Competitive Model Research Articles

Stochastic Optimal Strategies and Management of Electric Vehicles and Microgrids

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

Digital transformation and agile methodologies: An innovative model for competitiveness in the manufacturing sector

Digital transformation and agile methodologies: An innovative model for competitiveness in the manufacturing sector

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for diagnosis, prevention, prognosis, and drug development. Genes that encode proteins with similar sequences are often implicated in related diseases, as proteins causing identical or similar diseases tend to show limited variation in their sequences. Predicting gene-disease association (GDA) requires time-consuming and expensive experiments on a large number of potential candidate genes. Although methods have been proposed to predict associations between genes and diseases using traditional machine learning algorithms and graph neural networks, these approaches struggle to capture the deep semantic information within the genes and diseases and are dependent on training data. To alleviate this issue, we propose a novel GDA prediction model named FusionGDA, which utilizes a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models. Multi-modal representations are generated by the fusion module, which includes rich semantic information about two heterogeneous biomedical entities: protein sequences and disease descriptions. Subsequently, the pooling aggregation strategy is adopted to compress the dimensions of the multi-modal representation. In addition, FusionGDA employs a pre-training phase leveraging a contrastive learning loss to extract potential gene and disease features by training on a large public GDA dataset. To rigorously evaluate the effectiveness of the FusionGDA model, we conduct comprehensive experiments on five datasets and compare our proposed model with five competitive baseline models on the DisGeNet-Eval dataset. Notably, our case study further demonstrates the ability of FusionGDA to discover hidden associations effectively. The complete code and datasets of our experiments are available at https://github.com/ZhaohanM/FusionGDA.

Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.

Research on the Sex Ratio Variation of Sea Lampreys and Its Ecological Impact Based on the Lotka-Volterra Competition Model

Research on the Sex Ratio Variation of Sea Lampreys and Its Ecological Impact Based on the Lotka-Volterra Competition Model

Dynamics of a discrete-time mixed oligopoly Cournot-type model with three time delays

This paper analyzes a discrete-time Cournot competition model with time delays taking into consideration the dynamics of market interactions involving one public firm and multiple private firms. The model assumes that the production output of the public firm is influenced by the past output levels of the private firms. The output levels of the private firms are affected by the past production outputs of both the public firm and their private competitors. The study identifies two equilibrium states within the nonlinear system: a positive equilibrium and a boundary equilibrium. Through a stability analysis, it is established that the boundary equilibrium is a saddle point. In the absence of time delays, the stability region for the positive equilibrium is determined. The research explores various specific delay scenarios, finding conditions under which the positive equilibrium is asymptotic stability. Additionally, the occurrence of flip and Neimark–Sacker bifurcations are examined. In order to illustrate the complex dynamics, the paper provides a series of numerical examples.

Computing macroscopic reaction rates in reaction-diffusion systems using Monte Carlo simulations.

Stochastic reaction-diffusion models are employed to represent many complex physical, biological, societal, and ecological systems. The macroscopic reaction rates describing the large-scale, long-time kinetics in such systems are effective, scale-dependent renormalized parameters that need to be either measured experimentally or computed by means of a microscopic model. In a Monte Carlo simulation of stochastic reaction-diffusion systems, microscopic probabilities for specific events to happen serve as the input control parameters. To match the results of any computer simulation to observations or experiments carried out on the macroscale, a mapping is required between the microscopic probabilities that define the Monte Carlo algorithm and the macroscopic reaction rates that are experimentally measured. Finding the functional dependence of emergent macroscopic rates on the microscopic probabilities (subject to specific rules of interaction) is a very difficult problem, and there is currently no systematic, accurate analytical way to achieve this goal. Therefore, we introduce a straightforward numerical method of using lattice Monte Carlo simulations to evaluate the macroscopic reaction rates by directly obtaining the count statistics of how many events occur per simulation time step. Our technique is first tested on well-understood fundamental examples, namely, restricted birth processes, diffusion-limited two-particle coagulation, and two-species pair annihilation kinetics. Next we utilize the thus gained experience to investigate how the microscopic algorithmic probabilities become coarse-grained into effective macroscopic rates in more complex model systems such as the Lotka-Volterra model for predator-prey competition and coexistence, as well as the rock-paper-scissors or cyclic Lotka-Volterra model and its May-Leonard variant that capture population dynamics with cyclic dominance motifs. Thereby we achieve a more thorough and deeper understanding of coarse graining in spatially extended stochastic reaction-diffusion systems and the nontrivial relationships between the associated microscopic and macroscopic model parameters, with a focus on ecological systems. The proposed technique should generally provide a useful means to better fit Monte Carlo simulation results to experimental or observational data.

POCKET: Pruning random convolution kernels for time series classification from a feature selection perspective

In recent years, two competitive time series classification models, namely, ROCKET and MINIROCKET, have garnered considerable attention due to their low training cost and high accuracy. However, they rely on a large number of random 1-D convolutional kernels to comprehensively capture features, which is incompatible with resource-constrained devices. Despite the development of heuristic algorithms designed to recognize and prune redundant kernels, the inherent time-consuming nature of evolutionary algorithms hinders efficient evaluation. To efficiently prune models, this paper eliminates feature groups contributing minimally to the classifier, thereby discarding the associated random kernels without direct evaluation. To this end, we incorporate both group-level (l2,1-norm) and element-level (l2-norm) regularizations to the classifier, formulating the pruning challenge as a group elastic net classification problem. An ADMM-based algorithm is initially introduced to solve the problem, but it is computationally intensive. Building on the ADMM-based algorithm, we then propose our core algorithm, POCKET, which significantly speeds up the process by dividing the task into two sequential stages. In Stage 1, POCKET utilizes dynamically varying penalties to efficiently achieve group sparsity within the classifier, removing features associated with zero weights and their corresponding kernels. In Stage 2, the remaining kernels and features are used to refit a l2-regularized classifier for enhanced performance. Experimental results on diverse time series datasets show that POCKET prunes up to 60% of kernels without a significant reduction in accuracy and performs 11× faster than its counterparts. Our code is publicly available at https://github.com/ShaowuChen/POCKET.

Towards detailed combustor wall kinetics: An experimental and kinetic modeling study of hydrogen oxidation on Inconel

Gas-phase hydrogen combustion is ubiquitous in industrial processes, and the associated surface kinetics on heat-resistant alloys plays a crucial role in designing efficient low-carbon technologies. We conducted new temperature programmed reduction experiments to determine the reducibility of materials following an oxidation cycle. These experiments were modeled using a thermoconsistent multi-site microkinetic model for H2 heterogeneous oxidation on Inconels, which was validated against literature experiments. This competitive adsorption model considers iron bulk content, hydrogen spillover and subsurface oxygen migration on hydrogen surface oxidation kinetics. New X-ray diffraction experiments confirmed the postulated crystallographic structures in the Inconel samples, suggesting their presence on the surface scale. The phenomenological model was coupled with several state-of-the-art gas-phase oxidation mechanisms to assess gas/surface reactions interaction as a function of material and temperature. The results reveal a complex interaction in which surface removes H radicals from the gas-phase, while the Bradford (H2+OH) reaction converts OH into H2O, promoting water adsorption on Inconel. This interaction was found to give rise to gas-phase thermokinetic oscillations. The model predicts a non-monotonous effect of reactor area-to-volume ratio on reactivity and emphasizes the impact of Inconel composition on product selectivity. Overall, the multi-site model provides new insight into the contrasting reactivities among active sites, bridging the gap between material science and heterogeneous combustion modeling.

Taking the sting out of scorpions: Electrophysiological investigation of the relative efficacy of three antivenoms against medically significant Centruroides species

In this study, we report the innovative application of whole-cell patch-clamp electrophysiology in assessing broad-spectrum neutralisation by three different antivenoms, of venoms from the medically significant scorpion genus Centruroides. Envenomations by as many as 21 species from the Centruroides genus result in up to 300,000 envenomations per year in Mexico, which poses significant and potentially life-threatening pathophysiology. We first evaluated the in vitro manifestation of envenomation against two human voltage-gated sodium (hNaV) channel subtypes: hNaV1.4 and hNaV1.5, which are primarily expressed in skeletal muscles and cardiomyocytes, respectively. The neutralisation of venom activity was then characterised for three different antivenoms using a direct competition model against the more potent target, hNaV1.4. While broad-spectrum neutralisation was identified, variation in neutralisation arose for Centruroides elegans, C. limpidus, C. noxius and C. suffusus venoms, despite the presence of a number of these venoms within the immunising mixture. This raises questions regarding the truly “broad” neutralisation capacity of the antivenoms. This study not only extends previous validation of the in vitro investigation of antivenom efficacy utilising the whole-cell patch-clamp technique but also underscores the potential of this animal-free model in exploring cross-reactivity, experimental scalability, and most importantly, informing clinical management practices regarding the administration of antivenom in Mexico.

Production or transmission investments? A comparative analysis

The European electricity market is moving towards an integration process, and to facilitate that integration and the flow of electricity in that market, the European Transmission System Operator proposes different investments in transmission and production capacities. I develop a model of imperfect competition to study those investments, and I identify the economic forces that determine the equilibrium price and the flow of electricity: The size and the competitive effects. I prove formally when those forces operate simultaneously or independently, and how they determine consumers’ surplus and suppliers’ profits. I prove that an investment in transmission capacity outperforms an investment in production capacity by maximizing consumers’ welfare, but the performance in terms of suppliers’ profits is ambiguous.

Spatial Competition Models in Health Care Markets: A Review

Through time, spatial competition has become the natural environment for studying a number of problems, ranging from product differentiation to mail order versus traditional business, to service provision under various assumptions about the institutional setting. Health economics research has greatly benefited from the intuitions that underlie the Hotelling model and several authors have contributed to the literature on the architecture of the market for health care by proposing models within this framework. In this paper we explore how spatial competition has been used in health economics to improve the knowledge of the mechanisms of the market for health care through the derivation of significant policy implications. One of the most important messages is that in spite of competition, the regulator still should have a central role in the architecture of the market and policies have to be carefully tailored in order to avoid the onset of perverse, undesired effects on quality and welfare.

Dual‐Interface Competitive Fracture Model for Curvature‐Based Transfer Printing Method

AbstractTransfer printing is a key technology in the fabrication of flexible electronics. Transfer printing method based on curvature provides a simple and effective way to transfer films onto weakly adhesive or even adhesiveless surfaces, overcoming the shortage of traditional transfer printing methods that it is difficult to print functional materials from strong interface to weak interface and can only be applicable to surfaces with certain interfacial strength. So far, the theoretical principle of the curvature‐based transfer printing method has not yet been developed. In this article, a dual‐interface competitive fracture model is established to analyze the mechanism of printing and picking up processes and quantitatively provide the critical transfer printing radius in terms of material and geometric properties of the transfer printing system. This model is verified to be both correct and widely applicable by rich experimental results, providing a new and reliable theory for the fabrication of inorganic flexible electronics.

Move a Little Closer? Information Sharing and the Spatial Clustering of Bank Branches

Abstract We present a model of credit market competition to derive key hypotheses about how information sharing between banks influences the spatial clustering of their branches. We then test these hypotheses using data on 56,555 branches owned by 614 banks across 19 countries. We find that information sharing incentivizes banks to establish branches in localities that are new to them but that are already served by other banks. The resultant branch clustering is associated with reduced spatial credit rationing, as information sharing enables firms to access credit from more distant banks. These findings underscore how information sharing makes it more important for banks to move closer to each other rather than closer to their borrowers.

A mixed BAR(1) model driven by serially dependent innovation with application

To better analyze dependent count data with bounded support, this paper proposes a first-order mixture binomial autoregressive (BAR(1)) model, which is constructed via making use of the Pegram operator, where the innovation processes are assumed to be serially dependent to the observation rather than independent. The new model not only inherits the advantages of existing mixture BAR(1) models while also overcoming their shortcomings, but also does well in explaining binomial overdispersion and zero inflation. We infer various statistical properties of the process, including conditional moments, moments and autocovariance functions. The EM algorithm is employed to calculate the conditional maximum likelihood estimate. To evaluate the performance of obtained estimates, some simulation studies are conducted. Furthermore, the forecasting problem for the proposed model is also discussed. To show the applicability of our model, an application to proposition data set is conducted and we compare our model to some competitive integer-valued time series models with bounded support.

Nowcasting Norwegian household consumption with debit card transaction data

SummaryWe use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed‐frequency data, we estimate various quantile mixed‐data sampling (QMIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4–2019Q4. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high‐frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of 2020Q1, a quarter characterized by heightened uncertainty due to the COVID‐19 pandemic. We further show how debit card data have been useful in nowcasting consumption during the four subsequent quarters.

A retrospective study on the impact of radiotherapy on the survival outcomes of small cell lung cancer patients based on the SEER database

Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan–Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients’ overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery.

Exploring the ecological security evaluation of water resources in the Yangtze River Basin under the background of ecological sustainable development

The Yangtze River (hereafter referred to as the YZR), the largest river in China, is of paramount importance for ensuring water resource security. The Yangtze River Basin (hereafter referred to as the YRB) is one of the most densely populated areas in China, and complex human activities have a significant impact on the ecological security of water resources. Therefore, this paper employs theories related to ecological population evolution and the Driving Force-Pressure-State-Impact-Response (DPSIR) model to construct an indicator system for the ecological security of water resources in the YRB. The report evaluates the ecological security status of water resources in each province of the YRB from 2010 to 2019, clarifies the development trend of its water resource ecological security, and proposes corresponding strategies for regional ecological security and coordinated economic development. According to the results of the ecological population evolution competition model, the overall indicator of the ecological security of water resources in the YRB continues to improve, with the safety level increasing annually. Maintaining sound management of water resources in the YRB is crucial for sustainable socioeconomic development. To further promote the ecological security of water resources in the YRB and the coordinated development of the regional economy, this paper proposes policy suggestions such as promoting the continuous advancement of sustainable development projects, actively adjusting industrial structure, continuously enhancing public environmental awareness, and actively participating in international ecological construction and seeking cooperation among multiple departments.

Traffic prediction based on spatial-temporal disentangled generative models

Understanding the underlying spatial-temporal patterns in a traffic network is crucial for accurate predictions. However, current deep learning techniques often struggle to effectively identify the generative factors hidden in spatial-temporal traffic data, leading to a lack of interpretability. To address this challenge, we have developed an innovative generative model called Spatial-Temporal Disentangled Neural Relational Inference (STDNRI) for traffic prediction and disentangling interpretable generative factors within observed traffic network data. In this study, we assume that spatial-temporal data is dominated by three generative factors: time-correlated factors, spatial-correlated factors and spatial-temporal joint correlation factors. Based on information bottleneck theory, we establish a novel objective function to maximize the disentanglement of different factors. To model the probability distributions of three factors, we propose three graph neural network encoders to represent time-correlated factors using a graph containing only self-loops, and denote spatial-correlated factors as well as spatial-temporal joint correlation factors adopting a fully-connected graph without self-loops. Then, graph neural network decoder with a spatial-temporal message passing mechanism is used to generate the final predictions. Several comprehensive experiments were conducted based on two real datasets from NYC Yellow Taxi and SHMetro. The results indicate that our STDNRI outperforms ten existing competitive models in terms of high prediction accuracy and provides excellent interpretability for generative factors. Furthermore, the disentangled interpretable factors can afford valuable insights for planners, urban developers, and other stakeholders to gain a deeper understanding of the dynamics of the traffic network, thus enabling more informed decision-making.

Individualized, dynamic, and full-course vancomycin dosing prediction: a study on the customized dose model.

The single-point trough-based therapeutic drug monitoring (TDM) and Bayesian forecasting approaches are still limited in individualized and dynamic vancomycin delivery. Until recently, there has not yet been enough focus on the direct integration of pharmacokinetic/pharmacodynamic (PK/PD) and TDM to construct a customized dose model (CDM) for vancomycin to achieve individualized, dynamic, and full-course dose prediction from empirical to follow-up treatment. This study sought to establish CDM for vancomycin, test its performance and superiority in clinical efficacy prediction, formulate a CDM-driven full-course dosage prediction strategy to overcome the above challenge, and predict the empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various creatinine clearance rates (CLcr). The PK/PD and concentration models derived from our earlier research were used to establish CDM. The receiver operating characteristic (ROC) curve, with the area under ROC curve (AUCR) as the primary endpoint, for 21 retrospective cases was applied to test the performance and superiority of CDM in clinical efficacy prediction by comparison to the current frequently-used dose model (FDM). A model with an AUCR of at least 0.8 was considered acceptable. Based on the availability of TDM, the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy was formulated. Based on the CDM, Monte Carlo simulation was used to predict the empirical vancomycin dosages for the target populations and bacteria. Four CDMs and the strategy of CDM-driven individualized, dynamic, and full-course dose prediction for vancomycin therapy from empirical to follow-up treatment were constructed. Compared with FDM, CDM showed a greater AUCR value (0.807 vs. 0.688) in clinical efficacy prediction. The empirical vancomycin dosages for six Staphylococci populations and four strains in patients with various CLcr were predicted. CDM is a competitive individualized dose model. It compensates for the drawbacks of the existing TDM technology and Bayesian forecasting and offers a straightforward and useful supplemental approach for individualized and dynamic vancomycin delivery. Through mathematical modeling of the vancomycin dosage, this study achieved the goal of predicting doses individually, dynamically, and throughout, thus promoting "mathematical knowledge transfer and application" and also providing reference for quantitative and personalized research on similar drugs.