Competitive Model Research Articles

The complexity analysis of decision-making for horizontal fresh supply chains under a trade-off between fresh-keeping and carbon emission reduction

This study primarily focuses on the impact of carbon emissions generated during the fresh product fresh-keeping process on the traditional fresh supply chain (FSC1) and the low-carbon fresh supply chain with emission reduction behavior (FSC2). Its objective is to investigate how these supply chains dynamically adjust the production quantity, fresh-keeping level, and emission reduction level of fresh products when constrained by the dual-carbon policy. The decision competition model between the two supply chains was explored under two scenarios: the horizontal Nash (HN) game and the long-term Stackelberg (LS) game, wherein FSC1 assumes the role of the leader. The findings indicate that as the carbon tax and carbon trading price increase, the fresh supply chain tends to decrease fresh-keeping intensity while increasing investment in emission reduction. Carbon emissions generated during the fresh product fresh-keeping process have a smaller impact on FSC2's profit than on FSC1, and FSC2 exhibits stronger adaptability under the dual-carbon policy. Irrespective of the game structure, if the adjustment speed of FSC1's production decision exceeds the stable range, its own profit will decrease while the other party's profit will increase. However, if the adjustment speed of FSC2's production decision surpasses the stable range, it will lead to a decrease in profits for both sides. Interestingly, in the LS game, when the adjustment speed of FSC1's production decision exceeds the stable range, FSC1 demonstrates a leader advantage and experiences a rapid increase in profit as the adjustment speed of FSC2's production decision increases, this phenomenon suggests that in the LS game, the leader prevails amidst market chaos.

Challenges of Including Wet Grasslands with Variable Groundwater Tables in Large-Area Crop Production Simulations

Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing rapid species composition changes. We hypothesised that predicting above-ground biomass (AGB) remains challenging due to these dynamic responses. Ten years of data from four lysimeters at a German wet grassland site, with varying water table treatments, was utilised to test this hypothesis. Correlation analysis revealed a strong positive indirect effect of the water regime on AGB, with a one-year time lag (r = 0.97). The MONICA model initially exhibited fair agreement (d = 0.69) in simulating Leaf-Area-Index (LAI) but performed poorly in replicating AGB (d = 0.3). After removing the species composition change effect from the LAI and AGB datasets, the simulation notably improved, with the overall relative root mean square error (rRMSE) of AGB decreasing from 1.55 to 0.90 between the first and second simulations. This demonstrates MONICA’s ability to predict grass growth patterns amidst changing water supply levels for constant species composition. However, it needs a competition model to capture biomass growth changes with varying water supply.

Disclosure services and welfare gains in matching markets for indivisible assets

We present a competitive matching model in which indivisible assets are reallocated among many traders. The model has three features: (i) traders are heterogeneous in their prospects as buyers, sellers, and also in their stand-alone values with endowed assets, (ii) buyers do not know true values of assets sold, (iii) sellers can disclose values of their assets by paying fees. Despite its complexity, the model admits closed-form solutions. Two main results emerge. First, if full Disclosure is facilitated by a monopolist, it captures a large fraction of the welfare gains. Second, adding the option of minimum disclosure, when combined with a cap regulation on price-dependent fees for full disclosure, significantly weakens the monopolist’s power.

MEKANISME SISTEM PROPORSIONAL TERBUKA PADA PEMILIHAN DEWAN PERWAKILAN RAKYAT DI INDONESIA

The aim of this research is to analyze the mechanism of the open proportional system in the General Election of the People's Representative Council in Indonesia by referring to Law Number 7 of 2017 concerning General Elections which regulates the open list Proportional Election System, where this system can give rise to a polemic in the struggle for seats. in a very competitive parliament that has the effect of giving birth to an unhealthy model of competition between candidates that only aims to gain as many votes as possible from voters by relying solely on money and popularity. This research method uses normative juridical in answering the problems in this paper. Meanwhile, the results of the research obtained are that an open Proportional System makes it possible for anyone to nominate themselves as members of the legislature. In this system, a person who gets the most votes can take a seat as a member of the People's Representative Council even though the person concerned is at the bottom of the ballot paper.

Optimal contraception control for a nonlinear competitive vermin population model with spatial diffusion and size structure in a polluted environment

Optimal contraception control for a nonlinear competitive vermin population model with spatial diffusion and size structure in a polluted environment

Research on Complex Network Models and Characteristics of Building Material Enterprises Based on Competitive Relationships

Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with construction prefabricated component (PC) enterprises in the Beijing–Tianjin–Hebei region as research samples. Results show the competitive relationship network of PC enterprises in the Beijing–Tianjin–Hebei region is characterised by the development of a small-world network, and its cumulative degree and out-degree (in-degree) intensity distributions are single-scale networks with fast-decaying tails. The network has strong network clustering and assortativity, and it can truly reflect the competitive status and dynamics of the enterprises.

Competition in cargo and passenger between high-speed rail and airlines—considering the vertical structure of transportation

High-speed rail (HSR) services are experiencing rapid global expansion, posing competition not only in the traditional passenger sector but also challenging air cargo with their attributes of speed and punctuality. In this study, we establish a straightforward model for air-HSR competition in both passenger and cargo markets, taking into account the vertical structure of transportation supply chain. We explore various market equilibria based on the combination of three factors: 1) HSR's provision of cargo services, 2) the integration or separation of HSR's ownership and operation, and 3) uniform or differentiated airport administrative fees for passenger and cargo airlines. The analytical results of our model indicate that the entry of HSR into the cargo market consistently enhances social welfare and may even improve the profit of cargo airlines. However, if airports adopt uniform pricing or there is vertical separation in HSR sector, this could potentially weaken the positive impact on welfare enhancement. These findings have important implications for transportation policy, highlighting how strategic management of infrastructure can significantly influence the effectiveness and societal benefits of HSR's expansion into cargo services.

Compact biologically inspired camera with computational compound eye

Abstract The growing interests have been witnessed in the evolution and improvement of artificial compound eyes (CE) inspired by arthropods. However, the existing CE cameras are suffering from a defocusing problem due to the incompatibility with commercial CMOS cameras. Inspired by the CEs of South American Shrimps, we report a compact biologically inspired camera that enables wide-field-of-view (FOV), high-resolution imaging and sensitive 3D moving trajectory reconstruction. To overcome the defocusing problem, a deep learning architecture with distance regulation is proposed to achieve wide-range-clear imaging, without any hardware or complex front-end design, which greatly reduces system complexity and size. The architecture is composed of a variant of Unet and Pyramid-multi-scale attention, with designed short, middle and long distance regulation. Compared to the current competitive well-known models, our method is at least 2 dB ahead. Here we describe the high-resolution computational-CE camera with 271 ommatidia, with a weight of 5.4 g an area of 3 × 3 cm2 and 5-mm thickness, which achieves compatibility and integration of CE with commercial CMOS. The experimental result illustrates this computational-CE camera has competitive advantages in enhanced resolution and sensitive 3D live moving trajectory reconstruction. The compact camera has promising applications in nano-optics fields such as medical endoscopy, panoramic imaging and vision robotics.

The pro-competitive effects of trade agreements

How does trade policy affect competition? Using the universe of product exports by firms from eleven low and middle income countries, we document that tariff reductions under trade agreements have strong pro-competitive effects — they encourage entry and reduce the (tariff exclusive) price-cost markups of exporters. This finding, that markups fall with tariff cuts, contradicts a core prediction of standard oligopolistic competition models of trade. We extend a workhorse international pricing model of oligopolistic competition to include multiple countries and a rich preference structure. Our preference structure allows for fierce competition among firms from the same country and less intense competition among firms from different countries. We show a firm’s optimal markup after a tariff cut can rise or fall depending on the parameters of the preference structure and tariff-induced reallocation of market share among firms and across countries.

Misapplied management makes matters worse: Spatially explicit control leverages biotic interactions to slow invasion.

A wide range of approaches has been used to manage the spread of invasive species, yet invaders continue to be a challenge to control. In some cases, management actions have no effect or may even inadvertently benefit the targeted invader. Here, we use the mid-20th century management of the Red Imported Fire Ant, Solenopsis invicta, in the US as a motivating case study to explore the conditions under which such wasted management effort may occur. Introduced in approximately 1940, the fire ant spread widely through the southeast USand became a problematic pest. Historically, fire ants were managed with broad-spectrum pesticides; unfortunately, these efforts were largely unsuccessful. One hypothesis suggests that, by also killing native ants, mass pesticide application reduced competitive burdens thereby enabling fire ants to invade more quickly than they would in the absence of management. We use a mechanistic competition model to demonstrate the landscape-level effects of such management. We explicitly model the extent and location of pesticide applications, showing that the same pesticide application can have a positive, neutral, or negative effect on the progress of an invasion, depending on where it is applied on the landscape with respect to the invasion front. When designing management, the target species is often considered alone; however, this work suggests that leveraging existing biotic interactions, specifically competition with native species, can increase the efficacy of management. Our model not only highlights the potential unintended consequences of ignoring biotic interactions, but also provides a framework for developing spatially explicit management strategies that take advantage of these biotic interactions to work smarter, not harder.

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Blockchain-based cooperative game bilateral matching architecture for shared storage

The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered around pricing, leading to a scenario where node providers often engage in price competition to obtain greater rewards, resulting in an imbalance in resource allocation for both buyers and sellers. In contrast, a distributed storage model based on cooperative game theory can better facilitate cooperation and resource sharing among node providers. This paper designs a storage resource allocation algorithm based on the stable marriage matching algorithm, demonstrating the stability of this algorithm as a matching solution. The paper also analyzes the differences between cooperative and non-cooperative game models in the market, and explores an equilibrium pricing mechanism guided by supply and demand. Furthermore, the paper introduces a trading mechanism for storage resources, including publication standards and matching schemes, ensuring efficient and trustworthy interaction between storage suppliers and demanders in a decentralized network centered around storage resources, thus enabling the circulation of the value of storage resources. A prototype of a blockchain-based shared storage trading system is implemented in this paper, utilizing bilateral matching for tradings. System evaluation and experimental testing are conducted, with results showing that the average utility value of the matching trading mechanism proposed in this paper outperforms the Double Auction-based matching model under any Poisson distribution (λ = 0.1, 0.2, 0.3, …, 0.9) conditions set in the experiments. Additionally, compared to the traditional approach of directly storing complete data content on the chain, the design proposed in this paper effectively reduces on-chain storage consumption by approximately 27.06%.

Extension of the Lotka-Volterra competition model

In this paper, we introduce the (p,q)-Lotka-Volterra competition model which is extension of classical Lotka-Volterra competition model. The main purpose is to give some results on the existence and non-existence of positive solutions. Upper and lower solutions technique and comparison arguments plays a significant in our main proof.

The Effects of Infectious Diseases on the Consequences of Interspecific Competition in Grassland Communities

As infectious diseases have a severe impact on the individual survival and competitive ability of host species in grassland communities, competition between relevant species may have different consequences, potentially influencing the species composition of grassland communities and the functioning of grassland ecosystems. Understanding the impact of infectious diseases on competition is thus crucial for the health management of grassland ecosystems. How do infectious diseases affect the competitive coexistence of grassland plant species? In this study, by drawing on Tilman’s multispecies competition model and incorporating the spread characteristics of grassland plant diseases, we built three spatial competition models of two grass species subject to infectious diseases, and we analyzed the invasion conditions of infectious diseases and how they impact the population dynamics and competitive consequences of the species. Our model revealed the following: (1) Diseases with high transmission rates and low virulence are more likely to invade populations, while the presence of non-host species is detrimental to disease invasion. (2) Disease promotes the coexistence of competing species, breaking down the original competition–compromise trade-off mechanism for species coexistence and greatly expanding the range of parameters within which the two species can coexist. (3) Pathogen infections indirectly weaken the suppression of inferior species by dominant species, which is similar to the cascade effect seen in trophic interactions. Our findings highlight the importance of disease in species coexistence and grassland biodiversity maintenance.

Competition effects can mislead selection in eucalypt breeding trials

The assessment of plant performance and the accuracy of genetic selection can be significantly affected by genetic competition among individuals. In addition to genetic causes, competition is influenced by external factors such as environment and age. This research uses multi-location multi-age Eucalyptus dunii trials to answer four questions: i) Are there major changes when competition effects (both genetic and residual) are estimated in single-age and multi-age models? ii) What are the implications of considering competition effects on the early selection of eucalypt clones? iii) What are the impacts of considering the reliability of direct (DGE) and indirect genotypic effects (IGE) as a weight in a selection index?, and iv) Which clones hold the potential to form highly productive clonal plantations in Southern Brazil when deployed together as clonal composites? The dataset contained three trials established in different locations in Southern Brazil, where growth traits were measured at 3.5 and 7 years. We fitted single-age and multi-age spatial competition models for each trial. The multi-age spatial competition model was a valuable tool for selecting superior eucalypt clones. Neglecting the IGEs led to changes in clone ranking, reducing the effectiveness of early selection. Additionally, a selection index where DGEs and IGEs are weighted by their reliability was proposed. Therefore, our study contributes to understanding the magnitude of IGE's impact on the daily practice of eucalypt breeding, such as early selection and multi-age analyses, and proposes selection strategies that consider the quantity and quality of information provided by the models for each individual.

A mathematical model of competition between fiber and mucin degraders in the gut provides a possible explanation for mucus thinning

The human gut microbiota relies on complex carbohydrates (glycans) for energy and growth, primarily dietary fiber and host-derived mucins. We introduce a mathematical model of a glycan generalist and a mucin specialist in a two-compartment chemostat model of the human colon. Our objective is to characterize the influence of dietary fiber and mucin supply on the abundance of mucin-degrading species within the gut ecosystem. Current mathematical gut reactor models that include the enzymatic degradation of glycans do not differentiate between glycan types and their degraders. The model we present distinguishes between a generalist that can degrade both dietary fiber and mucin, and a specialist species that can only degrade mucin. The integrity of the colonic mucus barrier is essential for overall human health and well-being, with the mucin specialist Akkermanisa muciniphila being associated with a healthy mucus layer. Competition, particularly between the specialist and generalists like Bacteroides thetaiotaomicron, may lead to mucus layer erosion, especially during periods of dietary fiber deprivation. Our model treats the colon as a gut reactor system, dividing it into two compartments that represent the lumen and the mucus of the gut, resulting in a complex system of ordinary differential equations with a large and uncertain parameter space. To understand the influence of model parameters on long-term behavior, we employ a random forest classifier, a supervised machine learning method. Additionally, a variance-based sensitivity analysis is utilized to determine the sensitivity of steady-state values to changes in model parameter inputs. By constructing this model, we can investigate the underlying mechanisms that control gut microbiota composition and function, free from confounding factors.

Open Problems and Conjectures in the Evolutionary Periodic Ricker Competition Model

In this paper, we present a survey about the latest results in global stability concerning the discrete-time evolutionary Ricker competition model with n species, in both, autonomous and periodic models. The main purpose is to convey some arguments and new ideas concerning the techniques for showing global asymptotic stability of fixed points or periodic cycles in these kind of discrete-time models. In order to achieve this, some open problems and conjectures related to the evolutionary Ricker competition model are presented, which may be a starting point to study global stability, not only in other competition models, but in predator–prey models and Leslie–Gower-type models as well.

Determinants of COVID-19 Prevalence Rate in Asia: A study using Spatial Analysis

This study aims at finding out the important determinants of prevalence rate of COVID-19 in the Asian continent using spatial analysis. The impact of climatic, socioeconomic, demographic, and health status variables on the prevalence rate of COVID-19 is seen through various spatial models such as Spatial lag, Spatial error, Geographically Weighted regression model, and Multiscale Geographically Weighted regression model. The performance of the models is compared under different comparison criteria. It is found that among all, Multiscale Geographically Weighted regression model outperformed other competitive models. Findings also indicate that cardiovascular health, prevalence of smoking habit, human development index, and net migration rate played significant role in defining the prevalence rate of COVID-19 in Asia.

Search theory of imperfect competition with decreasing returns to scale

I study a version of the search-theoretic model of imperfect competition by Burdett and Judd (1983) in which sellers operate a production technology with decreasing rather than constant returns to scale. Equilibrium exists and is unique, and its structure depends on the extent of search frictions. If search frictions are large enough, the price distribution is non-degenerate and atomless. If search frictions are neither too large nor too small, the price distribution is non-degenerate with an atom at the lowest price. If search frictions are small enough, the price distribution is degenerate. Equilibrium is efficient if and only if the price distribution is degenerate. Generically, neither the structure nor the welfare properties of equilibrium are the same as in Burdett and Judd (1983). As in Burdett and Judd (1983), however, equilibrium outcomes span the spectrum from pure monopoly to perfect competition as search frictions decline.

FedCGS: Leverage Feature Compression and Conditional GAN for Privacy Protection and Efficient Communication in Federated Learning

Federated learning is a distributed machine learning technique that can train models from decentralized data, collaboratively building central models and protecting data privacy by passing partial model updates rather than raw data in each iteration of model learning. However, recent work has shown that the information during the exchange between the server and the client is still vulnerable to gradient-based privacy attacks, and traditional privacy protection methods can incur significant costs (such as communication consumption and performance evaluation). Therefore, we need an algorithm that can protect data privacy while reducing communication costs and performance losses. Here, we propose a federated learning framework FedCGS that leverages conditional generation adversarial networks and feature compression in order to achieve privacy protection while maintaining competitive model performance and effectively improving communication efficiency. We divided the local network of each client into feature extractor and public classifier, and kept the extractor local to protect privacy, and sent classifier and client generator to the server for aggregation and update through singular value decomposition, so as to improve the performance of the local network and efficient communication. A lot of experimental data prove that FedCGS can improve the performance of local model better than traditional federation learning, and improve the communication efficiency better than common conditional generation adversarial network. Therefore, we believe that FedCGS has better federal learning potential