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Journal articles on the topic 'Network thermodynamics'

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Published: 1 March 2025

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1

Wampler, Taylor, and Andre C. Barato. "Skewness and kurtosis in stochastic thermodynamics." Journal of Physics A: Mathematical and Theoretical 55, no. 1 (December 9, 2021): 014002. http://dx.doi.org/10.1088/1751-8121/ac3b0c.

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Abstract The thermodynamic uncertainty relation is a prominent result in stochastic thermodynamics that provides a bound on the fluctuations of any thermodynamic flux, also known as current, in terms of the average rate of entropy production. Such fluctuations are quantified by the second moment of the probability distribution of the current. The role of higher order standardized moments such as skewness and kurtosis remains largely unexplored. We analyze the skewness and kurtosis associated with the first passage time of thermodynamic currents within the framework of stochastic thermodynamics. We develop a method to evaluate higher order standardized moments associated with the first passage time of any current. For systems with a unicyclic network of states, we conjecture upper and lower bounds on skewness and kurtosis associated with entropy production. These bounds depend on the number of states and the thermodynamic force that drives the system out of equilibrium. We show that these bounds for skewness and kurtosis do not hold for multicyclic networks. We discuss the application of our results to infer an underlying network of states.

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2

Tasnim, Farita, and David H. Wolpert. "Stochastic Thermodynamics of Multiple Co-Evolving Systems—Beyond Multipartite Processes." Entropy 25, no. 7 (July 17, 2023): 1078. http://dx.doi.org/10.3390/e25071078.

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Many dynamical systems consist of multiple, co-evolving subsystems (i.e., they have multiple degrees of freedom). Often, the dynamics of one or more of these subsystems will not directly depend on the state of some other subsystems, resulting in a network of dependencies governing the dynamics. How does this dependency network affect the full system’s thermodynamics? Prior studies on the stochastic thermodynamics of multipartite processes have addressed this question by assuming that, in addition to the constraints of the dependency network, only one subsystem is allowed to change state at a time. However, in many real systems, such as chemical reaction networks or electronic circuits, multiple subsystems can—or must—change state together. Here, we investigate the thermodynamics of such composite processes, in which multiple subsystems are allowed to change state simultaneously. We first present new, strictly positive lower bounds on entropy production in composite processes. We then present thermodynamic uncertainty relations for information flows in composite processes. We end with strengthened speed limits for composite processes.

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3

Borlenghi, Simone, and Anna Delin. "Stochastic Thermodynamics of Oscillators’ Networks." Entropy 20, no. 12 (December 19, 2018): 992. http://dx.doi.org/10.3390/e20120992.

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We apply the stochastic thermodynamics formalism to describe the dynamics of systems of complex Langevin and Fokker-Planck equations. We provide in particular a simple and general recipe to calculate thermodynamical currents, dissipated and propagating heat for networks of nonlinear oscillators. By using the Hodge decomposition of thermodynamical forces and fluxes, we derive a formula for entropy production that generalises the notion of non-potential forces and makes transparent the breaking of detailed balance and of time reversal symmetry for states arbitrarily far from equilibrium. Our formalism is then applied to describe the off-equilibrium thermodynamics of a few examples, notably a continuum ferromagnet, a network of classical spin-oscillators and the Frenkel-Kontorova model of nano friction.

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4

Lewis, Edwin R. "Network thermodynamics revisited." Biosystems 34, no. 1-3 (1995): 47–63. http://dx.doi.org/10.1016/0303-2647(94)01456-h.

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5

Šesták, Jaroslav. "Studies in network thermodynamics." Thermochimica Acta 108 (November 1986): 393. http://dx.doi.org/10.1016/0040-6031(86)85106-1.

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6

Matsoukas, Themis. "Thermodynamics Beyond Molecules: Statistical Thermodynamics of Probability Distributions." Entropy 21, no. 9 (September 13, 2019): 890. http://dx.doi.org/10.3390/e21090890.

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Statistical thermodynamics has a universal appeal that extends beyond molecular systems, and yet, as its tools are being transplanted to fields outside physics, the fundamental question, what is thermodynamics, has remained unanswered. We answer this question here. Generalized statistical thermodynamics is a variational calculus of probability distributions. It is independent of physical hypotheses but provides the means to incorporate our knowledge, assumptions and physical models about a stochastic processes that gives rise to the probability in question. We derive the familiar calculus of thermodynamics via a probabilistic argument that makes no reference to physics. At the heart of the theory is a space of distributions and a special functional that assigns probabilities to this space. The maximization of this functional generates the mathematical network of thermodynamic relationship. We obtain statistical mechanics as a special case and make contact with Information Theory and Bayesian inference.

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7

Du, Bin, Daniel C. Zielinski, Jonathan M. Monk, and Bernhard O. Palsson. "Thermodynamic favorability and pathway yield as evolutionary tradeoffs in biosynthetic pathway choice." Proceedings of the National Academy of Sciences 115, no. 44 (October 11, 2018): 11339–44. http://dx.doi.org/10.1073/pnas.1805367115.

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The structure of the metabolic network contains myriad organism-specific variations across the tree of life, but the selection basis for pathway choices in different organisms is not well understood. Here, we examined the metabolic capabilities with respect to cofactor use and pathway thermodynamics of all sequenced organisms in the Kyoto Encyclopedia of Genes and Genomes Database. We found that (i) many biomass precursors have alternate synthesis routes that vary substantially in thermodynamic favorability and energy cost, creating tradeoffs that may be subject to selection pressure; (ii) alternative pathways in amino acid synthesis are characteristically distinguished by the use of biosynthetically unnecessary acyl-CoA cleavage; (iii) distinct choices preferring thermodynamic-favorable or cofactor-use–efficient pathways exist widely among organisms; (iv) cofactor-use–efficient pathways tend to have a greater yield advantage under anaerobic conditions specifically; and (v) lysine biosynthesis in particular exhibits temperature-dependent thermodynamics and corresponding differential pathway choice by thermophiles. These findings present a view on the evolution of metabolic network structure that highlights a key role of pathway thermodynamics and cofactor use in determining organism pathway choices.

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8

Reichl, L. E. "Book review:Studies in network thermodynamics." Journal of Statistical Physics 50, no. 1-2 (January 1988): 465. http://dx.doi.org/10.1007/bf01023005.

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9

Zhang, Mingjin, Peng Zhang, Yuhan Zhang, Minghai Yang, Xiaofeng Li, Xiaogang Dong, and Luchang Yang. "SAR-to-Optical Image Translation via an Interpretable Network." Remote Sensing 16, no. 2 (January 8, 2024): 242. http://dx.doi.org/10.3390/rs16020242.

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Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret by human visual perception. Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution. However, since there is a substantial domain difference between optical and SAR images, they suffer from low image quality and geometric distortion in the produced optical images. Motivated by the analogy between pixels during the S2O image translation and molecules in a heat field, a thermodynamics-inspired network for SAR-to-optical image translation (S2O-TDN) is proposed in this paper. Specifically, we design a third-order finite difference (TFD) residual structure in light of the TFD equation of thermodynamics, which allows us to efficiently extract inter-domain invariant features and facilitate the learning of nonlinear translation mapping. In addition, we exploit the first law of thermodynamics (FLT) to devise an FLT-guided branch that promotes the state transition of the feature values from an unstable diffusion state to a stable one, aiming to regularize the feature diffusion and preserve image structures during S2O image translation. S2O-TDN follows an explicit design principle derived from thermodynamic theory and enjoys the advantage of explainability. Experiments on the public SEN1-2 dataset show the advantages of the proposed S2O-TDN over the current methods with more delicate textures and higher quantitative results.

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10

Keegan, Michael, Hava T. Siegelmann, Edward A. Rietman, Giannoula Lakka Klement, and Jack A. Tuszynski. "Gibbs Free Energy, a Thermodynamic Measure of Protein–Protein Interactions, Correlates with Neurologic Disability." BioMedInformatics 1, no. 3 (December 14, 2021): 201–10. http://dx.doi.org/10.3390/biomedinformatics1030013.

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Modern network science has been used to reveal new and often fundamental aspects of brain network organization in physiological as well as pathological conditions. As a consequence, these discoveries, which relate to network hierarchy, hubs and network interactions, have begun to change the paradigms of neurodegenerative disorders. In this paper, we explore the use of thermodynamics for protein–protein network interactions in Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), traumatic brain injury and epilepsy. To assess the validity of using network interactions in neurological diseases, we investigated the relationship between network thermodynamics and molecular systems biology for these neurological disorders. In order to uncover whether there was a correlation between network organization and biological outcomes, we used publicly available RNA transcription data from individual patients with these neurological conditions, and correlated these molecular profiles with their respective individual disability scores. We found a linear correlation (Pearson correlation of −0.828) between disease disability (a clinically validated measurement of a person’s functional status) and Gibbs free energy (a thermodynamic measure of protein–protein interactions). In other words, we found an inverse relationship between disease disability and thermodynamic energy. Because a larger degree of disability correlated with a larger negative drop in Gibbs free energy in a linear disability-dependent fashion, it could be presumed that the progression of neuropathology such as is seen in Alzheimer’s disease could potentially be prevented by therapeutically correcting the changes in Gibbs free energy.

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Zhang, Mingjin, Handi Yang, Ke Yue, Xiaoyu Zhang, Yuqi Zhu, and Yunsong Li. "Thermodynamics-Inspired Multi-Feature Network for Infrared Small Target Detection." Remote Sensing 15, no. 19 (September 26, 2023): 4716. http://dx.doi.org/10.3390/rs15194716.

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Infrared small target detection (IRSTD) is widely used in many fields such as detection and guidance systems and is of great research importance. However, small targets in infrared images are typically small, blurry, feature-poor, and prone to being overwhelmed by noisy backgrounds, posing a significant challenge for IRSTD. In this paper, we propose a thermodynamics-inspired multi-feature network (TMNet) for the IRSTD task, which extracts richer and more essential semantic features of infrared targets through cross-layer and multi-scale feature fusion, along with the assistance of a thermodynamics-inspired super-resolution branch. Specifically, it consists of an attention-directed feature cross-aggregation encoder (AFCE), a U-Net backbone decoder, and a thermodynamic super-resolution branch (TSB). In the shrinkage path, the original encoder structure is reconstructed as AFCE, which contains two depth-weighted multi-scale attention modules (DMA) and a cross-layer feature fusion module (CFF). The DMA and CFF modules achieve self-feature-guided multi-scale feature fusion and cross-layer feature interaction by utilizing semantic features from different stages in the encoding process. In thermodynamics, the difference in the formation of different heat between particles leads to heat transfer between objects, which inspired us to analogize the feature extraction process of gradually focusing the network’s attention to an infrared target under the constraints of the loss function to the process of heat transfer. On the expansion path, the TSB module incorporates the Hamming equation of thermodynamics to mine infrared detail features through heat transfer-inspired high-resolution feature representations while assisting the low-resolution branch to learn high-resolution features. We conduct extensive experiments on the publicly available NUAA-SIRSST dataset and find that the proposed TMNet exhibits excellent detection performance in both pixel-level and object-level metrics. This discovery provides us with a relatively dependable guideline for formulating network designs aimed at IRSTD.

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12

Apostolova, R. D., O. V. Markevych, and E. M. Shembel. "Appraisal the effective diffusion coefficients of Li-ions by PITT and network thermodynamic methods in thin-layer Fe-sulfidic electrodes of Li-accumulator." Journal of Physics: Conference Series 2382, no. 1 (November 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2382/1/012007.

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In this work, we explored the possibilities of network thermodynamics to determine the most important kinetic parameter of the diffusion process, the diffusion coefficient of lithium ions (DLi) in iron sulfide. Iron sulfides have been electrolytically synthesized in thin aluminum-based layers for implementation in miniature lithium batteries. A comparison is made of the results obtained by the method of network thermodynamics and the method of potentiostatic pulse titration PITT. The theoretical aspects of both methods are presented. The task was reduced to obtaining curves I (current) - time (t), their analysis and calculation of coefficient values (DLi), using the theoretical foundations of diffusion and the methods used. At the early stages of applying the method of network thermodynamics to the study of diffusion in lithium current sources, it was not clear why the method is suitable for determining DLi not in the entire working range of potentials. A wide range of studies using the PITT method helps to answer questions related to the application of the network thermodynamic method. When using both methods, it is important to establish the potential range with diffusion kinetics and to reveal the accompanying electrode processes. Thus, it was found that both methods used are unable to provide reliable results in the FexSy electrode potential range of 2.8–1.8 V, since the diffusion nature of the electrode process is not a priority in this potential range.

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13

Zhang, Tao, and Shuyu Sun. "Thermodynamics-Informed Neural Network (TINN) for Phase Equilibrium Calculations Considering Capillary Pressure." Energies 14, no. 22 (November 18, 2021): 7724. http://dx.doi.org/10.3390/en14227724.

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The thermodynamic properties of fluid mixtures play a crucial role in designing physically meaningful models and robust algorithms for simulating multi-component multi-phase flow in subsurface, which is needed for many subsurface applications. In this context, the equation-of-state-based flash calculation used to predict the equilibrium properties of each phase for a given fluid mixture going through phase splitting is a crucial component, and often a bottleneck, of multi-phase flow simulations. In this paper, a capillarity-wise Thermodynamics-Informed Neural Network is developed for the first time to propose a fast, accurate and robust approach calculating phase equilibrium properties for unconventional reservoirs. The trained model performs well in both phase stability tests and phase splitting calculations in a large range of reservoir conditions, which enables further multi-component multi-phase flow simulations with a strong thermodynamic basis.

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14

van der Schaft, A. J., S. Rao, and B. Jayawardhana. "On the network thermodynamics of mass action chemical reaction networks." IFAC Proceedings Volumes 46, no. 14 (2013): 24–29. http://dx.doi.org/10.3182/20130714-3-fr-4040.00001.

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15

Imai, Y., H. Yoshida, M. Miyamoto, and H. Nakahari. "Network Thermodynamics of Membrane Transport System." Seibutsu Butsuri 27, no. 4 (1987): a159—a164. http://dx.doi.org/10.2142/biophys.27.a159.

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16

Ślęzak, Andrzej, and Sławomir M. Grzegorczyn. "Network Derivation of Liquid Junction Potentials in Single-Membrane System." Membranes 14, no. 6 (June 13, 2024): 140. http://dx.doi.org/10.3390/membranes14060140.

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Peusner’s network thermodynamics (PNT) is one of the more important formalisms of nonequilibrium thermodynamics used to describe membrane transport and the conversion of the internal energy of the system into energy dissipated in the environment and free energy used for the work involved in the transport of solution components in membrane processes. A procedure of transformation the Kedem–Katchalsky (K-K) equations for the transport of binary electrolytic solutions through a membrane to the Kedem–Katchalsky–Peusner (K-K-P) equations based on the PNT formalism for liquid junction potentials was developed. The subject of the study was a membrane used for hemodialysis (Ultra Flo 145 Dialyser) and aqueous NaCl solutions. The research method was the L version of the K-K-P formalism for binary electrolyte solutions. The Peusner coefficients obtained from the transformations of the K-K formalism coefficients for the transport of electrolyte solutions through the artificial polymer membrane were used to calculate the coupling coefficients of the membrane processes and to calculate the dissipative energy flux. In addition, the dissipative energy flux, as a function of thermodynamic forces, made it possible to investigate the energy conversion of transport processes in the membrane system.

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Gawthrop, Peter J., and Edmund J. Crampin. "Energy-based analysis of biochemical cycles using bond graphs." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2171 (November 8, 2014): 20140459. http://dx.doi.org/10.1098/rspa.2014.0459.

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Thermodynamic aspects of chemical reactions have a long history in the physical chemistry literature. In particular, biochemical cycles require a source of energy to function. However, although fundamental, the role of chemical potential and Gibb's free energy in the analysis of biochemical systems is often overlooked leading to models which are physically impossible. The bond graph approach was developed for modelling engineering systems, where energy generation, storage and transmission are fundamental. The method focuses on how power flows between components and how energy is stored, transmitted or dissipated within components. Based on the early ideas of network thermodynamics, we have applied this approach to biochemical systems to generate models which automatically obey the laws of thermodynamics. We illustrate the method with examples of biochemical cycles. We have found that thermodynamically compliant models of simple biochemical cycles can easily be developed using this approach. In particular, both stoichiometric information and simulation models can be developed directly from the bond graph. Furthermore, model reduction and approximation while retaining structural and thermodynamic properties is facilitated. Because the bond graph approach is also modular and scaleable, we believe that it provides a secure foundation for building thermodynamically compliant models of large biochemical networks.

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18

Pekař, Miloslav. "Thermodynamic framework for design of reaction rate equations and schemes." Collection of Czechoslovak Chemical Communications 74, no. 9 (2009): 1375–401. http://dx.doi.org/10.1135/cccc2009010.

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It has been shown previously that rational thermodynamics provides general foundations of mass-action kinetic law from the principles of continuum, irreversible thermodynamics. Practical outcomes of this phenomenological theory are analyzed and compared with traditional kinetic approaches on the example of N2O decomposition. It is revealed that classical rate equations are only simplified forms of a polynomial approximation to a general rate function proved by the continuum thermodynamics. It is also shown that various special considerations that have been introduced formerly as additional hypothesis to satisfactorily describe experimental data are naturally included in the thermodynamic approach. The method, in addition, makes it possible to obtain more general mass-action-type rate equations that give better description of experimental data than the traditional ones. The method even reverses the classical kinetic paradigm – reaction scheme directly follows from the rate equation. Data fitting by this method also indicates connections to distinctions between processes at the molecular level and their representation by some macroscopic reaction network. The role of dependent and independent reactions in reaction kinetics and reaction schemes is clarified. A selected example demonstrates that this thermodynamic methodology may improve our design and understanding of thermodynamically and mathematically necessary and sufficient reaction schemes. The phenomenological theory thus sheds new, “thermodynamic” light on what has been and is done by generations of kineticists and gives new hints how to do it in a way consistent with non-equilibrium thermodynamics.

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Liebermeister, Wolfram. "Structural Thermokinetic Modelling." Metabolites 12, no. 5 (May 11, 2022): 434. http://dx.doi.org/10.3390/metabo12050434.

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To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.

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20

Sarkar, Swarnavo, Joseph B. Hubbard, Michael Halter, and Anne L. Plant. "Information Thermodynamics and Reducibility of Large Gene Networks." Entropy 23, no. 1 (January 1, 2021): 63. http://dx.doi.org/10.3390/e23010063.

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Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system.

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21

Saifutdinov, Bulat R., and Aleksey K. Buryak. "Thermodynamic Characteristics and Selectivity of the Liquid-Phase Adsorption of Aromatic Compounds on Hypercrosslinked Polystyrene Networks with Ultimate-High Crosslinking Densities by Data of Liquid Chromatography." International Journal of Molecular Sciences 25, no. 3 (January 26, 2024): 1551. http://dx.doi.org/10.3390/ijms25031551.

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This study delves into the thermodynamics of liquid-phase adsorption on hypercrosslinked polystyrene networks (HPSNs), widely recognized for their distinct structure and properties. Despite the considerable progress in HPSN synthesis and characterization, gaps persist regarding the chromatographic retention mechanism, thermodynamics of adsorption, and their impact on the adsorption selectivity, especially in the case of networks with ultra-high crosslinking densities (up to 500%). Utilizing high-performance liquid chromatography (HPLC), we have explored, for the first time, the thermodynamic intricacies of liquid-phase adsorption onto HPSNs crosslinked in the entire range of the crosslinking degree from 100 to 500%. Our findings reveal the dependences of thermodynamic characteristics and selectivity of adsorption on the crosslinking degree, textural features, and liquid-phase composition in the region of extremely low adsorbent surface coverages (Henry’s range). We have detected that, in the case of HPSNs, the dependence of the thermodynamic characteristics of adsorption on the liquid-phase composition is different than for classical HPLC stationary phases. Moreover, we scrutinize the impact of the molecular structure of the studied aromatic compounds on the thermodynamic characteristics and selectivity of the liquid-phase adsorption on HPSNs. Investigating liquid-phase adsorption selectivity, we demonstrate the pivotal role of π-π interactions in separating aromatic compounds on HPSNs. Eventually, we unveil that the thermodynamic characteristics of adsorption peculiarly depend on the crosslinking degree due to the profound impact of the crosslinking on the electronic density in benzene rings in HPSNs, whereas the separation throughput peaks for the polymer with a 500% crosslinking degree, attributed to its exceptionally rigid network structure, moderate swelling and micropore volume, and minimum specific surface area.

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22

Lou, De Cang, Wen Guo, Zhi Guo Wang, and Yong Hong Wang. "Integrated Thermal Management System Design for Advanced Propulsion System." Applied Mechanics and Materials 232 (November 2012): 723–29. http://dx.doi.org/10.4028/www.scientific.net/amm.232.723.

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Thermal management system (TMS) design is considered to be a key technology for advanced aero engines and supersonic or hypersonic propulsion systems. In this paper, the concepts of coupling flow and thermodynamic networks are proposed for TMS design. In this method, the propulsion system is considered to be a zero-dimensional flow system. Components, subsystems and hence the entire engine system can be modelled using some basic flow and thermodynamics networks. The platform for TMS design, ThermalM, is developed based on this model. As an example, modelling for a Turbine Based Combined Cycle (TBCC) thermal management system is described. Performance of the fuel heat exchanger in the network is discussed in detail. With the TMS design technology, performance of the advanced propulsion system can be analysed.

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23

IMAI, YUSUKE. "Network thermodynamics of bio-membrane transport system." membrane 15, no. 3 (1990): 160–66. http://dx.doi.org/10.5360/membrane.15.160.

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Takaguchi, T., K. Ejima, and S. Miyazaki. "Network Analysis Based on Statistical-Thermodynamics Formalism." Progress of Theoretical Physics 124, no. 1 (July 1, 2010): 27–52. http://dx.doi.org/10.1143/ptp.124.27.

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Daoyun, Ji, Hu Beilai, and Chen Tianlun. "Dynamics and Thermodynamics of Layered Neural Network." Communications in Theoretical Physics 28, no. 3 (October 30, 1997): 283–88. http://dx.doi.org/10.1088/0253-6102/28/3/283.

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Soh, Keng Cher, and Vassily Hatzimanikatis. "Network thermodynamics in the post-genomic era." Current Opinion in Microbiology 13, no. 3 (June 2010): 350–57. http://dx.doi.org/10.1016/j.mib.2010.03.001.

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Imai, Yusuke. "Membrane transport system modeled by network thermodynamics." Journal of Membrane Science 41 (February 1989): 3–21. http://dx.doi.org/10.1016/s0376-7388(00)82387-x.

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Gröger, Roman. "Thermodynamics of Dislocation Pattern Formation at the Mesoscale." Key Engineering Materials 592-593 (November 2013): 79–82. http://dx.doi.org/10.4028/www.scientific.net/kem.592-593.79.

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We introduce a mesoscopic framework that is capable of simulating the evolution of dislocation networks and, at the same time, spatial variations of the stress, strain and displacement fields throughout the body. Within this model, dislocations are viewed as sources of incompatibility of strains. The free energy of a deformed solid is represented by the elastic strain energy that can be augmented by gradient terms to reproduce dispersive nature of acoustic phonons and thus set the length scale of the problem. The elastic strain field that is due to a known dislocation network is obtained by minimizing the strain energy subject to the corresponding field of incompatibility constraints. These stresses impose Peach-Koehler forces on all dislocations and thus drive the evolution of the dislocation network.

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IMAI, YUSUKE. "Network thermodynamics of bio-membrane transport system. II." membrane 15, no. 4 (1990): 196–202. http://dx.doi.org/10.5360/membrane.15.196.

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IMAI, YUSUKE. "Network thermodynamics of bio-membrane transport system. III." membrane 15, no. 5 (1990): 269–75. http://dx.doi.org/10.5360/membrane.15.269.

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IMAI, YUSUKE. "Network thermodynamics of bio-membrane transport system IV." membrane 15, no. 6 (1990): 305–13. http://dx.doi.org/10.5360/membrane.15.305.

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IMAI, YUSUKE. "Network Thermodynamics of Bio-Membrane Transport System. V." membrane 16, no. 3 (1991): 179–86. http://dx.doi.org/10.5360/membrane.16.179.

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IMAI, YUSUKE. "Network Thermodynamics of Bio-Membrane Transport System VII." membrane 16, no. 5 (1991): 306–17. http://dx.doi.org/10.5360/membrane.16.306.

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IMAI, YUSUKE. "Network Thermodynamics of Bio-Membrane Transport System. VIII." membrane 17, no. 1 (1992): 48–52. http://dx.doi.org/10.5360/membrane.17.48.

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35

Wang, Changda, Xiaowei Li, and Elisa Bertino. "Network Temperature: A Novel Statistical Index for Networks Measurement and Management." ACM Transactions on Internet Technology 22, no. 3 (August 31, 2022): 1–20. http://dx.doi.org/10.1145/3511093.

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Being able to monitor each packet path is critical for effective measurement and management of networks. However, such detailed monitoring can be very expensive especially for large-scale networks. To address such problem, inspired by thermodynamics, which uses the statistical characteristics of a large number of molecules’ motion but not each molecule’s trajectory for analysis, we propose the new concept of network temperature together with the notions of network-specific heat and network temperature gradient . Our approach does not only provide a statistical view of the current network state consisting of all the active packet paths at each time instant, but can be used to represent transitions among network states. Our network temperature-based methods have a broad applicability, such as to DDoS detection, dynamic node importance ranking, network stability and robustness evaluation, reliable packets routing, provenance compression assessment, and so on. Numerical and/or the experimental results show that our methods are effective.

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IMAI, Yusuke. "Network Thermodynamics: Analysis and Synthesis of Membrane Transport System." Japanese Journal of Physiology 46, no. 3 (1996): 187–99. http://dx.doi.org/10.2170/jjphysiol.46.187.

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37

Thoma, Jean, and Henri Atlan. "Osmosis and hydraulics by network thermodynamics and bond graphs." Journal of the Franklin Institute 319, no. 1-2 (January 1985): 217–26. http://dx.doi.org/10.1016/0016-0032(85)90075-4.

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Hubbard, Joseph B., Michael Halter, Swarnavo Sarkar, and Anne L. Plant. "The role of fluctuations in determining cellular network thermodynamics." PLOS ONE 15, no. 3 (March 11, 2020): e0230076. http://dx.doi.org/10.1371/journal.pone.0230076.

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MAKSE, H., and R. P. J. PERAZZO. "THE THERMODYNAMICS OF DYSLEXIC LEARNING." International Journal of Neural Systems 03, no. 04 (January 1992): 351–60. http://dx.doi.org/10.1142/s0129065792000267.

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The dyslexic behaviour of a layered network is interpreted as arising from its incomplete training using a cost function that is sensitive to the grouping of Boolean functions into symmetry classes. The training is envisaged as a simulated annealing and a partial learning as the interruption of the cooling schedule at a finite nonzero temperature. We present the thermodynamics of the process. The onset of dyslexic and normal behaviours arise from phase transitions that take place during the annealing schedule. We exemplify the theory with the numerical analysis of a schematic model.

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Ren, Likun, Haiqin Qin, Na Cai, Bianjiang Li, and Zhenbo Xie. "A Hybrid Degradation Evaluation Model for Aero-Engines." Sustainability 15, no. 1 (December 20, 2022): 29. http://dx.doi.org/10.3390/su15010029.

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The non-convergence and low efficiency of the thermodynamic model make them difficult to be used in the aero-engines degradation evaluation, while the negligence of the thermodynamics process of data-driven degradation evaluation methods makes them inaccurate and hard to analyze the actual degradation of air path components. So, we propose a thermodynamic-based and data-driven hybrid model for aero-engine degradation evaluation. Different from thermodynamic-based methods, the iteration calculation is converted to the forward flow in the proposed neural network, thus improving convergence. Moreover, a multi-objective loss function considering the components co-operation process and fusion training process fully taking advantage of simulation and degradation trajectory datasets are proposed to improve the degradation evaluation accuracy. The test case is carried out on NASA’s benchmark for aero-engine degradation evaluation. The result shows that the proposed method can improve the accuracy significantly, which suggests its effectiveness.

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Chu, Xin-Yi, Si-Ming Chen, Ke-Wei Zhao, Tian Tian, Jun Gao, and Hong-Yu Zhang. "Plausibility of Early Life in a Relatively Wide Temperature Range: Clues from Simulated Metabolic Network Expansion." Life 11, no. 8 (July 24, 2021): 738. http://dx.doi.org/10.3390/life11080738.

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The debate on the temperature of the environment where life originated is still inconclusive. Metabolic reactions constitute the basis of life, and may be a window to the world where early life was born. Temperature is an important parameter of reaction thermodynamics, which determines whether metabolic reactions can proceed. In this study, the scale of the prebiotic metabolic network at different temperatures was examined by a thermodynamically constrained network expansion simulation. It was found that temperature has limited influence on the scale of the simulated metabolic networks, implying that early life may have occurred in a relatively wide temperature range.

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Ahmadi, Mostafa, and Sebastian Seiffert. "Thermodynamic control over energy dissipation modes in dual-network hydrogels based on metal–ligand coordination." Soft Matter 16, no. 9 (2020): 2332–41. http://dx.doi.org/10.1039/c9sm02149c.

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Prechl, József. "Complex Physical Properties of an Adaptive, Self-Organizing Biological System." Biophysica 3, no. 2 (March 31, 2023): 231–51. http://dx.doi.org/10.3390/biophysica3020015.

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Physical modeling of the functioning of the adaptive immune system, which has been thoroughly characterized on genetic and molecular levels, provides a unique opportunity to define an adaptive, self-organizing biological system in its entirety. This paper describes a configuration space model of immune function, where directed chemical potentials of the system constitute a space of interactions. A mathematical approach is used to define the system that couples the variance of Gaussian distributed interaction energies in its interaction space to the exponentially distributed chemical potentials of its effector molecules to maintain its steady state. The model is validated by identifying the thermodynamic and network variables analogous to the mathematical parameters and by applying the model to the humoral immune system. Overall, this statistical thermodynamics model of adaptive immunity describes how adaptive biological self-organization arises from the maintenance of a scale-free, directed molecular interaction network with fractal topology.

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Horno, José, and Carlos F. González-Fernández. "Analysis of chemical reaction systems by means of network thermodynamics." Collection of Czechoslovak Chemical Communications 54, no. 9 (1989): 2335–44. http://dx.doi.org/10.1135/cccc19892335.

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The simple network thermodynamics approach is applied to chemical reaction systems, whereby chemical reactions can be studied avoiding complex mathematical treatment. Steady state reaction rates are obtained for two chemical reaction systems, viz. the decomposition of ozone and the reaction of hydrogen with bromine. The rate equations so obtained agree with those derived from the chemical kinetics concept.

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Pateras, Joseph, Ashwin Vaidya, and Preetam Ghosh. "Network Thermodynamics-Based Scalable Compartmental Model for Multi-Strain Epidemics." Mathematics 10, no. 19 (September 26, 2022): 3513. http://dx.doi.org/10.3390/math10193513.

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SARS-CoV-2 continues to upend human life by posing novel threats related to disease spread and mutations. Current models for the disease burden of SARS-CoV-2 consider the aggregate nature of the virus without differentiating between the potency of its multiple strains. Hence, there is a need to create a fundamental modeling framework for multi-strain viruses that considers the competing viral pathogenic pathways. Alongside the consideration that other viral pathogens may coexist, there is also a need for a generalizable modeling framework to account for multiple epidemics (i.e., multi-demics) scenarios, such as influenza and COVID-19 occurring simultaneously. We present a fundamental network thermodynamics approach for assessing, determining, and predicting viral outbreak severity, which extends well-known standard epidemiological models. In particular, we use historical data from New York City’s 2011–2019 influenza seasons and SARS-CoV-2 spread to identify the model parameters. In our model-based analysis, we employ a standard susceptible–infected–recovered (SIR) model with pertinent generalizations to account for multi-strain and multi-demics scenarios. We show that the reaction affinities underpinning the formation processes of our model can be used to categorize the severity of infectious or deceased populations. The spontaneity of occurrence captured by the change in Gibbs free energy of reaction (ΔG) in the system suggests the stability of forward occurring population transfers. The magnitude of ΔG is used to examine past influenza outbreaks and infer epidemiological factors, such as mortality and case burden. This method can be extrapolated for wide-ranging utility in computational epidemiology. The risk of overlapping multi-demics seasons between influenza and SARS-CoV-2 will persist as a significant threat in forthcoming years. Further, the possibility of mutating strains requires novel ways of analyzing the network of competing infection pathways. The approach outlined in this study allows for the identification of new stable strains and the potential increase in disease burden from a complex systems perspective, thereby allowing for a potential response to the significant question: are the effects of a multi-demic greater than the sum of its individual viral epidemics?

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Batko, Kornelia M., Izabella Slezak-Prochazka, Slawomir Grzegorczyn, and Andrzej Slezak. "MEMBRANE TRANSPORT IN CONCENTRATION POLARIZATION CONDITIONS: NETWORK THERMODYNAMICS MODEL EQUATIONS." Journal of Porous Media 17, no. 7 (2014): 573–86. http://dx.doi.org/10.1615/jpormedia.v17.i7.20.

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Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie E. Palmer, Michael J. Berry, and William Bialek. "Thermodynamics and signatures of criticality in a network of neurons." Proceedings of the National Academy of Sciences 112, no. 37 (September 1, 2015): 11508–13. http://dx.doi.org/10.1073/pnas.1514188112.

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The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance.

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Chiriki, Siva, Shweta Jindal, and Satya S. Bulusu. "Neural network potentials for dynamics and thermodynamics of gold nanoparticles." Journal of Chemical Physics 146, no. 8 (February 28, 2017): 084314. http://dx.doi.org/10.1063/1.4977050.

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Mikulecky, Donald C. "Network thermodynamics and complexity: a transition to relational systems theory." Computers & Chemistry 25, no. 4 (July 2001): 369–91. http://dx.doi.org/10.1016/s0097-8485(01)00072-9.

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50

Rusovs, D., L. Jansons, N. Zeltins, and I. Geipele. "Efficient Heat Recovery from Hydrogen and Natural Gas Blend Combustion Products." Latvian Journal of Physics and Technical Sciences 60, no. 2 (April 1, 2023): 31–42. http://dx.doi.org/10.2478/lpts-2023-0009.

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Abstract The introduction of hydrogen and natural gas blends in existing gas transportation and distribution networks would ensure faster and more efficient decarbonization of energy sector, but, at the same time, this process would request solution of many practical and technical problems. This paper explores thermodynamics of hydrogen and natural gas blend combustion products and estimates the amount of condensate and latent energy recovery from flue gas as a function of condensing temperature. The efficient energy recovery depends on network return temperature, and it is possible to overcome this limitation by implementation of heat pump for extraction of low temperature heat from flue gases. The case study considers operation of heat only boiler and flue gas condenser with integrated cascade of heat pumps, which consist of absorption lithium bromide-water chiller (in heat pump mode) and vapour compression unit. Presented results of energy recovery hence are limited by data collected from the natural gas combustion for district heating network energy supply. However, previous thermodynamic consideration allows extending the obtained results for case of hydrogen and natural gas blend combustion. A proof of concept of heat recovery by combination of flue gas condenser supported by a cascade of heat pumps demonstrates the efficiency in case of hydrogen and natural gas blend combustion.

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