This paper presents a novel multi-mode bionic robotic hand. Its bionic finger (BIF) ingeniously combines a magnetic-silica-gel skin with a rigid skeletal framework and integrates a vacuum suction cup at the fingertip. This design enables the bionic manipulator to execute multiple grasping modes, namely enveloping, parallel, and suction grasping. The proposed BIF emulates the skeletal structure of human fingers and equips the fingertip with suction-based grasping functionality, thus achieving both formal bionics and functional superiority. The overall grasping space range of the bionic manipulator can be determined through the computation of the offset of the steel wire, which corresponds to the bending angles of the three joints of the finger. Furthermore, by discerning the four phases within the bionic manipulator’s object-grasping process, in-depth exploration is carried out regarding the unique data characteristics of the magnetic-tactile sensing unit during the grasping operation. On this basis, an accurate prediction of the grasped object’s diameter is achieved. We constructed an autonomous grasping operation platform by integrating an external depth camera with the robotic arm to assess the fundamental performance of this robotic hand in grasping diverse objects.

As a well-established and extensively utilized model organism, Caenorhabditis elegans (C. elegans) serves as a crucial platform for investigating behavioral regulation mechanisms and their biological significance. However, manually tracking the locomotor behavior of large numbers of C. elegans is both cumbersome and inefficient. To address the above challenges, we innovatively propose an automated approach for analyzing C. elegans behavior through deep learning-based detection and tracking. Building upon existing research, we developed an enhanced worm detection framework that integrates YOLOv8 with ByteTrack, enabling real-time, precise tracking of multiple worms. Based on the tracking results, we further established an automated high-throughput method for quantitative analysis of multiple movement parameters, including locomotion velocity, body bending angle, and roll frequency, thereby laying a robust foundation for high-precision, automated analysis of complex worm behaviors. including movement speed, body bending angle, and roll frequency, thereby laying a robust foundation for high-precision, automated analysis of complex worm behaviors. Comparative evaluations demonstrate that the proposed enhanced C. elegans detection framework outperforms existing methods, achieving a precision of 99.5%, recall of 98.7%, and mAP50 of 99.6%, with a processing speed of 153 frames per second (FPS). The established framework for worm detection, tracking, and automated behavioral analysis developed in this study delivers superior detection and tracking accuracy while enhancing tracking continuity and robustness. Unlike traditional labor-intensive measurement approaches, our framework supports simultaneous tracking of multiple worms while maintaining automated extraction of various behavioral parameters with high precision. Furthermore, our approach advances the standardization of C. elegans behavioral parameter analysis, which can analyze the behavioral data of multiple worms at the same time, significantly improving the experimental throughput and providing an efficient tool for drug screening, gene function research and other fields.

As the scale of software projects increases, the code comments are more and more important for program comprehension. Unfortunately, many code comments are missing, mismatched or outdated due to tight development schedule or other reasons. Automatic code comment generation is of great help for developers to comprehend source code and reduce their workload. Thus, we propose a code comment generation tool (DeepCommenter) to generate descriptive comments for Java methods. DeepCommenter formulates the comment generation task as a machine translation problem and exploits a deep neural network that combines the lexical and structural information of Java methods. We implement DeepCommenter in the form of an Integrated Development Environment (i.e., Intellij IDEA) plug-in. Such plug-in is built upon a Client/Server architecture. The client formats the code selected by the user, sends request to the server and inserts the comment generated by the server above the selected code. The server listens for client’s request, analyzes the requested code using the pre-trained model and sends back the generated comment to the client. The pre-trained model learns both the lexical and syntactical information from source code tokens and Abstract Syntax Trees (AST) respectively and combines these two types of information together to generate comments. To evaluate DeepCommenter, we conduct experiments on a large corpus built from a large number of open source Java projects on GitHub. The experimental results on different metrics show that DeepCommenter outperforms the state-of-the-art approaches by a substantial margin.