Comment on: The cancer Warburg effect may be a testable example of the minimum entropy production rate principle

2018 ◽  
Vol 15 (2) ◽  
pp. 028001 ◽  
Author(s):  
Mostafa Sadeghi Ghuchani
Keyword(s):  
Entropy Production ◽  
Production Rate ◽  
Warburg Effect ◽  
Entropy Production Rate ◽  
Minimum Entropy ◽  
Minimum Entropy Production

Computational Intelligence for Robust Control Algorithms of Complex Dynamic Systems with Minimum Entropy Production

1999 ◽  
Vol 3 (2) ◽  
pp. 82-98 ◽  
Author(s):  
S.V. Ulyanov ◽  
◽  
K. Yamafuji ◽  
V.S. Ulyanov ◽  
I. Kurawaki ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Entropy Production ◽  
Production Rate ◽  
Dynamic Systems ◽  
Fitness Function ◽  
Object Motion ◽  
Entropy Production Rate ◽  
Minimum Entropy ◽  
Minimum Entropy Production

Our thermodynamic approach to the study and design of robust optimal control processes in nonlinear (in general global unstable) dynamic systems used soft computing based on genetic algorithms with a fitness function as minimum entropy production. Control objects were nonlinear dynamic systems involving essentially nonlinear stochastic differential equations. An algorithm was developed for calculating entropy production rate in control object motion and in control systems. Part 1 discusses relation of the Lyapunov function (measure of stability) and the entropy production rate (physical measure of controllability). This relation was used to describe the following qualitative properties and important relations: dynamic stability motion (Lyapunov function), Lyapunov exponent and Kolmogorov-Sinai entropy, physical entropy production rates, and symmetries group representation in essentially nonlinear systems as coupled oscillator models. Results of computer simulation are presented for entropy-like dynamic behavior for typical benchmarks of dynamic systems such as Van der Pol, Duffing, and Holmes-Rand, and coupled oscillators. Parts 2 and 3 discuss the application of this approach to simulation of dynamic entropy-like behavior and optimal benchmark control as a 2-link manipulator in a robot for service use and nonlinear systems under stochastic excitation.

Computational Intelligence with New Physical Controllability Measure for Robust Control Algorithm of Extension- Cableless Robotic Unicycle

1999 ◽  
Vol 3 (2) ◽  
pp. 136-147
Author(s):  
V.S. Ulyanov ◽  
◽  
K. Yamafuji ◽  
S.V. Ulyanov ◽  
K. Tanaka ◽  
...  
Keyword(s):  
Robust Control ◽  
Entropy Production ◽  
Production Rate ◽  
Fitness Function ◽  
Control Object ◽  
Object Motion ◽  
Entropy Production Rate ◽  
Minimum Entropy ◽  
Physical Measure ◽  
Minimum Entropy Production

The biomechanical robotic unicycle system uses internal world representation described by emotion, instinct, and intuition. The basic intelligent control concept for a complex nonlinear nonholonomic biomechanical systems, as benchmark the <I>extension-cableless robotic unicycle,</I> uses a <I>thermodynamic approach</I> to study optimum control processes in complex nonlinear dynamic systems is represented here. An algorithm for calculating the entropy production rate is developed. A new physical measure, the minimum entropy production rate, is used as a Genetic Algorithm (GA) fitness function to calculate robotic unicycle robustness controllability and intelligent behavior. The interrelation between the Lyapunov function - a measure of stochastic stability - and the entropy production rate - the physical measure of controllability - in the biomechanical model is the mathematical background for designing soft computing algorithms in intelligent robotic unicycle control. The principle of minimum entropy production rate in control systems and control object motion in general is a new physical concept of smart robust control for the complex nonlinear nonholonomic biomechanical system, as benchmark, <I>extension-cableless robotic unicycle.</I>

THE THERMODYNAMIC DESCRIPTION OF HETEROGENEOUS DISSIPATIVE SYSTEMS BY VARIATIONAL METHODS: III. AN APPLICATION OF THE PRINCIPLE OF MINIMUM ENTROPY PRODUCTION TO FLUID DYNAMICS

1964 ◽  
Vol 42 (8) ◽  
pp. 1437-1446 ◽  
Author(s):  
J. S. Kirkaldy
Keyword(s):  
Steady State ◽  
Entropy Production ◽  
Thermodynamic Description ◽  
Free Fall ◽  
Dissipative Systems ◽  
Entropy Production Rate ◽  
Stable Steady State ◽  
Minimum Entropy ◽  
Minimum Entropy Production ◽  
Steady State Rate

The stable free-fall flight of a maple seed gives an exceptionally graphic demonstration of the principle of minimum entropy production. Since the rate of entropy production is proportional to the steady-state rate of loss of potential energy, it is visually obvious that the stable rotary configuration represents a minimum of the entropy production rate relative to an unstable steady-state bomblike trajectory. Regarding this phenomenon as the prototype of many practical steady-state fluid-dynamical systems involving rotational modes, we formally demonstrate the possibility of mathematically defining the stable steady-state configuration by means of this variational principle.

A New Criterion for Assessing Heat Exchanger Performance

2010 ◽  
Author(s):  
Jiangfeng Guo ◽  
Mingtian Xu ◽  
Lin Cheng
Keyword(s):  
Heat Exchanger ◽  
Entropy Production ◽  
Optimization Design ◽  
Rectangular Channel ◽  
Entropy Production Rate ◽  
Local Entropy ◽  
Minimum Entropy ◽  
Minimum Entropy Production ◽  
Production Distribution ◽  
New Criterion

The principle of minimum entropy production has played an important role in the development of non-equilibrium thermodynamics. Inspired by this principle, Bejan derived the expression of the local entropy production rate for heat convection and established the entropy production minimization approach for the heat exchanger optimization design. Although one can obtain the entropy production distribution in the heat exchanger numerically, it can not directly been employed to examine the heat exchanger performance. Tondeur and Kvaalen found that the entropy production uniformity is closely related to the heat exchanger performance. In the present work, based on Tondear and Kvaalen’s work, an entropy production uniformity factor is defined, which quantifies the uniformity of the local entropy generation distribution in heat exchanger. Numerical results of the heat transfer in a rectangular channel show that the larger entropy production uniformity factor implies less irreversible loses. Therefore, this factor can serve as a thermodynamic figure of merit for assessing the heat exchanger performance.

Thermodynamic Modeling of the Competition Between Cancer and Normal Cells

2020 ◽  
Vol 45 (1) ◽  
pp. 19-25
Author(s):  
Mostafa Sadeghi Ghuchani
Keyword(s):  
Cancer Cells ◽  
Entropy Production ◽  
Production Rate ◽  
Thermodynamic Model ◽  
Warburg Effect ◽  
Entropy Production Rate ◽  
Metabolic Efficiency ◽  
Normal Cells ◽  
Functional Explanations ◽  
The Warburg Effect

AbstractOne of the recognized differences between normal and cancer cells is in their metabolic profile. Tumor cells tend to produce energy through glycolysis rather than the much more efficient oxidative phosphorylation pathway, which healthy cells generally prefer. This phenomenon is identified as the Warburg effect. Although several functional explanations have been proposed for the Warburg effect, the competitive advantage of it is still subject of debate. Here we present a thermodynamic model to simulate the competition of cancer and normal cells in terms of bioenergetics. Our model shows that the Warburg effect has an advantage because the entropy production rate is increased and metabolic efficiency is decreased for cancer cells. Although inefficiency is generally considered a competitive disadvantage for living organisms, the thermodynamic model shows that it is not always the case. Indeed, when the energy resources are abundant and the system has a limited ability to export entropy, the organism with a higher rate of entropy production will have a higher chance of survival despite its lower metabolic efficiency. This thermodynamic model predicts that as long as there are enough nutrients in circulating blood, there are two thermodynamic strategies to control cancer cell populations, i. e., (i) decreasing the entropy production rate of cancer cells and (ii) increasing normal cells’ entropy production rate.

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