Synthesis, Structure and Conductivity of the New Charge-transfer Salt (ET)_2(CH_2=CH-CH_2-SO_3)·H_2O

ET is one of the most famous electron-donor molecules, which forms charge-transfer complexes (abbr. CT-complexes) with various types of counterions. These complexes have received intense attention because a wide range of physical properties such as conductivity and superconductivity1, ferromagnetism2-4 and nonlinear optical properties5 was found in these materials. Although the majority of the ET-based CT-complexes were prepared by combining with inorganic counterions, CT-complexes with o…     

一种利用窗口结构提取红外弱小目标的方法

在分析红外弱小目标检测的检测概率和虚警率的基础上,引入了一种基于窗口结构的红外弱小目标检测方法.该方法无需进行图像差分,利用局部小范围内目标相对背景的灰度变化检测目标,可以在较低信噪比的情况下获得较高的检测概率和较低的虚警率.实验表明了该方法的有效性.     

Enantioselective Hydrogenation of Endocyclic Enones:the Solution to a Historical Problem

The enantioselective hydrogenation of endocyclic enones has been a historical problem for homogeneous catalysis.We herein report an efficient method to reduce e...     

水中硝酸盐氮稳定同位素比值测定前处理方法的优化

水体中过量的硝酸盐导致会严重的水生生态恶化和环境污染问题。氮稳定同位素技术为水体污染来源的判断及水生系统氮素转化机理研究提供了强有力的工具。在前人基础上,通过开发Cu2+-Cd复合催化结合超声波辅助加速反应,优化硝酸盐氮同位素比值测定前处理方法。考察了Cu2+添加量,超声功率以及反应时间变化对NO3--N转化生成N2O气体及其氮同位素比值的影响,在单因素实验基础上采用正交实验优化得到了最佳反应条件,并采用不同15N同位素比值的KNO3标准溶液结合气体预浓缩装置与稳定同位素质谱仪联用系统对新方法进行了验证。新方法单次反应体系中氮最低量为1.0 μg,其中自然丰度和高丰度样品δ15N分析精度小于1‰,富集样品的15N分析精度可达0.1 atom%以内(CV<1%);且所有标准样品的15N测定值与参考值基本一致。将优化后的方法应用于不同来源水样中硝酸盐氮稳定同位素比值测定,均可获得较好的精度,较原方法提高了前处理效率,且精度更优。综上,建立的方法准确可靠,操作简单,耗时短,适用于水中硝酸盐氮稳定同位素比值测定的批量、快速前处理。     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Development of a ~3He Gas Filling Station at the China Spallation Neutron Source

At the China Spallation Neutron Source(CSNS), we have developed a custom gas-filling station, a glassblowing workshop, and a spin-exchange optical pumping(SEOP) system for producing high-quality ~3He-based neutron spin filter(NSF) cells. The gas-filling station is capable of routinely filling ~3He cells made from GE180 glass of various dimensions, to be used as neutron polarizers and analyzers on beamlines at the CSNS. Performance tests on cells fabricated at our gas-filling station are conducted via neutron transmission and nuclear-magneticresonance measurements, revealing nominal filling pressures, and a saturated ~3He polarization in the region of 80%, with a lifetime of approximately 240 hours. These results demonstrate our ability to produce competitive NSF cells to meet the ever-increasing research needs of the polarized neutron research community.