Journal of Computational Science最新文献

{"title":"An exploration of the shift work consideration in production scheduling","authors":"Kuo-Ching Ying ,&nbsp;Pourya Pourhejazy ,&nbsp;Shih-Cheng Lin","doi":"10.1016/j.jocs.2025.102724","DOIUrl":"10.1016/j.jocs.2025.102724","url":null,"abstract":"<div><div>Scheduling problems predominantly assume that the same operators work fixed shifts during the day and night. Scheduling with a single-shift approach can result in infeasible or suboptimal production planning solutions when a multiple-shift system is implemented. This study introduces a new scheduling extension that incorporates shift work constraints. A new mathematical model based on the Permutation Flowshop Scheduling Problem is proposed, and the Iterated Greedy algorithm is adapted to solve it. The objective is to minimize the maximum completion time (makespan) and thereby improve the system performance while considering shift work constraints. Experiments reveal that the overall response time in 10-hour and 12-hour shifts is better than that of 8-hour shifts, despite the shorter overall active hours on the shop floor. Additional experiments confirm that the proposed Adjusted Iterated Greedy algorithm outperforms the Variable Neighbourhood Search algorithm in solving medium- and large-scale problems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102724"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219534","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":"Solving the dopant diffusion dynamics with physics-informed neural networks","authors":"Sungyeop Lee ,&nbsp;Jisu Ryu ,&nbsp;Young-Gu Kim ,&nbsp;Dae Sin Kim ,&nbsp;Hiroo Koshimoto ,&nbsp;Jaeshin Park","doi":"10.1016/j.jocs.2025.102695","DOIUrl":"10.1016/j.jocs.2025.102695","url":null,"abstract":"<div><div>Simulation plays a crucial role in the semiconductor chip manufacturing. In particular, process simulation is primarily used to solve the dopant diffusion dynamics, which describes the temporal evolution of doping profiles during the thermal annealing process. The diffusion dynamics constitutes a multiscale problem, formulated as a set of coupled partial differential equations (PDEs) with respect to the concentration of dopants and point defects. In this paper, we demonstrate that Physics-Informed Neural Networks (PINNs) can accurately predict not only the evolution of the doping profile, but also the unknown physical parameters, specifically the diffusivities appearing as PDE coefficients. Furthermore, we propose a physics-informed calibration method, which performs PDE-constrained optimization by leveraging a pre-trained PINN model. We experimentally verify that this post-processing significantly improves the accuracy of coefficients fine-tuning. To the best of our knowledge, this is the first demonstration of an annealing simulation for the semiconductor diffusion process using a physics-informed machine learning approach. This framework is expected to enable more efficient calibration of simulation parameters based on measurement data.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102695"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863514","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":"2025-12-01","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":"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":"2025-12-01","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":"An adaptive Hamiltonian circuit of virtual sample generation for a small dataset","authors":"Totok Sutojo ,&nbsp;Supriadi Rustad ,&nbsp;Muhamad Akrom ,&nbsp;Wahyu Aji Eko Prabowo ,&nbsp;De Rosal Ignatius Moses Setiadi ,&nbsp;Hermawan Kresno Dipojono ,&nbsp;Yoshitada Morikawa","doi":"10.1016/j.jocs.2025.102711","DOIUrl":"10.1016/j.jocs.2025.102711","url":null,"abstract":"<div><div>Small datasets often lead to poor performance of data-driven prediction models due to uneven data distribution and large data spacing. One popular approach to address this issue is to use virtual samples during machine learning (ML) model training. This study proposes a Hamiltonian Circuit Virtual Sample Generation (HCVSG) method to distribute virtual samples generated using interpolation techniques while integrating the K-Nearest Neighbors (KNN) algorithm in model development. The Hamiltonian circuit is chosen because it doesn’t depend on the distribution assumption and provides multiple circuits that allow adaptive sample distribution, allowing the selection of circuits that produce minimum errors. This method supports improving feature-target correlation, reducing the risk of overfitting, and stabilizing error values as model complexity increases. Applying this method to three datasets in material research (MLCC, PSH, and EFD) shows that HCVSG significantly improves prediction accuracy compared to conventional KNN and eight MTD-based methods. The distribution of virtual samples along the Hamiltonian circuit helps fill the information gap and makes the data distribution more even, ultimately improving the predictive model's performance.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102711"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891889","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 study on pest control models based on nonlinear threshold control","authors":"Yongfeng Li ,&nbsp;Leyan Liang ,&nbsp;Zhong Zhao","doi":"10.1016/j.jocs.2025.102694","DOIUrl":"10.1016/j.jocs.2025.102694","url":null,"abstract":"<div><div>The pest number trigger threshold strategy has been widely used in the control of pests in agricultural production. In this study, pest populations are managed by using an integrated nonlinear threshold function and a saturation function. The existence conditions of various equilibrium points and sliding sections in the system are derived. Theoretical analysis and numerical simulation results show the existence of boundary equilibrium bifurcations, tangency bifurcations and limit cycle bifurcations caused by discontinuous boundary. It is worth noting that persistence and non-smooth folding can be observed in the boundary equilibrium bifurcations. At the same time, because the nonlinear threshold control strategy is adopted in this study, the change of the sliding section of the model is more complicated. The numerical simulation results show that if there is an unstable focus in the model, a sliding homoclinic cycle will appear with the occurrence of boundary saddle point bifurcation, and then form a crossing limit cycle. The sensitivity analysis results of the system show that if the threshold level is too low, the control measures do not achieve the desired results. Too high threshold selection will cause unnecessary economic losses. Therefore, our results show that an appropriate threshold should be set to reduce economic losses while ensuring that the number of pests is in a lower stable state.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102694"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096008","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":"Helium focused ion beam damage in silicon: Physics-informed neural network modeling of helium bubble nucleation and early growth","authors":"Shupeng Gao ,&nbsp;Qi Li ,&nbsp;M.A. Gosalvez ,&nbsp;Xi Lin ,&nbsp;Yan Xing ,&nbsp;Zaifa Zhou ,&nbsp;Qianhuang Chen","doi":"10.1016/j.jocs.2025.102696","DOIUrl":"10.1016/j.jocs.2025.102696","url":null,"abstract":"<div><div>Currently, the time and cost required to obtain large datasets limit the application of data-driven machine learning in nanoscale manufacturing. Here, we focus on predicting the nanoscale damage induced by helium focused ion beams (He-FIBs) on silicon substrates. We briefly review the most relevant atomistic defects and the partial differential equations (PDEs), or rate equations, that describe the mutual creation and annihilation of the defects, eventually leading to the amorphization of the substrate and, the nucleation and early growth of helium bubbles. The novelty comes from the use of a physics-informed neural network (PINN) to simulate quantitatively the evolution of the bubbles, thus bypassing the dataset availability problem. As usual, the proposed PINN learns the underlying physics through the incorporation of the residuals of the PDEs and corresponding Initial Conditions (ICs) and Boundary Conditions (BCs) in the network’s loss function. Meanwhile, the system of PDEs poses some challenges to the PINN modeling strategy. We find that (i) hard constraints need to be imposed on the network output in order to satisfy both BCs and ICs, (ii) all the inputs and outputs of the PINN need to be cautiously normalized to ensure convergence during training, and (iii) customized weights need to be carefully applied to all the PDE loss terms in order to balance their contributions, thus improving the accuracy of the PINN predictions. Once trained, the network achieves good prediction accuracy over the entire space-time domain for various ion beam energies and doses. Comparisons are provided against previous experiments and traditional numerical simulations, which are also implemented in this study using the Finite Difference Method (FDM). While the L2 relative errors for all collocated points remain below 10%, the accuracy of the PINN decreases at lower beam energies and larger ion doses, due to the presence of higher numerical gradients.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102696"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863513","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":"2025-12-01","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":"2025-12-01","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}