Power plant performance can decrease along with its life span, and move away from the design and commissioning targets. Maintenance issues, operational practices, market restrictions, and financial objectives may lead to that behavior, and the knowledge of appropriate actions could support the system to retake its original operational performance. This paper applies unsupervised machine learning techniques to identify operating patterns based on the power plant’s historical data which leads to the identification of appropriate steam generator efficiency conditions. The selected operational variables are evaluated in respect to their impact on the system performance, quantified by the Variable Importance Index. That metric is proposed to identify the variables among a much wide set of monitored data whose variation impacts the overall power plant operation, and should be controlled with more attention. Principal Component Analysis (PCA) and k-means++ clustering techniques are used to identify suitable operational conditions from a one-year-long data set with 27 recorded variables from a steam generator of a 360MW thermal power plant. The adequate number of clusters is identified by the average Silhouette coefficient and the Variable Importance Index sorts nine variables as the most relevant ones, to finally group recommended settings to achieve the target conditions. Results show performance gains in respect to the average historical values of 73.5% and the lowest efficiency condition records of 68%, to the target steam generator efficiency of 76%.

These datasets include the relative abundances of planktonic foraminifera, relative abundances of drilled specimens, stable isotope data and age model results of sediment core SAT-048A.

Key Points: For the first time, drill holes in planktonic Foraminifera from the western South Atlantic were compared with paleoceanographic proxies. Paleoproductivity of the western South Atlantic was reconstructed using assemblage and geochemical indexes for the last 46 kyr. Paleoproductivity estimates and bioerosion rates of planktonic foraminiferal tests show a strong relation during late Quaternary.

Oral and oropharyngeal cancer is considered a public health problem in several countries due to its high incidence and mortality rate.

Objective:

This study aimed to analyze oral and oropharyngeal cancer mortality in Uruguay from 1997 to 2014 by age, sex and country region.

Methodology:

A time series ecological study using secondary data was performed. Data on mortality due to oral and oropharyngeal cancers were obtained from the Vital Statistics Department of Uruguay's Ministry of Public Health.

Results:

The cumulative mortality rate due to oral and oropharyngeal cancer over the study period was of 19.26/100,000 persons in women and 83.61/100.000 in men, with a mean annual rate of 1.75/100,000 in women and 7.60/100,000 in men. Mortality rate from both sites during the study period was 4.34 times higher in men than in women. Malignant neoplasms of other parts of the tongue and base of tongue showed the highest mortality rate. The means of the annual coefficients of deaths were higher for the age groups between 50 and 69 years. Higher mortality rates of oral and oropharyngeal cancer were observed in Artigas (4.63) and Cerro Largo (3.75).

Conclusions:

Our study described a high mortality rate for oral and oropharyngeal cancer in Uruguay from 1997 to 2014. According to the country's health department, men, tongue cancer, and oral cavity had higher mortality rates, with some variation. Prevention strategies with control of risk factors and early diagnosis are necessary to improve survival in the Uruguayan population.