Journal of Computational Science最新文献

{"title":"Intelligent computing for magnetohydrodynamic micropolar nanofluid with stratification using Levenberg–Marquardt backpropagation algorithm","authors":"Ikram Ul Haq ,&nbsp;Saira Shukat ,&nbsp;Ikram Ullah ,&nbsp;Waqar Ul Hassan ,&nbsp;Hong-Na Zhang ,&nbsp;Xiao-Bin Li ,&nbsp;Feng-Chen Li","doi":"10.1016/j.jocs.2025.102727","DOIUrl":"10.1016/j.jocs.2025.102727","url":null,"abstract":"<div><div>The magnetohydrodynamic (MHD) micropolar nanofluid with stratification is evaluated in this work by integrated numerical computing using the Levenberg Marquardt backpropagation (LMBB) optimization technique, an artificial neural network (ANN) approach. After that, model is condensed to a set of problems with boundary values, which are resolved utilizing the proposed method LMBB algorithm and a numerical technique BVP4c. The LMBB approach is an iterative approach for figuring out the least of a function that is not linear, is distinct as the addition of squares. The outcomes are also cross-checked against those of earlier studies and the MATLAB’s BVP4c solver for validation. The mapping of velocity, concentration and temperature profiles from the input to results is another use of neural networking. These results show the accuracy level of the predictions and improvements made by ANN. To generalize a dataset, the BVP4c techniques’ performance is utilized to lower error of mean square. Data based on the ratio of training (80 %), validation (10 %) and testing (10 %) is used by the ANN-based LMBB backpropagation optimization technique. Histograms and function fitness are utilized to verify the algorithm’s dependability. For fluid dynamics, numerical methods and ANN perform incredibly well together, and this could result in new developments across a wide range of fields. The results of this study may aid in the optimization of fluid systems, leading to higher productivity and efficiency in a range of engineering applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102727"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265397","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":"Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction","authors":"Paraskevas Dimitriou,&nbsp;Vasileios Karyotis","doi":"10.1016/j.jocs.2025.102719","DOIUrl":"10.1016/j.jocs.2025.102719","url":null,"abstract":"<div><div>Link prediction is a fundamental task in network analysis, aiming at predicting missing or future connections between nodes in a network. With the growing availability of complex network data in fields like social networks, biological systems, the Internet, and scientific collaboration networks, accurate link prediction methods are becoming increasingly critical. Neighborhood or graph based link prediction algorithms are applied identically to different types of networks so that any differences in their structures are not exploited efficiently. Machine or deep learning based link prediction algorithms apply to each kind of network differently depending on the type of network, due to the unique characteristics of each domain, but frequently, most of them give poor results. In this paper, we propose a novel approach for link prediction, leveraging the power of machine learning and evolutionary algorithms. Our method utilizes local network information by encoding the network topology into link embeddings through a heuristic machine learning architecture. We introduce a novel tool to extract features from network structure effectively and combine them in an effective way through an evolutionary algorithm improving the discriminative power of link embeddings. We evaluate our method on eleven benchmark datasets and demonstrate its superior performance compared to a series (eleven in total) of effective and state-of-the-art algorithms. Our approach advances the state-of-the-art in link prediction yielding better results than other methods in all the networks we have applied it to.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102719"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158210","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":"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":"2025-12-01","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":"Approach to global path planning and optimization for mobile robots based on multi-local gravitational potential fields bias-P-RRT*","authors":"Leiwen Yuan ,&nbsp;Jingwen Luo","doi":"10.1016/j.jocs.2025.102718","DOIUrl":"10.1016/j.jocs.2025.102718","url":null,"abstract":"<div><div>The sampling-based method has strong environmental adaptability and probability completeness, providing an effective solution for mobile robot path planning. However, the conventional rapidly-exploring random trees (RRT) algorithm often presents slow convergence and inefficient search paths. In this sense, this paper proposes a mobile robot path planning and optimization algorithm based on P-RRT* that incorporates multi-local gravitational potential fields and bias sampling, i.e., multi-local gravitational potential fields Bias-P-RRT* (MLGPFB-P-RRT*). The algorithm adds a local gravitational field between the starting point and the target point to better guide the direction of random tree growth, and directly connects the center of the last local gravitational field to the target point to accelerate the convergence of the random tree at the target point. Meanwhile, the introduction of bias sampling based on local potential fields to optimize the generation quality of random points, thereby improving the generation position of new nodes and reducing the randomness of sampling for mobile robots in the workspace. Then, a collision detection method between sampling nodes and obstacles was developed, which can quickly determine the feasibility of the sampling path. Finally, the generated path is optimized and smoothed through pruning optimization and quadratic B-spline function. A series of simulation studies and mobile robot experiments demonstrate the superior performance of the proposed algorithm.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102718"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095808","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":"Machine learning tree trimming for faster Markov reward game solutions","authors":"Burhaneddin İzgi ,&nbsp;Murat Özkaya ,&nbsp;Nazım Kemal Üre ,&nbsp;Matjaž Perc","doi":"10.1016/j.jocs.2025.102726","DOIUrl":"10.1016/j.jocs.2025.102726","url":null,"abstract":"<div><div>Existing methodologies for solving Markov reward games mostly rely on state–action frameworks and iterative algorithms to address these challenges. However, these approaches often impose significant computational burdens, particularly when applied to large-scale games, due to their inherent complexity and the need for extensive iterative calculations. In this paper, we propose a new neural network architecture for solving Markov reward games in the form of a decision tree with relatively large state and action sets, such as 2-actions-3-stages, 3-actions-3-stages, and 4-actions-3-stages, by trimming the decision tree. In this context, we generate datasets of Markov reward games with sizes ranging from <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> to <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> using the holistic matrix norm-based solution method and obtain the necessary components, such as the payoff matrices and the corresponding solutions of the games, for training the neural network. We then propose a vectorization process to prepare the outcomes of the matrix norm-based solution method and adapt them for training the proposed neural network. The neural network is trained using both the vectorized payoff and transition matrices as input, and the prediction system generates the optimal strategy set as output. In the model, we approach the problem as a classification task by labeling the optimal and non-optimal branches of the decision tree with ones and zeros, respectively, to identify the most rewarding paths of each game. As a result, we propose a novel neural network architecture for solving Markov reward games in real time, enhancing its practicality for real-world applications. The results reveal that the system efficiently predicts the optimal paths for each decision tree, with f1-scores slightly greater than 0.99, 0.99, and 0.97 for Markov reward games with 2-actions-3-stages, 3-actions-3-stages, and 4-actions-3-stages, respectively.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102726"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265399","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":"Detecting cardinal nodes in unweighted complex networks by examining their trajectories within Krylov subspace and various topological features","authors":"Ramraj Thirupathyraj","doi":"10.1016/j.jocs.2025.102713","DOIUrl":"10.1016/j.jocs.2025.102713","url":null,"abstract":"<div><div>Network Science, delving into complex networks with intricate topologies and structural interactions, plays a pivotal role in understanding various natural systems. Computational studies highlight the importance of influential nodes in capturing network characteristics and functionalities. Previous research underscores the inadequacy of relying on a single node characteristic to identify influence, emphasizing the need for integrating multiple characteristics. In this study, we propose an indicator by incorporating the network’s topological features into the Krylov subspace to effectively capture influence propagation among nodes and their neighbors. This new indicator, in an asymmetric form, considers distinct node influence effects and inherent dynamics asymmetry. Furthermore, when integrated with other locality-based measures, it enhances the cohesion of a unified model. This model is employed to identify influential nodes within complex networks. Empirical evaluations of Susceptible–Infected–Recovered (SIR) propagation dynamics across ten authentic networks demonstrate that our proposed unified model operates within polynomial time and surpasses numerous traditional methods in terms of accuracy. Utilizing this approach to identify influential nodes offers potential applications across a range of domains, such as social networks, malware analysis, and neuro-perception networks.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102713"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265400","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":"2025-12-01","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":"BAHA: Binary artificial hummingbird algorithm for feature selection","authors":"Ali Hamdipour ,&nbsp;Abdolali Basiri ,&nbsp;Mostafa Zaare ,&nbsp;Seyedali Mirjalili","doi":"10.1016/j.jocs.2025.102686","DOIUrl":"10.1016/j.jocs.2025.102686","url":null,"abstract":"<div><div>Datasets classification accuracy depends on their features. The presence of irrelevant and redundant features in the dataset leads to the reduction of classification accuracy. Identifying and removing such features is the main purpose in feature selection, which is an important step in the data science lifecycle. The objective of the Wrapper feature selection method is to reduce the number of selected feature (NSF) while improving the classification accuracy by working on a set of features. The feature selection is a challenging and computationally expensive problem that falls under the NP-complete category, so it requires computationally cheap and efficient algorithm to solve it. The artificial hummingbird algorithm (AHA) is a biological inspired optimization technique that mimics the unique flight capabilities and intelligent foraging tactics of hummingbirds in nature. Since feature selection is inherently a binary problem. In this paper, the binary form of the AHA meta-heuristic algorithm is proposed to show that binarizing the AHA meta-heuristic algorithm improves its performance for solving feature selection problems. The proposed method is tested on a standard benchmark dataset and compared with four state-of-the-art feature selection algorithms: Automata-based improved equilibrium optimizer with U-shaped transfer function (AIEOU), Whale optimization approaches for wrapper feature selection (WOA-CM), Ring theory-based harmony search (RTHS), and Adaptive switching gray-whale optimizer (ASGW). The results show the effectiveness of the proposed algorithm in searching for optimal features subset. The source code for the algorithm being proposed is accessible to the public on <span><span>https://github.com/alihamdipour/baha</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102686"},"PeriodicalIF":3.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863515","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 h-adaptive collocation method for Physics-Informed Neural Networks","authors":"Jan Trynda ,&nbsp;Paweł Maczuga ,&nbsp;Albert Oliver-Serra ,&nbsp;Luis Emilio García-Castillo ,&nbsp;Robert Schaefer ,&nbsp;Maciej Woźniak","doi":"10.1016/j.jocs.2025.102684","DOIUrl":"10.1016/j.jocs.2025.102684","url":null,"abstract":"<div><div>Despite their flexibility and success in solving partial differential equations, Physics-Informed Neural Networks (PINNs) often suffer from convergence issues, even failing to converge, particularly in problems with steep gradients or localized features. Several remedies have been suggested to solve this problem, but one of the most promising is the dynamical adaptation of the collocation points. This paper explores a novel adaptive sampling method, of a stochastic nature, based on the Adaptive Mesh Refinement used in the Finite Element Method. The error estimates in our refinement algorithm are based on the value of the residual loss function. We tested our method against a variety of 1D and 2D benchmark problems that exhibit steep gradients near certain boundaries, with promising results.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"91 ","pages":"Article 102684"},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722460","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":"2025-10-01","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}