Modern MT: A New Open-Source Machine Translation Platform for the Translation Industry

Modern MT (www.modernmt.eu) is a three-year Horizon 2020 innovation action (2015–2017) to develop new open-source machine translation technology for use in translation production environments, both fully automatic and as a back-end in interactive post-editing scenarios. Led by Translated srl, the project consortium also includes the Fondazione Bruno Kessler (FBK), the University of Edinburgh, and TAUS B.V. Modern MT has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 645487 (call ICT-17-2014)

Assessment of breath-by-breath alveolar gas exchange: an alternative view of the respiratory cycle

Breath-by-breath (BbB) determination of the O2 flux at alveolar level implies the identification of the start and end points of each respiratory cycle; Gr\uf8nlund defined them as the times in two successive breaths showing equal expiratory gas fractions. Alternatively, the start and end points of each breath might be linked to the ratio between the exchangeable and non-exchangeable gases. The alternative algorithm is described and evaluated with respect to the algorithm proposed by Gr\uf8nlund

Influence of the fitting window on the O2uptake kinetics at the onset of moderate intensity exercise

The O2uptake (V O2) data at the onset of an exercise are usually fitted with a mono-exponential function, after removal of the data pertaining to a conventional initial time period (DTr) lasting _20 s. We performed a thorough quantitative analysis on the effects of removing data pertaining to different DTr, aiming at identifying an objective method to establish the appropriate DTr. Breath-by-breath O2uptake responses, acquired from 25 healthy adults performing a step moderate-intensity exercise, and 104 simulated biexponential responses, were analyzed. For all the responses, the kinetic parameters of a mono-exponential function and the corresponding asymptotic standard errors (ASEs) were estimated by nonlinear regression, removing the data pertaining to progressively longer initial periods (1 s each) up to 60 s. Four methods to establish objectively DTr were compared. The minimum estimated s was obtained for DTr % 35 s in both the V O2and simulated data, that was about 30% lower compared with that obtained for DTr % 0s. The average ASE values remained quite constant up to DTr % 35 s, thereafter they increased remarkably. The s used to generate the simulated response fell within the confidence intervals of the estimated s in _85% of cases for DTr = 20 s ("20 s-w" method); this percentage increased to _92% of cases when DTr was established according to both the minimum s and its narrowest confidence interval ("Mixed" method). In conclusion, the effects of removing V O2data pertaining to different DTr are remarkable. The "Mixed" method provided estimated parameters close to those used to generate the simulated responses and is thus endorsed