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A Trapletti

Publications and source records attributed to A Trapletti.

2 recordsLinked to original sources

Breathing pattern and exercise endurance time after exhausting cycling or breathing.

The aim of the present study was to investigate whether the changes in breathing pattern that frequently occur towards the end of exhaustive exercise (i.e., increased breathing frequency, fb, with or without decreased tidal volume) may be caused by the respiratory work itself rather than by leg muscle work. Eight healthy, trained subjects performed the following three sessions in random order: (A) two sequential cycling endurance tests at 78% peak O2 consumption (VO2peak) to exhaustion (A1, A2); (B) isolated, isocapnic hyperpnea (B1) at a minute ventilation (VE) and an exercise duration similar to that attained during a preliminary cycling endurance test at 78% VO2peak, followed by a cycling endurance test at 78% VO2peak (B2); (C) isolated, isocapnic hyperpnea (C1) at a VE at least 20% higher than that of the preliminary cycling test and the same exercise duration as the preliminary cycling test, followed by a cycling endurance test at 78% VO2peak (C2). Neither of the two isocapnic hyperventilation tasks (B1 or C1) affected either the breathing pattern or the endurance times of the subsequent cycling tests. Only cycling test A2 was significantly shorter [mean (SD) 26.5 (8.3) min] than tests A1 [41.0(9.0) min], B2 [41.9 (6.0) min], and C2 [42.0 (7.5) min]. In addition, compared to test A1, only the breathing pattern of test A2 was significantly different [i.e., VE: + 10.5 (7.6) 1 min(-1), and fb: + 12.1 (8.5) breaths min(-1)], in contrast to the breathing patterns of cycling tests B2 [VE: -2.5 (6.2) 1 min(-1), f(b): +0.2 (3.6) breaths min(-1)] and C2 [VE: -3.0 (7.0) 1 min(-1), fb: +0.6 (6.1) breaths min(-1)]. In summary, these results suggest that the changes in breathing pattern that occur towards the end of an exhaustive exercise test are a result of changes in the leg muscles rather than in the respiratory muscles themselves.

Adult↗

Stationary and integrated autoregressive neural network processes.

We consider autoregressive neural network (AR-NN) processes driven by additive noise and demonstrate that the characteristic roots of the shortcuts-the standard conditions from linear time-series analysis-determine the stochastic behavior of the overall AR-NN process. If all the characteristic roots are outside the unit circle, then the process is ergodic and stationary. If at least one characteristic root lies inside the unit circle, then the process is transient. AR-NN processes with characteristic roots lying on the unit circle exhibit either ergodic, random walk, or transient behavior. We also analyze the class of integrated AR-NN (ARI-NN) processes and show that a standardized ARI-NN process "converges" to a Wiener process. Finally, least-squares estimation (training) of the stationary models and testing for nonstationarity is discussed. The estimators are shown to be consistent, and expressions on the limiting distributions are given.

Artifacts↗