Small World Effects in a Harmonious Unifying Hybrid Preferential Model Networks

Small world effects in the harmonious unifying hybrid preferential model (HUHPM) networks are studied both numerically and analytically. The idea and method of the HUHPM is applied to three typical examples of unweighted BA model, weighted BBV model, and the TDE model, so-called HUHPM-BA, HUHPM-BBV and HUHPM-TDE networks. Comparing the HUHPM with current typical models above, it is found that the HUHPM networks has the smallest average path length and the biggest average clustering coefficient. The results demonstrate that the HUHPM is more suitable not only for the un-weighted models but also for the weighted models.     

KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.     

Investigation Driven by Network Pharmacology on Potential Components and Mechanism of DGS,a Natural Vasoprotective Combination,for the Phytotherapy of Coronary Artery Disease

Phytotherapy offers obvious advantages in the intervention of Coronary Artery Disease (CAD), but it is difficult to clarify the working mechanisms of the medicinal materials it uses. DGS is a natural vasoprotective combination that was screened out in our previous research, yet its potential components and mechanisms are unknown. Therefore, in this study, HPLC-MS and network pharmacology were employed to identify the active components and key signaling pathways of DGS. Transgenic zebrafish and HUVECs cell assays were used to evaluate the effectiveness of DGS. A total of 37 potentially active compounds were identified that interacted with 112 potential targets of CAD. Furthermore, PI3K-Akt, MAPK, relaxin, VEGF, and other signal pathways were determined to be the most promising DGS-mediated pathways. NO kit, ELISA, and Western blot results showed that DGS significantly promoted NO and VEGFA secretion via the upregulation of VEGFR2 expression and the phosphorylation of Akt, Erk1/2, and eNOS to cause angiogenesis and vasodilation. The result of dynamics molecular docking indicated that Salvianolic acid C may be a key active component of DGS in the treatment of CAD. In conclusion, this study has shed light on the network molecular mechanism of DGS for the intervention of CAD using a network pharmacology-driven strategy for the first time to aid in the intervention of CAD.     

网络化《高等数学》学习系统的设计与实现

介绍了《高等数学》课程网络化学习系统的设计思路与实现方法.系统利用网络多媒体的灵活表现形式,双向互动等功能,将传统的教学学习环节与现代多媒体网络技术进行有机的结合,使学生学习变得生动有趣且不受时间、空间的限制,为学生课后自主学习提供了良好的学习环境.     

并联换热系统中冷媒分配的优化分析

为减小并联换热系统的质量,本文在一定的简化和假设之上,建立了其流动与传热的物理和数学模型;并以系统总换热面积最小为优化目标,通过对分配系数的变量转换,提出了一种可准确求解最佳分配系数的方法,并对计算结果进行了讨论,发现分配系数的结果与势容耗散最大基本一致.根据计算结果.本文给出了支路较少时分配系数与换热支路效能之间的近似关联式.     

Scale-Free Properties in Traffic System

In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model. The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution (i.e., the scale-free distribution) and random distribution etc.     

三维网络结构NiCo2S4@NiCo2O4复合电极的制备及其性能

超级电容器具有更大的功率密度、优秀的循环稳定性、极快的充放电速度、超长的循环寿命以及环境友好等突出特点,其性能与构件关系密切,其中最根本的就是组成它的电极材料。本研究主要采用传统的水热法制备出钴酸镍(NiCo₂O₄)电极材料,进而通过离子交换(二次水热)制得镍钴硫(NiCo₂S₄),最后利用化学浴沉积(CBD)法使其与钴酸镍复合,得到最终所需的三维网络结构NiCo₂S₄@NiCo₂O₄复合电极。经过表面形貌表征、循环伏安测试、恒电流充放电测试以及比电容计算分析等可以证明:三维网络结构NiCo₂S₄@NiCo₂O₄复合电极的比电容及循环稳定性等远远优于复合前单一的纯NiCo₂O₄电极材料,具有极大应用前景。     

同轴线-矩形波导结散射特性的数值分析

 提出了一种数值分析同轴线-矩形波导结散射特性的模式匹配方法。采用同轴线和矩形波导的本征模函数表示电磁场分量,通过同轴线-矩形波导结截面横向场分量匹配获得波导结的散射参数,引入电场模式匹配矩阵的解析形式提高了计算效率。给出了基于模式匹配法数值仿真的各种同轴线-矩形波导结散射特性及仿真结果分析,并与3维全电磁波分析软件HFSS的仿真结果进行了比较,二者非常吻合。模式匹配法计算效率高,能广泛应用于微波毫米波元器件及系统结构的设计与优化。     

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5–ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein–protein interaction.     

Prisoner’s Dilemma Game with Cooperation-Defection Dominance Strategies on Correlational Multilayer Networks

As multilayer networks are widely applied in modern society, numerous studies have shown the impact of a multilayer network structure and the network nature on the proportion of cooperators in the network. In this paper, we use Barabási–Albert scale-free networks (BA) and Watts and Strogatz networks (WS) to build a multilayer network structure, and we propose a new strategy-updating rule called “cooperation-defection dominance”, which can be likened to dominant and recessive traits in biogenetics. With the newly constructed multilayer network structure and the strategy-updating rules, based on the simulation results, we find that in the BA-BA network, the cooperation dominance strategy can make the networks with different rs show a cooperative trend, while the defection dominance strategy only has an obvious effect on the network cooperation with a larger r. When the BA network is connected to the WS network, we find that the effect of strategy on the proportion of cooperators in the network decreases, and the main influencing factor is the structure of the network. In the three-layer network, the cooperation dominance strategy has a greater impact on the BA network, and the proportion of the cooperators is enhanced more than under the natural evolution strategy, but the promotion effect is still smaller than that of the two-layer BA network because of the WS network. Under the defection dominance strategy, the WS layer appears different from the first two strategies, and we conclude through simulation that when the payoff parameter is at the middle level, its cooperator proportion will be suppressed, and we deduce that the proportion of cooperators and defectors, as well as the payoff, play an important role.