The Android platform provides a series of animation APIs, with which app developers can improve the implementation efficiency of UI animations—specifically, reducing the effort and time required to implement them. To assist app developers in quickly finding the suitable animation APIs, we have proposed two recommendation models called Animation2API and U-A2A. Animation2API has the capability to generate a list of available animation APIs for the UI animation task using the collaborative filtering algorithm. In contrast, U-A2A can encode both the animation API context and the UI animation task, and then predict the next animation API for the current animation implementation based on the joint encoding of the two modalities. Since U-A2A can provide real-time recommendations throughout the process of animation implementation, it is effective in assisting developers in using animation API resources. Nevertheless, U-A2A has three key limitations. First, its GRU encoder for the animation API context has difficulty in adequately capturing the long-distance dependencies and the global information. Second, its 3D CNN encoder for the UI animation task fails to effectively extract the long-distance dependencies between video frames and the spatiotemporal features at different scales. Third, U-A2A consistently treats the two modalities equally when fusing their encodings, despite the need to adaptively adjust their contribution levels according to the actual situation. To address these limitations, the paper introduces a novel animation API recommendation model named AC2Next. AC2Next adopts an encoder component based on the self-attention mechanism to encode the animation API context and the UI animation task. Specifically, it uses GRU with the self-attention mechanism as the encoder of the animation API context and applies ViViT, a Transformer architecture with self-attention mechanisms, to encode the UI animation task. Meanwhile, AC2Next utilizes its adaptive weight layer to assign appropriate weights to the animation API context and the UI animation task during the information fusion process. The experimental results show that AC2Next can outperform U-A2A in any stage of the animation implementation. When considering 1, 3, 5, and 10 animation APIs, AC2Next achieves an improvement of 31.56%, 10.01%, 5.57%, and 3.34% respectively in recommendation accuracy compared to U-A2A.

Magnetic helical micro-robots (microbots) have attracted strong interest due to their unique propulsion mechanisms and potential applications in biomedical fields, particularly in minimally-invasive surgical procedures. Earlier research primarily focused on studying helical microbots in viscous liquids, while their dynamic behavior in viscoelastic solids remains largely unexplored. Here, we present an experimental study of a helical microbot operating in a viscoelastic gelatin hydrogel. The robot is fabricated by two-photon polymerization and actuated by an external rotating magnetic field. We observe that in viscoelastic solids, the robot ruptures the gel and creates a three-dimensional (3D) helical trajectory, despite the rotational axis of the driving magnetic field being fixed. Largely distinct from the propulsion behavior in a Newtonian fluid, the precession angle of the helix is significantly enhanced in the viscoelastic gel and increases with a rising rotational frequency. A dynamic model is developed using the multipole expansion method, incorporating the gel’s complex viscosity and shear-thinning properties to capture the key characteristics of this dynamic response. These findings offer new insights into the behavior of helical microbots in viscoelastic media, expanding possible application scenarios of microbots in biomedicine.

Adaptive locomotion of microrobots can be achieved by using a smart polymer such as a hydrogel. For hydrogel-based bilayer helical microrobots, the change of environment such as temperature and pH can result in shape deformation into helical shapes differing from their initial state and hence swimming performance. In this work, we proposed a model for studying the parameters that affect the shape deformation of a hydrogel-based bilayer helical microrobot. Moreover, the dynamics of some examples of responsive helical swimming are compared before and after stimulation.