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In this paper, a fully distributed control protocol is presented based on adaptive technology to solve the time‐varying formation problem of multi‐unmanned aerial vehicle (UAV) systems, where the coupling weights between neighbouring UAVs can be adjusted through adaptive method. Firstly, the proposed control protocol eliminates the need for global information on the interaction topology of multi‐UAV systems, the calculation of Laplacian matrix eigenvalues is avoided and reduces the computational complexity of the system. Secondly, an algorithm with two steps is proposed to determine the distributed adaptive control gain matrices, where the feasibility condition of time‐varying formation of multi‐UAV systems is given. Then, the closed‐loop stability of multi‐UAV systems is proved through Lyapunov theory. Finally, numerical simulation is conducted to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register lazer scans and estimate LiDAR ego-motion, while they may be unreliable in dynamic or degraded environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of lazer sweeps (i.e., geometric, intensity and temporal characteristics). The specific content of this work includes method innovation and experimental verification. With respect to method innovation, we propose the cylindrical-image-based feature extraction scheme, which makes use of the characteristic of uniform spatial distribution of lazer points to boost the adaptive extraction of various types of features, i.e., ground, beam, facade and reflector. We propose a novel intensity-based point registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out in the resulting points map. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. With respect to experimental verification, extensive tests are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy with respect to the state-of-the-art in normal driving scenarios and outperforms geometric-based LO in unstructured environments. [ABSTRACT FROM AUTHOR]

Looking for early diagnostic markers and therapeutic targets is the key to ensuring prompt treatment of myocardial ischemia (MI). Here, a novel biomarker xanthurenic acid (XA) was identified based on metabolomics and exhibited high sensitivity and specificity in the diagnosis of MI patients. Additionally, the elevation of XA was proved to induce myocardial injury in vivo by promoting myocardial apoptosis and ferroptosis. Combining metabolomics and transcriptional data further revealed that kynurenine 3-monooxygenase (KMO) profoundly increased in MI mice, and was closely associated with the elevation of XA. More importantly, pharmacological or heart-specific inhibition of KMO obviously suppressed the elevation of XA and profoundly ameliorated the OGD-induced cardiomyocytes injury and the ligation-induced MI injury. Mechanistically, KMO inhibition effectively restrained myocardial apoptosis and ferroptosis by modulating mitochondrial fission and fusion. In addition, virtual screening and experimental validation were adopted to identify ginsenoside Rb3 as a novel inhibitor of KMO and exhibited great cardioprotective effects by regulating mitochondrial dynamical balance. Taken together, targeting KMO may provide a new approach for the clinical treatment of MI through maintaining mitochondrial fusion and fission balance, and ginsenoside Rb3 showed great potential to be developed as a novel therapeutic drug targeting KMO., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)