Modelling the Roles of Cewebrity Trust and Platform Trust in Consumers’Propensity of Live-Streaming:An Extended TAM Method

Live streaming is a booming industry in China,involving an increasing number of Internet users.Previous studies show that trust is a cornerstone to develop ecommerce.Trust in the streaming industry is different from that of other e-commerce areas.There are two major dimensions of trust in the live streaming context:platform trust and cewebrity trust,which are both important for customers to adopt and reuse a specific live streaming service.We collected questionnaire data from 520 participates who have used live streaming services in China.We model the collected data and identified factors that can influence users’propensity by an extended technology acceptance model(TAM)method.According to our analysis,both cewebrity trust and platform trust will greatly influence users’intention to reuse a certain platform.Moreover,results also indicate that cewebrity trust is far more important than platform trust.These findings can lead to several management strategies to improve the adherence of users to streaming platforms.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

IGF2BP3-induced activation of EIF5B contributes to progression of hepatocellular carcinoma cells

In this study,we investigated the functional role of eukaryotic initiation factor 5B(EIF5B)in hepatocellular carcinoma(HCC)and the underlying mechanisms.Bioinformatics analysis demonstrated that the EIF5B transcript and protein levels as well as the EIF5Bcopy number were significantly higher in the HCC tissues compared with the non-cancerous liver tissues.Down-regulation of EIF5B significantly decreased proliferation and invasiveness of the HCC cells.Furthermore,EIF5B knockdown suppressed epithelial-mesenchymal transition(EMT)and the cancer stem cell(CSC)phenotype.Down-regulation of EIF5B also increased the sensitivity of HCC cells to 5-fluorouracil(5-FU).In the HCC cells,activation of the NF-kappa B signaling pathway and IkB phosphorylation was significantly reduced by EIF5B silencing.IGF2BP3 increased the stability of the EIF5B mRNA in an m6A-dependent manner.Our data suggested that EIF5B is a promising prognostic biomarker and therapeutic target in HCC.

Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every day.How to closely monitor various KPIs,and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge,especially for unlabeled data.The generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are unlabeled.That is a time-consuming and laborious work to label anomaly for company engineers.Build an unsupervised model to detect unlabeled data is an urgent need at present.In this paper,unsupervised learning DBSCAN combined with feature extraction of data has been used,and for some KPIs,its best F-Score can reach about 0.9,which is quite good for solving the current problem.