Position error

Transient torques Gears with indexing or positioning errors. 

With the trolley displaeement and veloeity shown in Figures 10.15(e) and (d), the state feedbaek, although oseillatory, give the best results sinee there is no steady-state error. The positional error for both rulebases inereases with time, and there is a eonstant veloeity steady-state error for the 11 set rulebase, and inereasing error for the 22 set rulebase. Figure 10.15(e) shows the eontrol foree for eaeh of the three strategies. 

Tyler, G.A., Pried, D.L., 1982, Image-position error associated with a quadrant detector, JOSA 72, 804... 

We name Kerr the position error constant.1 For the error to approach zero, Kcrr must approach infinity. In Example 5.1, the error constant and steady state error are... 

Section II,B, this may be an overestimate of the false positive error rate because many of the apparent consecutive errors correspond to regions of disorder that are ordered in the crystal due to ligand binding or crystal contacts. Also, because disordered regions of length >40 residues are often missed due to false negative predictions of order, the data in... 

If the measured variable drops below the setpoint, a positive error is developed, and the control valve opens further. If the measured variable goes above the setpoint, a negative error is developed, and the control valve throttles down (opening is reduced). The 50% proportional band causes full stroke of the valve between a +50°F error and a -50°F error. 

If the measured variable decreases from its initial value of 50 gpm to a new value of 45 gpm, as seen in Figure 21, a positive error of 5% is produced and applied to the input of the integral controller. The controller has a constant of 0.1 seconds 1, so the controller output rate of change is 0.5% per second. 

Isaeva [181] described a phosphomolybdate method for the determination of phosphate in turbid seawater. Molybdenum titration methods are subject to extensive interferences and are not considered to be reliable when compared with more recently developed methods based on solvent extraction [182-187], such as solvent-extraction spectrophotometric determination of phosphate using molybdate and malachite green [188]. In this method the ion pair formed between malachite green and phosphomolybdate is extracted from the seawater sample with an organic solvent. This extraction achieves a useful 20-fold increase in the concentration of the phosphate in the extract. The detection limit is about 0.1 ig/l, standard deviation 0.05 ng-1 (4.3 xg/l in tap water), and relative standard deviation 1.1%. Most cations and anions found in non-saline waters do not interfere, but arsenic (V) causes large positive errors. 

Arsenic (V) causes large positive errors - arsenic (V) at a concentration of 10 pg/1 produces an absorbance of 0.07, but can be masked with tartaric acid (added in the reagent solution). When arsenic (V) was present at concentrations of 50 pg/1 it was masked with 0.1 ml of 1 x 10 4 M sodium thiosulfate added after the sulfuric acid. 

The numerator of Bayes theorem merely describes cell a (the tme-positive results). The probability of being in cell a is equal to the prevalence times the sensitivity, where XD+) is the prevalence (the probability of being in the effected column) and where XT + D+) is the sensitivity (the probability of being in the top row, given the fact of being in the effected column). The denominator of Bayes theorem consists of two terms, the first of which once again describes cell a (the true-positive results) and the second of which describes cell b (the false-positive error rate, or X I + D—), is multiplied by the prevalence of noneffected animals, or... 

Sensitivity of immunological stain = 96% = 0.96 False-negative error rate of the test = 4% = 0.04 Specificity of the test = 94% = 0.94 False-positive error rate of the test = 6% = 0.06 Prevalence of effect in the tissues = 1% = 0.01... 

Why did the posterior probability increase so much the second time One reason was that the prior probability was considerably higher in the second calculation than in the first (27% versus 2%), based on the fact that the first test yielded positive results. Another reason was that the specificity of the second test was quite high (98%), which markedly reduced the false-positive error rate and therefore increased the positive predictive value. 

A colorimetric version of the method uses a tetrazolium salt. The oxidation of the NADH is coupled to the reduction of 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyltetrazolium bromide by a diaphorase (EC 1.6.4.3) and a deep blue formazan develops. The absorbance is read at a wavelength between 540 and 600 nm. This is more sensitive than the ultraviolet method but is more prone to interference, especially from any reducing agents present in the sample, which will result in a positive error. 

This type of error equates to box B and is variously described as a type I error, a false-positive error or the a error. A type I error in a study result would lead to the incorrect rejection of the null hypothesis. 

Perhaps the most consistent effect of cannabis is consolidation of information from short-term memory (Dornbush et al 1971 Murray 1986). In contrast, cannabis does not appear to impair access to information already in long-term memory (Parley et al. 1977 Parker et al. 1980). While cannabis and placebo groups performed equally well on a word list recognition task, the cannabis group made more false positive errors (Abel 1970, 1971). Another effect on memory reported in severeal studies is increased intrusion of irrelevant material (Abel 1970, 1971 Clark et al. 1970 Tinklenberg et al. 1970 Pfefferbaum et al. 1977). 

AMI. While MNDO was widely accepted and extensively used, there were still some deficiencies in the model. In particular, excessive repulsions were observed in MNDO potential energy surfaces just outside chemical bonding distances. This deficiency manifested itself (5,7) in the inability of MNDO to model hydrogen bonding, as well as in large positive errors in the AHf of sterically crowded molecules and in heats of activation. Again Dewar set off to correct this deficiency. 

From the distribntion of signals of blank measnrements we can derive the critical value. This valne is defined by the one-tailed error probability a of the statistical distribntion. If the signal is above this critical value we have a low probability (a) for a false positive error. 

This low probability is shown in the left of the two distribntions in this graph, the statistical distribntion of blank measurements. At a signal corresponding to the critical value we have the low probability for a false positive error, described by the black part of the distribution. 

The LoD value is characterised by a low probability for a false positive error (a) and for a false negative error (p)... 

The results given in Table 12 also suggest, that the SMO-LMBPT formalism not only eliminates the basis set super-position error but also takes into account the beneficial part of the BSSE. 

Molecule Positivity Error Measured by the Lowest Negative Eigenvalue ... 

The concentration residuals are plotted as a function of the predicted concentration (Draper and Smith. 1981). One side note is that with replicate samples it can appear that there is structure in the residuals when in fact the model is adequate. This is demonstrated in Figure 5.15 where there are 10 replicates at three concentration levels. The residuals form slanted lines, but this is only because some samples arc predicted low (and so have positive errors), and ojher samples are predicted high (and so have negative errors). 

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