This paper presents the application of Bat and Cuckoo optimization algorithm methods to forecast Global CO
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emerged from energy consumption. The models are developed in two forms (linear and exponential) and used to estimate to develop Global CO2 emission model values based on the uses global oil, natural gas, coal, primary energy consumption. The available data are partly used for finding optimal, or near optimal values of weighting parameters (1980–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated with mean squared error (MSE), root mean squared error (RMSE), Mean absolute error (MAE). According to the simulation results obtained, there is a good agreement between the results obtained from BA Global CO_2 emission models (BA-GCO_2) and COA Global CO_2 emission models (COA-GCO_2) but COA- exponential model outperformed the other models. The modeling approach recommended a helpful and reliable method for forecasting global climate changes and environmental decision making.
The article provides a method for forecasting and climate policy decision making.
The method presented in this article can be useful for experts, policy planners and researchers who study greenhouse gases.
The analysis obtained herein by Metaheuristic Algorithms solver can serve as a standard benchmark for other researchers to compare their analysis of the other methods using this dataset.
In this paper, we develop a function of population, GDP, import, and export by applying a hybrid bat algorithm (BAT) and artificial neural network (ANN). We apply these methods to forecast oil consumption in Iran. For this purpose, an improved artificial neural network (ANN) structure, which is optimized by the BAT is proposed. The variables between 1980 and 2017 were used, partly for installing and testing the method. This method would be helpful in forecasting oil consumption and would provide a level playing field for checking the energy policy authority impacts on the structure of the energy sector in an economy such as Iran with high economic interventionism by the government. The result of the model shows that the findings are in close agreement with the observed data, and the performance of the method was excellent. We demonstrate that its efficiency could be a helpful and reliable tool for monitoring oil consumption.
Introduction:
The prevalence of carbapenem resistance in Acinetobacter baumannii (A. baumannii) has been increased in worldwide. Thus therapeutic options are extremely limited. We performed a systematic review to evaluate of phenotypic and genotypic carbapenem resistance in A.baumannii reported in Iran.
Methods:
We systematically searched Pub Med, Web of Science direct and Google scholar databases to identify studies addressing the carbapenem resistance of A. baumannii. From the first 71 papers. Selected papers were published between 2005 and November 2016 .Although sample collection year, between 2002 and 20116. To estimate the prevalence of the DerSimonian and Laird randomized models, 95% confidence interval was used. For heterogeneity check, I2 was used. The Egger test was used to check the propagation bias.
Results:
Analysis of data exposed that there was an increase in resistance to carbapenems from 4.5% to 2005 year until 100% to 2016 year prevalence rate 65.4 (95% CI: 58.8 – 71.6).
Conclusion:
According to result of this study, the rate of resistance to carbapenem in A.baumannii the increasing in Iran. Present of carbapenem resistant isolates are major concern because carbapenems are main drug against MDR isolates.
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