Ultrasonic gas flow meters are especially suitable for measurement in pipelines with large diameters. However, on the one hand, it is difficult to find a stable feature point to calculate the duration of propagation of the ultrasonic signal, through which we can obtain the real-time flow rate of the gas, and on the other hand, the computation incurred by signal processing methods to this end is burdensome and affects the real-time performance of the flow meter. To solve these problems, this study examines the characteristics of the stability of the echo signal and patterns of variation in the echo contour at different flow rates of gas. We found that peak points of the middle part of the rising segment of the echo signal were relatively stable, and the slope of the envelope of this part was always relatively large but constant, which indicates that peak points in this part were approximately distributed along a straight line. This finding is used to develop a signal processing method based on the connection fitting of the echo peak point with a large slope. This method is easy to implement, incurs a small amount of calculation, and has strong anti-interference ability. Moreover, it can guide research on signal processing methods and the stability of the echo signal. The proposed method was implemented on a dual-core hardware system, and the results of calibration show that it can attain 1.0-level accuracy over a measurable range of 30 m/h-1100 m/h.
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Sci Rep
January 2025
National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial.
View Article and Find Full Text PDFSci Rep
January 2025
The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, No. 26 Heping Road, Harbin, 150040, Heilongjiang, China.
Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting women of childbearing age, and we aimed to reveal its underlying molecular mechanisms. Gene expression profiles from GSE138518 and GSE155489, and single-cell RNA sequencing (scRNA-seq) data from PRJNA600740 were collected and subjected to bioinformatics analysis to identify the complex molecular mechanisms of PCOS. The expression of genes was detected by RT-qPCR.
View Article and Find Full Text PDFClin Gastroenterol Hepatol
January 2025
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. Campus Universitario de Rabanales, Albert Einstein Building. Ctra. N-IV, Km. 396. 14071, Córdoba, Spain; Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain. Av. Menéndez Pidal, s/n, Poniente Sur, 14004 Córdoba, Spain.
Background & Aims: We aimed to develop and validate an artificial intelligence score (GEMA-AI) to predict liver transplant (LT) waiting list outcomes using the same input variables contained in existing models.
Methods: Cohort study including adult LT candidates enlisted in the United Kingdom (2010-2020) for model training and internal validation, and in Australia (1998-2020) for external validation. GEMA-AI combined international normalized ratio, bilirubin, sodium, and the Royal Free Glomerular Filtration Rate in an explainable Artificial Neural Network.
PLoS One
January 2025
Computer Engineering, CCSIT, King Faisal University, Al Hufuf, Kingdom of Saudi Arabia.
This paper presents a low-power, second-order composite source-follower-based filter architecture optimized for biomedical signal processing, particularly ECG and EEG applications. Source-follower-based filters are recommended in the literature for high-frequency applications due to their lower power consumption when compared to filters with alternative topologies. However, they are not suitable for biomedical applications requiring low cutoff frequencies as they are designed to operate in the saturation region.
View Article and Find Full Text PDFNeuroinformatics
January 2025
Institute of Mathematics, University of Kassel, Heinrich-Plett-Str. 40, Kassel, 34132, Germany.
Accurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limited by the Heisenberg uncertainty principle. Inspired by the visual system's ability to identify objects and their locations, we propose a new method that integrates a visual system model with wavelet analysis to calculate both time and frequency features of local impulses in EEG signals. We develop a mathematical model based on invariant pattern recognition by the visual system, combined with wavelet analysis using Krawtchouk functions as the mother wavelet.
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