Objective: Whether it is manual suture or robotic automatic suture, the suture parameters of the kidney directly determine the recovery effect of the wound. However, there is a lack of quantitative research. The purpose of this paper is to reveal the influence of the different suture methods on the effect of the suture operation. Methods: Firstly, an editable three-dimensional model of the kidney was established using CT images. Subsequently, wounds were incised on the surface of the kidney entity and sutures were embedded. Secondly, the ABAQUS software was utilized to conduct a simulation analysis of the suturing process. Thirdly, experiments on the ex vivo porcine renal specimens were sutured manually in parallel to the simulation analysis. Meanwhile, the different tensile forces, under suture intervals, various wound conditions, and suturing methods, were measured to derive the tensile force values. Finally, a comparative study of finite element analysis and specimen experiments was performed. Results: The results showed that the 5 mm suture interval and the internal figure-of-eight suture are the optimal choice for suture and the suture of circular wounds is more difficult than elliptical wounds and rectangular wounds, the tensile force is around 0.3-0.5 N. Conclusion: This paper presents the variation law of the tensile force for different wounds and provides the optimal suturing method for both manual and robotic autonomous suturing. Significance: By revealing the influence of different suture methods and providing the optimal suturing method, it can contribute to improving the suture operation effect and promoting the development of suture technology.

In robot-assisted cell culture tasks, fluctuations in lighting conditions can result in blurred boundaries, intensified reflections, and pronounced refractions of transparent objects. These optical phenomena collectively escalate the complexity of image processing and target recognition. To address these challenges, this paper takes a dual-strategy approach. Firstly, it utilizes the Unity platform to construct a synthetic dataset (STTO-9k) containing 9,000 images of six types of transparent objects, providing abundant training samples for the detection and recognition of transparent objects. Secondly, it proposes an improved YOLOv8 visual detection algorithm (YOLO-Edge-Guided Lighting Adaptation, YL-EGLA). The algorithm realizes feature fusion by dynamically extracting the high-dimensional features of the input through the self-attention mechanism combined with the enhanced edge features extracted by the edge detection operator, and is equipped with adaptive image enhancement module to ensure stable detection under different lighting conditions. Algorithm comparison results demonstrate that the YL-EGLA can be fully trained on the synthetic dataset and directly applied to real-world scenarios without additional fine-tuning. Furthermore, physical experiments further validate the efficiency and practicality of this algorithm in transparent object manipulation, fully showcasing its significant value in practical applications.

With the global ecological environment facing continuous deterioration, effective monitoring of arboreal birds in complex canopy environments remains challenging due to limitations of conventional drones in endurance, size, and habitat disturbance. To address these challenges, this paper presents an ant-inspired micro quadrotor UAV equipped with a lightweight bistable gripper system mimicking the mandibular morphology of leafcutter ants. The design integrates shape memory alloy (SMA)-driven actuation and thermoplastic polyurethane (TPU)-based adaptive grippers, enabling rapid deformation (71 ms switching time) and energy-efficient operation (zero power consumption during perching). Experimental results demonstrate exceptional adaptability in grasping irregular objects (e.g., branches, pen caps) with an 8:1 payload-to-weight ratio. Field tests confirm stable navigation through dense foliage and reliable perching at heights exceeding 5 meters. The system’s compact dimensions (7 cm diameter, 70.5 g weight) and biomimetic approach offer a non-invasive solution for prolonged wildlife observation. This work advances bistable actuator design by combining bio-inspired structural optimization with rapid energy transition principles, showing potential in agile robotics and environmental sensing.

The security for many certificate-based encryption schemes was considered under the ideal condition, where the attackers rarely have the secret state for the solutions. However, with a side-channel attack, attackers can obtain partial secret values of the schemes. In order to make the scheme more practical, the security model for the certificate-based encryption which is resilient to continual leakage is first formalized. The attackers in the security model are permitted to get some secret information continuously through the side-channel attack. Based on the certificate-based key encapsulation scheme, a novel certificate-based encryption scheme is proposed, which is resilient to the continual leakage. In the standard model, the new scheme we propose is proved to be secure under the decisional truncated q-augmented bilinear Diffie–Hellman exponent hard problem and the decisional 1-bilinear Diffie–Hellman inversion hard problem. Additionally, the new scheme can resist the chosen-ciphertext attack. Moreover, a comparison is performed with other related schemes, where the proposed solution further considers the continual leakage-resilient property and exhibits less computation cost.

Deep learning is one of the most popular technologies and research areas in machine learning. Convolutional Neural Networks (CNNs) are a typical artificial neural network underpinning deep learning. They have been used in many fields including image recognition, natural language process, and through games such as AlphaGo. A CNN has many advantages such as efficient feature extraction, the simplicity of data format required and the small number of (hyper-)parameters that are required. This paper focuses on a particular application of deep learning: crowd counting. To address this, we apply a Multi-task, Cascaded Convolutional Neural Network (MTCNN). Compared to other models, this model has a good performance and requires a shorter inference time, with shallower network structure and smaller size. In order to demonstrate the value and feasibility of the technology and provide a friendly operating environment for users, the application was realised on both the iOS and Android platforms. A web platform was also developed to visualize the real-time data using a Firebase server.

Infrasound is ideally suited to provide essential information of these earthquakes without any invasive measures. This necessitates automatic processing of the data as the captured phenomena need to be sorted before further analysis can be undertaken. Extracting the physical characters of different signals by signal processing such as Fourier Transform and Wavelets Transform are generally employed to carry out the necessary expansion. This article reviews pattern recognition as it applies to earthquake prediction and discusses the concept of fuzzy logic approach as a means of seismic infrasound classification. An example is presented in which this approach was used for classifying preprocessed infrasound signals to identify precursory strong earthquake.

A fast algorithm is presented for determining the linear complexity and the minimal polynomial of a sequence with period 2p/sup n/ over GF (q), where p and q are odd prime, and q is a primitive root (mod p/sup 2/). The algorithm uses the fact that in this case the factorization of x/sup 2p(n)/-1 is especially simple.

A fast algorithm is presented for determining the linear complexity of a sequence with period p/sup n/ over GF (q), where p is an odd prime, and where q is a prime and a primitive root (mod p/sup 2/).