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Entropy, Volume 20, Issue 11 (November 2018)

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Open AccessArticle Magnetic Properties and Microstructure of FeCoNi(CuAl)0.8Snx (0 ≤ x ≤ 0.10) High-Entropy Alloys
Entropy 2018, 20(11), 872; https://doi.org/10.3390/e20110872 (registering DOI)
Received: 9 October 2018 / Revised: 8 November 2018 / Accepted: 8 November 2018 / Published: 13 November 2018
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Abstract
The present work exhibits the effects of Sn addition on the magnetic properties and microstructure of FeCoNi(CuAl)0.8Snx (0 ≤ x ≤ 0.10) high-entropy alloys (HEAs). The results show all the samples consist of a mixed structure of face-centered-cubic (FCC) phase
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The present work exhibits the effects of Sn addition on the magnetic properties and microstructure of FeCoNi(CuAl)0.8Snx (0 ≤ x ≤ 0.10) high-entropy alloys (HEAs). The results show all the samples consist of a mixed structure of face-centered-cubic (FCC) phase and body-centered-cubic (BCC) phase. The addition of Sn promotes the formation of BCC phase, and it also affects the shape of Cu-rich nano-precipitates in BCC matrix. It also shows that the Curie temperatures (Tc) of the FCC phase and the saturation magnetization (Ms) of the FeCoNi(CuAl)0.8Snx (0 ≤ x ≤ 0.10) HEAs increase greatly while the remanence (Br) decreases after the addition of Sn into FeCoNi(CuAl)0.8 HEA. The thermomagnetic curves indicate that the phases of the FeCoNi(CuAl)0.8Snx (0 ≤ x ≤ 0.10) HEAs will transform from FCC with low Tc to BCC phase with high Tc at temperature of 600–700 K. This work provides a new idea for FeCoNi(CuAl)0.8Snx (0 ≤ x ≤ 0.10) HEAs for their potential application as soft magnets to be used at high temperatures. Full article
(This article belongs to the Special Issue New Advances in High-Entropy Alloys)
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Open AccessArticle Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics
Entropy 2018, 20(11), 871; https://doi.org/10.3390/e20110871 (registering DOI)
Received: 8 October 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 12 November 2018
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Abstract
This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could
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This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn. Full article
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
Open AccessArticle Robust Signaling for Bursty Interference
Entropy 2018, 20(11), 870; https://doi.org/10.3390/e20110870 (registering DOI)
Received: 3 September 2018 / Revised: 25 October 2018 / Accepted: 6 November 2018 / Published: 12 November 2018
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Abstract
This paper studies a bursty interference channel, where the presence/absence of interference is modeled by a block-i.i.d. Bernoulli process that stays constant for a duration of T symbols (referred to as coherence block) and then changes independently to a new state. We consider
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This paper studies a bursty interference channel, where the presence/absence of interference is modeled by a block-i.i.d. Bernoulli process that stays constant for a duration of T symbols (referred to as coherence block) and then changes independently to a new state. We consider both a quasi-static setup, where the interference state remains constant during the whole transmission of the codeword, and an ergodic setup, where a codeword spans several coherence blocks. For the quasi-static setup, we study the largest rate of a coding strategy that provides reliable communication at a basic rate and allows an increased (opportunistic) rate when there is no interference. For the ergodic setup, we study the largest achievable rate. We study how non-causal knowledge of the interference state, referred to as channel-state information (CSI), affects the achievable rates. We derive converse and achievability bounds for (i) local CSI at the receiver side only; (ii) local CSI at the transmitter and receiver side; and (iii) global CSI at all nodes. Our bounds allow us to identify when interference burstiness is beneficial and in which scenarios global CSI outperforms local CSI. The joint treatment of the quasi-static and ergodic setup further allows for a thorough comparison of these two setups. Full article
(This article belongs to the Special Issue Multiuser Information Theory II)
Open AccessArticle Short-Time Propagators and the Born–Jordan Quantization Rule
Entropy 2018, 20(11), 869; https://doi.org/10.3390/e20110869 (registering DOI)
Received: 14 October 2018 / Revised: 6 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
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Abstract
We have shown in previous work that the equivalence of the Heisenberg and Schrödinger pictures of quantum mechanics requires the use of the Born and Jordan quantization rules. In the present work we give further evidence that the Born–Jordan rule is the correct
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We have shown in previous work that the equivalence of the Heisenberg and Schrödinger pictures of quantum mechanics requires the use of the Born and Jordan quantization rules. In the present work we give further evidence that the Born–Jordan rule is the correct quantization scheme for quantum mechanics. For this purpose we use correct short-time approximations to the action functional, initially due to Makri and Miller, and show that these lead to the desired quantization of the classical Hamiltonian. Full article
Open AccessArticle Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China
Entropy 2018, 20(11), 868; https://doi.org/10.3390/e20110868 (registering DOI)
Received: 12 October 2018 / Revised: 6 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
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Abstract
In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the
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In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the most landslide-prone areas in Shaanxi Province, China, Shangnan County was selected as the study area. Firstly, a series of reports, remote sensing images and geological maps were collected, and field surveys were carried out to prepare a landslide inventory map. A total of 348 landslides were identified in study area, and they were reclassified as a training dataset (70% = 244 landslides) and testing dataset (30% = 104 landslides) by random selection. Thirteen conditioning factors were then employed. Corresponding thematic data layers and landslide susceptibility maps were generated based on ArcGIS software. Finally, the area under the curve (AUC) values were calculated for the training dataset and the testing dataset in order to validate and compare the performance of the three models. For the training dataset, the AUC plots showed that the WOE model had the highest accuracy rate of 76.05%, followed by the SI model (74.67%) and the IOE model (71.12%). In the case of the testing dataset, the prediction accuracy rates for the SI, IOE and WOE models were 73.75%, 63.89%, and 75.10%, respectively. It can be concluded that the WOE model had the best prediction capacity for landslide susceptibility mapping in Shangnan County. The landslide susceptibility map produced by the WOE model had a profound geological and engineering significance in terms of landslide hazard prevention and control in the study area and other similar areas. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle Double Quantum Image Encryption Based on Arnold Transform and Qubit Random Rotation
Entropy 2018, 20(11), 867; https://doi.org/10.3390/e20110867 (registering DOI)
Received: 8 October 2018 / Revised: 1 November 2018 / Accepted: 8 November 2018 / Published: 10 November 2018
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Abstract
Quantum image encryption offers major advantages over its classical counterpart in terms of key space, computational complexity, and so on. A novel double quantum image encryption approach based on quantum Arnold transform (QAT) and qubit random rotation is proposed in this paper, in
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Quantum image encryption offers major advantages over its classical counterpart in terms of key space, computational complexity, and so on. A novel double quantum image encryption approach based on quantum Arnold transform (QAT) and qubit random rotation is proposed in this paper, in which QAT is used to scramble pixel positions and the gray information is changed by utilizing random qubit rotation. Actually, the independent random qubit rotation operates once, respectively, in spatial and frequency domains with the help of quantum Fourier transform (QFT). The encryption process accomplishes pixel confusion and diffusion, and finally the noise-like cipher image is obtained. Numerical simulation and theoretical analysis verify that the method is valid and it shows superior performance in security and computational complexity. Full article
(This article belongs to the collection Quantum Information)
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Open AccessArticle An Entropy-Guided Monte Carlo Tree Search Approach for Generating Optimal Container Loading Layouts
Entropy 2018, 20(11), 866; https://doi.org/10.3390/e20110866 (registering DOI)
Received: 4 October 2018 / Revised: 7 November 2018 / Accepted: 7 November 2018 / Published: 9 November 2018
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Abstract
In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having
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In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having “consistency” or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time. Full article
Open AccessArticle Optimization and Stability of Heat Engines: The Role of Entropy Evolution
Entropy 2018, 20(11), 865; https://doi.org/10.3390/e20110865 (registering DOI)
Received: 23 October 2018 / Revised: 5 November 2018 / Accepted: 7 November 2018 / Published: 9 November 2018
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Abstract
Local stability of maximum power and maximum compromise (Omega) operation regimes dynamic evolution for a low-dissipation heat engine is analyzed. The thermodynamic behavior of trajectories to the stationary state, after perturbing the operation regime, display a trade-off between stability, entropy production, efficiency and
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Local stability of maximum power and maximum compromise (Omega) operation regimes dynamic evolution for a low-dissipation heat engine is analyzed. The thermodynamic behavior of trajectories to the stationary state, after perturbing the operation regime, display a trade-off between stability, entropy production, efficiency and power output. This allows considering stability and optimization as connected pieces of a single phenomenon. Trajectories inside the basin of attraction display the smallest entropy drops. Additionally, it was found that time constraints, related with irreversible and endoreversible behaviors, influence the thermodynamic evolution of relaxation trajectories. The behavior of the evolution in terms of the symmetries of the model and the applied thermal gradients was analyzed. Full article
(This article belongs to the Special Issue Entropy Generation and Heat Transfer)
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Open AccessArticle Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades
Entropy 2018, 20(11), 864; https://doi.org/10.3390/e20110864 (registering DOI)
Received: 14 October 2018 / Revised: 3 November 2018 / Accepted: 7 November 2018 / Published: 9 November 2018
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Abstract
As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the
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As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this new perspective, a modified FMEA approach is proposed, where the subjective uncertainty of FMEA experts is handled in the framework of Dempster–Shafer evidence theory (DST). In the improved FMEA approach, the ambiguity measure (AM) which is an entropy-like uncertainty measure in DST framework is applied to quantify the uncertainty degree of each FMEA expert. Then, the classical risk priority number (RPN) model is improved by aggregating an AM-based weight factor into the RPN function. A case study based on the new RPN model in aircraft turbine rotor blades verifies the applicable and useful of the proposed FMEA approach. Full article
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Open AccessArticle Sample Entropy of sEMG Signals at Different Stages of Rectal Cancer Treatment
Entropy 2018, 20(11), 863; https://doi.org/10.3390/e20110863 (registering DOI)
Received: 11 October 2018 / Revised: 5 November 2018 / Accepted: 7 November 2018 / Published: 9 November 2018
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Abstract
Information theory provides a spectrum of nonlinear methods capable of grasping an internal structure of a signal together with an insight into its complex nature. In this work, we discuss the usefulness of the selected entropy techniques for a description of the information
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Information theory provides a spectrum of nonlinear methods capable of grasping an internal structure of a signal together with an insight into its complex nature. In this work, we discuss the usefulness of the selected entropy techniques for a description of the information carried by the surface electromyography signals during colorectal cancer treatment. The electrical activity of the external anal sphincter can serve as a potential source of knowledge of the actual state of the patient who underwent a common surgery for rectal cancer in the form of anterior or lower anterior resection. The calculation of Sample entropy parameters has been extended to multiple time scales in terms of the Multiscale Sample Entropy. The specific values of the entropy measures and their dependence on the time scales were analyzed with regard to the time elapsed since the operation, the type of surgical treatment and also the different depths of the rectum canal. The Mann–Whitney U test and Anova Friedman statistics indicate the statistically significant differences among all of stages of treatment and for all consecutive depths of rectum area for the estimated Sample Entropy. The further analysis at the multiple time scales signify the substantial differences among compared stages of treatment in the group of patients who underwent the lower anterior resection. Full article
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Open AccessFeature PaperArticle Emergence of Shear Bands in Confined Granular Systems: Singularity of the q-Statistics
Entropy 2018, 20(11), 862; https://doi.org/10.3390/e20110862 (registering DOI)
Received: 8 October 2018 / Revised: 31 October 2018 / Accepted: 1 November 2018 / Published: 9 November 2018
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Abstract
The statistics of grain displacements probability distribution function (pdf) during the shear of a granular medium displays an unusual dependence with the shear increment upscaling as recently evinced (see “experimental validation of a nonextensive scaling law in confined granular media”). Basically,
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The statistics of grain displacements probability distribution function (pdf) during the shear of a granular medium displays an unusual dependence with the shear increment upscaling as recently evinced (see “experimental validation of a nonextensive scaling law in confined granular media”). Basically, the pdf of grain displacements has clear nonextensive (q-Gaussian) features at small scales, but approaches to Gaussian characteristics at large shear window scales—the granulence effect. Here, we extend this analysis studying a larger system (more grains considered in the experimental setup), which exhibits a severe shear band fault during the macroscopic straining. We calculate the pdf of grain displacements and the dependency of the q-statistics with the shear increment. This analysis has shown a singular behavior of q at large scales, displaying a non-monotonic dependence with the shear increment. By means of an independent image analysis, we demonstrate that this singular non-monotonicity could be associated with the emergence of a shear band within the confined system. We show that the exact point where the q-value inverts its tendency coincides with the emergence of a giant percolation cluster along the system, caused by the shear band. We believe that this original approach using Statistical Mechanics tools to identify shear bands can be a very useful piece to solve the complex puzzle of the rheology of dense granular systems. Full article
(This article belongs to the Special Issue Nonadditive Entropies and Complex Systems)
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Open AccessArticle Regional Seismic Information Entropy to Detect Earthquake Activation Precursors
Entropy 2018, 20(11), 861; https://doi.org/10.3390/e20110861
Received: 2 October 2018 / Revised: 4 November 2018 / Accepted: 6 November 2018 / Published: 8 November 2018
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Abstract
A method is presented to detect earthquake precursors from time series data on earthquakes in a target region. The Regional Entropy of Seismic Information (RESI) is an index that represents the average influence of an earthquake in a target region on the diversity
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A method is presented to detect earthquake precursors from time series data on earthquakes in a target region. The Regional Entropy of Seismic Information (RESI) is an index that represents the average influence of an earthquake in a target region on the diversity of clusters to which earthquake foci are distributed. Based on a simple qualitative model of the dynamics of land crust, it is hypothesized that the saturation that occurs after an increase in RESI precedes the activation of earthquakes. This hypothesis is validated by the earthquake catalog. This temporal change was found to correlate with the activation of earthquakes in Japanese regions one to two years ahead of the real activation, more reliably than the compared baseline methods. Full article
Open AccessArticle Refined Multiscale Fuzzy Entropy to Analyse Post-Exercise Cardiovascular Response in Older Adults With Orthostatic Intolerance
Entropy 2018, 20(11), 860; https://doi.org/10.3390/e20110860
Received: 21 August 2018 / Revised: 20 October 2018 / Accepted: 30 October 2018 / Published: 8 November 2018
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Abstract
Orthostatic intolerance syndrome occurs when the autonomic nervous system is incapacitated and fails to respond to the demands associated with the upright position. Assessing this syndrome among the elderly population is important in order to prevent falls. However, this problem is still challenging.
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Orthostatic intolerance syndrome occurs when the autonomic nervous system is incapacitated and fails to respond to the demands associated with the upright position. Assessing this syndrome among the elderly population is important in order to prevent falls. However, this problem is still challenging. The goal of this work was to determine the relationship between orthostatic intolerance (OI) and the cardiovascular response to exercise from the analysis of heart rate and blood pressure. More specifically, the behavior of these cardiovascular variables was evaluated in terms of refined composite multiscale fuzzy entropy (RCMFE), measured at different scales. The dataset was composed by 65 older subjects, 44.6% (n = 29) were OI symptomatic and 55.4% (n = 36) were not. Insignificant differences were found in age and gender between symptomatic and asymptomatic OI participants. When heart rate was evaluated, higher differences between groups were observed during the recovery period immediately after exercise. With respect to the blood pressure and other hemodynamic parameters, most significant results were obtained in the post-exercise stage. In any case, the symptomatic OI group exhibited higher irregularity in the measured parameters, as higher RCMFE levels in all time scales were obtained. This information could be very helpful for a better understanding of cardiovascular instability, as well as to recognize risk factors for falls and impairment of functional status. Full article
(This article belongs to the Special Issue Entropy: From Physics to Information Sciences and Geometry)
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Open AccessArticle Optimization on theBuried Depth of Subsurface Drainage under Greenhouse Condition Based on Entropy Evaluation Method
Entropy 2018, 20(11), 859; https://doi.org/10.3390/e20110859
Received: 11 September 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 8 November 2018
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Abstract
Numerous indicators under the plant-soil system should be taken into consideration when developing an appropriate agricultural water conservancy project. Entropy evaluation method offers excellent prospects in optimizing agricultural management schemes. To investigate the impact of different buried depths (30, 45, 60, 75, 90,
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Numerous indicators under the plant-soil system should be taken into consideration when developing an appropriate agricultural water conservancy project. Entropy evaluation method offers excellent prospects in optimizing agricultural management schemes. To investigate the impact of different buried depths (30, 45, 60, 75, 90, and 105 cm) of subsurface drainage pipes on greenhouse plant-soil systems, the tomato was employed as plant material, and the marketable yield, fruit sugar to acid ratio, soil electrical conductivity, nitrogen loss rate, as well as crop water and fertilizer use efficiency were observed. Based on these indicators, the entropy evaluation method was used to select the optimal buried depth of subsurface drainage pipes. Both the calculation results of objective and subjective weights indicated that tomato yield and soil electrical conductivity were relatively more crucial than other indexes, and their comprehensive weights were 0.43 and 0.34, respectively. The 45 cm buried depth possessed the optimal comprehensive benefits, with entropy evaluation value of 0.94. Under 45 cm buried depth, the loss rate of soil available nitrogen was 13.9%, the decrease rate of soil salinity was 49.2%, and the tomato yield, sugar to acid ratio, nitrogen use efficiency, and water use efficiency were 112 kg·ha−1, 8.3, 39.7%, and 42.0%, respectively. Full article
(This article belongs to the Special Issue Applications of Information Theory in the Geosciences II)
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Open AccessArticle The Complexity and Entropy Analysis for Service Game Model Based on Different Expectations and Optimal Pricing
Entropy 2018, 20(11), 858; https://doi.org/10.3390/e20110858
Received: 3 October 2018 / Revised: 3 November 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
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Abstract
The internet has provided a new means for manufacturers to reach consumers. On the background of the widespread multichannel sales in China, based on a literature review of the service game and multichannel supply chain, this paper builds a multichannel dynamic service game
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The internet has provided a new means for manufacturers to reach consumers. On the background of the widespread multichannel sales in China, based on a literature review of the service game and multichannel supply chain, this paper builds a multichannel dynamic service game model where the retailer operates an offline channel and the manufacturer operates an online channel and offers customers the option to buy online and pick up from the retailer’s store (BOPS). The manufacturer and the retailer take maximizing the channel profits as their business objectives and make channel service game under optimal pricing. We carry on theoretical analysis of the model and perform numerical simulations from the perspective of entropy theory, game theory, and chaotic dynamics. The results show that the stability of the system will weaken with the increase in service elasticity coefficient and that it is unaffected by the feedback parameter adjustment of the retailer. The BOPS channel strengthens the cooperation between the manufacturer and the retailer and moderates the conflict between the online and the offline channels. The system will go into chaotic state and cause the system’s entropy to increase when the manufacturer adjusts his/her service decision quickly. In a chaotic state, the system is sensitive to initial conditions and service input is difficult to predict; the manufacturer and retailer need more additional information to make the system clear or use the method of feedback control to delay or eliminate the occurrence of chaos. Full article
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Open AccessArticle Building an Ensemble of Fine-Tuned Naive Bayesian Classifiers for Text Classification
Entropy 2018, 20(11), 857; https://doi.org/10.3390/e20110857
Received: 13 September 2018 / Revised: 25 October 2018 / Accepted: 31 October 2018 / Published: 7 November 2018
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Abstract
Text classification is one domain in which the naive Bayesian (NB) learning algorithm performs remarkably well. However, making further improvement in performance using ensemble-building techniques proved to be a challenge because NB is a stable algorithm. This work shows that, while an ensemble
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Text classification is one domain in which the naive Bayesian (NB) learning algorithm performs remarkably well. However, making further improvement in performance using ensemble-building techniques proved to be a challenge because NB is a stable algorithm. This work shows that, while an ensemble of NB classifiers achieves little or no improvement in terms of classification accuracy, an ensemble of fine-tuned NB classifiers can achieve a remarkable improvement in accuracy. We propose a fine-tuning algorithm for text classification that is both more accurate and less stable than the NB algorithm and the fine-tuning NB (FTNB) algorithm. This improvement makes it more suitable than the FTNB algorithm for building ensembles of classifiers using bagging. Our empirical experiments, using 16-benchmark text-classification data sets, show significant improvement for most data sets. Full article
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Open AccessArticle Ranking the Impact of Different Tests on a Hypothesis in a Bayesian Network
Entropy 2018, 20(11), 856; https://doi.org/10.3390/e20110856
Received: 31 August 2018 / Revised: 21 October 2018 / Accepted: 31 October 2018 / Published: 7 November 2018
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Abstract
Testing of evidence in criminal cases can be limited by temporal or financial constraints or by the fact that certain tests may be mutually exclusive, so choosing the tests that will have maximal impact on the final result is essential. In this paper,
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Testing of evidence in criminal cases can be limited by temporal or financial constraints or by the fact that certain tests may be mutually exclusive, so choosing the tests that will have maximal impact on the final result is essential. In this paper, we assume that a main hypothesis, evidence for it and possible tests for existence of this evidence are represented in the form of a Bayesian network, and use three different methods to measure the impact of a test on the main hypothesis. We illustrate the methods by applying them to an actual digital crime case provided by the Hong Kong police. We conclude that the Kullback–Leibler divergence is the optimal method for selecting the tests with the highest impact. Full article
(This article belongs to the Special Issue Bayesian Inference and Information Theory)
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Open AccessArticle Thermodynamics and Cosmic Censorship Conjecture in Kerr–Newman–de Sitter Black Hole
Entropy 2018, 20(11), 855; https://doi.org/10.3390/e20110855
Received: 7 October 2018 / Revised: 2 November 2018 / Accepted: 5 November 2018 / Published: 7 November 2018
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Abstract
We investigate the laws of thermodynamics and the validity of the cosmic censorship conjecture in the Kerr–Newman–de Sitter black hole under charged particle absorption. Here, the black hole undergoes infinitesimal changes because of the momenta carried by the particle entering it. The cosmic
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We investigate the laws of thermodynamics and the validity of the cosmic censorship conjecture in the Kerr–Newman–de Sitter black hole under charged particle absorption. Here, the black hole undergoes infinitesimal changes because of the momenta carried by the particle entering it. The cosmic censorship conjecture is tested by whether the black hole can be overcharged beyond the extremal condition under absorption. The changes in the black hole violate the second law of thermodynamics. Furthermore, this is related to the cosmic censorship conjecture. To resolve this violation, we impose a reference energy of the particle at the asymptotic region based on the first law of thermodynamics. Under imposition of the reference energy, the absorption satisfies the laws of thermodynamics, and the extremal black hole cannot be overcharged. Thus, the cosmic censorship conjecture is valid under the absorption. Full article
(This article belongs to the Section Astrophysics, Cosmology, and Black Holes)
Open AccessFeature PaperReview The Weak Reality That Makes Quantum Phenomena More Natural: Novel Insights and Experiments
Entropy 2018, 20(11), 854; https://doi.org/10.3390/e20110854
Received: 15 October 2018 / Revised: 2 November 2018 / Accepted: 3 November 2018 / Published: 7 November 2018
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Abstract
While quantum reality can be probed through measurements, the Two-State Vector Formalism (TSVF) reveals a subtler reality prevailing between measurements. Under special pre- and post-selections, odd physical values emerge. This unusual picture calls for a deeper study. Instead of the common, wave-based picture
[...] Read more.
While quantum reality can be probed through measurements, the Two-State Vector Formalism (TSVF) reveals a subtler reality prevailing between measurements. Under special pre- and post-selections, odd physical values emerge. This unusual picture calls for a deeper study. Instead of the common, wave-based picture of quantum mechanics, we suggest a new, particle-based perspective: Each particle possesses a definite location throughout its evolution, while some of its physical variables (characterized by deterministic operators, some of which obey nonlocal equations of motion) are carried by “mirage particles” accounting for its unique behavior. Within the time interval between pre- and post-selection, the particle gives rise to a horde of such mirage particles, of which some can be negative. What appears to be “no-particle”, known to give rise to interaction-free measurement, is in fact a self-canceling pair of positive and negative mirage particles, which can be momentarily split and cancel out again. Feasible experiments can give empirical evidence for these fleeting phenomena. In this respect, the Heisenberg ontology is shown to be conceptually advantageous compared to the Schrödinger picture. We review several recent advances, discuss their foundational significance and point out possible directions for future research. Full article
(This article belongs to the Special Issue Towards Ultimate Quantum Theory (UQT))
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Open AccessArticle Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures
Entropy 2018, 20(11), 853; https://doi.org/10.3390/e20110853
Received: 21 September 2018 / Revised: 31 October 2018 / Accepted: 5 November 2018 / Published: 6 November 2018
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Abstract
Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce
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Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model. Full article
(This article belongs to the Special Issue Permutation Entropy & Its Interdisciplinary Applications)
Open AccessArticle Estimation of Economic Indicator Announced by Government From Social Big Data
Entropy 2018, 20(11), 852; https://doi.org/10.3390/e20110852
Received: 7 August 2018 / Revised: 19 October 2018 / Accepted: 31 October 2018 / Published: 6 November 2018
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Abstract
We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each
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We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each candidate word to illustrate the economic index. From this large volume of words, our method automatically selects the words which have strong correlation with the economic indicator and resolves some difficulties in statistics such as the spurious correlation and overfitting. As a result, our model reasonably illustrates the real economy index. The announcement of an economic index from government usually has a time lag, while our proposed method can be real time. Full article
(This article belongs to the Special Issue Economic Fitness and Complexity)
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Open AccessFeature PaperArticle Modelling Study on Internal Energy Loss Due to Entropy Generation for Non-Darcy Poiseuille Flow of Silver-Water Nanofluid: An Application of Purification
Entropy 2018, 20(11), 851; https://doi.org/10.3390/e20110851
Received: 20 September 2018 / Revised: 29 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
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Abstract
In this paper, an analytical study of internal energy losses for the non-Darcy Poiseuille flow of silver-water nanofluid due to entropy generation in porous media is investigated. Spherical-shaped silver (Ag) nanosize particles with volume fraction 0.3%, 0.6%, and 0.9% are utilized. Four illustrative
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In this paper, an analytical study of internal energy losses for the non-Darcy Poiseuille flow of silver-water nanofluid due to entropy generation in porous media is investigated. Spherical-shaped silver (Ag) nanosize particles with volume fraction 0.3%, 0.6%, and 0.9% are utilized. Four illustrative models are considered: (i) heat transfer irreversibility (HTI), (ii) fluid friction irreversibility (FFI), (iii) Joule dissipation irreversibility (JDI), and (iv) non-Darcy porous media irreversibility (NDI). The governing equations of continuity, momentum, energy, and entropy generation are simplified by taking long wavelength approximations on the channel walls. The results represent highly nonlinear coupled ordinary differential equations that are solved analytically with the help of the homotopy analysis method. It is shown that for minimum and maximum averaged entropy generation, 0.3% by vol and 0.9% by vol of nanoparticles, respectively, are observed. Also, a rise in entropy is evident due to an increase in pressure gradient. The current analysis provides an adequate theoretical estimate for low-cost purification of drinking water by silver nanoparticles in an industrial process. Full article
(This article belongs to the Special Issue Entropy Generation in Nanofluid Flows II)
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Open AccessArticle Identification of Multiple Faults in Gearbox Based on Multipoint Optional Minimum Entropy Deconvolution Adjusted and Permutation Entropy
Entropy 2018, 20(11), 850; https://doi.org/10.3390/e20110850
Received: 25 June 2018 / Revised: 14 October 2018 / Accepted: 25 October 2018 / Published: 6 November 2018
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Abstract
The weak compound fault feature is difficult to extract from a gearbox because the signal components are complex and inter-modulated. An approach (that is abbreviated as MRPE-MOMEDA) for extracting the weak fault features of a transmission based on a multipoint optimal minimum entropy
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The weak compound fault feature is difficult to extract from a gearbox because the signal components are complex and inter-modulated. An approach (that is abbreviated as MRPE-MOMEDA) for extracting the weak fault features of a transmission based on a multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) and the permutation entropy was proposed to solve this problem in the present paper. The complexity of the periodic impact signal was low and the permutation entropy was relatively small. Moreover, the amplitude of the impact was relatively large. Based on these advantages, the multipoint reciprocal permutation entropy (MRPE) was proposed to track the impact fault source of the weak fault feature in gearbox compound faults. The impact fault period was indicated through MRPE. MOMEDA achieved signal denoising. The optimal filter coefficients were solved using MOMEDA. It exhibits an outstanding performance for noise suppression of gearbox signals with a periodic impact. The results from the transmission show that the proposed method can identify multiple faults simultaneously on a driving gear in the 4th gear of the transmission. Full article
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Open AccessArticle Effect of Annealing on Microstructure and Tensile Behavior of CoCrNi Medium Entropy Alloy Processed by High-Pressure Torsion
Entropy 2018, 20(11), 849; https://doi.org/10.3390/e20110849
Received: 19 September 2018 / Revised: 29 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
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Abstract
Annealing of severely plastic deformed materials is expected to produce a good combination of strength and ductility, which has been widely demonstrated in conventional materials. In the present study, high-pressure torsion processed CoCrNi medium entropy alloy consisting of a single face-centered cubic (FCC)
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Annealing of severely plastic deformed materials is expected to produce a good combination of strength and ductility, which has been widely demonstrated in conventional materials. In the present study, high-pressure torsion processed CoCrNi medium entropy alloy consisting of a single face-centered cubic (FCC) phase with a grain size of ~50 nm was subjected to different annealing conditions, and its effect on microstructure and mechanical behavior was investigated. The annealing of high-pressure torsion processed CoCrNi alloy exhibits partial recrystallization and near full recrystallization based on the annealing temperature and time. The samples annealed at 700 °C for 2 min exhibit very fine grain size, a high fraction of low angle grain boundaries, and high kernel average misorientation value, indicating partially recrystallized microstructure. The samples annealed for a longer duration (>2 min) exhibit relatively larger grain size, a low fraction of low angle grain boundaries, and low kernel average misorientation value, indicating nearly full recrystallized microstructure. The annealed samples with different microstructures significantly influence the uniform elongation, tensile strength, and work hardening rate. The sample annealed at 700 °C for 15 min exhibits a remarkable combination of tensile strength (~1090 MPa) and strain to failure (~41%). Full article
(This article belongs to the Special Issue New Advances in High-Entropy Alloys)
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Open AccessArticle Modeling of a Neural System Based on Statistical Mechanics
Entropy 2018, 20(11), 848; https://doi.org/10.3390/e20110848
Received: 28 August 2018 / Revised: 29 October 2018 / Accepted: 2 November 2018 / Published: 5 November 2018
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Abstract
The minimization of a free energy is often regarded as the key principle in understanding how the brain works and how the brain structure forms. In particular, a statistical-mechanics-based neural network model is expected to allow one to interpret many aspects of the
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The minimization of a free energy is often regarded as the key principle in understanding how the brain works and how the brain structure forms. In particular, a statistical-mechanics-based neural network model is expected to allow one to interpret many aspects of the neural firing and learning processes in terms of general concepts and mechanisms in statistical physics. Nevertheless, the definition of the free energy in a neural system is usually an intricate problem without an evident solution. After the pioneering work by Hopfield, several statistical-mechanics-based models have suggested a variety of definition of the free energy or the entropy in a neural system. Among those, the Feynman machine, proposed recently, presents the free energy of a neural system defined via the Feynman path integral formulation with the explicit time variable. In this study, we first give a brief review of the previous relevant models, paying attention to the troublesome problems in them, and examine how the Feynman machine overcomes several vulnerable points in previous models and derives the outcome of the firing or the learning rule in a (biological) neural system as the extremum state in the free energy. Specifically, the model reveals that the biological learning mechanism, called spike-timing-dependent plasticity, relates to the free-energy minimization principle. Basically, computing and learning mechanisms in the Feynman machine base on the exact spike timings of neurons, such as those in a biological neural system. We discuss the consequence of the adoption of an explicit time variable in modeling a neural system and the application of the free-energy minimization principle to understanding the phenomena in the brain. Full article
(This article belongs to the Special Issue Statistical Mechanics of Neural Networks)
Open AccessArticle Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier
Entropy 2018, 20(11), 847; https://doi.org/10.3390/e20110847
Received: 17 October 2018 / Revised: 2 November 2018 / Accepted: 3 November 2018 / Published: 5 November 2018
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Abstract
As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method
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As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies. Principle component analysis (PCA) is then used to reduce the dimension of the multi-feature entropy to achieve an effective fusion of features for selecting the feature vector. The detection of an unknown fault in HVCBs is achieved using support vector data description (SVDD) trained by normal-state samples and specific fault samples. On this basis, the identification and classification of the known states are realized by the support vector machine (SVM). Three faults (i.e., closing spring force decrease fault, buffer spring invalid fault, opening spring force decrease fault) are simulated on a real SF6 HVCB to test the feasibility of the proposed method. The detection accuracies of the unknown fault are 100%, 87.5%, and 100% respectively when each of the three faults is assumed to be the unknown fault. The comparative experiments show that SVM has no ability to detect the unknown fault, and that one-class support vector machine (OCSVM) has a weaker ability to detect the unknown fault than SVDD. For known-state classification, the adoption of the MFEF method achieved an accuracy of 100%, while the use of a single-feature method only achieved an accuracy of 75%. These results indicate that the proposed method combining MFEF with hybrid classifier is thus more efficient and robust than traditional methods. Full article
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Open AccessArticle MHD Free Convection and Entropy Generation in a Corrugated Cavity Filled with a Porous Medium Saturated with Nanofluids
Entropy 2018, 20(11), 846; https://doi.org/10.3390/e20110846
Received: 11 October 2018 / Revised: 23 October 2018 / Accepted: 31 October 2018 / Published: 5 November 2018
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Abstract
MHD free convection inside a triangular-wave-shaped corrugated porous cavity with Cu-water nanofluid is numerically studied with the finite element method. The influences of the Grashof number (104Gr106), Hartmann number (0Ha50
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MHD free convection inside a triangular-wave-shaped corrugated porous cavity with Cu-water nanofluid is numerically studied with the finite element method. The influences of the Grashof number ( 10 4 Gr 10 6 ), Hartmann number ( 0 Ha 50 ), Darcy number ( 10 4 Da 10 1 ) and solid volume fraction of the nanoparticle ( 0 ϕ 0.05 ) on the convective flow features are examined. It is observed that increasing the Grashof number and Darcy number enhances the heat transfer, while the effect is opposite for the Hartmann number. As the corrugation frequency of the triangular wave increases, the local and averaged heat transfer rates decrease, which is more effective for higher values of Grashof and Darcy numbers. Normalized total entropy generation increases as the Darcy number and solid volume fraction of the nanoparticles increase and decreases as the Hartmann number increases both for flat and corrugated wall configurations. Full article
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Open AccessArticle A Simple Explicit Expression for the Flocculation Dynamics Modeling of Cohesive Sediment Based on Entropy Considerations
Entropy 2018, 20(11), 845; https://doi.org/10.3390/e20110845
Received: 13 September 2018 / Revised: 25 October 2018 / Accepted: 2 November 2018 / Published: 4 November 2018
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Abstract
The flocculation of cohesive sediment plays an important role in affecting morphological changes to coastal areas, to dredging operations in navigational canals, to sediment siltation in reservoirs and lakes, and to the variation of water quality in estuarine waters. Many studies have been
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The flocculation of cohesive sediment plays an important role in affecting morphological changes to coastal areas, to dredging operations in navigational canals, to sediment siltation in reservoirs and lakes, and to the variation of water quality in estuarine waters. Many studies have been conducted recently to formulate a turbulence-induced flocculation model (described by a characteristic floc size with respect to flocculation time) of cohesive sediment by virtue of theoretical analysis, numerical modeling, and/or experimental observation. However, a probability study to formulate the flocculation model is still lacking in the literature. The present study, therefore, aims to derive an explicit expression for the flocculation of cohesive sediment in a turbulent fluid environment based on two common entropy theories: Shannon entropy and Tsallis entropy. This study derives an explicit expression for the characteristic floc size, assumed to be a random variable, as a function of flocculation time by maximizing the entropy function subject to the constraint equation using a hypothesis regarding the cumulative distribution function of floc size. It was found that both the Shannon entropy and the Tsallis entropy theories lead to the same expression. Furthermore, the derived expression was tested with experimental data from the literature and the results were compared with those of existing deterministic models, showing that it has good agreement with the experimental data and that it has a better prediction accuracy for the logarithmic growth pattern of data in comparison to the other models, whereas, for the sigmoid growth pattern of experimental data, the model of Keyvani and Strom or Son and Hsu model could be the better choice for floc size prediction. Finally, the maximum capacity of floc size growth, a key parameter incorporated into this expression, was found to exhibit an empirical power relationship with the flow shear rate. Full article
Open AccessArticle Generalized Distance-Based Entropy and Dimension Root Entropy for Simplified Neutrosophic Sets
Entropy 2018, 20(11), 844; https://doi.org/10.3390/e20110844
Received: 8 October 2018 / Revised: 1 November 2018 / Accepted: 2 November 2018 / Published: 4 November 2018
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Abstract
In order to quantify the fuzziness in the simplified neutrosophic setting, this paper proposes a generalized distance-based entropy measure and a dimension root entropy measure of simplified neutrosophic sets (NSs) (containing interval-valued and single-valued NSs) and verifies their properties. Then, comparison with the
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In order to quantify the fuzziness in the simplified neutrosophic setting, this paper proposes a generalized distance-based entropy measure and a dimension root entropy measure of simplified neutrosophic sets (NSs) (containing interval-valued and single-valued NSs) and verifies their properties. Then, comparison with the existing relative interval-valued NS entropy measures through a numerical example is carried out to demonstrate the feasibility and rationality of the presented generalized distance-based entropy and dimension root entropy measures of simplified NSs. Lastly, a decision-making example is presented to illustrate their applicability, and then the decision results indicate that the presented entropy measures are effective and reasonable. Hence, this study enriches the simplified neutrosophic entropy theory and measure approaches. Full article
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Open AccessArticle Improved Cryptanalysis and Enhancements of an Image Encryption Scheme Using Combined 1D Chaotic Maps
Entropy 2018, 20(11), 843; https://doi.org/10.3390/e20110843
Received: 4 October 2018 / Revised: 29 October 2018 / Accepted: 31 October 2018 / Published: 3 November 2018
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Abstract
This paper presents an improved cryptanalysis of a chaos-based image encryption scheme, which integrated permutation, diffusion, and linear transformation process. It was found that the equivalent key streams and all the unknown parameters of the cryptosystem can be recovered by our chosen-plaintext attack
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This paper presents an improved cryptanalysis of a chaos-based image encryption scheme, which integrated permutation, diffusion, and linear transformation process. It was found that the equivalent key streams and all the unknown parameters of the cryptosystem can be recovered by our chosen-plaintext attack algorithm. Both a theoretical analysis and an experimental validation are given in detail. Based on the analysis of the defects in the original cryptosystem, an improved color image encryption scheme was further developed. By using an image content–related approach in generating diffusion arrays and the process of interweaving diffusion and confusion, the security of the cryptosystem was enhanced. The experimental results and security analysis demonstrate the security superiority of the improved cryptosystem. Full article
(This article belongs to the Special Issue Entropy in Image Analysis)
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