maryam sharafi†

An important challenge in using progressive Type-II right censoring is to determine a removal scheme. It can be predetermined or randomly chosen per discrete distributions. This paper considers the random removal problem and proposes two scenarios for determining the removal vector without introducing any parameter to a model when progressively Type-II censored samples are available from the three-parameter Weibull distribution. The proposed scenarios are based on the normalized spacings with random and fixed coefficients according to progressively Type-II censored order statistics from an exponential distribution. The joint probability mass functions of removal vectors are provided as well as expected experimental time under the proposed two methods. Moreover, the maximum likelihood estimators (MLEs) and corrected maximum likelihood estimators (corrected MLEs) of parameters are obtained. The new approaches are compared with the patterns of removal derived from the discrete uniform and binomial distributions using a Monte Carlo simulation study. This comparison is based on their estimated biases, estimated mean squared errors and expected total time on the experiment. Finally, a real data example is given to show the practical applications of the paper.

Desalination of saline and brackish water as a relatively permanent resource is a reliable solution to compensate for water shortages. The Electrodialysis process, as one of the desalination methods, separates almost unsalted water from saline water and is becoming a solution for water scarcity all over the worldworldwide. Modeling methods allow provide the study of desalination of saline and brackish water by the Electro Dialysisdialysis, as well as predicting the behaviors and process patterns of these systems.  The purpose of this study is to model the Electrodialysis process by Design of Experiments and to evaluateing the GMDH neural network method in estimating the separation percentage and the output flux of the brackish water Electrodialysis cell.

The volumetric flow values in six levels (1, 2.5, 5, 10, 15, and 20 mL min-1), solute concentration in three levels (200, 500, and 1000 mg L-1), temperature in three levels (50 , 60, and 70°C), voltage at three levels (10, 20, and 30 V) and pressure at three levels (200, 400, and 800 Pa) were extracted. In order to model the effect of each above inputs on the separation percentage and output flux of brackish water Electrodialysis cells, The full factorial design and the GMDH neural network were are used to model the effect of each above inputs on the separation percentage and output flux of brackish water Electrodialysis cells. In modeling the output of the Electrodialysis cell output using the GMDH neural network, after determining the input variables, randomization, normalization and segmentation of input and output variables were performeddone. For modeling, 90% of the data (437 samples) were randomly assigned for training and 10% of the data (49 samples) were are used for validation. On the other hand, due to having specific levels for each of the inputs, it was possible to use a Full Factorial experimental design.

The linear results of statistical regression analysis table results showeshowd that there is a significant difference between all simple and interaction effects of the treatments for the response variable of separation percentage and flux at the one percent level. With decreasing volume flow and solute concentration, and increasing temperature, voltage, and pressure, the separation percentage and output flux showed a significant increase. The results of modeling with the GMDH neural network showed demonstrates that in the training step, the prediction accuracy of separation percentage (R2=0.90, MBE=-0.16 and RMSE=7.48) and flux (R2=0.79, MBE =0.001 and RMSE=0.08).  The These criteria same values in the testing step were are 0.85, -3, and 9.37 for separation percentage and 0.78, -0.008, and 0.07 for flux. Correlation of target and output data, proximity of target and output values to y = x diagram, low transgression of error values from zero value and proximity of error distribution (histogram) to normal distribution were obtained.

After determining the significant differences of all simple and interaction effects of treatments for response variables, the means were compared. It is ideal to Hhaveing more separation percentage and output flux response variables was ideal. Therefore, the results of comparing the means showed illustrate that the best volumetric flow level equal to 1 ml / min, input flow concentration of 200 mg / l, temperature of 70 ° C, input voltage of 30 volts and pressure of 800 Pascals, lead to the highest separation percentage and output flux. In addition, with decreasing volumetric flow,  and decreasing solute concentration, and increasing temperature, increasing voltage and increasing pressure, separation percentage and output flux showed a significant increaserise. Also, according to the statistical indicators for the whole data, appropriate correlation, slightly underestimation, and small error were are obtained. ThereforeThus, using the Design of Experiments and GMDH neural network has suitable accuracy for modeling desalination of brackish water using the Electrodialysis process.

One of the most critical challenges in progressively Type-II censored data is determining the removal plan. It can be fixed or random so that is chosen according to a discrete probability distribution. Firstly, this paper introduces two discrete joint distributions for random removals, where the lifetimes follow the two-parameter Weibull distribution. The proposed scenarios are based on the normalized spacings of exponential progressively Type-II censored order statistics. The expected total test time has been obtained under the proposed approaches. The parameters estimation are derived using different estimation procedures as the maximum likelihood, maximum product spacing and least-squares methods. Next, the proposed random removal schemes are compared to the discrete uniform, the binomial, and fixed removal schemes via a Monte Carlo simulation study in terms of their biases; root means squared errors of estimators and their expected experiment times. The expected experiment time ratio is also discussed under progressive Type-II censoring to the complete sampling plan.

 Determining and recognizing solid waste composition can be considered as the first and main step in planning and developing waste management processes in an area.

 The objective of this study was to investigate the production and determination of waste composition in Kermanshah School of Public Health, and evaluate the challenges ahead and present practical suggestions based on results for better solid waste management.

 Collection, separation, and weight measurement of solid wastes in the school of Public Health, and departments such as self-service, laboratories, buffet, office, and yard, were done during a working week from Saturday to Wednesday. Then the results and data were analyzed using Excel software version 2019.

 The results of data analysis, after collecting, separating, and weighing each solid waste component revealed that food waste accounts for 48% of the total solid waste production. Also, other waste components include 32% plastic, 14% paper and cardboard, 6% garden waste, and nearly 0% glass and metals, together comprising an average amount of waste produced daily from Saturday to Wednesday during a week. It was also found that the generated waste per capita was 104.29 grams per person per day. The highest and lowest amount of generated waste per capita was related to food waste with 49.45 grams per day and metal waste with 0.17 grams per day, respectively.

 Due to the predominant percentage of organic matter and the small portion of the glass and metal waste in the composition of waste produced in the school, the application of compost production methods seems to be a justifiable and economical way to dispose of the waste generated in the School of Public Health. Also, the presence of paper, cardboard, and plastic in the mentioned quantities warns us to pay attention to the recycling programs from the source.

The kappa distribution was first introduced by Mielke (1973) and Mielke and Johnson (1973) for describing and analyzing precipitation data. This distribution is positively skewed and is widely applied when studying precipitation, wind speed and the stream flow data in hydrology. The kappa distribution has some advantages over gamma and log-normal distributions in fitting historical rainfall. Data It is because, unlike the latter two distributions, it has closed forms for the cumulative distribution function and quantile function. Due to this important feature, the kappa distribution attracts the attention of several researchers. Park et al. (2009) introduced the three-parameter kappa distribution and provided a description of the mathematical properties of the distribution and estimated the parameters by three methods. Also, they illustrated its applicability for rainfall data from Seoul, Korea. In this paper, we study the distribution and the estimation methods for the parameters considered by Park et al. (2009), and propose a new estimation method. Then, we will compare these estimation methods using a Monte Calro simulation study and a real dataset.

In this scheme, first we consider the three-parameter kappa distribution and study some of its properties and then estimate the parameters of the distribution by four methods. These methods are method of moment (MM), L-moments (LM), maximum likelihood (ML) and maximum product of spacing method (MPS). Using a Monte Carlo simulation study and a real data set, performance of these methods are compared.

Comparing the performance of the proposed estimation methods in terms of bias and root of mean squares error (rmse), it can be concluded that the MPS method has a better performance due to its lower bias and rmse. The Kolmogorov-Smirnov test is applied for goodness-of-fit test in the three-parameter kappa distribution to the whole monthly rainfall data of Abali station in Tehran province. The results demonstrate that the MPSE method leads to better results than other mentioned methods.

The following conclusions were drawn from this research.The Monte Carlo simulation shows that the maximum product spacing method, which is proposed in this paper, is the best method for estimating the parameters of the three-parameter kappa distribution.The statistics and p-value of the Kolmogorov–Smirnov test show that the three-kappa distribution with the MPS method of estimation has better fit than the other methods.

In this article, first a density by the name ”The bimodal standard normal density” is introduced and denoted by bφ(z). Then, a definition for the kurtosis of bimodal densities relative to bφ(z) is presented. Finally, to illustrate the introduced kurtosis, a few examples are provided and a real data set is studied, too.

Calcareous deposits formed on the part of steel that cathodic reactions occur on their surface. The purpose of this research is an investigation of the effect of potential on crystal form of the calcareous deposits and effect of them on the corrosion resistance of the mild steel in natural seawater. The electrochemical tests such as impedance and polarization tofel tests were used in order to investigate the effect of applied cathodic potential on the corrosion resistance of mild steel in Persian gulf seawater. Also، scanning electron microscopy (SEM) and XRD analyzer were used to investigate the morphology and the type of deposits under applied cathodic protection.

The most of the results obtained about stochastic properties of generalized order statistics and their spacings in the literature are based on equal model parameters. In this paper, with less restrictive conditions on the model parameters, we prove some new multivariate likelihood ratio ordering results between two sub-vectors of GOS''s as well as two sub-vectors of $p$-spacings based on two continuous distribution functions. In particular, we apply the new results to obtain some computable bounds on the mean residual life of some unobserved progressive type II censored order statistics.

We present a new skew-normal distribution, denoted by NSN(λ). We first derive the density and moment generating function of NSN(λ). The properties of SN(λ), the known skew-normal distribution of Azzalini, and NSN(λ) are compared with each other. Finally, a numerical example for testing about the parameter in NSN(λ) is given.