Human-Centric Composite-Quality Modeling and Assessment for Virtual Desktop Clouds

There are several motivations, such as mobility, cost, and secu- rity, that are behind the trend of traditional desktop users transi- tioning to thin-client-based virtual desktop clouds （VDCs）. Such a trend has led to the rising importance of human-centric performance modeling and assessment within user communities that are increasingly making use of desktop virtualization. In this paper, we present a novel reference architecture and its eas- ily deployable implementation for modeling and assessing objec- tive user quality of experience （QoE） in VDCs. This architec- ture eliminates the need for expensive, time-consuming subjec- tive testing and incorporates finite-state machine representa- tions for user workload generation. It also incorporates slow-mo- tion benchmarking with deep-packet inspection of application task performance affected by QoS variations. In this way, a ＂composite-quality＂ metric model of user QoE can be derived. We show how this metric can be customized to a particular user group profile with different application sets and can be used to a） identify dominant performance indicators and troubleshoot bottlenecks and b） obtain both absolute and relative objective user QoE measurements needed for pertinent selection of thin-client encoding configurations in VDCs. We validate our composite-quality modeling and assessment methodology by us- ing subjective and objective user QoE measurements in a re- al-world VDC called VDPilot, which uses RDP and PCoIP thin-client protocols. In our case study, actual users are pres- ent in virtual classrooms within a regional federated university system.

A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure

Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA.In this work, we developed a new Hidden Markov Model（HMM） to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities：（1） encoding local structure of each position by jointly considering sequence and structure information, and（2）assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP,and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.

A case study of development and application of a streamlined control and response modeling system for PM_(2.5)attainment assessment in China

This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM_（2.5） attainment assessment in China.This method is capable of significantly reducing the dimensions required to establish a response surface model,as well as capturing more realistic response of PM_（2.5） to emission changes with a limited number of model simulations.The newly developed module establishes a data link between the system and the Software for Model Attainment Test—Community Edition（SMAT-CE）,and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface.The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta（YRD） in China.Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality（CMAQ） model simulation results with maximum mean normalized error 〈 3.5%.It is also demonstrated that primary emissions make a major contribution to ambient levels of PM_（2.5） in January and August（e.g.,more than50%contributed by primary emissions in Shanghai）,and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China＇s annual PM_（2.5）National Ambient Air Quality Standard.The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM_（2.5）（and potentially O_3 as well） and streamline air quality data for SMAT-CE to perform attainment assessments.