微区X射线荧光光谱成像法分析历史文物建筑混凝土材料成分及工艺

历史文物建筑修复与保护的首要步骤是对建材构件进行成分解析。但成型的混凝土构件成分复杂,传统技术难以直接分析。微区X射线荧光光谱具有速度快、无需前处理、可获得大面积高分辨的元素成像等优势,可用于此类混合物的原位分析。本文采用束斑为20μm的微区X射线荧光光谱仪,扫描成型混凝土构件以获得混合成分的元素分布图,结合基本参数定量法,对上海地区典型历史建筑混泥土构件进行元素定量。分析结果有效地解析了混凝土构建中骨料和凝胶材料的成分和含量,鉴别了涂层成分,解析了拌混工艺,鉴别了局部污染元素和致劣元素并分析致劣原因,为历史建筑修复材料选配、拌混工艺选择、除污及保护性预防劣化提供了科学的数据支撑。     

Speckle characteristics of simulated deep Fresnel region under shadowing effect

After the three-dimensional self-affine fractal random surface simulation, we use the optical scattering theory to calculate the deep Fresnel region speckle(DFRS) under consideration of the more strict shadowing effect. The evolution of DFRS with the scattering distance and the intensity probability distribution are studied. It is found that the morphology of the scatterer has an antisymmetric relationship with the intensity distribution of DFRS, and the effect of micro-lenses on the scattering surface causes the intensity probability distribution of DFRS to deviate from the Gaussian speckle in the high light intensity area.     

含高阶非线性效应的薛定谔方程的精确解研究

利用孤子理论,研究了含三次和五次非线性项的非线性薛定谔方程,在参数取不同值时得到了方程的新型亮孤子解、新型暗孤子解和新的三角函数周期解。     

Andrographolide Inhibits Biofilm and Virulence in Listeria monocytogenes as a Quorum-Sensing Inhibitor

Listeria monocytogenes is a major foodborne pathogen that can cause listeriosis in humans and animals. Andrographolide is known as a natural antibiotic and exhibits good antibacterial activity. We aimed to investigate the effect of andrographolide on two quorum-sensing (QS) systems, LuxS/AI-2 and Agr/AIP of L. monocytogenes, as well as QS-controlled phenotypes in this study. Our results showed that neither luxS expression nor AI-2 production was affected by andrographolide. Nevertheless, andrographolide significantly reduced the expression levels of the agr genes and the activity of the agr promoter P₂. Results from the crystal violet staining method, confocal laser scanning microscopy (CLSM), and field emission scanning electron microscopy (FE-SEM) demonstrated that andrographolide remarkably inhibited the biofilm-forming ability of L. monocytogenes 10403S. The preformed biofilms were eradicated when exposed to andrographolide, and reduced surviving cells were also observed in treated biofilms. L. monocytogenes treated with andrographolide exhibited decreased ability to secrete LLO and adhere to and invade Caco-2 cells. Therefore, andrographolide is a potential QS inhibitor by targeting the Agr QS system to reduce biofilm formation and virulence of L. monocytogenes.     

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.     

Synthesis of PNIPAAm-g-P4VP Microgel as Draw Agent in Forward Osmosis by RAFT Polymerization and Reverse Suspension Polymerization to Improve Water Flux

Microgels have unique and versatile properties allowing their use in forward osmosis areas as a draw agent. In this contribution, poly(4-vinylpyridine) (P4VP) was synthesized via RAFT polymerization and then grafted to a poly(N-Isopropylacrylamide) (PNIPAAm) crosslinking network by reverse suspension polymerization. P4VP was successfully obtained by the quasiliving polymerization with the result of nuclear magnetic resonance and gel permeation chromatography characterization. The particle size and particle size distribution of the PNIPAAm-g-P4VP microgels containing 0, 5, 10, 15 and 20 wt% P4VP were measured by means of a laser particle size analyzer. It was found that all the microgels were of micrometer scale and the particle size was increased with the P4VP load. Inter/intra-molecular-specific interactions, i.e., hydrogen bond interactions were then investigated by Fourier infrared spectroscopy. In addition, the water flux measurements showed that all the PNIPAAm-g-P4VP microgels can draw water more effectively than a blank PNIPAAm microgel. For the copolymer microgel incorporating 20 wt% P4VP, the water flux was measured to be 7.48 L∙m⁻²∙h⁻¹.     

Dynamic Properties of Two-Dimensional Polydisperse Granular Gases

We propose a two-dimensional model of polydisperse granular mixtures with a power-law size distribution in the presence of stochastic driving. A fractal dimension D is introduced as a measurement of the inhomogeneity of the size distribution of particles. We define the global and partial granular temperatures of the multi-component mixture. By direct simulation Monte Carlo, we investigate how the inhomogeneity of the size distribution influences the dynamic properties of the mixture, focusing on the granular temperature, dissipated energy, velocity distribution, spatial clusterization, and collision time. We get the following results: a single granular temperature does not characterize a multi-component mixture and each species attains its own "granular temperature"; The velocity deviation from Gaussian distribution becomes more and more pronounced and the partial density of the assembly is more inhomogeneous with the increasing value of the fractal dimension D; The global granular temperature decreases and average dissipated energy per particle increases as the value of D augments.     

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.     

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.