Journal of Computational Science最新文献

{"title":"Lattice Boltzmann simulation of pollutant transport in shallow water flows: Application to Nador lagoon","authors":"Ali Haddach ,&nbsp;Hassan Smaoui ,&nbsp;Bouchaib Radi","doi":"10.1016/j.jocs.2025.102538","DOIUrl":"10.1016/j.jocs.2025.102538","url":null,"abstract":"<div><div>This paper present a novel numerical method based on lattice Boltzmann and designed for simulating pollutant transport in Nador lagoon (Moroccan eastern coast of Mediterranean Sea). The model solves the shallow water equations coupled to the depth-averaged advection-diffusion equation. Solution of the Shallow water equations was performed by the multiple relaxation time lattice Boltzmann method, while the depth-averaged advection-diffusion equation was solved by the single relaxation time lattice Boltzmann method. To keep its role of mixing processes, the diffusion coefficients were determined by a linear relationship from the turbulent viscosity via the Schmit number. This relationship allowed the link of the relaxation time of hydrodynamics and the relaxation time of diffusion processes.</div><div>The results of the numerical hydrodynamic model were validated by comparison with laboratory measurements treating flow in two-branches channels. The analysis of this comparison showed that our numerical model reproduces this flow with high precision. Furthermore, the numerical solution of advection-diffusion equation was validated by comparison with both stationary and unsteady analytical solutions. The error analysis also showed that the proposed numerical model simulates the propagation of a contaminant with good accuracy.</div><div>After the validation phase, the numerical model was applied to simulate the propagation of pollutant for the real case of the Nador lagoon. For this case, the sources of pollution were identified at the positions of the different waterways bordering the southern shore of the lagoon. Two hydrodynamic scenarios were simulated: flow without wind and flow with wind. In the absence of measurement data on the area, the qualitative analysis of the simulation results showed consistency both with the literature on the study area and with the dynamics of the Eulerian circulation of the lagoon.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102538"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A soft sensor open-source methodology for inexpensive monitoring of water quality: A case study of NO3− concentrations","authors":"Antonio Jesús Chaves,&nbsp;Cristian Martín,&nbsp;Luis Llopis Torres,&nbsp;Manuel Díaz,&nbsp;Jaime Fernández-Ortega,&nbsp;Juan Antonio Barberá,&nbsp;Bartolomé Andreo","doi":"10.1016/j.jocs.2024.102522","DOIUrl":"10.1016/j.jocs.2024.102522","url":null,"abstract":"<div><div>Nitrate (NO₃⁻) concentrations in aquifers constitute a global problem affecting environmental integrity and public health. Unfortunately, deploying hardware sensors specifically for NO₃⁻ measurements can be expensive, thereby, limiting scalability. This research explores the integration of soft sensors with data streams through an use case to predict nitrate NO₃⁻ levels in real time. To achieve this objective, a methodology based on Kafka-ML is proposed, a framework designed to manage the pipeline of machine learning models using data streams. The study evaluates the effectiveness of this methodology by applying it to a real-world scenario, including the integration of low-cost sensor devices. Additionally, Kafka-ML is extended by integrating MQTT and other IoT data protocols. The methodology benefits include rapid development, enhanced control, and visualisation of soft sensors. By seamlessly integrating IoT and data analytics, the approach promotes the adoption of cost-effective solutions for managing NO₃⁻ pollution and improving sustainable water resource monitoring.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102522"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176115","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":"Bayesian approaches for revealing complex neural network dynamics in Parkinson’s disease","authors":"Hina Shaheen ,&nbsp;Roderick Melnik","doi":"10.1016/j.jocs.2025.102525","DOIUrl":"10.1016/j.jocs.2025.102525","url":null,"abstract":"<div><div>Parkinson’s disease (PD) belongs to the class of neurodegenerative disorders that affect the central nervous system. It is usually defined as the gradual loss of dopaminergic neurons in the substantia nigra pars compacta, which causes both motor and non-motor symptoms. Understanding the neuronal processes that underlie PD is critical for creating successful therapies. This study combines machine learning (ML), stochastic modelling, and Bayesian inference with connectomic data to analyse the brain networks involved in PD. We use modern computational methods to study large-scale neural networks to identify neuronal activity patterns related to PD development. We aim to define the subtle structural and functional connection changes in PD brains by combining connectomic with stochastic noises. Stochastic modelling approaches reflect brain dynamics’ intrinsic variability and unpredictability, shedding light on the origin and spread of pathogenic events in PD. We employ a novel hybrid model to assess how stochastic noise impacts the cortex-basal ganglia-thalamus (CBGTH) network, using data from the Human Connectome Project (HCP). Bayesian inference allows us to quantify uncertainty in model parameters, improving the accuracy of our predictions. Our findings reveal that stochastic disturbances increase thalamus activity, even under deep brain stimulation (DBS). Bayesian analysis suggests that reducing these disturbances could enhance healthy brain states, providing insights for potential therapeutic interventions. This approach offers a deeper understanding of PD dynamics and paves the way for personalized treatment strategies. This is an extended version of our work presented at the ICCS-2024 conference (Shaheen and Melnik, 2024)<span><span>[1]</span></span>.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102525"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176116","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":"Novel hits for targeting kidney failure in type 2 diabetes derived via in silico screening of the ZINC natural product database","authors":"Neda Shakour ,&nbsp;Saeideh Hoseinpoor ,&nbsp;Saghi Sepehri ,&nbsp;Mehrdad Iranshahi ,&nbsp;Mohaddeseh Badpeyma ,&nbsp;Farzin Hadizadeh","doi":"10.1016/j.jocs.2024.102497","DOIUrl":"10.1016/j.jocs.2024.102497","url":null,"abstract":"<div><div>Renal dysfunction is a common and potentially fatal consequence often noticed in persons who have been diagnosed with type 2 diabetes mellitus (T2DM). The gravity of this complication is underscored by the heightened likelihood of death linked to its advancement. Therefore, it is crucial to prioritize the prevention of the progress of renal impairment. The efficacy of sodium-glucose cotransporter 2 (SGLT2) inhibitors in retarding the advancement of renal dysfunction and albuminuria has been demonstrated, underscoring their potential utility in the management of renal problems. In a quest to unearth natural SGLT2 inhibitors, a comprehensive study was undertaken, encompassing structure-based virtual screening and a range of tools deployed to separate through the extensive ZINC database. Through the application of a pharmacophore model, a cohort of 11,336 natural compounds were discerned from the ZINC database that could potentially serve as SGLT2 inhibitors. Amid this collection, two compounds were singled out via a rigorous assessment of ADME (absorption, distribution, metabolism, and excretion) attributes, oral bioavailability, molecular dynamics parameters, and akin docking affinities to approved inhibitors. Compound <strong>580</strong> emerged as a promising candidate, validated by its congruence with metabolic processes and the absence of proclivities toward cardiotoxic effects. The findings from this investigation serve to reinforce the validation of SGLT2 inhibitors, thus paving the way for comprehensive in vitro and in vivo experimentation. Concurrently, these outcomes act as a catalyst for the innovation of novel inhibitors, heralding a new era of possibilities.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102497"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176120","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":"Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators","authors":"Magdalena Pabisz ,&nbsp;Judit Muñoz-Matute ,&nbsp;Maciej Paszyński","doi":"10.1016/j.jocs.2024.102493","DOIUrl":"10.1016/j.jocs.2024.102493","url":null,"abstract":"<div><div>We present a MATLAB code for exponential integrators method simulating the glioblastoma tumor growth. It employs the Fisher–Kolmogorov diffusion–reaction tumor brain model with logistic growth. The input is the MRI scans of the human head and the initial tumor location. The simulation uses the finite difference formulation in space and the ultra-fast exponential integrators method in time. The output from the code is the input data for ParaView visualization. While there are many brain tumor simulation codes, our method’s novelty lies in its implementation using exponential integrators. We propose a new algorithm for the fast computation of exponential integrators. Regarding execution time on a laptop with Win10, using MATLAB, with 11th Gen Intel(R) Core(TM) i5-11500H, 2.92 GHz, and 32 GB of RAM, the algorithm outperforms the state-of-the-art routines from Al-Mohy and Higham (2011). We also compare our method with an implicit, unconditionally stable Crank–Nicolson time integration scheme based on the finite difference method. We show that our method is two orders of magnitude faster than the Crank–Nicolson method with finite difference discretization in space on a laptop equipped with MATLAB. The brain tumor two-year future prediction using <span><math><mrow><mn>132</mn><mo>×</mo><mn>132</mn><mo>×</mo><mn>132</mn></mrow></math></span> computational grid and 100-time steps, built over the MRI scans of the human head, takes less than 15 minutes on the laptop.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102493"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176121","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":"MGDRGCN: A novel framework for predicting metabolite–disease connections using tripartite network and relational graph convolutional network","authors":"Pengli Lu,&nbsp;Ling Li","doi":"10.1016/j.jocs.2024.102477","DOIUrl":"10.1016/j.jocs.2024.102477","url":null,"abstract":"<div><div>Metabolites are fundamental to the existence of biomolecules, and numerous studies have demonstrated that uncovering the connections between metabolites and diseases can enhance our understanding of disease pathogenesis. Traditional biological methods can identify potential metabolite–disease relationships, but these approaches often require significant human and material resources. Consequently, computational methods have emerged as a more efficient alternative. However, most computational methods primarily rely on metabolite–disease associations and rarely explore the impact of more biological entities. To address this issue, we propose a novel computational framework based on a metabolite–gene–disease tripartite heterogeneous network and relational graph convolutional network (R-GCN), abbreviated as MGDRGCN. Specifically, we construct three types of similarity networks from multiple data sources, including metabolite and gene functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity for metabolites and diseases. Next, we use principal component analysis to further extract features and construct a tripartite heterogeneous network with genes as the bridge. This network structure comprehensively captures and represents the complex relationships among metabolites, genes and diseases. We employ R-GCN to extract higher-order information from the tripartite heterogeneous network. Finally, we input the embeddings learned from R-GCN into a residual network classifier to predict potential metabolite–disease associations. In five-fold cross-validation experiments, MGDRGCN exhibit outstanding performance, with both AUC (0.9866) and AUPR (0.9865) significantly surpassing other advanced methods. Additionally, case studies further demonstrate MGDRGCN’s superior performance in predicting metabolite–disease associations. Overall, the introduction of MGDRGCN provides new perspectives and methods for future biomedical research, offering promising potential for uncovering the mechanisms of complex biological systems.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102477"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176117","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":"Strategies for feature-assisted development of topology agnostic planar antennas using variable-fidelity models","authors":"Adrian Bekasiewicz ,&nbsp;Khadijeh Askaripour ,&nbsp;Mariusz Dzwonkowski ,&nbsp;Tom Dhaene ,&nbsp;Ivo Couckuyt","doi":"10.1016/j.jocs.2024.102521","DOIUrl":"10.1016/j.jocs.2024.102521","url":null,"abstract":"<div><div>Design of antennas for contemporary applications presents a complex challenge that integrates cognitive-driven topology development with the meticulous adjustment of parameters through rigorous numerical optimization. Nevertheless, the process can be streamlined by emphasizing the automatic determination of structure geometry, potentially reducing the reliance on traditional methods that heavily rely on engineering insight in the course of antenna development. In this work, which is an extension of our conference paper [1], a specification-oriented design of topologically agnostic antennas is considered by utilizing two strategies focused on bandwidth-specific design and bandwidth-enhanced optimization. The process is embedded within a variable-fidelity framework, where the low-fidelity optimization involves classification of randomly generated topologies, followed by their local tuning using a trust-region algorithm applied to a feature-based representation of structure response. The final result is then tuned using just a handful of high-fidelity simulations. The strategies under consideration were verified on a case study basis concerning automatic generation of three radiators with varying parameters. Benchmarks of the algorithm against more standard optimization methods, as well as comparisons of the obtained topologies with respect to state-of-the-art antennas from literature have also been considered.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102521"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177204","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":"Analytical and numerical methods for the solution to the rigid punch contact integral equations","authors":"N. Antoni","doi":"10.1016/j.jocs.2024.102492","DOIUrl":"10.1016/j.jocs.2024.102492","url":null,"abstract":"<div><div>In this article, the problem of a rigid punch pressed onto the surface of an elastic half-plane is studied. In a first instance, it is reminded that the standard frictionless hard contact situation, where the contact pressure is unbounded at contact ends, exhibits an analytical solution to the governing singular integral equation with Cauchy kernel. Thereafter, it is shown that the situation of contact regularization results in a singular integro-differential equation with Cauchy kernel. This latter case leads to bounded contact pressures at both contact ends, even in the frame of linear elasticity, which is of great interest in the presence of “peaking” phenomenon. However, this contact regularization requires a numerical treatment as opposed to the former. To that end, a novel simple but efficient numerical procedure, based on numerical integration in conjunction with a centered finite differences scheme, is presented and numerically illustrated through two examples at the end of the paper.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102492"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177207","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":"Microdroplet dynamics on solid surface: The effect of wetting and surface tension using lattice Boltzmann method","authors":"Salaheddine Channouf,&nbsp;Mohammed Jami,&nbsp;Ahmed Mezrhab","doi":"10.1016/j.jocs.2025.102537","DOIUrl":"10.1016/j.jocs.2025.102537","url":null,"abstract":"<div><div>In this study, we investigated the micro-scale dynamics of droplet impact on solid surfaces with varying wettability, using a 3D modeling approach to capture the intricate behavior of microdroplets. We employed the multi-relaxation times pseudopotential lattice Boltzmann method to simulate the interaction between fluids of different densities, with interface tension playing a key role. The analysis focused on two distinct wetting scenarios: hydrophobic (non-wetting) and hydrophilic (wetting) surfaces, examining the droplet dynamics during both the spreading (propagation) and recoiling phases of impact. By manipulating the bulk modulus parameter κ and the corresponding surface tension γ, we were able to explore how wettability and surface tension influence droplet behavior, including deformation and stability. The study also validated key aspects of our computational framework through reference validations such as contact angle measurements and Laplace's law. Our results provide valuable comprehension of the mixed effects of wettability and surface tension, offering a comprehensive understanding of droplet interactions on different surfaces. This work contributes to the broader knowledge of fluid dynamics and surface engineering, with implications for applications in fields such as inkjet printing, coating technologies, and material science.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"85 ","pages":"Article 102537"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177211","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":"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}