Journal of Computational Science最新文献

{"title":"PPEIM: A preference path-based early-stage influence accumulation model for influential nodes identification in locally dense multi-core networks","authors":"Yaofang Zhang ,&nbsp;Zibo Wang ,&nbsp;Yang Liu ,&nbsp;Ruohan Zhao ,&nbsp;Hongri Liu ,&nbsp;Bailing Wang","doi":"10.1016/j.jocs.2024.102479","DOIUrl":"10.1016/j.jocs.2024.102479","url":null,"abstract":"<div><div>The identification of influential nodes, a critical problem in the field of complex networks, has been extensively studied. However, previous research has primarily focused on maximizing terminal influence across all network structures indiscriminately, making it challenging to accurately identify influential nodes in specific structures. Moreover, overlooking the influence of the temporal dynamics of propagation significantly diminishes the benefits of identifying influential nodes. Therefore, we propose an influential nodes identification model, Preference Path-based Early-stage Influence Accumulation Model (PPEIM), tailored for typical locally dense multi-core networks. The key idea of PPEIM is to identify more influential nodes in early-stage propagation by aggregating dynamic influence propagation volumes superimposed on multiple paths. Specifically, early-stage influence performance is enhanced by sampling paths, mitigating the risk of dense influential nodes resulting from redundant relationships. Moreover, the K-shell, degree, influence distance and link direction are integrated to define connection strength between nodes to guide path selection. And the concept of influence propagation volume is introduced to accurately simulate the influence residuals and losses during the propagation process. To validate the effectiveness and superiority of PPEIM in locally dense multi-core networks, five sets of simulation experiments are conducted on seven real-world datasets. Experimental results demonstrate that PPEIM outperforms six state-of-the-art methods in overall propagation capability, early-stage influence capability, disintegration capability, node dispersion, and discrimination capability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102479"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177701","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":"Identifying pan-cancer and cancer subtype miRNAs using interpretable convolutional neural network","authors":"Joginder Singh ,&nbsp;Shubhra Sankar Ray ,&nbsp;Sukriti Roy","doi":"10.1016/j.jocs.2024.102510","DOIUrl":"10.1016/j.jocs.2024.102510","url":null,"abstract":"<div><h3>Background:</h3><div>MiRNAs are short-length (<span><math><mo>∼</mo></math></span>22nt) non-coding RNAs and are considered to be important biomarkers in pan-cancer analysis. Pan-cancer analysis is the study of finding the commonalities and differences in genetic and cellular alterations in various types of cancers. A common computational challenge in handling miRNA expression data is that it is high dimensional and complex (HDC) in nature. In this regard, convolutional neural networks are proven to be good performers due to their nature of finding patterns in complex data.</div></div><div><h3>Methodology:</h3><div>An interpretable convolutional neural network model (ICNNM) is developed for classifying miRNA expression based pan-cancer data. The ICNNM is a one dimensional model. The layers and other hyperparameters are optimized using Bayesian optimization with multivariate tree parzen estimator (BoMTPE). An interpretable approach is developed using SHapley Additive exPlanations (SHAP) values for explaining the behavior of ICNNM. This approach helps in introducing an attribution score for identifying relevant miRNAs using SHAP values. The attribution scores are assigned higher values for those miRNAs which help in the accurate prediction of tumor class of patients by utilizing the game theory concept in computing the SHAP values. The model is evaluated on 9 datasets among which 6 datasets (4 general pan cancer and two subtypes) are derived from a single TCGA pan-cancer dataset, one dataset is downloaded as Breast sub-type from TCGA, and two datasets, nasopharyngeal carcinoma and bone and soft tissue sarcoma, are downloaded from GEO as rare cancer ones.</div></div><div><h3>Results:</h3><div>The ICNNM is seen to perform better as compared to related techniques such as three variations of the CNN model, random forest RF, SVM, Gboost, XGboost, and Catboost. The performance is evaluated in terms of F1-score, discriminability power of expressions between normal and tumor classes, and biological significance of the selected miRNAs. The biological significance is established through existing literatures and online databases such as gene ontology and KEGG pathways after obtaining the target genes using miRDB database. While the performance of ICNNM in terms of F1-score varies from 0.95 to 0.99 for 4 general pan-cancer datasets, it varies from 0.91 to 0.99 for 3 subtype datasets and from 0.76 to 0.90 for rare cancer datasets. Many of the selected miRNAs are found to be the key biomarkers in various tumor classes according to existing investigations. Three miRNAs miR-503, miR-202, and miR-135a can be considered as novel predictions for cancer classes prostate and rectum, mesothelioma, and testicular germ cells, respectively, as their target genes are involved in related cancer pathways, obtained using miRDB database.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102510"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177704","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":"Species migration induced by fear triggers synchrony in two-habitat system","authors":"Shubhangi Dwivedi ,&nbsp;Bilel Elbetch ,&nbsp;Nitu Kumari","doi":"10.1016/j.jocs.2024.102512","DOIUrl":"10.1016/j.jocs.2024.102512","url":null,"abstract":"<div><div>We study the dynamics of a dual-habitat system using modified versions of the Hastings–Powell model, revealing the complex behavior within the system. In the first habitat, two predators hunt prey, with the top predator being an omnivore that hunts both the intermediate predator and the prey. In contrast, the second habitat features a solitary predator chasing prey that are protected by refugia (safe zones) and influenced by the Allee effect (a phenomenon where a prey’s growth rate decreases at low densities). In the first habitat, we incorporate the fear effect induced by the presence of both predators into the prey’s birth rate, reducing its growth. We study the system’s positivity, boundedness, dissipation, bifurcation, and extinction scenarios, alongside parameter analysis and chaos characterization. Our analysis shows that the fear of the intermediate predator causes a permanent shift in the system’s dynamics, while fear of the top predator leads to a shift from stable to chaotic dynamics. We introduce a linear migration function to couple both habitats, where species migrate from the habitat affected by fear to the one where prey growth is influenced by refugia and the Allee effect. Using singular perturbation theory and Tikhonov’s theorem, we analyze the system’s behavior as the migration rate approaches infinity. This study particularly focuses on investigating how varying migration levels across dimensions affect species synchrony between habitats. We test each possible case of the coupling dimension coefficient (CDC) to identify optimal coupling schemes for habitat sustainability. Instead of the traditional sine function, we use a cosine function to measure relative phase as a synchrony indicator. Our findings, validated through numerical simulations, suggest that ecological balance in the dual-habitat system can be achieved through single-species migration. Moreover, the presence of refugia and Allee effects supports species in attaining synchrony.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102512"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176114","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":"PDE-LDDMM meets NODEs: Introducing neural ordinary differential equation solvers in PDE-constrained Large Deformation Diffeomorphic Metric Mapping","authors":"Monica Hernandez","doi":"10.1016/j.jocs.2024.102507","DOIUrl":"10.1016/j.jocs.2024.102507","url":null,"abstract":"<div><div>Non-rigid image registration is a crucial task in various medical applications, allowing the alignment of images with complex spatial or temporal variations. This paper introduces NODEO-LDDMM and NODEO-PDE-LDDMM, two innovative deep-learning-based approaches that bridge the gap between Large Deformation Diffeomorphic Metric Mapping (LDDMM) and neural ordinary differential equations (NODEs). LDDMM and PDE-LDDMM offer mathematically well-established formulations for diffeomorphic registration, while NODEs provide the flexibility of deep-learning in the solution of the ODEs involved in both methods. Both NODEO-LDDMM and NODEO-PDE-LDDMM include the strengths of deep-learning into LDDMM, enabling a robust optimization with a good balance between accuracy and transformation smoothness in their solutions. Our proposed methods reached or outperformed their traditional counterparts and the nearly diffeomorphic deep-learning-based approaches selected as benchmarks. This work contributes to advancing non-rigid image registration techniques, with a methodology suited to overcome some of the limitations of deep-learning in medical image registration.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102507"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177703","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":"A fast method based on variable time steps for 2D nonlinear time-fractional generalized Benjamin–Bona–Mahony–Burgers equation: Error and stability analysis","authors":"Mojtaba Fardi ,&nbsp;Soheila Mohammadi ,&nbsp;Babak Azarnavid","doi":"10.1016/j.jocs.2024.102517","DOIUrl":"10.1016/j.jocs.2024.102517","url":null,"abstract":"<div><div>This paper presents the stability and convergence analysis of a hybrid scheme that combines the Galerkin-spectral method with a nonuniform time-stepping temporal discretization. The scheme is used to solve the two-dimensional nonlinear time-fractional generalized Benjamin–Bona–Mahony–Burgers equation with admissible regularities. It is widely accepted that fractional differential equations often exhibit singularity near the initial time, which makes uniform time-stepping methods unsuitable to approximate their solution. We convert the problem to an equivalent integral form and then solve it using a reliable time-stepping method with nonuniform time steps. We have developed an unconditionally stable discretization technique that approximates Riemann–Liouville fractional integrals with third-order convergence. Furthermore, we have used the spectral Galerkin method based on the Legendre polynomials for spatial discretization of the mentioned nonlinear problem in two dimensions. Also, we have proved the unconditional stability and convergence of the full-discretization scheme. Using the proposed method, we have conducted several numerical simulations which fully confirmed the theoretical results.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102517"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175503","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":"CICN: Higher-order link prediction with clustering mutual information of common neighbors","authors":"Yabing Yao ,&nbsp;Ziyu Ti ,&nbsp;Zhipeng Xu ,&nbsp;Yangyang He ,&nbsp;Zeguang Liu ,&nbsp;Wenxiang Liu ,&nbsp;Xiangzhen He ,&nbsp;Fuzhong Nian ,&nbsp;Jianxin Tang","doi":"10.1016/j.jocs.2024.102513","DOIUrl":"10.1016/j.jocs.2024.102513","url":null,"abstract":"<div><div>In complex networks, traditional link prediction focuses on pairwise interactions between node pairs to predict missing links and identify spurious interactions, which has a wide range of applications in the real world. However, with the continuous expansion of network scale, interactions within the network occur not only between pairs of nodes but also involve higher-order interactions among multiple nodes. However, traditional link prediction methods face challenges in directly predicting these higher-order structures. In this paper, we propose a novel higher-order link prediction method based on <u><strong>C</strong></u>lustering mutual <u><strong>I</strong></u>nformation of <u><strong>C</strong></u>ommon <u><strong>N</strong></u>eighbors (CICN) for the prediction of 3-cliques (triangles). CICN employs node information entropy and calculates the impact of common neighbors by integrating different-order clustering coefficients to predict 3-cliques in the network. Experiments on 9 empirical networks show that the higher-order clustering patterns of nodes can significantly improve the accuracy of predicting 3-cliques. Additionally, we investigate the stability of the proposed algorithm and the results indicate that the performance of the CICN remains favorable across training sets of varying sizes. The source codes of our method are publicly available at: <span><span>https://github.com/yabingyao/CICN4HigherOrderLinkPrediction</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102513"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177213","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":"Use of several non-Euclidean metrics to compute distances between every two points in a plane bounded convex set","authors":"Kazunori Iwata","doi":"10.1016/j.jocs.2024.102494","DOIUrl":"10.1016/j.jocs.2024.102494","url":null,"abstract":"<div><div>We consider movements between two chosen points in a plane-bounded set. The length of the movement between two points is measured by the Euclidean metric. We can then obtain the distribution of distances regarding the bounded set by repeatedly measuring the lengths of the movements. When we select two points uniformly in a bounded set, the shape of the bounded set affects the distribution. The distribution is easily computable by integral approximation and usefully remains invariant under the group of congruence transformations on the bounded set. For these reasons, some fundamental methods based on this distribution have been used in shape analysis. Thus, this distribution is important to some practical methods based on the Euclidean metric. Although some articles have been devoted to the study of non-Euclidean metrics in pattern recognition including shape analysis, only a few attempts have so far been made using the distribution of such non-Euclidean metrics. In this paper, we present the expression for the distributions with several non-Euclidean metrics using a density for the set of lines. This expression can be efficiently calculated as a sum of distributions, by generating a finite set of lines. We concentrate on a bounded convex set in the plane to define a non-Euclidean metric. In examples of such metrics on the convex set, we discuss those well-known in taxicab and projective geometries. In the experimental results using several convex sets, we visualize the distributions with the non-Euclidean metrics by plotting their graphs. Comparing the distribution based on the Euclidean metric with those based on the non-Euclidean metrics, we reveal several differences among the distributions.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102494"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177214","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":"Analytical assessment of workers’ safety concerning direct and indirect ways of getting infected by dangerous pathogen","authors":"Krzysztof Domino,&nbsp;Arkadiusz Sochan,&nbsp;Jarosław Adam Miszczak","doi":"10.1016/j.jocs.2024.102509","DOIUrl":"10.1016/j.jocs.2024.102509","url":null,"abstract":"<div><div>Developing safety policies to protect large groups of individuals working in indoor environments from disease spread is an important and challenging task. To address this issue, we investigate the scenario of workers becoming infected by a dangerous airborne pathogen in a near-real-life industrial environment. We present a simple analytical model based on observations made during the recent COVID-19 pandemic and business expectations concerning worker protection. The model can be adapted to address other epidemic or non-epidemic threats, including hazardous vapors from industrial processes. In the presented model, we consider both direct and indirect modes of infection. Direct infection occurs through direct contact with an infected individual, while indirect infection results from contact with a contaminated environment, including airborne pathogens in enclosed spaces or contaminated surfaces. Our analysis utilizes a simplified droplet/aerosol diffusion model, validated by droplet spread simulations. This model can be easily applied to new scenarios and has modest computational requirements compared to full simulations. Thus, it can be implemented within an automated protection ecosystem in an industrial setting, where rapid assessment of potential danger is required, and calculations must be performed almost in real-time. We validate general research findings on disease spread using a simple agent-based model. Based on our results, we outline a set of countermeasures for infection prevention, which could serve as the foundation for a prevention policy suited to industrial scenarios.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102509"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177705","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":"Dynamics of stage-structured ecological aquaculture management model with impulsive harvesting and nonlinearly releasing larval predators at different fixed moments","authors":"Lin Wu ,&nbsp;Jianjun Jiao ,&nbsp;Xiangjun Dai","doi":"10.1016/j.jocs.2025.102532","DOIUrl":"10.1016/j.jocs.2025.102532","url":null,"abstract":"<div><div>As a sustainable method of farming, ecological aquaculture aims to balance economic benefits and maintain biodiversity. Reasonable breeding strategies can reduce resource waste and sustain the persistent survival of populations. In this paper, we present a novel ecological aquaculture model with impulsive harvesting and nonlinear releasing larval predators at different fixed moments. The global stability of the prey-vanishing periodic solution and the permanence of system are analyzed by theory of impulsive differential equations. In addition, the existence conditions for positive periodic solutions are obtained through bifurcation theory. Finally, our findings are verified through numerical simulations.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102532"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376679","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":"Tensor ring subspace analysis method for hand movement classification from multichannel surface EMG signals","authors":"Rafał Zdunek","doi":"10.1016/j.jocs.2024.102520","DOIUrl":"10.1016/j.jocs.2024.102520","url":null,"abstract":"<div><div>Tensor ring (TR) decomposition is a linear combination of tensor train (TT) decomposition. As a circular-dimensional permutation-invariant tensor-decomposition model, it yields more powerful and general low-rank representations of multiway data with great potential for a variety of applications in machine learning and signal processing. Motivated by these applications, in this study, we extend the TT-based EMG signal classification strategy, which was introduced in our conference paper from ICCS 2023, to a more general and efficient version that takes advantage of the TR model. By combining it with tensor subspace analysis (TSA), which additionally allows us to extract more discriminant 2D features, we demonstrate that the proposed method outperforms many competitive approaches for the classification of multichannel sEMG signals registered during various hand movements.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102520"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177201","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}