| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330611 |
| 005 | | 20241101160137 |
| 008 | | 181129s2017 ||| | | | eng d |
| 020 | |
▼a 9780438091450 |
| 035 | |
▼a (MiAaPQ)AAI10871367 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1483392711778623 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 510 |
| 100 | 1 |
▼a Swang, Theodore William, II. |
| 245 | 12 |
▼a A Mathematical Model for the Energy Allocation Function of Sleep. |
| 260 | |
▼a [S.l.] :
▼b The Ohio State University.,
▼c 2017 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
| 300 | |
▼a 127 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Janet Best. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2017. |
| 520 | |
▼a The function of sleep remains one of the greatest unsolved questions in biology. Schmidt has proposed the unifying Energy Allocation Function of sleep, which posits that the ultimate function of sleep is effective energy allocation in the servic |
| 520 | |
▼a The fundamental quantity we model is called biological debt ( D). We define biological requirements (R) as the summation of maintenance obligations generated by all metabolic operations, biological investment (BI) as the summation of completed f |
| 520 | |
▼a We compare and contrast our model with the Borbely's two-process model of sleep and with empirical data of human neurobehavioural performance. |
| 520 | |
▼a We define a division of labor parameter (DOL) and use D to develop an algorithm to compute the energy saved by sleep-wake cycling compared to continuous wakefulness. We quantify the contributions to energy savings from DOL and from metabolic rat |
| 520 | |
▼a Some implications of the energy savings model include predictions that biological debt may govern sleep homeostasis |
| 520 | |
▼a We present an alternative energy savings calculation based on averaging theory and compare it to our original energy savings computation. |
| 520 | |
▼a Finally, we develop a Markov Decision Process with a reward of net energy intake in order to find an optimal sleep-wake policy under a variety of conditions. We use this Markov Decision Process to optimize a policy under three different sets of |
| 590 | |
▼a School code: 0168. |
| 650 | 4 |
▼a Mathematics. |
| 690 | |
▼a 0405 |
| 710 | 20 |
▼a The Ohio State University.
▼b Mathematics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0168 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000215
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |