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Algorithms
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State-of-the-Art Algorithms and Mathematical Models for Multidisciplinary Applications in Spain
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State-of-the-Art Algorithms and Mathematical Models for Multidisciplinary Applications in Spain

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 11231

Special Issue Editor

Prof. Dr. Francesc Pozo

E-Mail Website
Guest Editor
Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spanish researchers have played an important role in advancing the study of algorithms, mathematical models, and their applications. These applications range from computer-assisted diagnostic systems in a wide sense (medical, mechanical systems, civil engineering, chemical processes), evolutionary algorithms and machine learning, combinatorial optimization and network algorithms, and randomized and approximation algorithms.

This Special Issue on State-of-the-Art Algorithms and Mathematical Models for Multidisciplinary Applications in Spain aims to provide a comprehensive overview of algorithms and mathematical models in Spain. The Special Issue will include both original research articles and reviews covering novel approaches in the field.

We expect that this Special Issue will provide some insights into future directions of algorithms and mathematical models from Spain and provide the rest of the world with a snapshot of what is carried out in this field in our country.

Prof. Dr. Francesc Pozo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Algorithm engineering
  • Combinatorial optimization, mathematical programming, operations research, discrete mathematics, and graph theory
  • Computational geometry
  • Distributed and parallel algorithms
  • Image processing with applications
  • Interdisciplinary applications in other areas of mathematics and computer science
  • Machine learning
  • Markov chains and simulation
  • Numerical analysis
  • Performance and testing of algorithms
  • Randomized algorithms
  • Theory of algorithms

Published Papers (3 papers)

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Research

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24 pages, 11958 KiB  
Article
Simulation of Low-Speed Buoyant Flows with a Stabilized Compressible/Incompressible Formulation: The Full Navier–Stokes Approach versus the Boussinesq Model
by Guillermo Hauke and Jorge Lanzarote
Algorithms 2022, 15(8), 278; https://doi.org/10.3390/a15080278 - 05 Aug 2022
Cited by 2 | Viewed by 1448
Abstract
This paper compares two strategies to compute buoyancy-driven flows using stabilized methods. Both formulations are based on a unified approach for solving compressible and incompressible flows, which solves the continuity, momentum, and total energy equations in a coupled entropy-consistent way. The first approach [...] Read more.
This paper compares two strategies to compute buoyancy-driven flows using stabilized methods. Both formulations are based on a unified approach for solving compressible and incompressible flows, which solves the continuity, momentum, and total energy equations in a coupled entropy-consistent way. The first approach introduces the variable density thermodynamics of the liquid or gas without any artificial buoyancy terms, i.e., without applying any approximate models into the Navier–Stokes equations. Furthermore, this formulation holds for flows driven by high temperature differences. Further advantages of this formulation are seen in the fact that it conserves the total energy and it lacks the incompressibility inconsistencies due to volume changes induced by temperature variations. The second strategy uses the Boussinesq approximation to account for temperature-driven forces. This method models the thermal terms in the momentum equation through a temperature-dependent nonlinear source term. Computer examples show that the thermodynamic approach, which does not introduce any artificial terms into the Navier–Stokes equations, is conceptually simpler and, with the incompressible stabilization matrix, attains similar residual convergence with iteration count to methods based on the Boussinesq approximation. For the Boussinesq model, the SUPG and SGS methods are compared, displaying very similar computational behavior. Finally, the VMS a posteriori error estimator is applied to adapt the mesh, helping to achieve better accuracy for the same number of degrees of freedom. Full article
(This article belongs to the Special Issue State-of-the-Art Algorithms and Mathematical Models for Multidisciplinary Applications in Spain)
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10 pages, 5458 KiB  
Article
Adjacency Maps and Efficient Graph Algorithms
by Gabriel Valiente
Algorithms 2022, 15(2), 67; https://doi.org/10.3390/a15020067 - 20 Feb 2022
Cited by 1 | Viewed by 6557
Abstract
Graph algorithms that test adjacencies are usually implemented with an adjacency-matrix representation because the adjacency test takes constant time with adjacency matrices, but it takes linear time in the degree of the vertices with adjacency lists. In this article, we review the adjacency-map [...] Read more.
Graph algorithms that test adjacencies are usually implemented with an adjacency-matrix representation because the adjacency test takes constant time with adjacency matrices, but it takes linear time in the degree of the vertices with adjacency lists. In this article, we review the adjacency-map representation, which supports adjacency tests in constant expected time, and we show that graph algorithms run faster with adjacency maps than with adjacency lists by a small constant factor if they do not test adjacencies and by one or two orders of magnitude if they perform adjacency tests. Full article
(This article belongs to the Special Issue State-of-the-Art Algorithms and Mathematical Models for Multidisciplinary Applications in Spain)
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Review

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21 pages, 1308 KiB  
Review
Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
by Zouhair Haddi, Bouchra Ananou, Miquel Alfaras, Mustapha Ouladsine, Jean-Claude Deharo, Narcís Avellana and Stéphane Delliaux
Algorithms 2022, 15(7), 231; https://doi.org/10.3390/a15070231 - 01 Jul 2022
Cited by 2 | Viewed by 2110
Abstract
Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen [...] Read more.
Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF. Full article
(This article belongs to the Special Issue State-of-the-Art Algorithms and Mathematical Models for Multidisciplinary Applications in Spain)
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