No abstract

Reversibility, usually evaluated by Coulombic efficiency (CE) and limited by dendrite growth, has become the major roadblock toward the widespread commercialization of zincion batteries. Tailoring the Zn deposition behavior is vital to prevent dendrite growth. In this work, the facet‐terminator serine is introduced to modulate the interface and obstruct the rampant growth of the Zn (100) plane. The serine cation (Ser+) is revealed to preferentially adsorb onto the electrode/electrolyte interface, suppressing the interfacial parasitic reaction. Theoretical analysis and postmortem/operando experimental techniques indicate that the Ser+ bestows (100)‐dominated morphology to zinc anodes, enabling a highly reversible and dendrite‐free Zn anode. These features endow the Zn anode with a long cyclic life of more than 800 h for Zn//Zn batteries and a high average Coulombic efficiency of 99.8% at 5 mA cm−2 and 5 mAh cm−2 for Zn//Cu batteries. When assembling with commercial V2O5, the full battery delivers a high capacity of 345.1 mAh g−1 at 5 A g−1 with a retention of 74.1% over 2000 cycles.

Magnetic resonance imaging (MRI) can indirectly reflect microscopic changes in lesions on the spinal cord; however, the application of deep learning to MRI to classify and detect lesions for cervical spinal cord diseases has not been sufficiently explored. In this study, we implemented a deep neural network for MRI to detect lesions caused by cervical diseases. We retrospectively reviewed the MRI of 1,500 patients irrespective of whether they had cervical diseases. The patients were treated in our hospital from January 2013 to December 2018. We randomly divided the MRI data into three groups of datasets: disc group (800 datasets), injured group (200 datasets), and normal group (500 datasets). We designed the relevant parameters and used a faster‐region convolutional neural network (Faster R‐CNN) combined with a backbone convolutional feature extractor using the ResNet‐50 and VGG‐16 networks, to detect lesions during MRI. Experimental results showed that the prediction accuracy and speed of Faster R‐CNN with ResNet‐50 and VGG‐16 in detecting and recognizing lesions from a cervical spinal cord MRI were satisfactory. The mean average precisions (mAPs) for Faster R‐CNN with ResNet‐50 and VGG‐16 were 88.6 and 72.3%, respectively, and the testing times was 0.22 and 0.24 s/image, respectively. Faster R‐CNN can identify and detect lesions from cervical MRIs. To some extent, it may aid radiologists and spine surgeons in their diagnoses. The results of our study can provide motivation for future research to combine medical imaging and deep learning.

Autonomous unmanned aerial vehicle (UAV) swarm flights have been investigated widely. In the presence of a high airspace density and increasingly complex flight conditions, collision avoidance between UAV swarms is very important; however, this problem has not been fully addressed, particularly among self-organizing flight clusters. In this paper, we developed a method for avoiding collisions between different types of self-organized UAV clusters in various flight situations. The Reynolds rules were applied to self-organized flights of UAVs and a parameter optimization framework was used to optimize their organization, before developing a collision avoidance solution for UAV swarms. The proposed method can self-organize the flight of each UAV swarm during the overall process and the UAV swarm can continue to fly according to the self-organizing rules in the collision avoidance process. The UAVs in the airspace all make decisions according to their individual type. The UAVs in different UAV swarms can merge in the same space while avoiding collisions, where the UAV's self-organized flight process and collision avoidance process are very closely linked, and the trajectory is smooth to satisfy the actual operational needs. The numerical and experimental tests were conducted to demonstrate the effectiveness of the proposed algorithm. The results confirmed the effectiveness of this approach where self-organized flight cluster collision avoidance was successfully achieved by the UAV swarms.INDEX TERMS Collision avoidance, parameter optimization, Reynolds rules, self-organized, unmanned aerial vehicle cluster.