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In two-dimensional (2D) systems, high mobility is typically achieved in low-carrier-density semiconductors and semimetals. Here, we discover that the nanobelts of Weyl semimetal NbAs maintain a high mobility even in the presence of a high sheet carrier density. We develop a growth scheme to synthesize single crystalline NbAs nanobelts with tunable Fermi levels. Owing to a large surface-to-bulk ratio, we argue that a 2D surface state gives rise to the high sheet carrier density, even though the bulk Fermi level is located near the Weyl nodes. A surface sheet conductance up to 5–100 S per □ is realized, exceeding that of conventional 2D electron gases, quasi-2D metal films, and topological insulator surface states. Corroborated by theory, we attribute the origin of the ultrahigh conductance to the disorder-tolerant Fermi arcs. The evidenced low-dissipation property of Fermi arcs has implications for both fundamental study and potential electronic applications.High mobility and high carrier density are found in the Weyl semimetal NbAs. This is attributed to the low dissipation of disorder-tolerant Fermi arcs.

SignificanceThree-dimensional (3D) Dirac semimetals have attracted a lot of advanced research recently on many exotic properties and their association with crystalline and electronic structures under extreme conditions. As one of the fundamental state parameters, high pressure is an effective, clean way to tune lattice as well as electronic states, especially in quantum states, thus their electronic and magnetic properties. In this paper, by combining multiple experimental probes (synchrotron X-ray diffraction, low-temperature transport under magnetic field) and theoretical investigations, we discover the pressure-induced 3D Dirac semimetal to superconductor transition in ZrTe5. As a new type of topological materials, ZrTe5 shows many exotic properties under extreme conditions. Using resistance and ac magnetic susceptibility measurements under high pressure, while the resistance anomaly near 128 K is completely suppressed at 6.2 GPa, a fully superconducting transition emerges. The superconducting transition temperature Tc increases with applied pressure, and reaches a maximum of 4.0 K at 14.6 GPa, followed by a slight drop but remaining almost constant value up to 68.5 GPa. At pressures above 21.2 GPa, a second superconducting phase with the maximum Tc of about 6.0 K appears and coexists with the original one to the maximum pressure studied in this work. In situ high-pressure synchrotron X-ray diffraction and Raman spectroscopy combined with theoretical calculations indicate the observed two-stage superconducting behavior is correlated to the structural phase transition from ambient Cmcm phase to high-pressure C2/m phase around 6 GPa, and to a mixture of two high-pressure phases of C2/m and P-1 above 20 GPa. The combination of structure, transport measurement, and theoretical calculations enable a complete understanding of the emerging exotic properties in 3D topological materials under extreme environments.

Trajectory data uploaded by mobile devices is growing quickly. It represents the movement of an individual or a device based on the longitude and latitude coordinates collected by GPS. The location based service has a broad application prospect in the real world. As the traditional location prediction models which are based on the discrete state sequence cannot predict the locations in real time, we propose a Continuous Time Series Markov Model (CTS-MM) to solve this problem. The method takes the Gaussian Mixed Model (GMM) to simulate the posterior probability of a location in the continuous time series. The probability calculation method and state transition model of the Hidden Markov Model (HMM) are improved to get the precise location prediction. The experimental results on GeoLife data show that CTS-MM performs better for location prediction in exact minute than traditional location prediction models.

Aiming at solving the problem that it is challenging to choose the appropriate price adjustment strategy according to the market fluctuations, an adaptive price adjustment method based on dual deep fuzzy networks (DDFN) is designed. First, a price adjustment model based on DDFN is established. Through interactively learning the recycling market environment, the description of the mapping relationship between the market environment information and the price adjustment action is realized. Second, based on a greedy strategy to calculate the optimal price adjustment action, it is possible to make small adjustments based on the preliminary estimated value of the waste mobile phone, and complete the judgment of the mobile phone recycling price. Third, based on the market feedback, the gradient descent algorithm is used to update parameters of the model to improve the performance. The proposed adaptive price adjustment method based on DDFN is applied to the actual transaction process, and the results show that the proposed method can ensure the accuracy and reliability of the adjustment results of the mobile phone recycling price.

Phosphorene, as a novel two-dimensional material, has attracted a great interest due to its novel electronic structure. The pursuit of controlled magnetism in Phosphorene in particular has been persisting goal in this area. In this paper, an antiferromagnetic insulating state has been found in the zigzag phosphorene nanoribbons (ZPNRs) from the comprehensive density functional theory calculations. Comparing with other one-dimensional systems, the magnetism in ZPNRs display several surprising characteristics: (i) the magnetic moments are antiparallel arranged at each zigzag edge; (ii) the magnetism is quite stable in energy (about 29 meV/magnetic-ion) and the band gap is big (about 0.7 eV); (iii) the electronic and magnetic properties is almost independent on the width of nanoribbons; (iv) a moderate compressive strain will induce a magnetic to nonmagnetic as well as semiconductor to metal transition. All of these phenomena arise naturally due to one unique mechanism, namely the electronic instability induced by the half-filled one-dimensional bands which cross the Fermi level at around π/2a . The unusual electronic and magnetic properties in ZPNRs endow them possible potential for the applications in nanoelectronic devices.

Background Biomedical semantic indexing is important for information retrieval and many other research fields in bioinformatics. It annotates biomedical citations with Medical Subject Headings. In face of unbalanced category distribution in the training data, sampling methods are difficult to apply for semantic indexing task. Results In this paper, we present a novel deep serial multi-task learning model. The primary task treats the biomedical semantic indexing as a multi-label text classification issue that considers the relations of the labels. The auxiliary task is a regression task that predicts the MeSH number of the citation and provides hints for the network to make it converge faster. The experimental results on the BioASQ-Task5A open dataset show that our model outperforms the state-of-the-art solution “MTI”, proposed by the US National Library of Medicine. Further, it not only achieves the highest precision among all the solutions in BioASQ-Task5A but also has faster convergence speed compared with some naive deep learning methods. Conclusions Rather than parallel in an ordinary multi-task structure, the tasks in our model are serial and tightly coupled. It can achieve satisfied performance without any handcrafted feature.

Combining first-principles calculations and effective model analysis, we predict that CaTe is a topological node-line semimetal in the absence of the spin-orbit coupling. Using a slab model, we obtain the nearly flat drumhead surface state near the Fermi level. When the spin-orbit coupling is included, three node lines will evolve into a pair of Dirac points along the M − R line. These Dirac points are robust and protected by the C 4 rotation symmetry. Once this crystal symmetry is broken, the Dirac points will be eliminated, and the system becomes a strong topological insulator. Topological physics: a predicted node-line semimetal CaTe Topological insulators are materials with non-trivial topological order that are insulating in their bulk but conductive on their surface. Recent findings extend the topological states to three-dimensional semimetals that host exotic physical phenomena such as Weyl fermion quantum transport and Hall effects. Among the three types of topological semimetals, three-dimensional Dirac semimetals evolve to Weyl analogs upon breaking of time reversal or inversion symmetry. Here, the theoretical work by a team led by Professor Xiangang Wan from Nanjing University in China proposes a new phase that falls into the third category: node-line semimetals. Based on first-principles calculations and effective model analysis, CsCl structured CaTe is predicted to be a node-line semimetals with characteristic drumhead-like surface states if spin-orbit coupling is absent. When spin-orbit coupling is included, CaTe becomes a three-dimensional Dirac semimetal.

Weyl and Dirac semimetals recently stimulate intense research activities due to their novel properties. Combining first-principles calculations and effective model analysis, we predict that nonmagnetic compounds Ba Y Bi ( Y  = Au, Ag and Cu) are Dirac semimetals. As for the magnetic compound Eu Y Bi, although the time reversal symmetry is broken, their long-range magnetic ordering cannot split the Dirac point into pairs of Weyl points. However, we propose that partially substitute Eu ions by Ba ions will realize the Weyl semimetal.

The problem of fixed-time adaptive event-triggered control for uncertain nonlinear systems with input hysteresis is investigated. An adaptive dynamic threshold event-triggered control scheme is proposed to schedule the update of control signals and realize the online compensation of input hysteresis. Furthermore, a fixed-time adaptive event-triggered controller is proposed based on the fixed-time stability theorem. The controller can ensure that the tracking error converges into a small and adjustable set in a fixed time, and the convergence time is independent of the initial system states. Meanwhile, all the closed-loop signals are bounded, and the Zeno behavior is excluded. Finally, the feasibility of the method is verified by some simulation examples.

On the background of high morbidity and mortality of hepatocellular carcinoma (HCC) and rapid development of traditional Chinese medicine (TCM), we conducted a systematic review and meta-analysis of randomized clinical trials (RCTs) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to assess the clinical effectiveness and safety of traditional insect Chinese medicine and related preparation for non-surgical HCC. RCTs were searched based on standardized searching rules in mainstream medical databases from the inception up to May 2016. Ultimately, a total of 57 articles with 4,651 patients enrolled in this meta-analysis. We found that traditional insect Chinese medicine and related preparation combined chemotherapy show significantly effectiveness and safety in objective response rate ( P  < 0.001), survival time extension [12 months ( P  < 0.001); 18 months ( P  < 0.001); 24 months ( P  < 0.001); 36 months ( P  < 0.001)], amelioration for life quality [QoL scores improvement ( P  < 0.001); KPS improvement ( P  < 0.001); AFP improvement ( P  < 0.001)] and reduction of therapeutic toxicity [WBC decrease ( P  = 0.04); gastrointestinal adverse reactions ( P  < 0.001)]. In conclusion, traditional insect Chinese medicine and related preparations could be recommended as auxiliary therapy combined chemotherapy for HCC therapy.