Symmetry guaranteed Dirac-line semimetals in two-dimensions against strong spin-orbit coupling

Several intriguing electronic phenomena and electric properties were discovered in three-dimensional Dirac nodal line semimetals (3D-DNLSM), which are, however, easy to be perturbed under strong spin-orbit coupling (SOC). While two-dimensional (2D) layers are an emerging material category with many advantages, 2D-DNLSM against SOC is yet to be uncovered. Here, we report a 2D-DNLSM in odd-atomic-layer Bi (the brick phase, another Bi allotrope), whose robustness against SOC is protected by the little co-group C_2v \times Z^T_2, the unique protecting symmetry we found in 2D.Specially, (4n+2) valence electrons fill the electronic bands in the brick phase, so that the Dirac nodal line with fourfold degeneracy locates across the Fermi level. There are almost no other low energy states close to the Fermi level; this allows to feasibly observe the neat DNLSM-induced phenomena in transport measurements without being affected by other bands. In contrast, Other VA-group elements also form the brick phases, but their DNL states are mixed with the extra states around the Fermi level. This unprecedented category of layered materials allows for exploring nearly isolated 2DDNL states in 2D.Comment: Totally 25 pages including main text, methods and supporting information, 4 figures, 8 SI figure

Research on gas emission quantity prediction model based on EDA-IGA

In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic Algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the Estimation of Distribution Algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic Algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic Algorithm and Estimation of Distribution Algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission. Accurately predicting the gas emission quantity can provide guidance for safe mining in coal mines. The gas emission quantity can also be used as a safety indicator to reduce the possibility of coal mine accidents, ensure the personal safety of coal miners and reduce economic losses in coal mines

Research on Risk Identification of Coal and Gas Outburst Based on PSO-CSA

Aiming at the identification of coal and gas outburst risk, using the advantages of the clone selection algorithm (CSA), such as self-adaptation and robustness, and the characteristics of fast convergence of particle swarm optimization (PSO) algorithm, the complex decoding problem, and mutation process brought by CSA binary coding are used. It is difficult to control the problem. Using PSO optimization, the problem of abnormal detection and identification in coal and gas outburst monitoring is developed and studied, and a CSA coal and gas outburst risk anomaly detection and identification model based on PSO optimization variation is established. The model uses the coal and gas outburst index data as a collection of antigen-stimulated antibodies to achieve abnormal detection and identification of measured data. With the help of the measured data, the verification results show that the model can effectively detect and identify the risk of coal and gas outburst, and the identification results are consistent with the risk of coal and gas outburst in the field. It can be used as an effective risk identification model to guide coal mining work

Multiscale identification of urban functional polycentricity for planning implications : An integrated approach using geo-big transport data and complex network modeling

Polycentrism has gradually become a newly emergent dimension of global urbanization. Many countries worldwide have tailored plans suited to functional polycentricity, in light of the prevalent “ghost cities” or “empty towns” as lessons from the morphologically polycentric development practices. However, the subject of defining and measuring functional polycentricity is still in an initial development phase, both in theory and in methodology. This paper first establishes a general theoretical framework for understanding functional polycentricity from the lens of interactive human mobility among spatial units. Then, a new approach is proposed to identify and measure urban functional polycentricity from a multiscale perspective and further applied to the case of Shanghai, China. More specifically, the pick-up and drop-off points from taxi GPS data are used to examine the linkages among different urban units across various scales (e.g., census tract, 3000-m grid, 5000-m grid, and community). Complex network modeling, together with the sensitivity analysis, is further employed to identify the centers according to the spatial importance of each unit. The results show that (1) the approach proposed can effectively identify functional centers within urban setting; (2) an obvious polycentric structure exists in Shanghai and is sensitive to scale effects; (3) the estimates are more accurate and precise with the shrink of analysis unit size from community level to census tract level; and (4) under the same spatial scale, the grid-based analysis produces a more elaborated polycentric pattern compared with the traditional administration-based analysis. Finally, scale-dependent differences between morphological and functional polycentricity are distinguished for providing implications for urban planning. Our study is believed to renew the knowledge of polycentricity conceptualization

Low-voltage Extended Gate Organic Thin Film Transistors for Ion Sensing Based on Semi-conducting Polymer Electrodes

We report a low-voltage organic field-effect transistor consisting of an extended gate sensory area to detect various ions in a solution. The device distinguishes various ions by the shift in threshold voltage and is sensitive to multiple ions with various concentrations. X-ray photoelectron spectroscopy measurements and the resistance changes at the sensor area prove that the ions are doped into the sensitive film at the sensor area. Because of the effect of doping, the conductivity of the semiconductor polymer film changes thus causing a threshold voltage shift