Phenomenological Computational Model for the Development of a Population Outbreak of Insects with Its Bifurcational Completion

Perevaryukha, A. Yu.

doi:10.1134/s2070048218040117

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Consequently, these parameters influence the evolution and inheritance of species. In the current scientific landscape, it is notable that a substantial number of researchers have adopted the logistic model as a valuable tool for forecasting cumulative cases of infectious diseases, such as COVID-19, and for modeling the dynamics of insect outbreaks [16,17]. This mathematical model, originally introduced by Pierre François Verhulst and widely applied in epidemiology and population ecology, continues to be a favored choice for addressing these crucial scientific challenges.…”

Section: Growth Modelsmentioning

confidence: 99%

Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention

Boucharas,

Anastasiadou,

Karkabounas

et al. 2024

BioMedInformatics

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Cancer, which is currently rated as the second-leading cause of mortality across the globe, is one of the most hazardous disease groups that has plagued humanity for centuries. The experiments presented here span over two decades and were conducted on a specific species of mice, aiming to neutralize a highly carcinogenic agent by altering its chemical structure when combined with certain compounds. A plethora of growth models, each of which makes use of distinctive qualities, are utilized in the investigation and explanation of the phenomena of chemically induced oncogenesis and prevention. The analysis ultimately results in the formalization of the process of locating the growth model that provides the best descriptive power based on predefined criteria. This is accomplished through a methodological workflow that adopts a computational pipeline based on the Levenberg–Marquardt algorithm with pioneering and conventional metrics as well as a ruleset. The developed process simplifies the investigated phenomena as the parameter space of growth models is reduced. The predictability is proven strong in the near future (i.e., a 0.61% difference between the predicted and actual values). The parameters differentiate between active compounds (i.e., classification results reach up to 96% in sensitivity and other performance metrics). The distribution of parameter contribution complements the findings that the logistic growth model is the most appropriate (i.e., 44.47%). In addition, the dosage of chemicals is increased by a factor of two for the next round of trials, which exposes parallel behavior between the two dosages. As a consequence, the study reveals important information on chemoprevention and the cycles of cancer proliferation. If developed further, it might lead to the development of nutritional supplements that completely inhibit the expansion of cancerous tumors. The methodology provided can be used to describe other phenomena that progress over time and it has the power to estimate future results.

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Section: Growth Modelsmentioning

confidence: 99%

Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention

Boucharas,

Anastasiadou,

Karkabounas

et al. 2024

BioMedInformatics

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“…Ограничивающий показатель воздействия J, N(t) < J, ∀t, 0 < N(0) < J, в уравнении ( 14) зеркально симметричен по смыслу критическому порогу L из уравнения А. Д. Базыкина как нижней грани для существования локальной популяции 0 < N(0) < L, lim t→∞ N(t) = 0. В развитии модели логично представить, что J не является константой, но, возможно, станет ступенчатой триггерной функцией [Perevaryukha, 2018]. Например, в простом варианте порог J колеблется от смены сезонов по периодическому закону J(ω(t)).…”

Section: компьютерные исследования и моделированиеunclassified

Models of population process with delay and the scenario for adaptive resistance to invasion

Perevarukha¹

2022

CRM

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Changes in abundance for emerging populations can develop according to several dynamic scenarios. After rapid biological invasions, the time factor for the development of a reaction from the biotic environment will become important. There are two classic experiments known in history with different endings of the confrontation of biological species. In Gause's experiments with ciliates, the infused predator, after brief oscillations, completely destroyed its resource, so its r-parameter became excessive for new conditions. Its own reproductive activity was not regulated by additional factors and, as a result, became critical for the invader. In the experiments of the entomologist Uchida with parasitic wasps and their prey beetles, all species coexisted. In a situation where a population with a high reproductive potential is regulated by several natural enemies, interesting dynamic effects can occur that have been observed in phytophages in an evergreen forest in Australia. The competing parasitic hymenoptera create a delayed regulation system for rapidly multiplying psyllid pests, where a rapid increase in the psyllid population is allowed until the pest reaches its maximum number. A short maximum is followed by a rapid decline in numbers, but minimization does not become critical for the population. The paper proposes a phenomenological model based on a differential equation with a delay, which describes a scenario of adaptive regulation for a population with a high reproductive potential with an active, but with a delayed reaction with a threshold regulation of exposure. It is shown that the complication of the regulation function of biotic resistance in the model leads to the stabilization of the dynamics after the passage of the minimum number by the rapidly breeding species. For a flexible system, transitional regimes of growth and crisis lead to the search for a new equilibrium in the evolutionary confrontation.

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“…Mikhaylov used models based on algorithmic networks with thousands of nodes [11]. Our models can be calculated by using simple free program tools such as a laptop and a computational workbench tool Rand Model Designer [12].…”

Section: Mathematical Improvement Of a Biological Theory Of The Replenishment Of Stocks Formationmentioning

confidence: 99%

Development and scenario experiments with the new model of rapid bioresources crisis under expert control

Perevaryukha¹

2021

MMS

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This paper continues a series of studies dedicated to the analysis of the nonlinear dynamics of complex environmental processes through the use of computational methods. The construction of a computational structure that uses the forms of the hybrid time and the logic of redefined behavior of solutions of the special system of equations to describe important nonlinear phenomena in the man-agement of unstable biosystems is considered in the article. The difference between the described ap-proaches to building a model is that computational experiments based on differential equations and re-defined according to the rules simulate scenarios in the dynamics of controlled biological resources of different types. The form of time allows to operate on a discrete component of the trajectory to describe changes that are visible to experts from the monitoring statistics or from reports from the fishery. The computational structure logically corresponds to the life cycle of large marine fish. Continuous characteristics are used to manage changes in the life cycle model. The new models are intended to de-scribe in scenarios the phenomena of rapid degradation of valuable biological resources with a very small error in the regulation of the rate of removal from the stock. These models have shown that the tradi-tional methods of bioresources management by experts have fundamental shortcomings and problems. Experts overestimate the amount of stocks for commercial removal from the population. Regulation by setting quotas on fish catch does not prevent the fishery from collapsing. The approach is applicable for mathematical predicting of the rapidly inflowing phases of an ecological invasion in aquatic systems.

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Phenomenological Computational Model for the Development of a Population Outbreak of Insects with Its Bifurcational Completion

Cited by 7 publications

References 12 publications

Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention

Toward Cancer Chemoprevention: Mathematical Modeling of Chemically Induced Carcinogenesis and Chemoprevention

Models of population process with delay and the scenario for adaptive resistance to invasion

Development and scenario experiments with the new model of rapid bioresources crisis under expert control

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