Aprovecho is a non-profit research and education center dedicated to living,learning,organizing and educating to inspire a sustainable culture.Located on a forty acre land trust in the Coast Range outside of Cottage G...Aprovecho is a non-profit research and education center dedicated to living,learning,organizing and educating to inspire a sustainable culture.Located on a forty acre land trust in the Coast Range outside of Cottage Grove,Oregon,Aprovecho’s campus features a living demonstration of sustainable human settlement,organized around five core areas:food,shelter,water,forests,and energy.Aprovecho offers educational opportunities in all five of its core areas,including shelter through the Natural Building program.The Aprovecho Natural Building program trains students in the use of locally-sourced,non-toxic building materials for the construction of energy-efficient,affordable,healthy homes that work within natural communities and that enrich local economies.展开更多
Using environmental random vibration as the excitation,traditional accelerometer method,non-contact video method and non-contact laser method were employed to determine the natural frequency of Kunyu River footbridge....Using environmental random vibration as the excitation,traditional accelerometer method,non-contact video method and non-contact laser method were employed to determine the natural frequency of Kunyu River footbridge.All the results of these three methods are close to 2.70 Hz,which are concordant with each other and hence credible.展开更多
Soils are naturally radioactive, because of their mineral content. An effective dose delivered by photon emitted from natural radioactivity in soil （40K, 23SU and 232Th and their progenies） was calculated in this wo...Soils are naturally radioactive, because of their mineral content. An effective dose delivered by photon emitted from natural radioactivity in soil （40K, 23SU and 232Th and their progenies） was calculated in this work. Calculations were performed using the ORNL （Oak Ridge national laboratory） human phantom and Monte Carlo N-particle transport code MCNP-4C according to ICRPI03 recommendations. Optimum dimensions of each source were determined considering the incident photon energy. Then, these dimensions were employed in the MCNP code input for calculation of conversion factors which relate the effective dose rate and activity. The obtained factors of the 238U series, 232Th series and the 4~K in soil are 0.383, 0.314 and 0.019 nSv h-~ per Bq kg~, respectively. These results were compared with other studies and revealed that there is a good agreement exists between two sets of data. The estimation of the effective dose rates and the annual effective dose for the adult population has been derived in different regions of Iran, considering the natural radioactivity distribution in soil samples from these regions. Finally, the obtained results in this study were compared with UNSCEAR （United Nations Scientific Committee on the Effects of Atomic Radiation） 2008 report.展开更多
Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax...Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.展开更多
文摘Aprovecho is a non-profit research and education center dedicated to living,learning,organizing and educating to inspire a sustainable culture.Located on a forty acre land trust in the Coast Range outside of Cottage Grove,Oregon,Aprovecho’s campus features a living demonstration of sustainable human settlement,organized around five core areas:food,shelter,water,forests,and energy.Aprovecho offers educational opportunities in all five of its core areas,including shelter through the Natural Building program.The Aprovecho Natural Building program trains students in the use of locally-sourced,non-toxic building materials for the construction of energy-efficient,affordable,healthy homes that work within natural communities and that enrich local economies.
基金Ministry of Science and Technology of China for sponsoring the"Cooperation Research on the Dynamic Safety and Serviceability of Public Structures Servicing for Human"(No.2010DFB74280)between Beijing Institute of Technology and Ruhr-University Bochum
文摘Using environmental random vibration as the excitation,traditional accelerometer method,non-contact video method and non-contact laser method were employed to determine the natural frequency of Kunyu River footbridge.All the results of these three methods are close to 2.70 Hz,which are concordant with each other and hence credible.
文摘Soils are naturally radioactive, because of their mineral content. An effective dose delivered by photon emitted from natural radioactivity in soil （40K, 23SU and 232Th and their progenies） was calculated in this work. Calculations were performed using the ORNL （Oak Ridge national laboratory） human phantom and Monte Carlo N-particle transport code MCNP-4C according to ICRPI03 recommendations. Optimum dimensions of each source were determined considering the incident photon energy. Then, these dimensions were employed in the MCNP code input for calculation of conversion factors which relate the effective dose rate and activity. The obtained factors of the 238U series, 232Th series and the 4~K in soil are 0.383, 0.314 and 0.019 nSv h-~ per Bq kg~, respectively. These results were compared with other studies and revealed that there is a good agreement exists between two sets of data. The estimation of the effective dose rates and the annual effective dose for the adult population has been derived in different regions of Iran, considering the natural radioactivity distribution in soil samples from these regions. Finally, the obtained results in this study were compared with UNSCEAR （United Nations Scientific Committee on the Effects of Atomic Radiation） 2008 report.
文摘Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.
