Entropy2016, 18(9), 315; doi:10.3390/e18090315 - published 25 August 2016 Show/Hide Abstract
Abstract: The seizure process has been considered from the non-equilibrium thermodynamics and self-organization theory standpoints. It has been testified that, for the intensification of powder mix particles seizing with the substrate during spraying, it is required that relatively light components of the powder mix should be preferably transferred into the friction zone. The theory inferences have been experimentally confirmed, as exemplified by the gas dynamic spray of copper-zinc powders mix.
Entropy2016, 18(9), 316; doi:10.3390/e18090316 - published 25 August 2016 Show/Hide Abstract
Abstract: We demonstrate a vast expansion of the theory of evolutionary stability to finite populations with mutation, connecting the theory of the stationary distribution of the Moran process with the Lyapunov theory of evolutionary stability. We define the notion of stationary stability for the Moran process with mutation and generalizations, as well as a generalized notion of evolutionary stability that includes mutation called an incentive stable state (ISS) candidate. For sufficiently large populations, extrema of the stationary distribution are ISS candidates and we give a family of Lyapunov quantities that are locally minimized at the stationary extrema and at ISS candidates. In various examples, including for the Moran and Wright–Fisher processes, we show that the local maxima of the stationary distribution capture the traditionally-defined evolutionarily stable states. The classical stability theory of the replicator dynamic is recovered in the large population limit. Finally we include descriptions of possible extensions to populations of variable size and populations evolving on graphs.
Entropy2016, 18(9), 314; doi:10.3390/e18090314 - published 25 August 2016 Show/Hide Abstract
Abstract: Fossil fuels are still widely used for power generation. Nevertheless, it is possible to attain a short- and medium-term substantial reduction of greenhouse gas emissions to the atmosphere through a sequestration of the CO2 produced in fuels’ oxidation. The chemical-looping combustion (CLC) technique is based on a chemical intermediate agent, which gets oxidized in an air reactor and is then conducted to a separated fuel reactor, where it oxidizes the fuel in turn. Thus, the oxidation products CO2 and H2O are obtained in an output flow in which the only non-condensable gas is CO2, allowing the subsequent sequestration of CO2 without an energy penalty. Furthermore, with shrewd configurations, a lower exergy destruction in the combustion chemical transformation can be achieved. This paper focus on a second law analysis of a CLC combined cycle power plant with CO2 sequestration using syngas from coal and biomass gasification as fuel. The key thermodynamic parameters are optimized via the exergy method. The proposed power plant configuration is compared with a similar gas turbine system with a conventional combustion, finding a notable increase of the power plant efficiency. Furthermore, the influence of syngas composition on the results is investigated by considering different H2-content fuels.
Entropy2016, 18(8), 311; doi:10.3390/e18080311 - published 24 August 2016 Show/Hide Abstract
Abstract: Several recent works have shown that aggregating local descriptors to generate global image representation results in great efficiency for retrieval and classification tasks. The most popular method following this approach is VLAD (Vector of Locally Aggregated Descriptors). We present a novel image presentation called Distribution Entropy Boosted VLAD (EVLAD), which extends the original vector of locally aggregated descriptors. The original VLAD adopts only residuals to depict the distribution information of every visual word and neglects other statistical clues, so its discriminative power is limited. To address this issue, this paper proposes the use of the distribution entropy of each cluster as supplementary information to enhance the search accuracy. To fuse two feature sources organically, two fusion methods after a new normalization stage meeting power law are also investigated, which generate identically sized and double-sized vectors as the original VLAD. We validate our approach in image retrieval and image classification experiments. Experimental results demonstrate the effectiveness of our algorithm.
Entropy2016, 18(9), 313; doi:10.3390/e18090313 - published 24 August 2016 Show/Hide Abstract
Abstract: Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with and an amplitude-dependent tolerance of r = 80 μ. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment.
Entropy2016, 18(9), 272; doi:10.3390/e18090272 - published 23 August 2016 Show/Hide Abstract
Abstract: Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.