Impact of the COVID-19 Pandemic on Door-to-Device Time Segments and Clinical Outcomes for STEMI Patients in Northern Taiwan.

Background: Prompt primary percutaneous coronary intervention (pPCI) is crucial for the prognosis and reduction of myocardial damage in ST-segment elevation myocardial infarction (STEMI) patients. The Coronavirus Disease 2019 (COVID-19) pandemic had multifaceted impacts on healthcare. This study assessed the effects of the pandemic on pPCI procedures and clinical outcomes in emergency STEMI patients.

A Simple Surface Treatment for Improving the Adhesive Bonding Properties and Durability of an AlMg Alloy.

The structural adhesive bonding of aluminum is widely used in the aircraft and automotive industries. The surface preparation of aluminum prior to adhesive bonding plays a significant role in improving the bonding strength. Surface cleanliness, surface roughness, and surface chemistry can be controlled, primarily, by proper surface treatment methods.

Application of an Anchor Mapping of Alien Chromosome (AMAC) Fragment Localization Method in the Identification of Radish Chromosome Segments in the Progeny of Rape-Radish Interspecific Hybrids.

Rape () is an important oilseed crop widely cultivated worldwide. Due to its relatively short evolutionary and domestication history, its intra-species genetic diversity is limited. Radish (), belonging to a different genus but the same family as , possesses an abundance of excellent gene resources.

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions.

Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms.

Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture content maize kernel phenotypic features was constructed by extracting seven features (geometric and shape features).