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001 978-3-319-00753-3
003 DE-He213
005 20140220082507.0
007 cr nn 008mamaa
008 130827s2014 gw | s |||| 0|eng d
020 _a9783319007533
_9978-3-319-00753-3
024 7 _a10.1007/978-3-319-00753-3
_2doi
050 4 _aQC174.7-175.36
072 7 _aPHS
_2bicssc
072 7 _aPHDT
_2bicssc
072 7 _aSCI055000
_2bisacsh
082 0 4 _a621
_223
100 1 _aStankovski, Tomislav.
_eauthor.
245 1 0 _aTackling the Inverse Problem for Non-Autonomous Systems
_h[electronic resource] :
_bApplication to the Life Sciences /
_cby Tomislav Stankovski.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXV, 135 p. 48 illus., 26 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
505 0 _aTheoretical background: non-autonomous systems and synchronization -- Inference of time-evolving coupled dynamical systems in the presence of noise -- Application to life sciences -- Analogue simulation and synchronization analysis of non-autonomous oscillators.
520 _aThis thesis presents a new method for following evolving interactions between coupled oscillatory systems of the kind that abound in nature. Examples range from the subcellular level, to ecosystems, through climate dynamics, to the movements of planets and stars.  Such systems mutually interact, adjusting their internal clocks, and may correspondingly move between synchronized and non-synchronized states. The thesis describes a way of using Bayesian inference to exploit the presence of random fluctuations, thus analyzing these processes in unprecedented detail.  It first develops the basic theory of interacting oscillators whose frequencies are non-constant, and then applies it to the human heart and lungs as an example. Their coupling function can be used to follow with great precision the transitions into and out of synchronization. The method described has the potential to illuminate the ageing process as well as to improve diagnostics in cardiology, anesthesiology and neuroscience, and yields insights into a wide diversity of natural processes.
650 0 _aPhysics.
650 0 _aBiology
_xData processing.
650 0 _aDistribution (Probability theory).
650 0 _aEnvironmental sciences.
650 1 4 _aPhysics.
650 2 4 _aStatistical Physics, Dynamical Systems and Complexity.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aTheoretical, Mathematical and Computational Physics.
650 2 4 _aMath. Appl. in Environmental Science.
650 2 4 _aComputer Appl. in Life Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319007526
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-00753-3
912 _aZDB-2-PHA
999 _c92522
_d92522