艾叶精油化学成分研究

本文用水蒸气蒸馏法提取了不同产地艾叶精油,采用GC-MS技术分析了艾叶精油的化学成分。结果不同产地艾叶精油的质量收率为0.29%～0.56%,都含有特征成分:α-蒎烯、β-蒎烯、α-松油烯、γ-松油烯、桉叶素、蒿酮、蒿醇、2-环己烯-1-醇、樟脑、龙脑、4-松油醇、反式-石竹烯、丁子香酚。其中具有药效作用的成分有桉叶素(14.32%～26.12%,质量百分数,下同),樟脑(3.66%～14.97%)、龙脑(0.51%～10.53%)、甘菊环(0.00%～23.95%)等,有毒成分侧柏酮含量为0.32%～3.62%。     

IN SITU X-RAY PROBES FOR PIEZOELECTRICITY IN EPITAXIAL FERROELECTRIC CAPACITORS

ABSTRACT

Probing the piezoelectricity and ferroelectricity of thin film devices and nanostructures quantitatively has proven to be challenging because the appropriate experimental tools have had a limited range of usefulness. We show here that the piezoelectric and ferroelectric properties of epitaxial thin films can be measured quantitatively using time-resolved synchrotron x-ray microdiffraction. Microdiffraction combines structural specificity with the appropriate spatial resolution and ability to probe structures with electrical contacts. Our measurements of piezoelectric coefficients and coercive electric fields for Pb(Zr,Ti)O₃ capacitors using this approach are in excellent quantitative agreement with results obtained electrically and mechanically. The time and spatial resolution of microdiffraction probes are well-defined and decoupled from electrical and mechanical resonances of the ferroelectric capacitor.     

Determining the parameters of emotion by analyzing environmental images captured by a mobile device

Multimedia Tools and Applications - This paper proposes a method to quantitatively extract human emotions by analyzing images of the surrounding environment captured by a smartphone camera in real...     

Ultrasonic NDE discrimination with the gradient descent algorithm and SAFT image processing

Ultrasonic inspection methods are widely used for detecting flaws in materials. One of the most popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an approach which uses LMS method to determine the coordinates of the ultrasonic probe followed by the use of SAFT to estimate the location of the ultrasonic reflector. The method is employed for discriminating NDE signals from the steam generator tubes in a nuclear power plant. The results obtained by using this scheme, of discriminating the ultrasonic signals from cracks and deposits within steam generator tubes, are presented.     

Virtual machine consolidation based on interference modeling

Server consolidation is very attractive for cloud computing platforms to improve energy efficiency and resource utilization. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server. However, current virtualization technologies do not ensure performance isolation among guest virtual machines, which results in degraded performance due to contention in shared resources along with violation of service level agreement (SLA) of the cloud service. In that sense, minimizing performance interference among co-located virtual machines is the key factor of successful server consolidation policy in the cloud computing platforms. In this work, we propose a performance model that considers interferences in the shared last-level cache and memory bus. Our performance interference model can estimate how much an application will hurt others and how much an application will suffer from others. We also present a virtual machine consolidation method called swim which is based on our interference model. Experimental results show that the average performance degradation ratio by swim is comparable to the optimal allocation.     

Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems

Contemporary operating systems for single-ISA (instruction set architecture) multi-core systems attempt to distribute tasks equally among all the CPUs. This approach works relatively well when there is no difference in CPU capability. However, there are cases in which CPU capability differs from one another. For instance, static capability asymmetry results from the advent of new asymmetric hardware, and dynamic capability asymmetry comes from the operating system (OS) outside noise caused from networking or I/O handling. These asymmetries can make it hard for the OS scheduler to evenly distribute the tasks, resulting in less efficient load balancing. In this paper, we propose a user-level load balancer for parallel applications, called the ’capability balancer’, which recognizes the difference of CPU capability and makes subtasks share the entire CPU capability fairly. The balancer can coexist with the existing kernel-level load balancer without detrimenting the behavior of the kernel balancer. The capability balancer can fairly distribute CPU capability to tasks with very little overhead. For real workloads like the NAS Parallel Benchmark (NPB), we have accomplished speedups of up to 9.8% and 8.5% in dynamic and static asymmetries, respectively. We have also experienced speedups of 13.3% for dynamic asymmetry and 24.1% for static asymmetry in a competitive environment. The impacts of our task selection policies, FIFO (first in, first out) and cache, were compared. The use of the cache policy led to a speedup of 5.3% in overall execution time and a decrease of 4.7% in the overall cache miss count, compared with the FIFO policy, which is used by default.     

Energy-centric DVFS controlling method for multi-core platforms

Dynamic voltage and frequency scaling (DVFS) is a well-known and effective technique for reducing energy consumption in modern processors. However, accurately predicting the effect of frequency scaling on system performance is a challenging problem in real environments. In this paper, we propose a realistic DVFS performance prediction method, and a practical DVFS control policy (eDVFS) that aims to minimize total energy consumption in multi-core platforms. We also present power consumption estimation models for CPU and DRAM by exploiting a hardware energy monitoring unit. We implemented eDVFS in Linux, and our evaluation results show that eDVFS can save a substantial amount of energy compared with Linux “on-demand” CPU governor in diverse environments.     

A tool to help tune where computation is performed

We introduce a new performance metric, called load balancing factor (LBF), to assist programmers when evaluating different tuning alternatives. The LBF metric differs from traditional performance metrics since it is intended to measure the performance implications of a specific tuning alternative rather than quantifying where time is spent in the current version of the program. A second unique aspect of the metric is that it provides guidance about moving work within a distributed or parallel program rather than reducing it. A variation of the LBF metric can also be used to predict the performance impact of changing the underlying network. The LBF metric is computed incrementally and online during the execution of the program to be tuned. We also present a case study that shows that our metric can accurately predict the actual performance gains for a test suite of six programs