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Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.

This paper introduces the Q-Lindley distribution (QLD), a new one-parameter model for lifetime and reliability data derived from the affine exponential family. We derive its main statistical properties and estimate the parameter using maximum likelihood. The performance of the QLD is evaluated using Monte Carlo simulations and four real datasets, and its fit is compared to several classical and Lindley-type distributions. Model selection criteria and Goodness-of-Fit statistics indicate that the QLD provides competitive or superior performance despite its simple structure. These results suggest that the QLD is a useful addition to the family of lifetime distributions.

Fires are among the main drivers of forest degradation in Amazonia, causing multiple socioeconomic and environmental damages. Although human-ignited sources account for most of the fire events in Amazonia, extended droughts may magnify their occurrence and propagation. The southwestern Amazonia, a transnational region shared by Brazil, Peru, and Bolivia and known as the MAP region, has been articulating coordinated actions to prevent disasters, including fire, to reduce their negative impacts. Therefore, to understand the fire patterns in the MAP region, we investigated their main drivers and the changes in the suitability of fire occurrence for the years 2005, 2010, 2016, and 2020. We used a maximum entropy (MaxEnt) model approach based on active fire data from satellites, climatic data, and land use and land cover mapping to spatially quantify the suitability of fire occurrence and its drivers. We used the year 2015 to calibrate the models. For climatic data and active fire count, we only considered grid cells with active fire count over the third quartile. All our models had a satisfactory performance, with values of the area under the curve (AUC) above 0.75 and p < 0.05. Additionally, all models showed sensitivity rates higher than 0.8 and false positive rates below 0.25. We estimated that, on average, 38.5% of the study region had suitable conditions for fire occurrence during the study period. Most of the fire-prone areas belong to Acre, representing approximately 74% of the entire MAP region. The percentage of deforested areas, productive lands, forest edges, and high temperatures were the main drivers of fire occurrence in southwestern Amazonia, indicating the high vulnerability of fragmented landscapes extreme climatic conditions to fire occurrence. We observed that the modeling approach based on Maxint is useful for useful for evaluating the implications of climatic and anthropogenic variables on fire distribution. Furthermore, because the model can be easily employed to predict suitable and non-suitable locations for fire occurrence, it can to prevent potential impacts associated with large-scale wildfire in the future at regional levels.

Coastal regions are one of the most vulnerable areas to the effects of global warming, which is accompanied by an increase in mean sea level and changing shoreline configurations. In Indonesia, the socioeconomic importance of coastal regions where the most populated cities are located is high. However, shoreline changes in Indonesia are relatively understudied. In particular, detailed monitoring with remote sensing data is lacking despite the abundance of datasets and the availability of easily accessible cloud computing platforms such as the Google Earth Engine that are able to perform multi-temporal and multi-sensor mapping. Our study aimed to assess shoreline changes in East Java Province Indonesia from 2000 to 2019 using variables derived from a multi-sensor combination of optical remote sensing data (Landsat-7 ETM and Landsat-8 OLI) and radar data (ALOS Palsar and Sentinel-1 data). Random forest and GMO maximum entropy (GMO-Maxent) accuracy was assessed for the classification of land and water, and the land polygons from the best algorithm were used for deriving shorelines. In addition, shoreline changes were quantified using Digital Shoreline Analysis System (DSAS). Our results showed that coastal accretion is more profound than coastal erosion in East Java Province with average rates of change of +4.12 (end point rate, EPR) and +4.26 m/year (weighted linear rate, WLR) from 2000 to 2019. In addition, some parts of the shorelines in the study area experienced massive changes, especially in the deltas of the Bengawan Solo and Brantas/Porong river with rates of change (EPR) between −87.44 to +89.65 and −18.98 to +111.75 m/year, respectively. In the study areas, coastal erosion happened mostly in the mangrove and aquaculture areas, while the accreted areas were used mostly as aquaculture and mangrove areas. The massive shoreline changes in this area require better monitoring to mitigate the potential risks of coastal erosion and to better manage coastal sedimentation.

Proper knowledge of the wind characteristics of a site is of fundamental importance in estimating wind energy output from a selected wind turbine. The present paper focuses on assessing the suitability and accuracy of the fitted distribution function to the measured wind speed data for Baburband site in Sindh Pakistan. Comparison is made between the wind power densities obtained using the fitted functions based on Maximum Entropy Principle (MEP) and Weibull distribution. In case of MEP-based function a system of (N+1) non-linear equations containing (N+1) Lagrange multipliers is defined as probability density function. The maximum entropy probability density functions is calculated for 3–9 low order moments obtained from measured wind speed data. The annual actual wind power density (PA) is found to be 309.25 W/m2 while the Weibull based wind power density (PW) is 297.25 W/m2. The MEP-based density for orders 5, 7, 8 and 9 (PE) is 309.21 W/m2, whereas for order 6 it is 309.43 W/m2. To validate the MEP-based function, the results are compared with the Weibull function and the measured data. Kolmogorov–Smirnov test is performed between the cdf of the measured wind data and the fitted distribution function (Q95 = 0.01457 > Q = 10−4). The test confirms the suitability of MEP-based function for modeling measured wind speed data and for the estimation of wind energy output from a wind turbine. R2 test is also performed giving analogous behavior of the fitted MEP-based pdf to the actual wind speed data (R2 ~ 0.9). The annual energy extracted using the chosen wind turbine based on Weibull function is PW = 2.54 GWh and that obtained using MEP-based function is PE = 2.57–2.67 GWh depending on the order of moments.

In the modeling of successive arrival times with a monotone trend, the alpha-series process provides quite successful results. Both selecting the distribution of the first arrival time and making an optimal statistical inference play a crucial role in the modeling performance of the alpha-series process. In this study, when the distribution of the first arrival time is the generalized Rayleigh, the problem of statistical inference for the α , β , and λ parameters of the alpha-series process is considered. Further, in order to obtain optimal modeling performance from the mentioned alpha-series process, various estimators for the model parameters are obtained by employing different estimation methodologies such as maximum likelihood, modified maximum spacing, modified least-squares, modified moments, and modified L-moments. By a series of Monte Carlo simulations, the estimation efficiencies of the obtained estimators are evaluated through the different sample sizes. Finally, two real datasets are analyzed to illustrate the importance of modeling with the alpha-series process.

In this paper, we developed a new distribution, namely the two parameters Rani distribution (TPRD). Some statistical properties of the proposed distribution are derived including the moments, moment-generating function, reliability function, hazard function, reversed hazard function, odds function, the density function of order statistics, stochastically ordering, and the entropies. The maximum likelihood method is used for model parameters estimation. Following the same approach suggested by Bagdonavicius and Nikulin (2011), modified chi squared goodness-of-fit tests are constructed for right censored data and some tests for right data is considered. An application study is presented to illustrate the ability of the suggested model in fitting aluminum reduction cells sets and the strength data of glass of the aircraft window.

Background Accurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study. Results The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%. Conclusions The empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined.

Parametric models are proposed for the joint distribution of bivariate random variables when one variable is directional and one is scalar. These distributions are developed on the basis of the maximum entropy principle and by the specification of the marginal distributions. The properties of these distributions and the statistical analysis of regression models based on these distributions are explored. One model is extended to several variables in a form that justifies the use of least squares for estimation of parameters, conditional on the observed angles.