Network equilibrium models represent a versatile tool for the analysis of interconnected objects and their relationships. They have been widely employed in both science and engineering to study the behaviour of complex systems under various conditions, including external perturbations and damage. In this paper, network equilibrium models are revisited through graph-theory laws and attributes with special focus on systems that can sustain equilibrium in the absence of external perturbations (self-equilibrium). A new approach for the analysis of self-equilibrated networks is proposed; they are modelled as a collection of cells, predefined elementary network units that have been mathematically shown to compose any self-equilibrated network. Consequently, the equilibrium state of complex self-equilibrated systems can be obtained through the study of individual cell equilibria and their interactions. A series of examples that highlight the flexibility of network equilibrium models are included in the paper. The examples attest how the proposed approach, which combines topological as well as geometrical considerations, can be used to decipher the state of complex systems.
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http://dx.doi.org/10.1098/rspa.2020.0154 | DOI Listing |
Angew Chem Int Ed Engl
January 2025
UESTC: University of Electronic Science and Technology of China, Institute of Fundamental and Frontier Sciences, Jianshe Road, Chengdu, CHINA.
Catenated networks exclusively composed of intertwining rings were first envisioned as "Olympic gels" by Pierre-Gilles de Gennes four decades ago but have not been successfully prepared in artificial materials yet due to the challenge in synthesis. Herein, we present a bio-inspired, evaporation-assisted strategy to address this issue. In our design, the evaporation of liquid catalysts that induce ring-chain equilibrium of polymer systems drives macrocycles to encounter and assists their catenation through reversible cyclization.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Radiology, Ordos Central Hospital, Ordos, Inner Mongolia, China.
Background: Improvements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.
Objectives: The purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.
J Mater Chem B
January 2025
State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
Achieving microecological balance is a complex environmental challenge. This is because the equilibrium of microecological systems necessitates both the eradication of harmful microorganisms and preservation of the beneficial ones. Conventional materials predominantly target the elimination of pathogenic microorganisms and often neglect the protection of advantageous microbial species.
View Article and Find Full Text PDFChem Sci
January 2025
Department of Molecules and Materials, Faculty of Science and Technology, University of Twente Drienerlolaan 5 Enschede 7522 NH The Netherlands.
Network measures have proven very successful in identifying structural patterns in complex systems (, a living cell, a neural network, the Internet). How such measures can be applied to understand the rational and experimental design of chemical reaction networks (CRNs) is unknown. Here, we develop a procedure to model CRNs as a mathematical graph on which network measures and a random graph analysis can be applied.
View Article and Find Full Text PDFNanoscale
January 2025
Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China.
Superlattices are significant means to reduce the lattice thermal conductivity of thermoelectric materials and optimize their performance. In this work, using high-precision first-principles based neural network potentials combined with non-equilibrium molecular dynamics simulations and the phonon Boltzmann transport equation, the lattice thermal conductivities of BiTe monolayer and lateral BiTe/SbTe monolayer superlattices are thoroughly investigated. As the period length increases, the thermal conductivity shows a trend of an initial decrease followed by an increase, which aligns with conventional observations.
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