Journal of Computational Science最新文献

{"title":"Autonomous underwater vehicle path planning using fitness-based differential evolution algorithm","authors":"Shubham Gupta ,&nbsp;Ayush Kumar ,&nbsp;Vinay Kumar ,&nbsp;Shitu Singh ,&nbsp;Sachin ,&nbsp;Mayank Gautam","doi":"10.1016/j.jocs.2024.102498","DOIUrl":"10.1016/j.jocs.2024.102498","url":null,"abstract":"<div><div>The enhanced capabilities of autonomous underwater vehicles (AUVs) will facilitate sustainable exploration and utilization of maritime resources through improved precision in underwater mapping, resource extraction, and environmental surveillance. Enhanced navigation and communication systems will bolster the robustness and flexibility of AUVs, opening up new avenues for research and operations in demanding underwater conditions. The objective of this initiative is to optimize the performance of AUVs by developing sophisticated navigation methodologies specifically designed for complex marine environments. To achieve this goal, this paper proposes a modified structure of the well-known metaheuristic called differential evolution (DE). The proposed algorithm is denoted by a fitness-based differential evolution algorithm (FDE). Through the utilization of path planning techniques and the application of the proposed FDE to enhance navigation, this paper seeks to overcome obstacles such as underwater barriers, restricted communication, and limited visibility. These enhancements are anticipated to notably elevate the efficacy and cognitive capabilities of AUVs. The validation of the proposed FDE algorithm is conducted on nine case studies of the path planning of AUV, and the comparison is made with other metaheuristic algorithms. The comparison indicates the effectiveness of the FDE in solving the AUV path planning problem.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102498"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177700","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":"Numerical Analysis for a weakly coupled system of Singularly Perturbed Quasilinear Problem with non-smooth data","authors":"Ruby ,&nbsp;Vembu Shanthi ,&nbsp;Higinio Ramos","doi":"10.1016/j.jocs.2024.102475","DOIUrl":"10.1016/j.jocs.2024.102475","url":null,"abstract":"<div><div>This paper aims at solving a weakly coupled system of quasilinear convection diffusion equations with jump discontinuities in the convection and source terms. Due to the presence of a jump discontinuity in the convection term, the solution exhibits strong interior layers at the point of discontinuity. To approximate the solution of this problem, a hybrid difference technique is used and implemented on a Shishkin mesh. The proposed technique is proven to present an almost second order uniform convergence. To validate the theoretical results, some numerical examples are presented.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102475"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding top-r weighted k-wing communities in bipartite graphs","authors":"Jiahao He,&nbsp;Zijun Chen,&nbsp;Xue Sun,&nbsp;Wenyuan Liu","doi":"10.1016/j.jocs.2025.102530","DOIUrl":"10.1016/j.jocs.2025.102530","url":null,"abstract":"<div><div>Community search in bipartite graphs is an essential problem extensively studied, which aims at retrieving high-quality communities. And <span><math><mi>k</mi></math></span>-wing is a cohesive subgraph where butterflies (i.e., (2, 2)-biclique) are connected with each other. However, communities based on <span><math><mi>k</mi></math></span>-wing do not consider weights of edges. Motivated by this, in this paper, we investigate the problem of finding the top-<span><math><mi>r</mi></math></span> weighted <span><math><mi>k</mi></math></span>-wing communities in weighted bipartite graphs. To solve this problem, we propose two baseline algorithms, Globalsearch and Localsearch. The former tries to get results after finding all communities, while the latter aims to reduce the search space by utilizing a group of subgraphs of increasing size. Inspired by LocalSearch, we propose an offline index WNC-Index to filter out edges that are not in the results. In addition, we prove that butterfly connectivity can be transformed to bloom connectivity, thus the finding of <span><math><mi>k</mi></math></span>-wings can be accelerated by utilizing blooms. Based on this, we propose an online index BCC-Index, which can improve the key steps in our algorithms. Moreover, these two indexes can be used simultaneously to speed up the query process and reduce the space cost of BCC-Index. Finally, we have conducted extensive experiments on ten real-world datasets. The results demonstrate the efficiency and effectiveness of the proposed algorithms.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102530"},"PeriodicalIF":3.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177212","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":"Advances in directly computable multiphysics models","authors":"John G. Michopoulos,&nbsp;C. David Lewis II ,&nbsp;Jan Vandenbrande","doi":"10.1016/j.jocs.2025.102524","DOIUrl":"10.1016/j.jocs.2025.102524","url":null,"abstract":"","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"87 ","pages":"Article 102524"},"PeriodicalIF":3.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865067","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 relaxation scheme for the SIR and related compartmental models","authors":"Vo Anh Khoa ,&nbsp;Pham Minh Quan ,&nbsp;Ja’Niyah Allen ,&nbsp;Kbenesh W. Blayneh","doi":"10.1016/j.jocs.2024.102478","DOIUrl":"10.1016/j.jocs.2024.102478","url":null,"abstract":"<div><div>In this paper, we introduce a novel numerical approach for approximating the Susceptible–Infectious–Recovered (SIR) model in epidemiology. Our method enhances the existing linearization procedure by incorporating a suitable relaxation term to tackle the transcendental equation of nonlinear type. Developed within the continuous framework, our relaxation method is explicit and easy to implement, relying on a sequence of linear differential equations. This approach yields accurate approximations in both discrete and analytical forms. Through rigorous analysis, we prove that, with an appropriate choice of the relaxation parameter, our numerical scheme is non-negativity-preserving; moreover, it is strongly convergent to the true solution. We also extend the applicability of our relaxation method to handle some variations of the traditional SIR model. Finally, we present numerical examples using simulated data to demonstrate the effectiveness of our proposed method.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102478"},"PeriodicalIF":3.1,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745886","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 eXplainable machine learning framework for predicting the impact of pesticide exposure in lung cancer prognosis","authors":"Nitha V.R.,&nbsp;Vinod Chandra S.S.","doi":"10.1016/j.jocs.2024.102476","DOIUrl":"10.1016/j.jocs.2024.102476","url":null,"abstract":"<div><div>Lung cancer, the second most prevalent and lethal cancer, is caused by aberrant and uncontrolled cell division in the lungs. Once lung cancer spreads to surrounding tissues or organs, the likelihood of recovery declines; hence, early illness detection is vital. Machine learning has shown significant potential in several healthcare applications. Examining various factors and trends in the data, the machine learning model can predict lung cancer menace by pinpointing those more susceptible to the illness. Among the various causes of lung cancer, pesticide is a major contributor. ‘Pesticide’ refers to any chemical used in agriculture to manage pests like weeds and insects. Numerous health hazards, including the possibility of developing cancer, have been linked to exposure to specific pesticides. Our objective is to obtain the trust of medical professionals and patients depending on how interpretable machine learning models are in healthcare. This paper deals with implementing the proposed study by utilizing a public dataset from a Thai case study to predict the risk of lung cancer caused by pesticide exposure. Since the dataset was highly imbalanced, a hybrid normalization technique was utilized, combining the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbor (ENN). We applied a two-stage feature selection technique combined with Extra Tree Classifier and Principal Component Analysis. An eXplainable XGBoost Classifier is developed to predict lung cancer risk based on pesticide exposure. The robustness of the model is reflected in the results, with accuracy, sensitivity, and F1-Score as 99.00%, 98.87%, and 98.57%, respectively. Two public datasets were utilized to generalize the model, and the model performed well on both datasets. The model achieved accuracy, sensitivity, and F1-Score of 99.00%, 99.00%, and 99.33% on the ‘Lung Cancer Prediction’ dataset. The model is trained and tested on the ‘survey lung cancer’ dataset and obtained an accuracy, sensitivity, and F1-Score of 99.00%, 99.00%, 99.00%, respectively. The proposed model outperformed existing state-of-the-art methodologies regarding quality metrics. An illustration is done on the XAI (eXplainable Artificial Intelligence) model by utilizing SHapley Additive exPlanations (SHAP), thereby identifying the most relevant features contributing to the lung cancer menace.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102476"},"PeriodicalIF":3.1,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745801","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":"perms: Likelihood-free estimation of marginal likelihoods for binary response data in Python and R","authors":"Dennis Christensen ,&nbsp;Per August Jarval Moen","doi":"10.1016/j.jocs.2024.102467","DOIUrl":"10.1016/j.jocs.2024.102467","url":null,"abstract":"<div><div>In Bayesian statistics, the marginal likelihood (ML) is the key ingredient needed for model comparison and model averaging. Unfortunately, estimating MLs accurately is notoriously difficult, especially for models where posterior simulation is not possible. Recently, the idea of permutation counting was introduced, which provides an estimator which can accurately estimate MLs of models for exchangeable binary responses. Such data arise in a multitude of statistical problems, including binary classification, bioassay and sensitivity testing. Permutation counting is entirely likelihood-free and works for any model from which a random sample can be generated, including nonparametric models. Here we present <span>perms</span>, a package implementing permutation counting. Following optimisation efforts, <span>perms</span> is computationally efficient and can handle large data problems. It is available as both an R package and a Python library. A broad gallery of examples illustrating its usage is provided, which includes both standard parametric binary classification and novel applications of nonparametric models, such as changepoint analysis. We also cover the details of the implementation of <span>perms</span> and illustrate its computational speed via a simple simulation study.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102467"},"PeriodicalIF":3.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-the-fly mathematical formulation for estimating people flow from elevator load data in smart building virtual sensing platforms","authors":"Koichi Kondo ,&nbsp;Ryosuke Ohori ,&nbsp;Kiyotaka Matsue ,&nbsp;Hiroyuki Aizu","doi":"10.1016/j.jocs.2024.102488","DOIUrl":"10.1016/j.jocs.2024.102488","url":null,"abstract":"<div><div>This paper considers a new approach for people flow estimation in buildings from elevator trip records and corresponding load data, and the resulting model is used on the virtual sensing platform we have developed. People flow data can be used to improve elevator performance through optimal car assignments to hall calls by a group controller and are useful for estimating occupant distributions as heat loads allowing for optimized air-conditioning control to realize energy savings. Available data from an elevator controller is insufficient for exact people flow estimation and therefore this problem becomes under-defined. Our virtual sensing platform adopts equation-based modeling and optimization-based parameter estimation, which estimates application-related parameters from available sensor data, allowing for over- or under-defined situations among sensory information, but better mathematical formulation is essential for accurate parameter estimation on this virtual sensing platform. Accordingly, we propose a new method to define an elevator trip-wise mathematical formulation by modifying pre-defined base equations or defining additional equations. The key idea is that each elevator trip has different features, including sparsity, that are useful for improving accuracy and can be successfully formulated as simultaneous equations that our virtual sensing platform accepts. The procedure for defining a mathematical formulation is invoked after trip data are obtained and we refer this procedure as “on-the-fly mathematical formulation.” The formulated trip-wise equations are combined as simultaneous equations for estimating people flow over a given period on the virtual sensing platform by mathematical optimization.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102488"},"PeriodicalIF":3.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699238","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 learning aided surrogate modeling of the epidemiological models","authors":"Emel Kurul ,&nbsp;Huseyin Tunc ,&nbsp;Murat Sari ,&nbsp;Nuran Guzel","doi":"10.1016/j.jocs.2024.102470","DOIUrl":"10.1016/j.jocs.2024.102470","url":null,"abstract":"<div><div>The study of disease spread often relies on compartmental models based on nonlinear differential equations, which typically require computationally intensive numerical algorithms, especially for parameter estimation. This paper introduces a deep neural network-based surrogate modeling (DNN-SM) approach, engineered to accurately replicate the behavior of epidemiological models while significantly reducing computational demands. This approach adeptly handles the complexities inherent in nonlinear models and optimizes parameter estimation efficiency. We demonstrate the efficacy of the DNN-SM through its application to various disease models, including the Susceptible–Infected–Recovered (SIR), Susceptible–Exposed–Infected–Recovered (SEIR), and the more complex Susceptible–Exposed–Presymptomatic–Asymptomatic–Symptomatic–Reported (SEPADR) models. The results reveal that our DNN-SM not only forecasts solution trajectories with high accuracy but also operates approximately ten times faster than traditional ODE solvers for forward problems. By comparing the parameter estimation results of the DNN-SM and ODE solvers, we show that the DNN-SM produces highly accurate results with much less computational costs. The DNN-SM has been validated using both short-term and long-term COVID-19 data from several European countries. The results demonstrate that the DNN-SM provides accurate trajectories with significantly lower computational cost compared to traditional numerical methods.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102470"},"PeriodicalIF":3.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745800","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":"POD-Galerkin reduced order model coupled with neural networks to solve flow in porous media","authors":"C. Allery,&nbsp;C. Béghein,&nbsp;C. Dubot,&nbsp;F. Dubot","doi":"10.1016/j.jocs.2024.102471","DOIUrl":"10.1016/j.jocs.2024.102471","url":null,"abstract":"<div><div>This paper deals with the numerical modeling of flow around and through a porous obstacle by a reduced order model (ROM) obtained by Galerkin projection of the Navier–Stokes equations onto a Proper Orthogonal Decomposition (POD) reduced basis. In the few existing works dealing with model reduction techniques applied to flows in porous media, flows were described by Darcy’s law and the non linear Forchheimer term was neglected. This last term cannot be expressed in reduced form during the Galerkin projection phase. Indeed, at each new time step, the norm of the velocity needs to be recalculated and projected, which significantly increases the computational cost, rendering the reduced model inefficient. To overcome this difficulty, we propose to model the projected Forchheimer term with artificial neural networks. Moreover in order to build a stable ROM, the influence of unresolved modes and pressure variations are also modeled using a neural network. Instead of separately modeling each term, these terms were combined into a single term, which was modeled using the multilayer perceptron method (MLP). The validation of this approach was carried out for laminar flow past a porous obstacle in an unconfined channel. The proposed ROM coupled with MLP approach is able to accurately predict the dynamics of the flow while the standard ROM yields wrong results. Moreover, the ROM MLP method improves the prediction of flow for Reynolds numbers that are not included in the sampling and for times longer than sampling times. In the final part of the paper, the ROM MLP method was compared with purely data driven methods. It was shown that the MLP method is superior to the purely data driven methods.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"84 ","pages":"Article 102471"},"PeriodicalIF":3.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745799","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}