基于二元联系数距离的不确定语言多属性决策模型

研究了属性权重范围已知,方案主观偏好值为语言变量,决策信息为不确定语言决策矩阵的多属性决策问题.在给出不确定语言变量转换为二元联系数的公式以及二元联系数距离公式的基础上,将方案主观偏好语言评价值转换为二元联系数,将不确定语言决策矩阵转换为二元联系数决策矩阵,从而得到方案的二元联系数综合属性值,通过最小化方案的二元联系数综合属性值和主观偏好值之间距离,建立多目标优化模型,并将其转换为一个单目标规划模型计算出属性权重.然后,通过对方案的二元联系数综合属性值进行不确定性分析,得到各方案的排序总数,利用排序总数对方案进行排序择优.应用实例表明该决策方法可行有效.     

Unifying darko-lepto-genesis with scalar triplet inflation

We present a scalar triplet extension of the standard model to unify the origin of inflation with neutrino mass, asymmetric dark matter and leptogenesis. In presence of non-minimal couplings to gravity the scalar triplet, mixed with the standard model Higgs, plays the role of inflaton in the early Universe, while its decay to SM Higgs, lepton and dark matter simultaneously generate an asymmetry in the visible and dark matter sectors. On the other hand, in the low energy effective theory the induced vacuum expectation value of the triplet gives sub-eV Majorana masses to active neutrinos. We investigate the model parameter space leading to successful inflation as well as the observed dark matter to baryon abundance. Assuming the standard model like Higgs mass to be at 125–126 GeV, we found that the mass scale of the scalar triplet to be ?O(10⁹) GeV$?O({10}^9)\text{GeV}$ and its trilinear coupling to doublet Higgs is ?0.09 so that it not only evades the possibility of having a metastable vacuum in the standard model, but also lead to a rich phenomenological consequences as stated above. Moreover, we found that the scalar triplet inflation strongly constrains the quartic couplings, while allowing for a wide range of Yukawa couplings which generate the CP asymmetries in the visible and dark matter sectors.     

非平衡电桥温度计设计中串联电阻的研究

本文探讨了串联电阻型非平衡电桥温度设计中串联电阻与桥路电流、桥臂电阻的关系,并说明了串联电阻、桥路电流及桥臂电阻间的相互影响。     

Doi-Hopf模以及其上的平坦联络

对于任意Hopf代数,在其上建立了一种新的具有非交换微分几何形式的代数结构,并论证了Doi-hopf模与具有平坦联络的模之间是同构的,其中,联络对应于我们所给的微分算子.     

Polynomial algorithm for sharp upper bound of rainbow connection number of maximal outerplanar graphs

For a finite simple edge-colored connected graph G (the coloring may not be proper), a rainbow path in G is a path without two edges colored the same; G is rainbow connected if for any two vertices of G, there is a rainbow path connecting them. Rainbow connection number, rc(G), of G is the minimum number of colors needed to color its edges such that G is rainbow connected. Chakraborty et al. (2011) [5] proved that computing rc(G) is NP-hard and deciding if rc(G)=2 is NP-complete. When edges of G are colored with fixed number k of colors, Kratochvil [6] proposed a question: what is the complexity of deciding whether G is rainbow connected? is this an FPT problem? In this paper, we prove that any maximal outerplanar graph is k rainbow connected for suitably large k and can be given a rainbow coloring in polynomial time.     

Multiscale Hybrid Convolutional Deep Neural Networks with Channel Attention

Attention mechanisms can improve the performance of neural networks, but the recent attention networks bring a greater computational overhead while improving network performance. How to maintain model performance while reducing complexity is a hot research topic. In this paper, a lightweight Mixture Attention (MA) module is proposed to improve network performance and reduce the complexity of the model. Firstly, the MA module uses multi-branch architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Secondly, in order to reduce the number of parameters, each branch uses group convolution independently, and the feature maps extracted by different branches are fused along the channel dimension. Finally, the fused feature maps are processed using the channel attention module to extract statistical information on the channels. The proposed method is efficient yet effective, e.g., the network parameters and computational cost are reduced by 9.86% and 7.83%, respectively, and the Top-1 performance is improved by 1.99% compared with ResNet50. Experimental results on common-used benchmarks, including CIFAR-10 for classification and PASCAL-VOC for object detection, demonstrate that the proposed MA outperforms the current SOTA methods significantly by achieving higher accuracy while having lower model complexity.     

大学无机化学与高中化学知识衔接——以原子结构与元素周期表为例

原子结构理论是高中化学和大学无机化学的重要组成部分和学习难点,教学过程中如何较好地衔接高中阶段知识与大学无机化学课程尤为重要。本文以原子结构和元素周期表知识点为例,以2019版高中人教版教材和北师大版的无机化学教材内容讨论高中化学知识与大学化学知识的衔接问题,依据作者的教学实践提出若干建议,为大学无机化学原子结构与元素周期表知识点的教学提供参考,同时帮助大一学生顺利完成知识的衔接过渡和培养学生独立思考、自主学习的能力。     

舱内爆炸载荷下箱型舱室角隅连接结构设计

通过有限元软件LS-DYNA建立了舱内爆炸载荷下箱型舱室动响应数值模型,并借助文献试验结果验证了数值模型的可靠性,研究了平板型、内凹型、外凸型、箭头型、箭矢型、背面弧型等6种角隅连接结构对舱内爆炸载荷下箱型舱室变形、特征位置压力和破坏模式的影响,分析了内爆效应下角隅连接结构的失效机理。数值结果表明：舱壁角隅位置是舱内爆炸载荷作用下舱室易发生破坏撕裂的特征位置;相比无连接结构,平板型连接结构对舱壁最大塑性变形改善最大,降低幅度达到了31.9%;背面弧型连接结构能够使箱型舱室角隅等效塑性应变降低约60%;设置连接结构能够改变高塑性应变的发生位置,进而改变箱型舱室的破坏模式;采用平板型、内凹型、背面弧型连接结构的箱型舱室能够有效避免角隅失效破坏。

     

Classification of Hadamard matrices of order 44 with automorphisms of order 7

The Hadamard matrices of order 44 possessing automorphisms of order 7 are classified. The number of their equivalence classes is 384. The order of their full automorphism group is calculated. These Hadamard matrices yield 1683 nonisomorphic 3-(44,22,10) designs, 57932 nonisomorphic 2-(43,21,10) designs, and two inequivalent extremal binary self-dual doubly even codes of length 88 (one of them being new).     

基于集对分析的企业技术依赖预警及其演化趋势测度

国际经济交流与区域经济协同的深化加速了企业的外部技术获取。为避免外部技术的过度依赖,尤其是与技术内化不匹配的依赖,将模糊集与集对分析进行改进并运用到技术依赖预警的研究中。借助集对分析对我国各省份规模上工业企业技术依赖同一度计算,构建各省技术依赖预警集。利用模糊隶属度联系数化改进提取技术依赖联系数并对其进行演化测度,参照集对势表剖析和诠释技术依赖预警的风险排列与演化趋势。研究结果表明,模糊隶属度补数的改进及其联系数化在对成组数据进行联系数提取时简便有效,偏联系数对演化趋势的测度准确详尽;经技术依赖预警与演化趋势测度,发现现阶段外部技术对我国自主研发的挤出效应不明显,但由于企业外部技术创造性依赖能力不足导致的外部技术内化风险凸显。