Risk factor analysis of microvascular obstruction after percutaneous coronary intervention for ST-segment elevation myocardial infarction.

Objective: This study aimed to explore the risk factors of microvascular obstruction (MVO) after percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI).

Methods: A retrospective analysis was performed on 165 patients with STEMI who successfully underwent emergency PCI and completed cardiac magnetic resonance (CMR) within 1 week after PCI. Total ischemia time (symptom onset to wire, S2W), first medical contact to wire (FMC2W), and door to wire (D2W) were compared with the recommended critical time nodes for STEMI treatment.

Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging.

Currently, mechanical and chemical damage is the main way to carry out weed control. The use of chlorophyll fluorescence (CF) technology to nondestructively monitor the stress physiological state of weeds is significant to reveal the damage mechanism of mechanical and chemical stresses as well as complex stresses. Under simulated real field environmental conditions, different species and leaf age weeds (Digitaria sanguinalis 2-5 leaf age, and Erigeron canadensis 5-10 leaf age) were subjected to experimental treatments for 1-7 days, and fluorescence parameters were measured every 24 h using a chlorophyll fluorometer.

State Causality and Adaptive Covariance Decomposition Based Time Series Forecasting.

Time series forecasting is a very vital research topic. The scale of time series in numerous industries has risen considerably in recent years as a result of the advancement of information technology. However, the existing algorithms pay little attention to generating large-scale time series.

Deep Shearlet Residual Learning Network for Single Image Super-Resolution.

Recently, the residual learning strategy has been integrated into the convolutional neural network (CNN) for single image super-resolution (SISR), where the CNN is trained to estimate the residual images. Recognizing that a residual image usually consists of high-frequency details and exhibits cartoon-like characteristics, in this paper, we propose a deep shearlet residual learning network (DSRLN) to estimate the residual images based on the shearlet transform. The proposed network is trained in the shearlet transform-domain which provides an optimal sparse approximation of the cartoon-like image.

Low-Energy Data Collection in Wireless Sensor Networks Based on Matrix Completion.

Sparse sensing schemes based on matrix completion for data collection have been proposed to reduce the power consumption of data-sensing and transmission in wireless sensor networks (WSNs). While extensive efforts have been made to improve the recovery accuracy from the sparse samples, it is usually at the cost of running time. Moreover, most data-collection methods are difficult to implement with low sampling ratio because of the communication limit.