基于TOPSIS模型的机场网络节点重要度评估

在机场网络中单个机场节点的失效往往会对其他的节点产生影响,特别是关键节点的失效会波及整个网络.准确客观的识别重要节点机场关乎整个机场网络的安全运营.本文分析了机场网络拓扑特性中的度、集聚系数和接近度指标,考虑了机场旅客吞吐量和所在城市人口等交通经济特性指标,使用熵权法确定权重的基础上,应用TOPSIS法构建综合评价体系模型,最后以华东地区机场网络为例进行节点重要度排序.结果表明与单一指标的评估结果相比,该方法更加全面客观的确定不同属性指标的权重,避免了不同指标取值的差异性,使评价更加全面,更符合机场网络实际运营情况.     

Sentinel-2卫星的多光谱轻量级船舶目标检测算法

近年来深度卷积神经网络在可见光船舶检测方面取得了显著的进展，然而，大多数相关研究是通过改进大型的网络结构来提高检测性能，因此加大了对更高计算机性能的需求。此外，可见光图像难以在云、雾、海杂波、黑夜等复杂场景检测到船舶。针对以上问题，提出了一种融合红（red, R）、绿（green, G）、蓝（blue, B）和近红外（NIR）4个波段光谱信息的由粗到精细的轻量型船舶检测算法。与现有的方法中根据光谱特性利用水体检测算法提取水体区域不同之处是该算法是利用改进的水体检测算法来提取船舶候选区域。为获取更准确的候选区域，对船舶、厚云、薄云、平静海面、杂波海面5种场景中4个波段的像素值进行了统计分析，选取近红外大于阈值作为辅助判断，并以其中心点获取候选区域32×32大小的切片，并对切片进行非极大值抑制，由此获得了船舶粗检测结果。随后构建了轻量级LSGFNet网络对船舶候选区域切片进行精细识别。构建的网络融合了1×1卷积提取的波谱特征与3×3的提取几何特征，为防止光谱特征与几何特征的信息在融合时“信息不流通”，在LSGFNet网络中引入了ShuffleNet中的通道打乱机制，并减小了模型结构，与典型的轻量级网络相比具有更好的效果且模型较小。最后，利用Sentinel-2卫星多光谱10 m分辨率数据构建了512×512大小的1 120组数据进行粗检测，以及32×32大小的6 014组数据进行精细网络训练，其中候选区域粗提取的查全率为98.99%，精细识别网络精确度为96.04%，不同场景下的平均精确度为92.98%。实验表明该算法在抑制云层、海浪杂波等干扰的复杂背景下具有较高的检测效率，且训练时间短、计算机性能需求低。     

Stimulation of Microvascular Networks on Sulfonated Polyrotaxane Surfaces with Immobilized Vascular Endothelial Growth Factor

Modulation of material properties and growth factor application are critical in constructing suitable cell culture environments to induce desired cellular functions. Sulfonated polyrotaxane (PRX) surfaces with immobilized vascular endothelial growth factors (VEGFs) are prepared to improve network formation in vascular endothelial cells. Sulfonated PRXs, whereby sulfonated α‐cyclodextrins (α‐CDs) are threaded onto a linear poly(ethylene glycol) chain capped with bulky groups at both terminals, are coated onto surfaces. The molecular mobility of sulfonated PRX surfaces is modulated by tuning the number of threading α‐CDs. VEGF is immobilized onto surfaces with varying mobility. Low mobility and VEGF‐immobilization reinforce cell proliferation, yes‐associated protein activity, and rhoA, pdgf, ang‐1, and pecam‐1 gene expression. Highly mobile surfaces and soluble VEGF weakly affect these cell responses. Network formation is strongly stimulated in vascular endothelial cells only on low‐mobility VEGF‐immobilized surfaces, suggesting that molecular mobility and VEGF immobilization synergistically control cell function.     

The Scattering Problem in PT‐Symmetric Periodic Structures of 1D Two‐Material Waveguide Networks

A PT‐symmetric periodic structure with two‐material waveguide networks is constructed. In this study, how changing the number of cells affects the transmission properties is investigated. The results show that the PT‐unbroken (broken) region of the system is only determined by the cell structure, regardless of the number of unit cells. This means that any system has the same exceptional points (EPs), regardless of the number of cells and as long as the cell structure is consistent. In addition, it is confirmed that the coherent perfect absorbers and lasers (CPA lasers) occur in our model. The transfer matrix method is used to derive a sufficient condition for achieving the CPA laser point. A simple, effective formula for predicting the CPA laser state in an N unit cell system is derived.     

Origin of the Photoinduced Geometrical Change of Copper(I) Complexes from the Quantum Chemical Topology View

Copper(I) complexes (CICs) are of great interest due to their applications as redox mediators and molecular switches. CICs present drastic geometrical change in their excited states, which interferes with their luminescence properties. The photophysical process has been extensively studied by several time-resolved methods to gain an understanding of the dynamics and mechanism of the torsion, which has been explained in terms of a Jahn–Teller effect. Here, we propose an alternative explanation for the photoinduced structural change of CICs, based on electron density redistribution. After photoexcitation of a CIC (S₀→S₁), a metal-to-ligand charge transfer stabilizes the ligand and destabilizes the metal. A subsequent electron transfer, through an intersystem crossing process, followed by an internal conversion (S₁→T₂→T₁), intensifies the energetic differences between the metal and ligand within the complex. The energy profile of each state is the result of the balance between metal and ligand energy changes. The loss of electrons originates an increase in the attractive potential energy within the copper basin, which is not compensated by the associated reduction of the repulsive atomic potential. To counterbalance the atomic destabilization, the valence shell of the copper center is polarized (defined by ∇²ρ(r) and ∇²V_ne(r)) during the deactivation path. This polarization increases the magnitude of the intra-atomic nuclear–electron interactions within the copper atom and provokes the flattening of the structure to obtain the geometry with the maximum interaction between the charge depletions of the metal and the charge concentrations of the ligand.     

单重工作休假M/M/c排队驱动的流体模型分析

该文在M/M/c排队驱动系统中加入工作休假策略,研究了单重工作休假多服务台排队驱动的流体模型.利用拟生灭过程和矩阵几何解法得到驱动系统稳态队长分布.构建净输入率结构,导出流体模型的稳态联合分布函数满足的的矩阵微分方程组,进而利用Laplace-Stieltjes变换(LST)方法得到稳态下缓冲器库存量的空库概率及均值表达式.最后,给出模型在多信道无线Mesh网下的应用,通过数值例子展示参数变化对系统性能指标的影响.     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.