世界一流科技社团发展现状国际比较——基于实践动态的视角

 以世界一流科技社团2019年9月-2020年6月的新闻数据，分析总结了世界一流科技社团的实践动态及发展规律。研究发现：（1）国内科技社团强调党建引领，制度优势体现出强大的凝聚力；（2）国外科技社团更加善于聚合公共资源，并将产生的新知识转化为可供应用的产品和服务，推进科技与经济、创新链与产业链的融合；（3）国外科技社团善于通过多种形式发声，充分发挥决策者智库咨询的作用；（4）全球科技治理的实质性活动由国际化大型科技社团主导。中国科技社团需要重视决策咨询智库建设、为政府重大决策制定提供技术支撑；完善学术交流平台、创造有利于科技工作者的良好生态；为科技经济融合贡献智慧，推动互惠共享的国际科技交流合作。     

基于智能影像基因组学技术的阿尔茨海默病预测进展

 阿尔茨海默病（AD）起病隐匿，目前尚无有效控制病情进展以及治疗的药物或方法。在其发病轻度认知功能障碍阶段早期预测进行干预，则能有效控制其病程。从基于脑影像组学特征的AD早期临床诊断、基于人工智能影像组学技术的AD早期预测两个方面综述了AD的早期诊断与预测研究进展，提出结合多模脑核磁共振影像特征和组学特征，在深度学习的框架下，将影像学和基因组学联系在一起，构造高分类与预测性能的深度学习模型，可为AD早期筛查并干预提供支持。     

三维AoA目标跟踪的二次约束卡尔曼滤波算法

在基于到达角(angle of arrival, AoA)的三维目标跟踪中, 伪线性卡尔曼滤波具有稳定性高和计算复杂度低的优点, 但是严重的偏差问题使其跟踪精度迅速下降。针对该问题, 提出一种二次约束卡尔曼滤波(quadratic constraint Kalman filter, QCKF)算法。首先引入涉及所有观测噪声项的增广矩阵, 然后建立与线性卡尔曼滤波等价的目标函数并且附加含有二次项的约束条件, 以此降低偏差影响, 实现更准确的状态更新。QCKF算法采用广义特征值分解求解约束优化问题, 无法直接通过状态更新表达式推导其协方差矩阵, 因此利用约束条件以及矩阵扰动方法完成协方差矩阵更新。仿真分析表明, QCKF算法相较于其他非线性滤波算法具有更优的跟踪性能, 不仅在低噪声条件下可达到后验克拉美罗下界, 而且当噪声严重时能够显著降低跟踪误差, 并且计算开销不高。     

基于改进Lagrange乘子法的交通信号配时优化研究

为提高交叉口的机动车通行效率及环境效益,采用改进罚参数来构造一种新的Lagrange乘子法对交叉路口的交通信号进行优化配时。通过权重系数建立车辆延误与尾气排放的数学模型,利用改进Lagrange乘子法进行优化,将其结果与两种典型智能算法的优化结果进行对比,并利用VISSIM(Verkehr in Stadten Simulation)微观交通可视化仿真软件进行验证。实验结果表明,该方法优化的信号配时使车辆延误降低19.89%,尾气排放量降低2.379%,可见大比例优化了交叉口的车辆延误,同时可以降低尾气排放量。     

基于蓝牙的多功能台灯控制系统设计

针对台灯的个性化和智能化，设计了一套基于蓝牙的多功能台灯控制系统.首先用光敏传感器检测光照强度，用红外感应传感器检测是否有人，用超声波传感器检测使用者与台灯之间的距离，然后将这些检测信号传输到单片机，单片机将信号与设定值进行对比分析，再判断台灯的开/关状态，调节灯光亮度，对使用者的坐姿和使用时间作出语音提醒等.同时，相应参数实时显示在OLED屏和蓝牙终端，使用者通过蓝牙终端可以对台灯进行控制.与蓝牙的联机测试结果显示，系统功能强大，运行稳定.     

模具结构对连续纤维增强热塑性复合材料性能的影响

采用自行设计的两种不同结构的熔融浸渍模具制备了连续玻璃纤维增强聚丙烯预浸带，测试了模具结构对预浸带的孔隙率、纤维断裂率、界面形貌、纤维分散均匀度和拉伸强度的影响，建立了纤维浸渍模型和纤维断裂模型，并通过理论模型对预浸带的孔隙率和断裂率进行理论预测。结果表明，本文建立的数学模型能够有效预测预浸带的浸渍程度和纤维断裂率，可用于浸渍模具结构的优化设计；在本文范围内，与波浪形模具相比，斜齿形模具的多楔形区结构可以有效地降低预浸带孔隙率和提升纤维分散程度；波浪形模具的流道圆角半径较大，楔形区个数较少，与斜齿形模具相比，可有效降低纤维断裂率并提升拉伸性能。     

Improved hemimorphite flotation using xanthate as a collector with S(Ⅱ) and Pb(Ⅱ) activation

The flotation of hemimorphite using the S(Ⅱ)–Pb(Ⅱ)–xanthate process,which includes sulfidization with sodium sulfide,activation by lead cations,and subsequent flotation with xanthate,was investigated.The flotation results indicated that hemimorphite floats when the S(Ⅱ)–Pb(Ⅱ)–xanthate process is used; a maximum recovery of approximately 90% was obtained.Zeta-potential,contact-angle,scanning electron microscopy–energy-dispersive spectrometry(SEM–EDS),and diffuse-reflectance infrared Fourier transform spectroscopy(DRIFTS) measurements were used to characterize the activation products on the hemimorphite surface and their subsequent interaction with sodium butyl xanthate(SBX).The results showed that a Zn S coating formed on the hemimorphite surface after the sample was conditioned in an Na2 S solution.However,the formation of a Zn S coating on the hemimorphite surface did not improve hemimorphite flotation.With the subsequent addition of lead cations,Pb S species formed on the mineral surface.The formation of the Pb S species on the surface of hemimorphite significantly increased the adsorption capacity of SBX,forming lead xanthate(referred to as chemical adsorption) and leading to a substantial improvement in hemimorphite flotation.Our results indicate that the addition of lead cations is a critical step in the successful flotation of hemimorphite using the sulfidization–lead ion activation–xanthate process.     

Bidirectional Feedback Dynamic Particle Filter with Big Data for the Particle Degeneracy Problem

Particle Filter(PF) is a data assimilation method to solve recursive state estimation problem which does not depend on the assumption of Gaussian noise, and is able to be applied for various systems even with non-linear and non-Gaussian noise. However, while applying PF in dynamic systems, PF undergoes particle degeneracy,sample impoverishment, and problems of high computational complexity. Rapidly developing sensing technologies are providing highly convenient availability of real-time big traffic data from the system under study like never before. Moreover, some sensors can even receive control commands to adjust their monitoring parameters. To address these problems, a bidirectional dynamic data-driven improvement framework for PF(B3 DPF) is proposed.The B3 DPF enhances feedback between the simulation model and the big traffic data collected by the sensors,which means the execution strategies(sensor data management, parameters used in the weight computation,resampling) of B3 DPF can be optimized based on the simulation results and the types and dimensions of traffic data injected into B3 DPF can be adjusted dynamically. The first experiment indicates that the B3 DPF overcomes particle degeneracy and sample impoverishment problems and accurately estimates the state at a faster speed than the normal PF. More importantly, the new method has higher accuracy for multidimensional random systems.In the rest of experiments, the proposed framework is applied to estimate the traffic state on a real road network and obtains satisfactory results. More experiments can be designed to validate the universal properties of B3 DPF.