Dynamical Analysis of Carrier-Track Contact Model in a Braiding Machine

As vibrations caused by the coupling of carriers and tracks could affect the performance of braiding machines a lot, it is necessary to establish the carrier-track contact model and investigate its dynamic characteristics. Therefore, a dynamic model of carrier-track contact model has been developed. Based on the Hertz\u27s law and Newton\u27s law, the dynamical equations of carriers were established. The influence of contact related parameters to the carriers\u27 dynamic characteristics were discussed. Besides, a comparison of contact parameters with different contact models was presented. The results show that although carriers keep a low contact speed, collisions still exist during carriers transferring between tracks. To avoid/decrease collisions and shocks, some methods were proposed, e.g. controlling the angular velocity of horn gears variable, changing the contact surface to have a higher coefficient of restitution and reducing the vibration of frame and tracks, which could be helpful to the design of braiding machines

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

© 2021 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSI.2021.3081150Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.Peer reviewe

Excited-state spectroscopy of spin defects in hexagonal boron nitride

We used optically detected magnetic resonance (ODMR) technique to directly probe electron-spin resonance transitions in the excited state of negatively-charged boron vacancy (VB-) defects in hexagonal boron nitride (hBN) at room temperature. The data showed that the excited state has a zero-field splitting of ~ 2.1 GHz, a g factor similar to the ground state and two types of hyperfine splitting ~ 90 MHz and ~ 18.8 MHz respectively. Pulsed ODMR experiments were conducted to further verify observed resonant peaks corresponding to spin transitions in the excited state. In addition, negative peaks in photoluminescence and ODMR contrast as a function of magnetic field magnitude and angle at level anti-crossing were observed and explained by coherent spin precession and anisotropic relaxation. This work provided significant insights for studying the structure of VB- excited states, which might be used for quantum information processing and nanoscale quantum sensing

H+-pyrophosphatases enhance low nitrogen stress tolerance in transgenic Arabidopsis and wheat by interacting with a receptor-like protein kinase

IntroductionNitrogen is a major abiotic stress that affects plant productivity. Previous studies have shown that plant H+-pyrophosphatases (H+-PPases) enhance plant resistance to low nitrogen stress. However, the molecular mechanism underlying H+-PPase-mediated regulation of plant responses to low nitrogen stress is still unknown. In this study, we aimed to investigate the regulatory mechanism of AtAVP1 in response to low nitrogen stress.Methods and ResultsAtAVP1 in Arabidopsis thaliana and EdVP1 in Elymus dahuricus belong to the H+-PPase gene family. In this study, we found that AtAVP1 overexpression was more tolerant to low nitrogen stress than was wild type (WT), whereas the avp1-1 mutant was less tolerant to low nitrogen stress than WT. Plant height, root length, aboveground fresh and dry weights, and underground fresh and dry weights of EdVP1 overexpression wheat were considerably higher than those of SHI366 under low nitrogen treatment during the seedling stage. Two consecutive years of low nitrogen tolerance experiments in the field showed that grain yield and number of grains per spike of EdVP1 overexpression wheat were increased compared to those in SHI366, which indicated that EdVP1 conferred low nitrogen stress tolerance in the field. Furthermore, we screened interaction proteins in Arabidopsis; subcellular localization analysis demonstrated that AtAVP1 and Arabidopsis thaliana receptor-like protein kinase (AtRLK) were located on the plasma membrane. Yeast two-hybrid and luciferase complementary imaging assays showed that the AtRLK interacted with AtAVP1. Under low nitrogen stress, the Arabidopsis mutants rlk and avp1-1 had the same phenotypes.DiscussionThese results indicate that AtAVP1 regulates low nitrogen stress responses by interacting with AtRLK, which provides a novel insight into the regulatory pathway related to H+-pyrophosphatase function in plants

Overview of BioCreative II gene normalization

Background: The goal of the gene normalization task is to link genes or gene products mentioned in the literature to biological databases. This is a key step in an accurate search of the biological literature. It is a challenging task, even for the human expert; genes are often described rather than referred to by gene symbol and, confusingly, one gene name may refer to different genes (often from different organisms). For BioCreative II, the task was to list the Entrez Gene identifiers for human genes or gene products mentioned in PubMed/MEDLINE abstracts. We selected abstracts associated with articles previously curated for human genes. We provided 281 expert-annotated abstracts containing 684 gene identifiers for training, and a blind test set of 262 documents containing 785 identifiers, with a gold standard created by expert annotators. Inter-annotator agreement was measured at over 90%. Results: Twenty groups submitted one to three runs each, for a total of 54 runs. Three systems achieved F-measures (balanced precision and recall) between 0.80 and 0.81. Combining the system outputs using simple voting schemes and classifiers obtained improved results; the best composite system achieved an F-measure of 0.92 with 10-fold cross-validation. A 'maximum recall' system based on the pooled responses of all participants gave a recall of 0.97 (with precision 0.23), identifying 763 out of 785 identifiers. Conclusion: Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement. These results show promise as tools to link the literature with biological databases