No abstract

Nowadays, one of the biggest concern of human being is greenhouse gas emission, especially carbon dioxide emission in developed and under-developing countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO2 emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay. The studied region is modelled based on the available input data in terms of Million tons including oil (Million tons), gas (Million tons oil equivalent), coal (Million tons oil equivalent), $R_{ew}$ (Million tons oil equivalent), and Gross Domestic Product (GDP) in terms of billion US dollars.} Moreover, the available patents in the fields of climate change mitigation in six Latin American countries namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay has been reviewed and analyzed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies which account for 60% and 72% respectively.

Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO 2 emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay. The studied region is modelled based on the available input data in terms of million tons including oil (million tons), gas (million tons oil equivalent), coal (million tons oil equivalent), R e w (million tons oil equivalent) and Gross Domestic Product (GDP) in terms of billion U.S. dollars. Moreover, the available patents in the field of climate change mitigation in six Latin American countries, namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay, have been reviewed and analysed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies, which account for 60% and 72%, respectively.

Recently, energy‐related CO2 emissions are considered as one of the most crucial issues and are promptly augmented due to further urbanization. In this paper, in order to model and calculate yearly CO2 emission, an artificial neural network is used. For the first time, the IWO‐SVM method has been applied in modeling energy‐related CO2 emissions. In this regard, consumption of different energy sources such as renewable energy, natural gas, coal, and oil, and GDP of the G8 countries in various years (from 1990 to 2016) are regarded as input in the present study. For the aim of evaluating the exact ability of the SVM and SVM‐IWO models, the performance of these models in three different modes is carried out on the basis of the number of data in the test and train sections. For this purpose, implementations are split into three categories (a = 80% of the data for the train section and 20% for the test section; b = 70% of the data for the train section and 30% for the test section; and c = 60% of the data for the train section and 40% for the test section). Furthermore, five scenarios were selected on the basis of the number of input parameters and input parameters for achieving the best model. As indicated in the results in all scenarios, the correlation of the model with the hybrid invasive weed algorithm based on SVM is more favorable than that in the support vector machine model, due to better training of the SVM‐IWO model than the SVM model. Moreover, the technological orientations of the G8 countries to mitigate CO2 emissions are determined through patent analysis. While the patents have essential information, investigating the published patent by a country could be helpful for determination of technological orientations. Hence, all published patents by these countries are extracted and deeply analyzed. In the next step, to find out main technological approaches, all patents and their intents have been studied. Eventually, the technological life cycles and trends of each main technology are drawn.

In this paper, the numerical study on mechanical behavior of materials reinforced with shape memory alloys (SMAs) in framework of continuum damage mechanics has been investigated. The investigated structure is an aluminum notched piece reinforced with SMA under tension loading. Simulation of the structure has been conducted with nonlinear finite element method. In numerical simulations, the SMA is embedded in the aluminum material and it is assumed that there is no slip between the aluminum and SMAs. To properly account for the mechanical behavior with damage effects, Lemaitre constitutive model via UMAT code have been developed. Simulation results were verified by experimental results. The Brinson model is used to consider the thermomechanical behavior of SMA. The simulation results showed that the mechanical behavior of aluminum with reinforced aluminum is rather different. Also, the presence of SMAs in the notched piece leads to increasing the energy absorption, piece fracture in higher loadings and a decreasing in the damage growth rate.