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Low Energy Particle Accelerators and Laboratories
Published in Vlado Valković, Low Energy Particle Accelerator-Based Technologies and Their Applications, 2022
Where time constant is defined as τ = RC. This equation holds as long as the currents Ibelt and Ibeam and effective resistance R are constant and corona currents are negligible. The terminal will reach an equilibrium potential V0 where the charge delivered by the belt is equal to the charge leaving the terminal electrode. Modern machines use fast closed-loop controls to achieve a fast adjustment and a high stability of the terminal voltage V. In such a way, the time dependence of V is determined by the characteristics of the closed-loop control.
Mathematical Modeling and Analysis of Soft Tissue Viscoelasticity and Dielectric Relaxation
Published in A. Bakiya, K. Kamalanand, R. L. J. De Britto, Mechano-Electric Correlations in the Human Physiological System, 2021
A. Bakiya, K. Kamalanand, R. L. J. De Britto
This representation is known as the transfer function model and can be used to study the reaction of the system with various types of loads such as the Heaviside function, impulsive loads and dynamic or sinusoidal loads, as shown in Figure 1.10. The quantity is known as the time constant () of the system and is measured in seconds.
Flow Patterns During Acute Coronary Ischemia and Coronary Sinus Occlusion: A Window to the Mechanisms of “Hidden” Myocardial Perfusion
Published in Samuel Sideman, Rafael Beyar, Analysis and Simulation of the Cardiac System — Ischemia, 2020
Rafael Beyar, Henry R. Halperin, Joshua E. Tsitlik, Alan D. Guerci
A dynamic resistance behavior — In this approach it is assumed that the cross-sectional area of the microcirculation, which presents the major resistance to flow, is a function of the local average vessel transmural pressure and that the flow is simply related to the arteriovenous pressure gradient without assuming a “zero flow pressure”. As shown in Figure 2 (right panel, dashed line), the cross-sectional area rapidly increases at low transmural pressures and approaches a plateau at high pressures typical of a relatively stiff vessel at these pressure levels. It can be shown mathematically that for a quasi-steady-state case, the relation between the waterfall model resistence, Rw, and the pressure-dependent cross-sectional area, A, and nonwaterfall resistance, R, given in Figure 2 will result in a similar pressure flow relationship as in the waterfall model. An inherent assumption to such a description is that the local time constant for a change in the microcirculatory cross-sectional area is short relative to the cardiac cycle. It is very difficult at the current level of knowledge to accurately estimate what is the local time constant for changes in the cross-sectional area of the resistance vessels at the subendocardial layers as a function of the local intramyocardial pressure.
Oxygen uptake on-kinetics during six-minute walk test predicts short-term outcomes after off-pump coronary artery bypass surgery
Published in Disability and Rehabilitation, 2019
Isadora Salvador Rocco, Marcela Viceconte, Hayanne Osiro Pauletti, Bruna Caroline Matos-Garcia, Natasha Oliveira Marcondi, Caroline Bublitz, Douglas William Bolzan, Rita Simone Lopes Moreira, Michel Silva Reis, Nelson Américo Hossne, Walter José Gomes, Ross Arena, Solange Guizilini
Original breath-by-breath data were imported from the telemetric cardiopulmonary testing device. Raw data were pre-processed by averaging the breath-by-breath measurements over consecutive periods of 15 s. The onset of exercise curve was fitted using a monoexponential regression model [16]. f(t) representing VO2 at certain time (t); y0 is the lower limit at t = 0, representing the VO2 rest value corresponding to a mean value at the last minute of baseline prior to the test; y1 as the upper limit, i.e., VO2 steady-state (VO2SS); and τ when >0 determinates the steepness of the increase as t elapses, representing the time constant, i.e., the time take to reach 63% of the function [17].
Preliminary study of a control algorithm for radio-frequency-induced intestinal tissue fusion
Published in International Journal of Hyperthermia, 2019
Liangyong Tu, Yu Zhou, Chengli Song, Yuan Li, Lin Chen, Yinmin Xue
In stage A in Figure 2, two tasks had to be accomplished. The master control module adjusted the output parameters r(t) according to the different statuses of the tissue (bio-impedance and temperature). The feedback control module detected the output of the RF energy y(t) and adjusted it to be similar to r(t) in real time by using a proportional-integral-derivative (PID) controller, as displayed in Figure 3. For the PID controller, the relationship between the input e(t) and output u(t) is given as follows: e(t) = [r(t) − y(t)] is the error signal, KP is the proportional gain, KI is the integral gain, and KD is the derivative gain. The transfer function is given by as follows: TI = KP/KI is the integral time constant and TD = KD/KP is the derivative time constant.
Particle and inhalation exposure in human and monkey computational airway models
Published in Inhalation Toxicology, 2018
Nguyen Lu Phuong, Nguyen Dang Khoa, Kiao Inthavong, Kazuhide Ito
Figure 5 outlines the airways of the 100 sampled and representative particles, indicating the relationship between particle elapsed time and particle trajectory distance from its origin at the nostril to its eventual fate. The selected particles correspond to the parts-per-hundred probabilities of the 10,000 particles. The particle fates are a mixture of particle deposition and particles escaping the airway through the trachea exit. A nominal time constant (τ) is used, which is defined as airway volume [m3] divided by the inhaled airflow rate [m3/s]. This accounts for geometry and inhalation rate differences (Table 2). At the lower flow rates (2.2 L/min for monkey and 10 L/min for human), the nominal time constant for the monkey airway was τ = 0.31 s and, for the human, this was τ = 0.52 s.