As a significant source of global energy consumption and greenhouse gas emissions, the construction industry garners widespread attention due to its high carbon emissions. Anticipating its development trends is crucia...
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As a significant source of global energy consumption and greenhouse gas emissions, the construction industry garners widespread attention due to its high carbon emissions. Anticipating its development trends is crucial for energy conservation and emission reduction. In this paper, we utilize the carbon emission data from China 's national and provincial construction sectors from 2012 to 2021, employ the grey prediction model optimized by the particle swarm optimization algorithm, coupled with a metabolic algorithm, to forecast the carbon emissions of the construction industry across China and its provinces. The results demonstrate that: (1) The dynamic grey prediction model combined with the metabolism algorithm has a better prediction effect than the classical model, and the relative error is reduced from 5.103 % to 0.874 %. (2) The carbon emissions of China 's construction industry will continue to rise in the next decade, but the growth rate will decrease, and the proportion of indirect carbon emissions continues to increase. (3) There is a marked regional disparity in carbon emissions, with the eastern region exhibiting higher emission levels yet slower growth. In contrast, the western region has lower emission levels but experiences faster growth. These studies provide valuable insights for both the existing approaches to energy conservation and emission reduction, as well as for future policy improvements.
The objective of this work is to apply metabolic algorithm to the various items involved during the software development process. The metabolic algorithm is introduced in the rewriting mechanism of membrane or P syste...
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ISBN:
(纸本)9783037851517
The objective of this work is to apply metabolic algorithm to the various items involved during the software development process. The metabolic algorithm is introduced in the rewriting mechanism of membrane or P system considering many time varying functions. Rules for requirement evolution, reaction between items in the membrane, communications between data items, process speed-up and abort rule are being proposed. The metabolic algorithm is applied for the transformation of user requirements into system requirements which can be further segregated into functional as well as non-functional requirements. The requirement elicitation is illustrated and verified to obtain the most expected requirement objects using C# programming language.
P systems are used to compute predator-prey dynamics expressed in the traditional formulation by Lotka and Volterra. By governing the action of the transition rules in such systems using the regulatory features of the...
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P systems are used to compute predator-prey dynamics expressed in the traditional formulation by Lotka and Volterra. By governing the action of the transition rules in such systems using the regulatory features of the metabolic algorithm we come up with simulations of the Lotka-Volterra equations, whose robustness is comparable to that obtained using Runge-Kutta schemes and Gillespie's Stochastic Simulation algorithm. Besides their reliability, the results obtained using the metabolic algorithm on top of P systems have a clear biochemical interpretation concerning the role, of reactants or promoters, of the species involved. (C) 2007 Elsevier Ireland Ltd. All rights reserved.
metabolic P systems are a special class of P systems which seem to be adequate for expressing biological phenomena, especially, metabolism and signaling transduction. Basic motivations for their introduction are given...
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metabolic P systems are a special class of P systems which seem to be adequate for expressing biological phenomena, especially, metabolism and signaling transduction. Basic motivations for their introduction are given and their main aspects are explained by means of an example of biological modeling. A new kind of regulation mechanism is outlined, which could be the basis for a more efficient construction of computational models from experimental data of specific metabolic processes.
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