of nanomedicine. Compared with alternative driving mechanisms, magnetic actuation as a remote, highly permeable, and precisely controllable driving strategy endows nanomaterials with temporal and spatia mobility, making it possible to initiate and cease multiple forms of movement in vivo at will. When coupled with cutting‐edge diagnostic and treating techniques including but not limited to magnetic resonance imaging, magnetothermal therapy, and magnetoelectric stimulation, magnetically actuated nanomaterials offer the potential for visual analysis, provision of reliable molecular information, and effective disease or tissue damage intervention. This review comprehensively outlines the synthesis methodologies, functional strategies, and biomedical applications of magnetically actuated nanomaterials within nanomedicine. Additionally, the future developments and applications of biocompatible magnetically actuated nanomaterials, especially in response to time‐varying magnetic fields, are anticipated. [ABSTRACT FROM AUTHOR]

This study combined the Human-centered design (HCD) approach with the immersive VR techniques to design and evaluate a new evacuation alarm system with improved warning efficiency for building emergency evacuation, considering the negative influence of pre-emergency activity. Four main HCD design activities were performed, the context-of-use, user requirements were defined through a theoretical perspective, technology-based solution research, expert group meeting, and focus group were used to produce the final concept. A smart phone-based evacuation alarm system concept, consisting of a control center and a smartphone-based user interface, featuring warning redundancy and multimodality was proposed. A developed virtual environment (VE) was used to evaluate and refine the final design in an iteration manner. This study widened the application of the HCD approach by using alternative simulator methods for evaluation, and added a step for the validation of VR as an adequate tool for designing and evaluating products. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a type of cancer associated with a high rate of mortality and morbidity. In order to achieve precise HCC theranostics, it is important to develop excellent fluorescent probes. However, the existing probes are not sensitive or specific enough to accurately identify HCC margins and contours. For diagnosing HCC and identifying tumors during surgery, it is urgent to engineer highly sensitive and selective fluorescent probes. Liver tumor progression is closely associated with leucine aminopeptidase (LAP) overexpression, a biomarker of liver cancer. Herein, we have rationally designed a NIR fluorescent probe, NLAP, which is specially activated by LAP. The probe exhibited high sensitivity (detection limit = 6.8 mU L−1) and superior affinity (Km = 2.98 μM) for LAP. With this probe, we distinguished cancer cells overexpressing LAP from normal cells and applied it intraoperatively to guide liver tumor excisions. Furthermore, NLAP was employed to successfully detect the LAP of intestinal and splenic metastatic tumors in orthotopic liver tumor mice by "in situ spraying" and good performances were demonstrated. [ABSTRACT FROM AUTHOR]