A new five‐parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution′s structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type‐II censored data is used to derive the new distribution′s parameter estimators. The percentile bootstrap and bootstrap‐t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID‐19 real‐world data sets from France and the United Kingdom.

The goal of this cross‐sectional observational study was to assess dental students’ satisfaction regarding team‐based learning (TBL) methodology in prosthodontics courses taught at College of Dentistry, Princess Nourah bint Abdulrahman University, Saudi Arabia. Undergraduate dental students at second, third, fourth, and fifth years were taught prosthodontics courses through traditional and TBL pedagogies. TBL sessions consisted of preparation, readiness assurance, and application. At the end of each prosthodontics course, the students were asked to complete a self‐administered questionnaire that was divided into four sections to assess the effect of TBL on the following parameters: information acquisition, interpersonal skills improvement, classroom environment, and the students‐instructors interaction. The responses of the questionnaire followed the Likert scoring method (scaled from 1 to 5). The t‐test and ANOVA statistical analyses were performed using SPSS. Results. The response rate to the questionnaire was 86%. There were a significant relationship and correlation between TBL pedagogy and student satisfaction (P values ≤ 0.05) for all levels. The means of the responses for the second and fifth years were 4.36 and 4.56, respectively, where the means for the third and fourth years were 3.54 and 3.59, respectively. The parameter notably affected by TBL was interpersonal skills enhancement. All students strongly agreed that TBL enhances personal flexibility and boosts their self‐esteem. Conclusion. Students showed positive perceptions about TBL pedagogy in terms of active engagement, knowledge acquisition, and improvement of interpersonal skills leading to more efficient learning outcome.

In the field of biomedical research, data characteristics often exhibit significant variability, challenging the applicability of classical probability distributions for biomedical data modeling. In this research, we introduce a novel four‐parameter distribution called the odd beta prime Kumaraswamy (OBPK) distribution, derived from the odd beta prime generalized family of distributions and the Kumaraswamy distribution. This distribution exhibits greater flexibility compared with the traditional Kumaraswamy distribution. Importantly, the OBPK distribution offers symmetrical, right‐skewed, left‐skewed, bathtub, N‐shaped, J‐shaped, and reversed J‐shaped densities, as well as varied hazard functions, including increasing, decreasing, bathtub, increasing‐decreasing, upside‐down bathtub, and N‐shaped forms. These curvature patterns make it particularly useful for modeling biomedical data. We derive some basic properties of the OBPK distribution. Parameters are estimated using the maximum likelihood approach, and the performance of various estimators is assessed via Monte Carlo simulations. Finally, four applications to real data on COVID‐19 mortality rates from different countries are evaluated to demonstrate the importance of the OBPK model over other extended versions of the Kumaraswamy models. The empirical findings suggest that the OBPK model outperforms other extended versions of the Kumaraswamy models in these applications.

American scientific publishing group

The LINEX loss function, which climbs exponentially with one-half of zero and virtually linearly on either side of zero, is employed to analyze parameter analysis and prediction problems. It can be used to solve both underestimation and overestimation issues. This paper explained the Bayesian estimation of mean, Gamma distribution, and Poisson process. First, an improved estimator for is provided (which employs a variation coefficient). Under the LINEX loss function, a better estimator for the square root of the median is also derived, and an enhanced estimation for the average mean in such a negatively exponential function. Second, giving a gamma distribution as a prior and a likelihood function as posterior yields a gamma distribution. The LINEX method can be used to estimate an estimator using posterior distribution. After obtaining , the hazard function and the function of survival estimators are used. Third, the challenge of sequentially predicting the intensity variable of a uniform Poisson process with a linear exponentially (LINEX) loss function and a constant cost of production time is investigated using a Bayesian model. The APO rule is offered as an approximation pointwise optimal rule. LINEX is the loss function used. A variety of prior distributions have already been studied, and Bayesian estimation methods have been evaluated against squared error loss function estimation methods. Finally, compare the results of Maximum Likelihood Estimation (MLE) and LINEX estimation to determine which technique is appropriate for such information by identifying the lowest Mean Square Error (MSE). The displaced estimation method under the LINEX loss function was also examined in this research, and an improved estimation was proposed.