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SSRN 2024年

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xilong Jin, Neng-Zhi Zhang, Cai-Rong Sun, Xiao-Wei Zhang, Lei School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou730050 China

The physical properties and anisotropy of carbonate at high pressure are the basis of understanding the deep carbon cycle and storage. Here, the phase diagram of strontianite (SrCO3) was constructed by first-principles calculations and its physical properties and anisotropy at high pressure were investigated. The results show that SrCO3 only has the Pnma and Pmmn phases, and there is no R3m phase. SrCO3 polymorphs have large elastic anisotropy. Compared with CaCO3 polymorphs, SrCO3 polymorphs have lower wave velocity and smaller seismic anisotropy. The linear elastic compressibility combined with the volumetric thermal expansion coefficient was used to obtain the linear thermal expansion coefficient of SrCO3-Pmmn. © 2024, The Authors. All rights reserved.

关键词： Strontium compounds

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Study on the Structure, Elasticity, and Thermal Conductivity of Orthocarbonate Sr2CO4

SSRN

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SSRN 2024年

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xi-Long Jin, Neng-Zhi Zhang, Cai-Rong Wang, Xiao-Yun Wu, Liang School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou730050 China

Orthocarbonate Sr2CO4 is a recently discovered Sr-carbonate that plays a crucial role in understanding the global long-term carbon cycle. In this work, the structure, equation of state, elasticity, and thermal conductivity of Sr2CO4 were studied by first-principles calculations. The results show that the surfaces of the SrO9 and SrO11 polyhedrons, which make up Sr2CO4, are composed of triangles and quadrangles. By comparison with other alkaline earth metal orthocarbonates, SrCO3 polymorphs and the Preliminary Reference Earth Model (PREM), Sr2CO4 is found to be a carbonate with high density, low wave velocity, low anisotropy and low thermal conductivity in the entire Earth's mantle. This study may help to understand the high-pressure behavior of alkaline earth metal orthocarbonates in the Earth's interior. © 2024, The Authors. All rights reserved.

关键词： Elasticity

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SCALABLE MULTILEVEL MONTE CARLO METHODS EXPLOITING PARALLEL REDISTRIBUTION ON COARSE LEVELS

arXiv

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arXiv 2024年

作者： Fairbanks, Hillary R. Kalchev, Delyan Z. Lee, Chak Shing Vassilevski, Panayot S. Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Fariborz Maseeh Department of Mathematics and Statistics Portland State University PortlandOR United States

We study an element agglomeration coarsening strategy that requires data redistribution at coarse levels when the number of coarse elements becomes smaller than the used computational units (cores). The overall procedure generates coarse elements (general unstructured unions of fine grid elements) within the framework of element-based algebraic multigrid methods (or AMGe) studied previously. The AMGe generated coarse spaces have the ability to exhibit approximation properties of the same order as the fine-level ones since by construction they contain the piecewise polynomials of the same order as the fine level ones. These approximation properties are key for the successful use of AMGe in multilevel solvers for nonlinear partial differential equations as well as for multilevel Monte Carlo (MLMC) simulations. The ability to coarsen without being constrained by the number of available cores, as described in the present paper, allows to improve the scalability of these solvers as well as in the overall MLMC method. The paper illustrates this latter fact with detailed scalability study of MLMC simulations applied to model Darcy equations with a stochastic log-normal permeability *** Codes 65F50, 65Y05, 68W10 © 2024, CC BY.

关键词： Stochastic systems

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Density Clustering Algorithm Based on Nearest Neighbor Relationship and Relevant Space

SSRN

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SSRN 2024年

作者： Li, Xiangli Zhuang, Quan School of Mathematics and Computing Science Guilin University of Electronic Technology Guangxi541004 China Guangxi Colleges University Key Laboratory of Data Analysis and Computation Guangxi541004 China Center for Applied Mathematics of Guangxi Guangxi541004 China

Density-based clustering algorithms have the advantages of simplicity of thought and the ability to recognize clusters of arbitrary shapes as well as noise objects, and thus have been widely used in the fields of machine learning and data mining. However, traditional density-based clustering algorithms often have poor clustering performance when facing datasets with heterogeneous density and datasets with neighboring clusters. This is because the definition of density often relies solely on the computation of distance,so that sparse clusters are often incorrectly merged or ignored and the objects at the edge of adjacent clusters are incorrectly assigned. In order to solve the above problems, this paper proposes a density clustering algorithm based on nearest neighbor relationships and relevant spaces(NRDC). We introduce mutual nearest neighbors and reverse nearest neighbors into the algorithm,and also propose the concept of relevant space to define a new local density ,and propose a new core point identification method to identify the structure of clusters to form *** we propose a allocation strategy which based on relevant space for the remaining objects to obtain clustering results. Experimental results on 8 synthetic datasets and 12 real datasets on UCI show that the proposed algorithm NRDC exhibits good clustering performance. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Density Clustering Algorithm Based on Effective Neighbors and Multi-Stage Allocation Strategy

SSRN

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SSRN 2024年

作者： Zhuang, Quan Li, Xiangli School of Mathematics and Computing Science Guilin University of Electronic Technology Guangxi 541004 China Guangxi Colleges and University Key Laboratory of Data Analysis and Computation Guangxi 541004 China Center for Applied Mathematics of Guangxi Guangxi 541004 China

Density peak clustering (DPC) is an effective clustering method that is widely used in various *** idea of the density peak clustering is very simplistic, which can quickly select the cluster density peaks and the clustering process requires no iteration. However, DPC still has the following drawbacks:The calculation of the local density of the DPC algorithm is highly dependent on the calculation of the cut-off ***, the number of cluster centers is very challenging to determine and to select. Lastly, the straightforward allocation strategy makes the presence of domino effect, which greatly reduces the clustering performance of the DPC. To overcome the limitations presented above, we propose a density clustering algorithm based on effective neighbors and multistage allocation strategy(DCMS). We redeffne the density by introducing the concept of effective neighbors and identify the underlying structure of each *** addition, we combine the idea of outlier detection to divide the objects into different types of ***, we propose an multistage assignment strategy to generate initial clusters and assign the remaining points to obtain the ffnal clustering *** results on synthetic and UCI datasets validate that the proposed algorithm outperforms several famous clustering algorithms. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Comparing Constraint Evaluation Methods for Shape-Constrained Regression 19thth

Comparing Constraint Evaluation Methods for Shape-Constrain...

引用

19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Haider, Christian Bachinger, Florian Holzinger, Florian de França, Fabrício Olivetti Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Hagenberg Austria Johannes Kepler University Linz Austria Center for Mathematics Computing and Cognition Federal University of ABC Santo André Brazil

ISBN: (纸本)9783031829512

Shape-constrained regression is an important desideratum of data-based modeling when you want to enforce your model to possess an expected behavior despite the intrinsic noise of the collected data. Conventional data-based modeling approaches are solely reliant on empirical data, and thus, often fail to incorporate the essential physical constraints inherent to the underlying systems. This leads to a lack of trust in the model and a reduction of explanatory power. Recognizing these limitations of traditional data-based methodologies, the integration of shape constraints, derived from domain expertise or fundamental physical principles, emerges as a promising extension to enhance the quality of models. In this paper, we distinguish between single-objective and multi-objective approaches, as well as soft and hard constraint methods to evaluate the constraint violations of models. In the context of this research, the primary focus is on single-objective approaches that incorporate an expanded scope of features and dynamics for constraint handling. Instead of strictly enforcing hard constraints and discarding solutions solely based on constraint violation, the paper presents a dynamic method for calculating constraint violation and prediction error. The presented approach involves an incremental increase in the weighting of constraint violation during the modeling process, achieving an initial, wide-ranging exploration of the solution space followed by a comprehensive investigation of potential models through an in-depth search. Furthermore, we want to explore the critical issue of premature stagnation, a phenomenon wherein the search for a conformant model, plateaus before reaching an acceptable local optima solution. This paper tries to address this challenge through different strategies to maintain a certain diversity of solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Regression analysis

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Turbulent Length Scale for Multilayer RANS Model of Urban Canopy and Its Evaluation Based on Large-Eddy Simulations

Supercomputing Frontiers and Innovations

引用

Supercomputing Frontiers and Innovations 2021年第4期8卷 100-116页

作者： Glazunov, Andrey V. Debolskiy, Andrey V. Mortikov, Evgeny V. G.I. Marchuk Institute of Numerical Mathematics Russian Academy of Science Moscow Russia Moscow Center for Fundamental and Applied Mathematics Moscow Russia Research Computing Center Lomonosov Moscow State University Moscow Russia A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Science Moscow Russia

Large-Eddy Simulation (LES) numerical experiments of neutrally-stratified turbulent flow over an urban-type surface and passive scalar transport by this flow are performed. A simple parameterization of the turbulent length scale containing only one empirical constant is proposed. Multilayer Reynolds-Averaged Navier-Stokes (RANS) model of turbulent flow and turbulent scalar diffusion is constructed. The results of the RANS model are compared with the LES experiments. It is shown that the proposed approach allows predicting the average flow velocity and the scalar concentration inside and above the urban canopy. © The Authors 2021. This paper is published with open access at ***

关键词： Large eddy simulation

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Investigation on a quantum algorithm for linear differential equations

arXiv

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arXiv 2024年

作者： Dong, Xiaojing Peng, Yizhe Tang, Qili Yang, Yin Yu, Yue Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

Ref. [BCOW17] introduced a pioneering quantum approach (coined BCOW algorithm) for solving linear differential equations with optimal error tolerance. Originally designed for a specific class of diagonalizable linear differential equations, the algorithm was extended by Krovi in [Kro23] to encompass broader classes, including non-diagonalizable and even singular matrices. Despite the common misconception, the original algorithm is indeed applicable to non-diagonalizable matrices, with diagonalisation primarily serving for theoretical analyses to establish bounds on condition number and solution error. By leveraging basic estimates from [Kro23], we derive bounds comparable to those outlined in the Krovi algorithm, thereby reinstating the advantages of the BCOW approach. Furthermore, we extend the BCOW algorithm to address time-dependent linear differential equations by transforming non-autonomous systems into higher-dimensional autonomous ones, a technique also applicable for the Krovi algorithm. © 2024, CC BY-NC-ND.

关键词： Consensus algorithm

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Frequency-domain criterion on the stabilizability for infinite-dimensional linear control systems

arXiv

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arXiv 2023年

作者： Kunisch, Karl Wang, Gengsheng Yu, Huaiqiang Johann Radon Institute for Computational and Applied Mathematics ÖAW Altenbergerstrasse 69 Linz4040 Austria Institute of Mathematics and Scientific Computing University of Graz Heinrichstrasse 36 Graz8010 Austria Center for Applied Mathematics Tianjin University Tianjin300072 China School of Mathematics Tianjin University Tianjin300354 China

A quantitative frequency-domain condition related to the exponential stabilizability for infinite-dimensional linear control systems is presented. It is proven that this condition is necessary and sufficient for the stabilizability of special systems, while it is a necessary condition for the stabilizability in general. Applications are *** Codes 93D15, 93C25, 93C80, 93D20 © 2023, CC BY-NC-SA.

关键词： Linear control systems

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