概率语言多属性群决策方法及其在新型智慧城市市民获得感评价中的应用

针对公众参与的语言信息多属性群决策问题，研究了考虑参与者满意度的概率语言多属性群决策方法。首先，根据参与者的语言评价信息确定并规范化概率语言决策矩阵。然后，对大群体进行共识分析，由最大化参与者群体的满意度构建线性规划模型，确定参与者群组的权重；构造正、负理想方案的评价向量，构建多目标规划模型，用拉格朗日乘子法求解属性权重；定义各方案的加权贴近度，并以此对方案进行排序和优选。最后，通过新型智慧城市市民获得感评价案例验证了模型的可行性和有效性。     

石墨基液流电池复合双极板

液流电池是一种安全性高、使用寿命长、可扩展的大规模储能系统,可以协助电网调峰储能,提高能源利用率,发展前景广阔。双极板是液流电池的重要组成部分。功能上起到了分隔、串联电池、传导电流、为电堆提供结构支撑等作用。从成本构成角度看,双极板的价格占电堆成本的比重也较大。开发高性能、低成本的双极板对加快液流电池的商业化应用具有重要意义,也是目前业界的迫切需求。虽然文献上报道了许多针对液流电池双极板开发的工作,但是目前高性能、低成本的液流电池双极板产品仍无法充分满足市场需求。本文着重介绍了石墨基复合双极板的研究现状,介绍了材料选择、工艺流程对关键性能的影响,对相关工作进行了评述,并为液流电池双极板的开发提出了建议。     

Acridone-Based Host Materials for Green Phosphorescent and Thermally Activated Delayed Fluorescent OLEDs with Low-Efficiency Roll-Offs

By linking the carbazole unit to the nitrogen atom of acridone through phenyl or pyridyl, two compounds, named 10-(4-(9H-carbazol-9-yl)phenyl)acridin-9(10H)-one (AC-Ph-Cz) and 10-(5-(9H-carbazol-9-yl)pyridin-2-yl)acridin-9(10H)-one (AC-Py-Cz) were designed and synthesized. These two materials, characterized with highly twisted and rigid structure, good thermal stability, and balanced carrier-transporting properties, were employed as host materials for green phosphorescent and thermally activated delayed fluorescent organic light-emitting diodes (OLEDs). The carbazole group, despite its small contribution to the highest occupied molecular orbitals (HOMOs) of these two materials, plays an essential role as an intramolecular host in energy delivering and improving the hole transporting ability of these two hosts. The incorporation of the electron-deficient pyridyl group as a linking group slightly improves the electron transporting capability of AC-Py-Cz. The green phosphorescent OLED (PhOLED) based on AC-Py-Cz exhibited excellent device performance with a turn-on voltage of 2.5 V, a maximum power efficiency and an external quantum efficiency (η_ext) of 89.8 lm W⁻¹ and 25.2 %, respectively, benefitting from the better charge-balancing ability of AC-Py-Cz host due to the presence of the pyridyl bridge. More importantly, all the devices based on these two hosts showed low efficiency roll-off at high brightness due to the suppressed non-radiative transition in the emitting layer. In particular, the AC-Py-Cz-hosted green PhOLED exhibited an efficiency roll-off of 1.6 % from the maximum next at a high brightness of 1000 cd m⁻² and a roll-off of 15.9 % at an extremely high brightness of 10000 cd m⁻². This study manifests that acridone-based host materials have great potential in fabricating OLEDs with low efficiency roll-off.     

The character and the wave front set correspondence in the stable range

We relate the distribution characters and the wave front sets of unitary representation for real reductive dual pairs of type I in the stable range.     

On generalized perfect difference sets constructed from Sidon sets

Let $\mathbb{N}$ be the set of positive integers. For a nonempty set A of integers and every integer u, denote by $d_A(u)$ the number of $(a,a^{\prime })$ with $a,a^{\prime }\in A$ such that $u=a-a^{\prime }$. For a sequence S of positive integers, let $S(x)$ be the counting function of S. The set $A\subseteq \mathbb{N}$ is called a perfect difference set if $d_A(u)=1$ for every positive integer u. In 2008, Cilleruelo and Nathanson (2008) [4] constructed dense perfect difference sets from dense Sidon sets. In this paper, as a main result, we prove that: let $f:\mathbb{N}\to \mathbb{N}$ be an increasing function satisfying $f(n)\ge 2$ for any positive integer n, then for every Sidon set B and every function $\omega (x)\to \infty$, there exists a set $A\subseteq \mathbb{N}$ such that $d_A(u)=f(u)$ for every positive integer u and $B(x/3)-\omega (x)\le A(x)\le B(x/3)+\omega (x)$ for all $x\ge C_{f,B,\omega }$.     

基于串联两阶段网络DEA模型的供应链规模收益分析

数据包络分析(DEA)是评价供应链系统(Supply chain system)间相对有效性的一种重要的工具,但是传统的DEA不考虑供应链的内部结构,对系统效率评价偏高;而本文所研究两阶段串联供应链系统,考虑把部分中间产品作为最终产品输出,增加额外中间投入的情形.基于所提出的供应链系统结构,本文建立相应的串联结构下的网络DEA模型,并针对所建立模型进行相关理论的研究,给出了串联结构下的生产可能集和规模收益情况判定方法.最后,进行数值实验,以验证我们提出的结论.     

Definition and detection of data-based uniqueness in evaluating bilinear (two-way) chemical measurements

Multivariate curve resolution methods, frequently used in analyzing bilinear data sets, result in ambiguous decomposition in general. Implementing the adequate constraints may lead to reduce the so-called rotational ambiguity drastically, and in the most favorable cases to the unique solution. However, in some special cases, non-negativity constraint as minimal information of the system is a sufficient condition to resolve profiles uniquely. Although, several studies on exploring the uniqueness of the bilinear non-negatively constrained multivariate curve resolution methods have been made in the literature, it has still remained a mysterious question. In 1995, Manne published his profile-based theorems giving the necessary and sufficient conditions of the unique resolution. In this study, a new term, i.e., data-based uniqueness is defined and investigated in details, and a general procedure is suggested for detection of uniquely recovered profile(s) on the basis of data set structure in the abstract space. Close inspection of Borgen plots of these data sets leads to realize the comprehensive information of local rank, and these argumentations furnish a basis for data-based uniqueness theorem. The reported phenomenon and its exploration is a new stage (it can be said fundament) in understanding and describing the bilinear (matrix-type) chemical data in general.     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.