Entropy-based Models Research Articles

Fractional entropy-based models for S-type velocity distributions in turbulent open-channel flows and turbulent Couette flows

Fractional entropy-based models for S-type velocity distributions in turbulent open-channel flows and turbulent Couette flows

Surface water quality assessment for drinking and irrigation using DEMATEL, entropy-based models and irrigation hazard indices

Surface water quality assessment for drinking and irrigation using DEMATEL, entropy-based models and irrigation hazard indices

Fractional entropy-based models of one-dimensional velocity distributions in partially filled and fully filled pipe flows

Fractional entropy-based models of one-dimensional velocity distributions in partially filled and fully filled pipe flows

Evaluating the Veracity of Software Bug Reports using Entropy-based Measures

The wide usage of open source software (OSS) results in an increase of bug data forming an integral part of the extensive data ecosystem. This bug report data needs to be analyzed for bug fixing and prediction of various important attributes like bug severity, priority, fix time, assignees, etc. The increased volume of bug data and different bug reporters from different geographical locations make veracity an important concern. We assume that the bug reports (i.e., different bug attributes) reported in software bug repositories are trustworthy during the bug triaging process. In reality, the bug report data are not trustworthy regarding various aspects like integrity, authenticity, and trusted origin as the bugs are reported by users who may or may not have proper knowledge of the software. In this paper, we proposed entropy-based models for veracity estimation of different bug attributes.

Glucose metabolic heterogeneity correlates with pathological features and improves survival stratification of resectable lung adenocarcinoma.

We investigated whether glycolytic heterogeneity correlated with histopathology, and further stratified the survival outcomes pertaining to resectable lung adenocarcinoma. We retrospectively analyzed the 18F-fluorodeoxyglucose positron emission tomography-derived entropy and histopathology from 128 patients who had undergone curative surgery for lung adenocarcinoma. Disease-free survival (DFS) and overall survival (OS) were analyzed using univariate and multivariate Cox regression models. Independent predictors were used to construct survival prediction models. Entropy significantly correlated with histopathology, including tumor grades, lympho-vascular invasion, and visceral pleural invasion. Furthermore, entropy was an independent predictor of unfavorable DFS (p = 0.031) and OS (p = 0.004), while pathological nodal metastasis independently predicted DFS (p = 0.009). Our entropy-based models outperformed the traditional staging system (c-index = 0.694 versus 0.636, p = 0.010 for DFS; c-index = 0.704 versus 0.630, p = 0.233 for OS). The models provided further survival stratification in subgroups comprising different tumor grades (DFS: HR = 2.065, 1.315, and 1.408 for grade 1-3, p = 0.004, 0.001, and 0.039, respectively; OS: HR = 25.557, 6.484, and 2.570, for grade 1-3, p = 0.006, < 0.001, and = 0.224, respectively). The glycolytic heterogeneity portrayed by entropy is associated with aggressive histopathological characteristics. The proposed entropy-based models may provide more sophisticated survival stratification in addition to histopathology and may enable personalized treatment strategies for resectable lung cancer.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease.

To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease. We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival. Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies. Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

An Information Theoretic Approach to Symbolic Learning in Synthetic Languages.

An important aspect of using entropy-based models and proposed “synthetic languages”, is the seemingly simple task of knowing how to identify the probabilistic symbols. If the system has discrete features, then this task may be trivial; however, for observed analog behaviors described by continuous values, this raises the question of how we should determine such symbols. This task of symbolization extends the concept of scalar and vector quantization to consider explicit linguistic properties. Unlike previous quantization algorithms where the aim is primarily data compression and fidelity, the goal in this case is to produce a symbolic output sequence which incorporates some linguistic properties and hence is useful in forming language-based models. Hence, in this paper, we present methods for symbolization which take into account such properties in the form of probabilistic constraints. In particular, we propose new symbolization algorithms which constrain the symbols to have a Zipf–Mandelbrot–Li distribution which approximates the behavior of language elements. We introduce a novel constrained EM algorithm which is shown to effectively learn to produce symbols which approximate a Zipfian distribution. We demonstrate the efficacy of the proposed approaches on some examples using real world data in different tasks, including the translation of animal behavior into a possible human language understandable equivalent.

Evaluating Different Methods for Determining the Velocity-Dip Position over the Entire Cross Section and at the Centerline of a Rectangular Open Channel.

The velocity profile of an open channel is an important research topic in the context of open channel hydraulics; in particular, the velocity-dip position has drawn the attention of hydraulic scientists. In this study, analytical expressions for the velocity-dip position over the entire cross section and at the centerline of a rectangular open channel are derived by adopting probability methods based on the Tsallis and general index entropy theories. Two kinds of derived entropy-based expressions have the same mathematical form as a function of the lateral distance from the sidewall of the channel or of the aspect ratio of the channel. Furthermore, for the velocity-dip position over the entire cross section of the rectangular open channel, the derived expressions are compared with each other, as well as with two existing deterministic models and the existing Shannon entropy-based expression, using fifteen experimental datasets from the literature. An error analysis shows that the model of Yang et al. and the Tsallis entropy-based expression predict the lateral distribution of the velocity-dip position better than the other proposed models. For the velocity-dip position at the centerline of the rectangular open channel, six existing conventional models, the derived Tsallis and general index entropy-based expressions, and the existing Shannon entropy-based models are tested against twenty-one experimental datasets from the literature. The results show that the model of Kundu and the Shannon entropy-based expression have superior prediction accuracy with respect to experimental data compared with other models. With the exception of these models, the Tsallis entropy-based expression has the highest correlation coefficient value and the lowest root mean square error value for experimental data among the other models. This study indicates that the Tsallis entropy could be a good addition to existing deterministic models for predicting the lateral distribution of the velocity-dip position of rectangular open channel flow. This work also shows the potential of entropy-based expressions, the Shannon entropy and the Tsallis entropy in particular, to predict the velocity-dip position at the centerline of both narrow and wide rectangular open channels.

Mean Velocity and Entropy in Wide Channel Flows

AbstractFor decades many authors have used entropy-based models to predict velocity distribution in open channel flows. Such models use maximum and mean velocity in an assigned domain to obtain vel...

Theoretical-experimental investigation of temperature evolution in laminated composites due to fatigue loading

A method based on the first law of thermodynamics has been developed to predict the temperature evolution in the laminated composite during the fatigue process. The damage energy, frictional energy, and irreversible works are calculated to obtain the dissipated energy in each cycle. A lot of experiments on T300/EPL1012 carbon/epoxy laminates has been carried out to characterize and evaluate the proposed procedure. An IR thermography camera has been set up to monitor the temperature of the external surface of the specimen undergoing cyclic loading. The results show that the temperature increases rapidly in the first 20% of total life, then increases gradually until final failure and suddenly increases at the instance of fracture. The variations of the slope of temperature evolution during the fatigue process could be utilized for estimation of the stage of fatigue life. The results show that in 90° plies, the temperature grows more rapidly relative to 0° plies and increases about 20 °C up to final failure. The developed model has the capability to predict the temperature evolution of laminated composites during cyclic loading which can be used in entropy-based models of fatigue life assessment and also for evaluating the temperature-dependent properties of the matrix during fatigue process.

Distribution of sediment concentration in debris flow using Rényi entropy

Debris flow is a destructive natural phenomenon that normally occurs in mountainous regions or steeply sloping areas. Its destructive force is so high that whatever comes in its way is obliterated. It is therefore difficult to collect field observations of debris flow. This study developed a model of the vertical distribution of sediment concentration in debris flow by maximizing Rényi entropy. The model contains a parameter, called entropy parameter, which is a combination of Lagrange multipliers and has a physical justification. An expression for the equilibrium sediment concentration is also developed, and is found to be a function of the entropy parameter and an entropy index. The proposed model is validated with experimental data and is compared with other entropy-based models reported in the literature. It is observed that the prediction accuracy of the proposed models is superior to that of the other models. Errors, namely relative error and relative root-mean-squared error, are computed and are reported in the paper in support of the derived model.

Suspended Sediment Concentration and Discharge in Open Channels Using Rényi Entropy

The vertical distribution of suspended sediment concentration in an open channel is modeled using Rényi entropy. The model, derived by invoking the principle of maximum entropy, is tested against field and laboratory observations by taking into account nonzero sediment concentration at the surface, and is also compared with other entropy-based and deterministic sediment concentration models. The proposed model is found to be in good agreement with observed data, and its prediction accuracy is superior to that of the other models. It is expressed in terms of a dimensionless entropy parameter that is constant along any vertical in a given river cross section. Errors, namely relative bias (RBIAS) and root-mean-square error (RMSE), are calculated for each of the data sets to validate the model and establish its superiority. It is observed that the RMSE of the present model varies from 0.045 to 4.197 g/L, and from 16.438 to 74.470 g/L, for experimental and field data, respectively, which lie within reasonable limits. In addition, the suspended sediment discharge per unit width is calculated using the Rényi entropy-based models of velocity and sediment concentration. The computed and observed values of sediment discharge are calculated from experimental observations, and it is found that the model can estimate observed values well.

From Ecology to Finance (and Back?): A Review on Entropy-Based Null Models for the Analysis of Bipartite Networks

Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - systems. Nestedness, for example, has been observed in plants-pollinator as well as in country-product trade networks. This has raised questions about the significance of these patterns, which are often believed to constitute a genuine signature of self-organization. Here, we review several methods that have been developed for the analysis of such evidence. Due to the interdisciplinary character of complex networks, tools developed in one field, for example ecology, can greatly enrich other areas of research, such as economy and finance, and vice versa. With this in mind, we briefly review several entropy-based bipartite null models that have been recently proposed and discuss their application to several real-world systems. The focus on these models is motivated by the fact that they show three very desirable features: analytical character, general applicability and versatility. In this respect, entropy-based methods have been proven to perform satisfactorily both in providing benchmarks for testing evidence-based null hypotheses and in reconstructing unknown network configurations from partial information. On top of that, entropy-based models have been successfully employed to analyze ecological as well as economic systems, thus representing an ideal, interdisciplinary tool to approach the study of bipartite complex systems. [...]

Wavelet-fractal diagnosis model and its criterion for concrete dam crack status

The methods on fractal, phase space reconstruction and wavelet are integrated to analyze the observed data of concrete dam cracks. The diagnosis model and appropriate criterion for evolution characteristics of concrete dam crack are developed. Firstly, a wavelet threshold value de-noising algorithm is introduced to process the crack observations of concrete dam. The phase space reconstruction for data series after noise reduction is investigated and the parameter selection method is presented. Then the correlation dimension and Kolmogorov entropy-based models are built to evaluate the crack status of concrete dam. The corresponding criteria are given. Finally, the identification capability of the proposed approach is demonstrated with an actual case. It is indicated that the noise reduction of observed data has the important effect on improving the diagnosis accuracy of crack status. The observed behavior of dam crack has obvious fractal characteristics. The effective diagnosis for crack status of concrete dam can be implemented using the correlation dimension and Kolmogorov entropy-based criteria.

The M2 Model for Dose Simulation in Radiation Therapy

ABSTRACTThe transport of photons and electrons is studied in the field of radiotherapy to compute the dose, that is, the quantity of energy transferred to the medium by a beam of particles at each position. A kinetic model is proposed, and to decrease the computation times, it is reduced through a moment extraction. Entropy-based angular moment models of order up to two (M1 and M2 models) are shown to provide accurate results compared to a reference code with much lower computational costs.

Quantitative evaluation and prediction for preservation quality of cold shocked cucumber based on entropy

Abstract To evaluate and predict preservation quality of cold shocked cucumber and study the effects of cold shock parameters on the preservation quality, several entropy-based models were proposed. Cucumbers were cold shocked at different temperatures (0, 3, 6 °C) for different durations (20, 40, 60 min), and their preservation quality was evaluated by the proposed models. Results show that, the evaluation model can objectively evaluate preservation quality of cold shocked cucumber, and cold shocked at 3 °C for 40 min gets optimal preservation effect. Cold shock treatments at 0 °C and 3 °C improve preservation quality of cucumber effectively, while that at 6 °C fails. Therefore, entropy change caused by cold shock treatment should be higher than a certain critical value to improve preservation quality of cucumber effectively, and the duration is crucial for preservation quality when meeting this condition. The composite entropy change (S*) shows effect of both temperature and duration and characterizes cold shock intensity, and proposed prediction model of cold shock preservation quality can predict preservation quality of cucumber cold shocked at different temperatures and durations well. Industrial relevance Fresh fruits and vegetables contain rich essential substances to our bodies and play an active role in improving people's health. Since it has obvious seasonal regional characteristics on the fruits and vegetables production, pretreatment is very important for prolonging their storage periods. Cold shock treatment for many fruits and vegetables indicate that this method can effectively improve their preservation quality and have broad application space. This study lies in providing a series of methods based on entropy to characterize the cold shock intensity to fruits and vegetables, carrying out objective quantitative evaluation and prediction for preservation quality of the cold shock treatment, and based on which, the influence mechanism of cold shock intensity (temperature and duration) on preservation quality of fruits and vegetables was analyzed. The results will contribute to better design and optimization of the cold shock process for postharvest fruits storage.

Estimation of reliability of structures subject to fatigue loading using plastic strain energy and thermodynamic entropy generation

Plastic strain energy dissipation and thermodynamic entropy generation are explored as means of estimating the likelihood of fatigue crack initiation in reliability assessment of structures. A set of experimental data for aluminum alloy 7075-T651 used in airframe structures are presented to determine the correlation between plastic strain energy dissipation and the thermodynamic entropy generation versus fatigue crack initiation. Cumulative plastic strain energy and thermodynamic entropy generation measured from hysteresis energy and temperature rise proved to be valid physical indices for estimation of the probability of crack initiation. Crack initiation is considered as evidence of fatigue damage and loss of structural integrity. A Bayesian estimation and validation approach is used to determine systematic errors in the developed models as well as other model uncertainties. It was shown that the results from both methods are good predictors of crack initiation and comparable, but the entropy generation method was associated with fewer uncertainties than the plastic strain energy method. Comparisons of the energy-based and entropy-based models are presented and advantages of using one over the other are discussed.

Regional geomechanical-geodynamic control geoinformation system with entropy analysis of seismic events (In Terms of Kuzbass)

The presented geoinformation system is based on cloud service classification of natural and mining-induced seismic events. The data entropy-based models treat seismic signals as functions of a “waveguide” led from a seismic source to a seismic station. The quality similarity or distinction of the models is related with the genesis of seismic events. The service includes Google App Engine cloud calculating technologies, IRIS Data Management Center web-services and local seismic monitoring databases. The application of the cloud service to classifying unknown-genesis seismic events is illustrated by the examples.

Perturbed, entropy-based closure for radiative transfer

We derive a hierarchy of closures based on perturbations of well-known entropy-based closures;we therefore refer to them as perturbed entropy-based models. Our derivation reveals finalequations containing an additional convective and diffusive term which are added to theflux term of the standard closure. We present numerical simulations for the simplest member of the hierarchy, theperturbed $M_1$ or $PM_1$ model, in one spatial dimension. Simulations are performed using a Runge-Kuttadiscontinuous Galerkin method with special limiters thatguarantee the realizability of the moment variables and the positivity of the materialtemperature. Improvements to thestandard $M_1$ model are observed in cases where unphysical shocks develop in the $M_1$ model.

Improving Wetland Mitigation Site Identification Through Community Distribution Modeling and a Patch-Based Ranking Scheme

Current wetland mitigation practices do not fully recover wetland function, often due to poor mitigation site selection. Improved mitigation site selection methods are needed to efficiently assess the suitability and quality of potential wetland mitigation sites at broad spatial scales. We present a novel application of maximum entropy-based predictive distribution models coupled with a patch-based ranking scheme to identify potential wetland mitigation sites and contrast their effectiveness relative to a conventional “expert opinion” model. We used hydrogeologic and landscape features and widely available wetland community distribution data to predict locations of wetlands that were previously unknown, destroyed, or biologically rare in the Upper Susquehanna River Basin in the northeastern United States. An “expert opinion” model predicted wetland occurrence based on topographic slope and soil type. Maximum entropy-based models predicted an independent sample of wetland locations well (Area Under the Curve = 0.86–0.98; 92 % correct classification rate) and dramatically outperformed the “expert opinion” model (62 % correct classification rate). A patch-based ranking scheme, which incorporated additional influences on wetland quality, ranked sites with biologically important wetland plant communities highly among model-identified wetlands. We conclude that integration of maximum entropy-based predictive modeling and patch-based ranking can effectively identify high quality wetland mitigation sites.