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A relatively simple equation for the long term "time to decline" is presented, showing that it depends primarily on the level of fertility, secondarily on longevity, and only modestly on the mean age of fertility.
Thus, in the long term, time to decline t* can be expressed in terms of two parameters, the mean age of fertility, μ, and the intrinsic growth rate, r, with Eq. (5) relating r to μ and the NRR.
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The objectives of this study were to determine impact of reduced Relative Total Dose Intensity (RTDI) on short-term (Overall Response Rate, ORR) and long-term (Time-to-Disease Progression, TTDP and Overall Survival, OS) outcome measures, to identify potential predictors for TTDP and OS and determine optimal RTDI for MBC patients treated with the anthracycline and taxane-based first-line treatment.
The availability of long term time series data for 25 to 30 years containing daily rainfall and temperature observations is another challenge for many low income countries.
Six trials (2145 subjects) reported disease target results at a short term time point (SMD = −0.32 (95 % CI −0.71 to 0.07)) and five trials (2732 subjects) reported this outcome at a long term time point (SMD = −0.09 (95 % CI −0.24 to 0.07)) (Fig. 5).
Seventeen trials reported adherence outcome data, six (2306 subjects) reported dichotomous data at a short term time point (OR = 2.11 (95 % CI 0.90 to 4.97)), four trials (322 subjects) reported continuous data at a short term time point (Fig. 4) and eight trials (6019 subjects) reported dichotomous data at a long term time point (OR = 1.05 (95 % CI 0.82 to 1.34)) (Fig. 6).
In the long term, time is on the reformers' side.
Importantly, antibody responses induced by BoHV-1ΔgEβgal virus (inactivated or live) can be serologically distinguished from those induced by wild-type virus, even at long term time-points after being exposed to infectious virus (30 dpc).
It is critical for the investor with a long-term time horizon to understand the importance of accumulating stocks that grow both in price and dividend yield.
Finally, we appreciate that with such a long-term time horizon to 2050, that there exists many other uncertainties both in the political-economic future of the EU and within the structure of the transport system, through the introduction of new technologies (e.g. autonomous, smart and connected vehicles) and a possible move towards a sharing economy and mobility-as-a-service (MAAS).
Moreover, an effective tool should be able to evaluate legacies within a long-term time, and applicable to different geographical areas and contexts.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com