Porous ceramic composites with directional microchannels from micrometer to dozens of micrometer levels have attracted more and more attention in various fields including aerospace,biomedicines,and thermal insulation ...Porous ceramic composites with directional microchannels from micrometer to dozens of micrometer levels have attracted more and more attention in various fields including aerospace,biomedicines,and thermal insulation due to their excellent fluid permeability,mechanical properties,etc.In this article,we summarize the recent directional porous ceramics developments including their main processing routes and respective properties.Meanwhile,the properties get from different processing routes have been com-pared and analyzed in terms of microstructures,mechanical properties,and permeability.Emphasis has been given to the deeper understanding which can allow one to control the microstructural features of these porous ceramic composites to obtain the desired characteristics.This work can provide a useful reference for the development and application of porous ceramic composites with directional microchan-nels.展开更多
Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the ...Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.展开更多
The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to st...The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.展开更多
The phase field simulation has been actively studied as a powerful method to investigate the microstructural evolution during the solidification.However,it is a great challenge to perform the phase field simulation in...The phase field simulation has been actively studied as a powerful method to investigate the microstructural evolution during the solidification.However,it is a great challenge to perform the phase field simulation in large length and time scale.The developed graphics processing unit(GPU)calculation is used in the phase filed simulation,greatly accelerating the calculation efficiency.The results show that the computation with GPU is about 36 times faster than that with a single Central Processing Unit(CPU)core.It provides the feasibility of the GPU-accelerated phase field simulation on a desktop computer.The GPU-accelerated strategy will bring a new opportunity to the application of phase field simulation.展开更多
基金supported by the National Science and Technol-ogy Major Project(No.J2019-IV-0003-0070)the National Natural Science Foundation of China(Grant No.12202343)the China Postdoctoral Science Foundation(No.2021M702582).
文摘Porous ceramic composites with directional microchannels from micrometer to dozens of micrometer levels have attracted more and more attention in various fields including aerospace,biomedicines,and thermal insulation due to their excellent fluid permeability,mechanical properties,etc.In this article,we summarize the recent directional porous ceramics developments including their main processing routes and respective properties.Meanwhile,the properties get from different processing routes have been com-pared and analyzed in terms of microstructures,mechanical properties,and permeability.Emphasis has been given to the deeper understanding which can allow one to control the microstructural features of these porous ceramic composites to obtain the desired characteristics.This work can provide a useful reference for the development and application of porous ceramic composites with directional microchan-nels.
基金supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.
文摘The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.
基金supported by the China Postdoctoral Science Foundation(Grant No.2013M540772)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.61203233,51101124,51101125)
文摘The phase field simulation has been actively studied as a powerful method to investigate the microstructural evolution during the solidification.However,it is a great challenge to perform the phase field simulation in large length and time scale.The developed graphics processing unit(GPU)calculation is used in the phase filed simulation,greatly accelerating the calculation efficiency.The results show that the computation with GPU is about 36 times faster than that with a single Central Processing Unit(CPU)core.It provides the feasibility of the GPU-accelerated phase field simulation on a desktop computer.The GPU-accelerated strategy will bring a new opportunity to the application of phase field simulation.
