两类图的(d,1)-全标号

主要讨论了W_n与C_m的笛卡尔积和均衡完全r-部图K_r(n)的(d,1)-全标号,并得出了(d,1)-全数λ_d~T(W_n□C_m)和λ_d~T(K_(r(n)))的确切值.     

GARCH(1,1)模型的稳健估计比较及应用

首先阐述了GARCH(1,1)模型稳健估计的构造方法,然后在模型有无异常值扩散效应约束和异常值比例不同的情况下,比较了传统QMLE估计和多种稳健M估计的表现,结果表明:在数据无异常值下,QMLE估计较优;随着异常值比例增加,稳健Andrew估计表现更好;模型施加异常值扩散效应约束对估计有一定改善但不显著.最后选取波动程度不同的两个阶段沪深300指数的收益率,用模型拟合进行了实例比较,在波动程度较大时,Andrew估计效果较优,在波动相对平稳时,LAD估计较优.     

灰色GM(1,1)模型研究综述

GM(1,1)是灰色预测理论的核心模型和基础模型.从累加生成方法、初值优化、背景值优化、参数估计方法、模型性质和模型拓展的视角,梳理了GM(1,1)模型的研究进展.明确了有待进一步研究的问题,对GM(1,1)模型的未来研究方向提出了建议.     

d型工作休假的多台M/M/c排队及在HCS中的应用

在多服务台M/M/c排队系统中,引入半空竭服务的d型工作休假策略.当系统中有d个服务台空闲时,令d个空闲的服务台开始一次多重同步工作休假,休假期间的服务台继续慢速服务新到顾客,其余c-d个服务台正常工作.在工作休假期间,系统中顾客数小于等于c-d个时,一个顾客的离开是由正常工作速率服务台完成的.系统中顾客数多于c-d个时,一个服务的完成可能是接受了正常速率服务,也可能是接受了低速服务.利用拟生灭过程和矩阵几何解方法得到了稳态队长分布,给出模型在多层蜂窝系统(HCS)中的应用,并对影响系统性能指标的参数做出了数值分析.     

(∈,∈ Vq)-模糊软格

将模糊软集的概念与格相结合,引入了(∈,∈Vq)-模糊软格和(∈,∈Vq)-模糊软格的(∈,∈Vq)-模糊软子格的概念,给出了它们的若干代数性质.定义了格的模糊软同态概念,证明了(∈,∈Vq)-模糊软格在格的一个模糊软同态下的像与原像仍为(∈,∈Vq)-模糊软格的结论.     

广义行(列)酉对称矩阵的Moore-penrose逆

通过对母矩阵进行奇异值分解的方法得到广义行(列)酉对称矩阵的奇异值分解进一步得到其Moore-penrose逆;用谱分解方法得到母矩阵的Moore-penrose逆,进一步得到广义行(列)酉对称矩阵的Moore-penrose逆.     

基于改进的GM(1,1)-Markov链组合模型广东省单位GDP能耗预测

本文以灰色系统理论的GM(1,1)模型和随机过程理论的Markov链模型为基础构建了一个动态GM(1,1)-Markov链组合预测模型。该模型同时利用了GM(1,1)模型对序列趋势因素良好的拟合能力和Markov链模型对残差序列信息的提取能力。为进一步提高该模型的预测精度,用泰勒(Taylor)近似方法和新信息优先的思想对该模型进行了改进。最后,以1991-2014年广东省单位GDP能耗数据实证了该模型的预测效果。     

循环子空间的进一步应用

以向量的极小多项式作为出发点,深入分析了循环子空间的具体结构,得到了循环子空间进一步的应用.     

代数$\Omega$-范畴的等价范畴

本文基于$\Omega$-范畴研究了(连续) $\mathcal{I}$-余万备$\Omega$-范畴的一些性质. 我们给出了双完备$\Omega$-范畴和逼近双模的概念并讨论了它们的性质, 证明了任何$\mathcal{I}$-余万备$\Omega$-范畴都是双完备$\Omega$-范畴. 得到了代数$\Omega$-范畴范畴等价于双完备$\Omega$-范畴.     

碱性电解液中K3[Fe(CN)6]在锌阳极上的自发还原和吸附延长锌镍电池的循环寿命

In view of the continuously worsening environmental problems, fossil fuels will not be able to support the development of human life in the future. Hence, it is of great importance to work on the efficient utilization of cleaner energy resources. In this case, cheap, reliable, and eco-friendly grid-scale energy storage systems can play a key role in optimizing our energy usage. When compared with lithium-ion and lead-acid batteries, the excellent safety, environmental benignity, and low toxicity of aqueous Zn-based batteries make them competitive in the context of large-scale energy storage. Among the various Zn-based batteries, due to a high open-circuit voltage and excellent rate performance, Zn-Ni batteries have great potential in practical applications. Nevertheless, the intrinsic obstacles associated with the use of Zn anodes in alkaline electrolytes, such as dendrite, shape change, passivation, and corrosion, limit their commercial application. Hence, we have focused our current efforts on inhibiting the corrosion and dissolution of Zn species. Based on a previous study from our research group, the failure of the Zn-Ni battery was caused by the shape change of the Zn anode, which stemmed from the dissolution of Zn and uneven current distribution on the anode. Therefore, for the current study, we selected K₃[Fe(CN)₆] as an electrolyte additive that would help minimize the corrosion and dissolution of the Zn anode. In the alkaline electrolyte, [Fe(CN)₆]^3– was reduced to [Fe(CN)₆]^4– by the metallic Zn present in the Zn-Ni battery. Owing to its low solubility in the electrolyte, K₄[Fe(CN)₆] adhered to the active Zn anode, thereby inhibiting the aggregation and corrosion of Zn. Ultimately, the shape change of the anode was effectively eliminated, which improved the cycling life of the Zn-Ni battery by more than three times (i.e., from 124 cycles to more than 423 cycles). As for capacity retention, the Zn-Ni battery with the pristine electrolyte only exhibited 40% capacity retention after 85 cycles, while the Zn-Ni battery with the modified electrolyte (i.e., containing K₃[Fe(CN)₆]) showed 72% capacity retention. Moreover, unlike conventional organic additives that increase electrode polarization, the addition of K₃[Fe(CN)₆] not only significantly reduced the charge-transfer resistance in a simplified three-electrode system, but also improved the discharge capacity and rate performance of the Zn-Ni battery. Importantly, considering that this strategy was easy to achieve and minimized additional costs, K₃[Fe(CN)₆], as an electrolyte additive with almost no negative effect, has tremendous potential in commercial Zn-Ni batteries.