Board independence and fluctuations of corporate performance--A research from the angle of social relations of independent directors

Based on whether there is social relation between independent directors and CEOs,there are two types of independence in the board of directors:nominal independence and real independence.Their influences on the fluctuations of corporate performance are varied.First,the board independence will strengthen supervision and reduce the fluctuations in corporate performance.Second,the nominal board independence will reduce performance fluctuations but with inadequate significance;however,the real independence significantly reduces fluctuations of corporate performance;social relations between CEO and independent directors will encourage fluctuations of corporate performance.This also confirms that the independent directors can reduce the corporate performance instead of just holding the post and doing nothing.At the same time,in order to improve the real board independence,when recruiting independent directors,those gray directors who have social relations with CEO must be identified and excluded.

THE EFFECTS OF CORRELATED SENSOR SIGNAL FLUCTUATION ON THE STATISTICAL PERFORMANCE OF AN AR HIGH RESOLUTION ARRAY PROCESSOR

The statistical performance of AR high resolution array processor in presence of correlated sensor signal fluctuation is studied. Mean square inverse beam pattern and pointing error are examined. Special attention is paid to the effects of reference sensor and correlation between sensors. It is shown that fluctuation causes broadening or even distortion of the mean square inverse beam pattern. Phase fluctuation causes pointing error. Its standard variance is proportional to that of fluctuation and is related to the number of sensors of the array. Correlation between sensors has important effects on pointing error.

Optimizing storage performance in public cloud platforms

Cloud computing is an elastic computing model where users can lease computing and storage resources on demand from a remote infrastructure. It is gaining popularity due to its low cost, high reliability, and wide availability. With the emergence of public cloud storage platforms like Amazon, Microsoft, and Google, individual applications and enterprise storage are being deployed on Clouds. However, a serious impediment to its wider deployment is the relative lack of effective data management services. Our experiments, as well as industry reports, have shown that the performance and service-level agreement (SLA) cannot be guaranteed when the data is served over public Clouds. The relatively slow access to persistent data and large variability in cloud storage I/O performance can significantly degrade the performance of data-intensive applications. This paper addresses the issue of I/O performance fluctuation over public cloud platforms and we propose a middleware called CloudMW between the Cloud storage and clients to provide the storage services with better performance and SLA satisfaction. Some technologies, including data virtualization, data chunking, caching, and replication, are integrated into CloudMW to achieve a more stable and predictable performance, and permit flexible sharing of storage among the virtual machines (VMs). Experimental results based on Amazon Web Services (AWS) show that CloudMW is able to improve the stability and help provide better SLAs and data sharing for cloud storage.