Fabrication and characterization of Al-CuO nanocomposites prepared by sol-gel method

In this study, Al-CuO nanocomposites were fabricated by sol-gel method. As a contrast, the thermite was prepared by physical mixing at the equivalence ratio of 0.5, 1, 2, respectively. The intermediates and samples as prepared were characterized by SEM and XRD. The exothermic properties of the two samples prepared at different equivalence ratios were tested and the reaction products were characterized by XRD. The SEM results show that the sample prepared by the sol-gel method demonstrates a micron-sized agglomerated sphere formed by a mutual wrapping of Al NPs and CuO NPs, and the particles are evenly distributed in the agglomerate. In addition, when the content of Al powder is seriously insufficient, the heat release of the sample prepared by physical mixing is 1.6 times that of by sol-gel method. With the increase of Al powder content, the exothermic properties of Al/CuO NPs prepared by sol-gel method began to increase significantly compared with physical mixing and the difference is 1.5 times when the equivalence ratio increases to 2. It can be concluded that the reason for this result may be attributed to the different mass transfer modes of components due to the different morphologies of samples.     

RPL路由协议中基于投票法的黑洞攻击检测算法

物联网容易遭受多类拒绝服务攻击,其中黑洞攻击是最严重的攻击之一.为此,提出基于投票法的黑洞攻击检测算法(Voting Method-based Blackhole Attack Detection,VMBAD).通过VMBAD算法检测RPL协议上的黑洞攻击.先通过过滤器构建嫌疑节点,再验证嫌疑节点的行为,各节点作出自己的决策意见,并传输到边界路由(Bor-der Router,BR).接收后,BR利用投票机制融合各节点的决策意见,进行最终的决策,进而判断节点是否为黑洞攻击节点.一旦认定为黑洞攻击,就将黑洞攻击节点与网络隔离,使其不参与RPL路由.仿真结果表明,VMBAD算法能够有效地检测黑洞攻击节点,并且通过排除攻击节点,提高了数据包传递率.     

基于改进FPA和SVM的装备器材需求预测

精确预测已成为制约器材筹措决策的重难点问题.对此,在对国内外研究分析基础上,提出适用于复杂装备保障的消耗性器材需求预测方法.针对器材消耗规律,构建基于支持向量机的器材需求预测模型,并提出模型约束优化指标;针对器材需求预测约束优化的不确定性,将佳点集种群初始化、Deb可行性比较法、着约束法融入花朵授粉算法,设计面向SVM模型优化的花朵授粉求解算法;以某建制单位器材供应为例,验证方法的可行性和优越性.     

深度视觉语音生成研究进展与展望

为了进一步推进深度学习技术驱动的视觉语音生成相关科学问题的研究进展,阐述了视觉语音生成的研究意义与基本定义,并深入剖析了该领域面临的难点与挑战;在此基础上,介绍了目前视觉语音生成研究的现状与发展水平,基于生成框架的区别对近期主流方法进行了梳理、归类和评述;最后探讨视觉语音生成研究潜在的问题和可能的研究方向。     

Traffic channeling under uncertain conversion rates on e-commerce platforms

Traffic is the lifeblood of every e-commerce platform. The question of how to channel traffic to merchants operating on a platform lies at the heart of platform management. We consider a platform on which two independent merchants sell their products. Merchants compete on inventory in the sense that some of the unmet demand at one merchant will spill over to the other. The platform channels traffic based on products' conversion rates to maximize the total sale on the platform. We show that traffic channeling plays three roles. First, it allows more efficient allocation of traffic; that is, the merchant with a high conversion rate is given a higher priority in receiving traffic. Second, it allows the platform to control demand spillover between the merchants to maximize total sales. The platform either facilitates or prevents demand spillover, depending on product substitutability. Third, traffic channeling intensifies competition between the merchants and hence increases the total inventory. More efficient allocation of traffic and the increase in inventory increase sales inequality between the merchants. In contrast, demand spillover decreases sales inequality. While the platform always benefits from traffic channeling, the merchants do not benefit when their products are moderately substitutable. Interestingly, when the two products are owned and sold by the same merchant, the opposite happens–traffic channeling always benefits the merchant but may hurt the platform. Our study provides a basis for informed discussions on how platforms should channel traffic in response to conversion rates, and how traffic channeling affects the welfare of merchants and platforms.