基于DEA-ANP的快递企业绩效评价研究

在建立快递企业绩效评价模型时,提出了利用DEA和ANP相结合对快递企业绩效进行评价的方法.不同于之前研究,文中提出的方法使用ANP替换AHP,充分考虑了物流行业的"效益背反"现象.其解决了AHP确定权重时,无法体现元素之间关联性的问题,单独使用DEA评价时,无法考虑决策者偏好的弊端.首先,方法基于.ANP方法,求出每个指标的权重,再分别对每个因素使用DEA方法求出各系统的相对效率值;然后,将各指标权重和相对效率值结合,求出各快递企业中营业部的整体效率值并进行排序;最后通过实例分析,验证了此方法的实用性、可操作性及优越性.     

基于果蝇算法优化广义回归神经网络的矢量水听器的DOA估计

为了提高矢量水听器阵列对窄带信号的DOA估计精度,运用果蝇算法优化广义回归神经网络,通过对阵列协方差矩阵实值化,并提取信号子空间的基作为样本特征进行网络训练,构建了果蝇算法优化下的广义回归神经网络,实现了基于矢量水听器阵列的水下声源的DOA估计.仿真实验结果表明,方法泛化性能较好,能解决输入维数过大的问题,且运行时间短,DOA估计精度高,具有较强的工程应用价值.     

基于网络媒体信息的传染病传播模型及其仿真研究

当一种突发传染病开始流行时,政府、媒体会以各种形式告知民众,有防范意识的民众将采取一定的防范措施来降低感染率.考虑面对一种突发传染病,将易感群体划分为具有防范意识和不具有防范意识两种群体,利用生命周期理论,分析网络媒体信息报道对传染病传播的影响,以此为依据建立一种改进的传染病传播模型(MSI).利用网络大数据得到对传染病有防范意识群体的观测值信息,利用神经网络技术对模型MSI的参数进行反演.然后对模型MSI数值仿真得到传染病传播过程,提出了相应的控制措施.     

Crosslinking network structure governing particle shape and size distribution by one-step emulsion polymerization in the presence of particle coagulation

In this study, sub-200?nm, crosslinked latex particles with a narrow particle size distribution were prepared by one-step emulsion polymerization in the presence of particle coagulation. The relationship between the particle shape and particle coagulation was investigated by varying the time of crosslinking network structure formation and particle coagulation. Particles with irregular shapes such as doublet, triplet, and ellipsoid were obtained using divinylbenzene (DVB) and ethylene glycol dimethacrylate (EGDMA) as the crosslinking agents, because the crosslinking network structure of particles was formed before the particle coagulation. In contrast, latex particles with a uniform spherical shape were also prepared using triallyl isocyanurate (TAIC) or dihydrodicyclopentadienyl acrylate (DCPA) as the crosslinking agents by delaying the time of crosslinking network structure formation. Alternatively, uniform spherical latex particles were prepared by bringing forward the particle coagulation time using cationic initiator, 2, 2′-azobis (2-methylpropionamidine) dihydrochloride (AAPH). This study presents a new idea that would further broaden the application of particle coagulation in emulsion polymerization.     

Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation

In this paper, an experimental study and modeling by artificial neural networks were carried out to predict the generated microdroplet dimensionless size in a microfluidic system in order to formulate a water-in-oil emulsion. The various parameters that affect the size of microdroplets (flow rates, viscosities, surface tensions of both the two phases and the diameter of the microchannel) are studied and further grouped into dimensionless numbers; we used these numbers as input to the neural network and the dimensionless length as output. The better neural network architecture has 10 neurons in the hidden layer with a mean square error of 1.4 10^?6 and a determination’s coefficient near 1 value. The relative importance of inputs on the size of the microdroplets has been determined using the Garson algorithm and the results are in good agreement with other works.     

时滞离散网络模型的动力学分析与同步

复杂网络广泛存在于日常生活,首先,给出几类标准的网络模型;然后,利用稳定性控制方法设计并实现了具有时滞与非时滞耦合的复杂网络模型快速控制;最后,通过构造优化Lyapunov函数,讨论其模型的射影同步问题,得到了系统全局稳定的条件和有效的控制器,以实例数值验证其方法的可行性。     

Bipartite network analysis reveals metabolic gene expression profiles that are highly associated with the clinical outcomes of acute myeloid leukemia

Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR) = 1.58, p = 0.038) and in two test sets (HR = 1.69 and 1.56 and p = 0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.     

Aligned Poly(ε‐caprolactone) Nanofibers Guide the Orientation and Migration of Human Pluripotent Stem Cell‐Derived Neurons,Astrocytes, and Oligodendrocyte Precursor Cells In Vitro

Stem cell transplantations for spinal cord injury (SCI) have been studied extensively for the past decade in order to replace the damaged tissue with human pluripotent stem cell (hPSC)‐derived neural cells. Transplanted cells may, however, benefit from supporting and guiding structures or scaffolds in order to remain viable and integrate into the host tissue. Biomaterials can be used as supporting scaffolds, as they mimic the characteristics of the natural cellular environment. In this study, hPSC‐derived neurons, astrocytes, and oligodendrocyte precursor cells (OPCs) are cultured on aligned poly(ε‐caprolactone) nanofiber platforms, which guide cell orientation to resemble that of spinal cord in vivo. All cell types are shown to efficiently spread over the nanofiber platform and orient according to the fiber alignment. Human neurons and astrocytes require extracellular matrix molecule coating for the nanofibers, but OPCs grow on nanofibers without additional treatment. Furthermore, the nanofiber platform is combined with a 3D hydrogel scaffold with controlled thickness, and nanofiber‐mediated orientation of hPSC‐derived neurons is also demonstrated in a 3D environment. In this work, clinically relevant materials and substrates for nanofibers, fiber coatings, and hydrogel scaffolds are used and combined with cells suitable for developing functional cell grafts for SCI repair.

     

Well-dispersed sulfur anchored on interconnected polypyrrole nanofiber network as high performance cathode for lithium-sulfur batteries

Preparation of novel sulfur/polypyrrole (S/PPy) composite consisting well-dispersed sulfur particles anchored on interconnected PPy nanowire network was demonstrated. In such hybrid structure, the as-prepared PPy clearly displays a three-dimensionally cross-linked and hierarchical porous structure, which was utilized in the composite cathode as a conductive network trapping soluble polysulfide intermediates and enhancing the overall electrochemical performance of the system. Benefiting from this unique structure, the S/PPy composite demonstrated excellent cycling stability, resulting in a discharge capacity of 931 mAh g⁻¹ at the second cycle and retained about 54% of this value over 100 cycles at 0.1 C. Furthermore, the S/PPy composite cathode exhibits a good rate capability with a discharge capacity of 584 mAh g⁻¹ at 1  C.     

Compressing strongly connected subgroups in social networks: An entropy-based approach

To detect and study cohesive subgroups of actors is a main objective in social network analysis. What are the respective relations inside such groups and what separates them from the outside. Entropy-based analysis of network structures is an up-and-coming approach. It turns out to be a powerful instrument to detect certain forms of cohesive subgroups and to compress them to superactors without loss of information about their embeddedness in the net: Compressing strongly connected subgroups leaves the whole net’s and the (super-)actors’ information theoretical indices unchanged; i.e., such compression is information-invariant. The actual article relates on the reduction of networks with hundreds of actors. All entropy-based calculations are realized in an expert system shell.