Computational Study of the Magnetic Structure of Na2_2IrO3_3

The magnetic structure of honeycomb iridate Na$_2$IrO$_3$ is of paramount importance to its exotic properties. The magnetic order is established experimentally to be zigzag antiferromagnetic. However, the previous assignment of ordered moment to the $\bm{a}$-axis is tentative. We examine the magnetic structure of Na$_{2}$IrO$_{3}$ using first-principles methods. Our calculations reveal that total energy is minimized when the zigzag antiferromagnetic order is magnetized along $\bm{g}\approx\bm{a}+\bm{c}$. Such a magnetic configuration is explained by adding anisotropic interactions to the nearest-neighbor Kitaev-Heisenberg model. Spin-wave spectrum is also calculated, where the calculated spin gap of $10.4$ meV can in principle be measured by future inelastic neutron scattering experiments. Finally we emphasize that our proposal is consistent with all known experimental evidence, including the most relevant resonant x-ray magnetic scattering measurements [X. Liu \emph{et al.} {Phys. Rev. B} \textbf{83}, 220403(R) (2011)].Comment: 18 pages, 7 figure

The Online Data Quality Monitoring System at BESIII

The online Data Quality Monitoring (DQM) plays an important role in the data taking process of HEP experiments. BESIII DQM samples data from online data flow, reconstructs them with offline reconstruction software, and automatically analyzes the reconstructed data with user-defined algorithms. The DQM software is a scalable distributed system. The monitored results are gathered and displayed in various formats, which provides the shifter with current run information that can be used to find problems early. This paper gives an overview of DQM system at BESIII.Comment: Already submit to Chinese Physics

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

Global Value Chain Embeddedness, Digital Economy and Green Innovation- Evidence From Provincial-Level Regions of China

With globalization and digitalization, participating in Global Value Chain (GVC) and developing digital economy have had a profound impact, which transforms China’s economy into a green and innovative one. This paper studies the intrinsic influential mechanism of GVC embeddedness and digital economy on green innovation and proposes some research hypotheses. Based on panel data of 30 Chinese provinces from 2002 to 2016, we constructed some core indicators such as GVC embeddedness, digital economy and green innovation. The ordinary panel model and spatial panel model are used to empirically test the impact of GVC embeddedness and digital economy on China’s green innovation at the provincial level. The research findings are: First, GVC embeddedness and digital economy have significant promotion effects on green innovation. Second, the development of digital economy will not only directly promote green innovation, but also indirectly promote green innovation by effectively promoting the integration of provincial economy into GVC. The results of mediating effect test show that GVC embeddedness has a partial mediating effect in the influential mechanism of digital economy to promote green innovation. Third, GVC embeddedness and green innovation have significant spatial spillover effects. It indicates that Chinese provinces (cities¹) have significantly promoted green innovation in neighboring provinces through many possible channels and mechanisms in the process of participating in GVC, and the robustness test shows the stability of the spatial spillover mechanism. The findings provide useful policy implications for China’s deeply participating in GVC, vigorously developing digital economy and promoting green innovation