Journal of Computational Science最新文献

{"title":"An accurate and stable space-time radial basis function collocation method for transient coupled thermo-mechanical analysis","authors":"Xiaohan Jing ,&nbsp;Lin Qiu ,&nbsp;Hong Zhao ,&nbsp;Zeqian Zhang ,&nbsp;Yaoming Zhang ,&nbsp;Yan Gu","doi":"10.1016/j.jocs.2025.102720","DOIUrl":"10.1016/j.jocs.2025.102720","url":null,"abstract":"<div><div>In this study, an accurate and stable space-time radial basis function (STRBF) collocation method is developed to solve two- and three-dimensional dynamic coupled thermo-mechanical problems. The proposed method enhances numerical precision by strategically positioning source points beyond the computational domain through space-time scaling factors. To address the challenge of selecting the optimal shape parameter, a new coupled STRBF is formulated by combining the Multiquadric function with the conical spline. Furthermore, a multiscale computational strategy is implemented to mitigate numerical instability in the resulting linear system. The effectiveness of the developed approach is demonstrated through four numerical examples involving complex geometries and different initial and boundary conditions. Numerical results show that, compared to the traditional RBF collocation method, the developed scheme not only enhances computational accuracy but also significantly reduces the dependence on the choice of shape parameter, making it a promising method for dealing with transient coupled thermo-mechanical problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102720"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158211","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":"Optimal control applied to a dengue model incorporating symptomatic, asymptomatic, and severe cases with limited healthcare resources","authors":"Salamida Daudi ,&nbsp;Eva Lusekelo ,&nbsp;Mlyashimbi Helikumi ,&nbsp;Steady Mushayabasa","doi":"10.1016/j.jocs.2025.102733","DOIUrl":"10.1016/j.jocs.2025.102733","url":null,"abstract":"<div><div>Dengue remains the most common arboviral disease globally, but the public health implications of different dengue clinical manifestations and the insufficiency of public health infrastructure are not well understood. Accounting for these factors provides valuable insights for the effective management of the disease. This study develops a novel mathematical model for dengue fever that incorporates various clinical manifestations, constraints imposed by limited medical resources, and preventive control strategies. We computed the basic reproduction number and examined its correlation with model parameters. Dynamical analysis revealed that the model exhibits a backward bifurcation. Using numerical techniques, we investigated the influence of varying control strategies, modeled as both time-dependent and non-time-dependent functions, on epidemic dynamics. In both scenarios, we identified threshold levels of intervention and the timelines required for disease extinction. These findings underscore the complexity of dengue dynamics and highlight the necessity of tailored intervention approaches for effective disease management.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102733"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361611","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":"Multi-city modeling of epidemics using a topology-based SIR model: Neural network-enhanced SAIRD model","authors":"Achraf Zinihi ,&nbsp;Moulay Rchid Sidi Ammi ,&nbsp;Ahmed Bachir","doi":"10.1016/j.jocs.2025.102721","DOIUrl":"10.1016/j.jocs.2025.102721","url":null,"abstract":"<div><div>This paper presents a computationally efficient hybrid approach for multi-city epidemic modeling, utilizing a topology-based SIR model for individual cities coupled via empirical transportation networks to account for migration between them. Within each city, the epidemiological dynamics are described using an SAIRD model. This study introduces two key innovations: the self-consistent determination of coupling parameters to maintain the populations of individual cities, and the incorporation of distance-dependent temporal delays in migration. Our model is applied to China’s 3 populated cities. The results demonstrate the model’s effectiveness in capturing the complex dynamics of epidemic spread across multiple urban centers.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102721"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265396","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":"A cellular automaton towards structural balance—Long cycles of link dynamics","authors":"Malgorzata J. Krawczyk,&nbsp;Krzysztof Kułakowski","doi":"10.1016/j.jocs.2025.102712","DOIUrl":"10.1016/j.jocs.2025.102712","url":null,"abstract":"<div><div>A cellular automaton is defined on a line graph of a fully connected network. The automaton rule drives the system to a structural balance in most cases. Here, we investigate cycles with special symmetries, the so-called ’perfect cycles’ Burda et al. (2022). Two new characteristics of the cycles are investigated, as potential markers of perfect cycles: an equivalence of sets of states attained after external damage of links, and the homogeneity of the distribution of phase shifts between local trajectories. Only the second characteristic works as a criterion of the perfectness of the cycles. The results can be useful for generating pseudorandom numbers.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102712"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988917","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":"Wave phenomena in a wavelength-specific reflector for the Kundu–Mukherjee–Naskar system in optics and photonics","authors":"Ozlem Kirci ,&nbsp;Yusuf Pandir","doi":"10.1016/j.jocs.2025.102725","DOIUrl":"10.1016/j.jocs.2025.102725","url":null,"abstract":"<div><div>In this research, the coupled variation of the (2+1)-dimensional Kundu–Mukherjee–Naskar (KMN) equation, which governs the wave dynamics in fiber Bragg grating (FBG), is analyzed. This version models the interaction between two nonlinear waves, while the single mode of this equation characterizes nonlinear wave propagation in a single channel or medium where only one wave is considered. To find analytical solutions, the new version trial equation method (NVTEM) is regarded due to its wide range of solution structures. Analytic wave solutions are not just mathematical constructs but also help reveal the underlying physical mechanisms. Motivated by this, the present work derives and analyzes a variety of exact wave solutions to the coupled KMN equation, such as rogue-like soliton, double-peaked bound state, high-order rogue waves, and bright-lump solution supported by symbolic computation to ensure their validity. The KMN system is first converted to a nonlinear ordinary differential equation (NLODE) via the complex wave transform. Applying the proposed technique, rational, exponential, hyperbolic, and Jacobi elliptic type solutions have been acquired. The two and three-dimensional plots have been utilized to depict the dynamics of our constructed findings and to establish the abundance of the proposed analytical technique as well. Besides, some physical implications may be mentioned through interesting aspects in our findings.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102725"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265401","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":"A scalable composite Bayesian optimization framework for engineering design using deep learning reduced-order models","authors":"Abhijnan Dikshit,&nbsp;Leifur Leifsson","doi":"10.1016/j.jocs.2025.102722","DOIUrl":"10.1016/j.jocs.2025.102722","url":null,"abstract":"<div><div>Composite Bayesian optimization (CBO) methods are attractive methods for black-box optimization problems. Though CBO methods offer significant benefits, extending CBO to high-dimensional input and output spaces has been less explored. The limited scalability and accuracy of multi-output Gaussian process (GP) models makes them less attractive for engineering design problems. Standard neural network-based models provide an alternative, but require the implementation of expensive and complex uncertainty quantification methods to enable CBO. As such, this paper develops Bayesian optimization using non-intrusive reduced-order models (ROMBO), a framework for high-dimensional CBO using deep learning reduced-order models. The framework utilizes autoencoders to create a nonlinear embedding of the output space that is modeled using a multi-task GP model. A Monte Carlo expected improvement acquisition function is used to balance exploration of the design space and exploitation of the composite objective function. The proposed framework is characterized using three synthetic problems and an inverse design problem for a transonic airfoil. It is compared with a standard BO implementation and a CBO implementation that generates an embedding of the outputs using proper orthogonal decomposition (POD). The results demonstrate that the ROMBO framework can achieve up to one to four orders of magnitude lower objective function values as compared to the other two methods. Additionally, ROMBO is more sample efficient than the other two methods, achieving far lower objective function values in fewer sampling iterations. This work demonstrates that ROMBO is a promising framework for enabling the use of CBO for complex high-dimensional design problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102722"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265402","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":"SDEGCN: Syntactic dependency enhanced and integrated graph convolutional network for aspect-based sentiment analysis","authors":"Bo He,&nbsp;Hongqian Zhang,&nbsp;Ruoyu Zhao","doi":"10.1016/j.jocs.2025.102732","DOIUrl":"10.1016/j.jocs.2025.102732","url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) aims to identify the sentiment polarity of specific aspects within a sentence. Existing graph convolutional network (GCN) approaches often suffer from insufficient modeling of dependency relations on specific aspects and the shallow integration of syntactic information. To address these issues, this paper proposes a syntactic dependency enhanced and integrated graph convolutional network (SDEGCN), which aims to effectively mine syntactic dependency relations and deeply integrate them into the model. Firstly, in the syntactic dependency enhancement layer, the dependency location-aware algorithm highlights the core syntactic roles of aspect terms and their relevant context, while the syntactic consistency constraint guide the syntactic graph convolutional network to learn more effective representations. Then, the semantic encoding layer calculates attention scores through self-attention mechanisms to optimize the adjacency matrix input of the graph convolutional network, thereby capturing semantically relevant features of sentences. Finally, the feature fusion layer employs a biaffine attention transformation mechanism to fuse syntactic and semantic features, and after pooling and concatenation aggregation, the classification is completed. Extensive experimental results on five benchmark datasets demonstrate that the SDEGCN significantly outperforms existing graph convolutional baseline models, proving its effectiveness in ABSA tasks.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102732"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362164","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":"Private linear equation solving: An application to federated learning and extreme learning machines","authors":"Daniel Heinlein,&nbsp;Anton Akusok,&nbsp;Kaj-Mikael Björk,&nbsp;Leonardo Espinosa-Leal","doi":"10.1016/j.jocs.2025.102693","DOIUrl":"10.1016/j.jocs.2025.102693","url":null,"abstract":"<div><div>In federated learning, multiple devices compute each a part of a common machine learning model using their own private data. These partial models (or their parameters) are then exchanged in a central server that builds an aggregated model. This sharing process may leak information about the data used to train them. This problem intensifies as the machine learning model becomes simpler, indicating a higher risk for single-hidden-layer feedforward neural networks, such as extreme learning machines. In this paper, we establish a mechanism to disguise the input data to a system of linear equations while guaranteeing that the modifications do not alter the solutions, and propose two possible approaches to apply these techniques to federated learning. Our findings show that extreme learning machines can be used in federated learning with an extra security layer, making them attractive in learning schemes with limited computational resources.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102693"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908410","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":"Efficient numerical simulation of variable-order fractional diffusion processes with a memory kernel","authors":"Sabita Bera ,&nbsp;Mausumi Sen ,&nbsp;Sujit Nath","doi":"10.1016/j.jocs.2025.102705","DOIUrl":"10.1016/j.jocs.2025.102705","url":null,"abstract":"<div><div>Diffusion equations are fundamental in modeling the transport of heat, mass, or contaminants in porous media. However, classical models often fail to capture the anomalous diffusion behavior inherent in heterogeneous and memory-dependent materials. To address this, we investigate a fractional diffusion integro-differential equation involving variable-order derivatives in both time and space, subject to suitable conditions. The solutions are shown to exist and be unique through the rigorous application of fixed-point theorems. A finite difference-based numerical scheme is formulated to handle the variable-order fractional operators and convolution-type integral terms efficiently. Stability analysis confirms the accuracy and robustness of the method. In addition, approximate solutions are computed for three representative cases:(i) constant-order fractional diffusion (<span><math><mrow><mi>α</mi><mo>=</mo><mtext>constant</mtext></mrow></math></span>), (ii) time-dependent order <span><math><mrow><mi>α</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>, and (iii) fully variable-order <span><math><mrow><mi>α</mi><mrow><mo>(</mo><mi>x</mi><mo>,</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>. By incorporating variable order dynamics and integro-differential structures, this work extends conventional models and provides a unified framework for simulating complex transport processes in porous media.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102705"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095809","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":"CKDTA: A chemical knowledge-enhanced framework for drug–target affinity prediction","authors":"Xingran Zhao ,&nbsp;Yanbu Guo ,&nbsp;Bingyi Wang ,&nbsp;Weihua Li","doi":"10.1016/j.jocs.2025.102706","DOIUrl":"10.1016/j.jocs.2025.102706","url":null,"abstract":"<div><div>Accurate drug–target affinity (DTA) prediction is a cornerstone of efficient drug discovery, as it directly accelerates the screening of potential therapeutic candidates, reduces the cost of preclinical experiments, and shortens the development cycle of new drugs. However, existing deep learning-based methods face two main challenges: (I) Purely data-driven approaches struggle to capture the functional semantics of molecules, such as the role of specific functional regions and chemical element properties in binding interactions, due to the lack of integration with chemical prior knowledge, leading to unreliable predictions; (II) the integration of topological structure from graphs and long-range dependencies from sequences is insufficient, often failing to capture complementary features, limiting the model’s generalization ability, especially for novel drugs or targets commonly encountered in early drug discovery . To address these issues, we propose <strong>CKDTA</strong>, a <strong>C</strong>hemical <strong>K</strong>nowledge Enhanced framework for <strong>D</strong>rug-<strong>T</strong>arget <strong>A</strong>ffinity prediction. Our framework introduces two key innovations: (1) a chemical knowledge-enhanced molecular modeling approach, which constructs a multi-layer molecular graph incorporating atom-level features, chemical element information, and functional regions, enabling the capture of functional semantics through a hierarchical attention mechanism, while leveraging chemical prior knowledge; (2) a co-attention module designed to optimize sequence interaction information by leveraging graph-based interaction data, compensating for the lack of spatial structural information in sequence data. This module fully exploits the topological structure of graphs and the long-range dependencies in sequences, capturing complementary features. Extensive experiments on benchmark datasets demonstrate that CKDTA outperforms state-of-the-art methods. Furthermore, cold-start experiments validate its generalizability, highlighting its potential for drug discovery applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102706"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095812","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}