Measurement of respiratory impedance ([Formula: see text]) in intubated patients requires accurate compensation for pressure losses across the endotracheal tube (ETT). In this study, we compared time-domain (TD), frequency-domain (FD) and combined time-/frequency-domain (FT) methods for ETT compensation. We measured total impedance ([Formula: see text]) of a test lung in series with three different ETT sizes, as well as in three intubated porcine subjects. Pressure measurement at the distal end of the ETT was used to determine the true [Formula: see text]. For TD compensation, pressure distal to the ETT was obtained based on its resistive and inertial properties, and the corresponding [Formula: see text] was estimated. For FD compensation, impedance of the isolated ETT was obtained from oscillatory flow and pressure waveforms, and then subtracted from [Formula: see text]. For TF compensation, the nonlinear resistive properties of the ETT were subtracted from the proximal pressure measurement, from which the linear resistive and inertial ETT properties were removed in the frequency-domain to obtain [Formula: see text]. The relative root mean square error between the actual and estimated [Formula: see text] ([Formula: see text]) showed that TD compensation yielded the least accurate estimates of [Formula: see text] for the in vitro experiments, with small deviations observed at higher frequencies. The FD and TF compensations yielded estimates of [Formula: see text] with similar accuracies. For the porcine subjects, no significant differences were observed in [Formula: see text] across compensation methods. FD and TF compensation of the ETT may allow for accurate oscillometric estimates of [Formula: see text] in intubated subjects, while avoiding the difficulties associated with direct tracheal pressure measurement.
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http://dx.doi.org/10.1007/s10877-021-00788-9 | DOI Listing |
Sci Rep
January 2025
Faculty of Computers and Information, Minia University, Minia, Egypt.
This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency's Downscaler Model (DS) to predict Particulate Matter ([Formula: see text]) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time.
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January 2025
Department of Applied Physics, School of Engineering Sciences, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691, Stockholm, Sweden.
Non-trivial band topology along with magnetism leads to different novel quantum phases. When time-reversal symmetry is broken in three-dimensional topological insulators (TIs) through, e.g.
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January 2025
Changchun Automobile Economic & Technological Development Zone Employment Service Bureau, Jilin City, China.
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January 2025
TH-PPM Group, Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef, 62521, Egypt.
A wealth of details regarding an individual's state of health, like a person's respiratory and metabolic functioning, can be studied by analyzing the volatile molecules and atoms in human exhaled breath. Besides, the salinity of seawater is a crucial factor in understanding its characteristics because any variation in the salinity of seawater represents the variations in the hydrological, biological, and chemical distributions. In this paper, a symmetrical one-dimensional phononic structure is theoretically designed using two symmetrical crystals separated with a defective cavity.
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January 2025
Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, Riyadh, 11543, Saudi Arabia.
Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.
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