How the Design Leadership and Strategic Design Drive New Value in Enterprises and Organizations

Today's design has shifted from the original primary pursuit of appearance and function to processes and systems, creating new meanings for corporate strategies, products and services. Nowadays, enterprises are increasingly paying attention to the power of design, and regard design as the main innovation method and incorporate it into the organizations. Design began to be considered a key role at the top of large organizations. Although people recognize that design can bring good effects to enterprises and organizations, and there is growing interest in cultivating design thinking, what are the leaders who can lead enterprises, organizations and have design thinking? Their characteristics and how to formulate and plan strategic design still not fully elucidated. Therefore, this research uses related theories to understand the way of thinking and characteristics of design leaders, only in this way we formulate good strategic design for enterprises and organizations, and become an indispensable and important help in today rapidly changing world

Measurement-device-independent QKD with Modified Coherent State

The measurement-device-independent quantum key distribution (MDI-QKD) protocol has been proposed for the purpose of removing the detector side channel attacks. Due to the multi-photon events of coherent states sources, real-life implementations of MDI-QKD protocol must employ decoy states to beat the photon-number-splitting attack. Decoy states for MDI-QKD based on the weak coherent states have been studied recently. In this paper, we propose to perform MDI-QKD protocol with modified coherent states (MCS) sources. We simulate the performance of MDI-QKD with the decoy states based on MCS sources. And our simulation indicates that both the secure-key rate and transmission distance can be improved evidently with MCS sources.The physics behind this improvement is that the probability of multi-photon events of the MCS is lower than that of weak coherent states while at the same time the probability of single-photon is higher

Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents