Journal of Computational Science最新文献

Estimation of high-dimensional covariance matrices in latent factor models is an important topic in many fields and especially in finance. Since the number of financial assets grows while the estimation window length remains of limited size, the often used sample estimator yields noisy estimates which are not even positive definite. Under the assumption of latent factor models, the covariance matrix is decomposed into a common low-rank component and a full-rank idiosyncratic component. In this paper we focus on the estimation of the idiosyncratic component, under the assumption of a grouped structure of the time series, which may arise due to specific factors such as industries, asset classes or countries. We propose a generalized methodology for estimation of the block-diagonal idiosyncratic component by clustering the residual series and applying shrinkage to the obtained blocks in order to ensure positive definiteness. We derive two different estimators based on different clustering methods and test their performance using simulation and historical data. The proposed methods are shown to provide reliable estimates and outperform other state-of-the-art estimators based on thresholding methods.

We present Decapodes, a diagrammatic tool for representing, composing, and solving partial differential equations. Decapodes provides an intuitive diagrammatic representation of the relationships between variables in a system of equations, a method for composing systems of partial differential equations using an operad of wiring diagrams, and an algorithm for deriving solvers using hypergraphs and string diagrams. The string diagrams are in turn compiled into executable programs using the techniques of categorical data migration, graph traversal, and the discrete exterior calculus. The generated solvers produce numerical solutions consistent with state-of-the-art open source tools as demonstrated by benchmark comparisons with SU2. These numerical experiments demonstrate the feasibility of this approach to multiphysics simulation and identify areas requiring further development.

One of the goals of Industry 4.0 is the adoption of data-driven models to enhance various aspects of the manufacturing process, such as monitoring equipment conditions, ensuring product quality, detecting failures, and preparing optimal maintenance plans. However, many machine-learning algorithms require a large amount of training data to reach desired performance. In numerous industrial applications, such data is either not available or its acquisition is a costly process. In such cases, simulation frameworks are employed to replicate the behavior of real-world facilities and generate data for further analysis. Simulation frameworks typically provide high-quality data but are often slow which can be problematic when real-time decision-making is required. Control approaches based on simulation-based data commonly face challenges related to inflexibility, particularly in dynamic production environments undergoing frequent reconfiguration and upgrades. This paper introduces a method that seeks to strike a balance between the reliance on simulated data and the limited robustness of simulation-based control methods. This balance is achieved by supplementing available data with additional expert knowledge, enabling the matching of similar data sources and their combination for reuse. Furthermore, we augment the methods with an explainability layer, facilitating collaboration between the human expert and the AI system, leading to informed and actionable decisions. The performance of the proposed solution is demonstrated through a case study on gas production from an underground reservoir resulting in reduced downtime, heightened process reliability, and enhanced overall performance. This paper builds upon our conference paper (Kuk et al., 2023), addressing the same problem with an extended, more generic methodology, and presenting entirely new results.

Medical image analysis plays a crucial role in modern healthcare for accurate diagnosis and treatment. However, the inherent challenges and limitations posed by the complexity and variability of medical images, coupled with the shortcomings of existing methods, necessitate the development of novel approaches. In this study, we propose LiteFusionNet (Lightweight Fusion Network), a lightweight model that effectively addresses these challenges, offering the advantage of accurate and efficient medical image classification while mitigating the computational demands. The LitefusionNet leverages the power of deep convolutional neural networks (DCNNs) and feature fusion techniques to achieve improved performance in medical image classification. LitefusionNet combines the strengths of MobileNet and MobileNetV2 architectures to extract robust features from medical images. These features capture discriminative information from different levels of abstraction, enhancing the model's ability to capture fine-grained patterns. The fusion process employs a concatenation method to combine the extracted features, resulting in a more comprehensive representation that improves the model's classification accuracy. To evaluate the effectiveness of LitefusionNet, extensive experiments are conducted on a diverse set of publicly available medical image datasets, including brain MRI, skin, CT, X-ray, and histology. The results demonstrate that LitefusionNet outperforms several existing models in terms of classification accuracy, showcasing its efficacy in different medical imaging modalities. Furthermore, we provide interpretability to the model's predictions by performing Grad-CAM analysis, enabling insights into the important regions in the medical images that contribute to the classification decision. In addition, we compare LitefusionNet with five pre-trained models. LiteFusionNet excels in medical image classification, boasting impressive accuracies across diverse datasets: 97.33% for brain MRI, 91.11% for skin, 99.00% for CT, 98.15% for X-ray, and 92.11% for histology. These results underscore LiteFusionNet's robust and versatile performance, making it a compelling solution for accurate and efficient medical image analysis. Overall, LitefusionNet strikes a balance between accuracy, efficiency, and real-time performance. Our findings demonstrate its potential as a promising solution for accurate and efficient medical image analysis, with applications in diagnostic support systems and clinical decision-making.

This study developed a simulation model using a smoothed particle hydrodynamics (SPH) method targeted to seizure process at the mesoscale. The mechanisms of wear, adhesion, and heat generation leading to seizure at the mesoscale were modelized without assumptions or theories based on empirical rules. In particular, we targeted on flash temperature during seizure process, which is difficult to measure directly in experiment and has not been simulated without using friction theory. Our model consisted of both a macroscopic elastoplastic consideration and a microscopic interfacial interaction consideration, and the heat generation scheme that 90% of the plastic strain energy is converted to heat energy were adopted in the model. The simulation demonstrated the seizure process in which the contact state is maintained by the strong interfacial interaction as the plastic strain progresses and the temperature rapidly rises. The flash temperature by the simulation provided a reasonable quantitative match at order level to a temperature estimated by substituting true contact area and interfacial heat flux obtained by the simulation into a theoretical formula of flash temperature.

Medical image segmentation is an important technical tool, OTSU algorithm is a common method in threshold segmentation, but with the increase of the number of threshold segmentation, the selection of its threshold is a big problem, and the segmentation effect is difficult to be guaranteed. In order to solve this problem, this paper proposes a random collision whale optimization algorithm to optimize OTSU for reliable image segmentation. The algorithm is called RCWOA for short. Firstly, the Halton sequence is used to uniformly initialize the population to make the population position distribution uniform, and then the dimensional Opposition-based learning of small-hole imaging is introduced to update the whale position and find out the missing feasible solution. Finally, the random collision theory is used to update the position of the optimal individual to improve the quality of the solution, At the same time, it also improves the search ability of the algorithm. In 12 test functions, RCWOA was compared with 6 other algorithms, demonstrating the feasibility and novelty of RCWOA. In 8 experiments of fundus image segmentation, RCWOA was compared with 9 other algorithms. The results showed that RCWOA had a Friedman test composite ranking of 1.3516, ranking at the forefront, and exhibited significantly improved segmentation quality.

This article introduces the fractional variable order (VO) Gray–Scott model using the notion of VO fractional derivative in the Caputo sense. An efficient numerical method has been designed based on the Vieta–Lucas polynomial and the spectral collocation method for solving this model. The designed technique converts the concerned model into a nonlinear algebraic system of equations, which can be solved by Newton’s iterative method. In this article, we have illustrated the convergence analysis of the approximation and shown that a high order of convergence can be achieved despite a smaller number of approximations. A few numerical results are presented in order to verify the reliability and accuracy of the demonstrated scheme. The results of absolute errors for the considered Gray–Scott model with its exact solution show that the technique is very suitable for finding the solutions to the said kind of complex physical problem.

This research paper introduces a new hybrid block method to solve the third-order boundary value problems (BVPs). The method combines interpolation and collocation and uses a power series polynomial to find an approximate solution to the considered third-order BVPs. Some third-order BVP models are numerically solved to verify the performance and efficiency of the proposed method, and the approximate solution from the proposed method is more efficient when compared to some existing numerical methods. In summary, the proposed method provides reliable and efficient accuracy for solving third-order BVPs, making it a valuable contribution to the fields of numerical analysis and computational mathematics. The advantages of the proposed method include improved computational time efficiency and accuracy in terms of maximum absolute errors for solving third-order BVPs.

Clinical Pathways (CPs) consist of structured multidisciplinary guidelines and protocols used to model steps of clinical treatments. The main objective of applying CPs is that of optimizing both outcomes and efficiency — however, the actual implementation of CPs can be complex and result in important deviations and unexpected inefficiencies. In this paper, we develop an approach to identifying and understanding such problems by leveraging process mining techniques and background knowledge. We design specific data structures aimed at properly capturing the data produced during the implementation of CPs, including the treatment of more than one disease for a single patient. We then provide a methodology to discover and characterize congestion dynamics in CPs. Since the resulting process discovery problem is theoretically intractable, we develop heuristic algorithms that, based on an extensive experimental assessment, prove capable of discovering meaningful knowledge with a reasonable computational effort.

In this paper we present a novel method for solving the shallow-shelf equations in the presence of grounding lines. The shallow-self equations are a two-dimensional system of nonlinear elliptic PDEs with variable coefficients that are discontinuous across the grounding line, which we treat as a sharp interface between grounded and floating ice. The grounding line is “reconstructed” from ice thickness and basal topography data to provide necessary geometric information for our cut-cell, finite volume discretization. Our discretization enforces jump conditions across the grounding line and achieves high-order accuracy using stencils constructed with a weighted least-squares method. We demonstrate second and fourth order convergence of the velocity field, driving stress, and reconstructed geometric information.