48 research outputs found
Universal phase transitions of B1 structured stoichiometric transition-metal carbides
The high-pressure phase transitions of B1-structured stoichiometric
transition metal carbides (TMCs, TM=Ti, Zr, Hf, V, Nb, and Ta) were
systematically investigated using ab initio calculations. These carbides
underwent universal phase transitions along two novel phase-transition routes,
namely, B1\rightarrowdistorted TlI (TlI')\rightarrowTlI and/or
B1\rightarrowdistorted TiB (TiB')\rightarrowTiB, when subjected to pressures.
The two routes can coexist possibly because of the tiny enthalpy differences
between the new phases under corresponding pressures. Four new phases result
from atomic slips of the B1-structured parent phases under pressure. After
completely releasing the pressure, taking TiC as a representative of TMCs, only
its new TlI'-type phase is mechanically and dynamically stable, and may be
recovered.Comment: [email protected]
S1 Data -
With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women’s Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men’s Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.</div
S1 Text -
With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women’s Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men’s Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.</div
Fig 4 -
Evaluation of the CNN-BD data analysis performance (a is UT-Zap50K data set; b is Ai2 data set; c is Kaggle woman’s shoe data set; d is Kaggle man’s shoe data set).</p
Classification algorithm of BD.
With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women’s Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men’s Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.</div
Fig 5 -
Evaluation of model data labeling accuracy (a is UT-Zap50K dataset, b is Ai2 dataset, c is Kaggle woman’s shoe dataset, d is Kaggle man’s shoe dataset).</p
Fig 6 -
Comparison results of model performance (a is UT-Zap50K dataset; b is Ai2 dataset; c is Kaggle woman’s shoe dataset; d is Kaggle man’s shoe dataset).</p
Research process.
With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women’s Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men’s Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.</div
Fig 1 -
Technical composition and specific characteristics of BD (a is the technical composition; b is the characteristics).</p
Design of research environment.
With the emergence of Artificial Intelligence technology and the advancement of science and technology, the current mainstream path of social development is continuously updating and improving various industries using technology. Therefore, in order to promote the development of sneaker consumer culture, this study explores the use of technological means to improve the dissemination effect of symbolic culture in sneaker consumer culture. Firstly, the development concept and mainstream direction of sneaker consumer culture in the era of big data are discussed, and the application principle of big data technology is introduced. Then, a sneaker culture dissemination model based on big data technology is designed. Finally, the model is optimized using a Convolutional Neural Network (CNN), and its effectiveness is evaluated. The results show that the Convolutional Neural Network-Big Data (CNN-BD) model designed in this study has the highest fitting degree of 93% and a lowest fitting degree of 78% in the UT-Zap50K dataset. In the Ai2 dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 76%. In the Kaggle Women’s Shoe dataset, the highest fitting degree of the big data classification model is 92%, and the lowest is 77%. In the Kaggle Men’s Shoe dataset, the highest fitting degree of the big data classification model is 94%, and the lowest is 79%. The designed model has the highest accuracy rate of 93% in sneaker classification, while other models have the highest accuracy rate of around 82% in sneaker classification. Compared with traditional big data technology, the designed model has greatly improved and can adapt to more working environments. This study not only provides technical support for the application of big data technology but also contributes to improving the dissemination effect and promoting the comprehensive development of sneaker consumer culture.</div