Quantum Dynamical Approach to Predicting the Optical Pumping Threshold for Lasing in Organic Materials

We present a quantum dynamic study on organic lasing phenomena, which is a challenging issue in organic optoelectronics. Previously, phenomenological method has achieved success in describing experimental observation. However, it cannot directly bridge the laser threshold with molecular electronic structure parameters and cavity parameters. Quantum dynamics method for describing organic lasing and obtaining laser threshold is highly expected. In this Letter, we first propose a microscopic model suitable for describing the lasing dynamics of organic molecular system and we apply the time-dependent wave-packet diffusion (TDWPD) to reveal the microscopic quantum dynamical process for the optical pumped lasing behavior. Lasing threshold is obtained from the onset of output as a function of optical input pumping. We predict that the lasing threshold has an optimal value as function of the cavity volume and depends linearly on the intracavity photon leakage rate. The structure-property relationships between molecular electronic structure parameters (including the energy of molecular excited state, the transition dipole and the organization energy) and the laser threshold obtained through numerical calculations are in qualitative agreement the experimental results, which also confirms the reliability of our approach. This work is beneficial to understanding the mechanism of organic laser and optimizing the design of organic laser materials. TO

Profiling microRNA expression in Arabidopsis pollen using microRNA array and real-time PCR

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are ~22-nt small non-coding RNAs that regulate the expression of specific target genes in many eukaryotes. In higher plants, miRNAs are involved in developmental processes and stress responses. Sexual reproduction in flowering plants relies on pollen, the male gametophyte, to deliver sperm cells to fertilize the egg cell hidden in the embryo sac. Studies indicated that post-transcriptional processes are important for regulating gene expression during pollen function. However, we still have very limited knowledge on the involved gene regulatory mechanisms. Especially, the function of miRNAs in pollen remains unknown.</p> <p>Results</p> <p>Using miRCURY LNA array technology, we have profiled the expression of 70 known miRNAs (representing 121 miRBase IDs) in Arabidopsis mature pollen, and compared the expression of these miRNAs in pollen and young inflorescence. Thirty-seven probes on the array were identified using RNAs isolated from mature pollen, 26 of which showed significant differences in expression between mature pollen and inflorescence. Real-time PCR based on TaqMan miRNA assays confirmed the expression of 22 miRNAs in mature pollen, and identified 8 additional miRNAs that were expressed at low level in mature pollen. However, the expression of 11 miRNA that were identified on the array could not be confirmed by the Taqman miRNA assays. Analyses of transcriptome data for some miRNA target genes indicated that miRNAs are functional in pollen.</p> <p>Conclusion</p> <p>In summary, our results showed that some known miRNAs were expressed in Arabidopsis mature pollen, with most of them being low abundant. The results can be utilized in future research to study post-transcriptional gene regulation in pollen function.</p

A Stochastic Max Pooling Strategy for Convolutional Neural Network Trained by Noisy Samples

The deep convolutional neural network (CNN) has made remarkable progress in image classification. However, this network performs poorly and even cannot converge in many actual applications, where the training and test samples contain lots of noises. To solve the problems, this paper puts forward a network training strategy based on stochastic max pooling. Unlike the traditional max pooling, the proposed strategy first ranks all the values in each receptive field, and then selects a random value from the top-n values as the pooling result. Compared with common pooling methods, stochastic max pooling can limit the pooling selection to a larger value that represents the main information of the pooling area which reduces the chance of introducing noises into the network, and enhances the robustness of extracting noisy image features. Experimental results show that the CNN used stochastic max pooling Strategy can converge better than traditional CNN and classified noisy images much more accurately than traditional pooling methods

Railway Container Station Reselection Approach and Application: Based on Entropy-Cloud Model

Reasonable railway container freight stations layout means higher transportation efficiency and less transportation cost. To obtain more objective and accurate reselection results, a new entropy-cloud approach is formulated to solve the problem. The approach comprises three phases: Entropy Method is used to obtain the weight of each subcriterion during Phase  1, then cloud model is designed to form the evaluation cloud for each subcriterion during Phase  2, and finally during Phase  3 we use the weight during Phase  1 to multiply the initial evaluation cloud during Phase  2. MATLAB is applied to determine the evaluation figures and help us to make the final alternative decision. To test our approach, the railway container stations in Wuhan Railway Bureau were selected for our case study. The final evaluation result indicates only Xiangyang Station should be renovated and developed as a Special Transaction Station, five other stations should be kept and developed as Ordinary Stations, and the remaining 16 stations should be closed. Furthermore, the results show that, before the site reselection process, the average distance between two railway container stations was only 74.7 km but has improved to 182.6 km after using the approach formulated in this paper