Estimation of Mean Time between Failures of a Two Tandem Mill of a Sugar Plant

When minutes of down-time can negatively impact the bottom line of a business, it is crucial that the physical infrastructure supporting be reliable. The equipment reliability can be achieved with a solid understanding of mean time between failures. Mean time between failures (MTBF) has been used for years as a basis for various maintenance decisions supported by various methods and procedures for lifecycle predictions. To quantifying a maintainable system or reliability we can use MTBF. For developing the mean time between failures model we can use make use of Poisson distribution, Weibull model and Bayesian model. In this paper we will be talking about complexities and misconceptions of MTBF and clarify criteria that need to be consider in estimating MTBF in a sequential manner. This paper sheds light on MTBF using examples throughout in an effort to simplify complexity. Keywords: MTBF, Two Tandem Mill, Sugar Mill, Reliability, Maintenance

Hybrid Prepreg Tapes for Composite Manufacturing: A Case Study

The aim of this research was the preparation and characterization of hybrid prepreg tapes from glass multifilament roving (circular cross-section). The fiber, roving, and tape strength distribution was characterized by exploratory data analysis tools (especially quantile-quantile plot) and modeled by the three parameters’ Weibull distribution. For estimation of Weibull model parameters, the noniterative technique based on the so-called Weibull moments was used. It was shown that the prepared hybrid prepreg tapes prepared by controlled mechanical spreading technology developed by the authors improved mechanical tensile properties and can be used for the preparation of composites of complicated forms by robotic winding.

Brand Awareness via Online Media: An Evidence Using Instagram Medium with Statistical Analysis

Online marketing refers to the practices of promoting a company’s brand to its potential customers. It helps the companies to find new venues and trade worldwide. Numerous online media such as Facebook, YouTube, Twitter, and Instagram are available for marketing to promote and sell a company’s product. However, in this study, we use Instagram as a marketing medium to see its impact on sales. To carry out the computational process, the approach of linear regression modeling is adopted. Certain statistical tests are implemented to check the significance of Instagram as a marketing tool. Furthermore, a new statistical model, namely a new generalized inverse Weibull distribution, is introduced. This model is obtained using the inverse Weibull model with the new generalized family approach. Certain mathematical properties of the new generalized inverse Weibull model such as moments, order statistics, and incomplete moments are derived. A complete mathematical treatment of the heavy-tailed characteristics of the new generalized inverse Weibull distribution is also provided. Different estimation methods are discussed to obtain the estimators of the new model. Finally, the applicability of the new generalized inverse Weibull model is established via analyzing Instagram advertising data. The comparison of the new distribution is made with two other models. Based on seven analytical tools, it is observed that the new distribution is a better model to deal with data in the business, finance, and management sectors.

The lifetime analysis of the Weibull model based on Generalized Type-I progressive hybrid censoring schemes

<abstract><p>In this study, we estimate the unknown parameters, reliability, and hazard functions using a generalized Type-I progressive hybrid censoring sample from a Weibull distribution. Maximum likelihood (ML) and Bayesian estimates are calculated using a choice of prior distributions and loss functions, including squared error, general entropy, and LINEX. Unobserved failure point and interval Bayesian predictions, as well as a future progressive censored sample, are also developed. Finally, we run some simulation tests for the Bayesian approach and numerical example on real data sets using the MCMC algorithm.</p></abstract>

Time To Discharge and Its Predictors Among Admitted Patients in Felege Hiwot Comprehensive Specialized Hospital North-West Ethiopia, a Prospective Cohort Study

Background Hospital long stay is a serious social and economic problem, leading to elevated medical expenses, increased workload, and financial burden. Patients’ discharge time was different in admitted patients in hospitals. In Ethiopia, there was limited evidence why the patients' length of stay is different in roughly the same condition. Objective This study aimed to determine a time to discharge and its predictors among admissions in Felege Hiwot Hospital in Bahir Dar, Northwest Ethiopia, 2020 G.C Methods Prospective cohort study was conducted among 812 systematically selected study participants. The data collected using standardized questionary was entered into EPI DATA and exported into STATA for analysis. Appropriate descriptive statistics were stated. Incidence density to estimate time to discharge, and life table and Kaplan Meier curve to determine the cumulative survival of patients in the wards were used. Cox proportional hazard (Weibull) model was used to examine predictors of discharge. Variables from bivariate analysis with p<0.25 were a candidate for the final model. Adjusted Hazard Ratio with their 95% CI at p<0.05 was used to predict discharge. Model fitness was checked. Results A total of 812 admitted patients participated in the study giving a total response rate of 95%. Fifty-five presents were females; the median age was 32. The total times at admission wards in medical were 1827and in surgical 2050 days. The incidence densities of discharge in medical and surgical wards were 361/1827 and 359/2050-person days respectively. The median time to discharge of all participant admission was four days. Predictors of discharge were age (AHR 1.04(95% CI 1,02-1.06)), family care (AHR .69(.51-.94)), admission source (AHR 2.07(1.39-2.90)), admission time (AHR .57(.49-.67)). Lab.-result delay (AHR .67(.55-.81)), radiological result delay (AHR .78 (.64-.95)), presence of comorbidity (AHR.50(.42-.60)), disease burden group (AHR 2.15(1.84-2.66)), specialty (AHR 0.66 (0.56-0.78)), nurse-to-patient ratio (AHR 1.95 (1.57-2.43)), and medication adherence (AHR 2.83(2.29-3.51). Conclusion Surgical admitted patients discharged in longer time than medical. It is important to emphasize good communication of respective specialties on the lab. and radiology result test waiting time and also teaching the benefit of medication adherence to the patients.

Height-diameter allometry for tropical forest in northern Amazonia

Height measurements are essential to manage and monitor forest biomass and carbon stocks. However, accurate estimation of this variable in tropical ecosystems is still difficult due to species heterogeneity and environmental variability. In this article, we compare and discuss six nonlinear allometric models parameterized at different scales (local, regional and pantropical). We also evaluate the height measurements obtained in the field by the hypsometer when compared with the true tree height. We used a dataset composed of 180 harvested trees in two distinct areas located in the Amapá State. The functional form of the Weibull model was the best local model, showing similar performance to the pantropical model. The inaccuracy detected in the hypsometer estimates reinforces the importance of incorporating new technologies in measuring individual tree heights. Establishing accurate allometric models requires knowledge of ecophysiological and environmental processes that govern vegetation dynamics and tree height growth. It is essential to investigate the influence of different species and ecological gradients on the diameter/height ratio.

The Arctan-X Family of Distributions: Properties, Simulation, and Applications to Actuarial Sciences

The purpose of this paper is to investigate a new family of distributions based on an inverse trigonometric function known as the arctangent function. In the context of actuarial science, heavy-tailed probability distributions are immensely beneficial and play an important role in modelling data sets. Actuaries are committed to finding for such distributions in order to get an excellent fit to complex economic and actuarial data sets. The current research takes a look at a popular method for generating new distributions which are excellent candidates for dealing with heavy-tailed data. The proposed family of distributions is known as the Arctan-X family of distributions and is introduced using an inverse trigonometric function. For the specific purpose of the show of strength, we studied the Arctan-Weibull distribution as a special case of the developed family. To estimate the parameters of the Arctan-Weibull distribution, the frequentist approach, i.e., maximum likelihood estimation, is used. A rigorous Monte Carlo simulation analysis is used to determine the efficiency of the obtained estimators. The Arctan-Weibull model is demonstrated using a real-world insurance data set. The Arctan-Weibull is compared to well-known two-, three-, and four-parameter competitors. Among the competing distributions are Weibull, Kappa, Burr-XII, and beta-Weibull. For model comparison, we used the most precise tests used to know whether the Arctan-Weibull distribution is more useful than competing models.

AN APPLICATION OF THE POISSON-WEIBULL MODEL FOR CENSURED DATA

In this paper, we proposed the Poisson-Weibull distribution for the modeling of survival data. The motivation to study this model since, in addition to generalizing the Weibull distribution, which is widely used in several areas of knowledge among them the Survival and Reliability analysis, it presents great exibility in the forms of the hazard function. The Poisson-Weibull distribution was created in a composition of discrete and continuous distributions where there is no information about which factor was responsible for the component failure, only the minimum lifetime value among all risks is observed. The maximum likelihood approach was used to estimate the parameters of the model. Also was conducted a simulation study to examine the mean, the bias, and the root of the mean square error of the maximum likelihood estimates of the proposed model according to the censoring percentages and sample sizes. The model selection criteria were also applied, in addition to graphic techniques such as TTT-Plot and Kaplan-Meier. Application to the real data set was used to illustrate the usefulnessof the distribution.

Preparation and electrostrictive properties of polyurethane filled with polypyrrole-carbon black for the energy harvesting application

The polymer composites based on electrostrictive materials with high polarization are attracting scientists because of the prospect of application in energy conversion and electromechanical performance. Polyurethane is one of a good flexible dielectric polymers which is more interesting in electromechanical applications due to its easy fabrication, high dielectric constant, and high electrostrictive coefficient. This work focuses on the dielectric properties and electrostrictive properties of Polyurethane (PU) filled with Polypyrrole-Carbon Black (PPy-CB) various the different concentrations. All polymer composites were prepared by using a film-casting process in solution with DMF solvent. The dielectric properties and conductivity of thin films were investigated by using LCR meter at 1-105 Hz. The results showed that the dielectric constant of composites significantly increases when the concentration of PPy-CB was increased. Moreover, the electrostrictive coefficient of composites also increases when the concentration of PPy-CB was increased. Furthermore, the electrical breakdown stretches were analyzed by using the Weibull model, this result will discuss the relationship when the PPy-CB fillers were used.