The Application of the Cognitive Radio in the Aviation Communication Spectrum Management

AbstractIt is concerned that the aviation communication system is interfered by the inner and outside interference. Because of the electromagnetic spectrum is limited, it must be controlled and managed in order to use in aviation communication. The cognitive radio (CR) can perceive the electromagnetic environment automatically, search the spectrum holes, and adjust the signal parameters of both sides by communication protocols and algorithms to best situation. This paper discusses the CR and the application in the spectrum management of aviation communications

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA‑disease associations

Background: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNAdisease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results: We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion: Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNAdisease associations. The source code and data are available at https:// github. com/ zhang labNKU/ VGAEL DA

Distribution domains of the Pan-African event in East Antarctica and adjacent areas

The Pan-African event is widely distributed in East Antarctica (EA) craton, including both the coastal regions and interior of the EA. From aspects of the shear zones, granites, pegmatites, time of high-grade metamorphism and detrital zircon age peaks of the downflowing sediments from the inland, the Pan-African event in the EA and adjacent areas in the Gondwana reconstruction, like SE Africa, southern India and SW Australia, was described in the paper. The water or fluid available along the shear zones was responsible for retrogression of the earlier, e.g., Grenville age, high-grade outcrops to later Pan-African amphibolite to granulite facies metamorphism. In geochemistry, the granites are generally anorogenic, ocassionally with some gabbros or dolerite dykes, showing sign of bimodal feature. Meanwhile, the event has influenced most isotopic systems, including the U-Pb, Sm-Nd, Rb-Sr and Ar-Ar systems, giving Pan-African apparent ages. Spatially, the Pan-African event is demonstrated from possibly local granitic magmatism, to wider medium-high grade metamorphism, and mostly widespread in resetting for some isotope systems, suggesting the prevailing thermal effect of the event. Before Gondwana formation, local depressions in the EA may have been filled with sediments, implying the initial breakup period of the Rodinia. The later Pan-Gondwana counterrotating cogs shaped the interstitial fold belts between the continent blocks and formed a set of shear zones. The mafic underplating in the Gondwana may be responsible for the typical features of the Pan-African event. The event may be an overwhelmingly extensional and transcurrent tectonics in mechanism and is a possible response of the plate movement surrounding the continent swarms in the non-stable interior of the yet consolidated Gondwana

Benthic Habitat Quality Assessment in Estuarine Intertidal Flats Based on Long-Term Data with Focus on Responses to Eco-Restoration Activity

A long-term assessment of the benthic habitat quality of intertidal flats in Liaohe Estuary was conducted by three integrating ecological indices, AZTI’s Marine Biotic Index (AMBI), Multivariate-AMBI (M-AMBI), and Shannon–Wiener diversity index (H′) based on macrobenthos data from 2013 to 2020. The results showed that the macrobenthic communities were characterized by indifferent and sensitive species of AMBI ecological groups. The annual ranges of H′, AMBI, and M-AMBI were 0.77–1.56, 1.44–3.73 and 0.36–0.54, respectively. Noticeable differences were found among assessment obtained by these biotic indices. Approximately 100%, 24%, and 78% sampling sites had “moderate”, “poor”, and “bad” statuses as assessed by H′, AMBI, and M-AMBI, respectively. Compared with H′ and AMBI, M-AMBI may be more applicable to evaluate the benthic habitat quality of intertidal flats in Liaohe Estuary. Results suggest that the benthic habitat quality in the middle parts of intertidal flats still had an unacceptable status and has not improved radically to date after large-scale “mariculture ponds restored to intertidal flats”.publishedVersio

Construction of a Cross-layer Linked G-octamer via Conformational Control: A Stable G-quadruplex in H-bond Competitive Solvents

Methanol soluble and stable guanosine octamers were successfully achieved via H-bond self-assembly. Through structural conformational design, we developed a new class of guanosine derivatives with modification on guanine (8-aryl) and ribose (20 ,30 -isopropylidene). This unique design led to the formation of the first discrete G8-octamer with its structure characterized by single crystal X-ray diffraction, MS and NMR spectroscopy. The G8-octamer showed unique cation recognition properties, including the formation of a stable Rb+ templated G-quadruplex. Based on this observation, further modification on the 8-aryl moiety was performed to incorporate a cross-layer H-bond or covalent linkage. Similar G-octamers were obtained in both cases with structures confirmed by single crystal X-ray diffraction. Furthermore, the covalently linked G-quadruplex exhibited excellent stability even in MeOH and DMSO, suggesting a promising future for this new H-bond self-assembly system in biological and material applications

SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.Comment: 11 pages, 5 figures, ICAIS 202

Electroplating lithium transition metal oxides.

Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten salt electrodeposition approach to directly electroplate the important lithium-ion (Li-ion) battery cathode materials LiCoO2, LiMn2O4, and Al-doped LiCoO2. The crystallinities and electrochemical capacities of the electroplated oxides are comparable to those of the powders synthesized at much higher temperatures (700° to 1000°C). This new growth method significantly broadens the scope of battery form factors and functionalities, enabling a variety of highly desirable battery properties, including high energy, high power, and unprecedented electrode flexibility

A laboratory nanoseismological study on deep-focus earthquake micromechanics

Global earthquake occurring rate displays an exponential decay down to ~300 km and then peaks around 550 to 600 km before terminating abruptly near 700 km. How fractures initiate, nucleate, and propagate at these depths remains one of the greatest puzzles in earth science, as increasing pressure inhibits fracture propagation. We report nanoseismological analysis on high-resolution acoustic emission (AE) records obtained during ruptures triggered by partial transformation from olivine to spinel in Mg2GeO4, an analog to the dominant mineral (Mg,Fe)2SiO4 olivine in the upper mantle, using state-of-the-art seismological techniques, in the laboratory. AEs’ focal mechanisms, as well as their distribution in both space and time during deformation, are carefully analyzed. Microstructure analysis shows that AEs are produced by the dynamic propagation of shear bands consisting of nanograined spinel. These nanoshear bands have a near constant thickness (~100 nm) but varying lengths and self-organize during deformation. This precursory seismic process leads to ultimate macroscopic failure of the samples. Several source parameters of AE events were extracted from the recorded waveforms, allowing close tracking of event initiation, clustering, and propagation throughout the deformation/transformation process. AEs follow the Gutenberg-Richter statistics with a well-defined b value of 1.5 over three orders of moment magnitudes, suggesting that laboratory failure processes are self-affine. The seismic relation between magnitude and rupture area correctly predicts AE magnitude at millimeter scales. A rupture propagation model based on strain localization theory is proposed. Future numerical analyses may help resolve scaling issues between laboratory AE events and deep-focus earthquakes