Subjects -> ENGINEERING (Total: 2844 journals)
    - CHEMICAL ENGINEERING (259 journals)
    - CIVIL ENGINEERING (255 journals)
    - ELECTRICAL ENGINEERING (182 journals)
    - ENGINEERING (1420 journals)
    - ENGINEERING MECHANICS AND MATERIALS (454 journals)
    - HYDRAULIC ENGINEERING (60 journals)
    - INDUSTRIAL ENGINEERING (101 journals)
    - MECHANICAL ENGINEERING (113 journals)

ENGINEERING (1420 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 9)
3D Research     Hybrid Journal   (Followers: 22)
AAPG Bulletin     Hybrid Journal   (Followers: 11)
Abstract and Applied Analysis     Open Access   (Followers: 4)
Aceh International Journal of Science and Technology     Open Access   (Followers: 9)
ACS Nano     Hybrid Journal   (Followers: 452)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 10)
Acta Nova     Open Access   (Followers: 1)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 4)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access   (Followers: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 9)
Adsorption     Hybrid Journal   (Followers: 5)
Advanced Energy and Sustainability Research     Open Access   (Followers: 8)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 14)
Advanced Engineering Research     Open Access  
Advanced Journal of Graduate Research     Open Access   (Followers: 4)
Advanced Quantum Technologies     Hybrid Journal   (Followers: 1)
Advanced Science     Open Access   (Followers: 13)
Advanced Science Focus     Free   (Followers: 7)
Advanced Science Letters     Full-text available via subscription   (Followers: 13)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 11)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 5)
Advances in Catalysis     Full-text available via subscription   (Followers: 8)
Advances in Complex Systems     Hybrid Journal   (Followers: 12)
Advances in Engineering Software     Hybrid Journal   (Followers: 31)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 20)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 22)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 30)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 10)
Advances in Natural Sciences : Nanoscience and Nanotechnology     Open Access   (Followers: 36)
Advances in Operations Research     Open Access   (Followers: 14)
Advances in OptoElectronics     Open Access   (Followers: 6)
Advances in Physics Theories and Applications     Open Access   (Followers: 21)
Advances in Polymer Science     Hybrid Journal   (Followers: 54)
Advances in Porous Media     Full-text available via subscription   (Followers: 6)
Advances in Remote Sensing     Open Access   (Followers: 58)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aerobiologia     Hybrid Journal   (Followers: 4)
Aerospace Systems     Hybrid Journal   (Followers: 10)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 8)
AIChE Journal     Hybrid Journal   (Followers: 38)
Ain Shams Engineering Journal     Open Access   (Followers: 7)
Al-Nahrain Journal for Engineering Sciences     Open Access  
Al-Qadisiya Journal for Engineering Sciences     Open Access   (Followers: 2)
AL-Rafdain Engineering Journal     Open Access   (Followers: 3)
Alexandria Engineering Journal     Open Access   (Followers: 3)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 27)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 12)
American Journal of Engineering Education     Open Access   (Followers: 20)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 31)
Annals of Civil and Environmental Engineering     Open Access   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 6)
Annals of Regional Science     Hybrid Journal   (Followers: 10)
Annals of Science     Hybrid Journal   (Followers: 10)
Annual Journal of Technical University of Varna     Open Access   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 2)
Applications in Energy and Combustion Science     Open Access   (Followers: 3)
Applications in Engineering Science     Open Access   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 8)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 22)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Engineering Letters     Open Access   (Followers: 5)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 11)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 6)
Applied Physics Research     Open Access   (Followers: 7)
Applied Sciences     Open Access   (Followers: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Basic and Applied Sciences     Open Access  
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Archives of Thermodynamics     Open Access   (Followers: 13)
Arctic     Open Access   (Followers: 7)
Arid Zone Journal of Engineering, Technology and Environment     Open Access   (Followers: 2)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ArtefaCToS : Revista de estudios sobre la ciencia y la tecnología     Open Access   (Followers: 1)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 2)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 9)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 7)
Assembly Automation     Hybrid Journal   (Followers: 2)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
AURUM : Mühendislik Sistemleri ve Mimarlık Dergisi = Aurum Journal of Engineering Systems and Architecture     Open Access   (Followers: 1)
Australasian Journal of Engineering Education     Hybrid Journal   (Followers: 3)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Hybrid Journal   (Followers: 2)
Autocracy : Jurnal Otomasi, Kendali, dan Aplikasi Industri     Open Access  
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal   (Followers: 1)
Avances en Ciencias e Ingenierías     Open Access  
Avances: Investigación en Ingeniería     Open Access   (Followers: 6)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 2)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 6)
Batteries     Open Access   (Followers: 11)
Batteries & Supercaps     Hybrid Journal   (Followers: 7)
Bautechnik     Hybrid Journal   (Followers: 3)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 29)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
Beyond : Undergraduate Research Journal     Open Access  
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Bilge International Journal of Science and Technology Research     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 14)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 6)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 6)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 8)
Biomicrofluidics     Open Access   (Followers: 7)
Biotechnology Progress     Hybrid Journal   (Followers: 44)
Black Sea Journal of Engineering and Science     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 13)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers Droit, Sciences & Technologies     Open Access   (Followers: 1)
Calphad     Hybrid Journal   (Followers: 2)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 30)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 6)
Case Studies in Thermal Engineering     Open Access   (Followers: 8)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Catalysis Letters     Hybrid Journal   (Followers: 3)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 9)
Catalysis Science and Technology     Hybrid Journal   (Followers: 13)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal   (Followers: 6)
Cell Reports Physical Science     Open Access  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
Central European Journal of Engineering     Hybrid Journal  
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 3)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Journal of Population, Resources and Environment     Open Access  
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencia y Tecnología     Open Access  
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Hybrid Journal   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 27)
Clay Minerals     Hybrid Journal   (Followers: 9)
Coal Science and Technology     Full-text available via subscription   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 14)
Coastal Engineering Journal     Hybrid Journal   (Followers: 9)
Coastal Engineering Proceedings : Proceedings of the International Conference on Coastal Engineering     Open Access   (Followers: 2)
Coastal Management     Hybrid Journal   (Followers: 30)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 3)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Color Research & Application     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 17)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 20)
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering     Open Access  
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 28)
Composite Interfaces     Hybrid Journal   (Followers: 10)
Composite Structures     Hybrid Journal   (Followers: 335)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 279)
Composites Part B : Engineering     Hybrid Journal   (Followers: 312)
Composites Part C : Open Access     Open Access   (Followers: 3)
Composites Science and Technology     Hybrid Journal   (Followers: 247)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Computation     Open Access   (Followers: 1)
Computational Geosciences     Hybrid Journal   (Followers: 20)
Computational Optimization and Applications     Hybrid Journal   (Followers: 11)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 21)
Computers & Geosciences     Hybrid Journal   (Followers: 30)

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Computation
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2079-3197
Published by MDPI Homepage  [238 journals]
  • Computation, Vol. 9, Pages 94: EM Estimation for Zero- and k-Inflated
           Poisson Regression Model

    • Authors: Monika Arora, N. Rao Chaganty
      First page: 94
      Abstract: Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation–Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper.
      Citation: Computation
      PubDate: 2021-08-26
      DOI: 10.3390/computation9090094
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 95: Is There a Quadruple Fe-C Bond in
           FeC(CO)3'

    • Authors: Tommaso Nottoli, Filippo Lipparini
      First page: 95
      Abstract: A recent computational paper (Kalita et al., Phys. Chem. Chem. Phys. 2020, 22, 24178–24180) reports the existence of a quadruple bond between a carbon and an iron atom in the FeC(CO)3 molecule. In this communication, we perform several computations on the same system, using both density functional theory and post-Hartree–Fock methods and find that the results, and in particular the Fe-C bond length and stretching frequency depend strongly on the method used. We ascribe this behavior to a strong multireference character of the FeC(CO)3 ground state, which explains the non-conclusive results obtained with single-reference methods. We therefore conclude that, while the existence of a Fe-C quadruple bond is not disproved, further investigation is required before a conclusion can be drawn.
      Citation: Computation
      PubDate: 2021-08-30
      DOI: 10.3390/computation9090095
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 96: Performance of the Boost Converter
           Controlled with ZAD to Regulate DC Signals

    • Authors: Simeón Casanova Trujillo, John E. Candelo-Becerra, Fredy E. Hoyos
      First page: 96
      Abstract: This paper presents the performance of a boost converter controlled with a zero average dynamics technique to regulate direct current signals. The boost converter is modeled in a compact form, and a variable change is performed to depend only on the γ parameter. A new sliding surface is proposed, where it is possible to regulate both the voltage and the current with low relative errors with respect to the reference signals. It is analytically demonstrated that the approximation of the switching surface by a piecewise linear technique is efficient in controlling the system. It is shown numerically that for certain operating conditions, the system is evolved into a chaotic attractor. The zero average dynamics technique implemented in the boost converter has good regulation, due to the presence of zones in the bi-parametric space. Furthermore, the zero average dynamics technique regulates the voltage well and presents a chaotic attractor with low steady-state error.
      Citation: Computation
      PubDate: 2021-09-04
      DOI: 10.3390/computation9090096
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 97: Physics-Based Neural Network Methods for
           Solving Parameterized Singular Perturbation Problem

    • Authors: Tatiana Lazovskaya, Galina Malykhina, Dmitry Tarkhov
      First page: 97
      Abstract: This work is devoted to the description and comparative study of some methods of mathematical modeling. We consider methods that can be applied for building cyber-physical systems and digital twins. These application areas add to the usual accuracy requirements for a model the need to be adaptable to new data and the small computational complexity allows it to be used in embedded systems. First, we regard the finite element method as one of the “pure” physics-based modeling methods and the general neural network approach as a variant of machine learning modeling with physics-based regularization (or physics-informed neural networks) and their combination. A physics-based network architecture model class has been developed by us on the basis of a modification of classical numerical methods for solving ordinary differential equations. The model problem has a parameter at some values for which the phenomenon of stiffness is observed. We consider a fixed parameter value problem statement and a case when a parameter is one of the input variables. Thus, we obtain a solution for a set of parameter values. The resulting model allows predicting the behavior of an object when its parameters change and identifying its parameters based on observational data.
      Citation: Computation
      PubDate: 2021-09-06
      DOI: 10.3390/computation9090097
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 98: Understanding the Origin of Structural
           Diversity of DNA Double Helix

    • Authors: Valeri Poltev, Victor M. Anisimov, Veronica Dominguez, Andrea Ruiz, Alexandra Deriabina, Eduardo Gonzalez, Dolores Garcia, Francisco Rivas
      First page: 98
      Abstract: Deciphering the contribution of DNA subunits to the variability of its 3D structure represents an important step toward the elucidation of DNA functions at the atomic level. In the pursuit of that goal, our previous studies revealed that the essential conformational characteristics of the most populated “canonic” BI and AI conformational families of Watson–Crick duplexes, including the sequence dependence of their 3D structure, preexist in the local energy minima of the elemental single-chain fragments, deoxydinucleoside monophosphates (dDMPs). Those computations have uncovered important sequence-dependent regularity in the superposition of neighbor bases. The present work expands our studies to new minimal fragments of DNA with Watson–Crick nucleoside pairs that differ from canonic families in the torsion angles of the sugar-phosphate backbone (SPB). To address this objective, computations have been performed on dDMPs, cdDMPs (complementary dDMPs), and minimal fragments of SPBs of respective systems by using methods of molecular and quantum mechanics. These computations reveal that the conformations of dDMPs and cdDMPs having torsion angles of SPB corresponding to the local energy minima of separate minimal units of SPB exhibit sequence-dependent characteristics representative of canonic families. In contrast, conformations of dDMP and cdDMP with SPB torsions being far from the local minima of separate SPB units exhibit more complex sequence dependence.
      Citation: Computation
      PubDate: 2021-09-11
      DOI: 10.3390/computation9090098
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 99: Integrating Data Mining Techniques for
           Naïve Bayes Classification: Applications to Medical Datasets

    • Authors: Pannapa Changpetch, Apasiri Pitpeng, Sasiprapa Hiriote, Chumpol Yuangyai
      First page: 99
      Abstract: In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the performance of the latter. A classification tree was used to discretize quantitative predictors into categories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.
      Citation: Computation
      PubDate: 2021-09-13
      DOI: 10.3390/computation9090099
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 100: An Improved Robust Adaptive Controller for
           a Fed-Batch Bioreactor with Input Saturation and Unknown Varying Control
           Gain via Dead-Zone Quadratic Forms

    • Authors: Alejandro Rincón, Gloria María Restrepo, Óscar J. Sánchez
      First page: 100
      Abstract: In this work, a new adaptive controller is designed for substrate control of a fed-batch bioreactor in the presence of input saturation and unknown varying control gain with unknown upper and lower bounds. The output measurement noise and the unknown varying nature of reaction rate and biomass concentration and water volume are also handled. The design is based on dead zone quadratic forms. The designed controller ensures the convergence of the modified tracking error and the boundedness of the updated parameters. As the first distinctive feature, a new robust adaptive auxiliary system is proposed in order to tackle input saturation and control gain uncertainty. As the second distinctive feature, the modified tracking error converges to a compact region whose bound is user-defined, in contrast to related studies where the convergence region depends on upper bounds of either external disturbances, system states, model parameters or terms and model parameter values. Simulations confirm the properties of the closed loop behavior.
      Citation: Computation
      PubDate: 2021-09-16
      DOI: 10.3390/computation9090100
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 101: First-Principles Study of Linear and
           Nonlinear Optical Properties of Multi-Layered Borophene

    • Authors: Geeta Sachdeva, Sumandeep Kaur, Ravindra Pandey, Shashi P. Karna
      First page: 101
      Abstract: Anisotropic materials are of great interest due to their unique direction-dependent optical properties. Borophene, the two-dimensional analog of graphene consisting of boron atoms, has attracted immense research interest due to its exciting anisotropic electronic and mechanical properties. Its synthesis in several structural polymorphic configurations has recently been reported. The present work reports the layer-dependent optical absorption and hyperpolarizabilities of the buckled borophene (δ6-borophene). The results, based on density functional theory, show that multilayer borophene is nearly transparent with only a weak absorbance in the visible region, reflecting its anisotropic structural characteristics. The static first-order hyperpolarizability significantly increases with the number of layers, due mainly to interactions among the frontier orbitals in multilayer borophene. Transparency in the visible region combined with enhanced nonlinear optical properties makes the multilayer borophene important for future photonics technologies.
      Citation: Computation
      PubDate: 2021-09-18
      DOI: 10.3390/computation9090101
      Issue No: Vol. 9, No. 9 (2021)
       
  • Computation, Vol. 9, Pages 81: Interaction Network Provides Clues on the
           Role of Bcar1 in Cellular Response to Changes in Gravity

    • Authors: Johann Bauer, Erich Gombocz, Herbert Schulz, Jens Hauslage, Daniela Grimm
      First page: 81
      Abstract: When culturing cells in space or under altered gravity conditions on Earth to investigate the impact of gravity, their adhesion and organoid formation capabilities change. In search of a target where the alteration of gravity force could have this impact, we investigated p130cas/BCAR1 and its interactions more thoroughly, particularly as its activity is sensitive to applied forces. This protein is well characterized regarding its role in growth stimulation and adhesion processes. To better understand BCAR1′s force-dependent scaffolding of other proteins, we studied its interactions with proteins we had detected by proteome analyses of MCF-7 breast cancer and FTC-133 thyroid cancer cells, which are both sensitive to exposure to microgravity and express BCAR1. Using linked open data resources and our experiments, we collected comprehensive information to establish a semantic knowledgebase and analyzed identified proteins belonging to signaling pathways and their networks. The results show that the force-dependent phosphorylation and scaffolding of BCAR1 influence the structure, function, and degradation of intracellular proteins as well as the growth, adhesion and apoptosis of cells similarly to exposure of whole cells to altered gravity. As BCAR1 evidently plays a significant role in cell responses to gravity changes, this study reveals a clear path to future research performing phosphorylation experiments on BCAR1.
      Citation: Computation
      PubDate: 2021-07-23
      DOI: 10.3390/computation9080081
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 82: A Robust Observer—Based Adaptive Control
           of Second—Order Systems with Input Saturation via Dead-Zone Lyapunov
           Functions

    • Authors: Alejandro Rincón, Gloria M. Restrepo, Fredy E. Hoyos
      First page: 82
      Abstract: In this study, a novel robust observer-based adaptive controller was formulated for systems represented by second-order input–output dynamics with unknown second state, and it was applied to concentration tracking in a chemical reactor. By using dead-zone Lyapunov functions and adaptive backstepping method, an improved control law was derived, exhibiting faster response to changes in the output tracking error while avoiding input chattering and providing robustness to uncertain model terms. Moreover, a state observer was formulated for estimating the unknown state. The main contributions with respect to closely related designs are (i) the control law, the update law and the observer equations involve no discontinuous signals; (ii) it is guaranteed that the developed controller leads to the convergence of the tracking error to a compact set whose width is user-defined, and it does not depend on upper bounds of model terms, state variables or disturbances; and (iii) the control law exhibits a fast response to changes in the tracking error, whereas the control effort can be reduced through the controller parameters. Finally, the effectiveness of the developed controller is illustrated by the simulation of concentration tracking in a stirred chemical reactor.
      Citation: Computation
      PubDate: 2021-07-24
      DOI: 10.3390/computation9080082
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 83: Design of Computational Models for
           Hydroturbine Units Based on a Nonparametric Regression Approach with
           Adaptation by Evolutionary Algorithms

    • Authors: Vladimir Viktorovich Bukhtoyarov, Vadim Sergeevich Tynchenko
      First page: 83
      Abstract: This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment—hydroturbine units. A promising approach to the construction of regression models based on nonparametric Nadaraya–Watson kernel estimates is considered. A known problem in applying this approach is to determine the effective values of kernel-smoothing coefficients. Kernel-smoothing factors significantly impact the accuracy of the regression model, especially under conditions of variability of noise and parameters of samples in the input space of models. This fully corresponds to the characteristics of the problem of estimating the parameters of hydraulic turbines. We propose to use the evolutionary genetic algorithm with an addition in the form of a local-search stage to adjust the smoothing coefficients. This ensures the local convergence of the tuning procedure, which is important given the high sensitivity of the quality criterion of the nonparametric model. On a set of test problems, the results were obtained showing a reduction in the modeling error by 20% and 28% for the methods of adjusting the coefficients by the standard and hybrid genetic algorithms, respectively, in comparison with the case of an arbitrary choice of the values of such coefficients. For the task of estimating the parameters of the operation of a hydroturbine unit, a number of promising approaches to constructing regression models based on artificial neural networks, multidimensional adaptive splines, and an evolutionary method of genetic programming were included in the research. The proposed nonparametric approach with a hybrid smoothing coefficient tuning scheme was found to be most effective with a reduction in modeling error of about 5% compared with the best of the alternative approaches considered in the study, which, according to the results of numerical experiments, was the method of multivariate adaptive regression splines.
      Citation: Computation
      PubDate: 2021-07-28
      DOI: 10.3390/computation9080083
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 84: Artificial Intelligence-Based Optimization
           of a Bimorph-Segmented Tapered Piezoelectric MEMS Energy Harvester for
           Multimode Operation

    • Authors: Osor Pertin, Koushik Guha, Olga Jakšić
      First page: 84
      Abstract: This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimization. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications.
      Citation: Computation
      PubDate: 2021-07-29
      DOI: 10.3390/computation9080084
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 85: Response of Viscoelastic Turbulent Pipeflow
           Past Square Bar Roughness: The Effect on Mean Flow

    • Authors: Shubham Goswami, Arman Hemmati
      First page: 85
      Abstract: The influence of viscoelastic polymer additives on response and recovery of turbulent pipeflow over square bar roughness elements was examined using Direct Numerical Simulations at a Reynolds number of 5×103. Two different bar heights for the square bar roughness elements were examined, h/D=0.05 and 0.1. A Finitely Extensible Non-linear Elastic-Peterlin (FENE-P) rheological model was employed for modeling viscoelastic fluid features. The rheological parameters for the simulation corresponded to a high concentration polymer of 160 ppm. Recirculation regions formed behind the bar elements by the viscoelastic fluid were shorter than those associated with Newtonian fluid, which was attributed to mixed effects of viscous and elastic forces due to the added polymers. The recovery of the mean viscoelastic flow was faster. The pressure losses on the surface of the roughness were larger compared to the Newtonian fluid, and the overall contribution to local drag was reduced due to viscoelastic effects.
      Citation: Computation
      PubDate: 2021-07-30
      DOI: 10.3390/computation9080085
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 86: Dense Matrix Multiplication Algorithms and
           Performance Evaluation of HPCC in 81 Nodes IBM Power 8 Architecture

    • Authors: Eduardo Patricio Estévez Estévez Ruiz, Giovanny Eduardo Caluña Caluña Chicaiza, Fabian Rodolfo Jiménez Patiño, Joaquín Cayetano López López Lago, Saravana Prakash Thirumuruganandham
      First page: 86
      Abstract: Optimizing HPC systems based on performance factors and bottlenecks is essential for designing an HPC infrastructure with the best characteristics and at a reasonable cost. Such insight can only be achieved through a detailed analysis of existing HPC systems and the execution of their workloads. The “Quinde I” is the only and most powerful supercomputer in Ecuador and is currently listed third on the South America. It was built with the IBM Power 8 servers. In this work, we measured its performance using different parameters from High-Performance Computing (HPC) to compare it with theoretical values and values obtained from tests on similar models. To measure its performance, we compiled and ran different benchmarks with the specific optimization flags for Power 8 to get the maximum performance with the current configuration in the hardware installed by the vendor. The inputs of the benchmarks were varied to analyze their impact on the system performance. In addition, we compile and compare the performance of two algorithms for dense matrix multiplication SRUMMA and DGEMM.
      Citation: Computation
      PubDate: 2021-07-30
      DOI: 10.3390/computation9080086
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 87: Predicting Interfacial Thermal Resistance
           by Ensemble Learning

    • Authors: Mingguang Chen, Junzhu Li, Bo Tian, Yas Mohammed Al-Hadeethi, Bassim Arkook, Xiaojuan Tian, Xixiang Zhang
      First page: 87
      Abstract: Interfacial thermal resistance (ITR) plays a critical role in the thermal properties of a variety of material systems. Accurate and reliable ITR prediction is vital in the structure design and thermal management of nanodevices, aircraft, buildings, etc. However, because ITR is affected by dozens of factors, traditional models have difficulty predicting it. To address this high-dimensional problem, we employ machine learning and deep learning algorithms in this work. First, exploratory data analysis and data visualization were performed on the raw data to obtain a comprehensive picture of the objects. Second, XGBoost was chosen to demonstrate the significance of various descriptors in ITR prediction. Following that, the top 20 descriptors with the highest importance scores were chosen except for fdensity, fmass, and smass, to build concise models based on XGBoost, Kernel Ridge Regression, and deep neural network algorithms. Finally, ensemble learning was used to combine all three models and predict high melting points, high ITR material systems for spacecraft, automotive, building insulation, etc. The predicted ITR of the Pb/diamond high melting point material system was consistent with the experimental value reported in the literature, while the other predicted material systems provide valuable guidelines for experimentalists and engineers searching for high melting point, high ITR material systems.
      Citation: Computation
      PubDate: 2021-08-02
      DOI: 10.3390/computation9080087
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 88: Analyzing, Modeling, and Utilizing
           Observation Series Correlation in Capital Markets

    • Authors: Alexander Musaev, Dmitry Grigoriev
      First page: 88
      Abstract: In this paper, we consider the task of the analysis, modeling, and application of dependencies between asset quotes at various capital markets. As an example, we study the dependency between financial instrument observation series in the currency and stock markets. Our work intends to give a theoretical basis to asset management strategies that estimate an asset’s price via regression, taking into account its correlated assets in various markets. Furthermore, we provide a way to increase the estimate quality using an evolutionary algorithm.
      Citation: Computation
      PubDate: 2021-08-02
      DOI: 10.3390/computation9080088
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 89: Effects of Fractional Derivatives with
           Different Orders in SIS Epidemic Models

    • Authors: Caterina Balzotti, Mirko D’Ovidio, Anna Chiara Lai, Paola Loreti
      First page: 89
      Abstract: We study epidemic Susceptible–Infected–Susceptible (SIS) models in the fractional setting. The novelty is to consider models in which the susceptible and infected populations evolve according to different fractional orders. We study a model based on the Caputo derivative, for which we establish existence results of the solutions. Furthermore, we investigate a model based on the Caputo–Fabrizio operator, for which we provide existence of solutions and a study of the equilibria. Both models can be framed in the context of SIS models with time-varying total population, in which the competition between birth and death rates is macroscopically described by the fractional orders of the derivatives. Numerical simulations for both models and a direct numerical comparison are also provided.
      Citation: Computation
      PubDate: 2021-08-08
      DOI: 10.3390/computation9080089
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 90: Approximating Fixed Points Using a Faster
           Iterative Method and Application to Split Feasibility Problems

    • Authors: Kifayat Ullah, Junaid Ahmad, Muhammad Arshad, Zhenhua Ma
      First page: 90
      Abstract: In this article, the recently introduced iterative scheme of Hassan et al. (Math. Probl. Eng. 2020) is re-analyzed with the connection of Reich–Suzuki type nonexpansive (RSTN) maps. Under mild conditions, some important weak and strong convergence results in the context of uniformly convex Banach spaces are provided. To support the main outcome of the paper, we provide a numerical example and show that this example properly exceeds the class of Suzuki type nonexpansive (STN) maps. It has been shown that the Hassan et al. iterative scheme of this example is more useful than the many other iterative schemes. We provide an application of our main results to solve split feasibility problems in the setting of RSTN maps. The presented outcome is new and compliments the corresponding results of the current literature.
      Citation: Computation
      PubDate: 2021-08-11
      DOI: 10.3390/computation9080090
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 91: Parameter Estimation of Partially Observed
           Turbulent Systems Using Conditional Gaussian Path-Wise Sampler

    • Authors: Ziheng Zhang, Nan Chen
      First page: 91
      Abstract: Parameter estimation of complex nonlinear turbulent dynamical systems using only partially observed time series is a challenging topic. The nonlinearity and partial observations often impede using closed analytic formulae to recover the model parameters. In this paper, an exact path-wise sampling method is developed, which is incorporated into a Bayesian Markov chain Monte Carlo (MCMC) algorithm in light of data augmentation to efficiently estimate the parameters in a rich class of nonlinear and non-Gaussian turbulent systems using partial observations. This path-wise sampling method exploits closed analytic formulae to sample the trajectories of the unobserved variables, which avoid the numerical errors in the general sampling approaches and significantly increase the overall parameter estimation efficiency. The unknown parameters and the missing trajectories are estimated in an alternating fashion in an adaptive MCMC iteration algorithm with rapid convergence. It is shown based on the noisy Lorenz 63 model and a stochastically coupled FitzHugh–Nagumo model that the new algorithm is very skillful in estimating the parameters in highly nonlinear turbulent models. The model with the estimated parameters succeeds in recovering the nonlinear and non-Gaussian features of the truth, including capturing the intermittency and extreme events, in both test examples.
      Citation: Computation
      PubDate: 2021-08-13
      DOI: 10.3390/computation9080091
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 92: Stable, Explicit, Leapfrog-Hopscotch
           Algorithms for the Diffusion Equation

    • Authors: Ádám Nagy, Issa Omle, Humam Kareem, Endre Kovács, Imre Ferenc Barna, Gabriella Bognar
      First page: 92
      Abstract: In this paper, we construct novel numerical algorithms to solve the heat or diffusion equation. We start with 105 different leapfrog-hopscotch algorithm combinations and narrow this selection down to five during subsequent tests. We demonstrate the performance of these top five methods in the case of large systems with random parameters and discontinuous initial conditions, by comparing them with other methods. We verify the methods by reproducing an analytical solution using a non-equidistant mesh. Then, we construct a new nontrivial analytical solution containing the Kummer functions for the heat equation with time-dependent coefficients, and also reproduce this solution. The new methods are then applied to the nonlinear Fisher equation. Finally, we analytically prove that the order of accuracy of the methods is two, and present evidence that they are unconditionally stable.
      Citation: Computation
      PubDate: 2021-08-20
      DOI: 10.3390/computation9080092
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 93: Density Functional Theory of Coulombic
           Excited States Based on Nodal Variational Principle

    • Authors: Ágnes Nagy
      First page: 93
      Abstract: The density functional theory developed earlier for Coulombic excited states is reconsidered using the nodal variational principle. It is much easier to solve the Kohn–Sham equations, because only the correct number of nodes of the orbitals should be insured instead of the orthogonality.
      Citation: Computation
      PubDate: 2021-08-23
      DOI: 10.3390/computation9080093
      Issue No: Vol. 9, No. 8 (2021)
       
  • Computation, Vol. 9, Pages 74: Note on F-Graph Construction

    • Authors: Vladimir Liska, Robert Vrabel
      First page: 74
      Abstract: The center of an F-graph contains at least two vertices, and the distance between any two central vertices is equal to the radius. In this short note, we describe one way of constructing these graphs.
      Citation: Computation
      PubDate: 2021-06-24
      DOI: 10.3390/computation9070074
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 75: DG-GMsFEM for Problems in Perforated
           Domains with Non-Homogeneous Boundary Conditions

    • Authors: Valentin Alekseev, Maria Vasilyeva, Uygulaana Kalachikova, Eric T. Chung
      First page: 75
      Abstract: Problems in perforated media are complex and require high resolution grid construction to capture complex irregular perforation boundaries leading to the large discrete system of equations. In this paper, we develop a multiscale model reduction technique based on the Discontinuous Galerkin Generalized Multiscale Finite Element Method (DG-GMsFEM) for problems in perforated domains with non-homogeneous boundary conditions on perforations. This method implies division of the perforated domain into several non-overlapping subdomains constructing local multiscale basis functions for each. We use two types of multiscale basis functions, which are constructed by imposing suitable non-homogeneous boundary conditions on subdomain boundary and perforation boundary. The construction of these basis functions contains two steps: (1) snapshot space construction and (2) solution of local spectral problems for dimension reduction in the snapshot space. The presented method is used to solve different model problems: elliptic, parabolic, elastic, and thermoelastic equations with non-homogeneous boundary conditions on perforations. The concepts for coarse grid construction and definition of the local domains are presented and investigated numerically. Numerical results for two test cases with homogeneous and non-homogeneous boundary conditions are included, as well. For the case with homogeneous boundary conditions on perforations, results are shown using only local basis functions with non-homogeneous boundary condition on subdomain boundary and homogeneous boundary condition on perforation boundary. Both types of basis functions are needed in order to obtain accurate solutions, and they are shown for problems with non-homogeneous boundary conditions on perforations. The numerical results show that the proposed method provides good results with a significant reduction of the system size.
      Citation: Computation
      PubDate: 2021-06-29
      DOI: 10.3390/computation9070075
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 76: Improvement and Assessment of a Blind Image
           Deblurring Algorithm Based on Independent Component Analysis

    • Authors: Simone Fiori
      First page: 76
      Abstract: The aim of the present paper is to improve an existing blind image deblurring algorithm, based on an independent component learning paradigm, by manifold calculus. The original technique is based on an independent component analysis algorithm applied to a set of pseudo-images obtained by Gabor-filtering a blurred image and is based on an adapt-and-project paradigm. A comparison between the original technique and the improved method shows that independent component learning on the unit hypersphere by a Riemannian-gradient algorithm outperforms the adapt-and-project strategy. A comprehensive set of numerical tests evidenced the strengths and weaknesses of the discussed deblurring technique.
      Citation: Computation
      PubDate: 2021-07-01
      DOI: 10.3390/computation9070076
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 77: The Features of Building a Portfolio of
           Trading Strategies Using the SAS OPTMODEL Procedure

    • Authors: Oleksandr Terentiev, Tatyana Prosiankina-Zharova, Volodymyr Savastiyanov, Valerii Lakhno, Vira Kolmakova
      First page: 77
      Abstract: The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.
      Citation: Computation
      PubDate: 2021-07-06
      DOI: 10.3390/computation9070077
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 78: Wavelet Power Spectral Domain Functional
           Principal Component Analysis for Feature Extraction of Epileptic EEGs

    • Authors: Shengkun Xie
      First page: 78
      Abstract: Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems.
      Citation: Computation
      PubDate: 2021-07-07
      DOI: 10.3390/computation9070078
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 79: Alternating Direction Implicit (ADI)
           Methods for Solving Two-Dimensional Parabolic Interface Problems with
           Variable Coefficients

    • Authors: Chuan Li, Guangqing Long, Yiquan Li, Shan Zhao
      First page: 79
      Abstract: The matched interface and boundary method (MIB) and ghost fluid method (GFM) are two well-known methods for solving elliptic interface problems. Moreover, they can be coupled with efficient time advancing methods, such as the alternating direction implicit (ADI) methods, for solving time-dependent partial differential equations (PDEs) with interfaces. However, to our best knowledge, all existing interface ADI methods for solving parabolic interface problems concern only constant coefficient PDEs, and no efficient and accurate ADI method has been developed for variable coefficient PDEs. In this work, we propose to incorporate the MIB and GFM in the framework of the ADI methods for generalized methods to solve two-dimensional parabolic interface problems with variable coefficients. Various numerical tests are conducted to investigate the accuracy, efficiency, and stability of the proposed methods. Both the semi-implicit MIB-ADI and fully-implicit GFM-ADI methods can recover the accuracy reduction near interfaces while maintaining the ADI efficiency. In summary, the GFM-ADI is found to be more stable as a fully-implicit time integration method, while the MIB-ADI is found to be more accurate with higher spatial and temporal convergence rates.
      Citation: Computation
      PubDate: 2021-07-17
      DOI: 10.3390/computation9070079
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 80: Optimal Selection of Conductors in
           Three-Phase Distribution Networks Using a Discrete Version of the Vortex
           Search Algorithm

    • Authors: John Fernando Martínez-Gil, Nicolas Alejandro Moyano-García, Oscar Danilo Montoya, Jorge Alexander Alarcon-Villamil
      First page: 80
      Abstract: In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., Δ− and Y−connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function.
      Citation: Computation
      PubDate: 2021-07-18
      DOI: 10.3390/computation9070080
      Issue No: Vol. 9, No. 7 (2021)
       
  • Computation, Vol. 9, Pages 61: Accurate and Efficient Derivative-Free
           Three-Phase Power Flow Method for Unbalanced Distribution Networks

    • Authors: Oscar Danilo Montoya, Juan S. Giraldo, Luis Fernando Grisales-Noreña, Harold R. Chamorro, Lazaro Alvarado-Barrios
      First page: 61
      Abstract: The power flow problem in three-phase unbalanced distribution networks is addressed in this research using a derivative-free numerical method based on the upper-triangular matrix. The upper-triangular matrix is obtained from the topological connection among nodes of the network (i.e., through a graph-based method). The main advantage of the proposed three-phase power flow method is the possibility of working with single-, two-, and three-phase loads, including Δ- and Y-connections. The Banach fixed-point theorem for loads with Y-connection helps ensure the convergence of the upper-triangular power flow method based an impedance-like equivalent matrix. Numerical results in three-phase systems with 8, 25, and 37 nodes demonstrate the effectiveness and computational efficiency of the proposed three-phase power flow formulation compared to the classical three-phase backward/forward method and the implementation of the power flow problem in the DigSILENT software. Comparisons with the backward/forward method demonstrate that the proposed approach is 47.01%, 47.98%, and 36.96% faster in terms of processing times by employing the same number of iterations as when evaluated in the 8-, 25-, and 37-bus systems, respectively. An application of the Chu-Beasley genetic algorithm using a leader–follower optimization approach is applied to the phase-balancing problem utilizing the proposed power flow in the follower stage. Numerical results present optimal solutions with processing times lower than 5 s, which confirms its applicability in large-scale optimization problems employing embedding master–slave optimization structures.
      Citation: Computation
      PubDate: 2021-05-27
      DOI: 10.3390/computation9060061
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 62: Hybrid Feedback Control for Exponential
           Stability and Robust H∞ Control of a Class of Uncertain Neural Network
           with Mixed Interval and Distributed Time-Varying Delays

    • Authors: Charuwat Chantawat, Thongchai Botmart, Rattaporn Supama, Wajaree Weera, Sakda Noinang
      First page: 62
      Abstract: This paper is concerned the problem of robust H∞ control for uncertain neural networks with mixed time-varying delays comprising different interval and distributed time-varying delays via hybrid feedback control. The interval and distributed time-varying delays are not necessary to be differentiable. The main purpose of this research is to estimate robust exponential stability of uncertain neural network with H∞ performance attenuation level γ. The key features of the approach include the introduction of a new Lyapunov–Krasovskii functional (LKF) with triple integral terms, the employment of a tighter bounding technique, some slack matrices and newly introduced convex combination condition in the calculation, improved delay-dependent sufficient conditions for the robust H∞ control with exponential stability of the system are obtained in terms of linear matrix inequalities (LMIs). The results of this paper complement the previously known ones. Finally, a numerical example is presented to show the effectiveness of the proposed methods.
      Citation: Computation
      PubDate: 2021-05-28
      DOI: 10.3390/computation9060062
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 63: Numerical Investigation of a Radially
           Cooled Turbine Guide Vane Using Air and Steam as a Cooling Medium

    • Authors: Sondre Norheim, Shokri Amzin
      First page: 63
      Abstract: Gas turbine performance is closely linked to the turbine inlet temperature, which is limited by the turbine guide vanes ability to withstand the massive thermal loads. Thus, steam cooling has been introduced as an advanced cooling technology to improve the efficiency of modern high-temperature gas turbines. This study compares the cooling performance of compressed air and steam in the renowned radially cooled NASA C3X turbine guide vane, using a numerical model. The conjugate heat transfer (CHT) model is based on the RANS-method, where the shear stress transport (SST) k−ω model is selected to predict the effects of turbulence. The numerical model is validated against experimental pressure and temperature distributions at the external surface of the vane. The results are in good agreement with the experimental data, with an average error of 1.39% and 3.78%, respectively. By comparing the two coolants, steam is confirmed as the superior cooling medium. The disparity between the coolants increases along the axial direction of the vane, and the total volume average temperature difference is 30 K. Further investigations are recommended to deal with the local hot-spots located near the leading- and trailing edge of the vane.
      Citation: Computation
      PubDate: 2021-05-28
      DOI: 10.3390/computation9060063
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 64: Exploring the Influence of Social Media
           Usage for Academic Purposes Using a Partial Least Squares Approach

    • Authors: Jabar H. Yousif, Firdouse R. Khan, Safiya N. Al Al Jaradi, Aysha S. Alshibli
      First page: 64
      Abstract: Social media applications have been increasingly gaining significant attention from online education and training platforms. Social networking tools provide multiple advantages for communicating, exchanging opinions, and discussing specific issues. Social media also helps to improve the processes of teaching and learning through sharing educational programs. In this study, we used a quantitative research technique based on the partial least-squares (PLS) linear regression method to determine the influence of using social media as an online discussion and communication platform for academic purposes by assessing the relationships among the skills obtained through social media, the usage of social media, and the purpose of social media. A total of 200 students participated in this study (88% female and 12% males), and a purposive sampling technique was used to select a suitable population for the study. The results show that 61.5% of the participants use the web daily for more than five hours, mainly for social communication (meaningful dialog and discussion skills) and entertainment. The students agreed that social media develops their creative thinking, but it has no positive impact on their academic performance.
      Citation: Computation
      PubDate: 2021-05-29
      DOI: 10.3390/computation9060064
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 65: Computational Performance of Disparate
           Lattice Boltzmann Scenarios under Unsteady Thermal Convection Flow and
           Heat Transfer Simulation

    • Authors: Aditya Dewanto Hartono, Kyuro Sasaki, Yuichi Sugai, Ronald Nguele
      First page: 65
      Abstract: The present work highlights the capacity of disparate lattice Boltzmann strategies in simulating natural convection and heat transfer phenomena during the unsteady period of the flow. Within the framework of Bhatnagar-Gross-Krook collision operator, diverse lattice Boltzmann schemes emerged from two different embodiments of discrete Boltzmann expression and three distinct forcing models. Subsequently, computational performance of disparate lattice Boltzmann strategies was tested upon two different thermo-hydrodynamics configurations, namely the natural convection in a differentially-heated cavity and the Rayleigh-Bènard convection. For the purposes of exhibition and validation, the steady-state conditions of both physical systems were compared with the established numerical results from the classical computational techniques. Excellent agreements were observed for both thermo-hydrodynamics cases. Numerical results of both physical systems demonstrate the existence of considerable discrepancy in the computational characteristics of different lattice Boltzmann strategies during the unsteady period of the simulation. The corresponding disparity diminished gradually as the simulation proceeded towards a steady-state condition, where the computational profiles became almost equivalent. Variation in the discrete lattice Boltzmann expressions was identified as the primary factor that engenders the prevailed heterogeneity in the computational behaviour. Meanwhile, the contribution of distinct forcing models to the emergence of such diversity was found to be inconsequential. The findings of the present study contribute to the ventures to alleviate contemporary issues regarding proper selection of lattice Boltzmann schemes in modelling fluid flow and heat transfer phenomena.
      Citation: Computation
      PubDate: 2021-05-31
      DOI: 10.3390/computation9060065
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 66: 1D–2D Numerical Model for Wave
           Attenuation by Mangroves as a Porous Structure

    • Authors: Ikha Magdalena, Vivianne Kusnowo, Moh. Ivan Azis, Widowati
      First page: 66
      Abstract: In this paper, we investigate wave attenuation caused by mangroves as a porous media. A 1-D mathematical model is derived by modifying the shallow water equations (SWEs). Two approaches are used to involve the existing of mangrove: friction term and diffusion term. The model will be solved analytically using the separation of variables method and numerically using a staggered finite volume method. From both methods, wave transmission coefficient will be obtained and used to observe the damping effect induced by the porous media. Several comparisons are shown to examine the accuracy and robustness of the derived numerical scheme. The results show that the friction coefficient, diffusion coefficient and vegetation’s length have a significant effect on the transmission coefficient. Moreover, numerical observation is extended to a 2-D SWEs, where we conduct a numerical simulation over a real bathymetry profile. The results from the 2-D numerical scheme will be validated using the data obtained from the field measurement which took place in Demak, Central Java, Indonesia. The results from this research will be beneficial to determine the characteristics of porous structures used for coastal protection.
      Citation: Computation
      PubDate: 2021-06-07
      DOI: 10.3390/computation9060066
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 67: Improved Genetic Algorithm for
           Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave
           Optimization Approach

    • Authors: Oscar Danilo Montoya, Alexander Molina-Cabrera, Luis Fernando Grisales-Noreña, Ricardo Alberto Hincapié, Mauricio Granada
      First page: 67
      Abstract: This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function values.
      Citation: Computation
      PubDate: 2021-06-09
      DOI: 10.3390/computation9060067
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 68: Improved Equilibrium Optimization Algorithm
           Using Elite Opposition-Based Learning and New Local Search Strategy for
           Feature Selection in Medical Datasets

    • Authors: Zenab Mohamed Elgamal, Norizan Mohd Yasin, Aznul Qalid Md Sabri, Rami Sihwail, Mohammad Tubishat, Hazim Jarrah
      First page: 68
      Abstract: The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.
      Citation: Computation
      PubDate: 2021-06-10
      DOI: 10.3390/computation9060068
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 69: RFID Applications and Security Review

    • Authors: Cesar Munoz-Ausecha, Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez
      First page: 69
      Abstract: Radio frequency identification (RFID) is widely used in several contexts, such as logistics, supply chains, asset tracking, and health, among others, therefore drawing the attention of many researchers. This paper presents a review of the most cited topics regarding RFID focused on applications, security, and privacy. A total of 62,685 records were downloaded from the Web of Science (WoS) and Scopus core databases and processed, reconciling the datasets to remove duplicates, resulting in 40,677 unique elements. Fundamental indicators were extracted and are presented, such as the citation number, average growth rate, and average number of documents per year. We extracted the top topics and reviewed the relevant indicators using a free Python tool, ScientoPy. The results are discussed in the following sections: the first is the Applications Section, whose subsections are the Internet of Things (IoT), Supply Chain Management, Localization, Traceability, Logistics, Ubiquitous Computing, Healthcare, and Access Control; the second is the Security and Privacy section, whose subsections are Authentication, Privacy, and Ownership Transfer; finally, we present the Discussion section. This paper intends to provide the reader with a global view of the current status of trending RFID topics and present different analyses from different perspectives depending on motivations or background.
      Citation: Computation
      PubDate: 2021-06-10
      DOI: 10.3390/computation9060069
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 70: LMI-Based Results on Robust Exponential
           Passivity of Uncertain Neutral-Type Neural Networks with Mixed Interval
           Time-Varying Delays via the Reciprocally Convex Combination Technique

    • Authors: Nayika Samorn, Narongsak Yotha, Pantiwa Srisilp, Kanit Mukdasai
      First page: 70
      Abstract: The issue of the robust exponential passivity analysis for uncertain neutral-type neural networks with mixed interval time-varying delays is discussed in this work. For our purpose, the lower bounds of the delays are allowed to be either positive or zero adopting the combination of the model transformation, various inequalities, the reciprocally convex combination, and suitable Lyapunov–Krasovskii functional. A new robust exponential passivity criterion is received and formulated in the form of linear matrix inequalities (LMIs). Moreover, a new exponential passivity criterion is also examined for systems without uncertainty. Four numerical examples indicate our potential results exceed the previous results.
      Citation: Computation
      PubDate: 2021-06-10
      DOI: 10.3390/computation9060070
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 71: Analysing the Influential Parameters on the
           Monopile Foundation of an Offshore Wind Turbine

    • Authors: Adrien Jacomet, Ali Khosravifardshirazi, Iman Sahafnejad-Mohammadi, Mahdieh Dibaj, Akbar A. Javadi, Mohammad Akrami
      First page: 71
      Abstract: Countries around the world generate electricity from renewable resources to decarbonise their societies and reduce global warming. Some countries have already outlined their wishes to produce a part of their total energy consumption from renewable sources in the coming years and gradually reduce the use of nuclear energy and fossil fuel in favour of cleaner fuels. While renewable energies are significant factors in tackling climate change, the parameters that can influence their performance should be analysed in detail during the design process. One of these parameters is the foundation of an offshore wind turbine. Offshore wind turbines allow more energy to be produced than an onshore installation, and do not have any harmful effects on human beings, while their geotechnical aspects need to be clearly determined in advance. In this study, the influential parameters such as soil type, the number of bolts in the design, and the size of the structure were analysed using the finite element method for three different designs. The simulations showed that some soil properties, such as cohesion, do not influence the results, while Young’s modulus has a large influence on the designs. Additionally, the results of this study showed that the maximum stress concentrations are at the bolts and connection joints where they are too close to the steel’s yield stress. It also proves that the non-elastic behaviour of the soil does not require to be assigned for such analyses and it can be simplified only with its elastic behaviour. The embedded length affects the lateral displacement, while the number of bolts influences the structure’s resistance to external loads.
      Citation: Computation
      PubDate: 2021-06-12
      DOI: 10.3390/computation9060071
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 72: District-Heating-Grid Simulation in Python:
           DiGriPy

    • Authors: Lena Vorspel, Jens Bücker
      First page: 72
      Abstract: DiGriPy is a newly developed Python tool for the simulation of district heating networks published as open-source software in GitHub and offered as a Python package on PyPI. It enables the user to easily build a network model, run large-scale demand time series, and automatically compare different temperature-control conditions. In this paper, implementation details and usage instructions are given. Tests showing the results of different scenarios are presented and interpreted.
      Citation: Computation
      PubDate: 2021-06-16
      DOI: 10.3390/computation9060072
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 73: Density Functional Theory of Highly Excited
           States of Coulomb Systems

    • Authors: Ágnes Nagy
      First page: 73
      Abstract: The density functional theory proposed earlier for excited states of Coulomb systems is discussed. The localized Hartree–Fock (LHF) and the Krieger, Li, and Iafrate (KLI) methods combined with correlation are generalized for excited states. Illustrative examples include some highly excited states of Li and Na atoms.
      Citation: Computation
      PubDate: 2021-06-21
      DOI: 10.3390/computation9060073
      Issue No: Vol. 9, No. 6 (2021)
       
  • Computation, Vol. 9, Pages 49: Exact Reduction of the Generalized
           Lotka–Volterra Equations via Integral and Algebraic Substitutions

    • Authors: Rebecca E. Morrison
      First page: 49
      Abstract: Systems of interacting species, such as biological environments or chemical reactions, are often described mathematically by sets of coupled ordinary differential equations. While a large number β of species may be involved in the coupled dynamics, often only α<β species are of interest or of consequence. In this paper, we explored how to construct models that include only those given α species, but still recreate the dynamics of the original β-species model. Under some conditions detailed here, this reduction can be completed exactly, such that the information in the reduced model is exactly the same as the original one, but over fewer equations. Moreover, this reduction process suggests a promising type of approximate model—no longer exact, but computationally quite simple.
      Citation: Computation
      PubDate: 2021-04-22
      DOI: 10.3390/computation9050049
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 50: Numerical Investigation of a Thermal
           Ablation Porous Media-Based Model for Tumoral Tissue with Variable
           Porosity

    • Authors: Assunta Andreozzi, Luca Brunese, Marcello Iasiello, Claudio Tucci, Giuseppe Peter Vanoli
      First page: 50
      Abstract: Thermal ablation is a minimally or noninvasive cancer therapy technique that involves fewer complications, shorter hospital stays, and fewer costs. In this paper, a thermal-ablation bioheat model for cancer treatment is numerically investigated, using a porous media-based model. The main objective is to evaluate the effects of a variable blood volume fraction in the tumoral tissue (i.e., the porosity), in order to develop a more realistic model. A modified local thermal nonequilibrium model (LTNE) is implemented including the water content vaporization in the two phases separately and introducing the variable porosity in the domain, described by a quadratic function changing from the core to the rim of the tumoral sphere. The equations are numerically solved employing the finite-element commercial code COMSOL Multiphysics. Results are compared with the results obtained employing two uniform porosity values (ε = 0.07 and ε = 0.23) in terms of coagulation zones at the end of the heating period, maximum temperatures reached in the domain, and temperature fields and they are presented for different blood vessels. The outcomes highlight how important is to predict coagulation zones achieved in thermal ablation accurately. In this way, indeed, incomplete ablation, tumor recurrence, or healthy tissue necrosis can be avoided, and medical protocols and devices can be improved.
      Citation: Computation
      PubDate: 2021-04-23
      DOI: 10.3390/computation9050050
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 51: DrainCAN—A MATLAB Function for Generation
           of a HEC-RAS-Compatible Drainage Canal Network Model

    • Authors: Gordon Gilja, Antonija Harasti, Robert Fliszar
      First page: 51
      Abstract: The dimensioning of canal geometry in a surface drainage network influences the size and functionality of canal structures, reduces flood hazard, and consequently imposes restrictions on land use. Reliable free-surface flow calculation for optimization of the canal network can be challenging because numerous hydraulic structures and canal interactions influence the flow regime. The HEC-RAS software of the US Army Corps of Engineers’ Hydrologic Engineering Center is often used for this purpose as it allows the user to simulate the effect of numerous hydraulic structures on flow regime. This paper presents a MATLAB function, DrainCAN, for generating a HEC-RAS model from standard runoff input data, i.e., topographic data and canal design geometry (profile and slope). The DrainCAN function allows for fast optimization of the network geometry—it generates normal flow depth estimation and observed water levels in critical locations that need to be optimized. Advantages of the DrainCAN function are fast generation of the HEC-RAS hydraulic model files from simple input files, introduction of optimization variables in the model, and automatic adjustment of model geometry for computational junctions. This allows fast iteration of the canal design parameters, namely cross-sectional geometry, invert elevation, and longitudinal slope, and the evaluation of introduced changes on the flow regime.
      Citation: Computation
      PubDate: 2021-04-23
      DOI: 10.3390/computation9050051
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 52: Application of the Exp-Function and
           Generalized Kudryashov Methods for Obtaining New Exact Solutions of
           

    • Authors: Supaporn Kaewta, Sekson Sirisubtawee, Surattana Sungnul
      First page: 52
      Abstract: The key objective of this paper is to construct exact traveling wave solutions of the conformable time second integro-differential Kadomtsev–Petviashvili (KP) hierarchy equation using the Exp-function method and the (2 + 1)-dimensional conformable time partial integro-differential Jaulent–Miodek (JM) evolution equation utilizing the generalized Kudryashov method. These two problems involve the conformable partial derivative with respect to time. Initially, the conformable time partial integro-differential equations can be converted into nonlinear ordinary differential equations via a fractional complex transformation. The resulting equations are then analytically solved via the corresponding methods. As a result, the explicit exact solutions for these two equations can be expressed in terms of exponential functions. Setting some specific parameter values and varying values of the fractional order in the equations, their 3D, 2D, and contour solutions are graphically shown and physically characterized as, for instance, a bell-shaped solitary wave solution, a kink-type solution, and a singular multiple-soliton solution. To the best of the authors’ knowledge, the results of the equations obtained using the proposed methods are novel and reported here for the first time. The methods are simple, very powerful, and reliable for solving other nonlinear conformable time partial integro-differential equations arising in many applications.
      Citation: Computation
      PubDate: 2021-04-26
      DOI: 10.3390/computation9050052
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 53: Potential Water Recovery from Biomass
           Boilers: Parametric Analysis

    • Authors: Daniele Dondi, Cristina D. López Robles, Anna Magrini, Marco Cartesegna
      First page: 53
      Abstract: A fundamental component of the losses of convection boilers is localized in the warm fumes that are expelled. In the warm fumes, not only energy is lost, but water is also formed from the combustion reaction in the form of steam which is expelled through the exhaust. Modern fuel boilers recover both the heat from the fumes and the latent heat of condensation from water vapor. Depending on the chemical composition of the fuel, different amounts of steam are produced together with heat and different combustion conditions, such as air in excess. In this article, a computational tool was established to simulate a combustion system mainly (but not only) focusing on the prediction of the amount of water produced. In fact, while steam in fossil fuel boilers is commonly condensed, this is not so when the fuel is a biomass. Furthermore, biomasses could contain moisture in different amounts, thus affecting the production of water and the heat of combustion. The study shows that a ten-fold amount of water is formed from biomass combustion with respect to fossil fuels (when the same energy output is produced). As a result, the recovery of water is amenable in biomasses, both from the energetic point of view and for liquid water production. In fact, the water recovered from the fumes might be also reused in other processes such as the cleaning of fumes or agriculture (after treatment).
      Citation: Computation
      PubDate: 2021-04-27
      DOI: 10.3390/computation9050053
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 54: Integrated Multi-Model Face Shape and Eye
           Attributes Identification for Hair Style and Eyelashes Recommendation

    • Authors: Theiab Alzahrani, Waleed Al-Nuaimy, Baidaa Al-Bander
      First page: 54
      Abstract: Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial.
      Citation: Computation
      PubDate: 2021-04-27
      DOI: 10.3390/computation9050054
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 55: Stabilization of the Computation of
           Stability Constants and Species Distributions from Titration Curves

    • Authors: Stephan Daniel Schwoebel, Dominik Höhlich, Thomas Mehner, Thomas Lampke
      First page: 55
      Abstract: Thermodynamic equilibria and concentrations in thermodynamic equilibria are of major importance in chemistry, chemical engineering, physical chemistry, medicine etc. due to a vast spectrum of applications. E.g., concentrations in thermodynamic equilibria play a central role for the estimation of drug delivery, the estimation of produced mass of products of chemical reactions, the estimation of deposited metal during electro plating and many more. Species concentrations in thermodynamic equilibrium are determined by the system of reactions and to the reactions’ associated stability constants. In many applications the stability constants and the system of reactions need to be determined. The usual way to determine the stability constants is to evaluate titration curves. In this context, many numerical methods exist. One major task in this context is that the corresponding inverse problems tend to be unstable, i.e., the output is strongly affected by measurement errors, and can output negative stability constants or negative species concentrations. In this work an alternative model for the species distributions in thermodynamic equilibrium, based on the models used for HySS or Hyperquad, and titration curves is presented, which includes the positivity of species concentrations and stability constants intrinsically. Additionally, in this paper a stabilized numerical methodology is presented to treat the corresponding model guaranteeing the convergence of the algorithm. The numerical scheme is validated with clinical numerical examples and the model is validated with a Citric acid–Nickel electrolyte. This paper finds a stable, convergent and efficient methodology to compute stability constants from potentiometric titration curves.
      Citation: Computation
      PubDate: 2021-04-27
      DOI: 10.3390/computation9050055
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 56: Application of a Deep Neural Network to
           Phase Retrieval in Inverse Medium Scattering Problems

    • Authors: Soojong Lim, Jaemin Shin
      First page: 56
      Abstract: We address the inverse medium scattering problem with phaseless data motivated by nondestructive testing for optical fibers. As the phase information of the data is unknown, this problem may be regarded as a standard phase retrieval problem that consists of identifying the phase from the amplitude of data and the structure of the related operator. This problem has been studied intensively due to its wide applications in physics and engineering. However, the uniqueness of the inverse problem with phaseless data is still open and the problem itself is severely ill-posed. In this work, we construct a model to approximate the solution operator in finite-dimensional spaces by a deep neural network assuming that the refractive index is radially symmetric. We are then able to recover the refractive index from the phaseless data. Numerical experiments are presented to illustrate the effectiveness of the proposed model.
      Citation: Computation
      PubDate: 2021-04-28
      DOI: 10.3390/computation9050056
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 57: RuSseL: A Self-Consistent Field Theory Code
           for Inhomogeneous Polymer Interphases

    • Authors: Constantinos J. Revelas, Aristotelis P. Sgouros, Apostolos T. Lakkas, Doros N. Theodorou
      First page: 57
      Abstract: In this article, we publish the one-dimensional version of our in-house code, RuSseL, which has been developed to address polymeric interfaces through Self-Consistent Field calculations. RuSseL can be used for a wide variety of systems in planar and spherical geometries, such as free films, cavities, adsorbed polymer films, polymer-grafted surfaces, and nanoparticles in melt and vacuum phases. The code includes a wide variety of functional potentials for the description of solid–polymer interactions, allowing the user to tune the density profiles and the degree of wetting by the polymer melt. Based on the solution of the Edwards diffusion equation, the equilibrium structural properties and thermodynamics of polymer melts in contact with solid or gas surfaces can be described. We have extended the formulation of Schmid to investigate systems comprising polymer chains, which are chemically grafted on the solid surfaces. We present important details concerning the iterative scheme required to equilibrate the self-consistent field and provide a thorough description of the code. This article will serve as a technical reference for our works addressing one-dimensional polymer interphases with Self-Consistent Field theory. It has been prepared as a guide to anyone who wishes to reproduce our calculations. To this end, we discuss the current possibilities of the code, its performance, and some thoughts for future extensions.
      Citation: Computation
      PubDate: 2021-05-10
      DOI: 10.3390/computation9050057
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 58: Synthesis, Mass Spectroscopy Detection, and
           Density Functional Theory Investigations of the Gd Endohedral Complexes of
           C82 Fullerenols

    • Authors: Anastasia A. Shakirova, Felix N. Tomilin, Vladimir A. Pomogaev, Natalia G. Vnukova, Grigory N. Churilov, Nadezhda S. Kudryasheva, Olga N. Tchaikovskaya, Sergey G. Ovchinnikov, Pavel V. Avramov
      First page: 58
      Abstract: Gd endohedral complexes of C82 fullerenols were synthesized and mass spectrometry analysis of their composition was carried out. It was established that the synthesis yields a series of fullerenols Gd@C82Ox(OH)y (x = 0, 3; y = 8, 16, 24, 36, 44). The atomic and electronic structure and properties of the synthesized fullerenols were investigated using the density functional theory calculations. It was shown that the presence of endohedral gadolinium increases the reactivity of fullerenols. It is proposed that the high-spin endohedral fullerenols are promising candidates for application in magnetic resonance imaging.
      Citation: Computation
      PubDate: 2021-05-17
      DOI: 10.3390/computation9050058
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 59: Multi-Model Approach and Fuzzy Clustering
           for Mammogram Tumor to Improve Accuracy

    • Authors: Sarada Ghosh, Guruprasad Samanta, Manuel De la Sen
      First page: 59
      Abstract: Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.
      Citation: Computation
      PubDate: 2021-05-18
      DOI: 10.3390/computation9050059
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 60: Optimization Algorithm to Sequence the
           Management Processes in Information Technology Departments

    • Authors: Juan Luis Rubio Sánchez
      First page: 60
      Abstract: The most important standard in technology services management is the Information Technology Infrastructure Library (ITIL). The literature review developed shows that one of the most important questions to answer is finding the sequence of processes to be implemented, mainly in small companies with few resources. The purpose of this paper is to show a methodology that defines an optimal specific sequence of processes for each small company depending on internal and external parameters. The main contribution of this paper is a proven methodology to obtain a particular sequence of ITIL processes specifically adapted to each company, based on a mathematical and statistical model that uses data from a web survey. Its application generates an optimal sequence of ITIL processes. The methodology has been applied with successful results in a real case, and it shows specific benefits over the previous approaches. The main learning objective of this research is a proven method to obtain an optimal sequence of processes for the implementation of ITIL in small companies. Finally, some future works are presented.
      Citation: Computation
      PubDate: 2021-05-19
      DOI: 10.3390/computation9050060
      Issue No: Vol. 9, No. 5 (2021)
       
  • Computation, Vol. 9, Pages 39: Challenges in the Computational Modeling of
           the Protein Structure—Activity Relationship

    • Authors: Gabriel Del Río
      First page: 39
      Abstract: Living organisms are composed of biopolymers (proteins, nucleic acids, carbohydrates and lipid polymers) that are used to keep or transmit information relevant to the state of these organisms at any given time. In these processes, proteins play a central role by displaying different activities required to keep or transmit this information. In this review, I present the current knowledge about the protein sequence–structure–activity relationship and the basis for modeling this relationship. Three representative predictors relevant to the modeling of this relationship are summarized to highlight areas that require further improvement and development. I will describe how a basic understanding of this relationship is fundamental in the development of new methods to design proteins, which represents an area of multiple applications in the areas of health and biotechnology.
      Citation: Computation
      PubDate: 2021-03-24
      DOI: 10.3390/computation9040039
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 40: A Study on Shape-Dependent Settling of
           Single Particles with Equal Volume Using Surface Resolved Simulations

    • Authors: Robin Trunk, Colin Bretl, Gudrun Thäter, Hermann Nirschl, Márcio Dorn, Mathias J. Krause
      First page: 40
      Abstract: A detailed knowledge of the influence of a particle’s shape on its settling behavior is useful for the prediction and design of separation processes. Models in the available literature usually fit a given function to experimental data. In this work, a constructive and data-driven approach is presented to obtain new drag correlations. To date, the only considered shape parameters are derivatives of the axis lengths and the sphericity. This does not cover all relevant effects, since the process of settling for arbitrarily shaped particles is highly complex. This work extends the list of considered parameters by, e.g., convexity and roundness and evaluates the relevance of each. The aim is to find models describing the drag coefficient and settling velocity, based on this extended set of shape parameters. The data for the investigations are obtained by surface resolved simulations of superellipsoids, applying the homogenized lattice Boltzmann method. To closely study the influence of shape, the particles considered are equal in volume, and therefore cover a range of Reynolds numbers, limited to [9.64, 22.86]. Logistic and polynomial regressions are performed and the quality of the models is investigated with further statistical methods. In addition to the usually studied relation between drag coefficient and Reynolds number, the dependency of the terminal settling velocity on the shape parameters is also investigated. The found models are, with an adjusted coefficient of determination of 0.96 and 0.86, in good agreement with the data, yielding a mean deviation below 5.5% on the training and test dataset.
      Citation: Computation
      PubDate: 2021-03-25
      DOI: 10.3390/computation9040040
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 41: Electrostatic Circular Membrane MEMS: An
           Approach to the Optimal Control

    • Authors: Mario Versaci, Francesco Carlo Morabito
      First page: 41
      Abstract: The recovery of the membrane profile of an electrostatic micro-electro-mechanical system (MEMS) is an important issue, because, when an external electrical voltage is applied, the membrane deforms with the risk of touching the upper plate of the device producing an unwanted electrostatic effect. Therefore, it is important to know whether the movement admits stable equilibrium configurations especially when the membrane is closed to the upper plate. In this framework, this work analyzes the behavior of a two-dimensional (2D) electrostatic circular membrane MEMS device subjected to an external voltage. Specifically, starting from a well-known 2D non-linear second-order differential model in which the electrostatic field in the device is proportional to the mean curvature of the membrane, the stability of the only possible equilibrium configuration is studied. Furthermore, when considering that the membrane is equipped with mechanical inertia and that it must not touch the upper plate of the device, a useful range of possible values has been obtained for the applied voltage. Finally, the paper concludes with some computations regarding the variation of potential energy, identifying some optimal control conditions.
      Citation: Computation
      PubDate: 2021-03-25
      DOI: 10.3390/computation9040041
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 42: A Shoreline Evolution Model with a Groin
           Structure under Non-Uniform Breaking Wave Crest Impact

    • Authors: Pidok Unyapoti, Nopparat Pochai
      First page: 42
      Abstract: Beach erosion is a natural phenomenon that is not compensated by depositing fresh material on the shoreline while transporting sand away from the shoreline. There are three phenomena that have a serious influence on the coastal structure, such as increases in flooding, accretion, and water levels. In addition, the prediction of coastal evolution is used to investigate the topography of the beach. In this research, we present a one-dimensional mathematical model of shoreline evolution, and the parameters that influence this model are described on a monthly basis over a period of one year. Consideration is given to the wave crest impact model for evaluating the impact of the wave crest at that stage. It focuses on the evolution of the shoreline in environments where groins are installed on both sides. The initial and boundary condition setting techniques are proposed by the groins and their environmental parameters. The non-uniform influence of the crest of the breaking wave is so often considered. We then used the traditional forward time centered space technique and the Saulyev finite difference technique to estimate the monthly evolution of the shoreline for each year.
      Citation: Computation
      PubDate: 2021-03-26
      DOI: 10.3390/computation9040042
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 43: Modelling of a Bluff-Body Stabilised
           Premixed Flames Close to Blow-Off

    • Authors: Shokri Amzin, Mohd Fairus Mohd Yasin
      First page: 43
      Abstract: As emission legislation becomes more stringent, the modelling of turbulent lean premixed combustion is becoming an essential tool for designing efficient and environmentally friendly combustion systems. However, to predict emissions, reliable predictive models are required. Among the promising methods capable of predicting pollutant emissions with a long chemical time scale, such as nitrogen oxides (NOx), is conditional moment closure (CMC). However, the practical application of this method to turbulent premixed flames depends on the precision of the conditional scalar dissipation rate,ζ ⟨Nc ζ⟩, model. In this study, an alternative closure for this term is implemented in the RANS-CMC method. The method is validated against the velocity, temperature, and gas composition measurements of lean premixed flames close to blow-off, within the limit of computational fluid dynamic (CFD) capability. Acceptable agreement is achieved between the predicted and measured values near the burner, with an average error of 15%. The model reproduces the flame characteristics; some discrepancies are found within the recirculation region due to significant turbulence intensity.
      Citation: Computation
      PubDate: 2021-03-30
      DOI: 10.3390/computation9040043
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 44: Latent Class Model with Heterogeneous
           Decision Rule for Identification of Factors to the Choice of Drivers’
           Seat Belt Use

    • Authors: Mahdi Rezapour, Khaled Ksaibati
      First page: 44
      Abstract: The choice of not buckling a seat belt has resulted in a high number of deaths worldwide. Although extensive studies have been done to identify factors of seat belt use, most of those studies have ignored the presence of heterogeneity across vehicle occupants. Not accounting for heterogeneity might result in a bias in model outputs. One of the main approaches to capture random heterogeneity is the employment of the latent class (LC) model by means of a discrete distribution. In a standard LC model, the heterogeneity across observations is considered while assuming the homogeneous utility maximization for decision rules. However, that notion ignores the heterogeneity in the decision rule across individual drivers. In other words, while some drivers make a choice of buckling up with some characteristics, others might ignore those factors while making a choice. Those differences could be accommodated for by allowing class allocation to vary based on various socio-economic characteristics and by constraining some of those rules at zeroes across some of the classes. Thus, in this study, in addition to accounting for heterogeneity across individual drivers, we accounted for heterogeneity in the decision rule by varying the parameters for class allocation. Our results showed that the assignment of various observations to classes is a function of factors such as vehicle type, roadway classification, and vehicle license registration. Additionally, the results showed that a minor consideration of the heterogeneous decision rule resulted in a minor gain in model fits, as well as changes in significance and magnitude of the parameter estimates. All of this was despite the challenges of fully identifying exact attributes for class allocation due to the inclusion of high number of attributes. The findings of this study have important implications for the use of an LC model to account for not only the taste heterogeneity but also heterogeneity across the decision rule to enhance model fit and to expand our understanding about the unbiased point estimates of parameters.
      Citation: Computation
      PubDate: 2021-04-02
      DOI: 10.3390/computation9040044
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 45: The Influence of Thickness on the Magnetic
           Properties of Nanocrystalline Thin Films: A Computational Approach

    • Authors: Jose Darío Agudelo-Giraldo, Francy Nelly Jiménez-García, Elisabeth Restrepo-Parra
      First page: 45
      Abstract: A study of the magnetic behaviour of polycrystalline thin films as a function of their thickness is presented in this work. The grain volume was kept approximately constant in the virtual samples. The model includes the exchange interaction, magneto-crystalline anisotropy, surface anisotropy, boundary grain anisotropy, dipolar interaction, and Zeeman effect. The thickness-dependence of the critical temperature, blocking temperature, and irreversibility temperature are presented. Surface anisotropy exerts a great influence at very low thicknesses, producing a monodomain regime. As the thickness increases, the dipolar interaction produces a coupling in-plane of single domains per grain which favours superparamagnetic states. At higher thicknesses, the effects of the in-plane anisotropy produced by dipolar interaction and surface anisotropy decrease dramatically. As a result, the superparamagnetic states present three-dimensional local anisotropies by the grain.
      Citation: Computation
      PubDate: 2021-04-12
      DOI: 10.3390/computation9040045
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 46: Model and Analysis of Economic- and
           Risk-Based Objective Optimization Problem for Plant Location within
           Industrial Estates Using Epsilon-Constraint Algorithms

    • Authors: Niroot Wattanasaeng, Kasin Ransikarbum
      First page: 46
      Abstract: In many countries, a number of industrial estates have been established to support the growth of the industrial sector, which is an essential strategy to drive economic growth. Planning for the location of industrial factories within an industrial estate, however, becomes complex, given the various types of industrial plants and the requirements of utilities to support operations within an industrial park. In this research, we model and analyze bi-objective optimization for locating plants within an industrial estate by considering economic- and risk-based cost objectives. Whereas economic objectives are associated with utility distances between plant locations, risk-based cost is a surrogate criterion derived from safety considerations. Next, risk-based data are further generated from Areal Locations of Hazardous Atmospheres (ALOHA), the hazard modeling program, and solutions to the bi-objective model are obtained from the Epsilon-constraint algorithm. Finally, the model is applied to a regional case study in a Thailand industrial estate, and the Pareto frontier is evaluated to demonstrate the trade-off between objectives.
      Citation: Computation
      PubDate: 2021-04-14
      DOI: 10.3390/computation9040046
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 47: Non-Hydrostatic Discontinuous/Continuous
           Galerkin Model for Wave Propagation, Breaking and Runup

    • Authors: Lucas Calvo, Diana De Padova, Michele Mossa, Paulo Rosman
      First page: 47
      Abstract: This paper presents a new depth-integrated non-hydrostatic finite element model for simulating wave propagation, breaking and runup using a combination of discontinuous and continuous Galerkin methods. The formulation decomposes the depth-integrated non-hydrostatic equations into hydrostatic and non-hydrostatic parts. The hydrostatic part is solved with a discontinuous Galerkin finite element method to allow the simulation of discontinuous flows, wave breaking and runup. The non-hydrostatic part led to a Poisson type equation, where the non-hydrostatic pressure is solved using a continuous Galerkin method to allow the modeling of wave propagation and transformation. The model uses linear quadrilateral finite elements for horizontal velocities, water surface elevations and non-hydrostatic pressures approximations. A new slope limiter for quadrilateral elements is developed. The model is verified and validated by a series of analytical solutions and laboratory experiments.
      Citation: Computation
      PubDate: 2021-04-14
      DOI: 10.3390/computation9040047
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 48: XGRN: Reconstruction of Biological Networks
           Based on Boosted Trees Regression

    • Authors: Georgios N. Dimitrakopoulos
      First page: 48
      Abstract: In Systems Biology, the complex relationships between different entities in the cells are modeled and analyzed using networks. Towards this aim, a rich variety of gene regulatory network (GRN) inference algorithms has been developed in recent years. However, most algorithms rely solely on gene expression data to reconstruct the network. Due to possible expression profile similarity, predictions can contain connections between biologically unrelated genes. Therefore, previously known biological information should also be considered by computational methods to obtain more consistent results, such as experimentally validated interactions between transcription factors and target genes. In this work, we propose XGBoost for gene regulatory networks (XGRN), a supervised algorithm, which combines gene expression data with previously known interactions for GRN inference. The key idea of our method is to train a regression model for each known interaction of the network and then utilize this model to predict new interactions. The regression is performed by XGBoost, a state-of-the-art algorithm using an ensemble of decision trees. In detail, XGRN learns a regression model based on gene expression of the two interactors and then provides predictions using as input the gene expression of other candidate interactors. Application on benchmark datasets and a real large single-cell RNA-Seq experiment resulted in high performance compared to other unsupervised and supervised methods, demonstrating the ability of XGRN to provide reliable predictions.
      Citation: Computation
      PubDate: 2021-04-20
      DOI: 10.3390/computation9040048
      Issue No: Vol. 9, No. 4 (2021)
       
  • Computation, Vol. 9, Pages 25: The Use of Fragility Curves in the
           Life-Cycle Assessment of Deteriorating Bridge Structures

    • Authors: Elsa Garavaglia, Raffaella Pavani, Luca Sgambi
      First page: 25
      Abstract: Within the context of structure deterioration studies, we propose a new numerical method based on the use of fragility curves. In particular, the present work aims to theoretically study the degradation of concrete bridge structures subjected to aggressive environments. A simple probabilistic method based on fragility curves is presented which allows the forecasting of the lifetime of the considered structural system and the best monitoring time. The method was applied to investigate the degradation of a concrete bridge used as a case study. A Monte Carlo numerical procedure was used to simulate the variation over time of the residual resistant section and the ultimate bending moment of the deck of the case study. Within this context, fragility curves are used as reliable indicators of possible monitoring scenarios. In comparison with other methods, the main advantage of the proposed approach is the small amount of computing time required to obtain rapid assessment of reliability and deterioration level of the considered structure.
      Citation: Computation
      PubDate: 2021-02-25
      DOI: 10.3390/computation9030025
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 26: Modelling of Conditional Scalar Dissipation
           Rate in Turbulent Premixed Combustion

    • Authors: Amzin, Domagała
      First page: 26
      Abstract: In turbulent premixed flames, for the mixing at a molecular level of reactants and products on the flame surface, it is crucial to sustain the combustion. This mixing phenomenon is featured by the scalar dissipation rate, which may be broadly defined as the rate of micro-mixing at small scales. This term, which appears in many turbulent combustion methods, includes the Conditional Moment Closure (CMC) and the Probability Density Function (PDF), requires an accurate model. In this study, a mathematical closure for the conditional mean scalar dissipation rate, <Nc ζ>, in Reynolds, Averaged Navier–Stokes (RANS) context is proposed and tested against two different Direct Numerical Simulation (DNS) databases having different thermochemical and turbulence conditions. These databases consist of lean turbulent premixed V-flames of the CH4-air mixture and stoichiometric turbulent premixed flames of H2-air. The mathematical model has successfully predicted the peak and the typical profile of <Nc ζ> with the sample space ζ and its prediction was consistent with an earlier study.
      Citation: Computation
      PubDate: 2021-02-28
      DOI: 10.3390/computation9030026
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 27: Transient Pressure-Driven Electroosmotic
           Flow through Elliptic Cross-Sectional Microchannels with Various
           Eccentricities

    • Authors: Nattakarn Numpanviwat, Pearanat Chuchard
      First page: 27
      Abstract: The semi-analytical solution for transient electroosmotic flow through elliptic cylindrical microchannels is derived from the Navier-Stokes equations using the Laplace transform. The electroosmotic force expressed by the linearized Poisson-Boltzmann equation is considered the external force in the Navier-Stokes equations. The velocity field solution is obtained in the form of the Mathieu and modified Mathieu functions and it is capable of describing the flow behavior in the system when the boundary condition is either constant or varied. The fluid velocity is calculated numerically using the inverse Laplace transform in order to describe the transient behavior. Moreover, the flow rates and the relative errors on the flow rates are presented to investigate the effect of eccentricity of the elliptic cross-section. The investigation shows that, when the area of the channel cross-sections is fixed, the relative errors are less than 1% if the eccentricity is not greater than 0.5. As a result, an elliptic channel with the eccentricity not greater than 0.5 can be assumed to be circular when the solution is written in the form of trigonometric functions in order to avoid the difficulty in computing the Mathieu and modified Mathieu functions.
      Citation: Computation
      PubDate: 2021-03-01
      DOI: 10.3390/computation9030027
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 28: Reduced Model for Properties of Multiscale
           Porous Media with Changing Geometry

    • Authors: Malgorzata Peszynska, Joseph Umhoefer, Choah Shin
      First page: 28
      Abstract: In this paper, we consider an important problem for modeling complex coupled phenomena in porous media at multiple scales. In particular, we consider flow and transport in the void space between the pores when the pore space is altered by new solid obstructions formed by microbial growth or reactive transport, and we are mostly interested in pore-coating and pore-filling type obstructions, observed in applications to biofilm in porous media and hydrate crystal formation, respectively. We consider the impact of these obstructions on the macroscopic properties of the porous medium, such as porosity, permeability and tortuosity, for which we build an experimental probability distribution with reduced models, which involves three steps: (1) generation of independent realizations of obstructions, followed by, (2) flow and transport simulations at pore-scale, and (3) upscaling. For the first step, we consider three approaches: (1A) direct numerical simulations (DNS) of the PDE model of the actual physical process called BN which forms the obstructions, and two non-DNS methods, which we call (1B) CLPS and (1C) LP. LP is a lattice Ising-type model, and CLPS is a constrained version of an Allen–Cahn model for phase separation with a localization term. Both LP and CLPS are model approximations of BN, and they seek local minima of some nonconvex energy functional, which provide plausible realizations of the obstructed geometry and are tuned heuristically to deliver either pore-coating or pore-filling obstructions. Our methods work with rock-void geometries obtained by imaging, but bypass the need for imaging in real-time, are fairly inexpensive, and can be tailored to other applications. The reduced models LP and CLPS are less computationally expensive than DNS, and can be tuned to the desired fidelity of the probability distributions of upscaled quantities.
      Citation: Computation
      PubDate: 2021-03-03
      DOI: 10.3390/computation9030028
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 29: Origin of Irrational Numbers and Their
           Approximations

    • Authors: Ravi P. Agarwal, Hans Agarwal
      First page: 29
      Abstract: In this article a sincere effort has been made to address the origin of the incommensurability/irrationality of numbers. It is folklore that the starting point was several unsuccessful geometric attempts to compute the exact values of 2 and π. Ancient records substantiate that more than 5000 years back Vedic Ascetics were successful in approximating these numbers in terms of rational numbers and used these approximations for ritual sacrifices, they also indicated clearly that these numbers are incommensurable. Since then research continues for the known as well as unknown/expected irrational numbers, and their computation to trillions of decimal places. For the advancement of this broad mathematical field we shall chronologically show that each continent of the world has contributed. We genuinely hope students and teachers of mathematics will also be benefited with this article.
      Citation: Computation
      PubDate: 2021-03-09
      DOI: 10.3390/computation9030029
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 30: Traveling Wave Solutions of a Four
           Dimensional Reaction-Diffusion Model for Porcine Reproductive and
           Respiratory Syndrome with Time Dependent Infection Rate

    • Authors: Jeerawan Suksamran, Yongwimon Lenbury, Sanoe Koonprasert
      First page: 30
      Abstract: Porcine reproductive and respiratory syndrome virus (PRRSV) causes reproductive failure in sows and respiratory disease in piglets and growing pigs. The disease rapidly spreads in swine populations, making it a serious problem causing great financial losses to the swine industry. However, past mathematical models used to describe the spread of the disease have not yielded sufficient understanding of its spatial transmission. This work has been designed to investigate a mathematical model for the spread of PRRSV considering both time and spatial dimensions as well as the observed decline in infectiousness as time progresses. Moreover, our model incorporates into the dynamics the assumption that some members of the infected population may recover from the disease and become immune. Analytical solutions are derived by using the modified extended hyperbolic tangent method with the introduction of traveling wave coordinate. We also carry out a stability and phase analysis in order to obtain a clearer understanding of how PRRSV spreads spatially through time.
      Citation: Computation
      PubDate: 2021-03-09
      DOI: 10.3390/computation9030030
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 31: Variable Coefficient Exact Solutions for
           Some Nonlinear Conformable Partial Differential Equations Using an
           Auxiliary Equation Method

    • Authors: Sekson Sirisubtawee, Nuntapon Thamareerat, Thitthita Iatkliang
      First page: 31
      Abstract: The objective of this present paper is to utilize an auxiliary equation method for constructing exact solutions associated with variable coefficient function forms for certain nonlinear partial differential equations (NPDEs) in the sense of the conformable derivative. Utilizing the specific fractional transformations, the conformable derivatives appearing in the original equation can be converted into integer order derivatives with respect to new variables. As for applications of the method, we particularly obtain variable coefficient exact solutions for the conformable time (2+1)-dimensional Kadomtsev–Petviashvili equation and the conformable space-time (2+1)-dimensional Boussinesq equation. As a result, the obtained exact solutions for the equations are solitary wave solutions including a soliton solitary wave solution and a bell-shaped solitary wave solution. The advantage of the used method beyond other existing methods is that it provides variable coefficient exact solutions covering constant coefficient ones. In consequence, the auxiliary equation method based on setting all coefficients of an exact solution as variable function forms can be more extensively used, straightforward and trustworthy for solving the conformable NPDEs.
      Citation: Computation
      PubDate: 2021-03-10
      DOI: 10.3390/computation9030031
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 32: Pharmacophore-Guided Identification of
           Natural Products as Potential Inhibitors of Mycobacterium ulcerans
           Cystathionine γ-Synthase MetB

    • Authors: Samuel K. Kwofie, Nigel N. O. Dolling, Emmanuel Donkoh, Godwin M. Laryea, Lydia Mosi, Whelton A. Miller, Michael B. Adinortey, Michael D. Wilson
      First page: 32
      Abstract: Buruli ulcer caused by Mycobacterium ulcerans (M. ulcerans) is identified by a pain-free cyst or edema which develops into a massive skin ulcer if left untreated. There are reports of chemoresistance, toxicity, noncompliance, and poor efficacy of current therapeutic options. Previously, we used cheminformatics approaches to identify potential antimycobacterial compounds targeting major receptors in M. ulcerans. In this paper, we sought to identify potential bioactive compounds by targeting Cystathionine gamma-synthase (CGS) MetB, a key receptor involved in methionine synthesis. Inhibition of methionine synthesis restricts the growth of M. ulcerans. Two potent inhibitors Juglone (IC50 0.7 +/− 0.7 µmol/L) and 9-hydroxy-alpha-lapachone (IC50 0.9 +/− 0.1 µmol/L) were used to generate 3D chemical feature pharmacophore model via LigandScout with a score of 0.9719. The validated model was screened against a pre-filtered library of 2530 African natural products. Compounds with fit scores above 66.40 were docked against the structure of CGS to generate hits. Three compounds, namely Gentisic 5-O glucoside (an isolate of African tree Alchornea cordifolia), Isoscutellarein (an isolate of Theobroma plant) and ZINC05854400, were identified as potential bioactive molecules with high binding affinities of −7.1, −8.4 and −8.4 kcal/mol against CGS, respectively. Novel structural insight into the binding mechanisms was elucidated using LigPlot+ and molecular dynamics simulations. All three molecules were predicted to possess antibacterial, anti-ulcerative, and dermatological properties. These compounds have the propensity to disrupt the methionine synthesis mechanisms with the potential of stagnating the growth of M. ulcerans. As a result of reasonably good pharmacological profiling, the three drug-like compounds are potential novel scaffolds that can be optimized into antimycobacterial molecules.
      Citation: Computation
      PubDate: 2021-03-12
      DOI: 10.3390/computation9030032
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 33: Application of the Generalized Laplace
           Homotopy Perturbation Method to the Time-Fractional Black–Scholes
           Equations Based on the Katugampola Fractional Derivative in Caputo Type

    • Authors: Sirunya Thanompolkrang, Wannika Sawangtong, Panumart Sawangtong
      First page: 33
      Abstract: In the finance market, the Black–Scholes equation is used to model the price change of the underlying fractal transmission system. Moreover, the fractional differential equations recently are accepted by researchers that fractional differential equations are a powerful tool in studying fractal geometry and fractal dynamics. Fractional differential equations are used in modeling the various important situations or phenomena in the real world such as fluid flow, acoustics, electromagnetic, electrochemistry and material science. There is an important question in finance: “Can the fractional differential equation be applied in the financial market'”. The answer is “Yes”. Due to the self-similar property of the fractional derivative, it can reply to the long-range dependence better than the integer-order derivative. Thus, these advantages are beneficial to manage the fractal structure in the financial market. In this article, the classical Black–Scholes equation with two assets for the European call option is modified by replacing the order of ordinary derivative with the fractional derivative order in the Caputo type Katugampola fractional derivative sense. The analytic solution of time-fractional Black–Scholes European call option pricing equation with two assets is derived by using the generalized Laplace homotopy perturbation method. The used method is the combination of the homotopy perturbation method and generalized Laplace transform. The analytic solution of the time-fractional Black–Scholes equation is carried out in the form of a Mittag–Leffler function. Finally, the effects of the fractional-order in the Caputo type Katugampola fractional derivative to change of a European call option price are shown.
      Citation: Computation
      PubDate: 2021-03-12
      DOI: 10.3390/computation9030033
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 34: Experimental Analysis of Hyperparameters
           for Deep Learning-Based Churn Prediction in the Banking Sector

    • Authors: Edvaldo Domingos, Blessing Ojeme, Olawande Daramola
      First page: 34
      Abstract: Until recently, traditional machine learning techniques (TMLTs) such as multilayer perceptrons (MLPs) and support vector machines (SVMs) have been used successfully for churn prediction, but with significant efforts expended on the configuration of the training parameters. The selection of the right training parameters for supervised learning is almost always experimentally determined in an ad hoc manner. Deep neural networks (DNNs) have shown significant predictive strength over TMLTs when used for churn predictions. However, the more complex architecture of DNNs and their capacity to process huge amounts of non-linear input data demand more time and effort to configure the training hyperparameters for DNNs during churn modeling. This makes the process more challenging for inexperienced machine learning practitioners and researchers. So far, limited research has been done to establish the effects of different hyperparameters on the performance of DNNs during churn prediction. There is a lack of empirically derived heuristic knowledge to guide the selection of hyperparameters when DNNs are used for churn modeling. This paper presents an experimental analysis of the effects of different hyperparameters when DNNs are used for churn prediction in the banking sector. The results from three experiments revealed that the deep neural network (DNN) model performed better than the MLP when a rectifier function was used for activation in the hidden layers and a sigmoid function was used in the output layer. The performance of the DNN was better when the batch size was smaller than the size of the test set data, while the RemsProp training algorithm had better accuracy when compared with the stochastic gradient descent (SGD), Adam, AdaGrad, Adadelta, and AdaMax algorithms. The study provides heuristic knowledge that could guide researchers and practitioners in machine learning-based churn prediction from the tabular data for customer relationship management in the banking sector when DNNs are used.
      Citation: Computation
      PubDate: 2021-03-16
      DOI: 10.3390/computation9030034
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 35: Hybrid Mamdani Fuzzy Rules and
           Convolutional Neural Networks for Analysis and Identification of Animal
           Images

    • Authors: Hind R. Mohammed, Zahir M. Hussain
      First page: 35
      Abstract: Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.
      Citation: Computation
      PubDate: 2021-03-17
      DOI: 10.3390/computation9030035
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 36: An Application of Optimal Control to
           Sugarcane Harvesting in Thailand

    • Authors: Wisanlaya Pornprakun, Surattana Sungnul, Chanakarn Kiataramkul, Elvin James Moore
      First page: 36
      Abstract: The sugar industry is of great importance to the Thai economy. In general, the government sets sugarcane prices at the beginning of each harvesting season based on type (fresh or fired), sweetness (sugar content) and gross weight. The main aim of the present research is to use optimal control to find optimal sugarcane harvesting policies for fresh and fired sugarcane for the four sugarcane producing regions of Thailand, namely North, Central, East and North-east, for harvesting seasons 2012/13, 2013/14, 2014/15, 2017/18 and 2018/19. The optimality problem is to determine the harvesting policy which gives maximum profit to the farmers subject to constraints on the maximum amount that can be cut in each day, where a harvesting policy is defined as the amount of each type of sugarcane harvested and delivered to the sugar factories during each day of a harvesting season. The results from the optimal control methods are also compared with results from three optimization methods, namely bi-objective, linear programming and quasi-Newton. The results suggest that discrete optimal control is the most effective of the five methods considered. The data used in this paper were obtained from the Ministry of Industry and the Ministry of Agriculture and Co-operatives of the Royal Thai government.
      Citation: Computation
      PubDate: 2021-03-19
      DOI: 10.3390/computation9030036
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 37: Effect of Additional Order in Two-Stage
           Supply Chain Contract under the Demand Uncertainty

    • Authors: Suphannee Chueanun, Rawee Suwandechochai
      First page: 37
      Abstract: In this work, mathematical models are formulated in order to investigate the effect of the additional order on the expected total profit of a two-stage supply chain. A multi-period buyback contract between a supplier and a retailer under the demand uncertainty is considered. Under the contract, an advance order is submitted to the supplier in advance when the demand is unknown, and an additional order can be made at the beginning of each period after the previous period demand is realized. The impact of the coordination on the supply chain’s expected total profit is also considered. The results show that the additional order does not always increases the supply chain profit. The additional order increases the supply chain profit only when both the retailer and supplier are coordinated. Under the decentralized system with the buyback contract, the retailer tends to order less in an advance order to reduce the risk. This leads to the higher cost due the additional order after the demand is realized. As a result, it is lowers the supply chain profit. Moreover, the sensitivity analysis is performed using numerical studies in order to observe the behavior of the expected total profit of the supply chain.
      Citation: Computation
      PubDate: 2021-03-22
      DOI: 10.3390/computation9030037
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 38: Preliminary Evaluation of the Influence of
           Surface and Tooth Root Damage on the Stress and Strain State of a
           Planetary Gearbox: An Innovative Hybrid Numerical–Analytical Approach
           for Further Development of Structural Health Monitoring Models

    • Authors: Franco Concli, Athanasios Kolios
      First page: 38
      Abstract: Wind turbine gearboxes are known to be among the weakest components in the system and the possibility to study and understand the behavior of geared transmissions when subject to several types of faults might be useful to plan maintenance and eventually reduce the costs by preventing further damage. The aim of this work is to develop a high-fidelity numerical model of a single-stage planetary gearbox selected as representative and to evaluate its behavior in the presence of surface fatigue and tooth-root bending damage, i.e., pits and cracks. The planetary gearbox is almost entirely modelled, including shafts, gears as well as bearings with all the rolling elements. Stresses and strains in the most critical areas are analyzed to better evaluate if the presence of such damage can be somehow detected using strain gauges and where to place them to maximize the sensitivity of the measures to the damage. Several simulations with different levels, types and positions of the damage were performed to better understand the mutual relations between the damaged and the stress state. The ability to introduce the effect of the damage in the model of a gearbox represents the first indispensable step of a Structural Health Monitoring (SHM) strategy. The numerical activity was performed taking advantage of an innovative hybrid numerical–analytical approach that ensures a significant reduction of the computational effort. The developed model shows good sensitivity to the presence, type and position of the defects. For the studied configuration, the numerical results show clearly show a relation between the averaged rim stress and the presence of root cracks. Moreover, the presence of surface defects seems to produce local stress peaks (when the defects pass through the contact) in the instantaneous rim stress.
      Citation: Computation
      PubDate: 2021-03-23
      DOI: 10.3390/computation9030038
      Issue No: Vol. 9, No. 3 (2021)
       
  • Computation, Vol. 9, Pages 14: Dynamic Stability Enhancement of a Hybrid
           Renewable Energy System in Stand-Alone Applications

    • Authors: Ezzeddine Touti, Hossem Zayed, Remus Pusca, Raphael Romary
      First page: 14
      Abstract: Renewable energy systems have been extensively developed and they are attractive to become widespread in the future because they can deliver energy at a competitive price and generally do not cause environmental pollution. However, stand-alone energy systems may not be practical for satisfying the electric load demands, especially in places having unsteady wind speeds with high unpredictability. Hybrid energy systems seem to be a more economically feasible alternative to satisfy the energy demands of several isolated clients worldwide. The combination of these systems makes it possible to guarantee the power stability, efficiency, and reliability. The aim of this paper is to present a comprehensive analysis and to propose a technical solution to integrate a self-excited induction generator in a low power multisource system. Therefore, to avoid the voltage collapsing and the machine demagnetization, the various parameters have to be identified. This procedure allows for the limitation of a safe operating area where the best stability of the machine can be obtained. Hence, the load variation interval is determined. An improvement of the induction generator stability will be analyzed. Simulation results will be validated through experimental tests.
      Citation: Computation
      PubDate: 2021-02-01
      DOI: 10.3390/computation9020014
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 15: ESTA: Educating Adolescents in Sustainable
           Travel Urban Behavior through Mobile Applications Using Motivational
           Features

    • Authors: Maria Eftychia Angelaki, Theodoros Karvounidis, Christos Douligeris
      First page: 15
      Abstract: This paper proposes the use of motivational features in mobile applications to support adolescents’ education in sustainable travel urban behavior, so that they become more mindful of their environmental impact. To this effect, existing persuasive strategies are adopted, implemented, and integrated into six simulated screens of a prospective mobile application named ESTA, designed for this purpose through a user-centered design process. These screens are then assessed by secondary education pupils, the outcome of which is analyzed and presented in detail. The analysis takes into consideration the possibility for the daily use of ESTA in order for the adolescents to foster an eco-friendly and healthy transit attitude and make more sustainable mobility choices that will follow them throughout their life. The potential effectiveness of ESTA is demonstrated via two use cases: the “Daily Commuting” case is addressed towards adolescents who want to move within their area of residence or neighborhood following their daily routine and activities, while the “Weekend Entertainment” is addressed towards adolescents who want to move using the available public transport modes, encouraging them to adopt greener weekend travel habits.
      Citation: Computation
      PubDate: 2021-02-02
      DOI: 10.3390/computation9020015
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 16: An Elaborate Preprocessing Phase (p3) in
           Composition and Optimization of Business Process Models

    • Authors: George Tsakalidis, Kostas Georgoulakos, Dimitris Paganias, Kostas Vergidis
      First page: 16
      Abstract: Business process optimization (BPO) has become an increasingly attractive subject in the wider area of business process intelligence and is considered as the problem of composing feasible business process designs with optimal attribute values, such as execution time and cost. Despite the fact that many approaches have produced promising results regarding the enhancement of attribute performance, little has been done to reduce the computational complexity due to the size of the problem. The proposed approach introduces an elaborate preprocessing phase as a component to an established optimization framework (bpoF) that applies evolutionary multi-objective optimization algorithms (EMOAs) to generate a series of diverse optimized business process designs based on specific process requirements. The preprocessing phase follows a systematic rule-based algorithmic procedure for reducing the library size of candidate tasks. The experimental results on synthetic data demonstrate a considerable reduction of the library size and a positive influence on the performance of EMOAs, which is expressed with the generation of an increasing number of nondominated solutions. An important feature of the proposed phase is that the preprocessing effects are explicitly measured before the EMOAs application; thus, the effects on the library reduction size are directly correlated with the improved performance of the EMOAs in terms of average time of execution and nondominated solution generation. The work presented in this paper intends to pave the way for addressing the abiding optimization challenges related to the computational complexity of the search space of the optimization problem by working on the problem specification at an earlier stage.
      Citation: Computation
      PubDate: 2021-02-04
      DOI: 10.3390/computation9020016
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 17: Weighted Consensus Segmentations

    • Authors: Halima Saker, Rainer Machné, Jörg Fallmann, Douglas B. Murray, Ahmad M. Shahin, Peter F. Stadler
      First page: 17
      Abstract: The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves.
      Citation: Computation
      PubDate: 2021-02-05
      DOI: 10.3390/computation9020017
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 18: Least-Squares Finite Element Method for a
           Meso-Scale Model of the Spread of COVID-19

    • Authors: Fleurianne Bertrand, Emilie Pirch
      First page: 18
      Abstract: This paper investigates numerical properties of a flux-based finite element method for the discretization of a SEIQRD (susceptible-exposed-infected-quarantined-recovered-deceased) model for the spread of COVID-19. The model is largely based on the SEIRD (susceptible-exposed-infected-recovered-deceased) models developed in recent works, with additional extension by a quarantined compartment of the living population and the resulting first-order system of coupled PDEs is solved by a Least-Squares meso-scale method. We incorporate several data on political measures for the containment of the spread gathered during the course of the year 2020 and develop an indicator that influences the predictions calculated by the method. The numerical experiments conducted show a promising accuracy of predictions of the space-time behavior of the virus compared to the real disease spreading data.
      Citation: Computation
      PubDate: 2021-02-05
      DOI: 10.3390/computation9020018
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 19: A Power Dissipation Monitoring Circuit for
           Intrusion Detection and Botnet Prevention on IoT Devices

    • Authors: Dimitrios Myridakis, Paul Myridakis, Athanasios Kakarountas
      First page: 19
      Abstract: Recently, there has been a sharp increase in the production of smart devices and related networks, and consequently the Internet of Things. One concern for these devices, which is constantly becoming more critical, is their protection against attacks due to their heterogeneity and the absence of international standards to achieve this goal. Thus, these devices are becoming vulnerable, with many of them not even showing any signs of malfunction or suspicious behavior. The aim of the present work is to introduce a circuit that is connected in series with the power supply of a smart device, specifically an IP camera, which allows analysis of its behavior. The detection circuit operates in real time (real-time detection), sampling the supply current of the device, processing the sampled values and finally indicating any detection of abnormal activities, based on a comparison to normal operation conditions. By utilizing techniques borrowed by simple power analysis side channel attack, it was possible to detect deviations from the expected operation of the IP camera, as they occurred due to intentional attacks, quarantining the monitored device from the rest of the network. The circuit is analyzed and a low-cost implementation (under 5US$) is illustrated. It achieved 100% success in the test results, showing excellent performance in intrusion detection.
      Citation: Computation
      PubDate: 2021-02-06
      DOI: 10.3390/computation9020019
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 20: Deep Learning for Fake News Detection in a
           Pairwise Textual Input Schema

    • Authors: Despoina Mouratidis, Maria Nefeli Nikiforos, Katia Lida Kermanidis
      First page: 20
      Abstract: In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.
      Citation: Computation
      PubDate: 2021-02-17
      DOI: 10.3390/computation9020020
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 21: Modified Fast Inverse Square Root and
           Square Root Approximation Algorithms: The Method of Switching Magic
           Constants

    • Authors: Leonid V. Moroz, Volodymyr V. Samotyy, Oleh Y. Horyachyy
      First page: 21
      Abstract: Many low-cost platforms that support floating-point arithmetic, such as microcontrollers and field-programmable gate arrays, do not include fast hardware or software methods for calculating the square root and/or reciprocal square root. Typically, such functions are implemented using direct lookup tables or polynomial approximations, with a subsequent application of the Newton–Raphson method. Other, more complex solutions include high-radix digit-recurrence and bipartite or multipartite table-based methods. In contrast, this article proposes a simple modification of the fast inverse square root method that has high accuracy and relatively low latency. Algorithms are given in C/C++ for single- and double-precision numbers in the IEEE 754 format for both square root and reciprocal square root functions. These are based on the switching of magic constants in the initial approximation, depending on the input interval of the normalized floating-point numbers, in order to minimize the maximum relative error on each subinterval after the first iteration—giving 13 correct bits of the result. Our experimental results show that the proposed algorithms provide a fairly good trade-off between accuracy and latency after two iterations for numbers of type float, and after three iterations for numbers of type double when using fused multiply–add instructions—giving almost complete accuracy.
      Citation: Computation
      PubDate: 2021-02-17
      DOI: 10.3390/computation9020021
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 22: Improved Stability Criteria on Linear
           Systems with Distributed Interval Time-Varying Delays and Nonlinear
           Perturbations

    • Authors: Jitsin Piyawatthanachot, Narongsak Yotha, Kanit Mukdasai
      First page: 22
      Abstract: The problem of delay-range-dependent stability analysis for linear systems with distributed time-varying delays and nonlinear perturbations is studied without using the model transformation and delay-decomposition approach. The less conservative stability criteria are obtained for the systems by constructing a new augmented Lyapunov–Krasovskii functional and various inequalities, which are presented in terms of linear matrix inequalities (LMIs). Four numerical examples are demonstrated for the results given to illustrate the effectiveness and improvement over other methods.
      Citation: Computation
      PubDate: 2021-02-21
      DOI: 10.3390/computation9020022
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 23: Modified ALNS Algorithm for a Processing
           Application of Family Tourist Route Planning: A Case Study of Buriram in
           Thailand

    • Authors: Narisara Khamsing, Kantimarn Chindaprasert, Rapeepan Pitakaso, Worapot Sirirak, Chalermchat Theeraviriya
      First page: 23
      Abstract: This research presents a solution to the family tourism route problem by considering daily time windows. To find the best solution for travel routing, the modified adaptive large neighborhood search (MALNS) method, using the four destructions and the four reconstructions approach, is applied here. The solution finding performance of the MALNS method is compared with an exact method running on the Lingo program. As shown by various solutions, the MALNS method can balance travel routing designs, including when many tourist attractions are present in each path. Furthermore, the results of the MALNS method are not significantly different from the results of the exact method for small problem sizes. For medium and large problem sizes, the MALNS method shows a higher performance and a smaller processing time for finding solutions. The values for the average total travel cost and average travel satisfaction rating derived by the MALNS method are approximately 0.18% for a medium problem and 0.05% for a large problem, 0.24% for a medium problem, and 0.21% for a large problem, respectively. The values derived from the exact method are slightly different. Moreover, the MALNS method calculation requires less processing time than the exact method, amounting to approximately 99.95% of the time required for the exact method. In this case study, the MALNS algorithm result shows a suitable balance of satisfaction and number of tourism places in relation to the differences between family members of different ages and genders in terms of satisfaction in tour route planning. The proposed solution methodology presents an effective high-quality solution, suggesting that the MALNS method has the potential to be a great competitive algorithm. According to the empirical results shown here, the MALNS method would be useful for creating route plans for tourism organizations that support travel route selection for family tours in Thailand.
      Citation: Computation
      PubDate: 2021-02-22
      DOI: 10.3390/computation9020023
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 24: Criminal Intention Detection at Early
           Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

    • Authors: Guillermo A. Martínez-Mascorro, José R. Abreu-Pederzini, José C. Ortiz-Bayliss, Angel Garcia-Collantes, Hugo Terashima-Marín
      First page: 24
      Abstract: Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all the information in real-time. Human sight has critical limitations. Among those limitations, visual focus is one of the most critical when dealing with surveillance. For example, in a surveillance room, a crime can occur in a different screen segment or on a distinct monitor, and the surveillance staff may overlook it. Our proposal focuses on shoplifting crimes by analyzing situations that an average person will consider as typical conditions, but may eventually lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior—the one that may occur before the build-up phase of a crime—by detecting precise segments of a video with a high probability of containing a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily action and shoplifting samples. The results are encouraging as the model correctly classifies suspicious behavior in most of the scenarios where it was tested. For example, when classifying suspicious behavior, the best model generated in this work obtains precision and recall values of 0.8571 and 1 in one of the test scenarios, respectively.
      Citation: Computation
      PubDate: 2021-02-23
      DOI: 10.3390/computation9020024
      Issue No: Vol. 9, No. 2 (2021)
       
  • Computation, Vol. 9, Pages 113: Exact Boolean Abstraction of Linear
           Equation Systems

    • Authors: Emilie Allart, Joachim Niehren, Cristian Versari
      First page: 113
      Abstract: We study the problem of how to compute the boolean abstraction of the solution set of a linear equation system over the positive reals. We call a linear equation system ϕ exact for the boolean abstraction if the abstract interpretation of ϕ over the structure of booleans is equal to the boolean abstraction of the solution set of ϕ over the positive reals. interpretation over the booleans is thus complete for the boolean abstraction when restricted to exact linear equation systems, while it is not complete more generally. We present a new rewriting algorithm that makes linear equation systems exact for the boolean abstraction while preserving the solutions over the positive reals. The rewriting algorithm is based on the elementary modes of the linear equation system. The computation of the elementary modes may require exponential time in the worst case, but is often feasible in practice with freely available tools. For exact linear equation systems, we can compute the boolean abstraction by finite domain constraint programming. This yields a solution of the initial problem that is often feasible in practice. Our exact rewriting algorithm has two further applications. Firstly, it can be used to compute the sign abstraction of linear equation systems over the reals, as needed for analyzing function programs with linear arithmetics. Secondly, it can be applied to compute the difference abstraction of a linear equation system as used in change prediction algorithms for flux networks in systems biology.
      Citation: Computation
      PubDate: 2021-10-21
      DOI: 10.3390/computation9110113
      Issue No: Vol. 9, No. 11 (2021)
       
  • Computation, Vol. 9, Pages 102: P System–Based Clustering Methods
           Using NoSQL Databases

    • Authors: Péter Lehotay-Kéry, Tamás Tarczali, Attila Kiss
      First page: 102
      Abstract: Models of computation are fundamental notions in computer science; consequently, they have been the subject of countless research papers, with numerous novel models proposed even in recent years. Amongst a multitude of different approaches, many of these methods draw inspiration from the biological processes observed in nature. P systems, or membrane systems, make an analogy between the communication in computing and the flow of information that can be perceived in living organisms. These systems serve as a basis for various concepts, ranging from the fields of computational economics and robotics to the techniques of data clustering. In this paper, such utilization of these systems—membrane system–based clustering—is taken into focus. Considering the growing number of data stored worldwide, more and more data have to be handled by clustering algorithms too. To solve this issue, bringing these methods closer to the data, their main element provides several benefits. Database systems equip their users with, for instance, well-integrated security features and more direct control over the data itself. Our goal is if the type of the database management system is given, e.g., NoSQL, but the corporation or the research team can choose which specific database management system is used, then we give a perspective, how the algorithms written like this behave in such an environment, so that, based on this, a more substantiated decision can be made, meaning which database management system should be connected to the system. For this purpose, we discover the possibilities of a clustering algorithm based on P systems when used alongside NoSQL database systems, that are designed to manage big data. Variants over two competing databases, MongoDB and Redis, are evaluated and compared to identify the advantages and limitations of using such a solution in these systems.
      Citation: Computation
      PubDate: 2021-09-24
      DOI: 10.3390/computation9100102
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 103: Identifying Potential Machine Learning
           Algorithms for the Simulation of Binding Affinities to Molecularly
           Imprinted Polymers

    • Authors: Joseph W. Lowdon, Hikaru Ishikura, Malene K. Kvernenes, Manlio Caldara, Thomas J. Cleij, Bart van van Grinsven, Kasper Eersels, Hanne Diliën
      First page: 103
      Abstract: Molecularly imprinted polymers (MIPs) are synthetic receptors engineered towards the selective binding of a target molecule; however, the manner in which MIPs interact with other molecules is of great importance. Being able to rapidly analyze the binding of potential molecular interferences and determine the selectivity of a MIP can be a long tedious task, being time- and resource-intensive. Identifying computational models capable of reliably predicting and reporting the binding of molecular species is therefore of immense value in both a research and commercial setting. This research therefore sets focus on comparing the use of machine learning algorithms (multitask regressor, graph convolution, weave model, DAG model, and inception) to predict the binding of various molecular species to a MIP designed towards 2-methoxphenidine. To this end, each algorithm was “trained” with an experimental dataset, teaching the algorithms the structures and binding affinities of various molecular species at varying concentrations. A validation experiment was then conducted for each algorithm, comparing experimental values to predicted values and facilitating the assessment of each approach by a direct comparison of the metrics. The research culminates in the construction of binding isotherms for each species, directly comparing experimental vs. predicted values and identifying the approach that best emulates the real-world data.
      Citation: Computation
      PubDate: 2021-09-24
      DOI: 10.3390/computation9100103
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 104: On the Use of Composite Functions in the
           Simple Equations Method to Obtain Exact Solutions of Nonlinear
           Differential Equations

    • Authors: Nikolay K. Vitanov, Zlatinka I. Dimitrova, Kaloyan N. Vitanov
      First page: 104
      Abstract: We discuss the Simple Equations Method (SEsM) for obtaining exact solutions of a class of nonlinear differential equations containing polynomial nonlinearities. We present an amended version of the methodology, which is based on the use of composite functions. The number of steps of the SEsM was reduced from seven to four in the amended version of the methodology. For the case of nonlinear differential equations with polynomial nonlinearities, SEsM can reduce the solved equations to a system of nonlinear algebraic equations. Each nontrivial solution of this algebraic system leads to an exact solution of the solved nonlinear differential equations. We prove the theorems and present examples for the use of composite functions in the methodology of the SEsM for the following three kinds of composite functions: (i) a composite function of one function of one independent variable; (ii) a composite function of two functions of two independent variables; (iii) a composite function of three functions of two independent variables.
      Citation: Computation
      PubDate: 2021-09-27
      DOI: 10.3390/computation9100104
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 105: Novel Statistical Analysis in the Context
           of a Comprehensive Needs Assessment for Secondary STEM Recruitment

    • Authors: Norou Diawara, Sarah Ferguson, Melva Grant, Kumer Das
      First page: 105
      Abstract: There is a myriad of career opportunities stemming from science, technology, engineering, and mathematics (STEM) disciplines. In addition to careers in corporate settings, teaching is a viable career option for individuals pursuing degrees in STEM disciplines. With national shortages of secondary STEM teachers, efforts to recruit, train, and retain quality STEM teachers is greatly important. Prior to exploring ways to attract potential STEM teacher candidates to pursue teacher training programs, it is important to understand the perceived value that potential recruits place on STEM careers, disciplines, and the teaching profession. The purpose of this study was to explore students’ perceptions of the usefulness of STEM disciplines and their value in supporting students’ careers. A novel statistical method was utilized, combining exploratory-factor analysis, the analysis of variance, generalized estimating equation evaluations under the framework of a generalized linear model, and quantile regression. Using the outputs from each statistical measure, students’ valuation of each STEM discipline and their interest in pursuing teaching as a career option were assessed. Our results indicate a high correlation of liking and perceived usability of the STE disciplines relative to careers. Conversely, our results also display a low correlation of the liking and perceived usability of mathematics relative to future careers. The significance of these diametrically related results suggests the need for promotion of the interrelatedness of mathematics and STE.
      Citation: Computation
      PubDate: 2021-09-28
      DOI: 10.3390/computation9100105
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 106: Gene Expression Analysis through Parallel
           Non-Negative Matrix Factorization

    • Authors: Angelica Alejandra Serrano-Rubio, Guillermo B. Morales-Luna, Amilcar Meneses-Viveros
      First page: 106
      Abstract: Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.
      Citation: Computation
      PubDate: 2021-09-30
      DOI: 10.3390/computation9100106
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 107: A Language for Modeling and Optimizing
           Experimental Biological Protocols

    • Authors: Luca Cardelli, Marta Kwiatkowska, Luca Laurenti
      First page: 107
      Abstract: Automation is becoming ubiquitous in all laboratory activities, moving towards precisely defined and codified laboratory protocols. However, the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, while protocols define the steps carried out during an experiment: neither cover the domain of the other, although they both attempt to characterize the same phenomena. We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection. To this end, we present a language to model and optimize experimental biochemical protocols that facilitates such an integrated description, and that can be combined with experimental data. We provide probabilistic semantics for our language in terms of Gaussian processes (GPs) based on the linear noise approximation (LNA) that formally characterizes the uncertainties in the data collection, the underlying model, and the protocol operations. In a set of case studies, we illustrate how the resulting framework allows for automated analysis and optimization of experimental protocols, including Gibson assembly protocols.
      Citation: Computation
      PubDate: 2021-10-16
      DOI: 10.3390/computation9100107
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 108: A Class of Copula-Based Bivariate Poisson
           Time Series Models with Applications

    • Authors: Mohammed Alqawba, Dimuthu Fernando, Norou Diawara
      First page: 108
      Abstract: A class of bivariate integer-valued time series models was constructed via copula theory. Each series follows a Markov chain with the serial dependence captured using copula-based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins. The copula theory was also used again to capture the dependence between the two series using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible dependence structure that allows for positive and negative correlation, as well. In addition, the use of a copula permits applying different margins with a complicated structure such as the ZIP distribution. Likelihood-based inference was used to estimate the models’ parameters with the bivariate integrals of the Gaussian or t-copula functions being evaluated using standard randomized Monte Carlo methods. To evaluate the proposed class of models, a comprehensive simulated study was conducted. Then, two sets of real-life examples were analyzed assuming the Poisson and the ZIP marginals, respectively. The results showed the superiority of the proposed class of models.
      Citation: Computation
      PubDate: 2021-10-18
      DOI: 10.3390/computation9100108
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 109: Estimation of Daily Reproduction Numbers
           during the COVID-19 Outbreak

    • Authors: Jacques Demongeot, Kayode Oshinubi, Mustapha Rachdi, Hervé Seligmann, Florence Thuderoz, Jules Waku
      First page: 109
      Abstract: (1) Background: The estimation of daily reproduction numbers throughout the contagiousness period is rarely considered, and only their sum R0 is calculated to quantify the contagiousness level of an infectious disease. (2) Methods: We provide the equation of the discrete dynamics of the epidemic’s growth and obtain an estimation of the daily reproduction numbers by using a deconvolution technique on a series of new COVID-19 cases. (3) Results: We provide both simulation results and estimations for several countries and waves of the COVID-19 outbreak. (4) Discussion: We discuss the role of noise on the stability of the epidemic’s dynamics. (5) Conclusions: We consider the possibility of improving the estimation of the distribution of daily reproduction numbers during the contagiousness period by taking into account the heterogeneity due to several host age classes.
      Citation: Computation
      PubDate: 2021-10-18
      DOI: 10.3390/computation9100109
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 110: Forecasting Multivariate Chaotic Processes
           with Precedent Analysis

    • Authors: Alexander Musaev, Andrey Makshanov, Dmitry Grigoriev
      First page: 110
      Abstract: Predicting the state of a dynamic system influenced by a chaotic immersion environment is an extremely difficult task, in which the direct use of statistical extrapolation computational schemes is infeasible. This paper considers a version of precedent forecasting in which we use the aftereffects of retrospective observation segments that are similar to the current situation as a forecast. Furthermore, we employ the presence of relatively stable correlations between the parameters of the immersion environment as a regularizing factor. We pay special attention to the choice of similarity measures or distances used to find analog windows in arrays of retrospective multidimensional observations.
      Citation: Computation
      PubDate: 2021-10-19
      DOI: 10.3390/computation9100110
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 111: Metabolic Pathway Analysis in the Presence
           of Biological Constraints

    • Authors: Philippe Dague
      First page: 111
      Abstract: Metabolic pathway analysis is a key method to study a metabolism in its steady state, and the concept of elementary fluxes (EFs) plays a major role in the analysis of a network in terms of non-decomposable pathways. The supports of the EFs contain in particular those of the elementary flux modes (EFMs), which are the support-minimal pathways, and EFs coincide with EFMs when the only flux constraints are given by the irreversibility of certain reactions. Practical use of both EFMs and EFs has been hampered by the combinatorial explosion of their number in large, genome-scale systems. The EFs give the possible pathways in a steady state but the real pathways are limited by biological constraints, such as thermodynamic or, more generally, kinetic constraints and regulatory constraints from the genetic network. We provide results on the mathematical structure and geometrical characterization of the solution space in the presence of such biological constraints (which is no longer a convex polyhedral cone or a convex polyhedron) and revisit the concept of EFMs and EFs in this framework. We show that most of the results depend only on very general properties of compatibility of constraints with vector signs: either sign-invariance, satisfied by regulatory constraints, or sign-monotonicity (a stronger property), satisfied by thermodynamic and kinetic constraints. We show in particular that the solution space for sign-monotone constraints is a union of particular faces of the original polyhedral cone or polyhedron and that EFs still coincide with EFMs and are just those of the original EFs that satisfy the constraint, and we show how to integrate their computation efficiently in the double description method, the most widely used method in the tools dedicated to EFs computation. We show that, for sign-invariant constraints, the situation is more complex: the solution space is a disjoint union of particular semi-open faces (i.e., without some of their own faces of lesser dimension) of the original polyhedral cone or polyhedron and, if EFs are still those of the original EFs that satisfy the constraint, their computation cannot be incrementally integrated into the double description method, and the result is not true for EFMs, that are in general strictly more numerous than those of the original EFMs that satisfy the constraint.
      Citation: Computation
      PubDate: 2021-10-19
      DOI: 10.3390/computation9100111
      Issue No: Vol. 9, No. 10 (2021)
       
  • Computation, Vol. 9, Pages 112: Nonlinear Dynamics and Performance
           Analysis of a Buck Converter with Hysteresis Control

    • Authors: Carlos I. Hoyos Velasco, Fredy Edimer Hoyos Velasco, John E. Candelo-Becerra
      First page: 112
      Abstract: This paper presents the mathematical modeling and experimental implementation of a Buck converter with hysteresis control. The system is described using a state-space model. Theoretical and simulation studies show that the zero hysteresis control leads to an equilibrium point with the implication of an infinite commutation frequency, while the use of a constant hysteresis band induces a limit cycle with a finite switching frequency. There exists a tradeoff between voltage output ripple and transistor switching frequency. An experimental prototype for the Buck power converter is built, and theoretical results are verified experimentally. In general terms, the Buck converter with the hysteresis control shows a robust control with respect to load variations, with undesired high switching frequency taking place for a very narrow hysteresis band, which is solved by tuning the hysteresis band properly.
      Citation: Computation
      PubDate: 2021-10-19
      DOI: 10.3390/computation9100112
      Issue No: Vol. 9, No. 10 (2021)
       
 
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