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Abstract: Increase in refining demand and tighter environmental regulations have led to sharp increases in hydrogen consumption of oil refineries. Hydrogen conservation and effective use are of interest to refineries whose operations and profitability are constrained by hydrogen. Purification is widely used in hydrogen networks of refineries to reduce hydrogen production load. To minimize hydrogen utility consumption, it is necessary to optimize the hydrogen network with purification as a whole. In this paper, for hydrogen purification process, a triangle rule (which can be generalized to polygon rule) is proposed for graphical representation of its mass balance. The proposed procedure treats the product concentration and recovery rate of the purification process as adjustable parameters. An ensuing graphical method is developed for targeting the pinch point and minimum utility consumption of the hydrogen system with purification reuse. This graphical method can be used for any purification devices and in systems with any utility concentration. A refinery case is studied to demonstrate the optimization method. [ABSTRACT FROM AUTHOR]

Suggested here is a simple graphical method for studying the goodness of fit in Cox's regression model for survival data. The method is easy to use, as it does not require the estimation of alternative models and only involves quantities similar to those already appearing in the partial likelihood expression that is needed in the parameter estimation. The rationale behind the graphs is very intuitive: They make a direct comparison between observed and expected failure frequencies, as estimated from the model. In a correctly specified model one anticipates an approximate balance between such frequencies; otherwise there will typically be groups of individuals for which the expected frequencies are systematically too high or too low to match with the data, and this shows in the graphs introduced here. In the concrete applications of the method the individuals are stratified in a way that depends on what aspect of the model is being checked against data. There is always one graph for each stratum. Simulated and real data are used to illustrate the method. In the simulations two types of defect that can come up in a Cox's model are considered: (a) an influential covariate has been deleted from the model, and (b) a common baseline hazard for all individuals has been assumed in a case in which the individuals should be stratified according to baseline hazard. Serious defects in the model are relatively easy to detect from the diagnostic graphs. As concrete applications of the method, studied briefly are the fitting of Cox's model to the well-known Stanford heart transplant data and to a data set describing the survival of malignant melanoma patients after operation. The article concludes with some general observations concerning the randomness in the graphs. [ABSTRACT FROM AUTHOR]

In this study, the success of the numerical methods and a metaheuristic algorithm in parameter estimation of Weibull distribution, which is frequently used in wind energy applications, are compared. Numerical methods are Justus empirical method, moment method, graphical method, energy pattern factor method (EPM), energy trend method, maximum likelihood estimation method (MLE). The metaheuristic algorithm is manta ray foraging optimization method (MRFO). The wind data used in the study were recorded hourly in the Isparta region in the southwest of Turkey. A graphical user interface design has been made to easily perform the calculations in this study. The success of the methods was tested with four different statistical error analysis methods. According to the results of the analysis, the MRFO method was by far the most successful method. EPM and MLE methods were the most unsuccessful methods. [ABSTRACT FROM AUTHOR]

Unit distributions, exhibiting inherent symmetrical properties, have been extensively studied across various fields. A significant challenge in these studies, particularly evident in parameter estimations, is the existence and uniqueness of estimators. Often, it is challenging to demonstrate the existence of a unique estimator. The major issue with maximum likelihood and other estimator-finding methods that use iterative methods is that they need an initial value to reach the solution. This dependency on initial values can lead to local extremes that fail to represent the global extremities, highlighting a lack of symmetry in solution robustness. This study applies a very simple, and unique, estimation method for unit Weibull and unit Burr XII distributions that both attain the global maximum value. Therefore, we can conclude that the findings from the obtained propositions demonstrate that both the maximum likelihood and graphical methods are symmetrically similar. In addition, three real-world data applications are made to show that the method works efficiently. [ABSTRACT FROM AUTHOR]

Weibull distribution is an extensively used statistical distribution for analyzing wind speed and determining energy potential studies. Estimation of the wind speed distribution parameter is essential as it significantly affects the success of Weibull distribution application to wind energy. Various estimation methods viz. graphical method, moment method (MM), maximum likelihood method (ML), modified maximum likelihood method, and energy pattern factor method or power density method have been presented in various reported research studies for accurate estimation of distribution parameters. ML is the most preferred approach to study the parameter estimation. ML works on the principle of forming a likelihood function and maximizing the function for parameter estimation. ML generally uses the numerical based iterative method, such as Newton–Raphson. However, the iterative methods proposed in the literature are generally computationally intensive. In this paper, an efficient technique utilizing differential evolution (DE) algorithm to enhance the estimation accuracy of maximum likelihood estimation has been presented. The R 2 of GA-Weibull, SA-Weibull, and DE-Weibull is 0.958, 0.953, and 0.973 respectively, and value of RMSE of DE-Weibull 0.0083, GA-Weibull (0.0104), and SA-Weibull (0.0110), for the yearly wind speed data are obtained. The lowest root mean square error and larger regression value for both monthly and yearly wind speed data indicate that the DE-Weibull distribution has the best goodness of fit and advocate the DE algorithm for the parameter estimation. [ABSTRACT FROM AUTHOR]

Wind potential estimation is generally evaluated using two-parameter (k, c) Weibull distribution. Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Relative Error (RE) are computed in order to comparatively analyse fourteen methods of determining Weibull parameters. They are the Graphical Method, the Standard Deviation Method, the Empirical Method of Justus, the Empirical Method of Lysen, the Energy Pattern Factor Method, the Maximum Likelihood Method, the Modified Maximum Likelihood Method, the Alternative Maximum Likelihood Method, the Least Square Method, the Weighted Least squares Method, the Curve Fitting Method, the Wind Variability Method, the Moroccan Method and the Median and Quartile Method. These methods have been applied on three different windy sites (slightly, moderately and very windy sites) with hourly wind data over a period of 10 years (2005–2014), measured at 10 m height. As a result, compared to the other methods, Energy Pattern Factor method is the more suitable method applicable to assess the Weibull parameters for all wind speeds. However, the values obtained from RMSE, R2 and RE tests revealed that the WVM and MoroM methods are not suitable while all other methods are acceptable for the estimation of k and c. parameters. The determination of the wind power density and the gap between the predicted standard deviation by each method and the measured standard deviation for all the sites highlighted the relevance of EPFM method and the others methods. Moreover, this work reveals that the Weibull shape factor k decrease with height above ground level, while that of the scale factor c increase with height. [ABSTRACT FROM AUTHOR]

This is an extensive tutorial introducing the state of art in Software Reliability Engineering and associated metrics. Software Reliability Engineering (SRE) encompasses many tasks such as Reliability Growth Testing (RGT) based on software reliability growth modeling, assessment of operational reliability, software release time determination and determination of estimated testing time or required testing efforts and resources. The foremost activity in SRE is Software Reliability growth modeling to plan and execute RGT which is carried out as final testing activity in the software development life cycle (SDLC) and is known in different names such as system testing. This phase in SDLC requires extensive use of Computational Mathematics to estimate parameters of the software reliability growth model (SRGM). In this paper, we will briefly discuss the various SRGM parameter estimation methodologies and conclude that the Least Squares methodology provides a satisfactory solution in all situations. [ABSTRACT FROM AUTHOR]

• Inspiration on the heterogeneous nature of water adsorption in bentonites. • Noval (continuum) SWRC model considering adsorption and capillarity. • New model contains only 5 parameters, with high efficiency in parameter estimation. • A graphical method was suggested to estimate maximum adsorption capacity. Understanding soil–water retention behavior is a longstanding topic. Water retained in soils can be decomposed into adsorptive and capillary components, controlled by different physicochemical mechanisms. The capillary water retention was frequently discussed in literatures, but the adsorption role was rarely considered, especially for high active clays (e.g., bentonite). In this study, two novel equations for quantifying adsorptive and capillary water retentions are proposed, generating a twofold model to continuously simulate soil–water retention behavior of bentonites. Only 5 parameters, i.e., the maximum adsorption capacity, characteristic adsorptive and capillary suctions, and uniformities of adsorptive and capillary pores, are defined with clear physical meanings. A graphical method was suggested to firstly determine the maximum adsorption capacity, and the remaining parameters are efficiently estimated by non-linear curve fitting. The water retention data for various bentonites, representing a variety of hydration conditions, initial compactness, montmorillonite content and suction range, are used to assess the model performance. The predictions agree well with the measured total, adsorptive and capillary water contents of a Wyoming bentonite, and the fitting curves also match well with test data for other bentonites over the full suction range. Adsorption parameters are distributed within a narrow range, while the characteristic capillary suction is also distributed within a limited range under constant volume condition. The proposed model allows reasonable predictions about the capillary onset and the transition from adsorption to capillarity, revealing obvious superiority by comparison with other hybrid models. This work offers a new pathway to quantitively assess the soil–water retention curve of high active clays. [ABSTRACT FROM AUTHOR]

The use of wind as an energy source is becoming popular because of its non-polluting and renewable features. There is an urgent request to develop site-based estimation on wind engineering, which can be used for optimal design of wind turbines and wind farming. The wind speed for Zafarana Project in Suez Gulf, namely Site-3, based on monthly averaged data for 1 year as well as every 10 min for two days, one day in summer season and one day one winter season have been analyzed to estimate the most appropriate method to find Weibull distribution parameters for this site. The investigated methods are the mean wind speed method, the maximum likelihood method, the modified maximum likelihood method, the graphical method and the power density method. These methods results have been compared with the provided data to find their accuracy based on the root mean square errors. From the obtained results, the mean wind speed and the maximum likelihood method are recommended in estimating the wind speed distribution for the studied site in Zafarana wind farm. [ABSTRACT FROM AUTHOR]

Actuator rate limit phenomenon is ubiquitous in control systems, but it has always been neglected in practical engineering. Especially, few papers are published to investigate the system identification while considering the rate limit. Step function is commonly used in parameter estimation and its first‐order derivative at the step time goes to infinity, which is not affordable by real actuators. As a result, the rate limit slows down the system response speed, misleads the parameter identification, and subsequently affects the controller design and deteriorates the control performance. This study proposes both graphical method and curve fitting method to identify the first‐order plus time‐delay (FOPTD) systems with consideration of the actuator rate limit effect whose limit value is unknown. Quantitative analyses of the parameter mismatch in the traditional FOPTD model and parameter sensitivity analyses are also carried out. Both simulation results and experimental results have shown the necessity and feasibility of defining a new model structure which includes the rate limit value. [ABSTRACT FROM AUTHOR]

This study analyzes the wind speed characteristics, compares the six different methods (graphical, method of moment, wind energy pattern factor, empirical method of Justus and Lysen, and maximum likelihood method) of estimating Weibull parameters and calculates wind power density using daily mean wind speed data collected, at a height of 2 m, over a period of seven and eight years for Jumla and Okhaldhunga, respectively. Wind data were estimated at a height of 50 m to calculate average wind speed, Weibull parameters, and wind power density. Based on the results, Jumla has an average monthly maximum wind speed of 9.78 m/s in June and minimum wind speed of 6.71 m/s in December, whereas Okhaldhunga has an average monthly maximum wind speed of 10.95 m/s in April and minimum wind speed of 4.52 m/s in October. Jumla has an average annual wind speed of 8.11 m/s while Okhaldhunga has an average annual wind speed of 6.89 m/s. The accuracy of estimation methods was statistically tested using root mean square error and coefficient of determination. The empirical method of Justus and Lysen was found to be the best performing while the graphical method performed the poorest. By using the best method, an average wind power density has been estimated as 336.07 W/m 2 and 326.73 W/m 2 for Jumla and Okhaldhunga, respectively, indicating that both locations belong to wind power class III and have a moderate potential for wind energy harvesting. [ABSTRACT FROM AUTHOR]

In this study, we propose two estimators called the 3-step modified maximum likelihood (MML) and the combined estimators of the parameters of the modified Weibull distribution which is used in reliability models with bathtub-shaped failure rate function. The simulations show the superiority of both estimators over the graphical estimators. Particularly, the combined estimators are the better of the two. Two real-life data applications also show the superiority of the proposed estimators compared to the graphical estimators. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the Estonian Academy of Sciences is the property of Teaduste Akadeemia Kirjastus and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on choosing a spatial mixture model with implicitly includes the time to represent the relative risks of COVID-19 pandemic using an appropriate model selection criterion. For this purpose, a more recent criterion so-called the widely Akaike information criterion (WAIC) is used which we believe that its use so limitedly in the context of relative risk modelling. In addition, a graphical method is adopted that is based on a spatial-temporal predictive posterior distribution to select the best model yielding the best predictive accuracy. By applying this model selection criterion, we seek to identify the levels of relative risk, which implicitly represents the determination of the number of the model components of all regions over independent time periods. The estimation of parameters and the model selection are both performed in a Bayesian framework. Also, the means of estimated relative risk for the selected mixture model are mapped to give a clearer picture of distributing the disease risks in each district. [ABSTRACT FROM AUTHOR]

This paper is about dynamical modeling of an electroencephalographic (EEG) measurement chain. It is based on a home-made experimental setup consisting of an electrolytic medium, stimulated by external electrodes, the activity of which is measured at different points via other electrodes, connected to an EEG recording device. A novel noninteger-order model structure is then established with a so-called constant phase element, and a Nyquist-based graphical method is derived to identify the noninteger order. The study is completed with a recursive identification method for full parameter estimation in the model, and various applications on experimental data are presented, finally resulting in a fully identified noninteger order high-pass filter. [ABSTRACT FROM AUTHOR]

In this article we consider the problem of testing for parameter changes in ARIMA models based on the cusum test. The proposed test procedure is applicable to testing for the change from stationary models to non stationary models, and vice versa. The idea is to transform the time series via differencing to make the whole time series as a combination of stationary subseries. For this task, we propose a graphical method to identify the right order of differencing. Then the cusum test statistic proposed by Lee et al. (2003) is constructed based the differenced time series. Simulation study and real data analysis are provided for illustration. [ABSTRACT FROM AUTHOR]

This study focuses on the estimation of the two unknown parameters of Chen distribution, characterized by a bathtub-shaped hazard rate function, within an improved adaptive type-II progressive censored data framework. Maximum likelihood estimation is proposed for the two parameters, and the establishment of approximate confidence intervals is based on asymptotic normality. Bayesian estimation is also conducted under both symmetric and asymmetric loss functions, utilizing the proposed importance sampling and Metropolis–Hastings algorithm. Lastly, the performance of various estimation methods is evaluated through Monte Carlo simulation experiments, and the proposed estimation approach is illustrated using a real dataset. [ABSTRACT FROM AUTHOR]

In this paper, the Bayesian estimators for the Inverse Weibull Distribution (IWD) scale parameter are derived when the shape parameter of distribution is known. The Bayesian estimators for the parameter are obtained by using the Gamma prior under the different types of loss functions such as square error loss function (Self), Entropy loss function (Elf), Precautionary loss function (Plf), Linear exponential loss function (Linexlf) and nonlinear exponential loss function (Nlinexlf). A classical maximum likelihood estimator (mle) for the parameter is also derived. To compare the efficiency of the parameter estimation methods, a simulation study is carried out. The comparison is based on mean square error. [ABSTRACT FROM AUTHOR]

In this paper, dysentery diarrhea deterministic compartmental model is proposed. The local and global stability of the disease-free equilibrium is obtained using the stability theory of differential equations. Numerical simulation of the system shows that the backward bifurcation of the endemic equilibrium exists for R0>1. The system is formulated as a standard nonlinear least squares problem to estimate the parameters. The estimated reproduction number, based on the dysentery diarrhea disease data for Ethiopia in 2017, is R0=1.1208. This suggests that elimination of the dysentery disease from Ethiopia is not practical. A graphical method is used to validate the model. Sensitivity analysis is carried out to determine the importance of model parameters in the disease dynamics. It is found out that the reproduction number is the most sensitive to the effective transmission rate of dysentery diarrhea (βh). It is also demonstrated that control of the effective transmission rate is essential to stop the spreading of the disease. [ABSTRACT FROM AUTHOR]

In this paper, we propose an alternative generalization of a recent test for univariate normality which is based on the empirical moment generating function to the multivariate case. We show, among other properties, that the proposed weighted L²-class of statistics is affine invariant and consistent. The empirical critical values of the proposed test are evaluated for different sample sizes, variable dimensions and values of the smoothing parameter through large scale simulations. The empirical power comparison of the test with a strong competitor shows that the test has a considerably high power performance, especially at large sample sizes as well as under heavy-tailed alternative distributions. The application of the statistic, together with its competitor, to six real-life datasets also supports the considerable good power performance of the proposed statistic as well as its ease of application. [ABSTRACT FROM AUTHOR]

The Muskingum model is one of the most popular models for hydrologic channel flood routing. However, as the parameters of this model are estimated by analyzing both inflow and outflow data, the application of this model to an ungauged channel reach has been limited. In this study, as a solution to this problem, a methodology is proposed to estimate the parameters of the Muskingum model using the basin characteristics representing the inlet and outlet of the channel reach. That is, the Muskingum model could be expressed as a combination of the linear reservoir model and the Dirac delta function, whose weighting factors are determined as a function of the Muskingum model parameter x. It was also found that the storage coefficient of the linear reservoir model is expressed by both the Muskingum model parameters x and K. Finally, it was shown that the Muskingum model parameters can be estimated using the information of concentration time and storage coefficient representing the inlet and outlet of the given channel reach. In the application example with the observed data from the dam discharge experiment in the Geum River Basin, Korea, it was shown that the Muskingum model parameters could be reasonably estimated to be very close to those estimated by the conventional graphical method. [ABSTRACT FROM AUTHOR]

To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three wellknown methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method. [ABSTRACT FROM AUTHOR]

Flight delays may be decreased in a predictable way if the Weibull wind speed parameters of a runway, which are an important aspect of safety during the take-off and landing phases of aircraft, can be determined. One aim of this work is to determine the wind profile of Hasan Polatkan Airport (HPA) as a case study. Numerical methods for Weibull parameter determination perform better when the average wind speed estimation is the main objective. In this paper, a novel objective function that minimises the root-mean-square error by employing the cumulative distribution function is proposed based on the genetic algorithm and particle swarm optimisation. The results are compared with well-known numerical methods, such as maximum-likelihood estimation, the empirical method, the graphical method and the equivalent energy method, as well as the available objective function. Various statistical tests in the literature are applied, such as R2, Root-Mean-Square Error (RMSE) and $\chi$ 2. In addition, the Mean Absolute Error (MAE) and total elapsed time calculated using the algorithms are compared. According to the results of the statistical tests, the proposed methods outperform others, achieving scores as high as 0.9789 and 0.9996 for the R2 test, as low as 0.0058 and 0.0057 for the RMSE test, 0.0036 and 0.0045 for the MAE test and 3.53 × 10−5 and 3.50 × 10−5 for the $\chi$ 2 test. In addition, the determination of the wind speed characteristics at HPA show that low wind speed characteristics and regimes throughout the year offer safer take-off and landing schedules for target aircraft. The principle aim of this paper is to help establish the correct orientation of new runways at HPA and maximise the capacity of the airport by minimising flight delays, which represent a significant impediment to air traffic flow. [ABSTRACT FROM AUTHOR]

This paper presents a novel high-bandwidth control approach for piezo-actuated nanopositioning stages. A delayed position feedback (DPF) controller is first developed in the inner loop to damp the resonant mode of piezo-actuated stages. A generalized Runge–Kutta method (GRKM) is proposed to determine the parameters of the DPF controller with pole placement. The benefit of the DPF for active damping is its simple structure and ease of implementation. Then, a high-gain proportional-integral (PI) controller is designed in the outer loop to deal with the hysteresis nonlinearity, disturbance and modeling errors. The stability of the control system is analyzed via a graphical method. Finally, experiments are conducted to demonstrate the effectiveness and superiority of the proposed approach in terms of tracking accuracy at high speed as compared to the PI controller. [ABSTRACT FROM AUTHOR]

The destabilising effects of time delays on linear systems make the control design for such systems a challenging task. This study aims at developing a new graphical method to define the stable regions in the space of uncertain delay and controller coefficients. The features of the Nyquist plot are used to design the proportional-integral controllers, which guarantee the closed-loop stability for the first-order and second-order plants containing an uncertain time delay. The main aim is to determine the regions in the space of the uncertain delay and the controller coefficients for which the stable gain intervals exist. This is achieved by computing the values of the parameters at which the Nyquist plot gets tangent to the real axis. As a result, there is no need to sweep the gain or delay parameter to sketch the stability boundaries. Rekasius substitution is employed to convert the characteristic quasi-polynomial to a polynomial, and then the polynomial results are used to derive the stability boundaries. [ABSTRACT FROM PUBLISHER]

This work introduces a new approach for the evaluation of numerical methods used in calculating the Weibull parameters for the prediction of wind resource. In this evaluation, power is a key issue since it proves to be quite relevant in predicting the feasibility of a given site for efficient use of wind energy. Previous knowledge of such information is important for the decision process on the technical feasibility of installing industrial wind farms. The paper performed a statistical analysis of seven mathematical methods to estimate the shape parameter k and the scaling parameter c of the Weibull distribution. Wind speed and wind power data collected in two coastal cities (Icapuí and Camocim), State of Ceará, in the northeast region of Brazil were used. The methods used in the development of this research are the graphical method, maximum likelihood method, modified maximum likelihood method, empirical method, moment method, energy pattern factor method and the equivalent energy method. Comparative analysis of efficiency and accuracy involves the application of the following statistical tests: analysis of variance (R2), root mean square error (RMSE), and chi-square (X 2). The use of power as a parameter to be evaluated in this study by analyzing the Weibull distribution, is something different from what has been discussed in the literature, and indicates that methods which are well evaluated when the wind speed is used as a comparison factor can change position when ranked by power. It is important to emphasize that the values of k and c were calculated directly from the values of wind power (not the wind speed, as usual), and subsequently it was statistically tested the adequacy of the values in the prediction of wind speed and wind power. The presented results indicate that the values generated for the shape and scale parameters provided differences considering the databases formed by wind speed and wind power, namely the graphical method. The wind power data produced accurate results when submitted to statistical tests. [ABSTRACT FROM AUTHOR]

The flour milling industry—a vital component of global food production—is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters. [ABSTRACT FROM AUTHOR]

Statistical distributions are used to model wind speed, and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia, namely Aljouf, Rafha, Tabuk, Turaif, and Yanbo. Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data. Also, with six wind turbine technologies rating between 1 and 3MW, the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy ($/kWh) compared to the assessments by IOAs. The energy cost analyses show that Turaif is the windiest site, with an electricity cost of $0.016906/kWh. The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding $0.02739/kWh. Finally, the outcomes of this study exhibit the potential of wind energy in Saudi Arabia, and its environmental goals can be acquired by harvesting wind energy. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to estimate the economic, emissions, and energy benefits that could be obtained from a base loaded CHP system using screening parameters and system component efficiencies. On the basis of the location of the system and the facility power to heat ratio, the power that must be supplied by a base loaded CHP system in order to potentially achieve cost, emissions, or primary energy savings can be estimated. A base loaded CHP system is analyzed in nine US cities in different climate zones, which differ in both the local electricity generation fuel mix and local electricity prices. Its potential to produce economic, emissions, and energy savings is quantified on the basis of the minimum fraction of the useful heat to the heat recovered by the CHP system (ϕmin). The values for ϕmin are determined for each location in terms of cost, emissions, and energy. Results indicate that in terms of cost, four of the nine evaluated cities (Houston, San Francisco, Boulder, and Duluth) do not need to use any of the heat recovered by the CHP system to potentially generate cost savings. On the other hand, in cities such as Seattle, around 86% of the recovered heat needs to be used to potentially provide cost savings. In terms of emissions, only Chicago, Boulder, and Duluth are able to reduce emissions without using any of the recovered heat. In terms of primary energy consumption, only Chicago and Duluth do not require the use of any of the recovered heat to yield primary energy savings. For the rest of the evaluated cities, some of the recovered heat must be used in order to reduce the primary energy consumption with respect to the reference case. In addition, the effect of the efficiency of the power generation unit and the facility power to heat ratio on the potential of the CHP system to reduce cost, emissions, and primary energy is investigated, and a graphical method is presented for examining the trade-offs between power to heat ratio, base loading fraction, percentage of recovered heat used, and minimum ratios for cost, emissions, and primary energy. [ABSTRACT FROM AUTHOR]

This paper presents a framework of the digital generation of non-Gaussian spiky excitations. This study is focused on the random spikiness, featuring large excursions with considerable energy and monotonic (nonstochastic) variations in a local time history. A first-order non-Gaussian stochastic time series model and its spectral representation are employed for the local spiky features. The stochastic model generates not only autocorrelation properties but also a unique shape of peaks formed with random spikes and monotonic variations between spikes. The Fourier representation of the stochastic model enables an effective control of the peaks and provides a filtering operation for the local feature generation in the frame of stationary stochastic process. Several spectral models with stochastic or ensemble-averaged amplitudes and four added phase functions have been developed. Thus, the phase is different from the uncorrelated uniform phases in a conventional spectral method. The essential feature of the method is to utilize correlations in the structured phase that are responsible for the spikiness. A four-parameter system is developed that is capable of generating spiky features while simulating specified power spectra and higher-order moments. A simple procedure for the selection of phase parameter values by a graphical method is described with illustrations of surface pressure simulation. [ABSTRACT FROM AUTHOR]

Introduction: Introduction: the use of real images of food portions constitutes a useful and effective tool to help measure the amount of food consumed. Objective: to validate content and visual perception of the images of a photographic atlas of food portions designed for Ecuador. Methods: first, eight experts assessed the content in an atlas of food portions, using the Delphi technique. Then, 56 adults (aged 18-59) gave an assessment of their visual perception of about 35 portions of nine selected products. The concordance in the estimation using the atlas versus an estimation without the atlas was evaluated through the intra-class correlation coefficient (ICC), the Bland-Altman graphical method, and a hypothesis contrast. The differences between the real amounts and the estimation were assessed using the Wilcoxon test (p < 0.05). For each of the food items, the percentage of participants who chose the correct photograph, the one directly above or below was calculated. Results: the assessment carried out by experts showed that this instrument is relevant and appropriate. ICC values of between 0.576 and 0.956 were obtained using the atlas, as well as significant differences between the real amounts and the estimation without the atlas (p < 0.001). There was a sufficient correlation between the actual image and its perception for all food items except mayonnaise. The correct photograph was chosen in 66% of 500 estimations. Conclusions: the photographic atlas of food portions for Ecuador is an appropriate tool for helping to estimate the amount of food consumed by adults. [ABSTRACT FROM AUTHOR]

We derive optimal bias-robust L -estimators of a scale parameter $\sigma$ based on random samples from $F\lpar \lpar \cdot \!- \theta\rpar /\sigma\rpar$ , where $\theta$ and $\sigma$ are unknown and F is an unknown member of a $\varepsilon$ -contaminated neighborhood of a fixed symmetric error distribution $F_0$ . Within a very general class ${\cal S}$ of L -estimators which are Fisher-consistent at $F_0$ , we solve for: (i) the estimator with minimax asymptotic bias over the $\varepsilon$ -contamination neighborhood; and (ii) the estimator with minimum gross error sensitivity at $F_0$ [the limiting case of (i) as $\varepsilon \rightarrow 0$ ]. The solutions to problems (i) and (ii) are shown, using a generalized method of moment spaces, to be mixtures of at most two interquantile ranges. A graphical method is presented for finding the optimal bias-robust solutions, and examples are given. [ABSTRACT FROM AUTHOR]

Zero inflated distributions are used in a wide variety of phenomena, particularly when the response variable exhibits an excessive number of zeros. These phenomena can lead to an overdispersion in the data and the reduction of its mean. In this paper, we aim to address this issue by introducing the zero inflated Waring distribution (ZIW). The latter is regarded to fit the data more adequately in cases where the Waring distribution fails to accurately describe data behavior, especially when these contain a high frequency of observed zeros. Therefore, we propose a more flexible distribution than those found in the literature to address the presence of large tails to the right. This distribution encompasses both the overdispersion generated by the excess of zeros, and that generated by the Waring distribution. The estimation of parameters is calculated by using the maximum likelihood method. Monte Carlo simulation results are provided. Finally, a practical application with real data extracted from the literature is presented. [ABSTRACT FROM AUTHOR]

In this study we propose a new method called the cessation time approach (CTA) for interpreting recovery tests in confined aquifers, which is based on the Theis solution. The CTA method involves selecting a residual drawdown measurement from the recovery phase and linking it to its dimensionless counterpart through simple algebraic steps. This approach is then incorporated with a regression model to estimate aquifer parameters. The performance of several parametric polynomial and non-parametric machine learning regression models was investigated using various datasets. Results show that CTA with third-order multivariable polynomials produced highly accurate parameter estimates with a normalized root mean squared error (NRMSE) within 0.5% for a field dataset. Among the machine learning algorithms tested, the radial basis function and Gaussian process regression achieved the highest accuracy with NRMSEs of 0.6%. We conclude that CTA can be a viable interpretation tool for recovery tests due to its accuracy and simplicity. [ABSTRACT FROM AUTHOR]

We introduce a quantile-based multivariate log-normal distribution, providing a new multivariate skewed distribution with positive support. The parameters of this distribution are interpretable in terms of quantiles of marginal distributions and associations between pairs of variables, a desirable feature for statistical modeling purposes. We derive statistical properties of the quantile-based multivariate log-normal distribution involving the transformations, closed-form expressions for the mixed moments, expected value, covariance matrix, mode, Shannon entropy, and Kullback–Leibler divergence. We also present results on marginalization, conditioning, and independence. Additionally, we discuss parameter estimation and verify its performance through simulation studies. We evaluate the model fitting based on Mahalanobis-type distances. An application to children data is presented. [ABSTRACT FROM AUTHOR]

The exponential model is the simplest among all lifetime distribution models and it possesses a constant failure rate. Here we propose a new class of lifetime distribution having decreasing failure rate which we developed through compounding exponential distribution with the positive hyper-Poisson distribution. We investigate some of its statistical properties and employed various methods of estimation for estimating the parameters of the distribution along with certain test procedures. All the procedures discussed in the paper are illustrated with the help of real-life data sets. Further, a brief simulation study is conducted for examining the performance of the estimators of the parameters of the distribution. [ABSTRACT FROM AUTHOR]

Frequency analysis has long been an important theme of hydrology research. Although meteorological techniques (physical approaches) such as radar nowcasting, remote sensing, and forecasting heavy rainfall events using meteorological simulation models are quite effective for urban disaster prevention, statistical and stochastic theories that include frequency analysis, which are usually used in flood control plans, are also valuable for flood control plans for disaster prevention. Master plans for flood control projects in urban areas often use the concept of T-year hydrological values with a T-year return period. A flood control target is a "landside area that is safe against heavy rainfall or floods with a return period of T years". This review emphasizes discussions of parameter estimation of stochastic models and selection of optimal statistical models, which include evaluation of goodness-of-fit techniques of statistical models. Based on those results, the authors criticize Japanese standard procedures recommended by the central government. Consistency between parameter estimation and evaluation of goodness-of-fit is necessary. From this perspective, we recommend using the maximum likelihood method and AIC, both of which are related to Kullback–Leibler divergence. If one prefers using SLSC, we recommend not SLSC itself but SLSC's non-exceedance probability. One important purpose of this review is the introduction of well-used Japanese methods. Because some techniques that are slightly different from the international standard have been used for many years in Japan, we introduce those in the review article. [ABSTRACT FROM AUTHOR]

We provide unconstrained parameterisation for and model a covariance using covariates. The Cholesky decomposition of the inverse of a covariance matrix is used to associate a unique unit lower triangular and a unique diagonal matrix with each covariance matrix. The entries of the lower triangular and the log of the diagonal matrix are unconstrained and have meaning as regression coefficients and prediction variances when regressing a measurement on its predecessors. An extended generalised linear model is introduced for joint modelling of the vectors of predictors for the mean and covariance subsuming the joint modelling strategy for mean and variance heterogeneity, Gabriel's antedependence models, Dempster's covariance selection models and the class of graphical models. The likelihood function and maximum likelihood estimators of the covariance and the mean parameters are studied when the observations are normally distributed. Applications to modelling nonstationary dependence structures and multivariate data are discussed and illustrated using real data. A graphical method, similar to that based on the correlogram in time series, is developed and used to identify parametric models for nonstationary covariances. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for determining the Thévenin equivalent parameters of a power system at a node. A graphical method for deriving Thévenin equivalent parameters using online PMU measurements is developed based on representing and manipulating the measured voltage phasors in the complex voltage plane. The proposed method has the ability to use a large numbers of measurements for individual calculation thus providing greater tolerance of bad and erroneous measurements. The method is both computationally efficient and displays improved performance over similar methods when implemented using actual PMU data. [ABSTRACT FROM AUTHOR]

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches. [ABSTRACT FROM AUTHOR]

During the design of a wheeled mobile robot, the problem of the proper selection of the parameters of its motor controllers was encountered. Knowing the parameters of the robot's Permanent Magnet Direct Current (PMDC) motors, precise tuning of the controllers can be performed, which then results in improved robot dynamics. Among many methods of parametric model identification, optimization-based techniques, particularly genetic algorithms, have gained more and more interest recently. The articles on this topic present the results of parameter identification, but they do not refer to the search ranges for individual parameters. With too wide a range, genetic algorithms do not find solutions or are time-inefficient. This article introduces a method for determining the parameters of a PMDC motor. The proposed method performs an initial estimation of the range of searched parameters to shorten the estimation time of the bioinspired optimization algorithm. [ABSTRACT FROM AUTHOR]

Aquifer hydraulic parameters play a critical role in investigating various groundwater hydrology problems (e.g., groundwater depletion and groundwater transport), and the Theis formula for constant-rate pumping tests is commonly used to estimate them. However, the pumping rate in the field usually varies with time due to some factors, making the classical constant-rate model unsuitable for accurate parameter estimation. To address this issue, we developed a novel dimensionless-form analytical solution for variable-rate pumping tests involving piecewise-constant approximations for variable pumping rates. Analysis of the time–drawdown curves revealed that the first-step type curve was consistent with the Theis curve. However, the curves of subsequent steps deviated from the Theis curve and were associated with the first dimensionless inflection time (t1,D), which depended on the hydraulic conductivity (K) and specific storage (Ss) of the confined aquifers. On this basis, a new type curve method for estimating the aquifer K and Ss was proposed by matching the observed drawdown data with a series of type curves dependent on t1,D. Furthermore, this method can handle recovery drawdown data. We applied this method to a field site in Wuxi City, Jiangsu Province, China, by analyzing the drawdown data from four pumping tests. The hydraulic parameters estimated using this method were in close agreement with those calibrated via PEST. The calibrated K values were further validated by comparing them with lithology-based results. In summary, the geometric means of K and Ss were 6.62 m/d and 3.16 × 10−5 m−1 for the first confined aquifer and 0.92 m/d and 2.34 × 10−4 m−1 for the second confined aquifer. [ABSTRACT FROM AUTHOR]

The occurrence of missing observations is nearly unavoidable in longitudinal studies where repeated measurements are taken over time on the same subject who may miss appointments or drop out during the study period. In this article, we use the Gaussian estimating objective function to estimate the regression and correlation parameters and handle missing data using multiple imputation. The estimation of these parameters is carried out simultaneously using the iterative Newton-Raphson algorithm and the expectation-maximization algorithm. These ideas are implemented using two real data sets and both algorithms showed comparable results with respect to the standard errors of the parameters of interest. [ABSTRACT FROM AUTHOR]

The constant-discharge pumping test is a widely used aquifer characterization tool for estimating hydrogeological parameters. Various graphical and numerical solution techniques have been devised for interpretation of pumping test results. This study proposes an alternative regression-based solution approach for analyzing pumping tests conducted in nonleaky, confined aquifers. A regression model was developed by making use of the similarity solution that describes the temporal drawdown behavior at a monitoring well within a pumping period. The field curve obtained from a pumping test (drawdown versus time data) and the theoretical type curve (dimensionless drawdown versus dimensionless time) exhibit identical behavior in logarithmic scale. Correspondingly, based on the equivalence of the area above these two curves, the proposed regression equation establishes a link between a selected drawdown value and its dimensionless counterpart. Once this drawdown value is associated with the corresponding dimensionless drawdown, the explored aquifer transmissivity and storativity can be retrieved easily with the similarity solution. The proposed fit equation enables users to retrieve hydrogeological parameters effortlessly by eradicating the difficulties encountered while implementing conventional graphical or optimization-based methods. The method was tested for a large number of hypothetical noise-free and noise-perturbed time drawdown data sets, each synthesized with different aquifer parameters within a wide range, and in all these attempts the proposed algorithm achieved providing highly accurate parameter estimates. In addition, two published test cases were employed to assess the estimation performance of the method in comparison with the existing techniques. The developed solution scheme was revealed to yield results in very good agreement with those reported in previous works. Considering the accuracy of the method and the ease of its implementation, the proposed approach can be a practical tool for analyzing constant-discharge tests performed in confined aquifers. [ABSTRACT FROM AUTHOR]

Accurate and precise values of hydrodynamic parameters are needed for groundwater modeling and management. Pumping test in the aquifer is the standard method to estimate the transmissivity, hydraulic conductivity, and storage coefficient as the key hydrodynamic parameters. Analytical solutions with curve matching and numerical modeling are two methods to estimate these parameters in the aquifer. Graphical analyses are commonly applied to time-drawdown/water table data which are time-consuming and approximate. Graphical type-curve methods as promising tools are used extensively in water resources studies, while applying these methods is still new in pumping test analysis. In the current study, the first effort based on our knowledge, we have reviewed the literature type-curve graphical methods in pumping test analysis. To achieve this goal, we reviewed and compared the journal articles regarding the characteristics and capabilities of the modeling process from 2000 to 2022. We have clustered the reviewed papers into graphical, modeling, and hybrid categories. Then, a comprehensive review of the selected papers was presented to delineate the highlight of every paper. This review could guide researchers in pumping test analysis. Also, we have presented various recommendations for future research to improve the quality of hydrodynamic parameter estimation. [ABSTRACT FROM AUTHOR]

For the dewatering of deep excavation, the existing man-made waterproof curtain has a significant influence on flow response in confined aquifers; the effect of the waterproof curtain must be considered when using the field data for hydrogeological parameter estimation. In this study, a closed-form analytical solution for constant discharge pumping in a confined aquifer within a rectangular-shaped drop waterproof curtain is obtained by making use of the image method coupled with the superpose principle. A straight-lined method is presented to determine the value of the hydraulic parameters of the confined aquifer and the application of the obtained results is illustrated by the usefulness of a field pumping test in Wuhan, China. The results show that the predicted drawdowns developed by the estimated parameters are in good agreement with the measured drawdown in the field. The proposed solution and parameter estimation are reliable and can provide important help for the design of dewatering in deep foundation pit engineering. [ABSTRACT FROM AUTHOR]

This paper gives a proper study of 11 types of Weibull parameters estimation methods and their performance comparison for wind potential assessment on India's first offshore project in the Gulf of Khambhat, Gujarat. The Lidar (up to 200 m) and met mast (up to 100 m) are deployed to record wind data from Nov-2017 to Dec-2019 by the National Institute of Wind Energy (NIWE), Government of India. The estimation methods such as, modified maximum likelihood method (MMLM), maximum likelihood method (MLM), empirical method of Justus (EMJ), empirical method of Lysen (EML), graphical method (GM), first method of moment (MOM-1), second method of moment (MOM-2), third method of moment (MOM-3), alternative maximum likelihood method (AMLM), WAsP or equivalent energy method (EEM), energy pattern factor method (EPFM), found in literature survey are compared to evaluate the performance using statistical methods in the offshore region of India. The mathematical expressions of methods have been coded and implemented by MATLAB software through a proper data analysis. There is a need to explore more appropriate probabilistic distribution than Weibull distribution. The MMLM, MLM, and MOM versions perform nicely, followed by the empirical method of Justus and Lysen. The GM method underperformed and that needs modification. • The MMLM and MLM outperformed among all methods, followed by MOM, EMJ, and EML. • The WPD estimated by GM and AMLM gives misprediction over observed WPD, as compared to other methods. • The GM is one of the traditional methods, that underperformed among all methods followed by AMLM, and WAsP. • The WAsP method is only useful at high wind speed, while the lower windy region may give a significant error. [ABSTRACT FROM AUTHOR]

Statistical distribution approaches have been developed to describe wind data due to the intermittent and unpredictable nature of wind speed. The Weibull distribution with two parameters is thought to be the most accurate distribution for modeling wind data. This study seeks wind energy assessment via searching for the optimal estimation of the Weibull parameters. For this target, analytical and heuristic methods are investigated. The analytical methods involve the maximum likelihood, moment, energy pattern factor, and empirical methods, while the heuristic optimization algorithms include particle warm optimization and the Aquila optimizer (AO). Both analytical and heuristic methods are assessed together to fit the probability density function of wind data. In addition, nine models are submitted to find the most appropriate model to represent wind energy production. The error between actual and estimated wind energy density is computed to the model for each study site which has less error of energy. The fit test is performed with real data for the Zafarana and Shark El-Ouinate sites in Egypt for a year. Additionally, different indicators of fitness properties are assessed, such as the root mean square error, determination coefficient (R2), mean absolute error, and wind production deviation. The simulation results declare that the proposed AO optimization algorithm offers greater accuracy than several optimization algorithms in the literature for estimating the Weibull parameters. Furthermore, statistical analysis of the compared methods demonstrates the high stability of the AO algorithm. Thus, the proposed AO has greater accuracy and more stability in the obtained outcomes for Weibull parameters and wind energy calculations. [ABSTRACT FROM AUTHOR]