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Fig. 3 Comparison of overtime transaction ratio Fig. 4 Comparison of rejecting service time.
We mainly compare the system overhead, overtime transaction ratio (ratio of transactions that exceed the validity interval), and rejecting service time (downtime).
Next, in our proposed approach, we observe the changes of overtime transaction ratio under different "per_short" (short time-limited data ratio) and "threshold" (time series data state change threshold), and the results are in Figs. 5 and 6, respectively.
In Fig. 5, the order of overtime transaction ratios for different per_short is 0 > 0.5 > 0.1 > 0.3 > 0.2, which means that we must carefully select per_short to optimize the overtime transaction ratio.
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Fig. 5 Overtime transaction ratios for different "per_short" Fig. 6 Overtime transaction ratios for different "threshold".
In Fig. 6, the order of overtime transaction ratios for different threshold is the same as that of per_short, so we can have the same conclusion.
As a result, the appraisal to transaction price ratio is, for the whole period, 1.29.
This bimodal profile is not observed when we look at the distribution of the loan to transaction price ratio.
That is, the appraisal to transaction price ratio was 1.29, i.e. in other words there is an over-appraising of 29%%.
These two modes correspond to the regulatory thresholds mentioned above.17 Moreover, this bimodal profile is not observed when we look at the distribution of the loan to transaction price ratio.
To simplify this topic, six different costs will be evaluated: expense ratio, transaction costs (brokerage commissions, market impact cost, and spread cost), tax costs, cash drag, soft dollar cost and advisory fees.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com