Effects of CO2 Curing on the Properties of Pervious Concrete in Different Paste-Aggregate Ratios.

To improve the comprehensive performance of pervious concrete, the properties of pervious concrete in different paste-aggregate ratios were subjected to both early CO2 curing and uncarbonated curing conditions. The mechanical properties, water permeability, porosity, and chemical composition of pervious concrete under two curing conditions were investigated and compared. The effects of CO2 curing on the properties of pervious concrete with different paste-aggregate ratios were derived. Through mechanical experiments, it was revealed that early CO2 curing can enhance the mechanical strength of pervious concrete by about 15-18%. Meanwhile, with the increase in the paste-aggregate ratio, the improvement effect induced by early CO2 curing became more significant. The water resistance of carbonated concrete was not significantly reduced. And with the increase in the paste-aggregate ratio, the carbonation degree of pervious concrete was reduced; the differences in porosity and water resistance became less significant when the paste-aggregate ratio exceeded 0.39. Micro-structural analysis shows that the early CO2 curing reduced both total porosity and the volume of micropores with a pore diameter of less than 40 nm, while it increased the volume of pores with a diameter of more than 40 nm. This is also the main reason that the strength of pervious concrete under early CO2 curing is higher than that without CO2 curing. The effect of varying paste-aggregate ratio and curing methods adds to the limited knowledge of the performance of pervious concrete.

Automatic screening of patients with atrial fibrillation from 24-h Holter recording using deep learning.

Aims: As the demand for atrial fibrillation (AF) screening increases, clinicians spend a significant amount of time identifying AF signals from massive amounts of data obtained during long-term dynamic electrocardiogram (ECG) monitoring. The identification of AF signals is subjective and depends on the experience of clinicians. However, experienced cardiologists are scarce. This study aimed to apply a deep learning-based algorithm to fully automate primary screening of patients with AF using 24-h Holter monitoring. Methods and results: A deep learning model was developed to automatically detect AF episodes using RR intervals and was trained and evaluated on 23 621 (2297 AF and 21 324 non-AF) 24-h Holter recordings from 23 452 patients. Based on the AF episode detection results, patients with AF were automatically identified using the criterion of at least one AF episode lasting 6 min or longer. Performance was assessed on an independent real-world hospital-scenario test set (19 227 recordings) and a community-scenario test set (1299 recordings). For the two test sets, the model obtained high performance for the identification of patients with AF (sensitivity: 0.995 and 1.000; specificity: 0.985 and 0.997, respectively). Moreover, it obtained good and consistent performance (sensitivity: 1.000; specificity: 0.972) for an external public data set. Conclusion: Using the criterion of at least one AF episode of 6 min or longer, the deep learning model can fully automatically screen patients for AF with high accuracy from long-term Holter monitoring data. This method may serve as a powerful and cost-effective tool for primary screening for AF.

CCLOWW: A grade-level Chinese children's lexicon of written words.

In this article, we present the Chinese Children's Lexicon of Written Words (CCLOWW), the first grade-level database that provides frequency statistics of simplified Chinese characters and words for children. The database computes from a corpus of 34,671,424 character tokens and 22,427,010 word tokens (including single- and multicharacter words), extracted from 2131 books. It contains 6746 different character types and 153,079 different word types. CCLOWW provides several frequency indices of simplified Chinese for three grade levels (grade 2 and below, grades 3-4, grades 5-6) to profile children's experience with written Chinese in and outside of school. We describe in this article the distributions of frequency and contextual diversity of the characters and words, as well as word length and syntactic categories of the words in the corpus and the subcorpora. We also report results of correlation analyses with other written corpora and of several naming and lexicon decision experiments. The findings suggest that CCLOWW frequency measures correlate well with other corpora. Importantly, they could reliably predict children's and adults' naming and lexical decision performances. They could also explain variance in adults' visual word recognition, in addition to frequency measures computed in an adult corpus, indicating that early print exposure might influence readers' lexical processing later on beyond an age of acquisition effect. CCLOWW will help researchers in language processing and development as well as educators with selecting language materials appropriate for children's developmental stages. The database is freely available online at https://www.learn2read.cn/database/ .