Mathematical & Computational Applications最新文献_第8页

Entropy Generation of Cu–Al2O3/Water Flow with Convective Boundary Conditions through a Porous Stretching Sheet with Slip Effect, Joule Heating and Chemical Reaction 具有滑动效应、焦耳加热和化学反应的多孔拉伸片对流边界条件下Cu-Al2O3 /水流动的熵生成

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-02-02 DOI: 10.3390/mca28010018

Maria Immaculate Joyce, Jagan Kandasamy, S. Sivanandam

{"title":"Entropy Generation of Cu–Al2O3/Water Flow with Convective Boundary Conditions through a Porous Stretching Sheet with Slip Effect, Joule Heating and Chemical Reaction","authors":"Maria Immaculate Joyce, Jagan Kandasamy, S. Sivanandam","doi":"10.3390/mca28010018","DOIUrl":"https://doi.org/10.3390/mca28010018","url":null,"abstract":"Currently, the efficiency of heat exchange is not only determined by enhancements in the rate of heat transfer but also by economic and accompanying considerations. Responding to this demand, many scientists have been involved in improving heat transfer performance, which is referred to as heat transfer enhancement, augmentation, or intensification. This study deals with the influence on hybrid Cu–Al2CO3/water nanofluidic flows on a porous stretched sheet of velocity slip, convective boundary conditions, Joule heating, and chemical reactions using an adapted Tiwari–Das model. Nonlinear fundamental equations such as continuity, momentum, energy, and concentration are transmuted into a non-dimensional ordinary nonlinear differential equation by similarity transformations. Numerical calculations are performed using HAM and the outcomes are traced on graphs such as velocity, temperature, and concentration. Temperature and concentration profiles are elevated as porosity is increased, whereas velocity is decreased. The Biot number increases the temperature profile. The rate of entropy is enhanced as the Brinkman number is raised. A decrease in the velocity is seen as the slip increases.","PeriodicalId":53224,"journal":{"name":"Mathematical & Computational Applications","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46513169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Forecasting Financial and Macroeconomic Variables Using an Adaptive Parameter VAR-KF Model 使用自适应参数VAR-KF模型预测金融和宏观经济变量

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-02-02 DOI: 10.3390/mca28010019

Natnapa Promma, Nawinda Chutsagulprom

引用次数: 0

A Survey on the Hausdorff Dimension of Intersections 关于交叉口Hausdorff维数的研究

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-31 DOI: 10.3390/mca28020049

P. Mattila

引用次数: 4

Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives 多目标优化:一种减少目标数量的机器学习方法

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-30 DOI: 10.3390/mca28010017

A. Gaspar-Cunha, Paulo Costa, Francis A. Monaco, Alexandre Delbem

{"title":"Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives","authors":"A. Gaspar-Cunha, Paulo Costa, Francis A. Monaco, Alexandre Delbem","doi":"10.3390/mca28010017","DOIUrl":"https://doi.org/10.3390/mca28010017","url":null,"abstract":"Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since the types of algorithms generally used to solve these problems are based on the concept of non-dominance, which ceases to work as the number of objectives grows. This problem is known as the curse of dimensionality. Simultaneously, the existence of many objectives, a characteristic of practical optimization problems, makes choosing a solution to the problem very difficult. Different approaches are being used in the literature to reduce the number of objectives required for optimization. This work aims to propose a machine learning methodology, designated by FS-OPA, to tackle this problem. The proposed methodology was assessed using DTLZ benchmarks problems suggested in the literature and compared with similar algorithms, showing a good performance. In the end, the methodology was applied to a difficult real problem in polymer processing, showing its effectiveness. The algorithm proposed has some advantages when compared with a similar algorithm in the literature based on machine learning (NL-MVU-PCA), namely, the possibility for establishing variable–variable and objective–variable relations (not only objective–objective), and the elimination of the need to define/chose a kernel neither to optimize algorithm parameters. The collaboration with the DM(s) allows for the obtainment of explainable solutions.","PeriodicalId":53224,"journal":{"name":"Mathematical & Computational Applications","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42635611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Feature Paper Collection of Mathematical and Computational Applications—2022 数学与计算应用专题论文集——2022

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-28 DOI: 10.3390/mca28010016

G. Rozza, O. Schütze, N. Fantuzzi

引用次数: 0

Knowledge Transfer Based on Particle Filters for Multi-Objective Optimization 基于粒子滤波器的多目标优化知识传递

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-18 DOI: 10.3390/mca28010014

Xilu Wang, Yaochu Jin

{"title":"Knowledge Transfer Based on Particle Filters for Multi-Objective Optimization","authors":"Xilu Wang, Yaochu Jin","doi":"10.3390/mca28010014","DOIUrl":"https://doi.org/10.3390/mca28010014","url":null,"abstract":"Particle filters, also known as sequential Monte Carlo (SMC) methods, constitute a class of importance sampling and resampling techniques designed to use simulations to perform on-line filtering. Recently, particle filters have been extended for optimization by utilizing the ability to track a sequence of distributions. In this work, we incorporate transfer learning capabilities into the optimizer by using particle filters. To achieve this, we propose a novel particle-filter-based multi-objective optimization algorithm (PF-MOA) by transferring knowledge acquired from the search experience. The key insight adopted here is that, if we can construct a sequence of target distributions that can balance the multiple objectives and make the degree of the balance controllable, we can approximate the Pareto optimal solutions by simulating each target distribution via particle filters. As the importance weight updating step takes the previous target distribution as the proposal distribution and takes the current target distribution as the target distribution, the knowledge acquired from the previous run can be utilized in the current run by carefully designing the set of target distributions. The experimental results on the DTLZ and WFG test suites show that the proposed PF-MOA achieves competitive performance compared with state-of-the-art multi-objective evolutionary algorithms on most test instances.","PeriodicalId":53224,"journal":{"name":"Mathematical & Computational Applications","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48339689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acknowledgment to the Reviewers of MCA in 2022 感谢2022年MCA审稿人

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-18 DOI: 10.3390/mca28010015

引用次数: 0

Controllability Criteria for Nonlinear Impulsive Fractional Differential Systems with Distributed Delays in Controls 具有分布时滞的非线性脉冲分数阶微分系统的可控性准则

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-15 DOI: 10.3390/mca28010013

A. Debbouche, B. Vadivoo, V. Fedorov, V. Antonov

引用次数: 0

COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining 基于多目标进化算法的新冠肺炎数据分析因果关联规则挖掘

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-13 DOI: 10.3390/mca28010012

Santiago Sinisterra-Sierra, Salvador Godoy-Calderón, Miriam Pescador-Rojas

{"title":"COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining","authors":"Santiago Sinisterra-Sierra, Salvador Godoy-Calderón, Miriam Pescador-Rojas","doi":"10.3390/mca28010012","DOIUrl":"https://doi.org/10.3390/mca28010012","url":null,"abstract":"Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care.","PeriodicalId":53224,"journal":{"name":"Mathematical & Computational Applications","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45715441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Pseudo-Poisson Distributions with Concomitant Variables 伴随变量的伪泊松分布

IF 1.9

Mathematical & Computational Applications Pub Date : 2023-01-12 DOI: 10.3390/mca28010011

B. Arnold, B. G. Manjunath

引用次数: 0