Competitive Interaction Model Research Articles

Interconnection between density-regulation and stability in competitive ecological network

In natural ecosystems, species can be characterized by the nonlinear density-dependent self-regulation of their growth profile. Species of many taxa show a substantial density-dependent reduction for low population size. Nevertheless, many show the opposite trend; density regulation is minimal for small populations and increases significantly when the population size is near the carrying capacity. The theta-logistic growth equation can portray the intraspecific density regulation in the growth profile, theta being the density regulation parameter. In this study, we examine the role of these different growth profiles on the stability of a competitive ecological community with the help of a mathematical model of competitive species interactions. This manuscript deals with the random matrix theory to understand the stability of the classical theta-logistic models of competitive interactions. Our results suggest that having more species with strong density dependence, which self-regulate at low densities, leads to more stable communities. With this, stability also depends on the complexity of the ecological network. Species network connectance (link density) shows a consistent trend of increasing stability, whereas community size (species richness) shows a context-dependent effect. We also interpret our results from the aspect of two different life history strategies: r and K-selection. Our results show that the stability of a competitive network increases with the fraction of r-selected species in the community. Our result is robust, irrespective of different network architectures.

The link between multiplicative competitive interaction models and compositional data regression with a total

ABSTRACT This article sheds light on the relationship between compositional data (CoDa) regression models and multiplicative competitive interaction (MCI) models, which are two approaches for modeling shares. We demonstrate that MCI models are particular cases of CoDa models with a total and that a reparameterization links both. Recognizing this relation offers mutual benefits for the CoDa and MCI literature, each with its own rich tradition. The CoDa tradition, with its rigorous mathematical foundation, provides additional theoretical guarantees and mathematical tools that we apply to improve the estimation of MCI models. Simultaneously, the MCI model emerged from almost a century-long tradition in marketing research that may enrich the CoDa literature. One aspect is the grounding of the MCI specification in assumptions on the behavior of individuals. From this basis, the MCI tradition also provides credible justifications for heteroskedastic error structures – an idea we develop further and that is relevant to many CoDa models beyond the marketing context. Additionally, MCI models have always been interpreted in terms of elasticities, a method that has only recently emerged in CoDa. Regarding this interpretation, the CoDa perspective leads to a decomposition of the influence of the explanatory variables into contributions from relative and absolute information.

Mathematical Model of Phytoplankton Interspecific Competition for Food Resource

The phytoplankton in an aquatic ecosystem is the basis of its life activity and the main producing link. The functioning of phytoplankton in same time depends on environmental factors: mineral nutrition, photosynthetically active solar radiation, water temperature and other less significant ones. Sunlight is a stable factor, varying predictably over time and space. The water temperature is the small regulatory factor. Concentrations of mineral substances can change quite quickly and significantly, this much influences on plant organisms. Thus, mineral nutrition is a basic environmental factor of influence to phytoplankton. On the other hand, in large aquatic basin such as seas and oceanic areas the distribution of living organisms is very heterogeneous in space. These two aspects – nutrient and spatial heterogeneity – are the focus of this article. A model of competitive interaction is considered using the example of two species of phytoplankton. The phytoplankton move passively in water what is simulated by the diffusion process. The model contains one non-trivial stationary and spatially homogeneous equilibrium and two trivial ones, i.e. degenerate in at least one species of phytoplankton. Trivial equilibria are stable only in some “degenerate” situations. The non-trivial equilibrium in “normal” conditions is stable to temporal and spatial disturbances. The behavior of solutions near a nontrivial equilibrium for a stationary living environment and in cases of its nonstationary is studied. Perturbation of a nontrivial equilibrium in a stationary environment leads to relatively long-term deviations from equilibrium and a slow return to it. The instability of trivial equilibria increases the spatial heterogeneity of solutions. At the same time, the nontrivial equilibrium computationally demonstrates weak properties of global stability in time. The unsteadiness of the environment is simulated by the unsteadiness of the influx of nutrients. It has been shown that the distribution of nutrients can lead to significant heterogeneity in the distribution of individuals across the spatial habitat.

In vitro to clinical translation of combinatorial effects of doxorubicin and dexrazoxane in breast cancer: a mechanism-based pharmacokinetic/pharmacodynamic modeling approach.

Dexrazoxane (DEX) is the only drug clinically approved to treat Doxorubicin-induced cardiotoxicity (DIC), however its impact on the anticancer efficacy of DOX is not extensively studied. In this manuscript, a proof-of-concept in vitro study is carried out to quantitatively characterize the anticancer effects of DOX and DEX and determine their nature of drug-drug interactions in cancer cells by combining experimental data with modeling approaches. First, we determined the static concentration-response of DOX and DEX in breast cancer cell lines, JIMT-1 and MDA-MB-468. With a three-dimensional (3D) response surface analysis using a competitive interaction model, we characterized their interaction to be modestly synergistic in MDA-MB-468 or modestly antagonistic in JIMT-1 cells. Second, a cellular-level, pharmacodynamic (PD) model was developed to capture the time-course effects of the two drugs which determined additive and antagonistic interactions for DOX and DEX in MDA-MB-468 and JIMT-1, respectively. Finally, we performed in vitro to in vivo translation by utilizing DOX and DEX clinical dosing regimen that was previously identified to be maximally cardioprotective, to drive tumor cell PD models. The resulting simulations showed that a 10:1 DEX:DOX dose ratio over three cycles of Q3W regimen of DOX results in comparable efficacy based on MDA-MB-468 (additive effect) estimates and lower efficacy based on JIMT-1 (antagonistic effect) estimates for DOX + DEX combination as compared to DOX alone. Thus, our developed cell-based PD models can be used to simulate different scenarios and better design preclinical in vivo studies to further optimize DOX and DEX combinations.

A probabilistic model of competitive multi-ecological interactions for analyzing the impact of noise within sight of aquatic hyacinth

The article introduces a concept of probabilistic ecological competition from the aquatic hyacinth perspective, which often helps to track and analyze the sustainability of different species involved in the ecological system. A case study is presented to investigate the impact of the pH content in the eutrophication of aquatic hyacinths. To understand the interaction of aquatic species, we propose a mathematical model by considering the components of aquatic reserve structure in a maritime condition that involves two locales: aquatic hyacinth region and aquatic hyacinth liberated region. The model depicts a quantitative correlation between aquatic hyacinth and aquatic biomass. We analyze the stability of the steady states, which can help to understand the persistence and extinction of the species in the two regions mentioned above. Numerical simulation is carried out to conceive the distribution of aquatic species in two aquatic hyacinth regions. Furthermore, we impose white noise in the model to address the environmental heterogeneity of aquatic structures and subsequently inspect their influences on the inhabitants of the aquatic hyacinth region and aquatic hyacinth liberated region. A sensitivity analysis of the parameters is performed to identify the dominant parameters in the model. The analysis shows that numerical investigations comply with the stochasticity analysis of the system. Numerical results reveal that the amplitude of oscillations of the species of aquatic hyacinth region is higher than that of the aquatic hyacinth liberated region for any choice of the white noises, which has a reasonable agreement with the real aquatic biomass.

Lot-sizing and pricing decisions under attraction demand models and multi-channel environment: New efficient formulations

The presented paper considers the pricing and lot-sizing decisions for a manufacturer who produces and sells a single product in different selling channels i.e physical stock, website, mobile, etc. The objective is to find the production plan and prices of each channel to maximize the total profit defined from difference between the revenues and the productions, holding and setups costs. The consumers’ demand in each channel is represented by attraction demand models which include the multinomial logit (MNL), multiplicative competitive interaction (MCI) and linear demand models. The addressed problem is formulated as a non-convex mixed-integer nonlinear program (MINLP). Based on properties of attraction functions, an efficient reformulation which transforms the initial non-convex problem into a convex one is presented. Therefore, an optimization approach based on the outer approximation algorithm is presented to solve the problem. Numerical tests based on large benchmark of real inspired instances show the efficiency of the proposed approach to solve the addressed problem compared to the initial non-convex model.

Pharmacodynamic Modeling to Evaluate the Impact of Cimetidine, an OCT2 Inhibitor, on the Anticancer Effects of Cisplatin

Despite potent anticancer activity, the clinical utilization of cisplatin is limited due to nephrotoxicity. As Organic Cation Transporter 2 (OCT2) has been shown to be one of the key transporters involved in the uptake of cisplatin into renal proximal tubules, OCT2 inhibitors such as cimetidine have been explored to suppress cisplatin-induced nephrotoxicity. Nonetheless, the impact of OCT2 inhibition or cimetidine on the anti-cancer effects of cisplatin has not been extensively examined. The main objective of the present study was to quantitatively characterize the anticancer effects of cisplatin and cimetidine and determine their nature of interactions in two cancer cell lines, OCT2-negative hepatocellular carcinoma (HCC) cell line, Huh7, and OCT2-positive breast cancer cell line, MDA-MB-468. First, we determined the static concentration-response curves of cisplatin and cimetidine as single agents. Next, with the help of three-dimensional (3D) response surface analyses and a competitive interaction model, we determined their nature of interactions at static concentrations to be modestly synergistic or additive in Huh7 and antagonistic in MDA-MB-468. These results were consistent with the cell-level pharmacodynamic (PD) modeling analysis which leveraged the time-course effects of drugs as single agents and drug combinations. Our developed PD model can be further used to design future preclinical studies to further investigate the cisplatin and cimetidine combinations in different in vitro and in vivo cancer models.

Modulating Aggregation in Microemulsions: The Dispersion by Competitive Intermolecular Interaction Model.

A phenomenological model has been developed for the mechanism of action of phase modifiers as additives that control aggregation phenomena within water-in-oil emulsions. The "Dispersion by Competitive Intermolecular Interaction" model (DCI) explicitly considers the strength and prevalence of different intermolecular interactions that influence the molecular association of amphiphiles, the resulting distribution of aggregate size, and interaggregate interactions that influence phase phenomena. The existing "cosolvent" and "cosurfactant" association models, which describe the distribution of these amphiphiles within the solution, are re-examined in the context of intermolecular interactions. The different contributions of intermolecular interactions to the potential energy landscape of molecular association create distinct regimes within the DCI model that explain prior observations of cosolvent and cosurfactant behavior. The specific system under consideration, the N,N,N',N'-tetraoctyl diglycolamide amphiphile extractant with tributyl phosphate or dihexyl octanamide phase modifier additives, represents a new regime-labeled the polar disruption regime-where strong hydrogen bonding of the phase modifier with the polar-solutes disrupts the internal hydrogen bonding network of the polar micellar core, thereby decreasing aggregate size and narrowing the polydispersity in solution.

Salience by competitive and recurrent interactions: Bridging neural spiking and computation in visual attention.

Decisions about where to move the eyes depend on neurons in frontal eye field (FEF). Movement neurons in FEF accumulate salience evidence derived from FEF visual neurons to select the location of a saccade target among distractors. How visual neurons achieve this salience representation is unknown. We present a neuro-computational model of target selection called salience by competitive and recurrent interactions (SCRI), based on the competitive interaction model of attentional selection and decision-making (Smith & Sewell, 2013). SCRI selects targets by synthesizing localization and identification information to yield a dynamically evolving representation of salience across the visual field. SCRI accounts for neural spiking of individual FEF visual neurons, explaining idiosyncratic differences in neural dynamics with specific parameters. Many visual neurons resolve the competition between search items through feedforward inhibition between signals representing different search items, some also require lateral inhibition, and many act as recurrent gates to modulate the incoming flow of information about stimulus identity. SCRI was tested further by using simulated spiking representations of visual salience as input to the gated accumulator model of FEF movement neurons (Purcell et al., 2010, 2012). Predicted saccade response times fit those observed for search arrays of different set sizes and different target-distractor similarities, and accumulator trajectories replicated movement neuron discharge rates. These findings offer new insights into visual decision-making through converging neuro-computational constraints and provide a novel computational account of the diversity of FEF visual neurons. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Inferring the Effect of Species Interactions on Trait Evolution.

Models of trait evolution form an important part of macroevolutionary biology. The Brownian motion model and Ornstein–Uhlenbeck models have become classic (null) models of character evolution, in which species evolve independently. Recently, models incorporating species interactions have been developed, particularly involving competition where abiotic factors pull species toward an optimal trait value and competitive interactions drive the trait values apart. However, these models assume a fitness function rather than derive it from population dynamics and they do not consider dynamics of the trait variance. Here, we develop a general coherent trait evolution framework where the fitness function is based on a model of population dynamics, and therefore it can, in principle, accommodate any type of species interaction. We illustrate our framework with a model of abundance-dependent competitive interactions against a macroevolutionary background encoded in a phylogenetic tree. We develop an inference tool based on Approximate Bayesian Computation and test it on simulated data (of traits at the tips). We find that inference performs well when the diversity predicted by the parameters equals the number of species in the phylogeny. We then fit the model to empirical data of baleen whale body lengths, using three different summary statistics, and compare it to a model without population dynamics and a model where competition depends on the total metabolic rate of the competitors. We show that the unweighted model performs best for the least informative summary statistic, while the model with competition weighted by the total metabolic rate fits the data slightly better than the other two models for the two more informative summary statistics. Regardless of the summary statistic used, the three models substantially differ in their predictions of the abundance distribution. Therefore, data on abundance distributions will allow us to better distinguish the models from one another, and infer the nature of species interactions. Thus, our framework provides a conceptual approach to reveal species interactions underlying trait evolution and identifies the data needed to do so in practice. [Approximate Bayesian computation; competition; phylogeny; population dynamics; simulations; species interaction; trait evolution.]

Omitted variable bias in studies of plant interactions.

Models of plant-plant interactions underpin our understanding of species coexistence, invasive plant impacts, and plant community responses to climate change. In recent studies, models of competitive interactions failed predictive tests, thereby casting doubt on results of many past studies. We believe these model failures owe at least partly to heterogeneity in unmodeled factors (e.g., nutrients, soil pathogens) that affect both target plants and neighboring competitors. Such heterogeneity is ubiquitous, and models that do not account for it will suffer omitted variable bias. We used instrumental variables analysis to test for and correct omitted variable bias in studies that followed common protocols for measuring plant competition. In an observational study, omitted variables caused competition to seem like mutualism. In a quasi-experiment that partially controlled competitor abundances with seeding, omitted variables caused competition to seem about 35% weaker than it really was, even though the experiment occurred in an abandoned agricultural field where environmental heterogeneity was expected to be relatively low. Despite decades of research, consistently accurate estimates of competitive interactions remain elusive. The most foolproof way around this problem is true experiments that avoid omitted variable bias by completely controlling competitor abundances, but such experiments are rare.

Competitive interaction model with Holling type and application to IP network data packets forwarding

When network users are interacting intensively by sending large amount of packets during window time, handling and transmitting generated data can be challenging without violating quality of service (QoS) as network resources are limited. As data packets flow has different regime and incoming packets arrival rate depends on users’ behavior, we propose in this paper, to analyze the predator-prey interaction occurring between two users' packets flows traveling network segment considering incoming packets as newborn and queuing packets as adult population, using a modified Hutchinson interactive model. We focused our analysis on interaction outcome by performing qualitative studies of the proposed model. Theoretical analyses suggest adopting different queuing strategy and hybrid resources allocation by taking into account users’ behavior to enhance forwarding performance and improve transmission efficiency.

THE USE OF BENCHMARKING TOOL TO IMRPOVE EFFICIENCY OF COMPANY'S INNOVATION ACTIVITIES IN THE CONDITIONS OF DIGITAL ECONOMY

Today there is a necessity of developing a unique market behaviour strategy using own competitive advantages to reach the highest scores of real competitive ability of a sector. The driving force of the competition law requires companies to have thorough strategies to improve the quality of produced goods, to decrease production costs and to increase labour efficiency. In this context the use of benchmarking tools to improve innovation efficiency at companies is an important task. The present work considers decision-making models and methods regarding company's competitiveness management in terms of benchmarking. The model of competitive interaction introduced in the article in the form of a system of coupled Van-der-Pol equations with time lag was successfully tested in radio physics and now is successfully applied in economics. The article demonstrates the application of the given system to describe benchmarking of different economic companies.

Multiparticle Models of Brownian Dynamics for the Description of Photosynthetic Electron Transfer Involving Protein Mobile Carriers

The article presents a review of modeling the interaction of photosynthetic proteins using the multiparticle Brownian dynamics method developed at the Department of Biophysics, Biological Faculty, Lomonosov Moscow State University. The authors used multiparticle Brownian methods coupled to molecular dynamic simulations to reveal the role of electrostatic interactions and conformational changes in the transfer of an electron from the cytochrome complex to the molecule of the mobile carrier plastocyanin in plants, green algae, and cianobacteria. Taking into account the interior of photosynthetic membrane, they developed the model, combining events of protein diffusion along the thylakoid membrane, electrostatic interactions between proteins and the proteins with the membrane charges, formation of a multiprotein complex, electron transfer within a complex, and complex dissociation. They have also developed multiparticle models of competitive interactions between electron acceptors ferredoxin.

Copper(ii) complexes with 2,2':6',2''-terpyridine, 2,6-di(thiazol-2-yl)pyridine and 2,6-di(pyrazin-2-yl)pyridine substituted with quinolines. Synthesis, structure, antiproliferative activity, and catalytic activity in the oxidation of alkanes and alcohols with peroxides.

A series of 2,2':6',2''-terpyridine (terpy), 2,6-di(thiazol-2-yl)pyridine (dtpy) and 2,6-di(pyrazin-2-yl)pyridine (dppy) derivatives with n-quinolyl substituents (n = 2 and 4) was used to synthesize five-coordinate complexes [CuCl2(n-quinolyl-terpy)] (1-2), [CuCl2(n-quinolyl-dtpy)] (3-4) and [CuCl2(n-quinolyl-dppy)] (5-6), respectively. The main emphasis of the research was to investigate the impact of the triimine skeleton (terpy, dtpy and dppy) and n-quinolyl pendant substituent on the antiproliferative and catalytic properties of 1-6. The obtained Cu(ii) compounds were studied as antiproliferative agents against human colorectal (HCT116) and ovarian (A2780) carcinoma, and they were used as catalysts for the oxidation of alkanes and alcohols with peroxides under mild conditions. The kinetic characteristics of the oxidizing species generated by the catalytic system Cu(ii) complex-H2O2 in CH3CN were obtained from the dependence of the alkane oxidation rate on its initial concentration. A model of competitive interaction of hydroxyl radicals with CH3CN and RH in the catalyst cavity has been proposed which is based on the simultaneous study of kinetics and selectivity in alkane oxidations.

Explaining farm structural change in the European agriculture: a novel analytical framework

Abstract In this paper, we analyse the drivers of farm structural change in the EU-27, applying a novel analytical framework in the field of agricultural economics known as the multiplicative competitive interaction (MCI) model. MCI offers a more parsimonious specification for estimating models of regional farm group shares compared to the often-applied Markov approach. The MCI framework enables farm group-specific equations, which are used to account for drivers specific to certain farm groups. The MCI framework explains farm group shares at the regional level taken from the Farm Structure Survey (FSS) using socio-economic variables from the Farm Accountancy Data Network (FADN) and other databases for the period 1989–2013. We consider eight production specialisations and two size classes at the NUTS 2 regional level. The results indicate that the past farm structure explains approximately 36 per cent of the EU farm structure variation across regions and time, followed by natural conditions (16 per cent), agricultural prices (14 per cent), macroeconomic variables (9 per cent), subsidies (7 per cent), population (6 per cent) and agricultural income (6 per cent). Further, we have run a simulation experiment where we derived elasticities of structural change with respect to time-varying variables. The structural change appears to be the most elastic with respect to income and macroeconomic variables.

A Hurdle Model Approach of Store Choice and Market Area Analysis in Grocery Retailing

Store choice models play an important role in applied geography, especially in location analysis and retail impact assessment. Econometric store choice models enable the analysis of variables influencing store choice based on observed shopping behavior. Using these models—such as the multiplicative competitive interaction model or the discrete choice model—faces several methodical and conceptual problems limiting their explanatory power and applicability. Addressing these problems, a new approach of store choice analysis is developed, based on a generalized linear model for truncated data, the hurdle model. This approach is applied to grocery stores in Karlsruhe, based on empirical data from a customer survey. Three main insights can be gained: (1) The explanatory performance of the model is superior to the common models as it allows decomposing spatial shopping behavior into a store choice and the intensity of customer–store interactions, such as shopping trips or expenditures. (2) The hurdle approach allows for dealing with a large number of empirical customer–store interactions equal to zero, which is an important statistical feature. (3) The model results confirm previous findings; for example, that spatial shopping behavior in grocery retailing is mainly affected by the accessibility and size of the stores, which proves the approach’s plausibility.

Hexagonal nanoplates of high-quality γ-gallium oxide: controlled synthesis and good heterogeneous catalytic performance for thiophenes

Hexagonal nanoplates of high-quality γ-gallium oxide were successfully synthesized by using a competitive and cooperative interaction model.

Using compositional and Dirichlet models for market share regression

ABSTRACTWhen the aim is to model market shares, the marketing literature proposes some regression models which can be qualified as attraction models. They are generally derived from an aggregated version of the multinomial logit model. But aggregated multinomial logit models (MNL) and the so-called generalized multiplicative competitive interaction models (GMCI) present some limitations: in their simpler version they do not specify brand-specific and cross effect parameters. In this paper, we consider alternative models: the Dirichlet model (DIR) and the compositional model (CODA). DIR allows to introduce brand-specific parameters and CODA allows additionally to consider cross effect parameters. We show that these two models can be written in a similar fashion, called attraction form, as the MNL and the GMCI models. As market share models are usually interpreted in terms of elasticities, we also use this notion to interpret the DIR and CODA models. We compare the properties of the models in order to explain why CODA and DIR models can outperform traditional market share models. An application to the automobile market is presented where we model brands market shares as a function of media investments, controlling for the brands price and scrapping incentive. We compare the quality of the models using measures adapted to shares.

The magical number one-on-square-root-two: The double-target detection deficit in brief visual displays.

How limited representational capacity is divided when multiple items need to be processed simultaneously is a fundamental question in cognitive psychology. The double-target deficit is the finding that, when monitoring multiple locations or information streams for targets, identification of 2 simultaneous targets is substantially worse than is predicted from the cost of divided attention alone. This finding suggests that targets and nontargets are treated differently by the cognitive system. We investigated the double-target deficit in 4 different visual decision tasks using noisy, backwardly masked targets presented for a range of exposure durations to test the theory that the deficit reflects a capacity limitation of visual short-term memory (VSTM). We quantified the deficit using a sample-size model of VSTM and 2 different models of the decision process: a signal detection MAX model and an optimum likelihood ratio model. We found a double-target deficit in all 4 tasks which increased in magnitude for briefer displays, consistent with the capacity limits of VSTM. We explained the exposure dependency using a competitive interaction model in which nontargets compete for access to VSTM at a slower rate than targets. Our findings support 2-stage models of visual processing in which the most target-like stimuli gain priority access into VSTM before the decision process begins. (PsycINFO Database Record