Variable count time

However, some required methods of data collection may be unavailable from some vendors. For instance, at time of writing, Bede were the only known hardware or software vendor to include a general Variable Count Time (VCT) data collection capability. 

X Y, DBWS/Fullprof, Old DIA (ILL), DIB (ILL), Brookhaven sync, G4.1, D2B/3T2/G4.2, HRPT/ DMC (PSI), RX (Socabim), VCT/SR5 (Variable Count Time Madsen and Hill), GSAS, CPI, PANalytical, ISIS normalized, ESRF multi, LLB Saclay formats, UXD Multscans (Socabim) 6T2... 

Analysis of binding experiments required a careful comparison of (i) the MYKO 63 bands, either in the presence or absence of DNA bands and (ii) the DNA Raman bands, either in the presence or absence of MYKO 63 bands. This comparison was achieved by computer-subtracting variable amounts of one spectrum from another. Previously, the various spectra were normalized to the same relative Raman intensity, with the 934 cm band (CIO symmetric stretch) as an internal standard. The intensity of the CIO. scattering measures the combined effect of such experimental factors as counting time, optical alignment and laser power. 

Fig. 3. X-ray diffractogram of Class-F bituminous coal fly ash. Analytical conditions diffraction data were collected using a Philips X-ray powder diffractometer (45 kV/30-40 mA CuKa theta-compensating variable divergence slit diffracted-beam graphite monochromator scintillation detector) automated with an MDI/Radix Databox. The scan parameters were typically 0.02° step size for 1 s count times over a range of 5-60° 2-theta. All data were analysed and displayed using a data reduction and display code (JADE) from Materials Data Inc., livermore, CA.

Another factor to be taken into account is the degree of over determination, or the ratio between the number of observations and the number of variable parameters in the least-squares problem. The number of observations depends on many factors, such as the X-ray wavelength, crystal quality and size, X-ray flux, temperature and experimental details like counting time, crystal alignment and detector characteristics. The number of parameters is likewise not fixed by the size of the asymmetric unit only and can be manipulated in many ways, like adding parameters to describe complicated modes of atomic displacements from their equilibrium positions. Estimated standard deviations on derived bond parameters are obtained from the least-squares covariance matrix as a measure of internal consistency. These quantities do not relate to the absolute values of bond lengths or angles since no physical factors feature in their derivation. 

A very short program with only one variable counts up to 24 and then crashes. How could it possibly run out of memory The key is the GOSUB. Every time you go to a subroutine, the return address is saved on the stack. Since there are no RETURNS in the program, more and more addresses are saved, until finally there is no stack space left. 

There are, however, difficulties. Accurate correction for pulse loss depends upon the pulser pulses accurately mimicking the detector pulses. The rise time and the fall times of the pulser pulses should be identical to those of detector pulses. Leaving aside the fact that preamplifier output pulses have a variable rise time, none of the readily available pulsers allow detailed control of the fall time. Bear in mind that the fall time of the preamphfier pulses depends upon the time constant of the feedback circuit in the preamplifier, and that pole-zero cancellation within the amplifier matches the shaping circuits to the input pulse fall time. The consequence of this is that it may not be possible to pole-zero correct the pulser pulses and the detector pulses together. At anything more than a low count rate, many detector pulses may be incorrectly measured by the ADC if they occur close in time to a pulser pulse. 

Figure 13.6 Relative MDA versus relative efficiency fa- a fixed counting time, calculated from Equation (13.8), for cases where Bg is dominant, where is dominant and wha-e FWHM alters with size of detector (continuous lines, FWHM constant dashed lines, FWHM variable)...

An interesting, and useful, paper to consult in relation to this section is that by Bossew (2005), in which he discusses the gamma-rays detected in a spectrum representing 3.3 years of counting time. In fact, this spectrum is the sum of 333 separate background spectra, accumulated over 16 years. The paper also contains some discussion of the temporal variability of background, and in the case of the Rn daughters, the monthly variabdity. 

The prepared alpha sources were counted using passivated implanted planar silicon (PIPS) detectors (Canberra, Belgium, model A450). Counting time was variable depending on sample activity and errors for individual measurements were estimated from counting statistics. 

Estimated total variances were determined for each substrate by pooling the variances over lead concentrations. The precision (square root of the total variance) reported in the preceding table is that expected for an individual reading as opposed to that for the mean of three individual readings. The analysis of variance showed that essentially all variability was due to variability of individual replicate measurements. Hence, the precision of laboratory measurements could be improved by increasing the counting time. 

In the case of binary variables a G 0,1, we can let density p t) at time t represent the probability of finding value a = 1 at any site at time t. A simple counting of all possible configurations in an arbitrary neighborhood Af gives us the general mean-field expression for the time-dependence of p ... 

The variable ERRORmj n represents the error in the position of the mandrel over an increment in TIME, in seconds. ERRORman is calculated by subtracting the actual pulses accumulated, PULSEman, from the desired number of pulses that would be generated under perfect control. The desired number of pulses for perfect control is determined by the set point speed, RPSman, revolutions per second and the mechanical gear reduction. The constant 15630 is the product of encoder counts per revolution and the thirty to one gear reduction of the mandrel. 

This DATA step rearranges the counts data set created by PROC FREQ. The data set is essentially merged with itself three times in order to get each treatment into its proper column. A group variable is created to help separate the ANY MEDICATION row from the other true medications. Percentages are calculated, and the columns (coll-col3) are formatted as XXX (XXX%). Finally, the lastrec variable is created to help make a continuation flag in the PROC REPORT output. 

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