For accurate thermometry and to circumvent any emissivity effects

For accurate thermometry and to circumvent any emissivity effects of the measured heat source the temperature sensing probes were divided into a signal probe (CH-1) and a reference probe (CH-2). The inner part of the cap of the signal probe was coated with high emissivity (signal 1) black paint as an IR emitting material, while that of the reference EtOH probe was covered with a low emissivity polished stainless steel cap (reference 0.1) [19]. The black paint (Motip Dupli, Dupli-color Supertherm), which is similar to a blackbody, has a rough surface and a high IR emissivity compared with the polished stainless steel. In addition, it is highly temperature-resistant up to 800 ��C. The temperature of a heat source is determined by measuring the difference Inhibitors,Modulators,Libraries in intensity of the IR radiations emitted from the two kinds of materials in the caps.
Therefore, the IR intensity difference (��I) is a function of the temperature of two probes, as delineated in Equation (6):��I=Isignal?Ireference=(?signal??reference) ��eT4(6)Figure 3.Structure of the temperature sensing probe.Figure 4 Inhibitors,Modulators,Libraries shows the experimental setup employed for temperature measurements using the embedded IR fiber-optic sensor. The sign
Computerization of modern science and technology has raised many concerns about the methods traditionally used to ensure the integrity and utility of data ([1], and references therein). It is particularly important that sensor deployments and their associated information systems follow scientific fundamentals to ensure the integrity and validity of the data collected and presented.
These principles were recently formalized Inhibitors,Modulators,Libraries by the U.S. National Academy of Sciences Committee on Ensuring the Utility and Integrity of Research Inhibitors,Modulators,Libraries Data in the Digital Age in their report [2].Addressing the report recommendations and the basic principles of scientific research, this paper develops a methodology and implementation procedures for automatically assessing the quality of research data gathered from sensors. In particular, it concentrates on calculating and presenting data quality metrics along with the research data themselves, crucial as modern measurement theory and practice assumes getting a measurement result along with some characteristics of its uncertainty [3]. Since the early 1990s and in particular since the publication of the ISO Guide [4], there has been an increasing recognition that the uncertainty of a measurement is no less critical than the value of the measurement result itself.
Uncertainty may have a number of different components. Some of these may be evaluated Drug_discovery by statistical methods but others may require expert estimates, reasoning and judgment ([4], and references therein). This paper combines both approaches. It derives the total uncertainty characteristics from statistical processing of the available data and includes a contribution from domain expert judgment.

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