An unsymmetrical porphyrin and its metal complexes: synthesis, spectroscopy, thermal analysis and liquid crystal properties

The synthesis and characterization of a new unsymmetrical porphyrin liquid crystal, 5-(4-stearoyloxyphenyl)phenyl-10,15,20-triphenylporphyrin (SPTPPH2) and its transition metal complexes (SPTPPM, M(II) = Zn, Fe, Co, Ni, Cu or Mn) are reported. Their structure and properties were studied by elemental analysis, and UV–Vis, IR, mass and 1H-HMR spectroscopy. Their luminescent properties were studied by excitation and emission spectroscopy. The quantum yields of the S1 ® S0 fluorescence were measured at room temperature. According to thermal studies, the complexes have a higher thermal stability (no decomposition until 200 °C). Differential scanning calorimetry (DSC) data and an optical textural photograph, obtained using a polarizing microscope (POM), indicate that the porphyrin ligand had liquid crystalline character and that it exhibited more than one mesophase and a low-lying phase transition temperature, with transition temperatures of 19.3 and 79.4 °C; the temperature range of the liquid crystal (LC) phase of the ligand was 70.1 °C

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

Optimum design of a new VSS-NP chart with adjusting sampling inspection

The classical Shewhart NP control chart is used widely in industrial and service practice for the relative simplicity of handling attribute quality characteristics. However, the static strategies become less and less adequate for today's highly competitive industrial society because of their low efficiency to detect slight process changes promptly. To improve the capability of control charts, some adaptive schemes, such as variable sample size (VSS), variable sample interval (VSI), and variable control limits (VCL), have been extensively studied in the recent decade. In this paper, we propose a new VSS NP control chart with adjusting sampling inspection (called ASI-NP chart) and give the performance analysis using Markov chain. As the optimal model is related to an integer nonlinear program, genetic algorithms (GAs) are involved and Taguchi experiments are applied to configure the parameter of the GAs in numerical examples. The comparison study between classical NP chart and ASI-NP chart is conducted, and the result shows that ASI-NP chart's performance characteristics are significantly better than those of classical NP charts in all situations, especially, in processes with slight shift and high quality.NP chart Adaptive control chart Adjusting sampling inspection

A Data-Efficient Training Method for Deep Reinforcement Learning

Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often prohibitive to take weeks to train an algorithm. In this study, a data-efficient training method is proposed in which a DQN is used as a base algorithm, and an elaborate curriculum is designed for the agent in the simulation scenario to accelerate the training process. In the early stage of the training process, the distribution of the initial state is set close to the goal so the agent can obtain an informative reward easily. As the training continues, the initial state distribution is set farther from the goal for the agent to explore more state space. Thus, the agent can obtain a reasonable policy through fewer interactions with the environment. To bridge the sim-to-real gap, the parameters for the output layer of the neural network for the value function are fine-tuned. An experiment on UAV maneuver control is conducted in the proposed training framework to verify the method. We demonstrate that data efficiency is different for the same data in different training stages

A Data-Efficient Training Method for Deep Reinforcement Learning

Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often prohibitive to take weeks to train an algorithm. In this study, a data-efficient training method is proposed in which a DQN is used as a base algorithm, and an elaborate curriculum is designed for the agent in the simulation scenario to accelerate the training process. In the early stage of the training process, the distribution of the initial state is set close to the goal so the agent can obtain an informative reward easily. As the training continues, the initial state distribution is set farther from the goal for the agent to explore more state space. Thus, the agent can obtain a reasonable policy through fewer interactions with the environment. To bridge the sim-to-real gap, the parameters for the output layer of the neural network for the value function are fine-tuned. An experiment on UAV maneuver control is conducted in the proposed training framework to verify the method. We demonstrate that data efficiency is different for the same data in different training stages