Origin on Trial

Author:   Christopher H a Ting
Publisher:   Resource Publications (CA)
ISBN:  

9781666748246


Pages:   230
Publication Date:   27 January 2023
Format:   Paperback
Availability:   Available To Order   Availability explained
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Origin on Trial


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Overview

Do you know about the dark secrets in big evolution concerning the origin of the universe? Do you know that the Bible sets God's signature on his creation in the beginning? Not all fields of science are created equal. Some deal with past history rather than the present. Einstein's theory of gravity as curved spacetime is observable science. But some scientists use it with particle physics to tell a story of the origin of the universe. But can anyone see the moment of the Big Bang? Scientists themselves say the Big Bang model has big problems. The data they use to support their best model about the origin of the universe can also be used to undermine it. They started with A to build the model, but their data don't agree with A. Is there something fundamentally wrong? Great scientists make mistakes in science too. Hawking and others have made profound statements, but they don't always make sense. Big evolution has holes. It relies on deep time as god of the gap. Modern science began with Christians like Kepler and Galileo. They believed biblical creation had happened. It's time to bring science back to its genesis and the origin back to church.

Full Product Details

Author:   Christopher H a Ting
Publisher:   Resource Publications (CA)
Imprint:   Resource Publications (CA)
Dimensions:   Width: 15.20cm , Height: 1.20cm , Length: 22.90cm
Weight:   0.318kg
ISBN:  

9781666748246


ISBN 10:   1666748242
Pages:   230
Publication Date:   27 January 2023
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

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Christopher H. A. Ting earned his master's degree in experimental physics from the University of Tokyo and his PhD in theoretical physics from the University of Singapore. He is a professor at the School of Informatics and Data Science, Hiroshima University. He has written two books and published papers in different fields, including neural network, natural language processing, theoretical physics, and quantitative finance. His research interests are machine learning and applications of data science.

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