Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.     

How can the Cross-Link Adducts Formed by Novel Trans Platinum Drug be Influenced by Hydrogen Bond

A systematic quantum chemical characterization of intrinsic structure, energies and spectral properties of all the studied cross-link adducts formed by the novel trans platinum with thiazole ligand has been carried out at B3LYP/6-31G^＊ level of theory with the Lanl2dz pseudo potential basis set for the Pt atom. Special attention has been paid to the relative stability of these complexes and the factors that probably alter the order of the relative stability. The important influence of hydrogen bond on the structures, the energies and the spectral property was revealed. Other factors that contribute to relative stability including solvation effect, entropy and electronic delocalization energy were taken into account. The stability energy of the whole complex, and the interaction energy between two purine bases and the [Pt-（NH3）thiazole]^2＋ group were adopted to study the interplay among subsystems and their contribution to relative stability of all the studied cross-link model. Finally, basic spectral properties of these complexes including H（8） chemical shifts of all the studied complexes and the VCD （vibrational circular dichroism） spectra of two pairs of GG chelate enantiomers, were provided in order to define the structure of the most possible duplex bearing novel trans platinum drug lesions.     

融合课程思政的“自动化综合实验”教学探索

为将思政教育融入课程教学中,培养德能兼备的自动化专业优秀人才。在《自动化综合实验》课程中,首先明确提出了教学的专业目标和思政目标,然后将教学环节分为课程介绍、选题汇报、学生实践以及项目展示和答辩环节,挖掘出丰富的思政元素并融入到课程教学的每个环节,并选取典型案例,通过思维导图法剖析了课程思政的融入策略,最后介绍了课程思政考核模式和取得的初步成效。该课程思政教学模式可以为“专业实验课程-思政教育体系”的打造提供参考。     

Entanglement complexity of lattice ribbons

We consider a discrete ribbon model for double-stranded polymers where the ribbon is constrained to lie in a three-dimensional lattice. The ribbon can be open or closed, and closed ribbons can be orientable or nonorientable. We prove some results about the asymptotic behavior of the numbers of ribbons withn plaquettes, and a theorem about the frequency of occurrence of certain patterns in these ribbons. We use this to derive results about the frequency of knots in closed ribbons, the linking of the boundary curves of orientable closed ribbons, and the twist and writhe of ribbons. We show that the centerline and boundary of a closed ribbon are both almost surely knotted in the infinite-n limit. For an orientable ribbon, the expectation of the absolute value of the linking number of the two boundary curves increases at least as fast as n, and similar results hold for the twist and writhe.     

Robustness Evaluation of the Open Source Product Community Network Considering Different Influential Nodes

With the rapid development of Internet technology, the innovative value and importance of the open source product community (OSPC) is becoming increasingly significant. Ensuring high robustness is essential to the stable development of OSPC with open characteristics. In robustness analysis, degree and betweenness are traditionally used to evaluate the importance of nodes. However, these two indexes are disabled to comprehensively evaluate the influential nodes in the community network. Furthermore, influential users have many followers. The effect of irrational following behavior on network robustness is also worth investigating. To solve these problems, we built a typical OSPC network using a complex network modeling method, analyzed its structural characteristics and proposed an improved method to identify influential nodes by integrating the network topology characteristics indexes. We then proposed a model containing a variety of relevant node loss strategies to simulate the changes in robustness of the OSPC network. The results showed that the proposed method can better distinguish the influential nodes in the network. Furthermore, the network’s robustness will be greatly damaged under the node loss strategies considering the influential node loss (i.e., structural hole node loss and opinion leader node loss), and the following effect can greatly change the network robustness. The results verified the feasibility and effectiveness of the proposed robustness analysis model and indexes.     

Joint Detection of Community and Structural Hole Spanner of Networks in Hyperbolic Space

Community detection and structural hole spanner (the node bridging different communities) identification, revealing the mesoscopic and microscopic structural properties of complex networks, have drawn much attention in recent years. As the determinant of mesoscopic structure, communities and structural hole spanners discover the clustering and hierarchy of networks, which has a key impact on transmission phenomena such as epidemic transmission, information diffusion, etc. However, most existing studies address the two tasks independently, which ignores the structural correlation between mesoscale and microscale and suffers from high computational costs. In this article, we propose an algorithm for simultaneously detecting communities and structural hole spanners via hyperbolic embedding (SDHE). Specifically, we first embed networks into a hyperbolic plane, in which, the angular distribution of the nodes reveals community structures of the embedded network. Then, we analyze the critical gap to detect communities and the angular region where structural hole spanners may exist. Finally, we identify structural hole spanners via two-step connectivity. Experimental results on synthetic networks and real networks demonstrate the effectiveness of our proposed algorithm compared with several state-of-the-art methods.     

The effects of PMD and PDL on the performance of OOK and DPSK systems

Using multiple importance sampling （MIS） technique, the optical communication system performance affected by polarization-mode dispersion （PMD） and polarization-dependent loss （PDL） is simulated numerically. The power penalties and system outage probabilities are obtained for optical communication systems with on-off keying （OOK） or differential phase shift keying （DPSK） modulation. The simulation results show that DPSK always performs better than OOK. When the system has moderate PDL （about 1.5 dB）, it is necessary to compensate effectively the fiber PMD in order to enhance the system tolerance to the PMD and PDL. However, the efficiency of PMD compensation will degrade rapidly when the PMD of the whole optical link drops into the low value.     

基于流量工程的自动交换光网络新路由协议研究

为了研究基于流量工程的自动交换光网络(ASON)路由协议,采用基于源标签交换路由(LSR)路由泛供、仅广播链路可用带宽的增减的方法,得到了该协议能使LSR根据准确的带宽信息和波长可用信息选择路由的结果.结果表明,当链路中每个节点信息负荷量较小时,该协议拥有最小的链路阻塞率;与传统的路由机制相比,它能减少4～7倍的控制开销.     

A Useful Criterion on Studying Consistent Estimation in Community Detection

In network analysis, developing a unified theoretical framework that can compare methods under different models is an interesting problem. This paper proposes a partial solution to this problem. We summarize the idea of using a separation condition for a standard network and sharp threshold of the Erdös–Rényi random graph to study consistent estimation, and compare theoretical error rates and requirements on the network sparsity of spectral methods under models that can degenerate to a stochastic block model as a four-step criterion SCSTC. Using SCSTC, we find some inconsistent phenomena on separation condition and sharp threshold in community detection. In particular, we find that the original theoretical results of the SPACL algorithm introduced to estimate network memberships under the mixed membership stochastic blockmodel are sub-optimal. To find the formation mechanism of inconsistencies, we re-establish the theoretical convergence rate of this algorithm by applying recent techniques on row-wise eigenvector deviation. The results are further extended to the degree-corrected mixed membership model. By comparison, our results enjoy smaller error rates, lesser dependence on the number of communities, weaker requirements on network sparsity, and so forth. The separation condition and sharp threshold obtained from our theoretical results match the classical results, so the usefulness of this criterion on studying consistent estimation is guaranteed. Numerical results for computer-generated networks support our finding that spectral methods considered in this paper achieve the threshold of separation condition.     

Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data

Many similarity measure algorithms of nodes in weighted graph data have been proposed by employing the degree of nodes in recent years. Despite these algorithms obtaining great results, there may be still some limitations. For instance, the strength of nodes is ignored. Aiming at this issue, the relative entropy of the distance distribution based similarity measure of nodes is proposed in this paper. At first, the structural weights of nodes are given by integrating their degree and strength. Next, the distance between any two nodes is calculated with the help of their structural weights and the Euclidean distance formula to further obtain the distance distribution of each node. After that, the probability distribution of nodes is constructed by normalizing their distance distributions. Thus, the relative entropy can be applied to measure the difference between the probability distributions of the top d important nodes and all nodes in graph data. Finally, the similarity of two nodes can be measured in terms of this above-mentioned difference calculated by relative entropy. Experimental results demonstrate that the algorithm proposed by considering the strength of node in the relative entropy has great advantages in the most similar node mining and link prediction.