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With the widespread application of unmanned aerial vehicle technology in search and detection, express delivery and other fields, the requirements for unmanned aerial vehicle dynamic area coverage algorithms has become higher. For an unknown dynamic environment, an improved Dual-Attention Mechanism Double Deep Q-network area coverage algorithm is proposed in this paper. Firstly, a dual-channel attention mechanism is designed to deal with flight environment information. It can extract and fuse the features of the local obstacle information and full-area coverage information. Then, based on the traditional Double Deep Q-network algorithm, an adaptive exploration decay strategy and a coverage reward function are designed based on the real-time area coverage rate to meet the requirement of a low repeated coverage rate. The proposed algorithm can avoid dynamic obstacles and achieve global coverage under low repeated coverage rate conditions. Finally, with Python 3.12 and PyTorch 2.2.1 environment as the training platform, the simulation results show that, compared with the Soft Actor–Critic algorithm, the Double Deep Q-network algorithm, and the Attention Mechanism Double Deep Q-network algorithm, the proposed algorithm in this paper can complete the area coverage task in a dynamic and complex environment with a lower repeated coverage rate and higher coverage efficiency.

Wearable dry electrodes are needed for long-term biopotential recordings but are limited by their imperfect compliance with the skin, especially during body movements and sweat secretions, resulting in high interfacial impedance and motion artifacts. Herein, we report an intrinsically conductive polymer dry electrode with excellent self-adhesiveness, stretchability, and conductivity. It shows much lower skin-contact impedance and noise in static and dynamic measurement than the current dry electrodes and standard gel electrodes, enabling to acquire high-quality electrocardiogram (ECG), electromyogram (EMG) and electroencephalogram (EEG) signals in various conditions such as dry and wet skin and during body movement. Hence, this dry electrode can be used for long-term healthcare monitoring in complex daily conditions. We further investigated the capabilities of this electrode in a clinical setting and realized its ability to detect the arrhythmia features of atrial fibrillation accurately, and quantify muscle activity during deep tendon reflex testing and contraction against resistance. Reported wearable dry electrodes have limited long-term use due to their imperfect skin compliance and high motion artifacts. Here, the authors report an intrinsically conductive, stretchable polymer dry electrode with excellent self-adhesiveness for long-term high-quality biopotential detection.

In robotic-assisted surgery (RAS), traditional surgical instruments without sensing capability cannot perceive accurate operational forces during the task, and such drawbacks can be largely intensified when sophisticated tasks involving flexible and slender arms with small end-effectors, such as in gastrointestinal endoscopic surgery (GES). In this study, we propose a microelectromechanical system (MEMS) piezoresistive 3-axial tactile sensor for GES forceps, which can intuitively provide surgeons with online force feedback during robotic surgery. The MEMS fabrication process facilitates sensor chips with miniaturized dimensions. The fully encapsulated tactile sensors can be effortlessly integrated into miniature GES forceps, which feature a slender diameter of just 3.5 mm and undergo meticulous calibration procedures via the least squares method. Through experiments, the sensor’s ability to accurately measure directional forces up to 1.2 N in the Z axis was validated, demonstrating an average relative error of only 1.18% compared with the full-scale output. The results indicate that this tactile sensor can provide effective 3-axial force sensing during surgical operations, such as grasping and pulling, and in ex vivo testing with a porcine stomach. The compact size, high precision, and integrability of the sensor establish solid foundations for clinical application in the operating theater.

Planar perovskite solar cells (PSCs) have excellent photoelectric properties and show great commercialization potential. However, there are a lot of crystal defects in the perovskite films prepared by solution method, which reduces the development process of solar cells. In this work, alizarin red s (ARS) was doped into MAPbI3 films to passivate the defect. It was shown that the addition of ARS increased the quality of perovskite film and doped perovskite film exhibited improved light absorption. In addition, it was found that there was a strong interaction between ARS and perovskite, which reduced the density of defect states. The results showed that the passivated perovskite device had improved PL intensity, increased carrier lifetimes and reduced charge recombination. After passivation, the device obtained a higher open-circuit voltage (VOC) of 1.103 V where the control device was 1.055 V, and the best power conversion efficiency (PCE) of the doped device was 18.82%, which is 11.36% higher than that of the control device of 16.90%.

We present magnetostriction and thermal expansion measurements on multiferroic (Ni0.93Co0.07)3V2O8. The high field phase diagrams up to 33 T along the a, b and c directions are built. For H//a, as the magnetic field increases, two intermediate phases appear between the incommensurate phase and the paramagnetic phase at about 7 K, and then a magnetically induced phase appears above the paramagnetic phase. For H//b,thermal expansion measurement indicates a mutation in the spin lattice coupling of the high field phases. The interlaced phase boundary suggests a mixed state in the optical high field phase. For H//c, an intermediate phase between the commensurate phase and the incommensurate phase is detected. A nonlinear boundary between the intermediate phase and the low temperature incommensurate phase, and a clear boundary between the commensurate phase and the paramagnetic phase are found. These results indicate that doping Co2+ breaks the weak ferromagnetic moment of the commensurate phase, which exists in the parent compound Ni3V2O8 and (Ni0.9Co0.1)3V2O8. This nonlinear influence reflects complicated spin modulation in Ni3V2O8 by doping Co2+.

Purpose: Endoscopic surgical environments present challenges for dissection zone segmentation due to unclear boundaries between tissue types, leading to segmentation errors where models misidentify or overlook edges. This study aims to provide precise dissection zone suggestions during endoscopic submucosal dissection (ESD) procedures, enhancing ESD safety. Methods: We propose the Prompted-based Dissection Zone Segmentation (PDZSeg) model, designed to leverage diverse visual prompts such as scribbles and bounding boxes. By overlaying these prompts onto images and fine-tuning a foundational model on a specialized dataset, our approach improves segmentation performance and user experience through flexible input methods. Results: The PDZSeg model was validated using three experimental setups: in-domain evaluation, variability in visual prompt availability, and robustness assessment. Using the ESD-DZSeg dataset, results show that our method outperforms state-of-the-art segmentation approaches. This is the first study to integrate visual prompt design into dissection zone segmentation. Conclusion: The PDZSeg model effectively utilizes visual prompts to enhance segmentation performance and user experience, supported by the novel ESD-DZSeg dataset as a benchmark for dissection zone segmentation in ESD. Our work establishes a foundation for future research.

submucosal dissection (ESD) enables rapid resection of large lesions, minimizing recurrence rates and improving long-term overall survival. Despite these advantages, ESD is technically challenging and carries high risks of complications, necessitating skilled surgeons and precise instruments. Recent advancements in Large Visual-Language Models (LVLMs) offer promising decision support and predictive planning capabilities for robotic systems, which can augment the accuracy of ESD and reduce procedural risks. However, existing datasets for multi-level fine-grained ESD surgical motion understanding are scarce and lack detailed annotations. In this paper, we design a hierarchical decomposition of ESD motion granularity and introduce a multi-level surgical motion dataset (CoPESD) for training LVLMs as the robotic \\textbf{Co}-\\textbf{P}ilot of \\textbf{E}ndoscopic \\textbf{S}ubmucosal \\textbf{D}issection. CoPESD includes 17,679 images with 32,699 bounding boxes and 88,395 multi-level motions, from over 35 hours of ESD videos for both robot-assisted and conventional surgeries. CoPESD enables granular analysis of ESD motions, focusing on the complex task of submucosal dissection. Extensive experiments on the LVLMs demonstrate the effectiveness of CoPESD in training LVLMs to predict following surgical robotic motions. As the first multimodal ESD motion dataset, CoPESD supports advanced research in ESD instruction-following and surgical automation. The dataset is available at \\href{https://github.com/gkw0010/CoPESD}{https://github.com/gkw0010/CoPESD.}}

Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate prediction of dissection trajectories is crucial for better decision-making, reducing intraoperative errors, and improving surgical training. Nevertheless, predicting these trajectories is challenging due to variable tumor margins and dynamic visual conditions. To address this issue, we create the ESD Trajectory and Confidence Map-based Safety Margin (ETSM) dataset with \\(1849\\) short clips, focusing on submucosal dissection with a dual-arm robotic system. We also introduce a framework that combines optimal dissection trajectory prediction with a confidence map-based safety margin, providing a more secure and intelligent decision-making tool to minimize surgical risks for ESD procedures. Additionally, we propose the Regression-based Confidence Map Prediction Network (RCMNet), which utilizes a regression approach to predict confidence maps for dissection areas, thereby delineating various levels of safety margins. We evaluate our RCMNet using three distinct experimental setups: in-domain evaluation, robustness assessment, and out-of-domain evaluation. Experimental results show that our approach excels in the confidence map-based safety margin prediction task, achieving a mean absolute error (MAE) of only \\(3.18\\). To the best of our knowledge, this is the first study to apply a regression approach for visual guidance concerning delineating varying safety levels of dissection areas. Our approach bridges gaps in current research by improving prediction accuracy and enhancing the safety of the dissection process, showing great clinical significance in practice.

Gastrointestinal endoscopic surgery (GES) has high requirements for instruments' size and distal dexterity, because of the narrow endoscopic channel and long, tortuous human gastrointestinal tract. This paper utilized Nickel-Titanium (NiTi) wires to develop a miniature 3-DoF (pitch-yaw-translation) flexible parallel robotic wrist (FPRW). Additionally, we assembled an electric knife on the wrist's connection interface and then teleoperated it to perform an endoscopic submucosal dissection (ESD) on porcine stomachs. The effective performance in each ESD workflow proves that the designed FPRW has sufficient workspace, high distal dexterity, and high positioning accuracy.

2024年6月，在福建省莆田市仙游县采集到1号成年雄性白环蛇属（Lycodon）标本。经形态特征比对和基于线粒体Cyt b基因片段的系统发育重建，鉴定该白环蛇属标本为刘氏白环蛇（L.liuchengchaoi），系福建省蛇类分布新记录种。由于刘氏白环蛇曾长期被误定为双全白环蛇（L.fasciatus），所以本研究检视了福建省原记录的双全白环蛇凭证标本（CIB 8086），经形态比对，将该标本重新鉴定为刘氏白环蛇。本研究进一步扩展了对刘氏白环蛇分布范围的认识，重新总结了刘氏白环蛇和双全白环蛇的形态差异，并对福建省原记录的“双全白环蛇”的分类地位进行了讨论。