Biological machines that use biological cells and soft materials in combination to obtain a sense of the environment driven by bioenergy and generate driving force are called biohybrid actuators. With the development of tissue engineering and organoid technology, researchers have applied biohybrid actuators technology to the research of precision medicine and targeted drug delivery, but the research on feedback and evaluation of biohybrid actuation performance is limited to visual and simulation calculations. Therefore, we hope to develop an intelligent crawling skeleton for sensing function, which can be used to evaluate the actuation ability of muscle actuators, and eventually realize the high-precision control of biohybrid actuators. In this work, an intelligent crawling skeleton based on three-dimensional liquid metal is proposed to detect and feedback the crawling of C2C12 muscle actuators. Three-dimensional muscle tissue was composed of mixing hydrogels and cells, and the functionalization of muscle rings was promoted using static mechanical forces and external electric field stimulation. The composite crawling skeleton is fabricated by inverting mold and soft lithography technology. The skeleton can adapt to large deformations above 90 degrees and is more sensitive to deformations by adjusting materials with different elastic modulus. Inspired by the tendon-bone structure, the intelligent crawling skeleton can obtain the deformation degree of the biohybrid actuator in the crawling process according to the characteristics of the deformation from the muscle tissue, and put forward a good idea for the feedback and closed-loop control of the biohybrid actuators.

ABSTRACT Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi‐channel fusion network (AMCF‐Net) is proposed for computer‐aided diagnosis of lung nodules. First, a Multi‐Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi‐scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF‐Net, a novel adaptive depth‐wise separable convolution with a squeeze‐and‐excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF‐Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1‐score of 86.57%, a sensitivity of 86.49%, and a G‐mean of 87.72%. Compared with other advanced networks, AMCF‐net delivers high‐precision lung nodule classification with minimal inference cost. Related codes have been released at: https://github.com/GuYuIMUST/AMCF‐net .

The widely adopted Jacobi-Fourier moment (JFM) is limited by its inability to effectively capture spatial information. Although fractional-order JFM (FOJFM) introduces spatial information through a fractional-order parameter, the control of spatial information remains inadequate. This limitation stems from the insufficient control over zeros distribution associated with the used moment's radial kernel. To address this issue, we generalize both JFM and FOJFM into a transformed JFM. A transformed function with four parameters is designed, and adjustable JFM (AJFM) is proposed. Two parameters correlate to increasing velocities on the left and right parts of the transformed functions, enabling zeros quantities of radial kernel fall in the left and right parts of the interval. The other two parameters segment the transformed function, adjusting regions where different quantities of zeros fall in. This refined control over the radial kernel's zero distribution enhances the versatility of feature extraction by the AJFM, governed by the introduced parameters. Experimental results demonstrate that AJFM, with properly chosen parameters, can emphasize specific regions within an image more effectively.

Synthetic aperture radar automatic target recognition (SAR ATR) is a key technique of remote-sensing image recognition, which has many potential applications in the fields of military surveillance, national defense, civil application, and so on. With the development of science and technology, deep convolutional neural network (DCNN) has been widely applied for SAR ATR. However, it is difficult to use deep learning to train models with limited ray SAR images. To resolve this problem, we proposed an effectively lightweight attention mechanism CNN (AM-CNN) model for SAR ATR. Extensive experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrate that the AM-CNN model can achieve a superior recognition performance, and the average recognition accuracy can reach 99.35% on the classification of 10 class targets. Compared with the traditional CNN and the state-of-the-art method, our model is significantly superior to improve performance and efficiency.

In the above article[1], there was a typographical error in Fig. 2. The correct figure appears below:

In this paper, attention was paid to the impacts of RFI (Radio Frequency Interference) on SAR (Synthetic Aperture Radar) image ship detection with a proposed net structure based on deep learning. The RFI of different intensities can destroy the quality of SAR images, and pose difficulty for ship detection in marine environment. We acquired the dataset that contains numerous sample slices from two satellites, and took different ranges of SIR (Signal to Interference Ratio) to evaluate the robustness of the framework on the conditions. The experimental results show that the SAR images corrupted by RFI can affect the detection performance greatly. And it may provide suggest to researchers to conduct the interference properly when using the deep learning algorithm to monitor the ships in SAR images.

Bayesian meta-learning enables robust and fast adaptation to new tasks with uncertainty assessment. The key idea behind Bayesian meta-learning is empirical Bayes inference of hierarchical model. In this work, we extend this framework to include a variety of existing methods, before proposing our variant based on gradient-EM algorithm. Our method improves computational efficiency by avoiding back-propagation computation in the meta-update step, which is exhausting for deep neural networks. Furthermore, it provides flexibility to the inner-update optimization procedure by decoupling it from meta-update. Experiments on sinusoidal regression, few-shot image classification, and policy-based reinforcement learning show that our method not only achieves better accuracy with less computation cost, but is also more robust to uncertainty.