The MIR181A2HG/miR‐5680/VCAN‐CD44 Axis Regulates Gastric Cancer Lymph Node Metastasis by Promoting M2 Macrophage Polarization by Weijie Zang, Yongpu Yang, Junjie Chen, Qinsheng Mao, Wanjiang Xue, Yilin Hu

Get full text

INFLUENCING FACTOR ANALYSIS OF FATIGUE CRACK INITIATION LIFE ON CRANE LATTICE BOOM by CAI FuHai, YUAN WeiChen, WANG Xin, ZHAO FuLing, JIA XingJun

“…According to the model,three main factors which will influence the initiation life calculation were analyzed and compared. …”
Get full text

Towards Exploring the Influence of Community Structures on Information Dissemination in Sina Weibo Networks by Zhiwei Zhang, Aidong Fang, Lin Cui, Zhenggao Pan, Wanli Zhang, Chengfang Tan, Chao Wang

“…An in-depth examination of the results in terms of improved significant profile, the length of information propagation path, and the relevance of the nodes in the propagation path indirectly reveals the inhibitory effect of community structures on information propagation.…”
Get full text

The Influence of the Motor Traction Vibration on Fatigue Life of the Bogie Frame of the Metro Vehicle by Qiushi Wang, Jinsong Zhou, Dao Gong, Tengfei Wang, Jiangxue Chen, Taiwen You, Zhanfei Zhang

“…Some individual loads, such as motor vibration, have been ignored or wrongly understood, which leads to the occurrence of local insufficient fatigue life of the frame. To illustrate the influence of the motor vibration on the fatigue life of the bogie frame, a metro vehicle was taken as an example: first, the precise finite element model of the frame was established, and its correctness was verified; then, the vibration characteristics of the frame were analyzed by sweep frequency calculation; and finally, considering the vibration acceleration signal of the motor measured on a metro line as the excitation, the influence of the random vibration on the fatigue life of the frame under traction and idle running conditions was compared and analyzed by solving the power spectral density of the dynamic stress response at the weak fatigue nodes of the structure. …”
Get full text

Analysis of the Influence of Parameters in Asymmetric Gear with a Small Teeth Number on Temperature Field by Chen Ting

Get full text

Influence of Allee effect in prey populations on the dynamics of two-prey-one-predator model by Moitri Sen, Malay Banerjee, Yasuhiro Takeuchi

Get full text

Spatiotemporal Influence Analysis Through Traffic Speed Pattern Analysis Using Spatial Classification by Kyusoo Chong

Get full text

Temporal link prediction method based on community multi-features fusion and embedded representation by Yuhang ZHU, Lixin JI, Yingle LI, Haitao LI, Shuxin LIU

“…Dynamic networks integrates time attributes on the basis of static networks, and it contains multiple connotations such as the complexity and dynamics of the network structure.It is a better thinking object for studying complex network link prediction problems in the real world.Its high application value has attracted much attention in recent years.However, most of the research objects of traditional methods are still limited to static networks, and there are problems such as insufficient utilization of network time-domain evolution information and high time complexity.Combining sociological theory, a novel temporal link prediction method was proposed based on community multi-feature fusion embedding representation.The core idea of this method was to analyze the dynamic evolution characteristics of the network, learn the embedded representation vector of nodes within the community, and effectively fuse multiple features to measure the generation probability of the connection between nodes.The network was divided into several subgraphs by using community detection with collective influence weights and the Similarity index was proposed based on the collective influence.Then, the biased random walk and the Skip-gram were used to get the embedded vectors for every node and the Similarity index was proposed based on the random walk within the community.Integrating the collective influence, multiple central features of the community, and the representation vector learned within the community, the Similarity index was proposed based on the multi-features fusion.Compared with classical temporal link prediction methods, including moving average methods, embedded representation methods, and graph neural network methods, experimental results on six real data sets show that the proposed methods based on the random walk within the community and the multi-features fusion both achieve better prediction performance under the evaluation criteria of AUC.…”
Get full text

Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge by Kehong You, Sanyang Liu, Yiguang Bai

“…Abstract Influence Maximization (IM) stands as a central challenge within the domain of complex network analysis, with the primary objective of identifying an optimal seed set of a predetermined size that maximizes the reach of influence propagation. …”
Get full text

Connectivity analysis of IoV based on scale-free network by Tao HAN, Wei HE, Jun DAI, Yong ZUO, Yang YANG, Xiaohu GE

“…The connectivity of moving vehicles is one of the critical metrics in Internet of vehicles (IoV) that critically influence the performance of data transmission.Due to the high-frequency dynamic changes of the network topology of the IoV, which causes the link to be easily disconnected frequently.It is a critical issue to reduce the probability of link disconnection, and reducing the randomness of link establishment improves the overall network connectivity.To solve the above problems, the network’s connectivity was analyzed under the line of sight path loss model and the interference model, and the dynamic growth (DN) algorithm was designed through the characterization of the IoV link in the real world.Moreover, a scale-free VANET was built through the network’s addition and deletion of nodes and link preference connections.Simulation results indicate that the overall connectivity of the network is improved by 16%.…”
Get full text

Complex dynamics of a predator-prey fishery model: The impact of the Allee effect and bilateral intervention by Yuan Tian, Yang Liu, Kaibiao Sun

“…The existence, type, and stability of the boundary equilibria as well as the number of interior equilibria of the proposed model are discussed. Parameter influence: the bifurcations in the predation system are analyzed by selecting the capture rate of prey by the predator and Allee threshold as key parameters, and the results show that the system will undergo saddle-node bifurcation and Bogdanov-Takens bifurcation of codimension at least 2 and 3. …”
Get full text

A power load forecasting method using cosine similarity and a graph convolutional network by JI Shan, JIANG Wei, JING Xin

“…First， cosine similarity is utilized to learn similar patterns between load data from different nodes， allowing for the deep extraction of spatiotemporal correlation features. …”
Get full text

The Influence of Communication Range on Connectivity for Resilient Wireless Sensor Networks Using a Probabilistic Approach by Yuanjiang Huang, José-Fernán Martínez, Juana Sendra, Lourdes López

“…This paper focuses on the influence of communication range on the probability that all nodes are connected under two conditions, respectively: (1) all nodes have the same communication range, and (2) communication range of each node is a random variable. …”
Get full text

Research on influence spread of scientific research team based on scientific factor quantification of big data by Wenbin Zhao, Zhixian Yin, Tongrang Fan, Jishuang Luo

“…The experiment of influence spread is carried out by the comparison of unweighted network and weighted network, the comparison of single node and multiple nodes, and the comparison of influence spread and other index. …”
Get full text

Evaluating the influence of anti-PD-1 immunotherapy combined with IMRT on thyroid dysfunction in nasopharyngeal carcinoma by Liqianqi Chen, Zihuang Li, Xianming Li, Shihai Wu, Long Gong, Gang Xu, Shanyi Chen, Yucong Zhang

Get full text

Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance by Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï

“…Results revealed that the number of variables to be randomly selected for each split, the proportion of samples to train the model, the minimal number of samples within each terminal node, and RF regression performance are not influenced by the sample size, number, and type of predictors. …”
Get full text

STUDY ON BEND-TWIST COUPLING CHARACTERISTICS OF WEB OFFSET FOR LARGE WIND TURBINE BLADES (MT) by WANG HaiSheng, MIAO WeiPao, LI Chun, ZHANG Li, YAN YangTian, LI Zhi Hao, YUE Min Nan

“…In order to research the influence of the web deflection on the bend-twist coupling characteristics of large wind turbine blades, the CAD model of NREL 5 MW blade is established based on NX secondary development, the aerodynamic loads of the blade are obtained by CFD method, the blade bendind angle and twisting angle are solved by the node displacement method to analyze the influence of web deflection for bend-twist coupling blade. …”
Get full text

Comprehensive computational analysis of differentially expressed miRNAs and their influence on transcriptomic signatures in prostate cancer by Fahad M. Aldakheel, Hadeel Alnajran, Ayesha Mateen, Shatha A. Alduraywish, Mohammed S. Alqahtani, Rabbani Syed

“…Abstract Prostate cancer presents a major health issue, with its progression influenced by intricate molecular factors. Notably, the interplay between miRNAs and changes in transcriptomic patterns is not fully understood. …”
Get full text

Establishment of an Improved Elman Neural Network Model for Predicting the Corrosion Rate of 3C Steel in Marine Environment and Analysis of the Factors Affecting Model Accuracy by Wenbo Jin, Zhuo Chen, Wanying Liu, Qing Quan, Zongxiao Ren

“…The corrosion rates of 3C steel in different seawater environments were predicted, and the influences of the number of hidden layer nodes, the population sizes, and the number of iterations on the prediction results of the improved model were analyzed. …”
Get full text

The interchange stations siting model of urban rail transit network by Jiaao Guo, Qinghuai Liang, Jiaqi Zhao

“…Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. …”
Get full text