A model for the operations to render epidemic-free a hog farm infected by the Aujeszky
disease

Costamagna, Andrea; Drigo, Michele; Martini, Marco; Sona, B.; Venturino, Ezio

doi:10.21042/amns.2016.1.00016

Cited by 17 publications

(10 citation statements)

References 15 publications

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“…Control strategies based on vaccination and antiparasitic treatments have been performed [3]. But, due to residues and other problems, some vaccines and drugs have been eliminated from these strategies [4,5].…”

Section: Introductionmentioning

confidence: 99%

A discrete epidemic model for bovine Babesiosis disease and tick populations

Aranda¹,

Trejos²,

Valverde³

2017

Open Physics

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Abstract:In this paper, we provide and study a discrete model for the transmission of Babesiosis disease in bovine and tick populations. This model supposes a discretization of the continuous-time model developed by us previously. The results, here obtained by discrete methods as opposed to continuous ones, show that similar conclusions can be obtained for the discrete model subject to the assumption of some parametric constraints which were not necessary in the continuous case. We prove that these parametric constraints are not artificial and, in fact, they can be deduced from the biological significance of the model. Finally, some numerical simulations are given to validate the model and verify our theoretical study.

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Section: Introductionmentioning

confidence: 99%

A discrete epidemic model for bovine Babesiosis disease and tick populations

Aranda¹,

Trejos²,

Valverde³

2017

Open Physics

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“…The results of the filling are shown in Table 1: It can be seen from Table 1 that for any kind of missing combination, with the increase of data missing rate, the d 2 obtained by the algorithm FIMUS and DMI are decreasing, that is, the filling precision of the two algorithms for incomplete economic information decreases with the increase of data missing rate. As the data loss rate increases, the RMSE obtained by the algorithms FIMUS and DMI increases continuously, that is the filling accuracy of the two algorithms decreases as the data missing rate increases [23,24]. The algorithm proposed in this study has an RMSE fill value of less than 0.2.…”

Section: Experimental Process and Analysismentioning

confidence: 99%

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Li¹,

Shen²

2020

Symmetry

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Discretization based on rough sets is used to divide the space formed by continuous attribute values with as few breakpoint sets as possible, while maintaining the original indistinguishable relationship of the decision system, so as to accurately classify and identify related information. In this study, a discretization algorithm for incomplete economic information in rough set based on big data is proposed. First, the algorithm for filling-in incomplete economic information based on deep learning is used to supplement the incomplete economic information. Then, based on breakpoint discrimination, the algorithm for discretization in the rough set is used to implement the discretization based on rough set for supplementary economic information. The performance of this algorithm was tested using multiple sets of data and compared with other algorithms. Experimental results show that this algorithm is effective for discretization based on a rough set of incomplete economic information. When the number of incomplete economic information rough candidate breakpoints increases, it still has a higher computational efficiency and can effectively improve the integrity of incomplete economic information, and finally the application performance is superior.

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“…The decomposition based multi-objective ant colony optimization is to divide the ant colony into multiple subgroups and assign them to various targets. Each subgroup maintains a pheromone matrix, and each ant has its own heuristic information matrix [16][17][18][19][20][21][22]. Paretobased ant colony optimization (PACO) uses multiple pheromone matrices and a single heuristic information matrix.…”

Section: Solving Multi-objective Parameters Of Orc Cycle Based On Impmentioning

confidence: 99%

Multi-objective performance optimization of ORC cycle based on improved ant colony algorithm

Wei

Hassan

2019

Open Physics

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To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.

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A model for the operations to render epidemic-free a hog farm infected by the Aujeszky disease

Cited by 17 publications

References 15 publications

A discrete epidemic model for bovine Babesiosis disease and tick populations

A discrete epidemic model for bovine Babesiosis disease and tick populations

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Multi-objective performance optimization of ORC cycle based on improved ant colony algorithm

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