PREDICTING PRODUCT RETURN RATE WITH “TWEETS”

This study classifies posts into four distinct topics and uses their sentiment values to predict product return rate at e-commerce websites. The results reveal that the sentiments of posts related to e-commerce company news (i.e., objective posts) are negatively related to product return rate. On the contrary, the sentiments of social network posts related to product use, purchase and service experiences (i.e., subjective posts) are positively related to product return rate at a focal e-commerce website. The paper contributes to product return research as well as social network prediction research. Practitioners may use the method to predict product return rates using social network posts

Rapid characterization of microscopic two-level systems using Landau-Zener transitions in a superconducting qubit

This is the published version. Copyright 2015 American Institute of PhysicsWe demonstrate a fast method to detect microscopic two-level systems in a superconducting phase qubit. By monitoring the population leak after sweeping the qubit bias flux, we are able to measure the two-level systems that are coupled with the qubit. Compared with the traditional method that detects two-level systems by energy spectroscopy, our method is faster and more sensitive. This method supplies a useful tool to investigate two-level systems in solid-state qubits

Why Are People Addicted to SNS? Understanding the Role of SNS Characteristics in the Formation of SNS Addiction

Research has shown that many people use social networking sites (SNS) excessively, which may lead to various negative consequences. With the aim of reducing SNS addition, this study investigates the role of SNS characteristics in the formation of SNS addiction. By applying incentive sensitization theory in the context of SNS addiction, we suggest that the compulsive motivation for using an SNS is developed by pleasurable and rewarding SNS use experiences. Social network characteristics and communication characteristics, which determine the rewards that users obtain from SNS use, moderate the relationship between habitual SNS use and SNS addiction. We develop novel behavioral measures of habitual SNS use and SNS addiction based on SNS activity logs and empirically test the research model using a large and unique dataset. Besides contributing to the theoretical development of SNS addiction, the results of this study offer practical options to help prevent SNS addiction. Moreover, the measures of SNS addiction enable the automated monitoring of user behavior on SNS, which could be useful for detecting potential SNS addicts

The Therapeutic Effect of Cytokine-Induced Killer Cells on Pancreatic Cancer Enhanced by Dendritic Cells Pulsed with K-Ras Mutant Peptide

Objective. This study is to investigate the role of the CIKs cocultured with K-ras-DCs in killing of pancreatic cancer cell lines, PANC-1 (K-ras+) and SW1990 (K-ras−). Methods. CIKs induced by IFN-γ, IL-2, and anti-CD3 monoantibody, K-ras-DCCIKs obtained by cocultivation of k-ras-DCs and CIKs. Surface markers examined by FACS. IFN-γ IL-12 ,CCL19 and CCL22 detected by ELISA. Proliferation of various CIKs tested via 3H-TdR. Killing activities of k-ras-DCCIKs and CTLs examined with 125IUdR. Results. CD3+CD56+ and CD3+CD8+ were highly expressed by K-ras-DCCIKs. In its supernatant, IFN-γ, IL-12, CCL19 and CCL22 were significantly higher than those in DCCIK and CIK. The killing rate of K-ras-DCCIK was greater than those of CIK and CTL. CTL induced by K-ras-DCs only inhibited the PANC-1 cells. Conclusions. The k-ras-DC can enhance CIK's proliferation and increase the killing effect on pancreatic cancer cell. The CTLs induced by K-ras-DC can only inhibit PANC-1 cells. In this study, K-ras-DCCIKs also show the specific inhibition to PANC-1 cells, their tumor suppression is almost same with the CTLs, their total tumor inhibitory efficiency is higher than that of the CTLs