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