Step data collection sequence

Figure 2-10. Time scale of the periodic event, control signal and data collection sequence for the step-scan measurements.

The STEP procedure, described by Hendrick and Benner (1987), was developed from a research program on incident investigation methods. STEP is based on the mulHple events sequence method and is an investigative process which structures data collection, representation, and analysis. 

Data are the raw product of the scientific method of inquiry. By analysis, refinement and reduction which collectively constitute the fourth step in the sequence, data are converted to information about the nature of study systems. The conversion is accomplished by the Neymann-Pearson process of statistical hypotheses testing(g,). If the collected data are sufficient and pertinent enough to support rejecting or accepting the statistical hypothesis under test, a measurableO. 10) quantity of information about the study system has been extracted. If not, the data cannot be converted to information and therefore cannot contribute to the pool of accepted scientific knowledge. 

Generally, all practical reactions occur by a sequence of elementary steps that collectively constitute the mechanism. The rate equation for the overall reaction is developed from the mechanism and is then used in reactor design. Although there are cases where experimental data provide no information about intermediate chemical species, experimental data have provided researchers with useful guidelines in postulating reaction mechanisms. Information about intermediate species is essential in identifying the correct mechanism of reaction. Where many steps are used, different mechanisms can produce similar forms of overall rate expression. The overall rate equation is the result... 

A generic algorithm of data collection in the step scanning mode is shown in the form of a flowchart in Figure 3.42. It includes the following sequence of events ... 

The scientific method is not a rigid sequence of steps, but rather a dynamic process designed to expiain and predict reai phenomena. Observations (sometimes expressed as natural laws) lead to hypotheses about how or why something occurs. Hypotheses are tested in controlled experiments and adjusted if necessary. If all the data collected support a hypothesis, a model (theory) can be developed to explain the observations. A good model is useful in predicting related phenomena but must be refined if conflicting data appear. 

The structure of MPC is shown in the block diagram of Figure 12.40 [7]. A mathematical model of the process is used to predict the current values of the output (controlled) variables. The model is usually implemented in the form of a multi-variable linear or nonlinear difference equation. It is typically developed from data collected during special plant tests consisting of changing an input variable or a disturbance variable from one value to another using a series of step-changes with different durations, or more advanced protocols such as the pseudo random-binary sequence described in Ref 7. The residuals (that is, the difference between the pre-... 

Individual attributes and member variables, e.g. active lander units to power on, selectable power converters, unit priorities, data collection rates, data quotas, and also a pointer attribute to the list of relative time-tagged telecommands (TCs) to be executed in conjunction with this ESI. Further attributes define the conditions under which a particular ESI should be deactivated (e.g. terminated upon time-out or/and occurrence of nominal vs. off-nominal events) and the means of carrying on the sequencing (e.g. step to the next ESI, conditional vs. unconditional jump to another ESI),... 

The practical requirements of the pulse-induced critical scattering system are for instrumental control, data collection and data analysis. In its most recent form, it has therefore been convenient to integrate an IBM PC compatible computer into the design of the instrument (see Figure 8). A cruciform holder called a carousel is used to transport cells between two temperature-controlled baths. The rotary axis of the carousel is positioned within a stirred air bath at temperature. One of the four arms of the carousel projects through an aperture to a stirred liquid bath at temperature T2. A cell is mounted at the end of each arm and the carousel is positioned so that three cells lie within the air bath and one cell is completely immersed in the liquid bath at the lowest point of its carousel orbit. The other cells are immersed in turn, through a sequence of 90° rotational steps, to this same precise position in the liquid bath. Each remains there for a fixed period of time, and is then returned to the air bath by the following step. 

The second postprocessing step is the automated enhancement of the TrEMBL annotation to bring TrEMBL entries closer to SWISS-PROT standard. There is an increasing need for reliable automatic functional annotation to cope with the rapidly increasing amount of sequence data. Most of the current approaches are still based on sequence similarity searches against known proteins. Some groups try to collect the results of different prediction tools in a simple way, e.g., PEDANT (Frishman and Mewes, 1997) or GeneQuiz (Scharf et al., 1994). However, several pitfalls of these methods have been reported (Bork and Koonin, 1998). 

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