Purpose. Group B Streptococcus (GBS) is one of the major pathogens in severe materno-neonatal infections. We aimed to describe the clinical and molecular characteristics of GBS isolates causing infections in 45 maternal and 50 neonatal subjects, collected from eight healthcare centres in mainland China over the period 2010– 2017. Methodology. The phenotypic and genotypic features of the GBS isolates, including capsular polysaccharide (cps) serotypes, pilus island (PI) genes and antibiotic resistance profiles and genes, were characterized by both conventional and molecular methods. The clonal relationship between these strains was investigated using multilocus sequence typing (MLST). Results. Of the 95 isolates, the predominant serotypes were III (51, 53.7 %), V (13, 13.7 %) and Ib (13, 13.7 %). All GBS strains carried at least one pilus island, with 32.6 % carrying PI-2b and 67.4 % PI-2a, singly or in combination. The most frequently-detected pilus island pattern was the combination of PI-1 and PI-2a, accounting for 56.8 % (54 isolates), followed by PI-1 combined with PI-2b (28, 29.5 %), PI-2a (10, 10.5 %) and PI-2b (3, 3.2 %). The strains were classified into 17 individual sequence types, and further clustered into six clonal complexes (CCs). A high prevalence of CC17/PI-1 and PI-2b (17, 34.0 %) was detected in 50 GBS isolates causing neonatal infections. No strain was resistant to penicillin, ampicillin, ceftriaxone or vancomycin, whereas 78.9, 76.8 and 81.5 % were resistant to erythromycin, clindamycin and tetracycline, respectively. Conclusion. Our study highlights the high genotypic diversity of GBS strains causing materno-neonatal infections, and the CC17/PI-1 and PI-2b sublineages should be noted in neonatal infections.

This paper deals with stochastic comparisons of two parallel (series) systems with Type II half logistic-resilience scale (TIIHL-RS) distribution components with different baseline distribution functions. Under the conditions of interdependency and independency, the research shows that the system performance is better (worse) with the stronger component heterogeneity in the parallel (series) system under the usual stochastic order and the (reversed) hazard rate order.

This paper investigates the issue of stochastic comparison of multi-active redundancies at the component level versus the system level. Based on the assumption that all components are statistically dependent, in the case of complete matching and nonmatching spares, we present some interesting comparison results in the sense of the hazard rate, reversed hazard rate and likelihood ratio orders, respectively. And we also obtain two comparison results between relative agings of resulting systems at the component level and the system level. Several numerical examples are provided to illustrate the theoretical results.

In the reliability analysis of multicomponent stress-strength models, it is typically assumed that strengths are either independent or dependent on a common stress factor. However, this assumption may not hold true in certain scenarios. Therefore, accurately estimating the reliability of the stress-strength model becomes a significant concern when strengths exhibit interdependence with both each other and the common stress factor. To address this issue, we propose an Archimedean copula (AC)-based hierarchical dependence approach to effectively model these interdependencies. We employ four distinct semi-parametric methods to comprehensively estimate the reliability of the multicomponent stress-strength model and determine associated dependence parameters. Furthermore, we derive asymptotic properties of our estimator and demonstrate its effectiveness through both Monte Carlo simulations and real-life datasets. The main original contribution of this study is the first attempt to evaluate the reliability problem under dependent strengths and stress using a hierarchical AC approach.

This study investigates statistical inference for a heterogeneous competing risks model under an improved adaptive Type-II progressive censoring scheme, which effectively controls testing time while ensuring sufficient failure data. Assuming the latent lifetimes of distinct failure causes are independent and follow Chen and Weibull distributions, we develop both frequentist and Bayesian approaches to derive point and interval estimates for the unknown parameters. Interval estimators include approximate confidence intervals, bootstrap confidence intervals, and highest posterior density credible intervals. Under the Bayesian framework, Markov Chain Monte Carlo techniques are utilized to obtain numerical solutions under the squared error loss function, assuming independent gamma priors. Extensive Monte Carlo simulations and a real-world data application are presented to demonstrate the efficacy and practical utility of the proposed methodologies.