Numerical Evaluation on the Thermal Performance of the Solar External Cylinder Receiver using Monte Carlo Ray-Tracing Algorithm

The heat receiver is an essential part of the Concentrating Solar Power plant,directly affecting its operation and safety.In this paper,the Monte Carlo ray-tracing algorithm was introduced to evaluate a 50 MW(e)external cylindrical receiver’s thermal performance.The radiation heat flux concentrated from the heliostats field and the view factors between grids divided from the tubes were both calculated using Monte Carlo ray-tracing algorithm.Besides,an in-house code was developed and verified,including three modules of the view-factor calculation,thermal performance calculation,and thermal stress calculation.It was also employed to investigate the 50 MW(e)receiver,and the detailed 3D profiles of temperature and thermal stress in the receiver were analyzed.It was found that the molten salt was heated from 298℃to 565℃and the tube at the 50 MW(e)receiver’s outlet had a high temperature,while the high thermal stress came out at the receiver’s entrance.Finally,the over-temperature of the receiver was discussed,and an optimization algorithm was introduced.The tube wall temperature and film temperature at the overheated area matched the safety criteria,and the outlet molten salt temperature still reached 563℃after the optimization process,with only 2℃dropped.

PSA density improves the rate of prostate cancer detection in Chinese men with a PSA between 2.5-10.0 ng ml-1 and 10.1-20.0 ng ml-1： a multicenter study

Chinese men should have a higher prostate-specific antigen （PSA） ＂gray zone＂ than the traditional value of 2.5-10.0 ng ml-1 since the incidence of prostate cancer （PCa） in Chinese men is relative low. We hypothesized that PSA density （PSAD） could improve the rate of PCa detection in Chinese men with a PSA higher than the traditional PSA ＂gray zone.＂ A total of 461 men with a PSA between 2.5 and 20.0 ng ml-1, who had undergone prostatic biopsy at two Chinese centers were included in the analysis. The men were then further divided into groups with a PSA between 2.5-10.0 ng ml-1 and 10.1-20.0 ng ml-1. Receiver operating characteristic （ROC） curve was used to evaluate the efficacy of PSA and PSAD for the diagnosis of PCa. In men with a PSA of 2.5-10.0 ng ml-1 or 10.1-20.0 ng ml-z, the areas under the ROC curve were higher for PSAD than for PSA. This was consistent across both centers and the cohort overall. When the entire cohort was considered, the optimal PSAD cut-off for predicting PCa in men with a PSA of 2.5-10.0 ng m1-1 was 0.15 ng ml-2 ml-2, with a sensitivity of 64.4% and specificity of 64.6%. The optimal cut-off for PSAD in men with a PSA of 10.1-20.0 ng m1-1 was 0.33 ng ml-1 ml-1, with a sensitivity of 60.3% and specificity of 82.7%. PSAD can improve the effectiveness for PCa detection in Chinese men with a PSA of 2.5-10.0 ng ml-1 （traditional Western PSA ＂gray zone＂） and 10.1-20.0 ng ml-2 （Chinese PSA ＂gray zone＂）.

Comparison of Achievable Rates of OFDM and Single Carrier Communication Systems

This paper presents a theoretical comparison of a single carrier system and a multi-carrier system through an analysis of the achievable rate under frequency selective fading with channel state information at the receiver. A scheme was designed to compare the achievable rates of a single carrier system and an Or- thogonal Frequency Division Multiplexing （OFDM） system. A thorough theoretical analysis of the two-path channel was conducted, and simulations were also used to analyze practical stochastic channels. Analysis and simulation results show that the achievable rates of the two approaches are comparable when the channel is flat fading. However, when the channel is frequency selective fading, the single carrier system outperforms the OFDM system. The achievable rate of the OFDM system is about 10% lower than that of the single carrier system at higher SNRs.

Learning random forests for ranking

The random forests （RF） algorithm, which combines the predictions from an ensemble of random trees, has achieved significant improvements in terms of classification accuracy. In many real-world applications, however, ranking is often required in order to make optimal decisions. Thus, we focus our attention on the ranking performance of RF in this paper. Our experi- mental results based on the entire 36 UC Irvine Machine Learning Repository （UCI） data sets published on the main website of Weka platform show that RF doesn＇t perform well in ranking, and is even about the same as a single C4.4 tree. This fact raises the question of whether several improvements to RF can scale up its ranking performance. To answer this question, we single out an improved random forests （IRF） algorithm. Instead of the information gain measure and the maximum-likelihood estimate, the average gain measure and the similarity- weighted estimate are used in IRF. Our experiments show that IRF significantly outperforms all the other algorithms used to compare in terms of ranking while maintains the high classification accuracy characterizing RF.