Objective: Limited evidence exists concerning whether combined pharmacotherapy is more effective than monotherapy for increased smoking abstinence and post-cessation weight gain prevention. This research investigated the effect of combined pharmacotherapy on smoking abstinence and post-cessation weight change. Methods: A meta-analytic review of Randomized Controlled Trials (RCTs) published between January 1990 and July 2016 was conducted across PubMed, International Pharmaceutical Abstracts, Web of Science, and Cochrane Library. Aggregate fixed effects were estimated for continuous abstinence and mean post-cessation weight change. Keyword search terms included: “smoking cessation,” “naltrexone”, “varenicline”, and “bupropion”. Results: Eight RCTs with 2,513 participants were included. Aggregate fixed effect estimates revealed an increase in continuous smoking abstinence (OR = 1.81, p < .001) and mean decrease in post-cessation weight change (-.15 kg, p = .001). Decreased weight change was observed at 6-8 weeks follow-up (-.14 kg, p = .02). Increased mean weight change was observed among varenicline plus nicotine patch abstainers (.21 kg, p = .01), whereas bupropion plus NRT pharmacotherapies showed decreased mean weight change (-.15 kg, p = .01). Conclusion: Combination pharmacotherapy generates increased smoking abstinence and small short-term post-cessation weight change among abstainers, particularly among bupropion plus NRT when compared against varenicline plus nicotine patch.

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.