Journal Description
Entropy
Entropy
is an international and interdisciplinary peer-reviewed open access journal of entropy and information studies, published monthly online by MDPI. The International Society for the Study of Information (IS4SI) and Spanish Society of Biomedical Engineering (SEIB) are affiliated with Entropy and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), MathSciNet, Inspec, PubMed, PMC, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Physics, Multidisciplinary) / CiteScore - Q1 (Mathematical Physics)
- Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 18.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2022).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Entropy.
- Companion journals for Entropy include: Foundations, Thermo and MAKE.
Impact Factor:
2.738 (2021)
;
5-Year Impact Factor:
2.642 (2021)
Latest Articles
Double-Color-Image Compression-Encryption Algorithm Based on Quaternion Multiple Parameter DFrAT and Feature Fusion with Preferable Restoration Quality
Entropy 2022, 24(7), 941; https://doi.org/10.3390/e24070941 (registering DOI) - 06 Jul 2022
Abstract
To achieve multiple color images encryption, a secure double-color-image encryption algorithm is designed based on the quaternion multiple parameter discrete fractional angular transform (QMPDFrAT), a nonlinear operation and a plaintext-related joint permutation-diffusion mechanism. QMPDFrAT is first defined and then applied to encrypt multiple
[...] Read more.
To achieve multiple color images encryption, a secure double-color-image encryption algorithm is designed based on the quaternion multiple parameter discrete fractional angular transform (QMPDFrAT), a nonlinear operation and a plaintext-related joint permutation-diffusion mechanism. QMPDFrAT is first defined and then applied to encrypt multiple color images. In the designed algorithm, the low-frequency and high-frequency sub-bands of the three color components of each plaintext image are obtained by two-dimensional discrete wavelet transform. Then, the high-frequency sub-bands are further made sparse and the main features of these sub-bands are extracted by a Zigzag scan. Subsequently, all the low-frequency sub-bands and high-frequency fusion images are represented as three quaternion signals, which are modulated by the proposed QMPDFrAT with three quaternion random phase masks, respectively. The spherical transform, as a nonlinear operation, is followed to nonlinearly make the three transform results interact. For better security, a joint permutation-diffusion mechanism based on plaintext-related random pixel insertion is performed on the three intermediate outputs to yield the final encryption image. Compared with many similar color image compression-encryption schemes, the proposed algorithm can encrypt double-color-image with higher quality of image reconstruction. Numerical simulation results demonstrate that the proposed double-color-image encryption algorithm is feasibility and achieves high security.
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Open AccessArticle
Optical Flow-Aware-Based Multi-Modal Fusion Network for Violence Detection
Entropy 2022, 24(7), 939; https://doi.org/10.3390/e24070939 (registering DOI) - 06 Jul 2022
Abstract
Violence detection aims to locate violent content in video frames. Improving the accuracy of violence detection is of great importance for security. However, the current methods do not make full use of the multi-modal vision and audio information, which affects the accuracy of
[...] Read more.
Violence detection aims to locate violent content in video frames. Improving the accuracy of violence detection is of great importance for security. However, the current methods do not make full use of the multi-modal vision and audio information, which affects the accuracy of violence detection. We found that the violence detection accuracy of different kinds of videos is related to the change of optical flow. With this in mind, we propose an optical flow-aware-based multi-modal fusion network (OAMFN) for violence detection. Specifically, we use three different fusion strategies to fully integrate multi-modal features. First, the main branch concatenates RGB features and audio features and the optical flow branch concatenates optical flow features with RGB features and audio features, respectively. Then, the cross-modal information fusion module integrates the features of different combinations and applies weights to them to capture cross-modal information in audio and video. After that, the channel attention module extracts valuable information by weighting the integration features. Furthermore, an optical flow-aware-based score fusion strategy is introduced to fuse features of different modalities from two branches. Compared with methods on the XD-Violence dataset, our multi-modal fusion network yields APs that are 83.09% and 1.4% higher than those of the state-of-the-art methods in offline detection, and 78.09% and 4.42% higher than those of the state-of-the-art methods in online detection.
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(This article belongs to the Topic Machine and Deep Learning)
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Open AccessArticle
Research on Nickel Material Trade Redistribution Strategy Based on the Maximum Entropy Principle
Entropy 2022, 24(7), 938; https://doi.org/10.3390/e24070938 (registering DOI) - 06 Jul 2022
Abstract
In the double carbon background, riding the wind of new energy vehicles and the battery high nickelization, nickel resources rise along with the trend. In recent years, due to the influence of geopolitical conflicts and emergencies, as well as the speculation and control
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In the double carbon background, riding the wind of new energy vehicles and the battery high nickelization, nickel resources rise along with the trend. In recent years, due to the influence of geopolitical conflicts and emergencies, as well as the speculation and control of international capital with its advantages and rules, the world may face price and security supply risks to a certain extent. Therefore, to obtain the most objective trade redistribution strategy, this paper first constructs the nickel material trade network, identifies the core trading countries and the main trade relations of nickel material trade, and finds that the flow of nickel material mainly occurred between a few countries. On this basis, a trade redistribution model is constructed based on the maximum entropy principle. Taking Indonesia, the largest exporter, and the largest trade relationship (Indonesia exports to China) as examples, the nickel material redistribution between countries when different supply risks occur are simulated. The results can provide an important reference for national resource recovery after the risk of the nickel trade.
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(This article belongs to the Special Issue Complex Network Analysis in Econometrics)
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Open AccessArticle
Exponentially Weighted Multivariate HAR Model with Applications in the Stock Market
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Entropy 2022, 24(7), 937; https://doi.org/10.3390/e24070937 (registering DOI) - 06 Jul 2022
Abstract
This paper considers a multivariate time series model for stock prices in the stock market. A multivariate heterogeneous autoregressive (HAR) model is adopted with exponentially decaying coefficients. This model is not only suitable for multivariate data with strong cross-correlation and long memory, but
[...] Read more.
This paper considers a multivariate time series model for stock prices in the stock market. A multivariate heterogeneous autoregressive (HAR) model is adopted with exponentially decaying coefficients. This model is not only suitable for multivariate data with strong cross-correlation and long memory, but also represents a common structure of the joint data in terms of decay rates. Tests are proposed to identify the existence of the decay rates in the multivariate HAR model. The null limiting distributions are established as the standard Brownian bridge and are proven by means of a modified martingale central limit theorem. Simulation studies are conducted to assess the performance of tests and estimates. Empirical analysis with joint datasets of U.S. stock prices illustrates that the proposed model outperforms the conventional HAR models via OLSE and LASSO with respect to residual errors.
Full article
(This article belongs to the Special Issue Applications of Statistical Physics in Finance and Economics)
Open AccessArticle
Detecting Errors with Zero-Shot Learning
Entropy 2022, 24(7), 936; https://doi.org/10.3390/e24070936 (registering DOI) - 06 Jul 2022
Abstract
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Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of deep learning, some researchers focus their attention on
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Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of deep learning, some researchers focus their attention on learning the semantic distribution of data for error detection; however, the low error rate in real datasets makes it hard to collect negative samples for training supervised deep learning models. Most of the existing deep-learning-based error detection algorithms solve the class imbalance problem by data augmentation. Due to the inadequate sampling of negative samples, the features learned by those methods may be biased. In this paper, we propose an AEGAN (Auto-Encoder Generative Adversarial Network)-based deep learning model named SAT-GAN (Self-Attention Generative Adversarial Network) to detect errors in relational datasets. Combining the self-attention mechanism with the pre-trained language model, our model can capture semantic features of the dataset, specifically the functional dependency between attributes, so that no rules or constraints are needed for SAT-GAN to identify inconsistent data. For the lack of negative samples, we propose to train our model via zero-shot learning. As a clean-data tailored model, SAT-GAN tries to recognize error data as outliers by learning the latent features of clean data. In our evaluation, SAT-GAN achieves an average -score of 0.95 on five datasets, which yields at least 46.2% -score improvement over rule-based methods and outperforms state-of-the-art deep learning approaches in the absence of rules and negative samples.
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Open AccessEditorial
Physical-Layer Security, Quantum Key Distribution, and Post-Quantum Cryptography
Entropy 2022, 24(7), 935; https://doi.org/10.3390/e24070935 (registering DOI) - 06 Jul 2022
Abstract
The growth of data-driven technologies, 5G, and the Internet pose enormous pressure on underlying information infrastructure [...]
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(This article belongs to the Special Issue Physical-Layer Security, Quantum Key Distribution and Post-quantum Cryptography)
Open AccessFeature PaperArticle
The Role of Entropy in Construct Specification Equations (CSE) to Improve the Validity of Memory Tests: Extension to Word Lists
Entropy 2022, 24(7), 934; https://doi.org/10.3390/e24070934 (registering DOI) - 05 Jul 2022
Abstract
Metrological methods for word learning list tests can be developed with an information theoretical approach extending earlier simple syntax studies. A classic Brillouin entropy expression is applied to the analysis of the Rey’s Auditory Verbal Learning Test RAVLT (immediate recall), where more ordered
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Metrological methods for word learning list tests can be developed with an information theoretical approach extending earlier simple syntax studies. A classic Brillouin entropy expression is applied to the analysis of the Rey’s Auditory Verbal Learning Test RAVLT (immediate recall), where more ordered tasks—with less entropy—are easier to perform. The findings from three case studies are described, including 225 assessments of the NeuroMET2 cohort of persons spanning a cognitive spectrum from healthy older adults to patients with dementia. In the first study, ordinality in the raw scores is compensated for, and item and person attributes are separated with the Rasch model. In the second, the RAVLT IR task difficulty, including serial position effects (SPE), particularly Primacy and Recency, is adequately explained (Pearson’s correlation ) with construct specification equations (CSE). The third study suggests multidimensionality is introduced by SPE, as revealed through goodness-of-fit statistics of the Rasch analyses. Loading factors common to two kinds of principal component analyses (PCA) for CSE formulation and goodness-of-fit logistic regressions are identified. More consistent ways of defining and analysing memory task difficulties, including SPE, can maintain the unique metrological properties of the Rasch model and improve the estimates and understanding of a person’s memory abilities on the path towards better-targeted and more fit-for-purpose diagnostics.
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(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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Open AccessArticle
A Color Image Encryption Algorithm Based on Double Fractional Order Chaotic Neural Network and Convolution Operation
Entropy 2022, 24(7), 933; https://doi.org/10.3390/e24070933 - 05 Jul 2022
Abstract
A color image encryption algorithm based on double fractional order chaotic neural network (CNN), interlaced dynamic deoxyribonucleic acid (DNA) encoding and decoding, zigzag confusion, bidirectional bit-level diffusion and convolution operation is proposed. Firstly, two fractional order chaotic neural networks (CNNs) are proposed to
[...] Read more.
A color image encryption algorithm based on double fractional order chaotic neural network (CNN), interlaced dynamic deoxyribonucleic acid (DNA) encoding and decoding, zigzag confusion, bidirectional bit-level diffusion and convolution operation is proposed. Firstly, two fractional order chaotic neural networks (CNNs) are proposed to explore the application of fractional order CNN in image encryption. Meanwhile, spectral entropy (SE) algorithm shows that the sequence generated by the proposed fractional order CNNs has better randomness. Secondly, a DNA encoding and decoding encryption scheme with evolutionary characteristics is adopted. In addition, convolution operation is utilized to improve the key sensitivity. Finally, simulation results and security analysis illustrate that the proposed algorithm has high security performance and can withstand classical cryptanalysis attacks.
Full article
Open AccessArticle
Information Geometry in Roegenian Economics
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Entropy 2022, 24(7), 932; https://doi.org/10.3390/e24070932 - 05 Jul 2022
Abstract
We characterise the geometry of the statistical Roegenian manifold that arises from the equilibrium distribution of an income of noninteracting identical economic actors. The main results for ideal income are included in three subsections: partition function in distribution, scalar curvature, and geodesics. Although
[...] Read more.
We characterise the geometry of the statistical Roegenian manifold that arises from the equilibrium distribution of an income of noninteracting identical economic actors. The main results for ideal income are included in three subsections: partition function in distribution, scalar curvature, and geodesics. Although this system displays no phase transition, its analysis provides an enlightening contrast with the results of Van der Waals Income in Roegenian Economics, where we shall examine the geometry of the economic Van der Waals income, which does exhibit a “monetary policy as liquidity—income” transition. Here we focus on three subsections: canonical partition function, economic limit, and information geometry of the economic Van der Waals manifold.
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Open AccessArticle
Research on Computer-Aided Diagnosis Method Based on Symptom Filtering and Weighted Network
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Entropy 2022, 24(7), 931; https://doi.org/10.3390/e24070931 - 05 Jul 2022
Abstract
In the process of disease identification, as the number of diseases increases, the collection of both diseases and symptoms becomes larger. However, existing computer-aided diagnosis systems do not completely solve the dimensional disaster caused by the increasing data set. To address the above
[...] Read more.
In the process of disease identification, as the number of diseases increases, the collection of both diseases and symptoms becomes larger. However, existing computer-aided diagnosis systems do not completely solve the dimensional disaster caused by the increasing data set. To address the above problems, we propose methods of using symptom filtering and a weighted network with the goal of deeper processing of the collected symptom information. Symptom filtering is similar to a filter in signal transmission, which can filter the collected symptom information, further reduce the dimensional space of the system, and make the important symptoms more prominent. The weighted network, on the other hand, mines deeper disease information by modeling the channels of symptom information, amplifying important information, and suppressing unimportant information. Compared with existing hierarchical reinforcement learning models, the feature extraction methods proposed in this paper can help existing models improve their accuracy by more than 10%.
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(This article belongs to the Topic Machine and Deep Learning)
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Open AccessReview
Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition
Entropy 2022, 24(7), 930; https://doi.org/10.3390/e24070930 - 05 Jul 2022
Abstract
The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions
[...] Read more.
The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the “higher-order” information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure–function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.
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(This article belongs to the Special Issue Information Theory in Computational Biology)
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Open AccessFeature PaperArticle
Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation
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Entropy 2022, 24(7), 929; https://doi.org/10.3390/e24070929 - 04 Jul 2022
Abstract
On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some
[...] Read more.
On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an estimation by adjusting the data produced by the output layer of the NN on the related estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the . Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.
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(This article belongs to the Special Issue Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science)
Open AccessArticle
From Random Numbers to Random Objects
Entropy 2022, 24(7), 928; https://doi.org/10.3390/e24070928 - 04 Jul 2022
Abstract
Many security-related scenarios including cryptography depend on the random generation of passwords, permutations, Latin squares, CAPTCHAs and other types of non-numerical entities. Random generation of each entity type is a different problem with different solutions. This study is an attempt at a unified
[...] Read more.
Many security-related scenarios including cryptography depend on the random generation of passwords, permutations, Latin squares, CAPTCHAs and other types of non-numerical entities. Random generation of each entity type is a different problem with different solutions. This study is an attempt at a unified solution for all of the mentioned problems. This paper is the first of its kind to pose, formulate, analyze and solve the problem of random object generation as the general problem of generating random non-numerical entities. We examine solving the problem via connecting it to the well-studied random number generation problem. To this end, we highlight the challenges and propose solutions for each of them. We explain our method using a case study; random Latin square generation.
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(This article belongs to the Section Multidisciplinary Applications)
Open AccessArticle
Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
Entropy 2022, 24(7), 927; https://doi.org/10.3390/e24070927 - 03 Jul 2022
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In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation
[...] Read more.
In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.
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Open AccessArticle
Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection
by
and
Entropy 2022, 24(7), 926; https://doi.org/10.3390/e24070926 (registering DOI) - 03 Jul 2022
Abstract
(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical
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(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.
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(This article belongs to the Special Issue Statistical Methods for Medicine and Health Sciences)
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Open AccessEditorial
Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics
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Entropy 2022, 24(7), 925; https://doi.org/10.3390/e24070925 (registering DOI) - 03 Jul 2022
Abstract
This editorial is intended to provide a brief history of the application of Information Theory to the fields of Computational Biology and Bioinformatics; to succinctly summarize the current state of associated research, and open challenges; and to describe the scope of the invited
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This editorial is intended to provide a brief history of the application of Information Theory to the fields of Computational Biology and Bioinformatics; to succinctly summarize the current state of associated research, and open challenges; and to describe the scope of the invited content for this Special Issue of the journal Entropy with the theme of “Information Theory in Computational Biology” [...]
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(This article belongs to the Special Issue Information Theory in Computational Biology)
Open AccessArticle
On the Distribution of the Information Density of Gaussian Random Vectors: Explicit Formulas and Tight Approximations
Entropy 2022, 24(7), 924; https://doi.org/10.3390/e24070924 - 02 Jul 2022
Abstract
Based on the canonical correlation analysis, we derive series representations of the probability density function (PDF) and the cumulative distribution function (CDF) of the information density of arbitrary Gaussian random vectors as well as a general formula to calculate the central moments. Using
[...] Read more.
Based on the canonical correlation analysis, we derive series representations of the probability density function (PDF) and the cumulative distribution function (CDF) of the information density of arbitrary Gaussian random vectors as well as a general formula to calculate the central moments. Using the general results, we give closed-form expressions of the PDF and CDF and explicit formulas of the central moments for important special cases. Furthermore, we derive recurrence formulas and tight approximations of the general series representations, which allow efficient numerical calculations with an arbitrarily high accuracy as demonstrated with an implementation in Python publicly available on GitLab. Finally, we discuss the (in)validity of Gaussian approximations of the information density.
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(This article belongs to the Section Information Theory, Probability and Statistics)
Open AccessArticle
Numerical Study on Entropy Generation of the Multi-Stage Centrifugal Pump
Entropy 2022, 24(7), 923; https://doi.org/10.3390/e24070923 - 02 Jul 2022
Abstract
The energy loss of the multi-stage centrifugal pump was investigated by numerical analysis using the entropy generation method with the RNG k-ε turbulence model. Entropy generation due to time-averaged motion and velocity fluctuation was mainly considered. It was found that the entropy generation
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The energy loss of the multi-stage centrifugal pump was investigated by numerical analysis using the entropy generation method with the RNG k-ε turbulence model. Entropy generation due to time-averaged motion and velocity fluctuation was mainly considered. It was found that the entropy generation of guide vanes and impellers account for 71.2% and 23.3% of the total entropy generation under the designed flow condition. The guide vanes are the main hydraulic loss domains and their entropy generation is about 9 W/K, followed by impellers. There are vortices at the tongue of the guide vane inlet as well as flow separations in the impellers, which lead to entropy generation. The fluid impacts the outer surface of the guide vanes, resulting in the increase in entropy generation. There are refluxes near the guide vane tongues which also increase the entropy generation of this part. The entropy generation distribution of the guide vanes and impellers was investigated, which found that the positive guide vane has more entropy generation compared with the reverse guide. The entropy generation of the blade suction surface is higher compared with the pressure surface. This study indicated that the entropy generation method has distinct advantages in the assessment of hydraulic loss.
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Open AccessCommunication
Numerical Investigation of Exergy Loss of Ammonia Addition in Hydrocarbon Diffusion Flames
Entropy 2022, 24(7), 922; https://doi.org/10.3390/e24070922 - 01 Jul 2022
Abstract
In this paper, a theoretical numerical analysis of the thermodynamics second law in ammonia/ethylene counter-flow diffusion flames is carried out. The combustion process, which includes heat and mass transfer, as well as a chemical reaction, is simulated based on a detailed chemical reaction
[...] Read more.
In this paper, a theoretical numerical analysis of the thermodynamics second law in ammonia/ethylene counter-flow diffusion flames is carried out. The combustion process, which includes heat and mass transfer, as well as a chemical reaction, is simulated based on a detailed chemical reaction model. Entropy generation and exergy loss due to various reasons in ammonia/ethylene and argon/ethylene flames are calculated. The effects of ammonia addition on the thermodynamics efficiency of combustion are investigated. Based on thermodynamics analysis, a parameter, the lowest emission of pollutant (LEP), is proposed to establish a relationship between the available work and pollutant emissions produced during the combustion process. Chemical reaction paths are also analyzed by combining the chemical entropy generation, and some important chemical reactions and substances are identified. The numerical results reveal that ammonia addition has a significant enhancement on heat transfer and chemical reaction in the flames, and the total exergy loss rate increases slightly at first and then decreases with an increase in ammonia concentration. Considering the factors of thermodynamic efficiency, the emissions of CO2 and NOx reach a maximum when ammonia concentration is near 10% and 30%, respectively. In terms of the chemical reaction path analysis, ammonia pyrolysis and nitrogen production increase significantly, while ethylene pyrolysis and carbon monoxide production decrease when ammonia is added to hydrocarbon diffusion flames.
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(This article belongs to the Special Issue Entropy Generation Analysis in Near-Wall Turbulent Flow)
Open AccessArticle
Regularity in Stock Market Indices within Turbulence Periods: The Sample Entropy Approach
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Entropy 2022, 24(7), 921; https://doi.org/10.3390/e24070921 - 01 Jul 2022
Abstract
The aim of this study is to assess and compare changes in regularity in the 36 European and the U.S. stock market indices within major turbulence periods. Two periods are investigated: the Global Financial Crisis in 2007–2009 and the COVID-19 pandemic outbreak in
[...] Read more.
The aim of this study is to assess and compare changes in regularity in the 36 European and the U.S. stock market indices within major turbulence periods. Two periods are investigated: the Global Financial Crisis in 2007–2009 and the COVID-19 pandemic outbreak in 2020–2021. The proposed research hypothesis states that entropy of an equity market index decreases during turbulence periods, which implies that regularity and predictability of a stock market index returns increase in such cases. To capture sequential regularity in daily time series of stock market indices, the Sample Entropy algorithm (SampEn) is used. Changes in the SampEn values before and during the particular turbulence period are estimated. The empirical findings are unambiguous and confirm no reason to reject the research hypothesis. Moreover, additional formal statistical analyses indicate that the SampEn results are similar both for developed and emerging European economies. Furthermore, the rolling-window procedure is utilized to assess the evolution of SampEn over time.
Full article
(This article belongs to the Special Issue Entropy-Based Applications in Economics, Finance, and Management)
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Energies, Materials, Applied Sciences, Entropy, Nanoenergy Advances
Thermoelectric Energy Harvesting
Topic Editors: Amir Pakdel, David BerthebaudDeadline: 31 July 2022
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Entropy, Sensors
Advances in Biomedical Engineering from the Annual Conference of SEIB 2021
Topic Editors: Raúl Alcaraz, Elisabete Aramendi, Raimon Jane, Gema García-Sáez, Gema Prats-Boluda, Javier Reina-Tosina, Roberto Hornero, Patricia Sánchez-GonzálezDeadline: 30 August 2022
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Algorithms, Entropy, Fractal Fract, Mathematics, Physics
Mathematical Modeling in Physical Sciences
Topic Editors: Dimitrios Vlachos, George KastisDeadline: 15 November 2022
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Complex Data Analytics and Computing with Real-World Applications
Topic Editors: S. Ejaz Ahmed, Shuangge Steven Ma, Peter X.K. SongDeadline: 22 November 2022

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Entropy
The Foundations of Thermodynamics
Guest Editor: David SandsDeadline: 15 July 2022
Special Issue in
Entropy
Uncertainty in Large Neural Systems: Validation, Explanation and Correction of Multidimensional Intelligence in a Multidimensional World
Guest Editors: Alexander Gorban, Ivan TyukinDeadline: 21 July 2022
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Entropy and Complexity in Quantum Dynamics
Guest Editor: Pavan HosurDeadline: 31 July 2022
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Statistical Methods for Complex Systems
Guest Editors: Irad E. Ben-Gal, Amichai PainskyDeadline: 15 August 2022
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Algorithmic Information Dynamics: A Computational Approach to Causality from Cells to Networks
Collection Editors: Hector Zenil, Felipe Abrahão
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Entropy in Image Analysis
Collection Editor: Amelia Carolina Sparavigna