粉末压片-X荧光光谱法测定过磷酸钙中硫含量

硫是过磷酸钙中重要营养指标之一,为准确快速测定过磷酸钙中硫的含量,试验采用粉末压片-X荧光光谱法,将过磷酸钙试样充分干燥后研磨至粒度小于74 μm,采用硼酸镶边,在压力18 Mpa条件下保压30 s,制成样片。通过在过磷酸钙样品中添加不同质量的纯物质硫酸钙（质量分数范围1.52 %～17.21 %）,经过专用混匀设备混合均匀后,与试样压片相同条件下压制标准样片,作为过磷酸钙中硫的标准样品,建立硫标准曲线,曲线线性相关系数R2为0.9995,采用经验系数法校正干扰,建立了粉末压片-波长色散X射线荧光光谱法测定过磷酸钙中硫含量的方法, 方法检出限为0.002 %。对3个不同硫含量的过磷酸钙样品采用本实验方法重复测量7次,RSD在1.4 %~3.1 %,方法精密度性好,同时用高温燃烧红外光谱法和电感耦合等离子发射光谱法对比,三者测量结果相对极差小于2.0 %,测量结果无显著性差异。此方法不需要对样品进行熔融或溶解,样品制备简单,数据准确度和稳定性好,分析效率高,适合大批量样品中硫的测定。     

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.     

蝙蝠喉部发声组织建模及发声机理*

建立蝙蝠发声组织模型对超声机理研究及在智能设备的应用具有重要意义。根据蝙蝠喉部发声组织结构特点,通过有限元方法构建了蝙蝠的3种不同发声组织模型,分析了尺寸、材料力学参数、组织结构和张力4个因素对发声组织特征频率的影响。结果表明,如果用人类声带,按比例缩小构建蝙蝠喉部组织模型,蝙蝠无法发出超声波。构建组织结构含甲状软骨和声带的半鼓状模型和只含声带的条状模型,两种模型的特征频率相近且在合理的参数域内均无法达到超声范围。而含膜条状模型的特征频率可以通过张力进行超声频率的调节,这与文献的实验结果一致。因此,可基于含膜条状模型对蝙蝠喉管发声组织进行建模及其发声机理研究。     

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.     

粗糙线性空间

将粗糙集思想引入到线性空间。提出线性空间中子集的上近似和下近似的概念,给出上、下粗线性子空间与上、下粗不变子空间的概念,讨论粗糙集线性空间的有关性质。     

信息时代的铌酸锂晶体:进展与展望

铌酸锂晶体具有非线性效应、电光效应、声光效应、光折变效应、压电效应与热释电效应等多种物理特性,在表面声波器件、光电器件、声光器件等方面获得广泛的应用.经历了六十多年的发展,铌酸锂晶体历久弥新,随着材料特性的不断开发,新功能、新器件、新应用层出不穷,尤其是铌酸锂单晶薄膜在薄膜滤波器、集成光电器件等领域的性能具有明显优势,...     

Microwave Pyrolysis and Electrochemical Supercapacitor of S-doped g-C_3N_4 Nanoparticles

In this study, S-doped g-C_3N_4 nanoparticles were successfully prepared by one-step solid-state microwave synthesis. The detailed characterizations through XRD, FT-IR, SEM and XPS were studied. In addition, the electrochemical properties as supercapacitor of the sample were tested by cyclic voltammetry(CV), galvanostatic charge-discharge(GCD) and electrochemical impedance spectroscopy(EIS) techniques. The results showed a high specific capacitance of 691 F/g at current density of 4 A/g in 2 M KOH + 0.15 M K_3[Fe(CN)_6] electrolyte. This study shows that the microwave synthesis is a promising way to design carbon-based electrodes for supercapacitor.     

Characterization of Roasting Time on Sensory Quality,Color, Taste,and Nonvolatile Compounds of Yuan An Yellow Tea

Roasting is crucial for producing Yuan An yellow tea (YAYT) as it substantially affects sensory quality. However, the effect of roasting time on YAYT flavor quality is not clear. To investigate the effect of roasting time on the sensory qualities, chemical components, odor profiles, and metabolic profile of YAYTs produced with 13 min roasting, 16 min roasting, 19 min roasting, 22 min roasting, and 25 min roasting were determined. The YAYTs roasted for 22 min got higher sensory scores and better chemical qualities, such as the content of gallocatechin (GC), gallocatechin gallate (GCG), free amino acids, solutable sugar, meanwhile the lightness decreased, the hue of tea brew color (b) increased, which meant the tea brew got darker and yellower. YAYTs roasted for 22 min also increased the contents of key odorants, such as benzaldehyde, nonanal, β-cyclocitral, linalool, nerol, α-cedrol, β-ionone, limonene, 2-methylfuran, indole, and longiborneol. Moreover, non-targeted metabolomics identified up to 14 differentially expressed metabolites through pair-wise comparisons, such as flavonoids, phenolic acids, sucrose, and critical metabolites, which were the main components corresponding to YAYT roasted for 22 min. In summary, the current results provide scientific guidance for the production of high quality YAYT.