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How to Stop Creating Pedophiles Explained With phm Analysis: Ending the Epidemic of Pedophilia (en Inglés)
Len Losik Ph. D.
(Autor)
·
Createspace Independent Publishing Platform
· Tapa Blanda
How to Stop Creating Pedophiles Explained With phm Analysis: Ending the Epidemic of Pedophilia (en Inglés) - Losik Ph. D., Len
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Reseña del libro "How to Stop Creating Pedophiles Explained With phm Analysis: Ending the Epidemic of Pedophilia (en Inglés)"
The U.S. is experiencing an epidemic in many mental health related disorders including childhood autism, pedophilia and gender expansion. The author discovered the physiological process that creates these disorders while working for the Canadian Space Agency's Predictive Medicine Group in Ottawa Canada in 2012 by using his prognostics and health management (PHM) technology's prognostic analysis that identifies the presence of premature aging. The author was asked by the Canadian Space Agency personnel to determine if his predictive algorithms could identify the astronauts in training that would acquire a mental illness on future planned deep space missions to Mars for replacement with an astronaut that would not. The author's research results identified the cause of mental illness and the physiological information needed from each astronaut to predict which would acquire a mental illness. The same research results explains how other mental disorders and altered brain states are created including pedophiles and how to stop them from being created, stopping the current epidemic of pedophilia, childhood autism and gender confusion. Just as an engineering diagnostic analysis identifies spacecraft equipment failures from premature aging after they occur as well as health issues caused from premature aging after they occur in a medical diagnostic analysis, the author's PHM's prognostic analysis identifies the spacecraft equipment information needed to predict spacecraft equipment failures from premature aging with 100% certainty and brain disorders and altered states needed to predict them with certainty such as mental illness, autism, arthritis, diabetes, cancer, gender confusion and pedophilia. The author was successful by applying his proprietary predictive algorithms to identify the presence of premature aging in physiological information. By using PHM's prognostic analysis, the author was able to identify the presence premature aging in health data and identify how the same changes in the brain that cause mental illness, autism and gender expansion creates pedophiles and what is needed to stop creating these disorders and altered brain states as well as all other auto-immune and immune system diseases including as cancer and childhood autism, arthritis and diabetes and he identified which ones can be stopped, cured and/or how to recover from them.
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