Journal of Computational Science最新文献

{"title":"Sensitivity analysis of high-dimensional models with correlated inputs","authors":"Juraj Kardoš ,&nbsp;Wouter Edeling ,&nbsp;Diana Suleimenova ,&nbsp;Derek Groen ,&nbsp;Olaf Schenk","doi":"10.1016/j.jocs.2025.102681","DOIUrl":"10.1016/j.jocs.2025.102681","url":null,"abstract":"<div><div>Sensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input–output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions in the state-of-the-art sensitivity analysis methods. Consequently, the results obtained ignoring the correlations provide values which do not reflect the true contributions of the input parameters. This study proposes an approach to address the parameter correlations using a polynomial chaos expansion method and Rosenblatt and Cholesky transformations to reflect the parameter dependencies. Treatment of the correlated variables is discussed in context of variance and derivative-based sensitivity analysis. We demonstrate that the sensitivity of the correlated parameters can not only differ in magnitude, but even the sign of the derivative-based index can be inverted, thus significantly altering the model behavior compared to the prediction of the analysis disregarding the correlations. Numerous experiments are conducted using workflow automation tools within the VECMA toolkit.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102681"},"PeriodicalIF":3.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739413","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 computational analysis of traffic cluster dynamics using a percolation-based approach in urban road networks","authors":"Yongsung Kwon ,&nbsp;Minjin Lee ,&nbsp;Mi Jin Lee ,&nbsp;Seung-Woo Son","doi":"10.1016/j.jocs.2025.102675","DOIUrl":"10.1016/j.jocs.2025.102675","url":null,"abstract":"<div><div>Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102675"},"PeriodicalIF":3.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711667","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 structural feature-based approach for comprehensive graph classification","authors":"Saiful Islam ,&nbsp;Md. Nahid Hasan ,&nbsp;Pitambar Khanra","doi":"10.1016/j.jocs.2025.102679","DOIUrl":"10.1016/j.jocs.2025.102679","url":null,"abstract":"<div><div>The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity often hinders practical implementation. In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties. We demonstrate that these features, despite their simplicity, are powerful enough to capture the intrinsic characteristics of graphs within the same class. We explore the efficacy of our approach using three distinct machine learning methods, highlighting how our feature-based classification leverages the inherent structural similarities of graphs within the same class to achieve accurate classification. A key advantage of our approach is its simplicity, which makes it accessible and adaptable to a broad range of applications, including social network analysis, bioinformatics, and cybersecurity. Furthermore, we conduct extensive experiments to validate the performance of our method, showing that it not only reveals a competitive performance but in some cases surpasses the accuracy of more complex, state-of-the-art techniques. Our findings suggest that a focus on fundamental graph features can provide a robust and efficient alternative for graph classification, offering significant potential for both research and practical applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102679"},"PeriodicalIF":3.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779729","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":"Mathematical modeling of smoking addiction control: Impact of treatment, news, and media campaigns","authors":"Abu Safyan Ali ,&nbsp;Muhammad Awais ,&nbsp;Shumaila Javeed","doi":"10.1016/j.jocs.2025.102677","DOIUrl":"10.1016/j.jocs.2025.102677","url":null,"abstract":"<div><div>Smoking dynamics created a global health crisis with major socioeconomic repercussions. It presents one of the most pressing issues the world has encountered for decades, affecting the social fabric, economy, and health globally. Sufficient treatment plans paired with significant coverage on radio, in print media, and social media as information sources may cause people to become more aware of the risks caused by smoking due to which individuals change their behavior and attitude toward smoking dynamics. In this study, we propose novel deterministic models for analyzing and controlling smoking dynamics. The model classifies the total population into five distinct sub-populations. Initially, we implement treatment for smokers, then the impact of media coverage of smokers on a daily basis along with proper treatment of smokers applies, and last one is about the combined effectiveness of TV, Radio, and all social media platforms (SMP) advertisement and treatment to addicted smokers. The disease-free equilibrium (DFE) and endemic equilibrium (EEP) states of proposed model one are qualitatively formulated, with stability analyses indicating local stability of DFE when <span><math><mi>R</mi></math></span> ₀ <span><math><mrow><mo>&lt;</mo><mn>1</mn></mrow></math></span> and of EEP when <span><math><mi>R</mi></math></span> ₀ <span><math><mrow><mo>&gt;</mo><mn>1</mn></mrow></math></span>. Global stability of the steady states is further examined using the Lyapunov function and Castillo-Chavez theorems. Sensitivity analysis of models is evaluated through the Normalized Sensitivity Index and Partial Rank Correlation Coefficient (PRCC) techniques. Furthermore, numerical simulations are used to verify the theoretical predictions of the proposed deterministic models. The simulation results suggest that targeted media coverage across different sources, including conventional and social media, together with competent medical care by treatment, may successfully lower the incidence of smoking. Through the use of awareness campaigns and advertising slogans, we can greatly increase public knowledge and eventually encourage quitting smoking.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102677"},"PeriodicalIF":3.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722461","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":"Artificial neural network discrete-time biomass controller for a continuous stirred tank reactor","authors":"Hale Hapoglu ,&nbsp;Egemen Ander Balas ,&nbsp;Semin Altuntaş","doi":"10.1016/j.jocs.2025.102688","DOIUrl":"10.1016/j.jocs.2025.102688","url":null,"abstract":"<div><div>The employment of stirred tank reactors in the field of treatment technology is well-established. In this regard, a bioreactor model is commonly utilized for conducting simulations, identifying parameters, and developing control applications. Control of biomass concentration is independent of scale through manipulation of the dilution rate. To enable discrete-time control, an equivalent model incorporating a zero-order hold element and a 0.1-h sampling time has been formulated for controlling biomass concentration. In this study, the various well-known controllers performed effectively to track set points. Further, to mitigate the effects of load disturbances, the generalized predictive controller, the proportional integral derivative controller, and the controllers designed based on pole placement have been employed to obtain process control responses. The performance of these controllers has been evaluated through a weighted aggregate sum product assessment technique that employs an analytical hierarchy process. Due to the significant nonlinearity present in the closed loop bioprocess with substrate inhibition, the feedforward artificial neural network controller is trained using a closed-loop dataset, and its performances are compared with the conventional controllers. The controller has demonstrated its ability to manage realistic feed fluctuations without risking upset to the culture. The biomass concentration showed only minor deviations, settling swiftly back to the desired value by smoothly adjusting the dilution rate. This controller with tansig and purelin functions overcomes nonlinearities and time delays better than conventional controllers. The results suggest that the artificial neural network controller offers the desired simplicity and effectiveness for industrial applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102688"},"PeriodicalIF":3.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711668","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":"Integrating fuzzy rough set-based entropies for identifying drug-resistant miRNAs in cancer","authors":"Joginder Singh ,&nbsp;Shubhra Sankar Ray","doi":"10.1016/j.jocs.2025.102673","DOIUrl":"10.1016/j.jocs.2025.102673","url":null,"abstract":"<div><div>MicroRNAs (miRNAs) are key biomarkers in cancer diagnosis and treatment. Identification of drug-resistant miRNAs may help in effective treatment of cancer. Two new z score based fuzzy rough relevance and redundancy entropies are developed and then a weighted framework is introduced to integrate the entropies for ranking and selecting miRNAs in classifying control and drug resistant patients. Here, two key components of soft computing, fuzzy set and rough set are utilized. The methodology is called a weighted framework for integrating fuzzy rough set-based relevance and redundancy entropies (WFIFRRRE). The z score is used to compute the fuzzy membership of expression values required for both entropies. Fuzziness deals with the overlapping nature of miRNA expression profiles and rough set helps in determining the exact class size. The weights in WFIFRRRE, assigned to relevance and redundancy entropies, are determined in a supervised manner to maximize the <span><math><mi>F</mi></math></span> score used for validating the classification performance in discriminating the control and drug-resistant patients. The weights are varied from 0 to 1 in steps of 0.01 which enables an integration between relevance and redundancy entropies. A subset of miRNAs is selected from the ranked list and the performance is evaluated using three benchmark classifiers on eight drug-resistant cancer datasets. Experimental results show that WFIFRRRE provides better prediction accuracy than the popular methods used for comparison. The classification accuracy in terms of <span><math><mi>F</mi></math></span> score, achieved by WFIFRRRE, ranges from 0.74 to 1.0, 0.75 to 1.0, and 0.73 to 1.0 using random forest, Naive Bayes, and linear SVM classifiers, respectively. The resultant set of miRNAs obtained using WFIFRRRE is also verified with the help of existing biological studies. The source code of WFIFRRRE is available at <span><span>https://www.isical.ac.in/ shubhra/WFIFRRRE.html</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102673"},"PeriodicalIF":3.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724960","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":"Corrosion-induced multiscale damage behavior of ultrahigh strength steel: An integrated simulation and experiment study","authors":"Weizheng Lu ,&nbsp;Zouxueyin Wang ,&nbsp;Libo Yu ,&nbsp;Shaohua Xing ,&nbsp;Andong Wang ,&nbsp;Yong Zhang ,&nbsp;Jia Li ,&nbsp;Qihong Fang","doi":"10.1016/j.jocs.2025.102676","DOIUrl":"10.1016/j.jocs.2025.102676","url":null,"abstract":"<div><div>Corrosion is an aggravating problem to cause the premature failure of structure materials, ultimately impacting the safety and operational expenses of equipment. However, the corrosion-induced multiscale damage evolution in the ultrahigh-strength steel is not clearly revealed from atomic scale to macroscopic scale. Here, corrosion-induced multiscale damage mechanism of ultrahigh strength steel plate is investigated using the experiments combined with multiscale simulation, including molecular dynamic simulation, cellular automaton simulation, and phase field finite element method. The experiment shows that the high angle grain boundaries are particularly vulnerable to corrosion, grain refinement takes place during the process of corrosion, and the exposed surface displays significant cracks in the surface of plate. From molecular dynamic simulation, the thickness of the passivation film and the corrosion rate go up with the increasing temperature, which accelerates the early passivation. The corrosion-induced cracks promote the local healing of surface roughness, leading to low strain softening at the nanoscale. By cellular automaton simulation, the passivation film, formed by the corrosion products, serves to hinder the anodic dissolution of the matrix, thereby reducing the average depth of the corrosion pits. Through phase field finite element simulation, the concentration of local strain plays a crucial role in accelerating the rupture rate of the passive film and increasing the corrosion rate at the tip of a pit. Additionally, strong local strains have a significant impact on the longitudinal advancement of corrosion, leading to the progression from a corrosion pit to a crack. These findings not only give a deep understanding of the corrosion-induced cracking behavior, but also provide valuable insights for the development of steel plate with enhanced mechanical properties.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102676"},"PeriodicalIF":3.1,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632998","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":"Deep Link Strength Prediction: Leveraging line graph transformations and neural networks","authors":"Zhixin Ming ,&nbsp;Jie Li ,&nbsp;Jing Wang","doi":"10.1016/j.jocs.2025.102661","DOIUrl":"10.1016/j.jocs.2025.102661","url":null,"abstract":"<div><div>Predicting link strengths in complex networks is a fundamental challenge, crucial for understanding network dynamics and optimizing real-world applications. Traditional approaches often rely on shallow structural features, limiting their ability to model intricate dependencies. To address these limitations, we propose Deep Link Strength Prediction (DLSP), a novel framework that integrates line graph transformations with graph convolutional networks (GCNs) to enhance the predictive capability of link weight estimation. DLSP redefines the task by transforming edge-centric information into node-level representations, facilitating effective learning of complex structural patterns. DLSP follows a multi-phase approach: first, a localized subgraph around the target link is extracted and encoded using a weighted node labeling scheme, preserving local structural and attribute-driven properties. Next, the labeled subgraph undergoes a line graph transformation, mapping link dependencies into node representations, thereby enabling a structured embedding space. A GCN is then employed to extract rich hierarchical representations, capturing both micro and macro-level graph structures. Finally, these learned embeddings are passed through a dense neural network to estimate the target link strength, framing the problem as a continuous-valued regression task. Unlike existing methods that rely on handcrafted features or isolated node embeddings, DLSP explicitly models link dependencies through graph-aware transformations, leading to superior predictive performance. Extensive experiments conducted on six diverse network datasets demonstrate that DLSP consistently outperforms state-of-the-art methods, showcasing its robustness, scalability, and potential for real-world applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102661"},"PeriodicalIF":3.1,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632597","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":"Fractional order malaria epidemic model: Qualitative and computational study to determine the dynamics for sensitivity prevalence","authors":"Muhammad Farman ,&nbsp;Nezihal Gokbulut ,&nbsp;Aamir Shehzad ,&nbsp;Kottakkaran Sooppy Nisar ,&nbsp;Evren Hincal ,&nbsp;Aceng Sambas","doi":"10.1016/j.jocs.2025.102656","DOIUrl":"10.1016/j.jocs.2025.102656","url":null,"abstract":"<div><div>In this study, we created a nonlinear mathematical model with eight compartments to understand the dynamics of malaria transmission in North Cyprus region using the Caputo fractional operator. Because of their memory and genetic features, fractional-order models are regarded to be more adaptable than integer-order models. To explore the malaria compartmental model, we use the stability theory of fractional-order differential equations with the Caputo operator. A full explanation of the proposed model’s qualitative and quantitative analysis is offered, as well as a brief overview of its essential aspects and a theoretical evaluation. The Lipschitz criterion and well-known fixed point theorems are used to prove the existence and uniqueness of solutions. In addition to establishing equilibrium points, sensitivity analysis of reproductive number parameters is carried out. The proposed system has been validated in terms of Ulam–Hyers–Rassias. To deal with chaotic circumstances a linear feedback control strategy directs system dynamics near equilibrium points. To verify the existence of bifurcation, we apply bifurcation principles. The study uses numerical methodology based on Newton polynomial interpolation method to graphically model the solutions. The study analyzes system behavior by investigating parameter alterations at various fractional orders while retaining model stability. The long-term memory effect, represented by the Caputo fractional order derivative, has no influence on steady point stability, but solutions get closer to equilibrium faster at higher fractional-orders.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102656"},"PeriodicalIF":3.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579929","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":"Data classification with dynamically growing and shrinking neural networks","authors":"Szymon Świderski ,&nbsp;Agnieszka Jastrzębska","doi":"10.1016/j.jocs.2025.102660","DOIUrl":"10.1016/j.jocs.2025.102660","url":null,"abstract":"<div><div>The issue of data-driven neural network model construction is one of the core problems in the domain of Artificial Intelligence. A standard approach assumes a fixed architecture with trainable weights. A conceptually more advanced assumption is that we not only train the weights but also find out the optimal model architecture. We present a new method that realizes just that. This article is an extended version of our conference paper titled “Dynamic Growing and Shrinking of Neural Networks with Monte Carlo Tree Search (Świderski and Jastrzebska, 2024). In the paper, we show in detail how to create a neural network with a procedure that allows dynamic shrinking and growing of the model while it is being trained. The decision-making mechanism for the architectural design is governed by the Monte Carlo tree search procedure, which simulates network behavior and allows comparing several candidate architecture changes to choose the best one. The proposed method was validated using both visual and time series datasets, demonstrating its particular effectiveness in multivariate time series classification. This is attributed to the architecture’s ability to adapt dynamically, allowing independent modifications for each time series. To enhance the reproducibility of our method, we publish open-source code of the proposed method. It was prepared in Python. Experimental evaluations in visual pattern and multivariate time series classification tasks revealed highly promising performance, underscoring the method’s robustness and adaptability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102660"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564047","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}