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A method of estimating the annual wind energy potential of a selected site using short term measurements related to one year's recorded wind data at another reference site is presented. The proposed method utilizes the 1-year measured wind speed of one site to extrapolate the annual wind speed at a new site, using an artificial neural network (ANN). In this study, concurrent measurements from target and reference sites over periods of 1-month and 2-month were used to 'train' the ANN. Topographical details of other meteorological data are not required for this approach. After derivation of the simulated wind speed time series for the target site, its mean value and its corresponding Weibull distribution parameters are calculated. The derived Weibull distribution of the simulated wind speed is used to make an assessment of the annual wind energy resource in the new area with respect to a particular wind turbine model. Three pairs of measuring stations in the southwest of Ireland were examined, where the wind potential is high and technically exploitable. Analysis of the measurements showed a reasonable cross-correlation coefficient of the wind speed between the sites. Results indicate that with this method, only a short time period of wind data acquisition in a new area might provide the information required for a satisfactory assessment of the annual wind energy resource. To evaluate the accuracy of the method, simulation results of the 1-month and 2-month training periods are compared to the corresponding actual values recorded at the sites. Also, a comparison with the results of a commercial wind energy assessment software package is presented showing similar results. Keywords: Wind; Energy assessment; Correlation; Simulation; Neural networks

Background: The effect of delivering nutrition at different calorie levels during critical illness is uncertain, and patients typically receive less than the recommended amount., Methods: We conducted a multicenter, double-blind, randomized trial, involving adults undergoing mechanical ventilation in 46 Australian and New Zealand intensive care units (ICUs), to evaluate energy-dense (1.5 kcal per milliliter) as compared with routine (1.0 kcal per milliliter) enteral nutrition at a dose of 1 ml per kilogram of ideal body weight per hour, commencing at or within 12 hours of the initiation of nutrition support and continuing for up to 28 days while the patient was in the ICU. The primary outcome was all-cause mortality within 90 days., Results: There were 3957 patients included in the modified intention-to-treat analysis (1971 in the 1.5-kcal group and 1986 in the 1.0-kcal group). The volume of enteral nutrition delivered during the trial was similar in the two groups; however, patients in the 1.5-kcal group received a mean (±SD) of 1863±478 kcal per day as compared with 1262±313 kcal per day in the 1.0-kcal group (mean difference, 601 kcal per day; 95% confidence interval [CI], 576 to 626). By day 90, a total of 523 of 1948 patients (26.8%) in the 1.5-kcal group and 505 of 1966 patients (25.7%) in the 1.0-kcal group had died (relative risk, 1.05; 95% CI, 0.94 to 1.16; P=0.41). The results were similar in seven predefined subgroups. Higher calorie delivery did not affect survival time, receipt of organ support, number of days alive and out of the ICU and hospital or free of organ support, or the incidence of infective complications or adverse events., Conclusions: In patients undergoing mechanical ventilation, the rate of survival at 90 days associated with the use of an energy-dense formulation for enteral delivery of nutrition was not higher than that with routine enteral nutrition. (Funded by National Health and Medical Research Institute of Australia and the Health Research Council of New Zealand; TARGET ClinicalTrials.gov number, NCT02306746 .).