The manuscript. Funding: This operate was supported in component by the
The manuscript. Funding: This work was supported in part by the National Natural Science Foundation of China (62003289), in portion by the China Postdoctoral Science Foundation (2021M690400), in aspect by the Doctoral Foundation of Xinjiang University (BS180207), in component by the Tianshan Youth Program (2018Q068), and in component by the Tianshan Innovation Group Plan (2020D14017). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.AbbreviationsThe following abbreviations are made use of in this manuscript: MASs FONMASs SMC Multi-agent systems First-order nonlinear multi-agent systems Sliding mode manage
Academic Editor: Hector Zenil Received: 2 September 2021 Accepted: 26 October 2021 Published: 28 OctoberPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Copyright: 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is definitely an open access article distributed under the terms and conditions of the Inventive Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ four.0/).Not too long ago there has been a fast enhance in analysis to create theory and methods for calculating the entropy of landscape patterns (e.g., [1]). The original applications of entropy in ecological research at the same time as image processing had been primarily based on Shannon entropy [5]. These approaches are limited in that they’re not explicitly sensitive to distinctive configurations in the technique (e.g., they measure compositional rather than configurational entropy, sensu [1]). Application of entropy measures to landscape ecology demands explicit attention to and quantification of spatial patterns (configuration as well as composition). Recently, there has been a concentrated work to move away from non-spatial informationentropy approaches, rooted in Shannon entropy, to explicit calculation of Boltzmann entropy of landscape patterns, that is rooted in counting the frequency of microstates across a full distribution of feasible landscape configurations [1,two,6]. The important distinction among these two lines of research is the fact that the latter straight focuses on the entropy of distinctive configurations of landscape patterns. Several approaches have already been proposed to directly quantify the configurational entropy of Seclidemstat Protocol landscapes, including an method to directly apply the Boltzmann relation to permuted landscape patterns (the Cushman approach [1,2]) as well as a quantity of far more complicated approaches, like making use of multi-resolution analysis (the Gao strategy [3,4,7]) as well as other option entropy formulations (for example Wassenstein entropy [80]). All of these approaches have theoretical strengths and differ in complexity and also the measurements they generate. Till now there has been small info, Tenidap Protocol however, on theEntropy 2021, 23, 1420. https://doi.org/10.3390/ehttps://www.mdpi.com/journal/entropyEntropy 2021, 23,2 ofthermodynamic consistency of the various solutions. The Gao method(s) have been evaluated and identified to be partly thermodynamically consistent, following modifications [6]. In addition, the Wassenstein approach has been evaluated and found to be consistent with several criteria of thermodynamic consistency following clarification and modification by [10]. Within this paper, I evaluate the thermodynamic consistency on the Cushman technique of calculating the.