Journal of Computational Science最新文献

{"title":"Data assimilation by cellular neural network applied to Lorenz-63 system","authors":"César Magno Leite de Oliveira Júnior ,&nbsp;Antonio Mauro Saraiva ,&nbsp;Alexandre Cláudio Botazzo Delbem ,&nbsp;Haroldo Fraga de Campos Velho ,&nbsp;Gerônimo Gallarreta Zubiaurre Lemos ,&nbsp;Fabrício Pereira Härter","doi":"10.1016/j.jocs.2025.102587","DOIUrl":"10.1016/j.jocs.2025.102587","url":null,"abstract":"<div><div>Data assimilation is an important process to compute the best initial condition for a computational prediction system, combining a previous prediction (<em>background</em>) with observation. The result from this procedure is the computed <em>analysis</em>. A cellular neural network (Cell-NN) is applied as a data assimilation (DA) method. The Cell-NN is also employed to integrate dynamic systems in time. Different Cell-NN configurations are developed for the DA process and as an integration scheme. The Lorenz system under a chaotic dynamical regime is used for testing with Cell-NN. Data assimilation with the 3D variational (3D-Var) method is also implemented for comparison. Cell-NN belongs to the class of unsupervised neural networks. The performance for computing the analysis by Cell-NN presented a similar error magnitude to the 3D-Var technique.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102587"},"PeriodicalIF":3.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive time-varying output formation tracking control for multi-agent system with dynamic event-triggered strategies","authors":"Jie Wu ,&nbsp;Xiaoyi Zhang ,&nbsp;Xisheng Zhan ,&nbsp;Tao Han ,&nbsp;Huaicheng Yan","doi":"10.1016/j.jocs.2025.102630","DOIUrl":"10.1016/j.jocs.2025.102630","url":null,"abstract":"<div><div>This paper investigates the output feedback time-varying formation(OFTVF) tracking issue for general linear multi-agent systems(MASs). To address this issue, novel dynamic event-triggered(DET) strategies are proposed to manage the inter-agent communication effectively. It removes the assumption that constant interaction is required between agents, and therefore communication cost is reduced significantly. Then under the proposed DET strategies, an adaptive OFTVF tracking control algorithms is designed for general linear MASs. Using Lyapunov stability theory, it is demonstrated that under proper conditions the proposed protocol is implementable. Furthermore, for the constructed DET scheme, no agent exhibit the Zeno behavior. Simulation example is presented at the end of the paper to demonstrate the effectiveness of designed DET control mechanism.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102630"},"PeriodicalIF":3.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AGDFCDA: Adaptive graph convolutional network and dual feature for circRNA-disease association prediction","authors":"Yan Zhao ,&nbsp;Xin He ,&nbsp;Junliang Shang ,&nbsp;Daohui Ge ,&nbsp;Jin-Xing Liu","doi":"10.1016/j.jocs.2025.102615","DOIUrl":"10.1016/j.jocs.2025.102615","url":null,"abstract":"<div><div>Circular RNA (circRNA) is a special type of RNA molecule whose structure presents as a closed loop. Numerous studies have demonstrated that abnormal expression of circRNA is closely associated with the development of diverse diseases. Accurately predicting the association between the circRNA and disease is important for understanding the pathogenesis of disease and discovering potential biomarkers. However, the high cost and complexity of traditional biological experiments limit the development of research. By constructing computational models and performing bioinformatics analysis, it is possible to identify disease-related circRNA more efficiently and reveal its potential mechanism. This paper presents AGDFCDA, a computational model for circRNA-disease association prediction, featuring a dual feature extraction strategy. On the one hand, the strategy applies the fully connected neural network to reduce the redundant information in the initial features, while the hidden information of circRNA and disease is preliminarily extracted. On the other hand, the strategy introduces adaptive graph convolutional network to learn more comprehensive representation of circRNA and disease to realize further extraction of features. AGDFCDA is assessed using five-fold cross-validation, and the results indicate that it outperforms the comparison methods in predicting circRNA-disease associations. In addition, the results of case studies can provide reliable candidate circRNA for wet experiments to be carried out with effective cost savings.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102615"},"PeriodicalIF":3.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New model of HIV-AIDS dynamics based on the Caputo–Fabrizio derivative: Optimal strategies for controlling the spread","authors":"Nassira Madani ,&nbsp;Zakia Hammouch ,&nbsp;EL-Houssine Azroul","doi":"10.1016/j.jocs.2025.102612","DOIUrl":"10.1016/j.jocs.2025.102612","url":null,"abstract":"<div><div>The goal of this study is to introduce a new model to better understand the spread of HIV/AIDS, with a particular focus on individuals who are unaware of their infection status. We propose and analyze a new Caputo–Fabrizio fractional model, examining its local stability around the equilibrium point using the abdicate method tailored for Caputo–Fabrizio derivatives. To assess global stability, we employ linear matrix inequalities (LMI). Furthermore, we formulate a fractional optimal control problem to identify effective strategies for controlling the disease. Numerical simulations are conducted to confirm the stability of the equilibrium and to demonstrate the behavior of the proposed solutions.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"90 ","pages":"Article 102612"},"PeriodicalIF":3.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal consistency of cloud-microphysical parameter sensitivity in a warm-conveyor belt","authors":"Maicon Hieronymus ,&nbsp;Annika Oertel ,&nbsp;Annette K. Miltenberger ,&nbsp;André Brinkmann","doi":"10.1016/j.jocs.2025.102614","DOIUrl":"10.1016/j.jocs.2025.102614","url":null,"abstract":"<div><div>A good representation of clouds and precipitation processes is essential in numerical weather and climate models. Subgrid-scale processes, such as cloud physics, are parameterized and inherently introduce uncertainty into models. Traditionally, the sensitivities of the model state to specific uncertain parameters are quantified through perturbations to a few selected parameters, limited by computational resources. Algorithmic Differentiation (AD) enables the efficient and simultaneous estimation of sensitivities for a large number of parameters, thereby overcoming the previous limitations and significantly enhancing the efficiency of the analysis. This framework provides an objective way to identify processes where more precise representations have the largest impact on model accuracy. AD-estimated sensitivities can also address the underdispersiveness of perturbed ensemble simulations by guiding the parameter selection or the perturbation itself. In our study, we applied AD to 169 uncertain parameters identified in the two-moment microphysics scheme of the numerical weather prediction (NWP) model ICON of the German Weather Service. This application of AD allowed us to evaluate the sensitivities of specific humidity, latent heating, and latent cooling along several thousand warm conveyor belt trajectories. This coherent, strongly ascending Lagrangian flow feature is crucial for the cloud and precipitation structure and the evolution of extratropical cyclones. The quantification of individual parameter sensitivities shows that only 38 parameters are of primary importance for the investigated model state variables. These parameters are associated with rain evaporation, hydrometeor diameter-mass relations, and fall velocities. Moreover, the parameter sensitivities systematically vary with different microphysical regimes, ascent behavior, and ascent stages of the WCB airstream. Finally, several parameters impact an extended region in the extratropical cyclone, illustrating the spatiotemporal consistency of cloud microphysical parameter uncertainty in the applied NWP model and microphysics scheme.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102614"},"PeriodicalIF":3.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilient authentication protocol for electronic healthcare enabled wireless body area networks using distributed ledger","authors":"Munir Hussain ,&nbsp;Amjad Mehmood ,&nbsp;Muhammad Altaf Khan ,&nbsp;Jaime Lloret ,&nbsp;Carsten Maple","doi":"10.1016/j.jocs.2025.102617","DOIUrl":"10.1016/j.jocs.2025.102617","url":null,"abstract":"<div><div>The recent developments in telecommunication technologies and monitoring devices have brought many changes in modern electronic healthcare systems (EHSs) by improving quality and decreasing healthcare expenses. Despite the benefits, they have privacy and security issues because the communication between patients and service providers takes place generally over public channels. Several user authentication protocols using distributed ledger technology (DLT) have recently been proposed to address these issues in EHSs. However, many are still vulnerable to a single point of failure (SPoF), privacy, and security attacks. Besides, they suffered from high communication and computational costs. Therefore, in this paper, we proposed a user authentication protocol using DLT to avoid these issues. A Burrows-Abadi-Needham (BAN) logic proof method has been used to check the security of the proposed protocol and ensure it achieves the desired security goals. In addition, an informal security analysis has been conducted to verify its important security requirements. A formal security analysis has been performed via the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Real-or-Random (ROR) model for further security strength. The results demonstrate that the proposed user authentication protocol is SAFE against all types of Man-in-the-Middle (MitM) attacks, impersonation, replay, and forgery attacks. Finally, performance analysis has been performed and results show that it achieves better performance by consuming 29.63 % and 13.21 % less communication and computational overheads as compared to existing related user authentication protocols. The security and performance analysis make it a more appropriate choice for the EHSs.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102617"},"PeriodicalIF":3.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time detection and classification of active regions from solar images using sector-based hashing","authors":"Rafał Grycuk ,&nbsp;Rafał Scherer ,&nbsp;Giorgio De Magistris ,&nbsp;Christian Napoli","doi":"10.1016/j.jocs.2025.102604","DOIUrl":"10.1016/j.jocs.2025.102604","url":null,"abstract":"<div><div>We present a new approach for real-time retrieval and classification of solar images using a proposed sector-based image hashing technique. To this end, we generate intermediate hand-crafted features from automatically detected active regions in the form of layer-sector-based descriptors. Additionally, we employ a small fully-connected autoencoder to encode and finally obtain the concise Layer-Sector Solar Hash. By reducing the amount of data required to describe the Sun images, we achieve almost real-time retrieval speed of similar images to the query image. Since solar AIA images are not labeled, for the purposes of the presented test experiments, we consider images produced within a short time frame (typically up to several hours) to be similar. This approach has several potential applications, including searching, classifying, and retrieving solar flares, which are of critical importance for many aspects of life on Earth.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102604"},"PeriodicalIF":3.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From simulations to surrogates: Neural networks enhancing burn wound healing predictions","authors":"Ioannis Papapanagiotou ,&nbsp;Roland V. Bumbuc ,&nbsp;H. Ibrahim Korkmaz ,&nbsp;Valeria Krzhizhanovskaya ,&nbsp;Vivek M. Sheraton","doi":"10.1016/j.jocs.2025.102593","DOIUrl":"10.1016/j.jocs.2025.102593","url":null,"abstract":"<div><div>Burn injuries trigger substantial inflammation, complicating wound healing and potentially leading to severe systemic complications. Understanding the immune response to burns is crucial for improving treatment. Although agent-based models (ABMs) are valuable for studying these interactions, they are computationally demanding. This paper explores the integration of neural networks (NNs) as surrogate models to approximate and forecast ABM simulation results in predicting cytokine concentrations over time and space. We present the development of a baseline ABM using the CompuCell3D software, simulating the innate immune response and generating extensive cytokine concentration data. This data is processed and prepared for neural network training, involving data cleaning, transformation into suitable formats, and a time-series-aware train-test split. We then implement and assess various neural network architectures. Each model is designed to capture the temporal and spatial dynamics of cytokine concentrations, with adjusted model architectures (kernels, number of layers, neurons per layer) to better suit this problem. The models are evaluated using Mean Squared Error, R-squared, and Mean Absolute Percentage Error. In this paper, we assess how different NN architectures (convolutional neural networks (CNNs), long short-term memory (LSTM) neural networks, attention mechanisms, and physics-informed neural networks (PINNs)) predict the concentration of cytokines in this biological system. We find that STA-LSTM generally performs best across statistical metrics.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102593"},"PeriodicalIF":3.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved local radial basis function method for pricing options under the time-fractional Black–Scholes model","authors":"Omid Nikan ,&nbsp;Jalil Rashidinia ,&nbsp;Hossein Jafari","doi":"10.1016/j.jocs.2025.102610","DOIUrl":"10.1016/j.jocs.2025.102610","url":null,"abstract":"<div><div>The time-fractional Black–Scholes model (T-FBSM) is developed to assess price fluctuations in a correlated fractal transmission system. It is applied to price American and European call and put options on non-dividend-paying stocks. This study focuses on numerically solving the T-FBSM for option pricing using a local compact integrated radial basis function method (LCIRBFM). The temporal discretization is accomplished using the second-order shifted Grünwald scheme, while the spatial derivatives are discretized by using a combination of an integrated RBF interpolation and a compact scheme within a sub-domain (stencil). This approach utilizes second derivatives and nodal function values to construct a link between the RBF weights and the physical domain. The convergence and unconditional stability of the semi-discretized time formulation are proven via the energy method in the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> space. The proposed method demonstrates efficiency, and the numerical results validate the theoretical formulation.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102610"},"PeriodicalIF":3.1,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solution of stress and deformation field and inversion of material parameter for gravity dams based on physics-informed neural networks","authors":"Danni Luo ,&nbsp;Haixing Mo ,&nbsp;Qingbin Li ,&nbsp;Xinxin Jin","doi":"10.1016/j.jocs.2025.102613","DOIUrl":"10.1016/j.jocs.2025.102613","url":null,"abstract":"<div><div>The stress and deformation analysis of concrete gravity dams is a core component of evaluating the structural safety of dam bodies. To obtain accurate and efficient solutions for the stress<img>deformation fields of gravity dams and fast inversion of material parameter, based on physics-informed neural networks (PINNs), we develop a residual minimization-based PINN model and a potential energy minimization-based EPINN model specifically targeted at gravity dams, which are grounded in the elasticity theory of solid mechanics. Through various case studies involving gravity dams, the computational accuracy and efficiency of different PINN models are compared. The results show the following: (1) The EPINN model demonstrates superior solving capability and computational efficiency—it is approximately 20 times faster than the PINN model—when dealing with complex geometries and boundary conditions. Conversely, the PINN model achieves higher computational accuracy for simpler geometries, with its precision being approximately twice that of the EPINN model. (2) Both models exhibit strong capabilities in material parameter inversion. In particular, the PINN model achieves accurate inversion of material properties via extremely limited data samples, with errors of only 0.46 % for the elastic modulus <em>E</em> and 2.32 % for Poisson's ratio <em>μ</em>. (3) The convergence performance of PINNs is influenced by factors such as the number of hidden layers, the number of neurons, and the test displacement functions. Overall, PINNs serve as a machine learning method that enables the direct construction of mechanistic models for gravity dams, contributing to the rapid and intelligent assessment of dam structural safety.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102613"},"PeriodicalIF":3.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}