Special Issue "Entropy and Its Applications across Disciplines II"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Dr. Francesco Villecco
E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy
Interests: industrial design; entropy; fuzzy logic; computer-aided design (CAD); axiomatic design; MaxInf principle
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yusif S. Gasimov
E-Mail Website
Co-Guest Editor
Institute of Technical Thermodynamics, Azerbaijan University, Jeyhun Hajibeyli str., 71, AZ1007 Baku, Azerbaijan
Interests: spectral theory; inverse problems; variable domain eigenvalue problems; “shape” optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicola Cappetti
E-Mail Website
Co-Guest Editor
Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy
Interests: mechanical design and technical drawings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In modern research, many problems are characterized by complexity and dependence on multiple parameters. The entropy of a system is a direct measure of its complexity. Other complexity-related mathematical functions include the Hurst exponent, long-range correlation, fractals, stochastic processes, probability, and fuzzy probability. These models may be seen in various fields of science, such as physics, engineering, mechanics, biology, economics, and some more mathematical applications.

The aim of this Special Issue is to discuss, from both theoretical and applied points of view, the physical and engineering properties of the entropy- and complexity-based models arising in nature and applied sciences.

Topics of interest are given below, and papers related to these fields are welcome.

  • Entropy and complexity of mathematical models with fractional and integer order.
  • New analytical and numerical methods in the analysis of problems where entropy and complexity are the main features.
  • Entropy and complexity in computational methods for differential models.
  • Entropy and complexity in engineering, fluid dynamics, and thermal engineering problems, as well as problems related to physics, applied sciences, and computer science.
  • Deterministic and stochastic fractional order models.
  • Entropy and complexity models in physics and engineering.
  • Entropy and complexity in analytical and numerical solutions.
  • Nonlinear dynamical complex systems.
  • Entropic measure of epistemic uncertainties.

Dr. Francesco Villecco
Prof. Dr. Yusif S. Gasimov
Prof. Dr. Nicola Cappetti
Guest Editors

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (10 papers)

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Research

Article
Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony
Entropy 2021, 23(5), 617; https://doi.org/10.3390/e23050617 - 16 May 2021
Cited by 1 | Viewed by 644
Abstract
The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type [...] Read more.
The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
Electrical Power Diversification: An Approach Based on the Method of Maximum Entropy in the Mean
Entropy 2021, 23(3), 281; https://doi.org/10.3390/e23030281 - 26 Feb 2021
Viewed by 429
Abstract
Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much [...] Read more.
Electrical energy is generated in different ways, each located at some specific geographical area, and with different impact on the environment. Different sectors require heterogeneous rates of energy delivery, due to economic requirements. An important problem to solve is to determine how much energy must be sent from each supplier to satisfy each demand. Besides, the energy distribution process may have to satisfy ecological, technological, or economic cost constraints. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
Article
On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys
Entropy 2021, 23(2), 135; https://doi.org/10.3390/e23020135 - 21 Jan 2021
Cited by 7 | Viewed by 993
Abstract
Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and [...] Read more.
Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. Conclusions: the present study confirms the possibility of continuously monitoring the driver’s status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
Partial Exactness for the Penalty Function of Biconvex Programming
Entropy 2021, 23(2), 132; https://doi.org/10.3390/e23020132 - 21 Jan 2021
Cited by 1 | Viewed by 482
Abstract
Biconvex programming (or inequality constrained biconvex optimization) is an important model in solving many engineering optimization problems in areas like machine learning and signal and information processing. In this paper, the partial exactness of the partial optimum for the penalty function of biconvex [...] Read more.
Biconvex programming (or inequality constrained biconvex optimization) is an important model in solving many engineering optimization problems in areas like machine learning and signal and information processing. In this paper, the partial exactness of the partial optimum for the penalty function of biconvex programming is studied. The penalty function is partially exact if the partial Karush–Kuhn–Tucker (KKT) condition is true. The sufficient and necessary partially local stability condition used to determine whether the penalty function is partially exact for a partial optimum solution is also proven. Based on the penalty function, an algorithm is presented for finding a partial optimum solution to an inequality constrained biconvex optimization, and its convergence is proven under some conditions. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
Article
The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing Environments
Entropy 2020, 22(12), 1410; https://doi.org/10.3390/e22121410 - 15 Dec 2020
Viewed by 631
Abstract
This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a [...] Read more.
This paper applies the entropy-based fractal indexing scheme that enables the grid environment for fast indexing and querying. It addresses the issue of fault tolerance and load balancing-based fractal management to make computational grids more effective and reliable. A fractal dimension of a cloud of points gives an estimate of the intrinsic dimensionality of the data in that space. The main drawback of this technique is the long computing time. The main contribution of the suggested work is to investigate the effect of fractal transform by adding R-tree index structure-based entropy to existing grid computing models to obtain a balanced infrastructure with minimal fault. In this regard, the presented work is going to extend the commonly scheduling algorithms that are built based on the physical grid structure to a reduced logical network. The objective of this logical network is to reduce the searching in the grid paths according to arrival time rate and path’s bandwidth with respect to load balance and fault tolerance, respectively. Furthermore, an optimization searching technique is utilized to enhance the grid performance by investigating the optimum number of nodes extracted from the logical grid. The experimental results indicated that the proposed model has better execution time, throughput, makespan, latency, load balancing, and success rate. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
Reconstructing Nonparametric Productivity Networks
Entropy 2020, 22(12), 1401; https://doi.org/10.3390/e22121401 - 11 Dec 2020
Viewed by 619
Abstract
Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to [...] Read more.
Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
Noise Reduction in Spur Gear Systems
Entropy 2020, 22(11), 1306; https://doi.org/10.3390/e22111306 - 16 Nov 2020
Cited by 4 | Viewed by 775
Abstract
This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, [...] Read more.
This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
A New Adaptive Entropy Portfolio Selection Model
Entropy 2020, 22(9), 951; https://doi.org/10.3390/e22090951 - 28 Aug 2020
Viewed by 854
Abstract
In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange [...] Read more.
In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors’ expectations. Our model applies equally well to portfolio optimizations for other financial markets. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
Modeling of Eddy Current Welding of Rail: Three-Dimensional Simulation
Entropy 2020, 22(9), 947; https://doi.org/10.3390/e22090947 - 28 Aug 2020
Cited by 2 | Viewed by 910
Abstract
In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the [...] Read more.
In this paper is given a three-dimensional numerical simulation of the eddy current welding of rails where the longitudinal two directions are not ignored. In fact, usually it is considered a model where, in the two-dimensional numerical simulation of rail heat treatment, the longitudinal directions are ignored for the magnetic induction strength and temperature, and only the axial calculation is performed. Therefore, we propose the electromagnetic-thermal coupled three-dimensional model of eddy current welding. The induced eddy current heat is obtained by adding the z-axis spatial angle to the two-dimensional electromagnetic-thermal, thus obtaining some new results by coupling the numerical simulation and computations of the electric field and magnetic induction intensity of the three-dimensional model. Moreover, we have considered the objective function into a weak formulation. The three-dimensional model is then meshed by the finite element method. The electromagnetic-thermal coupling has been numerically computed, and the parametric dependence to the eddy current heating process has been fully studied. Through the numerical simulation with different current densities, frequencies, and distances, the most suitable heat treatment process of U75V rail is obtained. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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Article
An Efficient Method Based on Framelets for Solving Fractional Volterra Integral Equations
Entropy 2020, 22(8), 824; https://doi.org/10.3390/e22080824 - 28 Jul 2020
Cited by 8 | Viewed by 1055
Abstract
This paper is devoted to shedding some light on the advantages of using tight frame systems for solving some types of fractional Volterra integral equations (FVIEs) involved by the Caputo fractional order derivative. A tight frame or simply framelet, is a generalization of [...] Read more.
This paper is devoted to shedding some light on the advantages of using tight frame systems for solving some types of fractional Volterra integral equations (FVIEs) involved by the Caputo fractional order derivative. A tight frame or simply framelet, is a generalization of an orthonormal basis. A lot of applications are modeled by non-negative functions; taking this into account in this paper, we consider framelet systems generated using some refinable non-negative functions, namely, B-splines. The FVIEs we considered were reduced to a set of linear system of equations and were solved numerically based on a collocation discretization technique. We present many important examples of FVIEs for which accurate and efficient numerical solutions have been accomplished and the numerical results converge very rapidly to the exact ones. Full article
(This article belongs to the Special Issue Entropy and Its Applications across Disciplines II)
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