Between 1999 and 2020, gastrointestinal cancers were responsible for over three million deaths, emphasizing the critical role of minimally invasive surgical techniques like Endoscopic Submucosal Dissection (ESD) in managing such life-threatening conditions. ESD, which dissects the connective tissue between the mucosal and muscular layers using an electrosurgical knife connected to an endoscope, requires a constant traction force to stabilize tissues and expose underlying anatomical structures. This paper introduces a miniature magnetic flexible robot, actuated by a permanent magnet on a robotic manipulator, designed to enhance ESD by providing traction forces consistently on lesions. The robot was fabricated by casting magnetic silicone composites, and its safe deployment through the endoscope instrument channel was successfully demonstrated, avoiding tissue contact. Experiments in a rubber intestine model validated the feasibility of providing constant traction and 2 DOF orientation control via the robot, allowing real-time fine-tuning of the force direction. This reduces the difficulty and improves the precision and safety of ESD. This research presents a practical method for achieving stable force output in medical miniature robots, particularly in gastrointestinal procedures.

As application migration to the cloud becomes the mainstream way of application deployment, application runtime management presents a significant need for large-scale workload prediction technology. Existing large-scale workload approaches commonly construct training sets based on all the training samples generated from the original data to generate prediction models. However, due to the similar behavior between different container instances of the microservice application, training and modeling in this way results in a huge number of redundant samples, which produces a significant redundant training overhead. Therefore, this paper proposes Less, a largescale workload forecasting model based on multiple sequence compression. First, based on the grouping results of similar containers, a container workload feature recognition algorithm is proposed to determine the common and individual features of container workloads in each prediction period, so as to guide the compression of workload sequences within each group; second, a fitness function that takes into account the common features, individual features, and the number of sequences are designed, and the optimal compressed sequences are solved by the Whale Optimization Algorithm to efficiently reduce the number of redundant training workload sequences, and then the Bidirectional Gated Recurrent Unit model is built and trained based on the compressed sequences, which effectively reduces the model complexity and overhead while ensuring the accuracy. Finally, we validate the comprehensive advantages of Less in terms of accuracy and overhead based on public datasets and verify the effectiveness of each subpart of our model through ablation experiments.