Bacterial Resistance Algorithm. An Application to CVRP

Orellana, María José; Contreras, A Ricardo

doi:10.1007/978-3-030-19651-6_20

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Extra chromosomal resistance is when transmission of genetic material occurs through plasmids, transposons, and integrons [ 64 ], which are extra chromosomal material. This type of resistance is also known as horizontal genetic transmission mutation [ 58 , 65 , 66 , 67 , 68 ].…”

Section: Antimicrobial Resistancementioning

confidence: 99%

Antimicrobial Resistance and Inorganic Nanoparticles

Balderrama-González

Piñón-Castillo

Ramírez-Valdespino

et al. 2021

IJMS

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Antibiotics are being less effective, which leads to high mortality in patients with infections and a high cost for the recovery of health, and the projections that are had for the future are not very encouraging which has led to consider antimicrobial resistance as a global health problem and to be the object of study by researchers. Although resistance to antibiotics occurs naturally, its appearance and spread have been increasing rapidly due to the inappropriate use of antibiotics in recent decades. A bacterium becomes resistant due to the transfer of genes encoding antibiotic resistance. Bacteria constantly mutate; therefore, their defense mechanisms mutate, as well. Nanotechnology plays a key role in antimicrobial resistance due to materials modified at the nanometer scale, allowing large numbers of molecules to assemble to have a dynamic interface. These nanomaterials act as carriers, and their design is mainly focused on introducing the temporal and spatial release of the payload of antibiotics. In addition, they generate new antimicrobial modalities for the bacteria, which are not capable of protecting themselves. So, nanoparticles are an adjunct mechanism to improve drug potency by reducing overall antibiotic exposure. These nanostructures can overcome cell barriers and deliver antibiotics to the cytoplasm to inhibit bacteria. This work aims to give a general vision between the antibiotics, the nanoparticles used as carriers, bacteria resistance, and the possible mechanisms that occur between them.

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Section: Antimicrobial Resistancementioning

confidence: 99%

Antimicrobial Resistance and Inorganic Nanoparticles

Balderrama-González

Piñón-Castillo

Ramírez-Valdespino

et al. 2021

IJMS

View full text Add to dashboard Cite

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A Bacteria-Based Metaheuristic as a Tool for Group Formation

Ricardo Contreras,

Valentina Hernández,

Pinacho-Davidson

et al. 2022

Bio-Inspired Systems and Applications: From Robotics to Ambient Intelligence

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Extension of CMSA with a Learning Mechanism: Application to the Far from Most String Problem

Pinacho-Davidson,

Blum,

Pinninghoff

et al. 2024

Int J Comput Intell Syst

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One of the problems with exact techniques for solving combinatorial optimization problems is that they tend to run into problems with growing problem instance size. Nevertheless, they might still be very usefully employed, even in the context of large problem instances, as a sub-ordinate method within so-called hybrid metaheuristics. “Construct, Merge, Solve and Adapt” (Cmsa) is a hybrid metaheuristic technique that allows the application of exact methods to large-scale problem instances through intelligent instance reduction. However, Cmsa does not make use of an explicit learning mechanism. In this work, an algorithm called $$\textsc {Learn}\_\textsc {Cmsa}$$ L E A R N _ C M S A is presented for the application to the far from most string problem (FFMSP), which is an NP-hard combinatorial optimization problem from the field of string consensus problems. $$\textsc {Learn}\_\textsc {Cmsa}$$ L E A R N _ C M S A results from hybridization between Cmsa and a population-based algorithm. By means of this hybridization, explicit learning is introduced to Cmsa. Even though the FFMSP is a well-studied problem, $$\textsc {Learn}\_\textsc {Cmsa}$$ L E A R N _ C M S A achieves superior performance when compared to current state-of-the-art solvers.

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Bacterial Resistance Algorithm. An Application to CVRP

Cited by 3 publications

References 7 publications

Antimicrobial Resistance and Inorganic Nanoparticles

Antimicrobial Resistance and Inorganic Nanoparticles

A Bacteria-Based Metaheuristic as a Tool for Group Formation

Extension of CMSA with a Learning Mechanism: Application to the Far from Most String Problem

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