Competitive Model Research Articles

Microfinance institution and moneylenders in a segmented rural credit market

PurposeThe effectiveness of microfinance institutions (MFIs) in rescuing poor borrowers from “clutches of” moneylenders has been a much-debated topic over the past few decades. This paper aims to contribute by presenting a model of competition between a socially motivated MFI and profit-maximizing moneylenders when market segmentation exists.Design/methodology/approachA principal–agent model is used to characterize equilibrium conditions under scenarios where only moneylenders operate, only MFI operates and when both co-exist to pose comparative results effectively.FindingsThe authors find unambiguous benefits arising when a welfare-maximizing MFI enters the market. However, there are benefits to having local agents like moneylenders on the ground who also have informational advantages.Originality/valueTo the best of authors’ knowledge, this study is the first to evaluate the competition between MFI and moneylenders under the framework of captive and noncaptive segments with a mandatory savings requirement.

Inhibitory effects characteristics of polysaccharide of Polygonati Rhizome on cytochrome P450 enzymes.

As the major active ingredient of Polygonatum sibriricum Red., polysaccharide of Polygonati Rhizome (PSP) has been reported to possess various pharmacological activities, especially for the treatment of chronic disorders in the elderly. This study evaluated the effect of PSP on the activities of major cytochrome P450 enzymes (CYP450) isoforms, aiming to provide theoretical reference for its co-prescription with other drugs and prevent the risk of adverse drug-drug interaction. The activities of CYP450 isoforms were assessed in human liver microsomes with specific probe substances. Through Lineweaver-Burk fitting models, the effect of PSP on the activity of inhibited CYP450 isoforms was characterized by competitive and non-competitive models. Dose-dependent and time-dependent experiments were also performed to completely understand the inhibition. Among experimental isoforms, PSP significantly inhibited the activities of CYP2C9, 2D6, and 3A4 in a concentration-dependent manner with IC50 values of 14.01, 17.63, and 5.33 μM. The inhibition of CYP2C9 and 2D6 was best fitted with the competitive model with the Ki values of 7.15 and 8.92 μM, respectively, while the inhibition of CYP3A4 was non-competitive with the Ki value of 2.63 μM. Additionally, the inhibition of CYP3A4 by PSP also showed time-dependent characteristics with the inhibition parameters, KI of 1.273 μM-1 and Kinact of 0.036 min-1. PSP exerted moderate inhibition on CYP2C9 and 2D6 and strong inhibition of CYP3A4. The dosage of CYP2C9-, 2D6-, and 3A4-metabolized drugs should be adjusted when co-administrated with PSP and its sourced herbs.

Artificial intelligence in healthcare forecasting: Enhancing market strategy with predictive analytics

This research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting. Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare Strategy

Fixing imbalanced binary classification: An asymmetric Bayesian learning approach.

Most statistical and machine learning models used for binary data modeling and classification assume that the data are balanced. However, this assumption can lead to poor predictive performance and bias in parameter estimation when there is an imbalance in the data due to the threshold election for the binary classification. To address this challenge, several authors suggest using asymmetric link functions in binary regression, instead of the traditional symmetric functions such as logit or probit, aiming to highlight characteristics that would help the classification task. Therefore, this study aims to introduce new classification functions based on the Lomax distribution (and its variations; including power and reverse versions). The proposed Bayesian functions have proven asymmetry and were implemented in a Stan program into the R workflow. Additionally, these functions showed promising results in real-world data applications, outperforming classical link functions in terms of metrics. For instance, in the first example, comparing the reverse power double Lomax (RPDLomax) with the logit link showed that, regardless of the data imbalance, the RPDLomax model assigns effectively lower mean posterior predictive probabilities to failure and higher probabilities to success (21.4% and 63.7%, respectively), unlike Logistic regression, which does not clearly distinguish between the mean posterior predictive probabilities for these two classes (36.0% and 39.5% for failure and success, respectively). That is, the proposed asymmetric Lomax approach is a competitive model for differentiating binary data classification in imbalanced tasks against the Logistic approach.

Perceptual Expertise and Attention: An Exploration using Deep Neural Networks.

Perceptual expertise and attention are two important factors that enable superior object recognition and task performance. While expertise enhances knowledge and provides a holistic understanding of the environment, attention allows us to selectively focus on task-related information and suppress distraction. It has been suggested that attention operates differently in experts and in novices, but much remains unknown. This study investigates the relationship between perceptual expertise and attention using convolutional neural networks (CNNs), which are shown to be good models of primate visual pathways. Two CNN models were trained to become experts in either face or scene recognition, and the effect of attention on performance was evaluated in tasks involving complex stimuli, such as superimposed images containing superimposed faces and scenes. The goal was to explore how feature-based attention (FBA) influences recognition within and outside the domain of expertise of the models. We found that each model performed better in its area of expertise-and that FBA further enhanced task performance, but only within the domain of expertise, increasing performance by up to 35% in scene recognition, and 15% in face recognition. However, attention had reduced or negative effects when applied outside the models' expertise domain. Neural unit-level analysis revealed that expertise led to stronger tuning towards category-specific features and sharper tuning curves, as reflected in greater representational dissimilarity between targets and distractors, which, in line with the biased competition model of attention, leads to enhanced performance by reducing competition. These findings highlight the critical role of neural tuning at single as well as network level neural in distinguishing the effects of attention in experts and in novices and demonstrate that CNNs can be used fruitfully as computational models for addressing neuroscience questions not practical with the empirical methods.

DYNAMICAL BEHAVIOR IN THE COMPETITIVE MODEL INCORPORATING THE FEAR EFFECT OF PREY DUE TO ALLELOPATHY WITH SHARED BIOTIC RESOURCES

This research develops a mathematical model of a natural phenomenon, namely sea snails that can release toxins (allelopathy) so that non-toxic sea snails become afraid. In addition, toxic and non-toxic sea snails share biotic resources. Based on the existing phenomenon, the model of fear effect caused by allelopathy in the competitive interaction model with shared biotic resources will be studied. In this system, three equilibrium points are obtained: extinction point of prey, extinction point of predator, and coexisting point under certain conditions. Analysis of local stability at equilibrium points by linearization shows that all equilibrium points are asymptotically stable with certain conditions. Numerical simulations at the equilibrium point show the same results as the analysis results. Then, numerical continuity was carried out by selecting variation of the fear effect parameter for Numerical continuity results show that changes in these parameters affect the population of toxic and non-toxic species, marked by the emergence of Transcritical bifurcations, Bifurcation occurs at the first Saddle-Node bifurcation at and the second Saddle-Node bifurcation at

Multi-Source Data Fusion for Vehicle Maintenance Project Prediction

Ensuring road safety is heavily reliant on the effective maintenance of vehicles. Accurate predictions of maintenance requirements can substantially reduce ownership costs for vehicle owners. Consequently, this field has attracted increasing attention from researchers in recent years. However, existing studies primarily focus on predicting a limited number of maintenance needs, predominantly based solely on vehicle mileage and driving time. This approach often falls short, as it does not comprehensively monitor the overall health condition of vehicles, thus posing potential safety risks. To address this issue, we propose a deep fusion network model that utilizes multi-source data, including vehicle maintenance record data and vehicle base information data, to provide comprehensive predictions for vehicle maintenance projects. To capture the relationships among various maintenance projects, we create a correlation representation using the maintenance project co-occurrence matrix. Furthermore, building on the correlation representation, we propose a deep fusion network that employs the attention mechanism to efficiently merge vehicle mileage and vehicle base information. Experiments conducted on real data demonstrate the superior performance of our proposed model relative to competitive baseline models in predicting vehicle maintenance projects.

Adaptive synchronization and anti-synchronization of Julia sets generated by the competitive model

In this paper, we study the fractal behaviour of a competitive model that describes the interaction of plankton allelopathy. This paper aims to establish synchronization and anti-synchronization of Julia sets of two competitive systems with some different parameters by using an adaptive control strategy. Firstly, a discrete version of the competitive model is obtained, and then the Julia set of the discrete version is generated by using the escape-time algorithm. Adaptive controllers and parameter update laws for unknown parameters are designed to achieve synchronization and anti-synchronization of Julia sets. Furthermore, we can determine unknown parameters of the competitive system by using this adaptive control technique. Here, the adaptive synchronization and anti-synchronization of Julia sets are accomplished by its trajectories synchronization and anti-synchronization due to the close relation of trajectories of the system with the Julia set of the system. Numerical simulations are carried out to validate several key theoretical results as well as the efficacy and accuracy of the applied methodologies. Moreover, with the help of this analysis, we can study other models of a similar type.

Coexistence states for a Lotka–Volterra competitive model with aggressive movement strategy

This paper is concerned with a model of reaction–diffusion–advection equations arising in ecology. The equations model a situation in which the foreign species and the native species are both assumed to be competing for space in the same ecological niche, and the foreign species is just random diffusion while the native species is “braver” — a combination of random diffusion and a directed movement up the gradient of the foreign species. Our aim is to give the necessary and sufficient conditions for the coexistence of the foreign species and native species. For this, we first establish an a priori estimate result. Then we apply eigenvalue theory and homogenization principle to derive the necessary conditions for the coexistence. Finally, the sufficient conditions for the coexistence are given by using a global bifurcation method.

A joint Cournot equilibrium model for the hydrogen and electricity markets

Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers.

Paradojas dialécticas del modelo de competencia perfecta de la teoría económica neoclásica

Neoclassical economic theory holds, with apparent certainty, that the model of perfect competition is an idealized form of real markets. Although difficult to find, it is of enormous importance as a scientific concept and as a guide to practice. In this article we propose that this statement is partially incorrect. Neither is perfect competition an idealized form of real markets, nor can it serve as an adequate guide for an economic policy on the ordering of markets. Instead, perfect competition and other similar concepts are topological variants of spontaneous order. From the two previous considerations, a set of paradoxes and inconsistencies derive that, however, do not totally invalidate the search for transcendental concepts, if we correctly locate their place in the social sciences.

Very high cycle fatigue behavior of TC4 titanium alloy: Faceting cracking mechanism and life prediction based on dislocation characterization

This research analyzes the very-high-cycle-fatigue behavior of TC4 titanium alloy through fatigue tests at R = −1, −0.3, and 0.1. The results show that the S-N curves are all bilinear and exhibit three failure modes as surface slip failure, surface cleavage failure and interior cleavage failure. Transmission electron microscopy analysis reveals the dislocation structure in interior cleavage failure and suggests that the deformation mechanism of faceting cracking involves both anti-phase boundary shearing and stacking fault shearing mechanisms. It concludes that interior failure results from cleavage fracture of α grains due to dislocation slip. Based on the stress intensity factor of the maximum defect, a slip-cleavage competitive failure model was developed by considering factors such as control volume, defect size, external loading, and grain content, with good predictive results. Additionally, on the basis of the failure mechanism and crack propagation rate model, considering the coupled effects of crack tip blunting, stress ratio, Vickers hardness, and material fracture toughness on crack propagation, the crack propagation life prediction model is constructed. The life prediction model is further modified to be more conservative and accurate in predicting life by consideration the maximum defect size, providing important theoretical support and practical guidance for engineering applications.

Exact solitary wave solutions and their comparisons under the effect of noise: an allelopathic phytoplankton competition model

Exact solitary wave solutions and their comparisons under the effect of noise: an allelopathic phytoplankton competition model

The Impact of Tariffs on a Transnational Supply Chain under Different Power Structures in China

In the trade environment of a globalized economy, tariffs play a crucial role in transnational supply chains. At the same time, the power structure of the supply chain also plays an important role in the decision making and income distribution of a transnational supply chain. Therefore, we construct game-theoretic models to analyze the impacts of tariffs and power structures on the decision making and revenue distribution of transnational supply chains. First, we consider a bilateral monopoly model consisting of a Chinese manufacturer and a U.S. retailer and analyze the effects of tariffs and power structures on decision making and revenue distributions in this supply chain. Then, we extend the model to a duopoly competition model consisting of two Chinese manufacturers and one American retailer, further analyzing the roles of tariffs and power structures. The results indicate that in the bilateral monopoly model, the impact of tariffs on the manufacturer’s profits is always greater than on the retailer’s profits under a manufacturer-led circumstance. However, in a competitive model, when the market size is large, the impact of tariffs on the manufacturer’s profits exceeds that of the retailer’s profits; conversely, when the market size is smaller, the impact of tariffs on the retailer’s profits is greater than on the manufacturer’s profits. Furthermore, we find that in the duopoly competition model, under the manufacturer-led circumstance, both the manufacturer and the retailer earn the highest profits.

The Competition Model and Adult Second Language Learning: Cross-Language Similarity and L2 Morphosyntax Learning

Adult second language learners arrive at the language learning situation with an already formed first language grammar system in place. The study of cross-language similarity across the first and second languages explores how the similarities and differences in the two languages make learning more or less difficult, particularly for adult learners. From a Competition Model (MacWhinney, 1997) framework, the authors and their colleagues have conducted research examining the relationship between cross-language similarity and adult second language learning and processing with actual learners and in training paradigms with naïve learners; converging methodological approaches have been used (eye tracking, event related brain potentials, self- paced reading, and grammaticality judgments). We review these studies and describe the predictions derived from the Competition Model framework as a function of whether grammatical constructions are formed similarly or differently in the two languages or are unique to the second language. We conclude with a description of Brian MacWhinney’s influence on this body of research.

Probe substrates assay estimates the effect of polyphyllin H on the activity of cytochrome P450 enzymes in human liver microsomes.

Cytochrome P450 enzymes (CYPs) play a crucial role in phase I metabolic reactions. The activity of CYPs would affect therapeutic efficacy and may even induce toxicity. Given the complex components of traditional Chinese medicine, it is important to understand the effect of active ingredients on CYPs activity to guide their prescription. This study aimed to evaluate the effect of polyphyllin H on the activity of CYPs major isoforms providing a reference for the clinical prescription of polyphyllin H and its source herbs. The effects of polyphyllin H were evaluated in pooled human liver microsomes using probe substrates of CYP1A2, 2A6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4 to determine their activities. The Lineweaver-Burk was used to model the inhibition, and a time-dependent inhibition experiment was performed to understand the characteristics of the inhibition. Polyphyllin H significantly suppressed the activity of CYP1A2, 2D6, and 3A4 with IC50 values of 6.44, 13.88, and 4.52 μM, respectively. The inhibition of CYP1A2 and 2D6 was best fitted with a competitive model, yielding the inhibition constant (Ki) values of 3.18 and 6.77 μM, respectively. The inhibition of CYP3A4 was fitted with the non-competitive model with the Ki value of 2.38 μM. Moreover, the inhibition of CYP3A4 was revealed to be time-dependent with the inhibition parameters inhibition constant (KI) and inactivation rate constant (Kinact) values of 2.26 μM-1 and 0.045 min-1. Polyphyllin H acted as a competitive inhibitor of CYP1A2 and 2D6 and a non-competitive and time-dependent inhibitor of CYP3A4.

Improved outcomes with leadless vs. single-chamber transvenous pacemaker in haemodialysis patients.

Cardiac conduction disorders are common in haemodialysis patients, with a relatively high rate of pacemaker implantations. Pacemaker-related complications, especially lead infections and central venous stenosis, pose significant challenges in this population. This study aims to compare single-chamber leadless pacemaker to single-chamber transvenous pacemakers in terms of survival and related complications in haemodialysis patients. This retrospective study included adult haemodialysis patients who received a first single-chamber transvenous or leadless pacemaker between January 2017 and December 2020. Data were obtained from the French national REIN registry matched to the national health databases (Système National des Données de Santé). Propensity score matching was used to balance baseline characteristics. Survival and complications were compared between groups by Cox regression and by competitive risk models, respectively. One hundred and seventy-eight patients were included after propensity score matching, with 89 patients in each group. The median follow-up time was 24 (range 7-37) months. Leadless pacemakers were associated with significantly lower all-cause mortality rates compared to transvenous pacemakers [hazard ratio (HR) = 0.68, 95% confidence interval (CI) (0.47-0.99)]. Device-related infections are significantly lower with leadless pacemakers throughout the follow-up period (HR 0.43, 95% CI 0.21-0.86). Leadless pacemaker recipients also required fewer vascular access interventions [odds ratio 0.53, 95% CI (0.33-0.68)] on arteriovenous fistula. With the limitations of its observational design, this study suggests that leadless pacemakers are associated with a lower rate of complications and better survival as compared with transvenous VVI pacemakers in haemodialysis patients, supporting to consider their preferential use in this population.

Clinical outcomes of allogeneic hematopoietic stem cell transplantation from matched sibling donor for myelofibrosis

Objective: To evaluate the efficacy and safety of matched sibling donor allogeneic hematopoietic stem cell transplantation (allo-HSCT) for the treatment of myelofibrosis (MF). Methods: In this case series, the clinical data of 18 patients with MF who received allo-HSCT in the Department of Hematology, Peking University People's Hospital from December 2008 to December 2023 were retrospectively studied. Kaplan-Meier survival analysis and competitive risk model were used to evaluate the probabilities of 3-year overall survival (OS), disease-free survival (DFS), cumulative incidence of relapse (CIR), and transplant related mortality (TRM). The transplant related complications were also analyzed. Results: Among the 18 patients included, there were 12 males and 6 females, with a median age of 50 (range: 28-64) years. All 18 patients achieved neutrophil engraftment, and the time of neutrophil engraftment [M (Q1, Q3)] was 16.0 (11.8, 18.0) days. Twelve patients achieved platelet engraftment, and the platelet engraftment time was 21.0 (16.2, 43.2) days. Six patients had grade Ⅱ to Ⅳ acute graft-versus-host disease (GVHD), and six patients had chronic GVHD. The 3-year OS rate and DFS rate after transplantation were 62.2% and 52.2%, respectively. The 3-year CIR and TRM were 29.7% and 24.6%, respectively. Four patients died during follow-up, with the main cause of death being infections. Conclusion: Matched sibling allo-HSCT is a feasible option for the treatment of MF.

Strategic changes and challenges of private dental clinics and practitioners in Israel: adapting to a competitive environment

BackgroundIn the 21st century’s digital age, health organizations face challenges from reduced public healthcare spending, creating a competitive market that impacts healthcare management. The struggle is hardest for small organizations, such as private clinics. Competing under unfavorable conditions, these small businesses must run as independent, profitable units in a government-regulated market where they are subject to numerous restrictions yet receive no financial support. In a world of increasing dependence on digital technologies, these small businesses must adopt competitive business models and be adaptive and flexible in embracing change.MethodsThis qualitative study is based on a thematic qualitative analysis of 20 in-depth, 45-minute-long interviews with dentists and owners of private dental clinics in various specialties. The study employs the strategic change model to examine how dentists who run private dental clinics implement new strategies and technologies to adjust to changes and create a competitive edge.ResultsSix main categories emerged from the analysis of the interviews: changes in the organization’s environment; instituting and assimilating changes; obstacles in embracing change; added value obtained from embracing the change; quality of care and service; and cost-benefit considerations. The categories were map and evaluated in light of the strategic change model. The analysis indicated that digital strategies have been only partially adopted, suggesting an absence of a cohesive, long-term strategic vision for the organizations.ConclusionsThe study explored the actions, perceptions, and challenges of adapting to a competitive digital market in dental private clinics. Based on these insights, recommendations have been provided for global change management, aiming for a sustainable and stable healthcare system that benefits the broader community.

Modeling and analysis of COVID-19 spreading based on complex network theory

Complex networks can effectively describe interactions within real-world complex systems. In researches of epidemic spreading, scientists constructed various physical contact networks between individuals on the microscopic scale and the metapopulation networks on the macroscopic scale. These different types of network structures significantly impact the propagation dynamics of epidemic in human society. For instance, population flows in global airline networks influence the speed and arrival time of epidemics across large-scale space. In this paper we review the epidemic spreading models on various network structures, including fully mixed networks, three types of lower-order networks, three types of higher-order networks, metapopulation networks, and multiple strains competitive epidemic spreading models. We also provide an overview of the application of complex network theory in the COVID-19 pandemic, covering topics of prediction, prevention, and control of the epidemic. Finally, we discuss the strengths and limitations of these models and propose perspectives for future research.