Journal of Computational Science最新文献

{"title":"Spatio-temporal consistency of cloud-microphysical parameter sensitivity in a warm-conveyor belt","authors":"Maicon Hieronymus ,&nbsp;Annika Oertel ,&nbsp;Annette K. Miltenberger ,&nbsp;André Brinkmann","doi":"10.1016/j.jocs.2025.102614","DOIUrl":"10.1016/j.jocs.2025.102614","url":null,"abstract":"<div><div>A good representation of clouds and precipitation processes is essential in numerical weather and climate models. Subgrid-scale processes, such as cloud physics, are parameterized and inherently introduce uncertainty into models. Traditionally, the sensitivities of the model state to specific uncertain parameters are quantified through perturbations to a few selected parameters, limited by computational resources. Algorithmic Differentiation (AD) enables the efficient and simultaneous estimation of sensitivities for a large number of parameters, thereby overcoming the previous limitations and significantly enhancing the efficiency of the analysis. This framework provides an objective way to identify processes where more precise representations have the largest impact on model accuracy. AD-estimated sensitivities can also address the underdispersiveness of perturbed ensemble simulations by guiding the parameter selection or the perturbation itself. In our study, we applied AD to 169 uncertain parameters identified in the two-moment microphysics scheme of the numerical weather prediction (NWP) model ICON of the German Weather Service. This application of AD allowed us to evaluate the sensitivities of specific humidity, latent heating, and latent cooling along several thousand warm conveyor belt trajectories. This coherent, strongly ascending Lagrangian flow feature is crucial for the cloud and precipitation structure and the evolution of extratropical cyclones. The quantification of individual parameter sensitivities shows that only 38 parameters are of primary importance for the investigated model state variables. These parameters are associated with rain evaporation, hydrometeor diameter-mass relations, and fall velocities. Moreover, the parameter sensitivities systematically vary with different microphysical regimes, ascent behavior, and ascent stages of the WCB airstream. Finally, several parameters impact an extended region in the extratropical cyclone, illustrating the spatiotemporal consistency of cloud microphysical parameter uncertainty in the applied NWP model and microphysics scheme.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102614"},"PeriodicalIF":3.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184583","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":"Resilient authentication protocol for electronic healthcare enabled wireless body area networks using distributed ledger","authors":"Munir Hussain ,&nbsp;Amjad Mehmood ,&nbsp;Muhammad Altaf Khan ,&nbsp;Jaime Lloret ,&nbsp;Carsten Maple","doi":"10.1016/j.jocs.2025.102617","DOIUrl":"10.1016/j.jocs.2025.102617","url":null,"abstract":"<div><div>The recent developments in telecommunication technologies and monitoring devices have brought many changes in modern electronic healthcare systems (EHSs) by improving quality and decreasing healthcare expenses. Despite the benefits, they have privacy and security issues because the communication between patients and service providers takes place generally over public channels. Several user authentication protocols using distributed ledger technology (DLT) have recently been proposed to address these issues in EHSs. However, many are still vulnerable to a single point of failure (SPoF), privacy, and security attacks. Besides, they suffered from high communication and computational costs. Therefore, in this paper, we proposed a user authentication protocol using DLT to avoid these issues. A Burrows-Abadi-Needham (BAN) logic proof method has been used to check the security of the proposed protocol and ensure it achieves the desired security goals. In addition, an informal security analysis has been conducted to verify its important security requirements. A formal security analysis has been performed via the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Real-or-Random (ROR) model for further security strength. The results demonstrate that the proposed user authentication protocol is SAFE against all types of Man-in-the-Middle (MitM) attacks, impersonation, replay, and forgery attacks. Finally, performance analysis has been performed and results show that it achieves better performance by consuming 29.63 % and 13.21 % less communication and computational overheads as compared to existing related user authentication protocols. The security and performance analysis make it a more appropriate choice for the EHSs.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102617"},"PeriodicalIF":3.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168985","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":"Real-time detection and classification of active regions from solar images using sector-based hashing","authors":"Rafał Grycuk ,&nbsp;Rafał Scherer ,&nbsp;Giorgio De Magistris ,&nbsp;Christian Napoli","doi":"10.1016/j.jocs.2025.102604","DOIUrl":"10.1016/j.jocs.2025.102604","url":null,"abstract":"<div><div>We present a new approach for real-time retrieval and classification of solar images using a proposed sector-based image hashing technique. To this end, we generate intermediate hand-crafted features from automatically detected active regions in the form of layer-sector-based descriptors. Additionally, we employ a small fully-connected autoencoder to encode and finally obtain the concise Layer-Sector Solar Hash. By reducing the amount of data required to describe the Sun images, we achieve almost real-time retrieval speed of similar images to the query image. Since solar AIA images are not labeled, for the purposes of the presented test experiments, we consider images produced within a short time frame (typically up to several hours) to be similar. This approach has several potential applications, including searching, classifying, and retrieving solar flares, which are of critical importance for many aspects of life on Earth.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102604"},"PeriodicalIF":3.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184584","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":"From simulations to surrogates: Neural networks enhancing burn wound healing predictions","authors":"Ioannis Papapanagiotou ,&nbsp;Roland V. Bumbuc ,&nbsp;H. Ibrahim Korkmaz ,&nbsp;Valeria Krzhizhanovskaya ,&nbsp;Vivek M. Sheraton","doi":"10.1016/j.jocs.2025.102593","DOIUrl":"10.1016/j.jocs.2025.102593","url":null,"abstract":"<div><div>Burn injuries trigger substantial inflammation, complicating wound healing and potentially leading to severe systemic complications. Understanding the immune response to burns is crucial for improving treatment. Although agent-based models (ABMs) are valuable for studying these interactions, they are computationally demanding. This paper explores the integration of neural networks (NNs) as surrogate models to approximate and forecast ABM simulation results in predicting cytokine concentrations over time and space. We present the development of a baseline ABM using the CompuCell3D software, simulating the innate immune response and generating extensive cytokine concentration data. This data is processed and prepared for neural network training, involving data cleaning, transformation into suitable formats, and a time-series-aware train-test split. We then implement and assess various neural network architectures. Each model is designed to capture the temporal and spatial dynamics of cytokine concentrations, with adjusted model architectures (kernels, number of layers, neurons per layer) to better suit this problem. The models are evaluated using Mean Squared Error, R-squared, and Mean Absolute Percentage Error. In this paper, we assess how different NN architectures (convolutional neural networks (CNNs), long short-term memory (LSTM) neural networks, attention mechanisms, and physics-informed neural networks (PINNs)) predict the concentration of cytokines in this biological system. We find that STA-LSTM generally performs best across statistical metrics.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102593"},"PeriodicalIF":3.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107173","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 improved local radial basis function method for pricing options under the time-fractional Black–Scholes model","authors":"Omid Nikan ,&nbsp;Jalil Rashidinia ,&nbsp;Hossein Jafari","doi":"10.1016/j.jocs.2025.102610","DOIUrl":"10.1016/j.jocs.2025.102610","url":null,"abstract":"<div><div>The time-fractional Black–Scholes model (T-FBSM) is developed to assess price fluctuations in a correlated fractal transmission system. It is applied to price American and European call and put options on non-dividend-paying stocks. This study focuses on numerically solving the T-FBSM for option pricing using a local compact integrated radial basis function method (LCIRBFM). The temporal discretization is accomplished using the second-order shifted Grünwald scheme, while the spatial derivatives are discretized by using a combination of an integrated RBF interpolation and a compact scheme within a sub-domain (stencil). This approach utilizes second derivatives and nodal function values to construct a link between the RBF weights and the physical domain. The convergence and unconditional stability of the semi-discretized time formulation are proven via the energy method in the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> space. The proposed method demonstrates efficiency, and the numerical results validate the theoretical formulation.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102610"},"PeriodicalIF":3.1,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107172","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":"Solution of stress and deformation field and inversion of material parameter for gravity dams based on physics-informed neural networks","authors":"Danni Luo ,&nbsp;Haixing Mo ,&nbsp;Qingbin Li ,&nbsp;Xinxin Jin","doi":"10.1016/j.jocs.2025.102613","DOIUrl":"10.1016/j.jocs.2025.102613","url":null,"abstract":"<div><div>The stress and deformation analysis of concrete gravity dams is a core component of evaluating the structural safety of dam bodies. To obtain accurate and efficient solutions for the stress<img>deformation fields of gravity dams and fast inversion of material parameter, based on physics-informed neural networks (PINNs), we develop a residual minimization-based PINN model and a potential energy minimization-based EPINN model specifically targeted at gravity dams, which are grounded in the elasticity theory of solid mechanics. Through various case studies involving gravity dams, the computational accuracy and efficiency of different PINN models are compared. The results show the following: (1) The EPINN model demonstrates superior solving capability and computational efficiency—it is approximately 20 times faster than the PINN model—when dealing with complex geometries and boundary conditions. Conversely, the PINN model achieves higher computational accuracy for simpler geometries, with its precision being approximately twice that of the EPINN model. (2) Both models exhibit strong capabilities in material parameter inversion. In particular, the PINN model achieves accurate inversion of material properties via extremely limited data samples, with errors of only 0.46 % for the elastic modulus <em>E</em> and 2.32 % for Poisson's ratio <em>μ</em>. (3) The convergence performance of PINNs is influenced by factors such as the number of hidden layers, the number of neurons, and the test displacement functions. Overall, PINNs serve as a machine learning method that enables the direct construction of mechanistic models for gravity dams, contributing to the rapid and intelligent assessment of dam structural safety.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102613"},"PeriodicalIF":3.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107171","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":"Evolution of the computational science community: The dynamics of topics and collaborations in 24 years of ICCS and JoCS publications","authors":"Lijing Luo ,&nbsp;Klavdiya Bochenina ,&nbsp;Tesfamariam M. Abuhay ,&nbsp;Nachyn Dorzhu ,&nbsp;George Kampis ,&nbsp;Sergey Kovalchuk ,&nbsp;Valeria Krzhizhanovskaya ,&nbsp;Maciej Paszynski ,&nbsp;Clélia de Mulatier ,&nbsp;Jack Dongarra ,&nbsp;Peter M.A. Sloot","doi":"10.1016/j.jocs.2025.102609","DOIUrl":"10.1016/j.jocs.2025.102609","url":null,"abstract":"<div><div>We analyze the topic structure of 10,299 publications from the International Conference on Computational Science (ICCS) between 2001 and 2024 as well as the Journal of Computational Science (JoCS) between 2010 and 2023, using natural language processing techniques and network analysis. The computational science classification corpus was created into 15 main disciplines and 256 sub-disciplines sourced from Wikipedia. Among the 15 main disciplines, machine learning became the most popular topic after 2019, surpassing parallel &amp; distributed computing, which peaked in the early 2010s. ICCS and JoCS show differences in research popularity in both first and second-level disciplines. Algorithm theory, Mathematical modeling, and network science are the most dominant topics in both ICCS and JoCS. Different disciplines present different trends in ICCS and JoCS. In the past 24 years, machine learning related topics have gained the most attention in both ICCS and JoCS. We also examined and compared the correlation between the trends in ICCS and Google search Trends. The collaboration of disciplinary networks of second-level disciplines exhibits a scale-free characteristic, and the network structures have undergone significant evolution over 24 years. Moreover, different disciplinary communities exhibit different ”introverted” and ”extroverted” community characteristics within the network. Additionally, we examined the life span of thematic workshops and the evolution of authors’ collaborations inside and after ICCS.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"89 ","pages":"Article 102609"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115345","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 virtual population cohort approach for fetal cardiac valve modeling","authors":"Bettine G. van Willigen ,&nbsp;Nick van Osta ,&nbsp;M. Beatrijs van der Hout-van der Jagt ,&nbsp;Frans N. van de Vosse ,&nbsp;Wouter Huberts","doi":"10.1016/j.jocs.2025.102606","DOIUrl":"10.1016/j.jocs.2025.102606","url":null,"abstract":"<div><h3>Introduction:</h3><div>Mathematical models of fetal cardiovascular physiology provide valuable insights when studying the fetal circulatory system. In 0D and 1D models, fetal cardiac valves are often represented as diodes, offering simplicity and scalability but failing to capture realistic valvular behavior and can result in unrealistic pressure drops. More accurate models based on the Bernoulli equation capture valvular dynamic behavior more realistically, but they require constant tuning for specific cases, challenging simulation of fetal cardiac growth.</div></div><div><h3>Method:</h3><div>This study introduces a virtual population cohort approach informed by Bayesian inference as a solution to this challenge. By applying this method to a standardized aortic valve model of a 40-week-old fetus, it demonstrates its effectiveness in identifying input parameter distributions that reflect healthy fetal aortic valve behavior.</div></div><div><h3>Results:</h3><div>The approach involves defining a template model and determining an appropriate parameter space to simulate physiological behavior. Bayesian inference method facilitates identification of these parameters, resulting in a virtual population cohort that closely represents real physiological relevant fetal aortic valve conditions.</div></div><div><h3>Conclusion:</h3><div>The findings show that this approach successfully identifies a virtual population cohort of the fetal aortic valve model, including uncertainty of model parameters and their correlations with model outcomes. This approach offers a widely applicable framework with potential for models that can adapt to the evolving physiological conditions of fetal growth.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"88 ","pages":"Article 102606"},"PeriodicalIF":3.1,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917202","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":"Error-based correlation coefficient: An alternative to combine error and coefficient of correlation and its application in geophysical data","authors":"Waskito Pranowo ,&nbsp;Adhitya Ryan Ramadhani","doi":"10.1016/j.jocs.2025.102611","DOIUrl":"10.1016/j.jocs.2025.102611","url":null,"abstract":"<div><div>The coefficient of correlation and error values are two standard metrics for determining the similarity between two data sets. Correlated errors in experimental data are a common problem that is often overlooked. In addition, traditional error estimation approaches do not consider pattern similarity. An error-based method for estimating correlation coefficients is proposed, combining the fundamental principles of ' 'Pearson's correlation coefficient and error. This method represents a generalised form of the concordance correlation coefficient (CCC). The experiment with synthetic geophysical data pairs demonstrates that the suggested method effectively evaluates pattern and amplitude similarity. The proposed error-based correlation coefficient is comparable to the concordance coefficient of correlation but with some modifications. These modifications increase the new method's sensitivity to scale shifting, a vital element in geophysical data processing and analysis.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"88 ","pages":"Article 102611"},"PeriodicalIF":3.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899651","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":"Edge computing driven forest fire spread simulation: An energy-aware study","authors":"Carlos Carrillo,&nbsp;Tomàs Margalef,&nbsp;Antonio Espinosa,&nbsp;Ana Cortés","doi":"10.1016/j.jocs.2025.102605","DOIUrl":"10.1016/j.jocs.2025.102605","url":null,"abstract":"<div><div>An accurate and fast prediction of forest fire evolution is a crucial issue to minimize its impact. One of the challenges facing forest fire spread simulators is the uncertainty surrounding the input data. While high-performance computing (HPC) platforms help reduce these uncertainties, their accessibility during emergencies is limited due to infrastructure constraints. real time data collection using sensors onboard Unmanned Aerial Vehicles (UAVs) in real time can significantly reduce their uncertainty. However, transmitting this data to HPC environments and returning the results to firefighters remains difficult, especially in areas with poor connectivity. We propose using Edge Computing to address these challenges, leveraging low-consumption GPU-accelerated embedded systems for <em>in situ</em> data processing and wildfire spread simulation. For simulation purposes, the FARSITE forest fire spread simulator has been used. This work aims to demonstrate the feasibility of leveraging Embedded Systems with low-consumption GPUs to simulate <em>short-term</em> forest fire spread evolution (up to 5 hours) at high resolution (5 meters). The obtained results highlight that these devices can gather data, simulate the hazard, and deliver prediction results <em>in situ</em>, even without connectivity, opening up the possibility of monitoring and predicting fire behavior at high resolution without employing HPC platforms.</div><div>(This paper is an extension version of the best poster paper award in ICCS-2024 entitled “From HPC to Edge Computing: A new Paradigm in Forest Fire Spread Simulation”.)</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"88 ","pages":"Article 102605"},"PeriodicalIF":3.1,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903471","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}