Competitive Model Research Articles

Research on the new power system dispatching model considering clean energy market and power‐section stabilization

AbstractWith the introduction of dual carbon targets, clean energy is connected to the grid on a large scale, and the stochastic and volatility of its output further affects the stable operation of the new power system. Meanwhile, in order to solve the problem that it is difficult to take into account the participation of clean energy in market‐oriented trading and complete consumption in the construction of power market, the new type of power system source–load balance dispatching model considering the market of clean energy and cross‐section stabilization is proposed. Firstly, a clean energy market price competition model based on the market clearing mechanism is proposed to realize the alternative consumption of clean energy. Secondly, considering the section security of inter‐system power transmission, a new power system source‐load balance two‐level optimization model is established by considering the cross‐section power rate and real‐time trading of clean energy market. Finally, the advantages of the model in enhancing the high proportion of clean energy consumption, rationally clearing the clean energy market and reducing the cross‐section load ratio of the contact line are verified through simulation.

Learning Disentangled Graph Convolutional Networks Locally and Globally.

Graph convolutional networks (GCNs) emerge as the most successful learning models for graph-structured data. Despite their success, existing GCNs usually ignore the entangled latent factors typically arising in real-world graphs, which results in nonexplainable node representations. Even worse, while the emphasis has been placed on local graph information, the global knowledge of the entire graph is lost to a certain extent. In this work, to address these issues, we propose a novel framework for GCNs, termed LGD-GCN, taking advantage of both local and global information for disentangling node representations in the latent space. Specifically, we propose to represent a disentangled latent continuous space with a statistical mixture model, by leveraging neighborhood routing mechanism locally. From the latent space, various new graphs can then be disentangled and learned, to overall reflect the hidden structures with respect to different factors. On the one hand, a novel regularizer is designed to encourage interfactor diversity for model expressivity in the latent space. On the other hand, the factor-specific information is encoded globally via employing a message passing along these new graphs, in order to strengthen intrafactor consistency. Extensive evaluations on both synthetic and five benchmark datasets show that LGD-GCN brings significant performance gains over the recent competitive models in both disentangling and node classification. Particularly, LGD-GCN is able to outperform averagely the disentangled state-of-the-arts by 7.4% on social network datasets.

AGProto: Adaptive Graph ProtoNet towards Sample Adaption for Few-Shot Malware Classification

Traditional malware-classification methods reliant on large pre-labeled datasets falter when encountering new or evolving malware types, particularly when only a few samples are available. And most current models utilize a fixed architecture; however, the characteristics of the various types of malware differ significantly. This discrepancy results in notably inferior classification performance for certain categories or samples with uncommon features, but the threats of these malware samples are of equivalent significance. In this paper, we introduce Adaptive Graph ProtoNet (AGProto), a novel approach for classifying malware in the field of Few-Shot Learning. AGProto leverages Graph Neural Networks (GNNs) to propagate sample features and generate multiple prototypes. It employs an attention mechanism to calculate the relevance of each prototype to individual samples, resulting in a customized prototype for each case. Our approach achieved optimal performance on two few-shot malware classification datasets, surpassing other competitive models with an accuracy improvement of over 2%. In extremely challenging scenarios—specifically, 20-class classification tasks with only five samples per class—our method notably excelled, achieving over 70% accuracy, significantly outperforming existing advanced techniques.

The Complexities in the R&D Competition Model with Spillover Effects in the Supply Chain

This study aims to investigate the research and development (R&D) competition within the supply chain, focusing on two aspects: R&D competition at the manufacturing level and competition in pricing strategies. This paper establishes a dynamic game model of R&D competition, comprising two manufacturers and two retailers, with both manufacturers exhibiting bounded rationality. The key findings are as follows: (1) an increase in the adjustment speed positively affects the chaotic nature of the R&D competition system, leading to a state of disorder. This chaotic state has adverse implications for manufacturing profitability. (2) The spillover effect exhibits a positive relationship with the level of chaos in the R&D competition system. A greater spillover effect contributes to a more turbulent environment, which subsequently impacts the profitability of manufacturers. (3) R&D cost parameters exert a positive influence on the stability of the R&D competition system. When the system reaches a state of equilibrium, an escalation in the R&D cost parameters poses a threat to manufacturer profitability. (4) Retailer costs play a detrimental role in the stability of the R&D competition system. As retailer costs increase, there is a decline in R&D levels, thereby diminishing manufacturer profitability. (5) To mitigate the chaotic state, we propose the implementation of the time-delayed feedback control (TDFC) method, which reflects a more stable state in the R&D competition system.

Widespread analytical pitfalls in empirical coexistence studies and a checklist for improving their statistical robustness

Abstract Modern coexistence theory (MCT) offers a conceptually straightforward approach for connecting empirical observations with an elegant theoretical framework, gaining popularity rapidly over the past decade. However, beneath this surface‐level simplicity lie various assumptions and subjective choices made during data analysis. These can lead researchers to draw qualitatively different conclusions from the same set of experiments. As the predictions of MCT studies are often treated as outcomes, and many readers and reviewers may not be familiar with the framework's assumptions, there is a particular risk of ‘researcher degrees of freedom’ inflating the confidence in results, thereby affecting reproducibility and predictive power. To tackle these concerns, we introduce a checklist consisting of statistical best practices to promote more robust empirical applications of MCT. Our recommendations are organised into four categories: presentation and sharing of raw data, testing model assumptions and fits, managing uncertainty associated with model coefficients and incorporating this uncertainty into coexistence predictions. We surveyed empirical MCT studies published over the past 15 years and discovered a high degree of variation in the level of statistical rigour and adherence to best practices. We present case studies to illustrate the dependence of results on seemingly innocuous choices among competition model structure and error distributions, which in some cases reversed the predicted coexistence outcomes. These results demonstrate how different analytical approaches can profoundly alter the interpretation of experimental results, underscoring the importance of carefully considering and thoroughly justifying each step taken in the analysis pathway. Our checklist serves as a resource for authors and reviewers alike, providing guidance to strengthen the empirical foundation of empirical coexistence analyses. As the field of empirical MCT shifts from a descriptive, trailblazing phase to a stage of consolidation, we emphasise the need for caution when building upon the findings of earlier studies. To ensure that progress made in the field of ecological coexistence is based on robust and reliable evidence, it is crucial to subject our predictions, conclusions and generalisability to a more rigorous assessment than is currently the trend.

A competition model with impulsive interventions and environmental perturbations in moving environments

In order to understand how impulsive interventions and environmental perturbations affect dynamics of competitors, we focus on a diffusive competition model with free boundaries and periodic pulses in a temporally heterogeneous environment with upward or downward advection. The dependence of the principal eigenvalue of corresponding periodic impulsive eigenvalue problem on advection rates, habitat sizes and pulses is investigated, which gives precise conditions that classify the dynamics into four types of competition outcomes including coexistence, co-extinction, two different competition exclusions for small or negative advection rates. Some sufficient conditions on pulses or initial habitats for species spreading or vanishing, and spreading speeds are then established. Our results not only extend the existing ones to the case with pulses, but also reveal the effects of human and natural factors, that is, impulsive interventions factors including positive or negative impulsive effect, pulse intensity and timing can significantly affect and alter the competition outcomes. The different performances of the superior and inferior affected by environmental perturbations are also reflected in the simulations.

Dancemania: Analyzing the Commercialization of Dance Crew Competitions

This research delves into the intricate landscape of commercialization within dance crew competitions, employing a focused examination of prominent television series, notably the widely recognized "Dancemania." As the dance competition model gains unprecedented popularity and financial success, there is a discernible risk that the core authenticity of this expressive art form may be compromised. Rather than adhering to artistic values, there is a growing concern that these competitions may veer towards catering primarily to mass entertainment tastes. To comprehensively unravel the increasing commercial influences and economic incentives shaping the competition format, this study employs a multifaceted approach. Interviews and surveys are conducted, drawing perspectives from dance professionals, competitors, and television producers intimately involved in the dance competition milieu. Through qualitative insights and firsthand accounts, the paper aims to shed light on the evolving dynamics propelled by commercial interests. Quantitative data analysis forms a critical component of this research, uncovering correlations that exist between success metrics and influential factors, such as viewership ratings, which may take precedence over the technical prowess of dance ability. The findings from this analysis serve as a catalyst for discussions surrounding potential policy and structural adjustments. The overarching objective is to realign the priorities of competitive dance with a renewed emphasis on artistic values, countering the prevailing trend of prioritizing commercial interests. This research signals the need for holistic interventions and shifts in the competition landscape to preserve the inherent artistic integrity of dance within a commercialized context.

Dimensions of Sufism Within The Islamic Religious Education Curriculum in Higher Education

Education is currently increasingly directing students toward a passion for competition. Competitive model education can blunt emotional and spiritual intelligence to inhibit the growth of vibrant and affective traits in student personalities. Therefore, the Dimensions of Sufism in education can function as an alternative to stem the disruption and problems of millennial education. This study uses a multi-case study type of Higher Education in East Java, Indonesia. The dimensions of Sufism in Islamic religious education in higher education show their uniqueness. The unique fact is the teaching of Sufism at Darul Ulum University (UNDAR) Jombang, which prioritizes the teaching of the Trisula course with three pillars: the concept of boarding schools, Sufi order, and campus. Sufism values are taught in all study programs and are internalized practically through the ritual of the Sufi order in the structured campus activity program. At the same time, the uniqueness of Sufism teaching in the other six higher educations emphasizes the basis of the cluster in the field of science. This study argues that the injection of Sufism values into the structure of the higher education curriculum is a primary need to equip intellect intelligence to control themselves against the dynamics of life.

Global dynamics of two-species reaction–diffusion competition model with Gompertz growth

ABSTRACT In this paper, we investigate a two-species reaction–diffusion competition model with Gompertz growth, where the intrinsic growth rates and carrying capacities of environments are heterogeneous. At firstly, assuming two competing species only admit different diffusive rates, we show that ‘slower diffuser prevails’, which is consistent with the well-known result in Dockery J, Hutson V, Mischaikow K, Pernarowski M. [The evolution of slow dispersal rates: a reaction–diffusion model. J Math Biol. 1998;37(1):61–83; Hastings A. Can spatial variation alone lead to selection for dispersal? Theor Popul Biol. 1983;24:244–251]. Then, for the “weak competition” case, we establish a prior estimate, which combined with the theory of monotone dynamical system and spectral analysis implies that the model admits a unique coexistence steady state, which is globally asymptotically stable. Finally, for the “strong–weak competition” case, we give the expression of critical competition intensity and the weak competitor will be wiped out.

The role of geographic market definition in analysis of grocery retailing

AbstractWe examine how estimates of household food demand elasticities and store profit margins vary with alternative geographic market extents using structural models of household store choice and retailer competition. Our consumer store choice model is novel, simultaneously accounting for the heterogeneity of store choice sets, households' travel distance to stores, and their store‐specific shopping basket prices. We estimate the models using a unique combination of datasets on grocery purchases. We find that the geographic market extent is positively associated with household demand elasticity and negatively associated with store profit margins. The maximum market extent at which changes in demand elasticities become statistically insignificant varies by retailers, ranging between 10 and 16 km. These findings are robust to alternative assumptions of store competition. Our results imply that overlooking the locality of retail competition can result in overestimating the magnitudes of household demand elasticities while underestimating store profit margins, characterizing a relatively more competitive market.

Analysis of Hemodialisa (HD) Services Based on Comparison of Calculations Hospital Rates with Rates BPJS Kesehatan

For hospitals to grow and develop in order to be managed efficiently and effectively, it is necessary to face intense competition in the world of health services. If they want to survive in competition, hospitals need to recalculate their existing hospital rates, namely by using a competitive tariff model calculation. Based on the results of interviews with the head of the hemodialysis (HD) unit, it was stated that 95% of HD patients seeking treatment at Wangaya Hospital were BPJS patients and the rest were general patients. There is a difference in cost sharing between HD rates at hospitals and InaCBG's (BPJS) rates. Wangaya Hospital previously set a fee for HD patients at Rp. 990,000, while the BPJS has set a tariff for InaCBG's HD patients at IDR 887,100. Between HD rates from hospitals and BPJS, there is a difference in cost sharing. Researchers in this case only compared HD rates based on hospital rates and rates based on BPJS, so that the cost sharing value was small. So far, the hospital has never recalculated the difference based on these two rates. Based on this calculation, hospitals will incur losses in HD services in 2020, thus further research is needed on the topic above.

Prediction of bronchopulmonary dysplasia in very preterm infants: competitive risk model nomogram.

To develop predictive clinical models of bronchopulmonary dysplasia (BPD) through competing risk analysis. Retrospective observational cohort study, including preterm newborns ≤32 weeks gestational age, conducted between January 1, 2013 and September 30, 2022 in a third-level Neonatal Intensive Care Unit in Spain. A prediction study was carried out using competing risk models, where the event of interest was BPD and the competing event was death. A multivariate competing risk model was developed separately for each postnatal day (days 1, 3, 7 and 14). Nomograms to predict BPD risk were developed from the coefficients of the final models and internally validated. A total of 306 patients were included in the study, of which 73 (23.9%) developed BPD and 29 (9.5%) died. On day 1, the model with the greatest predictive capacity was that including birth weight, days since rupture of membranes, and surfactant requirement (area under the receiver operating characteristic (ROC) curve (AUC), 0.896; 95% CI, 0.792-0.999). On day 3, the final predictive model was based on the variables birth weight, surfactant requirement, and Fraction of Inspired Oxygen (FiO2) (AUC, 0.891; 95% CI, 0.792-0.989). Competing risk analysis allowed accurate prediction of BPD, avoiding the potential bias resulting from the exclusion of deceased newborns or the use of combined outcomes. The resulting models are based on clinical variables measured at bedside during the first 3 days of life, can be easily implemented in clinical practice, and can enable earlier identification of patients at high risk of BPD.

Perceptual Image Quality Prediction: Are Contrastive Language–Image Pretraining (CLIP) Visual Features Effective?

In recent studies, the Contrastive Language–Image Pretraining (CLIP) model has showcased remarkable versatility in downstream tasks, ranging from image captioning and question-answering reasoning to image–text similarity rating, etc. In this paper, we investigate the effectiveness of CLIP visual features in predicting perceptual image quality. CLIP is also compared with competitive large multimodal models (LMMs) for this task. In contrast to previous studies, the results show that CLIP and other LMMs do not always provide the best performance. Interestingly, our evaluation experiment reveals that combining visual features from CLIP or other LMMs with some simple distortion features can significantly enhance their performance. In some cases, the improvements are even more than 10%, while the prediction accuracy surpasses 90%.

Cost subsidy or environmental regulation? The effects of government interventions on environmental quality and 3BL performance

Green product development and innovation play a crucial role in addressing environmental issues. Governments often promote innovation by motivating manufacturers to enhance their products' environmental qualities (i.e., greenness, energy efficiency, recyclability). This paper investigates the implications of cost subsidy and environmental regulation for manufacturers’ pricing and environmental quality decisions, and triple-bottom-line (3BL) performance. We develop theoretical models involving one social welfare-maximizing government and one or two manufacturers. In the monopoly model, environmental regulation can achieve the same level of social welfare and environmental performance as cost subsidy when consumers have low environmental awareness. In the competitive model, market scale asymmetry between manufacturers drives differences in product price and environmental quality. Specifically, the manufacturer with a larger market share is also the leader in environmental quality and profit. Furthermore, cost subsidy and environmental regulation have distinct effects on environmental quality competition. A higher subsidy rate widens the gap between environmental qualities, helping competing manufacturers better differentiate their products. Conversely, a stricter regulatory standard narrows the gap between environmental qualities, hindering product differentiation. Moreover, numerical analysis shows that the dominating policy in the competitive model depends on environmental and market characteristics. Finally, we check the robustness of the results by considering two extensions of our models and find that the main insights remain valid.

Competitive random sequential adsorption of binary mixtures of disks and discorectangles

The two-dimensional (2D) packings of binary mixtures of disks with diameter d and discorectangles with aspect ratio ɛ (length-to-width ratio ε=l/d ) were studied numerically. The competitive random sequential adsorption (RSA) with simultaneous deposition of particles was considered. The aspect ratio was changed within the range ε=1−10 . In the competitive model, the particle was selected with probability pd (disks) and pε=1−pd (discorectangles), and then they were placed sequentially on a solid surface without overlapping with previously placed particles. Behavior of the total coverage in jamming state ϕ T at different values of pd and ɛ was analyzed. For core–shell structure of the particles the percolation connectivity of films was also discussed.

Persistence in power of long-lived parties

This paper presents a dynamic model of electoral competition in which parties are long-lived organizations. In each period, the incumbent chooses between two policies. The competitive policy yields a greater reelection probability, but absent electoral effects, the incumbent would prefer the accommodative policy. The analysis reveals that parties’ incentives to win reelection feed on themselves via a dynamic strategic complementarity effect: the incumbent’s incentives to prioritize reelection today increase if it expects its rival will prioritize reelection once in power. This complementarity effect is more relevant when competitiveness harms the opposition electorally- and policy-wise. As a result of this effect, checks and balances disincentivize parties’ competitiveness not only under divided governments but also when government is unified. Lastly, competitiveness is disincentivized by political turnover, party discipline, and parties’ impatience. The model applies both to settings of “virtuous” and “perverse” accountability, in which the competitive policy is socially better or worse, respectively.

An improved clustering method using particle swarm optimization algorithm and mitochondrial fusion model (PSO-MFM)

Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity.

Taking firms' margin targets seriously in a model of competition in supply functions

AbstractThis article deals with the integration of industry‐level markup targets into oligopoly theory. It proposes a behavioral competition model in which firms use the average cost‐plus price to determine their supplies. Specifically, firms are assumed to increase (resp., decrease) their supplies as the market price rises over (resp., falls below) this reference price. The equilibrium market outcome lies between those corresponding to Bertrand and Cournot competition. It depends on the industry's margin target, which determines the slope of firms' supply functions. The more significant the markup target is, the lower are the firms' equilibrium supplies at any price level and the higher is the equilibrium market price. An industry‐wide commitment to targeting a markup thus reduces competition in equilibrium. The reduction in competition is more pronounced than when firms commit to linear supply functions.

An optimal control problem for a Lotka-Volterra competition model with chemo-repulsion

An optimal control problem for a Lotka-Volterra competition model with chemo-repulsion

Spontaneous Dynamics of Hippocampal Place Fields in a Model of Combinatorial Competition among Stable Inputs.

We present computer simulations illustrating how the plastic integration of spatially stable inputs could contribute to the dynamic character of hippocampal spatial representations. In novel environments of slightly larger size than typical apparatus, the emergence of well-defined place fields in real place cells seems to rely on inputs from normally functioning grid cells. Theoretically, the grid-to-place transformation is possible if a place cell is able to respond selectively to a combination of suitably aligned grids. We previously identified the functional characteristics that allow a synaptic plasticity rule to accomplish this selection by synaptic competition during rat foraging behavior. Here, we show that the synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time, when the model is run in larger environments and the topographical/modular organization of grid inputs is taken into account. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization-ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models. In sum, spontaneous remapping could arise from rapid synaptic learning involving inputs that are functionally homogeneous, spatially stable, and minimally stochastic.