By Bill Green
“This brilliant clinical memoir captures the romance and sweetness of analysis in special poetic prose that's as lovely and evocative as something written by way of Rilke, painted via Seurat, or performed by means of Casals." —Mary Doria Russell, writer of document and The Sparrow
“A radiant love letter to technology from a scientist with a poet's soul . . . eco-friendly is a lovely author, and his fierce concentration and mastery of fashion are equivalent to the biologist and essayist Lewis Thomas." —Kirkus Reviews
In Boltzmann's Tomb, invoice eco-friendly interweaves the tale of his personal lifelong evolution as a scientist, and his paintings within the Antarctic, with a travelogue that could be a own and common background of technological know-how. Like Richard Holmes' The Age of Wonder—this e-book serves as a wonderful advent to the good figures of technology. besides lyrical meditations at the tragic lifetime of Galileo, the wildly eccentric Tycho Brahe, and the visionary Sir Isaac Newton, Green's ruminations go back all through to the lesser-known determine of Ludwig Boltzmann. utilizing Boltzmann's theories of randomness and entropy as a bigger metaphor for the unpredictable paths that our lives take, eco-friendly exhibits us that technology, like paintings, is a lived adventure.
Bill eco-friendly is a geochemist and professor emeritus at Miami college in Oxford, Ohio. he's additionally the writer of Water, Ice & Stone: technological know-how and reminiscence at the Antarctic Lakes which bought the yankee Museum of common History's John Burroughs Award for Nature Writing, used to be a finalist for the PEN/Martha Albrand Award, and used to be excerpted within the Ends of the Earth: An Anthology of the best Writing at the Arctic and the Antarctic, edited via Elizabeth Kolbert.
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Additional resources for Boltzmann's Tomb: Travels in Search of Science
Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Trans. Power Syst. 13(3), 1115–1120 (1998) 12. : Nonparametric regression based short-term load forecasting. IEEE Trans. Power Syst. 13(3), 725–730 (1998) 13. : An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities. IEEE Trans. Power Syst. 10(3), 1716–1722 (1995) 14. : ANNSTLF- neural network-based electric load forecasting system. IEEE Trans. Neural Netw.
The use of validation test set is necessary as it helps to achieve better generalization results and it seems to have similar behaviour with the test set rather than the training set. Fig. 11 MAPE of training, validation and test sets versus the training years. J. Tsekouras et al. Fig. 12 MAPE of training, validation and test sets versus the percentage of the data used for ANN evaluation. Scaled conjugate gradient training algorithm is used Another important issue that should be carefully studied is the percentage of the data used for the evaluation of the model.
In Fig. 9 the prediction errors of a typical summer day for Greek interconnected power system of the year 2000 (Thursday 8-6-2000) are presented for the training, evaluation and test sets respectively, while in Fig. J. Tsekouras et al. Fig. 10 Chronological active load curves of the measured load, the estimated load, the estimated load with the 5 % lower limit with respect to evaluation set, the estimated load with the 5 % upper limit with respect to evaluation set, the estimated load with the 5 % lower limit with respect to test set, the estimated load with the 5 % upper limit with respect to test set for the best ANN model for 8-6-2000 in Greek interconnected power system the respective measured and estimated load values are presented together with the 90 % confidence intervals of the evaluation and the test data sets.