A test system has been developed that can be used to calibrate and determine the time response, linearity and temperature sensitivity of a fibre optic oxygen sensor. The simple system obviates the need for precision gas standards and the requirement to generate a true square wave step response, which is seldom achievable. The sensor is mounted in a small chamber containing air or a known fraction of oxygen. By means of a computer-controlled switch, the absolute pressure within the chamber can be changed rapidly to a new steady state value. The partial pressure of oxygen changes in direct proportion to the absolute pressure, and so the accuracy and linearity and response time of the PO(2) calibration are limited only by those of the absolute pressure sensor. The temperature sensitivity of a commercial sensor and a means of correction are also described.
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http://dx.doi.org/10.1088/0967-3334/31/4/N02 | DOI Listing |
Life (Basel)
December 2024
Exercise and Metabolism Research Center, College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua 321004, China.
Background: The objective of this study was to examine the impacts of absolute cuff pressure blood flow restriction (A-BFR) training and incremental cuff pressure blood flow restriction (I-BFR) training, under equal cuff pressures, on body composition and maximal strength among untrained adults. Additionally, we aimed to compare these effects with those observed in high-load resistance training (HL-RT).
Methods: Thirty-three adults without prior professional sports or resistance training experience were recruited and randomly assigned to three groups ( = 11 per group) for an 8-week training program, held three times weekly.
Sci Rep
January 2025
Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Department of Biomedical Engineering, Indian Institute of Technology, Ropar, Punjab, India.
Blood pressure (BP) is one of the vital physiological parameters, and its measurement is done routinely for almost all patients who visit hospitals. Cuffless BP measurement has been of great research interest over the last few years. In this paper, we aim to establish a method for cuffless measurement of BP using ultrasound.
View Article and Find Full Text PDFHeliyon
December 2024
School of Materials Science and Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
This paper presents the preparation of the parental experimental alloy, featuring a standard composition of TiYZrFeNiMn, via the vacuum induction melting technique. Subsequently, the TiYZrFeNiMn alloy, with an addition of 2 wt% Ni, underwent mechanical ball milling to yield a TiFe-based composite for experimental purposes. The results of the experimental tests indicate that the composite alloy's phase composition comprises the TiFe primary phase, with a minor quantity of ZrMn phase segregated on the surface of the primary TiFe phase, as well as Ni phase.
View Article and Find Full Text PDFACS Omega
December 2024
China University of Petroleum-Beijing, Changping, Beijing 102249, China.
One of the key points in the construction of smart oil and gas fields is the effective utilization of data. Virtual Flow Metering (VFM), as one of the representative research directions for digital transformation, can obtain real-time production from oil and gas wells without the need for additional field instrumentation, utilizing pressure and temperature data obtained from sensors and employing multiphase flow mechanism models. The data-driven VFM demonstrates a commendable capacity in capturing the nonlinear relationship between sensor data and flow rates, while circumventing the necessity for rigorous analysis of the underlying mechanistic processes.
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