Journal of Computational Science最新文献

{"title":"Hybridized iterative scheme for solving non-linear collisional-breakage equation","authors":"Shweta ,&nbsp;Saddam Hussain ,&nbsp;Rajesh Kumar","doi":"10.1016/j.jocs.2025.102731","DOIUrl":"10.1016/j.jocs.2025.102731","url":null,"abstract":"<div><div>The non-linear collision induced fragmentation plays a crucial role in modeling several engineering and physical problems. In contrast to linear breakage, it has not been thoroughly investigated in the existing literature. This study introduces an innovative iterative method that leverages the Elzaki integral transform as a preparatory step to enhance the accuracy and convergence of adomian decomposition, used alongside the projected differential transform method to obtain closed-form or series approximations of solutions for the collisional breakage equation (CBE). A significant advantages of this technique is its capability to directly address both linear and nonlinear differential equations without the need for discretization or linearization. The mathematical framework is reinforced by a thorough convergence analysis, applying fixed point theory within an adequately defined Banach space. Additionally, error estimates for the approximated solutions are derived, offering more profound insights into the accuracy and dependability of the proposed method. The validity of this approach is demonstrated by comparing the obtained results with exact or finite volume approximated solutions considering several physical examples. Interestingly, the proposed algorithm yields accurate approximations for the number density functions as well as moments with fewer terms and maintains higher precision over extended time periods.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102731"},"PeriodicalIF":3.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320025","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":"Glioma classification in MRI using a hybrid deep learning framework with majority vote ensemble","authors":"Sonam Saluja ,&nbsp;Munesh Chandra Trivedi","doi":"10.1016/j.jocs.2025.102729","DOIUrl":"10.1016/j.jocs.2025.102729","url":null,"abstract":"<div><div>Glioma diagnosis remains a critical challenge, often plagued by subjectivity and inconsistent grading. This study explores the potential of deep learning to overcome these limitations, proposing a novel hybrid convolutional neural network (CNN) approach in classifying low-grade (LGG) and high-grade (HGG) tumors on T2-weighted magnetic resonance imaging (T2-W MRI) data. Five pre-trained convolutional neural networks (AlexNet, VGG-16, SqueezeNet, GoogLeNet, and ResNet-50) were fine-tuned and combined through ensemble methods: Majority Voting (MJ), Weighted Voting (WV), and Stacked Ensemble (SE). On the BraTS 2018 dataset, the ensembles demonstrated excellent performance, with the SE method achieving up to 99.35 % accuracy, 99.50 % sensitivity, 99.45 % specificity, and 99.40 % AUC. Testing on the external BraTS 2020 dataset showed strong generalization, with SE achieving 97.90 % accuracy, 98.05 % sensitivity, 97.80 % specificity, and 97.85 % AUC.The proposed ensemble techniques outperformed individual models and existing approaches, illustrating improved robustness and reliability. These findings establishes a foundation for subsequent research to explore diverse imaging sequences, segmented data analyses, and multi-institutional studies, thereby enhancing the scope and applicability of the findings in advancing automated grading systems and holding significant promise for real-world clinical use.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102729"},"PeriodicalIF":3.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320020","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":"High-level synthesis acceleration for an FPGA implementation of an optimized automatic target detection and classification algorithm for hyperspectral image analysis with Intel oneAPI","authors":"R. Macias,&nbsp;S. Bernabé,&nbsp;C. González","doi":"10.1016/j.jocs.2025.102723","DOIUrl":"10.1016/j.jocs.2025.102723","url":null,"abstract":"<div><div>For many years, hyperspectral images have been one of the most well-established technologies in remote sensing. The main difficulty in hyperspectral image analysis lies in the spectral mixing of materials within a single pixel, which hinders the identification of pure components or endmembers. This task is essential for various Earth observation applications, such as agriculture, mining, and environmental management. From the whole process, the endmember identification or target detection is usually one of the most time-consuming stages, so high-performance computing (HPC) platforms such as multicore processors, graphics processing units (GPUs) or field-programmable gate arrays (FPGAs) are necessary and essential for its exploitation in time-critical scenarios. In this article, we present a high-level synthesis (HLS) acceleration for an FPGA implementation of the automatic target detection and classification algorithm (ATDCA) using the Gram–Schmidt (GS) method for hyperspectral images with the Intel oneAPI Toolkit and DPC++ instead of traditional hardware description languages (HDL). Optimization strategies were applied in terms of parallelism and efficiency in the use of hardware resources on a Stratix 10 SX 2800 FPGA, resulting in a significant performance improvement. Experimental results showed that the optimized implementation through HLS achieved a significant reduction in processing times, demonstrating that the use of optimization techniques for FPGA platforms, combined with the DPC++ environment, provides an effective and flexible solution for spectral unmixing of hyperspectral images.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102723"},"PeriodicalIF":3.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320024","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":"2025-10-09","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":"Detecting cardinal nodes in unweighted complex networks by examining their trajectories within Krylov subspace and various topological features","authors":"Ramraj Thirupathyraj","doi":"10.1016/j.jocs.2025.102713","DOIUrl":"10.1016/j.jocs.2025.102713","url":null,"abstract":"<div><div>Network Science, delving into complex networks with intricate topologies and structural interactions, plays a pivotal role in understanding various natural systems. Computational studies highlight the importance of influential nodes in capturing network characteristics and functionalities. Previous research underscores the inadequacy of relying on a single node characteristic to identify influence, emphasizing the need for integrating multiple characteristics. In this study, we propose an indicator by incorporating the network’s topological features into the Krylov subspace to effectively capture influence propagation among nodes and their neighbors. This new indicator, in an asymmetric form, considers distinct node influence effects and inherent dynamics asymmetry. Furthermore, when integrated with other locality-based measures, it enhances the cohesion of a unified model. This model is employed to identify influential nodes within complex networks. Empirical evaluations of Susceptible–Infected–Recovered (SIR) propagation dynamics across ten authentic networks demonstrate that our proposed unified model operates within polynomial time and surpasses numerous traditional methods in terms of accuracy. Utilizing this approach to identify influential nodes offers potential applications across a range of domains, such as social networks, malware analysis, and neuro-perception networks.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102713"},"PeriodicalIF":3.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265400","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":"Machine learning tree trimming for faster Markov reward game solutions","authors":"Burhaneddin İzgi ,&nbsp;Murat Özkaya ,&nbsp;Nazım Kemal Üre ,&nbsp;Matjaž Perc","doi":"10.1016/j.jocs.2025.102726","DOIUrl":"10.1016/j.jocs.2025.102726","url":null,"abstract":"<div><div>Existing methodologies for solving Markov reward games mostly rely on state–action frameworks and iterative algorithms to address these challenges. However, these approaches often impose significant computational burdens, particularly when applied to large-scale games, due to their inherent complexity and the need for extensive iterative calculations. In this paper, we propose a new neural network architecture for solving Markov reward games in the form of a decision tree with relatively large state and action sets, such as 2-actions-3-stages, 3-actions-3-stages, and 4-actions-3-stages, by trimming the decision tree. In this context, we generate datasets of Markov reward games with sizes ranging from <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> to <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> using the holistic matrix norm-based solution method and obtain the necessary components, such as the payoff matrices and the corresponding solutions of the games, for training the neural network. We then propose a vectorization process to prepare the outcomes of the matrix norm-based solution method and adapt them for training the proposed neural network. The neural network is trained using both the vectorized payoff and transition matrices as input, and the prediction system generates the optimal strategy set as output. In the model, we approach the problem as a classification task by labeling the optimal and non-optimal branches of the decision tree with ones and zeros, respectively, to identify the most rewarding paths of each game. As a result, we propose a novel neural network architecture for solving Markov reward games in real time, enhancing its practicality for real-world applications. The results reveal that the system efficiently predicts the optimal paths for each decision tree, with f1-scores slightly greater than 0.99, 0.99, and 0.97 for Markov reward games with 2-actions-3-stages, 3-actions-3-stages, and 4-actions-3-stages, respectively.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102726"},"PeriodicalIF":3.7,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265399","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 portfolio selection through preference aggregation with Quicksort and the Bradley–Terry model","authors":"Yurun Ge ,&nbsp;Lucas Böttcher ,&nbsp;Tom Chou ,&nbsp;Maria R. D’Orsogna","doi":"10.1016/j.jocs.2025.102728","DOIUrl":"10.1016/j.jocs.2025.102728","url":null,"abstract":"<div><div>Allocating limited resources to a set of alternatives with uncertain long-term benefits is a common challenge in innovation management, research funding, and participatory budgeting. Related problems arise in emerging applications such as ranking outputs of large language models and coordinating decisions in agentic systems. All settings include multiple agents tasked with estimating the true value of a potentially large number of alternatives. These estimates, or quantities derived from them, are then aggregated to select a final portfolio that maximizes overall benefit, ideally using efficient methods. Standard sorting algorithms are ill-suited as they do not account for uncertainties associated with each agent’s estimate. Furthermore, the cognitive load on agents can be demanding, especially if the number of alternatives to evaluate is large. Building on the Quicksort algorithm and the Bradley–Terry model, we develop four new, efficient aggregation protocols based on agent-assigned win probabilities of pairwise comparisons that are then globally aggregated. The pairwise comparisons we introduce not only reduce cognitive load on agents, but lead to aggregation protocols that outperform existing ones, which we confirm via numerical simulations. Our methods can be combined with sampling strategies to further reduce the number of pairwise comparisons.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102728"},"PeriodicalIF":3.7,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265398","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":"2025-10-04","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 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":"2025-10-04","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":"Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm","authors":"Ikram Ul Haq ,&nbsp;Saira Shukat ,&nbsp;Ikram Ullah ,&nbsp;Waqar Ul Hassan ,&nbsp;Hong-Na Zhang ,&nbsp;Xiao-Bin Li ,&nbsp;Feng-Chen Li","doi":"10.1016/j.jocs.2025.102727","DOIUrl":"10.1016/j.jocs.2025.102727","url":null,"abstract":"<div><div>The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102727"},"PeriodicalIF":3.7,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265397","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}