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Journal articles on the topic 'Optimal reaction networks'
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1
Feng, Xiao-jiang, and Herschel Rabitz. "Optimal Identification of Biochemical Reaction Networks." Biophysical Journal 86, no. 3 (2004): 1270–81. http://dx.doi.org/10.1016/s0006-3495(04)74201-0.
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Liu, Chen, Shupeng Gao, Mingrui Song, Yue Bai, Lili Chang, and Zhen Wang. "Optimal control of the reaction–diffusion process on directed networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (2022): 063115. http://dx.doi.org/10.1063/5.0087855.
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Reaction–diffusion processes organized in networks have attracted much interest in recent years due to their applications across a wide range of disciplines. As one type of most studied solutions of reaction–diffusion systems, patterns broadly exist and are observed from nature to human society. So far, the theory of pattern formation has made significant advances, among which a novel class of instability, presented as wave patterns, has been found in directed networks. Such wave patterns have been proved fruitful but significantly affected by the underlying network topology, and even small topological perturbations can destroy the patterns. Therefore, methods that can eliminate the influence of network topology changes on wave patterns are needed but remain uncharted. Here, we propose an optimal control framework to steer the system generating target wave patterns regardless of the topological disturbances. Taking the Brusselator model, a widely investigated reaction–diffusion model, as an example, numerical experiments demonstrate our framework’s effectiveness and robustness. Moreover, our framework is generally applicable, with minor adjustments, to other systems that differential equations can depict.
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Liu, Chen, Shupeng Gao, Mingrui Song, Yue Bai, Lili Chang, and Zhen Wang. "Optimal control of the reaction–diffusion process on directed networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (2022): 063115. http://dx.doi.org/10.1063/5.0087855.
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Reaction–diffusion processes organized in networks have attracted much interest in recent years due to their applications across a wide range of disciplines. As one type of most studied solutions of reaction–diffusion systems, patterns broadly exist and are observed from nature to human society. So far, the theory of pattern formation has made significant advances, among which a novel class of instability, presented as wave patterns, has been found in directed networks. Such wave patterns have been proved fruitful but significantly affected by the underlying network topology, and even small topological perturbations can destroy the patterns. Therefore, methods that can eliminate the influence of network topology changes on wave patterns are needed but remain uncharted. Here, we propose an optimal control framework to steer the system generating target wave patterns regardless of the topological disturbances. Taking the Brusselator model, a widely investigated reaction–diffusion model, as an example, numerical experiments demonstrate our framework’s effectiveness and robustness. Moreover, our framework is generally applicable, with minor adjustments, to other systems that differential equations can depict.
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Dourado, Hugo, Wolfram Liebermeister, Oliver Ebenhöh, and Martin J. Lercher. "Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth." PLOS Computational Biology 19, no. 6 (2023): e1011156. http://dx.doi.org/10.1371/journal.pcbi.1011156.
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The physiology of biological cells evolved under physical and chemical constraints, such as mass conservation across the network of biochemical reactions, nonlinear reaction kinetics, and limits on cell density. For unicellular organisms, the fitness that governs this evolution is mainly determined by the balanced cellular growth rate. We previously introduced growth balance analysis (GBA) as a general framework to model and analyze such nonlinear systems, revealing important analytical properties of optimal balanced growth states. It has been shown that at optimality, only a minimal subset of reactions can have nonzero flux. However, no general principles have been established to determine if a specific reaction is active at optimality. Here, we extend the GBA framework to study the optimality of each biochemical reaction, and we identify the mathematical conditions determining whether a reaction is active or not at optimal growth in a given environment. We reformulate the mathematical problem in terms of a minimal number of dimensionless variables and use the Karush-Kuhn-Tucker (KKT) conditions to identify fundamental principles of optimal resource allocation in GBA models of any size and complexity. Our approach helps to identify from first principles the economic values of biochemical reactions, expressed as marginal changes in cellular growth rate; these economic values can be related to the costs and benefits of proteome allocation into the reactions’ catalysts. Our formulation also generalizes the concepts of Metabolic Control Analysis to models of growing cells. We show how the extended GBA framework unifies and extends previous approaches of cellular modeling and analysis, putting forward a program to analyze cellular growth through the stationarity conditions of a Lagrangian function. GBA thereby provides a general theoretical toolbox for the study of fundamental mathematical properties of balanced cellular growth.
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Yang, Bin, Chuan Zhu Liao, Ming Yan Jiang, and Dong Feng Yuan. "Delayed Stochastic Biochemical Reactions Reconstruction Based on Additive Reaction Model." Advanced Materials Research 894 (February 2014): 280–83. http://dx.doi.org/10.4028/www.scientific.net/amr.894.280.
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Stochastic dynamics and delayed time of biochemical reactions play an important role in the biological networks such as gene regulatory and metabolic networks. This paper presents a new model, called additive reaction model (ARM), to capture the stochastic dynamical and delayed behavior. The new evolutionary strategy is used to search the optimal biochemical model, in which genetic algorithm (GA) and particle swarm optimization (PSO) are employed to evolve the architecture and parameters of biochemical reactions, respectively. The results reveal that the delayed biochemical reaction modeling problems could be solved effectively and efficiently using our proposed new model and new evolutionary strategy.
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Tamura, Takeyuki, Kazuhiro Takemoto, and Tatsuya Akutsu. "Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets." International Journal of Knowledge Discovery in Bioinformatics 1, no. 1 (2010): 14–31. http://dx.doi.org/10.4018/jkdb.2010100202.
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In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.
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Duan, Chenru, Guan-Horng Liu, Yuanqi Du, et al. "Optimal transport for generating transition states in chemical reactions." Nature Machine Intelligence 7, no. 4 (2025): 615–26. https://doi.org/10.1038/s42256-025-01010-0.
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Abstract Transition states (TSs) are transient structures that are key to understanding reaction mechanisms and designing catalysts but challenging to capture in experiments. Many optimization algorithms have been developed to search for TSs computationally. Yet, the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation of 0.053 Å and median barrier height error of 1.06 kcal mol−1 requiring only 0.4 s per reaction. The root mean square deviation and barrier height error are further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.
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Smith, Robert W., Sluijs Bob van, and Christian Fleck. "Designing synthetic networks in silico: a generalised evolutionary algorithm approach." BMC Systems Biology 11, no. 1 (2017): 118. https://doi.org/10.1186/s12918-017-0499-9.
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<strong>Background: </strong>Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised <i>in silico</i> evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes).<strong>Results: </strong>The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction.<strong>Conclusions: </strong>In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.
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Göb, S., E. Oliveros, S. H. Bossmann, A. M. Braun, C. A. O. Nascimento, and R. Guardani. "Optimal experimental design and artificial neural networks applied to the photochemically enhanced Fenton reaction." Water Science and Technology 44, no. 5 (2001): 339–45. http://dx.doi.org/10.2166/wst.2001.0321.
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Among advanced oxidation processes (AOPs), the photochemically enhanced Fenton reaction may be considered as one of the most efficient for the degradation of contaminants in industrial wastewater. This process involves a series of complex reactions. Therefore, an empirical model based on artificial neural networks has been developed for fitting the experimental data obtained in a laboratory batch reactor for the degradation of 2,4-dimethyl aniline (2,4-xylidine), chosen as a model pollutant. The model describes the evolution of the pollutant concentration during irradiation time as a function of the process conditions. It has been used for simulating the behavior of the reaction system in sensitivity studies aimed at optimizing the amounts of reactants employed in the process, an iron(III) salt and hydrogen peroxide, as well as the temperature. The results show that the process is most sensitive to the concentration of iron(III) salt and temperature, whereas the concentration of hydrogen peroxide has a minor effect.
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Rätze, K. H. G., K. McBride, and K. Sundmacher. "Optimal experimental design with Bayesian parameter identification for chemical reaction networks." Chemie Ingenieur Technik 92, no. 9 (2020): 1354. http://dx.doi.org/10.1002/cite.202055028.
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Malashin, Ivan, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, and Aleksei Borodulin. "Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions." International Journal of Molecular Sciences 25, no. 7 (2024): 3860. http://dx.doi.org/10.3390/ijms25073860.
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This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e−x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.
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Bjelovic, Zoran, Ivan Ristic, Jaroslava Budinski-Simendic, et al. "The investigation reaction kinetic for polyurethanes based on different types of diisocyanate and castor oil." Chemical Industry 66, no. 6 (2012): 841–51. http://dx.doi.org/10.2298/hemind111216014b.
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The formation of polyurethanes based on vegetable oils is very complex and thus for industrial production of this materials it is important to determine the optimal temperature for polymerisation and finally to obtain materials with the proper mechanical properties. The goal of this work was to assess the kinetic of catalysed and noncatalysed reactions for polyurethanes based on castor oil as the polyol component and different types of diisocyanates. Due to the presences of hydroxyl groups on ricinoleic acid, castor oil is suitable for polyurethane preparation. The differential scanning calorimetry has been employed to study the polyurethane formation reaction using Ozawa isoconversion method. It was estimated that the catalyst addition decreases the activation energy. The highest reduction of activation energy was observed for the reactive systems with hexamethylene diisocyanate. Validity of obtained kinetic model was examined by FTIR spectroscopy following the apsorption of reactive groups. Obtained results of mechanical characteristics of the polyuretahane networks (with different NCO/OH ratio) confirmed that applied method could be used for prediction of optimal reaction condition in polyurethane networks synthesis.
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Flassig, R. J., and K. Sundmacher. "Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks." Bioinformatics 28, no. 23 (2012): 3089–96. http://dx.doi.org/10.1093/bioinformatics/bts585.
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Venkataraman, Gaurav G., Eric A. Miska, and David J. Jordan. "Processive and distributive non-equilibrium networks discriminate in alternate limits." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 8 (2022): 083206. http://dx.doi.org/10.1088/1742-5468/ac85e8.
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Abstract We study biochemical reaction networks capable of product discrimination inspired by biological proofreading mechanisms. At equilibrium, product discrimination, the selective formation of a ‘correct’ product with respect to an ‘incorrect product’, is fundamentally limited by the free energy difference between the two products. However, biological systems often far exceed this limit, by using discriminatory networks that expend free energy to maintain non-equilibrium steady states. Non-equilibrium systems are notoriously difficult to analyze and no systematic methods exist for determining parameter regimes which maximize discrimination. Here we introduce a measure that can be computed directly from the biochemical rate constants which provides a condition for proofreading in a broad class of models, making it a useful objective function for optimizing discrimination schemes. Our results suggest that this measure is related to whether a network is processive or distributive. Processive networks are those that have a single dominant pathway for reaction progression, such as a protein complex that must be assembled sequentially. While distributive networks are those that have many effective pathways from the reactant to the product state; e.g. a protein complex in which the subunits can associate in any order. Non-equilibrium systems can discriminate using either binding energy (energetic) differences or activation energy (kinetic) differences. In both cases, proofreading is optimal when dissipation is maximized. In this work, we show that for a general class of proofreading networks, energetic discrimination requires processivity and kinetic discrimination requiring distributivity. Optimal discrimination thus requires both maximizing dissipation and being in the correct processive/distributive limit. Sometimes, adjusting a single rate may put these requirements in opposition and in these cases, the error may be a non-monotonic function of that rate. This provides an explanation for the observation that the error is a non-monotonic function of the irreversible drive in the original proofreading scheme of Hopfield and Ninio. Finally, we introduce mixed networks, in which one product is favored energetically and the other kinetically. In such networks, sensitive product switching can be achieved simply by spending free energy to drive the network toward either the processive limit or the distributive limit. Biologically, this corresponds to the ability to select between products by driving a single reaction without network fine tuning. This may be used to explore alternate product spaces in challenging environments.
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M�llney, Michael, Wolfgang Wiechert, Dirk Kownatzki, and Albert A. de Graaf. "Bidirectional reaction steps in metabolic networks: IV. Optimal design of isotopomer labeling experiments." Biotechnology and Bioengineering 66, no. 2 (1999): 86–103. http://dx.doi.org/10.1002/(sici)1097-0290(1999)66:2<86::aid-bit2>3.0.co;2-a.
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Liu, Qiyu, Qunxiong Zhu, and Longjin Lv. "Computational optimal control for the time fractional convection-diffusion-reaction system." Cluster Computing 20, no. 4 (2017): 2943–53. http://dx.doi.org/10.1007/s10586-017-0929-x.
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Cordoni, Francesco, and Luca Di Persio. "Gaussian estimates on networks with dynamic stochastic boundary conditions." Infinite Dimensional Analysis, Quantum Probability and Related Topics 20, no. 01 (2017): 1750001. http://dx.doi.org/10.1142/s0219025717500011.
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In this paper we prove the existence and uniqueness for the solution to a stochastic reaction–diffusion equation, defined on a network, and subjected to nonlocal dynamic stochastic boundary conditions. The result is obtained by deriving a Gaussian-type estimate for the related leading semigroup, under rather mild regularity assumptions on the coefficients. An application of the latter to a stochastic optimal control problem on graphs, is also provided.
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Laniau, Julie, Clémence Frioux, Jacques Nicolas, et al. "Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks." PeerJ 5 (October 12, 2017): e3860. http://dx.doi.org/10.7717/peerj.3860.
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BackgroundThe emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network.ResultsWe propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of thephenotypic essential metabolite(PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool,Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach.ConclusionThe exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, theConquestspython package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.
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Hao, Yaobin, and Fangying Song. "Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations." Fluids 9, no. 11 (2024): 258. http://dx.doi.org/10.3390/fluids9110258.
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In this paper, we used Fourier Neural Operator (FNO) networks to solve reaction–diffusion equations. The FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. We applied the FNO to the Surface Quasi-Geostrophic (SQG) equation, and we tested the model with two significantly different initial conditions: Vortex Initial Conditions and Sinusoidal Initial Conditions. Furthermore, we explored the generalization ability of the model by evaluating its performance when trained on Vortex Initial Conditions and applied to Sinusoidal Initial Conditions. Additionally, we investigated the modes (frequency parameters) used during training, analyzing their impact on the experimental results, and we determined the most suitable modes for this study. Next, we conducted experiments on the number of convolutional layers. The results showed that the performance of the models did not differ significantly when using two, three, or four layers, with the performance of two or three layers even slightly surpassing that of four layers. However, as the number of layers increased to five, the performance improved significantly. Beyond 10 layers, overfitting became evident. Based on these observations, we selected the optimal number of layers to ensure the best model performance. Given the autoregressive nature of the FNO, we also applied it to solve the Gray–Scott (GS) model, analyzing the impact of different input time steps on the performance of the model during recursive solving. The results indicated that the FNO requires sufficient information to capture the long-term evolution of the equations. However, compared to traditional methods, the FNO offers a significant advantage by requiring almost no additional computation time when predicting with new initial conditions.
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Smith, Eric, Harrison B. Smith, and Jakob Lykke Andersen. "Rules, hypergraphs, and probabilities: The three-level analysis of chemical reaction systems and other stochastic stoichiometric population processes." PLOS Complex Systems 1, no. 4 (2024): e0000022. https://doi.org/10.1371/journal.pcsy.0000022.
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We consider problems in the functional analysis and evolution of combinatorial chemical reaction networks as rule-based, or three-level systems. The first level consists of rules, realized here as graph-grammar representations of reaction mechanisms. The second level consists of stoichiometric networks of molecules and reactions, modeled as hypergraphs. At the third level is the stochastic population process on molecule counts, solved for dynamics of population trajectories or probability distributions. Earlier levels in the hierarchy generate later levels combinatorially, and as a result constraints imposed in earlier and smaller layers can propagate to impose order in the architecture or dynamics in later and larger layers. We develop general methods to study rule algebras, emphasizing system consequences of symmetry; decomposition methods of flows on hypergraphs including the stoichiometric counterpart to Kirchhoff’s current decomposition and work/dissipation relations studied by Wachtel et al.; and the large-deviation theory for currents in a stoichiometric stochastic population process, deriving additive decompositions of the large-deviation function that relate a certain Kirchhoff flow decomposition to the extended Pythagorean theorem from information geometry. The latter result allows us to assign a natural probabilistic cost to topological changes in a reaction network of the kind produced by selection for catalyst-substrate specificity. We develop as an example a model of biological sugar-phosphate chemistry from a rule system published by Andersen et al. It is one of the most potentially combinatorial reaction systems used by biochemistry, yet one in which two ancient, widespread and nearly unique pathways have evolved in the Calvin-Benson cycle and the Pentose Phosphate pathway, which are additionally nearly reverses of one another. We propose a probabilistic accounting in which physiological costs can be traded off against the fitness advantages that select them, and which suggests criteria under which these pathways may be optimal.
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Muñoz, Raül, Ramon Casellas, Ricard Vilalta, and Ricardo Martínez. "Dynamic and Adaptive Control Plane Solutions for Flexi-grid Optical Networks based on Stateful PCE." Journal of Lightwave Technology 32, no. 16 (2016): 2703–15. https://doi.org/10.5281/zenodo.57996.
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Adaptive flexi-grid optical networks should be able to autonomously decide where and when to dynamically setup, reoptimize, and release elastic optical connections, in reaction to network state changes. A stateful path computation element (PCE) is a key element for the introduction of dynamics and adaptation in generalized multiprotocol label switching (GMPLS)-based distributed control plane for flexi-grid DWDM networks (e.g., global concurrent reoptimization, defragmentation, or elastic inverse-multiplexing), as well as for enabling the standardized deployment of the GMPLS control plane in the software defined network control architecture. First, this paper provides an overview of passive and active stateful PCE architectures for GMPLS-enabled flexi-grid DWDM networks. A passive stateful PCE allows for improved path computation considering not only the network state (TED) but also the global connection state label switched paths database (LSPDB), in comparison with a (stateless) PCE. However, it does not have direct control (modification, rerouting) of path reservations stored in the LSPDB. The lack of control of these label switched paths (LSPs) may result in the suboptimal performance. To this end, an active stateful PCE allows for optimal path computation considering the LSPDB for the control of the state (e.g., increase of LSP bandwidth, LSP rerouting) of the stored LSPs. More recently, an active stateful PCE architecture has also been proposed that exposes the capability of setting up and releasing new LSPs. It is known as active stateful PCE with instantiation capabilities. This paper presents the first prototype implementation and experimental evaluation of an active stateful PCE with instantiation capabilities for the GMPLS-controlled flexi-grid DWDM network of the ADRENALINE testbed.
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Ruess, Jakob, Andreas Milias-Argeitis, and John Lygeros. "Designing experiments to understand the variability in biochemical reaction networks." Journal of The Royal Society Interface 10, no. 88 (2013): 20130588. http://dx.doi.org/10.1098/rsif.2013.0588.
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Exploiting the information provided by the molecular noise of a biological process has proved to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single-cell measurements. However, quantifying this additional information a priori , to decide whether a single-cell experiment might be beneficial, is currently only possible in systems where either the chemical master equation is computationally tractable or a Gaussian approximation is appropriate. Here, we provide formulae for computing the information provided by measured means and variances from the first four moments and the parameter derivatives of the first two moments of the underlying process. For stochastic kinetic models for which these moments can be either computed exactly or approximated efficiently, the derived formulae can be used to approximate the information provided by single-cell distribution experiments. Based on this result, we propose an optimal experimental design framework which we employ to compare the utility of dual-reporter and perturbation experiments for quantifying the different noise sources in a simple model of gene expression. Subsequently, we compare the information content of a set of experiments which have been performed in an engineered light-switch gene expression system in yeast and show that well-chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.
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Dutta, Susanta, Provas Kumar Roy, and Debashis Nandi. "Optimal Allocation of Static Synchronous Series Compensator Controllers using Chemical Reaction Optimization for Reactive Power Dispatch." International Journal of Energy Optimization and Engineering 5, no. 3 (2016): 43–62. http://dx.doi.org/10.4018/ijeoe.2016070103.
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Static synchronous series compensator (SSSC) is one of the most effective flexible AC transmission systems (FACTS) devices used for enhancing power system security. In this paper, optimal location and sizing of SSSC are investigated for solving the optimal reactive power dispatch (ORPD) problem in order to minimize the active power loss in the transmission networks. A new and efficient chemical reaction optimization (CRO) is proposed to find the feasible optimal solution of the SSSC based optimal reactive power dispatch (ORPD) problem. The proposed approach is carried out on the standard IEEE 30 bus and IEEE 57 bus test systems. The optimization results obtained by the proposed CRO are analyzed and compared with the same obtained from genetic algorithm (GA), teaching learning based optimization (TLBO), quasi-oppositional TLBO (QOTLBO) and strength pareto evolutionary algorithm (SPEA). The results demonstrate the capabilities of the proposed approach to generate true and well-distributed optimal solutions.
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XIA, ShaoJun, LinGen CHEN, and Chao WANG. "Optimal current paths for a class of generalized electrochemical reaction systems." SCIENTIA SINICA Technologica 51, no. 9 (2020): 1025–39. http://dx.doi.org/10.1360/sst-2020-0074.
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Pizoń, Zofia, Shinji Kimijima, and Grzegorz Brus. "Integrating Experimental and Numerical Data for Improved Steam Reforming Simulation with Deep Learning." Journal of Physics: Conference Series 2812, no. 1 (2024): 012024. http://dx.doi.org/10.1088/1742-6596/2812/1/012024.
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Abstract In this paper, a data-driven methane steam reforming simulation is developed and used to predict the post-reaction mixture composition. Until today, methane steam reforming remains a predominant hydrogen production method, yet modeling its complex reactions remains a significant challenge due to the intricate interplay of process variables. Here, we show an artificial neural network simulator that can effectively model these reactions, offering precise predictions based on parameters like temperature, inlet gas composition, methane flow, and nickel catalyst mass. Our approach to data curation integrates experimental, interpolated, and theoretically calculated values and refining the model by assessing the relative importance of each data category. Various neural network structures were tested before ultimately identifying an optimal architecture with a 5-6-8-6-4 network structure. The network underwent 6000 epochs of training, leading to a model that demonstrates excellent agreement with experimental observations, as evidenced by the mean squared error of 0.000217 and the Pearson correlation coefficient of 0.965. Moreover, all process trajectories predicted by the network are characterized by a smooth course and are within a physical range of values. Therefore, this work overcomes a common challenge in chemical process simulation using neural networks and also sets a possible direction for future research in this field.
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BROWN, ERIC, JUAN GAO, PHILIP HOLMES, RAFAL BOGACZ, MARK GILZENRAT, and JONATHAN D. COHEN. "SIMPLE NEURAL NETWORKS THAT OPTIMIZE DECISIONS." International Journal of Bifurcation and Chaos 15, no. 03 (2005): 803–26. http://dx.doi.org/10.1142/s0218127405012478.
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We review simple connectionist and firing rate models for mutually inhibiting pools of neurons that discriminate between pairs of stimuli. Both are two-dimensional nonlinear stochastic ordinary differential equations, and although they differ in how inputs and stimuli enter, we show that they are equivalent under state variable and parameter coordinate changes. A key parameter is gain: the maximum slope of the sigmoidal activation function. We develop piecewise-linear and purely linear models, and one-dimensional reductions to Ornstein–Uhlenbeck processes that can be viewed as linear filters, and show that reaction time and error rate statistics are well approximated by these simpler models. We then pose and solve the optimal gain problem for the Ornstein–Uhlenbeck processes, finding explicit gain schedules that minimize error rates for time-varying stimuli. We relate these to time courses of norepinephrine release in cortical areas, and argue that transient firing rate changes in the brainstem nucleus locus coeruleus may be responsible for approximate gain optimization.
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Ananda, Ridho, Kauthar Mohd Daud, and Suhaila Zainudin. "RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain." PeerJ Computer Science 11 (May 27, 2025): e2880. https://doi.org/10.7717/peerj-cs.2880.
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Over the last 20 years, researchers have proposed regulatory-metabolic network models to integrate gene regulatory networks (GRNs) and metabolic networks in in silico metabolic engineering, aiming to enhance the production rate of desired metabolites. However, the proposed models are unable to comprehensively include the Boolean rules in the empirical gene regulatory networks (GRNs) and gene-protein-reaction (GPR) interactions. Thus, the types of gene interactions, such as inhibition and activation, are disregarded from the analysis. This may result in sub-optimal model performance. Hence, this article presented a novel model using reliability theory to include Boolean rules in empirical GRNs and GPR rules in the integrating process. The proposed algorithm of this model is termed as a reliability-based integrating (RBI) algorithm. The suggested algorithm had three variants: RBI-T1, RBI-T2, and RBI-T3. The performance of the RBI algorithms was assessed by comparing them with the existing algorithms, using empirical results and validated transcription factors (TF) knockout schemes, and their complexity time was identified. Also, the RBI method was implemented in the design of optimal mutant strains of Escherichia coli and Saccharomyces cerevisiae. The simulation results indicated that the effectiveness and efficiency of the RBI algorithms are adequately strong and competitive relative to the existing algorithms. Furthermore, the RBI algorithm effectively identified eight schemes capable of enhancing succinate and ethanol production rates by maintaining the survival of microbial strains. Those results demonstrated that the RBI algorithms are recommended for the construction of optimum mutant strains in in silico metabolic engineering.
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Alavi, Seyed Arash, Valentin Ilea, Alireza Saffarian, Cristian Bovo, Alberto Berizzi, and Seyed Ghodratollah Seifossadat. "Feasible Islanding Operation of Electric Networks with Large Penetration of Renewable Energy Sources considering Security Constraints." Energies 12, no. 3 (2019): 537. http://dx.doi.org/10.3390/en12030537.
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The high penetration of Renewable Energy Sources into electric networks shows new perspectives for the network’s management: among others, exploiting them as resources for network’s security in emergency situations. The paper focuses on the frequency stability of a portion of the grid when it remains islanded following a major fault. It proposes an optimization algorithm that considers the frequency reaction of the relevant components and minimizes the total costs of their shedding. The algorithm predicts the final frequency of the island and the active power profiles of the remaining generators and demands. It is formulated as a Mixed-Integer Non-Linear Programming problem and the high computation time due to a large-size problem is mitigated through a simplified linear version of the model that filters the integer variables. The algorithm is designed to operate on-line and preventively compute the optimal shedding actions to be engaged when islanding occurs. The algorithm is validated for a typical distribution grid: the minimum amount of shedding actions is obtained while the most frequency reactive resources are maintained in operation to assure a feasible frequency. Finally, time-domain simulations show that the optimal solution corresponds to the one at the end of the network’s transients following the islanding.
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De Martino, A., D. Granata, E. Marinari, C. Martelli, and V. Van Kerrebroeck. "Optimal Fluxes, Reaction Replaceability, and Response to Enzymopathies in the Human Red Blood Cell." Journal of Biomedicine and Biotechnology 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/415148.
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Characterizing the capabilities, key dependencies, and response to perturbations of genome-scale metabolic networks is a basic problem with important applications. A key question concerns the identification of the potentially most harmful reaction knockouts. The integration of combinatorial methods with sampling techniques to explore the space of viable flux states may provide crucial insights on this issue. We assess the replaceability of every metabolic conversion in the human red blood cell by enumerating the alternative paths from substrate to product, obtaining a complete map of he potential damage of single enzymopathies. Sampling the space of optimal steady state fluxes in the healthy and in the mutated cell reveals both correlations and complementarity between topologic and dynamical aspects.
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Deng, Yong, Guiyi Wei, Mande Xie, and Jun Shao. "Cooperation Dynamics on Mobile Crowd Networks of Device-to-Device Communications." Mobile Information Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8686945.
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The explosive use of smart devices enabled the emergence of collective resource sharing among mobile individuals. Mobile users need to cooperate with each other to improve the whole network’s quality of service. By modeling the cooperative behaviors in a mobile crowd into an evolutionary Prisoner’s dilemma game, we investigate the relationships between cooperation rate and some main influence factors, including crowd density, communication range, temptation to defect, and mobility attributes. Using evolutionary game theory, our analysis on the cooperative behaviors of mobile takes a deep insight into the cooperation promotion in a dynamical network with selfish autonomous users. The experiment results show that mobile user’s features, including speed, moving probability, and reaction radius, have an obvious influence on the formation of a cooperative mobile social network. We also found some optimal status when the crowd’s cooperation rate reaches the best. These findings are important if we want to establish a mobile social network with a good performance.
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Kulkarni, Nandkumar Prabhakar, Neeli Rashmi Prasad, and Ramjee Prasad. "An Evolutionary Mobility Aware Multi-Objective Hybrid Routing Algorithm for Heterogeneous WSNs." International Journal of Rough Sets and Data Analysis 4, no. 3 (2017): 17–32. http://dx.doi.org/10.4018/ijrsda.2017070102.
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Researchers have faced numerous challenges while designing WSNs and protocols in numerous applications. Amongst all sustaining connectivity and capitalizing on the network lifetime is a serious deliberation. To tackle these two problems, the authors have considered Mobile Wireless Sensor Networks (MWSNs). In this paper, the authors put forward an Evolutionary Mobility aware multi-objective hybrid Routing Protocol for heterogeneous wireless sensor networks (EMRP). EMRP selects the optimal path from source node to sink by means of various metrics such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The Performance of EMRP when equated with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing Algorithm (DyMORA) using parameters such as Average Residual Energy (ARE), Delay and Normalized Routing Load. EMRP improves AES by a factor of 4.93% as related to SHRP and 5.15% as related to DyMORA. EMRP has a 6% lesser delay as compared with DyMORA.
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Anzeneder, Sofia, Cäcilia Zehnder, Anna Lisa Martin-Niedecken, Mirko Schmidt, and Valentin Benzing. "Acute exercise and children's cognitive functioning: What is the optimal dose of cognitive challenge?" Psychology of Sport and Exercise 66, no. 102404 (2023): 1–9. https://doi.org/10.5281/zenodo.13284956.
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Acute bouts of exercise have the potential to benefit children's cognition. Inconsistent evidence on the role of qualitative exercise task characteristics calls for further investigation of the cognitive challenge level in exercise. Thus, the study aim was to investigate which "dose" of cognitive challenge in acute exercise benefits children's cognition, also exploring the moderating role of individual characteristics. In a within-subject experimental design, 103 children (Mage = 11.1, SD = 0.9, 48% female) participated weekly in one of three 15-min exergames followed by an Attention Network task. Exergame sessions were designed to keep physical intensity constant (65% HRmax) and to have different cognitive challenge levels (low, mid, high; adapted to the ongoing individual performance). ANOVAs performed on variables that reflect the individual functioning of attention networks revealed a significant effect of cognitive challenge on executive control efficiency (reaction time performances; p = .014, ƞ2p = .08), with better performances after the high-challenge condition compared to lower ones (ps < .015), whereas alerting and orienting were unaffected by cognitive challenge (ps > .05). ANOVAs performed on variables that reflect the interactive functioning of attention networks revealed that biological sex moderated cognitive challenge effects. For males only, the cognitive challenge level influenced the interactive functioning of executive control and orienting networks (p = .004; ƞ2p = .07). Results suggest that an individualized and adaptive cognitively high-challenging bout of exercise is more beneficial to children’s executive control than less challenging ones. For males, the cognitive challenge in an acute bout seems beneficial to maintain executive control efficiency also when spatial attention resources cannot be validly allocated in advance. Results are interpreted referring to the cognitive stimulation hypothesis and arousal theory.
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FÖRSTER, A., A. GUDERIAN, K. P. ZEYER, G. DECHERT, and F. W. SCHNEIDER. "STOCHASTIC RESONANCE AND TIME ADVANCE CODING IN CHEMICAL REACTIONS." International Journal of Neural Systems 07, no. 04 (1996): 385–91. http://dx.doi.org/10.1142/s012906579600035x.
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Stochastic Resonance (SR) is a phenomenon wich may be found in nonlinear systems close to an excitation threshold. SR is a means for enhancing a weak periodic subthreshold signal from its noisy background by adding stochastic fluctuations, i.e. in biological and physical systems. It has been proposed that SR is important for the ability of neural systems to detect weak periodic signals. In the present work we show experimentally that SR occurs in two nonlinear chemical reactions, namely in the enzymatic Peroxidase-Oxidase (PO) reaction and in the Belousov–Zhabotinskii (BZ) reaction. A small sinusoidal signal with increasing noise is imposed on the focal steady state near a subcritical Hopf bifurcation. When the threshold is crossed beyond a certain noise amplitude, the system responds with spikes. The resulting interspike histogram and the plot of the signal to noise ratio, which is evaluated from the respective Fourier spectra, pass through a maximum at an optimal external noise level. An alternative way to cross the excitation threshold without noise is the variation of the bias value of the sinusoidal signal. The variation of the bias value causes the spikes to appear earlier if the sinusoidal function is moved closer towards the threshold. This so-called time advance coding is shown experimentally for the first time in the BZ reaction by imposing sinusoidal flow rate variations using different bias values. The phenomenon has been proposed by Hopfield11 to be a means for analog pattern recognition.
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Rabinovich, Mihail, Pablo Varona, and Valentin Afraimovich. "Information dynamics in neural systems: computing with separatrices." Izvestiya VUZ. Applied Nonlinear Dynamics 11, no. 1 (2003): 86–97. http://dx.doi.org/10.18500/0869-6632-2003-11-1-86-97.
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Information processing in neural networks by computation with attractors (steady states, limit cycles, strange attractors) has been extensively discussed in application to many neural systems: central pattern generators, sensory systems (e.g. visual, olfactory), hippocampus, etc. Computation with attractors in a traditional way faces а fundamental contradiction between robustness and sensitivity. In this paper we discuss а new direction in information neurodynamics based on experiments performed in the locust olfactory system, the orientation sensory system of the marine mollusk Clione апа the hippocampal place cell networks. This new concept uses the transformation of the incoming spatial or identity information into spatio-temporal output based on the intrinsic switching dynamics of neural networks with nonsymmetric inhibitory connections. This is called the Winner-Less Competition Principle (WLC). The key feature of а network that computes with separatrices is the robustness against noise and the simultancous sensitivity of the sequence of switching to the incoming information. We present rigorous results about the stability of the sequential switching in the framework оf the Lottka-Volterra model. Because оf their fast reaction, the discussed neural networks are able 10 change their intrinsic dynamics to respond to new incoming information and solve many different functional tasks. Computation with separatrices can also be an optimal principle for the design of new paradigms of artificial neural networks.
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Maaraoui, Kayla V., Gregory Ellson, and Walter Voit. "Hybrid cured thiol-ene/epoxy networks for core-shell semiconductor packaging." MRS Advances 1, no. 1 (2016): 57–62. http://dx.doi.org/10.1557/adv.2016.59.
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ABSTRACTThis research describes thiol-ene/epoxy hybrid networks for core-shell encapsulation of semiconductor devices. A thiol-ene network was formed using ultraviolet-induced radical polymerization, with unreacted thiols and epoxide monomers remaining in the network. Immersion in tributylamine catalyzed the thiol-epoxy coupling to produce a diffusion-limited hard outer shell. Tensile testing shows that the initial thiol-ene product (core) has elastomeric behavior, while the secondary curing creates a glassy material (shell) at room temperature due to thiol-epoxy coupling. Bulk samples of the material form a hard outer shell surrounding a soft core depending on the secondary cure conditions. There are positive relationships between wall thickness and secondary cure temperature and cure time, enabling control of shell thickness by varying reaction conditions. Shell thicknesses were measured up to 1.8 mm when immersed in tributylamine for up to 150 minutes and up to 140 °C. The ability to control core-shell thickness of dual-cured networks is applicable in device encapsulation processes. Core-shell encapsulants for microelectronics may provide further shock and impact protection for durable electronic devices. Further aging and operational studies will be needed to determine time-stability and optimal processing of the core-shell structure.
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Li, Xue Yong, Jun Hui Fu, Guo Hong Gao, and Shi Yong Li. "The Safety Control of Computer Systems and Networks Based on Adaptive Approach." Advanced Materials Research 121-122 (June 2010): 699–704. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.699.
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The safety control tools in CSN perform supervision and analysis of the parameters of security systems, so these tools are an important part of the security system. The crucial problem is to develop effective methods and tools for the adaptive control of CSN safety. The suggested adaptive safety control module for CSN based on the safety monitoring tools provides the flexible, correct and effective reaction of security system to the intruder actions. This module allows dynamical evaluation of the information value of each CSN resource and setting the optimal parameters of security system. As a result, the suggested tools allow decrease of the possible damage to the CSN resource from successful intrusions, along with decreasing of security system implementation cost.
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Daniel, Gil-Vera Victor. "Smart Grid Stability Prediction with Machine Learning." WSEAS TRANSACTIONS ON POWER SYSTEMS 17 (October 6, 2022): 297–305. http://dx.doi.org/10.37394/232016.2022.17.30.
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Smart grids refer to a grid system for electricity transmission, which allows the efficient use of electricity without affecting the environment. The stability estimation of this type of network is very important since the whole process is time-dependent. This paper aimed to identify the optimal machine learning technique to predict the stability of these networks. A free database of 60,000 observations with information from consumers and producers on 12 predictive characteristics (Reaction times, Power balances, and Price-Gamma elasticity coefficients) and an independent variable (Stable / Unstable) was used. This paper concludes that the Random Forests technique obtained the best performance, this information can help smart grid managers to make more accurate predictions so that they can implement strategies in time and avoid collapse or disruption of power supply.
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ADAMATZKY, ANDREW, and JEFF JONES. "ROAD PLANNING WITH SLIME MOULD: IF PHYSARUM BUILT MOTORWAYS IT WOULD ROUTE M6/M74 THROUGH NEWCASTLE." International Journal of Bifurcation and Chaos 20, no. 10 (2010): 3065–84. http://dx.doi.org/10.1142/s0218127410027568.
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Plasmodium of Physarum polycephalum is a single cell visible by unaided eye. During its foraging behavior the cell spans spatially distributed sources of nutrients with a protoplasmic network. Geometrical structure of the protoplasmic networks allows the plasmodium to optimize transfer of nutrients between remote parts of its body, to distributively sense its environment, and make a decentralized decision about further routes of migration. We consider the ten most populated urban areas in United Kingdom and study what would be an optimal layout of transport links between these urban areas from the "plasmodium's point of view". We represent geographical locations of urban areas by oat flakes, inoculate the plasmodium in Greater London area and analyze the plasmodium's foraging behavior. We simulate the behavior of the plasmodium using a particle collective which responds to the environmental conditions to construct and minimize transport networks. Results of our scoping experiments show that during its colonization of the experimental space the plasmodium forms a protoplasmic network isomorphic to a network of major motorways except the motorway linking England with Scotland. We also imitate the reaction of transport network to disastrous events and show how the transport network can be reconfigured during natural or artificial cataclysms. The results of the present research lay a basis for future science of bio-inspired urban and road planning.
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Liebermeister, Wolfram. "Structural Thermokinetic Modelling." Metabolites 12, no. 5 (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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Okrasa, Lidia, Magdalena Włodarska, Maciej Kisiel, and Beata Mossety-Leszczak. "Modification of the Dielectric and Thermal Properties of Organic Frameworks Based on Nonterminal Epoxy Liquid Crystal with Silicon Dioxide and Titanium Dioxide." Polymers 16, no. 10 (2024): 1320. http://dx.doi.org/10.3390/polym16101320.
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A nonterminal liquid crystal epoxy monomer is used to create an epoxy–amine network with a typical diamine 4,4′diaminodiphenylmethane. The plain matrix is compared to matrices modified with inorganic fillers: TiO2 or SiO2. Conditions of the curing reaction and glass transition temperatures in the cured products are determined through differential scanning calorimetry and broadband dielectric spectroscopy. The curing process is also followed through optical and electrical observations. The dielectric response of all investigated networks reveals a segmental α-process related to structural reorientation (connected to the glass transition). In all products, a similar process associated with molecular motions of polar groups also appears. The matrix modified with TiO2 exhibits two secondary relaxation processes (β and γ). Similar processes were observed in the pure monomer. An advantage of the network with the TiO2 filler is a shorter time or lower temperature required for optimal curing conditions. The physical properties of cured matrices depend on the presence of a nematic phase in the monomer and nonterminal functional groups in the aliphatic chains. In effect, such cured matrices can have more flexibility and internal order than classical resins. Additional modifiers used in this work shift the glass transition above room temperature and influence the fragility index in both cases.
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Dai, Feng, and Bin Liu. "Optimal control problem for a general reaction–diffusion tumor–immune system with chemotherapy." Journal of the Franklin Institute 358, no. 1 (2021): 448–73. http://dx.doi.org/10.1016/j.jfranklin.2020.10.032.
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Klimenko, V. M., and T. T. Suprun. "METHANATION TECHNOLOGIES FOR PRODUCING SYNTHETIC RENEWABLE METHANE." Thermophysics and Thermal Power Engineering 46, no. 3 (2022): 63–72. http://dx.doi.org/10.31472/ttpe.3.2022.6.
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Methanation, or the generation of synthetic methane through the combination of carbon dioxide and hydrogen, has been attracting more and more attention of researchers and energy scientists in recent years due to the fact that the development of an effective and economically feasible technology for the implementation of this process will allow solving a number of energy and environmental problems. First, it is the accumulation of excess renewable electricity from solar and wind power plants by using it in the creation of another energy-intensive product, namely synthetic natural gas, which removes the problem of coordinating unstable sources of electricity with energy networks. Secondly, methanation becomes another technology for enriching biogas and turning it into biomethane, which will allow it to be used through existing gas networks and contribute to solving the problem of natural gas shortage.
 The development and improvement of methanation technologies are engaged in many organizations of the world - Germany, Denmark, France, the USA, Japan and others. Research is conducted in two main directions: catalytic methanation and biological methanation. In the first direction, methanation is carried out through the Sabatier reaction using catalysts. The problems of such methanation are: the development of catalysts with high activity, selectivity and resistance to the heat of reaction, the provision of optimal reaction modes, in particular temperature and pressure, through the use of various methods of reactor cooling, control of the reaction mechanism, the use of three-phase reactors, changing their structure, and so on. Biological methanation is carried out using of biological methanogens - so-called archaea, which act as a kind of catalyst. The methanation is carried out either directly in the biomass anaerobic digestion reactor (in-situ methanation) or in a separate reactor into which biogas and hydrogen are fed separately (ex-situ methanation). One of the main problems of in-situ methanation is the simultaneous provision of optimal conditions for both acetoclastic and hydrogenotrophic methanogens. This problem is solved by ex-situ methanation, in which the optimal conditions for anaerobic digestion and methanation processes are provided separately. It is clear that optimal conditions are also provided for biomethanation of pure CO2 and H2, when the «broth» for archaea is created separately. A comparison of catalytic and biological methanation technologies shows that catalytic methanation provides higher energy efficiency and requires much smaller reactor sizes than biological methanation for the same methane yield. However, the latter has a higher resistance to harmful impurities than the catalytic one.
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Gavrilescu, Marius, Sabina-Adriana Floria, Florin Leon, and Silvia Curteanu. "A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering." Mathematics 10, no. 19 (2022): 3581. http://dx.doi.org/10.3390/math10193581.
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Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs.
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Llaguno-Roque, José-Luis, Rocio-Erandi Barrientos-Martínez, Héctor-Gabriel Acosta-Mesa, Tania Romo-González, and Efrén Mezura-Montes. "Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips." Mathematical and Computational Applications 28, no. 3 (2023): 72. http://dx.doi.org/10.3390/mca28030072.
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Breast cancer has become a global health problem, ranking first in incidences and fifth in mortality in women around the world. In Mexico, the first cause of death in women is breast cancer. This work uses deep learning techniques to discriminate between healthy and breast cancer patients, based on the banding patterns obtained from the Western Blot strip images of the autoantibody response to antigens of the T47D tumor line. The reaction of antibodies to tumor antigens occurs early in the process of tumorigenesis, years before clinical symptoms. One of the main challenges in deep learning is the design of the architecture of the convolutional neural network. Neuroevolution has been used to support this and has produced highly competitive results. It is proposed that neuroevolve convolutional neural networks (CNN) find an optimal architecture to achieve competitive ranking, taking Western Blot images as input. The CNN obtained reached 90.67% accuracy, 90.71% recall, 95.34% specificity, and 90.69% precision in classifying three different classes (healthy, benign breast pathology, and breast cancer).
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Laborde, María Fernanda, Medardo Serna Gonzalez, Ana María Pagano, and María Cristina Gely. "Technical-Economic Study of the Esterification Process of Used Vegetable Oils (UVOs) Using Heat Exchange Networks (HENs)." Advanced Materials Research 1139 (July 2016): 40–45. http://dx.doi.org/10.4028/www.scientific.net/amr.1139.40.
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The objective in this study was to conduct a technical-economic study of the esterification process of used vegetable oils (UVOs) for the production of biodiesel from the point of view of energy savings achieved by implementing heat exchange networks (HENs). Used vegetable oils (UVOs) can be employed as an input in the production of biodiesel by catalytic transesterification. But, previously it is necessary to reduce its level of free fat acids (FFA) by the acid-catalyzed esterification process in order to prevent undesirable saponification reaction. To carrying out an optimal design of the technology required in the process, simulation tools have an important role for process engineering and optimization of resources. Computer programs such as Aspen PlusTM and Aspen Energy AnalyzerTM provide an environment to perform process modeling and network design optimal heat exchange. In this paper, from the Aspen PlusTM simulation of the process of catalytic esterification in acid medium of UVOs, the technical-economic evaluation process was conducted with and without network of heat exchange in order to analyze the different investment options. The comparison of the two projects (with and without the implementation of HENs) was performed by determining the net present value (NPV). On the scale set for the project, the total cost of the equipment of heat exchange for the esterification process designed with HENs was US$ 4,782.50 higher than the corresponding to the process without HENs application. However, it should be noted that the cost of services decreased by 30% annually, and on the other hand, comparing the process, it was observed that the NPV of the HENs process was 29.5% higher, which leads to the conclusion that the project which includes heat exchange networks is technically and economically feasible.
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Nurjanah, Siti, Falentino Sembiring, and Rieska Rahayu Ayuningsih. "INTEGRATION OF TELEGRAM BOT AND UPTIME KUMA FOR WI-FI NETWORK MONITORING USING MIKROTIK." Jurnal Pilar Nusa Mandiri 20, no. 2 (2024): 118–26. http://dx.doi.org/10.33480/pilar.v20i2.5535.
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Wi-Fi network monitoring is a crucial aspect in ensuring the availability and security of widely used internet services. This research confirms the use of Telegram bots for notification integration with the Uptime-Kuma monitoring program and proxy devices for Wi-Fi network monitoring. The main router and network controller in this study were Mikrotik devices, while Uptime Kuma was used as a monitoring tool to track the performance and availability of the Wi-Fi network. When important events are discovered on the network, network administrators can receive quick notifications through integration with Telegram bots. In the context of Wi-Fi networks, the Security Policy Development Life Cycle (SPDLC) method is used to design relevant and effective security policies. It includes the stages of planning, implementation, monitoring, evaluation and regular updating of security policies to maintain optimal levels of security. The results show that the integration of Telegram bots with the Kuma Uptime monitoring tool can improve network availability. This allows quick reaction to Uptime and Downtime reports in network conditions, which record the percentage of time network services are available. Thus, administrators do not need to wait for complaints from users if the connection is suddenly lost, because changes in connection status will automatically send a notification to Telegram.
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Peschanskii, Vladyslav, та Yevgeniya Sulema. "ПОШУК КЛЮЧОВИХ ТОЧОК НА ЗОБРАЖЕННЯХ ДЛЯ СТВОРЕННЯ ЦИФРОВИХ ДВІЙНИКІВ МЕДИКО-БІОЛОГІЧНИХ ОБ'ЄКТІВ". System technologies 6, № 149 (2024): 3–10. http://dx.doi.org/10.34185/1562-9945-6-149-2023-01.
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The paper presents an analysis of optimal tools for creating a digital twin of human or-gans (on the example of otolaryngology) based on streaming video data received in real time from the camera of a medical device. Two main methods were studied: algorithmic recon-struction and the use of a neural network. The comparison of methods was performed accord-ing to the following criteria: efficiency, accuracy, speed of reaction and practicality of appli-cation in the medical environment. Special attention is paid to approaches based on neural networks due to their high adaptability, accuracy and ability to efficiently process noisy and incomplete data. The main advantages and features of this method in the context of medical application are determined. The results of the study confirm the high potential of neural net-works in creating accurate digital models of internal organs, which opens up new perspec-tives for the development of software for the creation of digital twins of medical and biologi-cal objects.
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Wang, Zichen, Parth Natekar, Challana Tea, Sharon Tamir, Hiroyuki Hakozaki, and Johannes Schöneberg. "MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data." PLOS Computational Biology 19, no. 4 (2023): e1011060. http://dx.doi.org/10.1371/journal.pcbi.1011060.
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Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. Dysregulation of this four-dimensional (4D: x,y,z,time) temporal network is implicated in numerous diseases ranging from cancer to neurodegeneration. While lattice light-sheet microscopy has recently made it possible to image mitochondria in 4D, quantitative analysis methods for the resulting datasets have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking assignment. To validate the accuracy of tracking, we created a reaction-diffusion simulation to model mitochondrial network motion and remodeling events. We found that our tracking is >90% accurate for the ground-truth simulations and agrees well with published motility results for experimental data. We used MitoTNT to quantify 4D mitochondrial networks from human induced pluripotent stem cells. First, we characterized sub-fragment motility and analyzed network branch motion patterns. We revealed that the skeleton node motion is correlated along branch and uncorrelated in time. Second, we identified fission and fusion events with high spatiotemporal resolution. We found that mitochondrial skeleton nodes near the fission/fusion sites move nearly twice as fast as random skeleton nodes and that microtubules play a role in mediating selective fission/fusion. Finally, we developed graph-based transport simulations that model how material would distribute on experimentally measured mitochondrial temporal networks. We showed that pharmacological perturbations increase network reachability but decrease network resilience through a combination of altered mitochondrial fission/fusion dynamics and motility. MitoTNT’s easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analysis aim at making temporal network tracking accessible to the wider mitochondria research community.
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K.C, Chandrika, T. Niranjana Prabhu, R. R. Siva Kiran, and R. Hari Krishna. "Applications of artificial neural network and Box-Behnken Design for modelling malachite green dye degradation from textile effluents using TiO2 photocatalyst." Environmental Engineering Research 27, no. 1 (2021): 200553–0. http://dx.doi.org/10.4491/eer.2020.553.
Full textAbstract:
Most of the photocatalytic studies for pollutant degradation are based on optimizing a single parameter that results in a non-linear relationship between the overall parameters and the photo-degradation reactions. To address this critical problem, herein, we report the use of Response Surface Methodology based on the Box-Behnken Design (BBD) for modeling the photocatalysis degradation of Malachite Green (MG) dye using nano TiO2 as photocatalyst. The catalyst characterizations are carried out using XRD, SEM, and TEM, indicating that the TiO2 prepared by sol-gel synthesis possesses Anatase phase with particles in the nano regime and porous surface morphology. The optimum operating conditions for degradation of MG was identified by the interactive effects of variable factors such as initial dye concentration 10-30 ppm (x1), catalyst dosage 1-3 mg (x2), contact time 20-60 min (x3) using the Box-Behnken method. Furthermore, the degradation reactions are also evaluated by Artificial Neural Networks (ANN). Their predicted results have been validated by the experimental studies and found to be acceptable. Their optimal results to achieve 90% degradation efficiency at TiO2 nanoparticle dosage (3 mg), reaction time (60 min), and initial dye concentration (20 ppm) have been validated by the experimental studies and found to be acceptable.
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Gao, Shupeng, Lili Chang, Ivan Romić, Zhen Wang, Marko Jusup, and Petter Holme. "Optimal control of networked reaction–diffusion systems." Journal of The Royal Society Interface 19, no. 188 (2022). http://dx.doi.org/10.1098/rsif.2021.0739.
Full textAbstract:
Patterns in nature are fascinating both aesthetically and scientifically. Alan Turing’s celebrated reaction–diffusion model of pattern formation from the 1950s has been extended to an astounding diversity of applications: from cancer medicine, via nanoparticle fabrication, to computer architecture. Recently, several authors have studied pattern formation in underlying networks, but thus far, controlling a reaction–diffusion system in a network to obtain a particular pattern has remained elusive. We present a solution to this problem in the form of an analytical framework and numerical algorithm for optimal control of Turing patterns in networks. We demonstrate our method’s effectiveness and discuss factors that affect its performance. We also pave the way for multidisciplinary applications of our framework beyond reaction–diffusion models.