Preparation and some properties of cholesterol oxidase from Rhodococcus sp. R14-2

Rhodococcus sp. R14-2, isolated from Chinese Jin-hua ham, produces a novel extracellular cholesterol oxidase (COX). The enzyme was extracted from fermentation broth and purified 53.1-fold based on specific activity. The purified enzyme shows a single polypeptide band on SDS-PAGE with an estimated molecular weight of about 60 kDa, and has a pI of 8.5. The first 10 amino acid residues of the NH2-terminal sequence of the enzyme are A-P-P-V-A-S-C-R-Y-C, which differs from other known COXs. The enzyme is stable over a rather wide pH range of 4.0¿10.0. The optimum pH and temperature of the COX are pH 7.0 and 50°C, respectively. The COX rapidly oxidizes 3ß-hydroxysteroids such as cholesterol and phytosterols, but is inert toward 3¿-hydroxysteroids. Thus, the presence of a 3ß-hydroxyl group appears to be essential for substrate activity. The Michaelis constant (Km) for cholesterol is estimated at 55 ¿M; the COX activity was markedly inhibited by metal ions such as Hg2+ and Fe3+ and inhibitors such as p-chloromercuric benzoate, mercaptoethanol and fenpropimorph. Inhibition caused by p-chloromercuric benzoate, mercuric chloride, or silver nitrate was almost completely prevented by the addition of glutathione. These suggests that -SH groups may be involved in the catalytic activity of the present CO

Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, Multi-user Computation Partitioning Problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10% on average in terms of application delay. Based on SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces. Index Terms—mobile cloud computing; offloading; computation partitioning; job schedulin

Ubiquitous intelligent object : modeling and applications

2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A middleware support for agent-based application mobility in pervasive environments

Version of RecordPublishe

Multi-user Computation Partitioning for Latency Sensitive Mobile Cloud Applications

Elastic partitioning of computations between mobile devices and cloud is an important and challenging research topic for mobile cloud computing. Existing works focus on the single-user computation partitioning, which aims to optimize the application completion time for one particular single user. These works assume that the cloud always has enough resources to execute the computations immediately when they are offloaded to the cloud. However, this assumption does not hold for large scale mobile cloud applications. In these applications, due to the competition for cloud resources among a large number of users, the offloaded computations may be executed with certain scheduling delay on the cloud. Single user partitioning that does not take into account the scheduling delay on the cloud may yield significant performance degradation. In this paper, we study, for the first time, Multi-user Computation Partitioning Problem (MCPP), which considers the partitioning of multiple users’ computations together with the scheduling of offloaded computations on the cloud resources. Instead of pursuing the minimum application completion time for every single user, we aim to achieve minimum average completion time for all the users, based on the number of provisioned resources on the cloud. We show that MCPP is different from and more difficult than the classical job scheduling problems. We design an offline heuristic algorithm, namely SearchAdjust, to solve MCPP. We demonstrate through benchmarks that SearchAdjust outperforms both the single user partitioning approaches and classical job scheduling approaches by 10% on average in terms of application delay. Based on SearchAdjust, we also design an online algorithm for MCPP that can be easily deployed in practical systems. We validate the effectiveness of our online algorithm using real world load traces. Index Terms—mobile cloud computing; offloading; computation partitioning; job schedulin

Remedial injected harmonic current operation of redundant flux-switching permanent magnet motor drives

published_or_final_versio

Single Transverse Spin Asymmetries at Parton Level

Two factorization approaches have been proposed for single transverse spin asymmetries. One is the collinear factorization, another is the transverse-momentum-dependent factorization. They have been previously derived in a formal way by using diagram expansion at hadron level. If the two factorizations hold or can be proven, they should also hold when we replace hadrons with parton states. We examine these two factorizations at parton level with massless partons. It is nontrivial to generate these asymmetries at parton level with massless partons because the asymmetries require helicity-flip and nonzero absorptive parts in scattering amplitudes. By constructing suitable parton states with massless partons we derive the two factorizations for the asymmetry in Drell-Yan processes. It is found from our results that the collinear factorization derived at parton level is not the same as that derived at hadron level. Our results with massless partons confirm those derived with single massive parton state in our previous works.Comment: shortened version to match published versio

Research on hot extrusion forming of 7075 aluminum alloy wheel profile

Design the wheel mold according to the cross-sectional view of the lightweight aluminum alloy wheel profile, determine the length of its working belt and use HyperXtrude software to simulate it, verify the rationality of the working belt design, analyze the flow velocity and temperature of the mold outlet, and determine the 7075 aluminum alloy The alloy wheel profile is most reasonable to be produced on a 10 MN extruder. Finally, the optimized working belt length is used for production. The quality of hot extrusion profile is qualified, which proves the accuracy of the simulation