1,183 research outputs found
Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion
The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration (Nc) and spectral dispersion (ɛ), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ɛ−Nc relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ɛ is an increasing function of Nc, the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ɛ is a decreasing function of Nc. For the ɛ−Nc negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ɛ−Nc relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions.
Keywords: aerosol indirect effects, cloud droplet spectral dispersion, autoconversion parameterization, deep convective systems, two-moment bulk microphysics schem
Quasiparticle interference of C2-symmetric surface states in LaOFeAs parent compound
We present scanning tunneling microscopy studies of the LaOFeAs parent
compound of iron pnictide superconductors. Topographic imaging reveals two
types of atomically flat surfaces, corresponding to the exposed LaO layer and
FeAs layer respectively. On one type of surface, we observe strong standing
wave patterns induced by quasiparticle interference of two-dimensional surface
states. The distribution of scattering wavevectors exhibits pronounced two-fold
symmetry, consistent with the nematic electronic structure found in the
Ca(Fe1-xCox)2As2 parent state.Comment: 13 pages, 4 figure
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant to leak any parameters of the trained object detectors. 10 years ago,
Avidan & Butman introduced Blind Vision, which is a method for securely
evaluating a Viola-Jones type object detector. Blind Vision uses standard
cryptographic tools and is painfully slow to compute, taking a couple of hours
to scan a single image. The purpose of this work is to explore an efficient
method that can speed up the process. We propose the Random Base Image (RBI)
Representation. The original image is divided into random base images. Only the
base images are submitted randomly to the cloud server. Thus, the content of
the image can not be leaked. In the meanwhile, a random vector and the secure
Millionaire protocol are leveraged to protect the parameters of the trained
object detector. The RBI makes the integral-image enable again for the great
acceleration. The experimental results reveal that our method can retain the
detection accuracy of that of the plain vision algorithm and is significantly
faster than the traditional blind vision, with only a very low probability of
the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE
International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul
14, 2017, Hong Kong, Hong Kon
Liquid air energy storage: process optimization and performance enhancement
Liquid Air Energy Storage (LAES) aims to large scale operations a~d-:_has caught the attention due to the advantages of high energy density, a highly competitive capital cost, no geographical constraints and environmental friendliness. However, the situation is getting more challenging due to its disappointed performance in the current configuration. This thesis focuses increase the system performance of the LAES technology, particularly through developing novel thermodynamic cycles for an increased use of the thermal energy and system optimization strategies. The improvements to the LAES mainly aim at two points: increasing power output by using compression heat and rising the liquification rate through external cold sources. To effectively use the heat, three integrated LAES systems with the Organic
Rankine Cycle (ORC) are proposed, termed LAES-ORC-VCRC system, LAES-ORC-ARC system and LAES-ORC system respectively according to different cooling methods. External cold sources, such as Liquefied Natural Gas (LNG), can be used to enhance air liquefication, and hence two integrated LAES systems, termed the LAES-LNG and the LAES-LNG-CS, are investigated and optimized
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