可自修复的高延性混凝土(ECC)在机场道面的适用性分析

在飞机荷载与环境荷载长期共同作用下,机场道面易出现裂缝、接缝和竖向位移类病害,使运营阶段维修成本高.提出将具有自修复功能的高延性水泥基材料(ECC)用于机场道面,以控制裂缝发展、实现无缝连接;并利用ECC因二次反应而使微裂缝愈合的特性,减少路面维护工作,延长使用寿命.通过试验手段研究了ECC材料应用于机场道面的力学行为、自修复行为和早期开裂潜能.试验结果表明:机场道面用ECC材料在拉伸荷载作用下,其拉伸应变可达3.67％(约为普遍混凝土的400倍);在弯曲荷载作用下,其竖向弯曲变形可达6.33 mm;抗压强度与弯曲强度分别为43.9 MPa和12.68 MPa,可满足机场道面的使用要求.在受约束条件下,ECC表现出较低的早期开裂潜能.由于自修复能力,裂损ECC的拉伸刚度、拉伸应变与拉伸强度几乎可恢复至未裂时的程度;其水渗透系数随自修复进程逐渐降低,最终达到未裂时的渗透水平.研究结果表明ECC材料用于无缝机场道面具有较高适用性.     

独蒜兰优质种苗快速培育研究

建立独蒜兰优质种苗快繁体系,为实现独蒜兰植物工业化育苗、保护野生植物资源和发展人工育种技术提供新的途径.本研究从不同基本培养基对独蒜兰种子萌发影响、丛生芽诱导增殖培养和无根苗生根壮苗等方面开展研究.筛选出影响独蒜兰种子萌发的最佳基本培养基为B5,培养30 d统计种子萌发率为92％;丛生芽诱导增殖的最佳培养基为B5+TDZ 2.0 mg/L+KT 0.5 mg/L+200.0 mg/L肌醇+2.0 g/L活性炭+20.0 g/L蔗糖,培养60 d丛生芽率诱导率为98.00％,平均产生丛生芽数为6.80个;最佳生根壮苗培养基为1/2MS+IBA 0.2 mg/L+6-BA 0.1 mg/L+2.0 g/L活性炭+30.0 g/L土豆泥+20.0 g/L蔗糖,培养60 d后统计生根率为100％,平均生根数为3.20条,平均鲜重为21.11 g.     

A New Method of Portfolio Optimization Under Cumulative Prospect Theory

In this paper,the portfolio selection problem under Cumulative Prospect Theory(CPT) is investigated and a model of portfolio optimization is presented.This model is solved by coupling scenario generation techniques with a genetic algorithm.Moreover,an Adaptive Real-Coded Genetic Algorithm(ARCGA) is developed to find the optimal solution for the proposed model.Computational results show that the proposed method solves the portfolio selection model and that ARCGA is an effective and stable algorithm.We compare the portfolio choices of CPT investors based on various bootstrap techniques for scenario generation and empirically examine the effect of reference points on investment behavior.     

A Novel Routing Method for Social Delay-Tolerant Networks

The lack of continuous connectivity and a complete path from source to destination makes node communication quite difficult in Delay-Tolerant Networks(DTNs). Most studies focus on routing problems in idealized network environments without considering social properties. Communication devices are carried by individuals in many DTNs; therefore, DTNs are unique social networks to some extent. To design efficient routing protocols for DTNs, it is important to analyze their social properties. In this paper, a more accurate and comprehensive metric for detecting the quality of the relationships between nodes is proposed, by considering the contact time, contact frequency, and contact regularity. An overlapping hierarchical community detection method is designed based on this new metric, and a tree structure is built. Furthermore, we exploit the overlapping community structure and the tree structure to provide message-forwarding paths from the source node to the destination node.The simulation results show that our Routing method based on Overlapping hierarchical Community Detection(ROCD) achieves better delivery rate than SimBet and Bubble Rap, the classic routing protocols, without affecting the average delay.     

统一碳税和差异化碳税下的再制造绩效评价

分别在无碳税、统一碳税和差异化碳税三种政策下,构建了垄断制造商的利润最大化决策模型,研究实施不同碳税政策对制造商的再制造生产决策、碳排放和社会福利的影响.研究表明:相比于无碳税政策,政府征收碳税减少了制造商的利润和碳排放,在一定条件下有利于提高社会福利;实施碳税政策有利于鼓励更多类型的制造商参与再制造,并且差异化碳税比统一碳税具有更大的灵活性.进一步地,结合理论比较和数值试验,研究了政府在统一碳税的基础上分别做单向和双向差异化碳税调整后的绩效变化情况,结论表明:统一碳税与差异化碳税不存在绝对占优的碳税形式.政府应根据再制造品特点、单位环境污染损失、统一碳税基准水平和调整幅度等特点,采取合理的碳税调整方式以优化社会福利.     

基于鲸鱼优化算法的类图重构研究

为了提高软件质量,组合使用重构技术、软件度量和元启发式搜索可以有效改进软件的结构而不影响其功能.本文提出一种基于鲸鱼优化算法的类图重构方法,并结合耦合、继承、抽象3个指标所构建的质量模型来指导类图重构序列寻优.在6个不同的开源程序上使用鲸鱼优化方法对类图进行重构,研究结果表明:基于鲸鱼优化算法的类图重构在质量增益上优于模拟退火算法和爬山算法,可有效提高重构质量.     

2021年经济景气形势监测、分析和预测

我国经济增长在遭遇前所未有的疫情冲击后进入为期一年的超常快速扩张期，并于2021年2月形成扩张高峰，此后转入经济周期收缩阶段。2020年11月至2021年7月经济运行已恢复至“正常”景气区间，但需求端的恢复弱于供给端。2021年3季度的综合警情指数明显下滑，发出“偏冷”预警信号，且4季度可能继续下行,但物价总体保持稳定。预计全年GDP增长8.1%左右（两年平均增长5.2%左右），全年CPI上涨0.9%左右。建议宏观调控应做好跨周期设计，注意处理好稳增长、防风险和节能环保的关系，提高疫情应对的精准性，适度加大稳增长力度，努力保持经济在合理区间的平稳运行。     

基于观点动力学的水库移民文化适应机制研究

从观点动力学的全新视角出发,首先厘清非自愿移民文化适应概念框架,然后据此构建出“社会心理建模+复杂网络分析+多Agent仿真”的文化演化计算模型,最后通过模拟实验量化分析了水库移民文化适应的影响因素和发生机制。结果表明:移民身份认同中的文化容忍度和易感性能加速文化适应进程;扩大移民在安置区的社交范围和精英移民的引导,能辅助其与原住民间的文化交互;政策满意度比文化环境的营造更为重要,能通过调节文化冲击方式来改变移民文化观念的更替速率,进而影响移民文化适应过程的平稳性与极化趋势。     

基于特征选取与树状Parzen估计的入侵检测

针对目前网络空间安全形势快速变化带来的新风险和新挑战, 提出一种基于相关性分析的特征选取和树状Parzen估计优化的入侵检测方法。首先, 通过基于相关性分析的数据特征选取方法对数据维度进行压缩。其次, 对原始数据集进行特征筛选, 生成新的特征子集。最终, 使用序列模型优化算法中的树状Parzen估计算法对随机森林算法进行模型优化。实验结果表明, 相比其他应用机器学习算法的入侵检测方法, 所提方法在提升综合性能的同时拥有更高的检测效率, 有效地提升了入侵检测技术的实用性。     

Variable Selection via Generalized SELO-Penalized Cox Regression Models

The seamless-L_0(SELO) penalty is a smooth function that very closely resembles the L_0 penalty, which has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection. In this paper, the authors first generalize the SELO penalty to a class of penalties retaining good features of SELO, and then develop variable selection and parameter estimation in Cox models using the proposed generalized SELO(GSELO) penalized log partial likelihood(PPL) approach. The authors show that the GSELO-PPL procedure possesses the oracle property with a diverging number of predictors under certain mild, interpretable regularity conditions. The entire path of GSELO-PPL estimates can be efficiently computed through a smoothing quasi-Newton(SQN) with continuation algorithm. The authors propose a consistent modified BIC(MBIC) tuning parameter selector for GSELO-PPL, and show that under some regularity conditions, the GSELOPPL-MBIC procedure consistently identifies the true model. Simulation studies and real data analysis are conducted to evaluate the finite sample performance of the proposed method.