Mark Fleming – författare

1976 - Growing Up Bipolar is a disturbing, but darkly humorous and life-affirming mental health memoir by Mark Fleming, a Scottish writer and musician. Diagnosed with bipolar disorder in his 20s, Fleming takes the reader into bipolar''s depths of depression and unnatural highs of mania, and is candid about his experiences of locked psych wards, the debilitating side-effects of anti-psychotic drugs, and the terrifying places his deluded mind took him to.

Much of the book''s timescale overlaps with Grant McPhee''s award-winning documentary, Big Gold Dream: The Sound of Young Scotland 1977-1985. 1976 - Growing Up Bipolar also celebrates Scotland''s electrifying indie and post-punk music and cultural scenes, with anecdotes about gigging, songwriting, recording sessions at BBC''s Maida Vale studios, and teenage obsessions with sex, drugs, and rock ''n'' roll. The cathartic impact of John Peel''s BBC Radio 1 show and a long-time devotion to Manchester post-punk legends The Fall figure prominently.

Delving deep into the psyche of a chemically-imbalanced mind, he relives a tragic event during the 1976 heatwave that inflicted the mental injury that might have triggered his bipolar disorder.

This book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems.  Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry leveltextbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.