Mathematics and Computers in Simulation最新文献_第7页

PDE models for vegetation biomass and autotoxicity 植被生物量和自毒性的 PDE 模型

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-12 DOI: 10.1016/j.matcom.2024.07.004

Mudassar Abbas , Francesco Giannino , Annalisa Iuorio , Zubair Ahmad , Francesco Calabró

{"title":"PDE models for vegetation biomass and autotoxicity","authors":"Mudassar Abbas , Francesco Giannino , Annalisa Iuorio , Zubair Ahmad , Francesco Calabró","doi":"10.1016/j.matcom.2024.07.004","DOIUrl":"10.1016/j.matcom.2024.07.004","url":null,"abstract":"<div><div>Numerical techniques are widely used to simulate population dynamics in space. In vegetation dynamics, these techniques are very useful to investigate how plants grow, compete for resources, and react to environmental factors within the ecosystem. Plant–soil feedback (PSF) refers to the process where plants or a community alter the biotic and abiotic characteristics of soil that affects the growth of plants or community subsequently growing in that soil. During the last three decades, PSF has been recognized as an important driver for the emergence of vegetation patterns. The importance of studying such vegetation patterns is that they provide an insight into potential ecological changes and illustrate the flexibility and resilience of an ecosystem. Despite the fact that water depletion was once thought to be a major factor in the development of vegetation patterns the existence of patterns in ecosystems without water limitations serves as evidence that this is not the case. In this study, we examine how negative plant–soil feedback contributes to the dynamics of plant biomass. We provide a comparison of different reaction–diffusion PDE models explaining the dynamics of plant biomass in the presence of autotoxicity produced by litter decomposition. We introduce different growth terms, including logistic and exponential, along with additional factors such as extra mortality and inhibitor terms, and develop six distinct models to investigate their individual and combined effects on biomass toxicity distribution. By applying appropriate numerical techniques, we solve the proposed reaction–diffusion PDE models in MATLAB to predict the impact of soil toxicity on plant biomass.</div></div>","PeriodicalId":49856,"journal":{"name":"Mathematics and Computers in Simulation","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of the stress–strength reliability for the exponential-Rayleigh distribution 估计指数-雷利分布的应力-强度可靠性

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-10 DOI: 10.1016/j.matcom.2024.09.005

M.S. Kotb , M.A. Al Omari

引用次数: 0

An exponential spectral deferred correction method for multidimensional parabolic problems 多维抛物线问题的指数谱延迟修正法

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-10 DOI: 10.1016/j.matcom.2024.09.003

Yurun Wang, Fei Liu

引用次数: 0

News of IMACS IMACS 新闻

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-05 DOI: 10.1016/S0378-4754(24)00333-1

引用次数: 0

A quadrature method for Volterra integral equations with highly oscillatory Bessel kernel 具有高振荡贝塞尔核的 Volterra 积分方程的正交方法

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-05 DOI: 10.1016/j.matcom.2024.09.002

Longbin Zhao , Pengde Wang , Qiongqi Fan

引用次数: 0

IMACS Calendar of Events IMACS 活动日历

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-05 DOI: 10.1016/S0378-4754(24)00334-3

引用次数: 0

Modeling low-intensity ultrasound mechanotherapy impact on growing cancer stem cells 模拟低强度超声机械疗法对生长中的癌症干细胞的影响

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-04 DOI: 10.1016/j.matcom.2024.08.030

Beatriz Blanco , Roberto Palma , Manuel Hurtado , Gema Jiménez , Carmen Griñán-Lisón , Juan Melchor , Juan Antonio Marchal , Hector Gomez , Guillermo Rus , Juan Soler

{"title":"Modeling low-intensity ultrasound mechanotherapy impact on growing cancer stem cells","authors":"Beatriz Blanco , Roberto Palma , Manuel Hurtado , Gema Jiménez , Carmen Griñán-Lisón , Juan Melchor , Juan Antonio Marchal , Hector Gomez , Guillermo Rus , Juan Soler","doi":"10.1016/j.matcom.2024.08.030","DOIUrl":"10.1016/j.matcom.2024.08.030","url":null,"abstract":"<div><p>Targeted therapeutic interventions utilizing low-intensity ultrasound (LIUS) exhibit substantial potential for hindering the proliferation of cancer stem cells. This investigation introduces a multiscale model and computational framework to comprehensively explore the therapeutic LIUS on poroelastic tumor dynamics, thereby unraveling the intricacies of mechanotransduction mechanisms at play. Our model includes both macroscopic timescales encompassing days and rapid timescales spanning from microseconds to seconds, facilitating an in-depth comprehension of tumor behavior. We unveil the discerning suppression or reorientation of cancer cell proliferation and migration, enhancing a notable redistribution of cellular phases and stresses within the tumor microenvironment. Our findings defy existing paradigms by elucidating the impact of LIUS on cancer stem cell behavior. This endeavor advances our fundamental understanding of mechanotransduction phenomena in the context of LIUS therapy, thus underscoring its promising as a targeted therapeutic modality for cancer treatment. Furthermore, our results make a substantial contribution to the broader scientific community by shedding light on the intricate interplay between mechanical forces, cellular responses, and the spatiotemporal evolution of tumors. These insights hold the promising to promote a new perspective for the future development of pioneering and highly efficacious therapeutic strategies for combating cancer in a personalized manner.</p></div>","PeriodicalId":49856,"journal":{"name":"Mathematics and Computers in Simulation","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378475424003434/pdfft?md5=193e442ad0d077d63895faae24bfc727&pid=1-s2.0-S0378475424003434-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computing parameter planes of iterative root-finding methods with several free critical points 计算具有多个自由临界点的迭代寻根方法的参数平面

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-03 DOI: 10.1016/j.matcom.2024.08.013

Beatriz Campos , Jordi Canela , Alberto Rodríguez-Arenas , Pura Vindel

引用次数: 0

Discretisation of the Hough parameter space for fitting and recognising geometric primitives in 3D point clouds 用于拟合和识别三维点云中几何基元的 Hough 参数空间的离散化

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-02 DOI: 10.1016/j.matcom.2024.08.033

Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti

{"title":"Discretisation of the Hough parameter space for fitting and recognising geometric primitives in 3D point clouds","authors":"Chiara Romanengo, Bianca Falcidieno, Silvia Biasotti","doi":"10.1016/j.matcom.2024.08.033","DOIUrl":"10.1016/j.matcom.2024.08.033","url":null,"abstract":"<div><p>Research in recognising and fitting simple geometric shapes has been ongoing since the 1970s, with various approaches proposed, including stochastic methods, parameter methods, primitive-based registration techniques, and more recently, deep learning. The Hough transform is a method of interest due to its demonstrated robustness to noise and outliers, ability to handle missing data, and support for multiple model instances. Unfortunately, one of the main limitations of the Hough transform is how to properly discretise its parameter space, as increasing their number or decreasing the sampling frequency can make it computationally expensive.</p><p>The relationship between the approximation accuracy and the parameter space’s discretisation is investigated to address this. We present two distinct discretisations to illustrate how the fitting and recognition quality can be improved by selecting an appropriate parameter discretisation. Our parameter-driven space discretisation is shown to significantly improve the parameter recognition quality over the classical method and reduce computational time and space by decreasing the discretisation’s dimension, as demonstrated by an extensive validation on a benchmark of geometric primitives. Preliminary experiments are also presented on segmenting datasets from urban buildings and CAD objects.</p></div>","PeriodicalId":49856,"journal":{"name":"Mathematics and Computers in Simulation","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0378475424003458/pdfft?md5=808410d9aafec4900a4d7d00f9dcdd3b&pid=1-s2.0-S0378475424003458-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An asymptotically optimal algorithm for generating bin cardinalities 生成二进制万有引力的渐进最优算法

IF 4.4 2区数学

Mathematics and Computers in Simulation Pub Date : 2024-09-02 DOI: 10.1016/j.matcom.2024.08.034

Luc Devroye , Dimitrios Los

{"title":"An asymptotically optimal algorithm for generating bin cardinalities","authors":"Luc Devroye , Dimitrios Los","doi":"10.1016/j.matcom.2024.08.034","DOIUrl":"10.1016/j.matcom.2024.08.034","url":null,"abstract":"<div><p>In the balls-into-bins setting, <span><math><mi>n</mi></math></span> balls are thrown uniformly at random into <span><math><mi>n</mi></math></span> bins. The naïve way to generate the final load vector takes <span><math><mrow><mi>Θ</mi><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> time. However, it is well-known that this load vector has with high probability bin cardinalities of size <span><math><mrow><mi>Θ</mi><mrow><mo>(</mo><mfrac><mrow><mo>log</mo><mi>n</mi></mrow><mrow><mo>log</mo><mo>log</mo><mi>n</mi></mrow></mfrac><mo>)</mo></mrow></mrow></math></span>. Here, we present an algorithm in the RAM model that generates the bin cardinalities of the final load vector in the optimal <span><math><mrow><mi>Θ</mi><mrow><mo>(</mo><mfrac><mrow><mo>log</mo><mi>n</mi></mrow><mrow><mo>log</mo><mo>log</mo><mi>n</mi></mrow></mfrac><mo>)</mo></mrow></mrow></math></span> time in expectation and with high probability.</p><p>Further, the algorithm that we present is still optimal for any <span><math><mrow><mi>m</mi><mo>∈</mo><mrow><mo>[</mo><mi>n</mi><mo>,</mo><mi>n</mi><mo>log</mo><mi>n</mi><mo>]</mo></mrow></mrow></math></span> balls and can also be used as a building block to efficiently simulate more involved load balancing algorithms. In particular, for the <span>Two-Choice</span> algorithm, which samples two bins in each step and allocates to the least-loaded of the two, we obtain roughly a quadratic speed-up over the naïve simulation.</p></div>","PeriodicalId":49856,"journal":{"name":"Mathematics and Computers in Simulation","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0