Integrated Role of Nutrition and Physical Activity for Lifelong Health

• Main Author: Koehler, Karsten (auth)
• Other Authors: Drenowatz, Clemens (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: History of engineering & technology; n/a; sport games; compensatory eating; breath test; exercise-induced anorexia; Fitbit; nutrition education; senescence; body weight; sport nutrition; fitness; long-term follow-up; self-efficacy; protein; food consumption; cytokines; physical exercise; cardiometabolic health; mediation analyses; obesity; food choice; telomere length; sporting events; carbohydrates; free radicals; marathon; adolescents; movement skills; naturally enriched 13C-milk proteins; behavioral intervention; acute exercise; skeletal muscle damage; Protein; TERRA; nutrition; telemonitoring; Latino; adolescent; macronutrients; metabolism; mechanisms of impact; anabolic competence; endurance exercise; food healthiness; exercise; health promotion; behavioral economics; immunity; stunting; lifestyle intervention; running; telomerase; dietary intake; inflammation; vegetarian; recreational athlete; physical performance; out-of-home eating; diet; overweight; dietary restriction; soccer; dual burden of malnutrition; protein shake; childhood memories; chronic diseases; music events; oxidation; added sugar; lifestyle change; vegan; nudges; food; physical activity; half-marathon; health conscious; free or reduced lunch; low-income; diet quality; National School Lunch Program; older adults; aging; sport spectators; chronic low-grade systemic inflammation; sport; food intake
• Online Access: DOAB: download the publication; DOAB: description of the publication

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.