Analytic expressions for the partial waves of the off-shell two body coulomb T-matrix

Closed form expressions for all partial waves of the pure Coulomb off-shell T-matrix p¦ T_c,l.(k ²) ¦p are obtained. All singularities appear through simple special functions, which makes it possible to study the analytic properties of p¦ T_c,l(k²) ¦p as a function not only of one of the momentap, p but even of both of them. With use of the renormalization procedure found by Zorbas the transition to half-and on-shell values is performed reproducing known expressions. By the same method simple expressions for the partial waves of the Coulomb wave function in the momentum representation are found.     

对全入射角负折射二维光子晶体结构和电磁参量的研究

利用二维光子晶体的等频线分析原理和平面波展开方法,得到了使二维光子晶体产生全入射角负折射(All-Angle Negative Refraction,AANR)现象时,入射电磁波的频率取值范围.同时,分析了AANR频率范围随着结构参量(晶格类型、介质棒半径与晶格周期的比值)和电磁参量(介质柱介电常量、本底介电常量、入射电磁波偏振方向)变化的行为.结果表明:固定组成光子晶体的一种介质的介电常量,另一介质的介电常量只有达到一定阈值,才有可能使光子晶体出现AANR现象.在给定两种介质介电常量的条件下,存在使AANR频率范围最大化的结构参量和电磁参量.     

蓝绿光敏感的非水溶性光致聚合物特性研究

报导了一种新型蓝绿光敏感的非水溶性光致聚合物材料,这种材料以聚醋酸乙烯酯为成膜物,9-乙烯基咔唑(NVC),甲基丙烯酸2-苯氧乙脂(POEA)和双季戊四醇五丙烯酸酯为单体,Dye3和Dye4为光敏剂,邻氯代六芳基双咪唑(O-CL-HABI)为共引发剂.本材料能同时对蓝光和绿光敏感,用蓝光和绿光分别或同时对该材料曝光,可得到清晰图像.测得的两种衍射效率分别不低于80%.     

用于彩色液晶显示器的色分离光栅的优化设计

利用光栅实现液晶显示器中的色分离可以提高液晶显示器对光能的利用率。为了提高光能利用率,对光栅台阶数进行了优化设计,经矢量衍射理论计算,优化后的多台阶色分离光栅的能量利用率达到了65.3%,与采用滤色片的方法相比,光能利用率提高了近一倍;与三台阶相比,光能利用率提高了9.3%。     

基于激光诱导击穿光谱(LIBS)的煤灰熔点快速检测

炉内结渣是影响火电机组和气化工艺可靠运行的关键因素之一,准确预测灰熔点可以提前调整炉膛出口温度以避免结渣。本论文采用激光诱导击穿光谱（LIBS）采集煤灰样中金属元素的光谱,分别建立煤灰中的金属元素的谱线强度与煤灰熔点的随机森林模型、支持向量机回归模型和线性回归模型,直接预测煤灰熔点温度。采用基于马氏距离(MD)的异常数据剔除算法和基于稀疏矩阵的基线估计与降噪算法（BEADS）,对粉煤灰样的全光谱数据进行了预处理。随机森林模型对粉煤灰熔点的预测平均相对误差（MRE）为54.74%,支持向量机回归模型的预测平均相对误差为60.08%,而线性回归模型的预测平均相对误差达到了9.78%。研究结果表明,线性回归模型对煤灰熔点的预测结果更准确。     

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。     

A general electron donor–acceptor complex for photoactivation of arenes via thianthrenation

General photoactivation of electron donor–acceptor (EDA) complexes between arylsulfonium salts and 1,4-diazabicyclo[2.2.2]octane with visible light or natural sunlight was discovered. This practical and efficient mode enables the production of aryl radicals under mild conditions, providing an unrealized opportunity for two-step para-selective C–H functionalization of complex arenes. The novel mode for generating aryl radicals via an EDA complex was well supported by UV-vis absorbance measurements, nuclear magnetic resonance titration experiments, and density functional theory (DFT) calculations. The method was applied to the regio- and stereo-selective arylation of various N-heterocycles under mild conditions, yielding an assembly of challengingly linked heteroaryl–(hetero)aryl products. Remarkably, the meaningful couplings of bioactive molecules with structurally complex drugs or agricultural pharmaceuticals were achieved to display favorable in vitro antitumor activities, which will be of great value in academia or industry.

General photoactivation of EDA complexes between arylsulfonium salts and 1,4-diazabicyclo[2.2.2]octane was discovered. This practical mode enables the generation of aryl radicals for C–H functionalization of arenes.     

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.     

圆柱类构件超声相控阵聚焦模型

在利用平面相控阵超声换能器对圆柱类构件检测时,受曲界面结构引起的入射波和回波时延的影响,扫描声束的波阵面产生弯曲,在利用传统迭代遍历算法计算延迟时间时效率低,无法发挥相控阵换能器检测优势。针对上述问题,建立了一种基于圆柱类构件特征和耦合介质特性的超声相控阵扫描成像的聚焦模型。该模型基于换能器、耦合介质、圆柱类构件材料特性和几何关系以及声线模型和折射定律,建立了耦合介质及被检构件的声速、曲面曲率半径、阵列与曲面间的距离等关联的延迟时间聚焦控制模型。利用该模型计算出的延时时间,对各阵元发射时间进行控制,从而实现扫描声束的聚焦。该文以液体和有机玻璃制介质楔块为例,对圆柱钢曲面的相控阵声束聚焦进行了仿真实验,结果表明该文方法计算效率显著提升,同时声束可以在预设位置实现聚焦,验证了该模型在计算效率上的优势与有效性。