Bayesian spline method for assessing extreme loads on wind turbines

This study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level imposed on a turbine structure that the turbine would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic simulation studies. In general, data obtained in either case are not sufficient to represent various loading responses under all possible weather conditions. An appropriate extrapolation is necessary to characterize the structural loads in a turbine's service life. This study devises a Bayesian spline method for this extrapolation purpose, using load data collected in a period much shorter than a turbine's service life. The spline method is applied to three sets of turbine's load response data to estimate the corresponding extreme loads at the roots of the turbine blades. Compared to the current industry practice, the spline method appears to provide better extreme load assessment.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS670 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Condition-based Selective Maintenance Optimization for a Large-scale Non-Markovian System

We consider selective maintenance that repairs severely degraded units in the system consisting of massive units. Under the assumption that units degrade independently in a finite number of states, we derive a fluid model that approximates the mean behavior of the system’s health condition. Our simulation study indicates that even if only a subset of units gets repaired, the system would asymptotically become a regenerative process as the maintenance operations are repeated over time. Based on this observation, we optimize the maintenance scheduling that triggers the maintenance operations based on the fraction of units at each degradation state in order to minimize long-run maintenance costs.1

Synergistic inhibitory effect of cationic peptides and antimicrobial agents on the growth of oral streptococci

Although chlorhexidine is one of the most efficacious antimicrobial agents used for the prevention of dental caries, side effects limit its application. The effects of gaegurin 6 (GGN6), an animal-derived cationic peptide, and its derivatives PTP6 and PTP12 on the growth of oral streptococci were investigated to assess the potential of these agents for use in the prevention of dental caries. The minimal inhibitory concentrations of the peptides for inhibition of the growth of oral streptococci <(Streptococcus mutans, S. sobrinus, S. sanguis <and <S. gordonii)< ranged from 1.2 to 8.2 [mu]<M<. The peptides also exhibited marked synergistic antibacterial effects with chlorhexidine or xylitol. The most effective combinations (fractional inhibitory concentration index of 0.5) were xylitol with GGN6 against <S. gordonii <10558 and chlorhexidine with either GGN6 or PTP6 against <S. sobrinus <OMZ-175. These results indicate that cationic peptides alone or in combination with chlorhexidine or xylitol might prove effective for the inhibition of the growth of cariogenic oral streptococci in situ

A nomogram for predicting three or more axillary lymph node involvement before breast cancer surgery

Based on the American College of Surgeons Oncology Group (ACOSOG)-Z0011, a useful nomogram has been constructed to identify patients who do not require intraoperative frozen sections to evaluate sentinel lymph nodes in the previous study. This study investigated the developed nomogram by ultrasonography (US) and positron emission tomography (PET)/computed tomography (CT) as a modality. In the training set, 89/1030 (8.6%) patients had three or more positive nodes. Larger tumor size, higher grade ultrasonographic ALN classification, and findings suspicious of positive ALN on PET/CT were associated in multivariate analysis. The areas under the receiver operating characteristic curve (AUC) of the nomogram were 0.856 [95% CI 0.815-0.897] in the training set. The AUC in the validation set was 0.866 [95% CI 0.799-0.934]. Application of the nomogram to 1067 patients who met the inclusion criteria of ACOSOG-Z0011 showed that 90 (8.4%) patients had scores above the cut-off and a false-negative result was 37 (3.8%) patients. And the specificity was 93.8%, and the negative predictive value was 96.4%. The upgraded nomogram improved the predictive accuracy, using only US and PET/CT. This nomogram is useful for identifying patients who do not require intraoperative analysis of sentinel lymph nodes and considering candidates for identifying neoadjuvant chemotherapy. The patients consisted of clinical T1-2 and node-negative invasive breast cancer. The training and validation set consisted of 1030 and 781 patients, respectively. A nomogram was constructed by analyzing factors related to three or more axillary lymph node metastases. The patients who matched the ACOSOG-Z0011 criteria were selected and applied to the new nomogram.N

Development of a Nomogram to Predict the Recurrence Score of 21-Gene Prediction Assay in Hormone Receptor–Positive Early Breast Cancer

© 2019 Elsevier Inc.Introduction: A 21-gene prediction assay (Oncotype DX) is helpful to estimate benefit from adjuvant chemotherapy in patients with hormone receptor–positive, lymph node–negative early breast cancer. This study was conducted to develop a model to estimate high recurrence score (RS) using easily available clinicopathologic parameters in limited-resource countries. Patients and Methods: Hormone receptor–positive, lymph node–negative early breast cancer patients who underwent Oncotype DX were enrolled onto the training set (n = 192). The risk category range of the RS was the same as in the TAILORx study. The multivariable logistic regression model was used to identify significant variables associated with high RS. The independent validation set (n = 264) was established from patients of a different time period. Results: The median age in the training set was 47 years, and 78.0% were premenopausal. The number of patients with low RS ( 25) were 42 (22.0%), 122 (63.9%), and 27 (14.1%), respectively. High nuclear grade, no progesterone receptor expression, and high Ki-67 were associated with high RS, and these variables were used to construct the nomogram. It had significant discriminatory power in internal validation (area under the curve = 0.856) and in the validation set (area under the curve = 0.828). The calibration plot showed optimal agreement between predicted and actual probabilities in both sets. Conclusion: A nomogram was successfully developed with 3 simple parameters. The probability of high RS can be easily and conveniently estimated using our nomogram. It might be useful to determine whether or not Oncotype DX is conducted in the TAILORx era. Future large-scale prospective studies are warranted. A model to predict the recurrence scores of a 21-gene prediction assay (Oncotype DX) in patients with hormone receptor–positive, lymph node–negative early breast cancer is needed in limited-resource countries. A nomogram using a predictive model with easily available clinicopathologic parameters (nuclear grade, progesterone receptor, and Ki-67) was developed using a training set (n = 191) and was validated using 264 independent cases