Blastocyst quality and reproductive and perinatal outcomes : a multinational multicentre observational study

Funding H.Z. is supported by a Monash Research Scholarship. B.W.J.M. is supported by an NHMRC Investigator grant (GNT1176437). R.W. is supported by an NHMRC Emerging Leadership Investigator grant (2009767).Peer reviewedPublisher PD

A robust adaptive Capon beamforming

Adaptive beamforming suffers from performance degradation in the presence of mismatch between the actual and presumed array steering vector of the desired signal. We propose a novel approach to adaptive beamforming that is robust to array errors. The proposed method involves two steps: the first step is to estimate the actual steering vector of the desired signal, and the second is to obtain optimal weight by utilizing the estimated steering vector. Our method belongs to the class of diagonal loading, but the optimal amount of diagonal loading level can be precisely calculated based on the uncertainty set of the steering vector. Moreover, the proposed robust Capon beamforming has an explicit closed-form solution. Computer simulation results demonstrate its excellent performance. (c) 2005 Elsevier B.V. All rights reserved

Economic Development Forecast of China’s General Aviation Industry

Aiming at solving the problem of system external impact on China’s general aviation industry, combining functional theory and grey system theory, and applying Bayesian network reasoning technology, a grey Bayesian network reasoning prediction model of system impact and system control is established. Based on the dynamic deduction of the functional analysis factor of system impact evolution, the flight time of general aviation production operation is selected to predict the development trend of the system. Based on the current period information of the general aviation industry, the grey Bayesian network inference prediction model is used to predict the current and future trends, so as to predict the economic development trend of the general aviation industry in China. The prediction results are more accurate than those of other existing models

Numerical study on the smoke movement and control in main roadway for mine fires occurred in branch

Mine exogenous fire is the main disaster in coal mines. Owing to the complicated structure and the ventilation network, the smoke movement and control in branched roadway fires of coal mines is more complicated than that in traffic tunnel fires. In this study, the smoke backlayering length and critical velocity in a main roadway for fires that occurred in a branch were studied with varying fire locations. The results suggest that the smoke from the branch does not spread along the width centerline of the main roadway, but forms an early “snake-shaped” structure. The variation of dimensionless backlayering length with the dimensionless variable ln(Q˙*1/3/V*) is divided into two regions with different slopes by the line of L* = 1.6. Besides, branched roadway fires have a lower backlayering length and critical velocity compared to single-hole tunnel fires. These two parameters increase with decreasing the fire-node distance. Combined with dimensionless analysis and simulation results, calculation models considering fire location were proposed to estimate the smoke backlayering length and critical velocity. The credibility of prediction models is validated by comparing them with simulation results. The outcomes of the current study guide smoke control in similar-structured mine roadways and traffic tunnels

SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow