Pub Date : 2025-09-17DOI: 10.1016/j.jocs.2025.102704
Walid Remili , Wen-Xiu Ma
This study investigates the numerical solution of the biological Susceptible–Infectious–Recovered model for COVID-19 over extended time intervals using the shifted Chebyshev polynomial collocation method. Initially, the original problem is reformulated into a nonlinear Volterra integral equation for the susceptible population. The shifted Chebyshev polynomials are then employed to derive the numerical solution. A comprehensive convergence analysis of the collocation method is conducted to ensure the reliability and accuracy of the proposed approach. Finally, numerical simulations are performed for various parameter configurations that influence the system’s coefficients. Our method is compared with existing approaches, providing insights into the model’s dynamics under different conditions.
{"title":"Numerical solution of the biological SIR model for COVID-19 with convergence analysis","authors":"Walid Remili , Wen-Xiu Ma","doi":"10.1016/j.jocs.2025.102704","DOIUrl":"10.1016/j.jocs.2025.102704","url":null,"abstract":"<div><div>This study investigates the numerical solution of the biological Susceptible–Infectious–Recovered model for COVID-19 over extended time intervals using the shifted Chebyshev polynomial collocation method. Initially, the original problem is reformulated into a nonlinear Volterra integral equation for the susceptible population. The shifted Chebyshev polynomials are then employed to derive the numerical solution. A comprehensive convergence analysis of the collocation method is conducted to ensure the reliability and accuracy of the proposed approach. Finally, numerical simulations are performed for various parameter configurations that influence the system’s coefficients. Our method is compared with existing approaches, providing insights into the model’s dynamics under different conditions.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102704"},"PeriodicalIF":3.7,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095810","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}
The rapid development of Digital Rock Physics (DRP) requires the elaboration of robust techniques for closing the gaps between different scales of rock studies (upscaling). The upscaling workflows are especially needed to support the applicability of DRP for heterogeneous rocks. Basically, DRP involves two primary stages: model construction and simulation of physical processes on the models created. For heterogeneous rocks, there is an inherent trade-off between the spatial resolution of the data and the representativeness of the model size. The primary objective of this study was to implement and test a technique for upscaling digital core models from microscale to macroscale, enabling the computation of rock properties while accounting for heterogeneity of various scales. The upscaling is based on establishing correlations between tomography data of different resolutions and transforming low-resolution tomography into a multi-class model according to the defined correlation. The convolutional neural network for high-resolution tomography data was considered as the optimal algorithm for transforming low-resolution tomography into a multi-class model. The output of the neural network was an upscaled model of lower resolution than the original tomography image. Each cell in the upscaled model belonged to one of several types of formation, whose generalized characteristics were determined on the basis of the analysis of high-resolution tomography data. To validate the upscaling technique, we constructed a digital model of a complex carbonate reservoir based on data from multi-scale microtomography (CT). A Darcy-scale model has been used and validated as a multi-class model, enabling the computation of flows in pore samples of various scales. By incorporating diverse pore space structures as different classes in the Darcy-scale model, it is possible to preserve the substantial physical size of the model while enhancing its level of complexity.
{"title":"Darcy-scale digital core models for rock properties upscaling and computational domain reduction","authors":"Denis Orlov, Batyrkhan Gainitdinov, Dmitry Koroteev","doi":"10.1016/j.jocs.2025.102715","DOIUrl":"10.1016/j.jocs.2025.102715","url":null,"abstract":"<div><div>The rapid development of Digital Rock Physics (DRP) requires the elaboration of robust techniques for closing the gaps between different scales of rock studies (upscaling). The upscaling workflows are especially needed to support the applicability of DRP for heterogeneous rocks. Basically, DRP involves two primary stages: model construction and simulation of physical processes on the models created. For heterogeneous rocks, there is an inherent trade-off between the spatial resolution of the data and the representativeness of the model size. The primary objective of this study was to implement and test a technique for upscaling digital core models from microscale to macroscale, enabling the computation of rock properties while accounting for heterogeneity of various scales. The upscaling is based on establishing correlations between tomography data of different resolutions and transforming low-resolution tomography into a multi-class model according to the defined correlation. The convolutional neural network for high-resolution tomography data was considered as the optimal algorithm for transforming low-resolution tomography into a multi-class model. The output of the neural network was an upscaled model of lower resolution than the original tomography image. Each cell in the upscaled model belonged to one of several types of formation, whose generalized characteristics were determined on the basis of the analysis of high-resolution tomography data. To validate the upscaling technique, we constructed a digital model of a complex carbonate reservoir based on data from multi-scale microtomography (<span><math><mi>μ</mi></math></span>CT). A Darcy-scale model has been used and validated as a multi-class model, enabling the computation of flows in pore samples of various scales. By incorporating diverse pore space structures as different classes in the Darcy-scale model, it is possible to preserve the substantial physical size of the model while enhancing its level of complexity.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102715"},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095811","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}
Pub Date : 2025-09-15DOI: 10.1016/j.jocs.2025.102706
Xingran Zhao , Yanbu Guo , Bingyi Wang , Weihua Li
Accurate drug–target affinity (DTA) prediction is a cornerstone of efficient drug discovery, as it directly accelerates the screening of potential therapeutic candidates, reduces the cost of preclinical experiments, and shortens the development cycle of new drugs. However, existing deep learning-based methods face two main challenges: (I) Purely data-driven approaches struggle to capture the functional semantics of molecules, such as the role of specific functional regions and chemical element properties in binding interactions, due to the lack of integration with chemical prior knowledge, leading to unreliable predictions; (II) the integration of topological structure from graphs and long-range dependencies from sequences is insufficient, often failing to capture complementary features, limiting the model’s generalization ability, especially for novel drugs or targets commonly encountered in early drug discovery . To address these issues, we propose CKDTA, a Chemical Knowledge Enhanced framework for Drug-Target Affinity prediction. Our framework introduces two key innovations: (1) a chemical knowledge-enhanced molecular modeling approach, which constructs a multi-layer molecular graph incorporating atom-level features, chemical element information, and functional regions, enabling the capture of functional semantics through a hierarchical attention mechanism, while leveraging chemical prior knowledge; (2) a co-attention module designed to optimize sequence interaction information by leveraging graph-based interaction data, compensating for the lack of spatial structural information in sequence data. This module fully exploits the topological structure of graphs and the long-range dependencies in sequences, capturing complementary features. Extensive experiments on benchmark datasets demonstrate that CKDTA outperforms state-of-the-art methods. Furthermore, cold-start experiments validate its generalizability, highlighting its potential for drug discovery applications.
{"title":"CKDTA: A chemical knowledge-enhanced framework for drug–target affinity prediction","authors":"Xingran Zhao , Yanbu Guo , Bingyi Wang , Weihua Li","doi":"10.1016/j.jocs.2025.102706","DOIUrl":"10.1016/j.jocs.2025.102706","url":null,"abstract":"<div><div>Accurate drug–target affinity (DTA) prediction is a cornerstone of efficient drug discovery, as it directly accelerates the screening of potential therapeutic candidates, reduces the cost of preclinical experiments, and shortens the development cycle of new drugs. However, existing deep learning-based methods face two main challenges: (I) Purely data-driven approaches struggle to capture the functional semantics of molecules, such as the role of specific functional regions and chemical element properties in binding interactions, due to the lack of integration with chemical prior knowledge, leading to unreliable predictions; (II) the integration of topological structure from graphs and long-range dependencies from sequences is insufficient, often failing to capture complementary features, limiting the model’s generalization ability, especially for novel drugs or targets commonly encountered in early drug discovery . To address these issues, we propose <strong>CKDTA</strong>, a <strong>C</strong>hemical <strong>K</strong>nowledge Enhanced framework for <strong>D</strong>rug-<strong>T</strong>arget <strong>A</strong>ffinity prediction. Our framework introduces two key innovations: (1) a chemical knowledge-enhanced molecular modeling approach, which constructs a multi-layer molecular graph incorporating atom-level features, chemical element information, and functional regions, enabling the capture of functional semantics through a hierarchical attention mechanism, while leveraging chemical prior knowledge; (2) a co-attention module designed to optimize sequence interaction information by leveraging graph-based interaction data, compensating for the lack of spatial structural information in sequence data. This module fully exploits the topological structure of graphs and the long-range dependencies in sequences, capturing complementary features. Extensive experiments on benchmark datasets demonstrate that CKDTA outperforms state-of-the-art methods. Furthermore, cold-start experiments validate its generalizability, highlighting its potential for drug discovery applications.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102706"},"PeriodicalIF":3.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095812","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}
Pub Date : 2025-09-15DOI: 10.1016/j.jocs.2025.102718
Leiwen Yuan , Jingwen Luo
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.
{"title":"Approach to global path planning and optimization for mobile robots based on multi-local gravitational potential fields bias-P-RRT*","authors":"Leiwen Yuan , 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":"2025-09-15","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}
Pub Date : 2025-09-12DOI: 10.1016/j.jocs.2025.102705
Sabita Bera , Mausumi Sen , Sujit Nath
Diffusion equations are fundamental in modeling the transport of heat, mass, or contaminants in porous media. However, classical models often fail to capture the anomalous diffusion behavior inherent in heterogeneous and memory-dependent materials. To address this, we investigate a fractional diffusion integro-differential equation involving variable-order derivatives in both time and space, subject to suitable conditions. The solutions are shown to exist and be unique through the rigorous application of fixed-point theorems. A finite difference-based numerical scheme is formulated to handle the variable-order fractional operators and convolution-type integral terms efficiently. Stability analysis confirms the accuracy and robustness of the method. In addition, approximate solutions are computed for three representative cases:(i) constant-order fractional diffusion (), (ii) time-dependent order , and (iii) fully variable-order . By incorporating variable order dynamics and integro-differential structures, this work extends conventional models and provides a unified framework for simulating complex transport processes in porous media.
{"title":"Efficient numerical simulation of variable-order fractional diffusion processes with a memory kernel","authors":"Sabita Bera , Mausumi Sen , Sujit Nath","doi":"10.1016/j.jocs.2025.102705","DOIUrl":"10.1016/j.jocs.2025.102705","url":null,"abstract":"<div><div>Diffusion equations are fundamental in modeling the transport of heat, mass, or contaminants in porous media. However, classical models often fail to capture the anomalous diffusion behavior inherent in heterogeneous and memory-dependent materials. To address this, we investigate a fractional diffusion integro-differential equation involving variable-order derivatives in both time and space, subject to suitable conditions. The solutions are shown to exist and be unique through the rigorous application of fixed-point theorems. A finite difference-based numerical scheme is formulated to handle the variable-order fractional operators and convolution-type integral terms efficiently. Stability analysis confirms the accuracy and robustness of the method. In addition, approximate solutions are computed for three representative cases:(i) constant-order fractional diffusion (<span><math><mrow><mi>α</mi><mo>=</mo><mtext>constant</mtext></mrow></math></span>), (ii) time-dependent order <span><math><mrow><mi>α</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>, and (iii) fully variable-order <span><math><mrow><mi>α</mi><mrow><mo>(</mo><mi>x</mi><mo>,</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>. By incorporating variable order dynamics and integro-differential structures, this work extends conventional models and provides a unified framework for simulating complex transport processes in porous media.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102705"},"PeriodicalIF":3.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095809","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}
Pub Date : 2025-09-11DOI: 10.1016/j.jocs.2025.102714
Riski Kurniawan , Sri Redjeki Pudjaprasetya , Rani Sulvianuri
Sediment transport plays a crucial role in the evolution of bed morphology through deposition and erosion. This study presents numerical simulations of two-dimensional sediment transport induced by fluid flow. The fluid-sediment interaction is governed by a capacity model, i.e., the coupled system of shallow water and Exner equations, a simplification of more physically advanced non-capacity models. The system is solved using a momentum-conserving staggered grid (MCS) scheme. Model validation is performed using the Meyer-Peter and Müller (MPM) bedload transport formula, applied to experimental data from dam-break flows in various channel configurations. The proposed method successfully reproduces trends in the evolution of the water surface and quasi-steady sediment profiles. In general, the MCS scheme provides more accurate water level predictions than the numerical benchmark schemes. Although the predictions of maximum depths of deposition and erosion are less accurate, the overall results are consistent with those obtained from non-capacity models. Furthermore, the model is applied to the Kampar River estuary to simulate sediment transport due to the tidal bore.
{"title":"Numerical study of two-dimensional sediment transport using momentum-conserving staggered grid scheme","authors":"Riski Kurniawan , Sri Redjeki Pudjaprasetya , Rani Sulvianuri","doi":"10.1016/j.jocs.2025.102714","DOIUrl":"10.1016/j.jocs.2025.102714","url":null,"abstract":"<div><div>Sediment transport plays a crucial role in the evolution of bed morphology through deposition and erosion. This study presents numerical simulations of two-dimensional sediment transport induced by fluid flow. The fluid-sediment interaction is governed by a capacity model, i.e., the coupled system of shallow water and Exner equations, a simplification of more physically advanced non-capacity models. The system is solved using a momentum-conserving staggered grid (MCS) scheme. Model validation is performed using the Meyer-Peter and Müller (MPM) bedload transport formula, applied to experimental data from dam-break flows in various channel configurations. The proposed method successfully reproduces trends in the evolution of the water surface and quasi-steady sediment profiles. In general, the MCS scheme provides more accurate water level predictions than the numerical benchmark schemes. Although the predictions of maximum depths of deposition and erosion are less accurate, the overall results are consistent with those obtained from non-capacity models. Furthermore, the model is applied to the Kampar River estuary to simulate sediment transport due to the tidal bore.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102714"},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049295","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}
Pub Date : 2025-09-11DOI: 10.1016/j.jocs.2025.102694
Yongfeng Li , Leyan Liang , Zhong Zhao
The pest number trigger threshold strategy has been widely used in the control of pests in agricultural production. In this study, pest populations are managed by using an integrated nonlinear threshold function and a saturation function. The existence conditions of various equilibrium points and sliding sections in the system are derived. Theoretical analysis and numerical simulation results show the existence of boundary equilibrium bifurcations, tangency bifurcations and limit cycle bifurcations caused by discontinuous boundary. It is worth noting that persistence and non-smooth folding can be observed in the boundary equilibrium bifurcations. At the same time, because the nonlinear threshold control strategy is adopted in this study, the change of the sliding section of the model is more complicated. The numerical simulation results show that if there is an unstable focus in the model, a sliding homoclinic cycle will appear with the occurrence of boundary saddle point bifurcation, and then form a crossing limit cycle. The sensitivity analysis results of the system show that if the threshold level is too low, the control measures do not achieve the desired results. Too high threshold selection will cause unnecessary economic losses. Therefore, our results show that an appropriate threshold should be set to reduce economic losses while ensuring that the number of pests is in a lower stable state.
{"title":"A study on pest control models based on nonlinear threshold control","authors":"Yongfeng Li , Leyan Liang , Zhong Zhao","doi":"10.1016/j.jocs.2025.102694","DOIUrl":"10.1016/j.jocs.2025.102694","url":null,"abstract":"<div><div>The pest number trigger threshold strategy has been widely used in the control of pests in agricultural production. In this study, pest populations are managed by using an integrated nonlinear threshold function and a saturation function. The existence conditions of various equilibrium points and sliding sections in the system are derived. Theoretical analysis and numerical simulation results show the existence of boundary equilibrium bifurcations, tangency bifurcations and limit cycle bifurcations caused by discontinuous boundary. It is worth noting that persistence and non-smooth folding can be observed in the boundary equilibrium bifurcations. At the same time, because the nonlinear threshold control strategy is adopted in this study, the change of the sliding section of the model is more complicated. The numerical simulation results show that if there is an unstable focus in the model, a sliding homoclinic cycle will appear with the occurrence of boundary saddle point bifurcation, and then form a crossing limit cycle. The sensitivity analysis results of the system show that if the threshold level is too low, the control measures do not achieve the desired results. Too high threshold selection will cause unnecessary economic losses. Therefore, our results show that an appropriate threshold should be set to reduce economic losses while ensuring that the number of pests is in a lower stable state.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102694"},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096008","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}
Pub Date : 2025-09-11DOI: 10.1016/j.jocs.2025.102716
Yiwei Liu, Yinggan Tang, Changchun Hua
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.
{"title":"Hybrid nutcracker optimization algorithm for multi-objective energy scheduling in grid-connected microgrid systems","authors":"Yiwei Liu, Yinggan Tang, Changchun Hua","doi":"10.1016/j.jocs.2025.102716","DOIUrl":"10.1016/j.jocs.2025.102716","url":null,"abstract":"<div><div>The growing demand for clean and sustainable energy has driven rapid advancements in hybrid microgrid systems to mitigate climate change and environmental degradation. This paper proposes a novel multi-objective scheduling framework for hybrid microgrids aimed at minimizing operational costs while maximizing environmental benefits. To efficiently solve this complex optimization problem, we introduce a Hybrid Nutcracker Optimization Algorithm (HNOA), which combines the recently developed Nutcracker Optimization Algorithm (NOA) with the Bat Algorithm (BAT). This hybridization enhances NOA’s exploration–exploitation balance and search capability, as demonstrated by rigorous validation on 12 benchmark functions. HNOA achieves superior accuracy and computational efficiency compared to several state-of-the-art metaheuristics. The proposed HNOA is then applied to solve the scheduling of a grid-connected hybrid microgrid under various scenarios to evaluate its performance. Simulation results indicate that the optimal microgrid configuration, consisting of PV/WT/turbine/diesel/battery, achieves an investment cost of 80,789.02 yuan. The findings of this study offer valuable insights for advancing renewable energy integration and promoting environmental sustainability.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102716"},"PeriodicalIF":3.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-02DOI: 10.1016/j.jocs.2025.102712
Malgorzata J. Krawczyk, Krzysztof Kułakowski
A cellular automaton is defined on a line graph of a fully connected network. The automaton rule drives the system to a structural balance in most cases. Here, we investigate cycles with special symmetries, the so-called ’perfect cycles’ Burda et al. (2022). Two new characteristics of the cycles are investigated, as potential markers of perfect cycles: an equivalence of sets of states attained after external damage of links, and the homogeneity of the distribution of phase shifts between local trajectories. Only the second characteristic works as a criterion of the perfectness of the cycles. The results can be useful for generating pseudorandom numbers.
{"title":"A cellular automaton towards structural balance—Long cycles of link dynamics","authors":"Malgorzata J. Krawczyk, Krzysztof Kułakowski","doi":"10.1016/j.jocs.2025.102712","DOIUrl":"10.1016/j.jocs.2025.102712","url":null,"abstract":"<div><div>A cellular automaton is defined on a line graph of a fully connected network. The automaton rule drives the system to a structural balance in most cases. Here, we investigate cycles with special symmetries, the so-called ’perfect cycles’ Burda et al. (2022). Two new characteristics of the cycles are investigated, as potential markers of perfect cycles: an equivalence of sets of states attained after external damage of links, and the homogeneity of the distribution of phase shifts between local trajectories. Only the second characteristic works as a criterion of the perfectness of the cycles. The results can be useful for generating pseudorandom numbers.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102712"},"PeriodicalIF":3.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.jocs.2025.102697
Xinyi Wei , Hao Meng , Lizhen Shao , Dongmei Fu , Lingwei Ma , Dawei Zhang
With the intensification of environmental air pollution, the impact of air pollutants on both the ecological environment and human health has attracted widespread attention. However, due to the relatively late introduction of environmental monitoring systems, there were long consecutive missing values in early pollutant data. In this paper, we propose a decomposition-based imputation method for long consecutive missing pollution data. Firstly, wavelet coherence analysis is employed to investigate the periodic relationship between the pollution data and the relevant air data, decomposing them into periodic and non-periodic components. Then, machine learning and transfer learning are used to impute the periodic and non-periodic components, respectively. Furthermore, the effectiveness of the method is validated on artificially missing and concentration data from five regions of China. Comparison results show that the proposed method significantly outperforms some other imputation methods in the literature in terms of both mean absolute error and mean absolute percentage error. Finally, the proposed imputation method is applied in the study of accelerated aging of polycarbonate materials. Experimental results show that the predictive accuracy of the aging model is improved when using the imputed pollutant data.
{"title":"A decomposition based imputation algorithm for long consecutive missing atmospheric pollution data and its application","authors":"Xinyi Wei , Hao Meng , Lizhen Shao , Dongmei Fu , Lingwei Ma , Dawei Zhang","doi":"10.1016/j.jocs.2025.102697","DOIUrl":"10.1016/j.jocs.2025.102697","url":null,"abstract":"<div><div>With the intensification of environmental air pollution, the impact of air pollutants on both the ecological environment and human health has attracted widespread attention. However, due to the relatively late introduction of environmental monitoring systems, there were long consecutive missing values in early pollutant data. In this paper, we propose a decomposition-based imputation method for long consecutive missing pollution data. Firstly, wavelet coherence analysis is employed to investigate the periodic relationship between the pollution data and the relevant air data, decomposing them into periodic and non-periodic components. Then, machine learning and transfer learning are used to impute the periodic and non-periodic components, respectively. Furthermore, the effectiveness of the method is validated on artificially missing <span><math><msub><mrow><mi>NO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>SO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> concentration data from five regions of China. Comparison results show that the proposed method significantly outperforms some other imputation methods in the literature in terms of both mean absolute error and mean absolute percentage error. Finally, the proposed imputation method is applied in the study of accelerated aging of polycarbonate materials. Experimental results show that the predictive accuracy of the aging model is improved when using the imputed pollutant data.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"92 ","pages":"Article 102697"},"PeriodicalIF":3.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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