Statistical distributions are very useful in describing and predicting real world phenomena. Consequently, the choice of the most suitable statistical distribution for modeling given data is very important. In this paper, we propose a new class of lifetime distributions called the Weibull Topp-Leone Generated (WTLG) family. The proposed family is constructed via compounding the Weibull and the Topp-Leone distributions. It can provide better fits and is very flexible in comparison with the various known lifetime distributions. Several general statistical properties of the WTLGfamily are studied in details including density and hazard shapes, limit behavior, mixture representation, skewness and kurtosis, moments, moment generating function, incomplete moment. Di erent methods have been used to estimate its parameters. The performances of the estimators are numerically investigated. We have discussed inference on the new family based on the likelihood ratio statistics for testing some lifetime distributions. We assess the performance of themaximumlikelihood estimators in terms of the biases and mean squared errors by means of a simulation study. The importance and flexibility of the new family are illustrated by means of two applications to real data sets.

In this study, we introduce a new model called the Extended Exponentiated PowerLindley distribution which extends the Lindley distribution and has increasing, bathtub andupside down shapes for the hazard rate function. It also includes the power Lindley distributionas a special case. Several statistical properties of the distribution are explored, such as thedensity, hazard rate, survival, quantile functions, and moments. Estimation using the maximumlikelihood method and inference on a random sample from this distribution are investigated. Asimulation study is performed to compare the performance of the di® erent parameter estimatesin terms of bias and mean square error. We apply a real data set to illustrate the applicabilityof the new model. Empirical ¯ ndings show that proposed model provides better ¯ ts than otherwell-known extensions of Lindley distributions.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) strains have been listed as one of the major clinical concerns. Objectives: We investigated CPKP isolates from non-tertiary hospitals tofinddisseminated clones and analyze extensive phenotypic and genetic diversity in this study. Methods: In this cohort study, a total of 49 CRKP isolates from 3 hospitals in the same region were collected in 2021. The prevalence and antimicrobial susceptibility patterns were analyzed. Clinical data were retrieved from electronic medical record systems. The molecular types, antimicrobial resistance (AMR) profiles, plasmid replicons, and virulence factors were analyzed. The maximumlikelihood phylogenetic tree and transmission networks were constructed using single-nucleotide polymorphisms (SNPs). Results: The median age of patients (N = 49) was 66. 0 years, and 85. 7% were male. The mostcommonCRKP infection was nosocomial pneumonia (75. 5%), followed by bacteremia (10. 2%). More than 53% of isolates were resistant to ceftazidime-avibactam (CAZ/AVI). Forty-five isolates were successfully sequenced,the predominant carbapenem-resistant gene was blaKPC-2 (93. 3%). The 30-day mortality in our cohort was 24. 5%. The most dominant sequence type (ST) was ST11 (60. 0%), followed by ST15 (13. 3%). Whole genome sequencing (WGS) analysis exhibited dissemination of ST11 strain clones, ST420, and ST15 clones, both within and outside the given hospital. Conclusions: In this surveillance study, several dissemination chains of CRKP were discovered in the hospital and the region, as ST11 was the main epidemic clone. Our findings suggest that effective infection control practices and antimicrobial stewardship are needed in non-tertiary hospitals in China.

In order to assess of suitable distribution along with statistical period length changes as well as to select the best frequency distribution to estimate peak discharge with certain occurance probability, 23 gauging station wich have the more complete data, were selected among hydrometrical gauge stations of central Alborz region. A 20 years statical period has been selected after analyzing scatter data. complete the missing data and divided the prepared data in to 10, 15 and 20 years period. in every station 11 series of 10 years’ time period, 6 series of 15 years’ time period and A sery of 20 years’ time period and in all stations 414 series were analyzed. Then the best frequency distribution was selected for statistical series by data analysis in time series (according to HYFA output) and goodness of fit of relative residual square mean (RMS). trend of statistical distribution changes with change of statistical period length is in such way that no distribution can be selected definitely and based on the best fitted distribution in 10, 15 and 20 years’ time period, three parameters log pearson (moment) with 45.4 percent, three parameters log pearson (moment) with 35.5 percent and three parameters log pearson (moment) with 47.8 percent were the best distributions respectively. Second disribution for 10, 15 and 20 years’ time periods were three parameters Pearson (moment), two parameters Gama (maximum likelihood) and two parmeters log normal (maximum likelihood), respectively. on based on selecting best distribution with moment and maximum likelihood methods separately. three parameters log pearson was the best in moment method and based on maximumlikelihood in 10 and 15 years time Periods two parameters gama distribution and in 20 years time period three parameters log normal were the best distributions.