Journal of Computational Science最新文献

{"title":"Hybrid nutcracker optimization algorithm for multi-objective energy scheduling in grid-connected microgrid systems","authors":"Yiwei Liu,&nbsp;Yinggan Tang,&nbsp;Changchun Hua","doi":"10.1016/j.jocs.2025.102716","DOIUrl":"10.1016/j.jocs.2025.102716","url":null,"abstract":"<div><div>The growing demand for clean and sustainable energy has driven rapid advancements in hybrid microgrid systems to mitigate climate change and environmental degradation. This paper proposes a novel multi-objective scheduling framework for hybrid microgrids aimed at minimizing operational costs while maximizing environmental benefits. To efficiently solve this complex optimization problem, we introduce a Hybrid Nutcracker Optimization Algorithm (HNOA), which combines the recently developed Nutcracker Optimization Algorithm (NOA) with the Bat Algorithm (BAT). This hybridization enhances NOA’s exploration–exploitation balance and search capability, as demonstrated by rigorous validation on 12 benchmark functions. HNOA achieves superior accuracy and computational efficiency compared to several state-of-the-art metaheuristics. The proposed HNOA is then applied to solve the scheduling of a grid-connected hybrid microgrid under various scenarios to evaluate its performance. Simulation results indicate that the optimal microgrid configuration, consisting of PV/WT/turbine/diesel/battery, achieves an investment cost of 80,789.02 yuan. The findings of this study offer valuable insights for advancing renewable energy integration and promoting environmental sustainability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102716"},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049294","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":"2025-09-02","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":"A decomposition based imputation algorithm for long consecutive missing atmospheric pollution data and its application","authors":"Xinyi Wei ,&nbsp;Hao Meng ,&nbsp;Lizhen Shao ,&nbsp;Dongmei Fu ,&nbsp;Lingwei Ma ,&nbsp;Dawei Zhang","doi":"10.1016/j.jocs.2025.102697","DOIUrl":"10.1016/j.jocs.2025.102697","url":null,"abstract":"<div><div>With the intensification of environmental air pollution, the impact of air pollutants on both the ecological environment and human health has attracted widespread attention. However, due to the relatively late introduction of environmental monitoring systems, there were long consecutive missing values in early pollutant data. In this paper, we propose a decomposition-based imputation method for long consecutive missing pollution data. Firstly, wavelet coherence analysis is employed to investigate the periodic relationship between the pollution data and the relevant air data, decomposing them into periodic and non-periodic components. Then, machine learning and transfer learning are used to impute the periodic and non-periodic components, respectively. Furthermore, the effectiveness of the method is validated on artificially missing <span><math><msub><mrow><mi>NO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>SO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> concentration data from five regions of China. Comparison results show that the proposed method significantly outperforms some other imputation methods in the literature in terms of both mean absolute error and mean absolute percentage error. Finally, the proposed imputation method is applied in the study of accelerated aging of polycarbonate materials. Experimental results show that the predictive accuracy of the aging model is improved when using the imputed pollutant data.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102697"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926023","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":"2025-08-26","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":"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":"2025-08-22","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":"Tuning sensitivity of black phosphorene surface doped SnS, SnSe, GeS, and GeSe quantum dots toward water molecule and other small toxic molecules","authors":"Mamori Habiba ,&nbsp;Moatassim Hajar ,&nbsp;El Kenz Abdallah ,&nbsp;Benyoussef Abdelilah ,&nbsp;Taleb Abdelhafed ,&nbsp;Abdel Ghafour El Hachimi ,&nbsp;Zaari Halima","doi":"10.1016/j.jocs.2025.102707","DOIUrl":"10.1016/j.jocs.2025.102707","url":null,"abstract":"<div><div>In this work, Density Functional Theory (DFT) was employed to investigate the impact of SnS, GeS, SnSe, and GeSe quantum dots doped black phosphorene on the sensitivity of black phosphorene toward various adsorbed gas molecules namely NO₂ and H₂S. The interaction of H₂O molecule with doped black phosphorene surface is also investigated to evaluate the impact of humidity on the sensing response. The results revealed the large electronic changes in bands distribution upon exposure to the selected gas molecules, giving rise to a variation in the electronic band nature from hole to electron doping which can promote the electrical conductivity and the sensing properties of the doped phosphorene structures.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102707"},"PeriodicalIF":3.7,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889883","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":"Making hierarchically aware decisions on short findings for automatic summarisation","authors":"Emrah Inan","doi":"10.1016/j.jocs.2025.102692","DOIUrl":"10.1016/j.jocs.2025.102692","url":null,"abstract":"<div><div>An impression in a typical radiology report emphasises critical information by providing a conclusion and reasoning based on the findings. However, the findings and impression sections of these reports generally contain brief texts, as they highlight crucial observations derived from the clinical radiograph. In this scenario, abstractive summarisation models often experience a degradation in performance when generating short impressions. To address this challenge in the summarisation task, our work proposes a method that combines well-known fine-tuned text classification and abstractive summarisation language models. Since fine-tuning a language model requires an extensive, well-defined training dataset and is a time-consuming task dependent on high GPU resources, we employ prompt engineering, which uses prompt templates to programme language models and improve their performance. Our method first predicts whether the given findings text is normal or abnormal by leveraging a fine-tuned language model. Then, we apply a radiology-specific BART model to generate the summary for abnormal findings. In the zero-shot setting, our method achieves remarkable results compared to existing approaches on a real-world dataset. In particular, our method achieves scores of 37.43 for ROUGE-1, 21.72 for ROUGE-2, and 35.52 for ROUGE-L.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102692"},"PeriodicalIF":3.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852163","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":"2025-08-15","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":"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":"2025-08-13","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":"Variational Bayes for analysis of masked data","authors":"Himanshu Rai ,&nbsp;Sanjeev K. Tomer","doi":"10.1016/j.jocs.2025.102690","DOIUrl":"10.1016/j.jocs.2025.102690","url":null,"abstract":"<div><div>Bayesian competing risks analysis in presence of masked data often leads to an intractable posterior, for which Markov chain Monte Carlo (MCMC) methods are frequently utilized to evaluate various estimators of interest. However, while analyzing several risks simultaneously, MCMC methods may consume substantial amount of computation time. This paper introduces Variational Bayes, a machine learning technique, as an efficient alternative to MCMC for Bayesian analysis of competing risk data. Variational Bayes demonstrates faster convergence than MCMC, particularly in the context of extensive competing risk datasets. We compare the performance of variational Bayes over Gibbs sampling with respect to the number of considered risks through a simulation study. Additionally, we apply the two methods to analyze a real data set of computer hard drives.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102690"},"PeriodicalIF":3.7,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842213","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}