Upgrade theme to v1.2.1

zhonger · Jul 6, 2023 · 0bf733d · 0bf733d · vercel · Jul 6, 2023
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diff --git a/_config.production.yml b/_config.production.yml
@@ -162,6 +162,9 @@ foreverblog: true
 # Wormhole
 wormhole: true
 
+# NotByAI
+notbyAI: true
+
 # Alive time
 alivetime: true
 alivestart: "08/08/2015"

diff --git a/_config.tsukuba.yml b/_config.tsukuba.yml
@@ -158,6 +158,9 @@ foreverblog: true
 # Wormhole
 wormhole: true
 
+# NotByAI
+notbyAI: true
+
 # Alive time
 alivetime: true
 alivestart: "08/08/2015"

diff --git a/_config.yml b/_config.yml
@@ -158,6 +158,9 @@ foreverblog: true
 # Wormhole
 wormhole: true
 
+# NotByAI
+notbyAI: true
+
 # Alive time
 alivetime: true
 alivestart: "08/08/2015"

diff --git a/pages/en_index.md b/pages/en_index.md
@@ -0,0 +1,47 @@
+---
+layout: page
+home-title: Welcome to zhonger's blog!
+description: Writing, writing, writing ...
+permalink: /en/index.html
+langs: ["zh-Hans", "en"]
+lang: "en"
+---
+
+# Introduction
+
+## About me
+
+&emsp;&emsp;I'm Li Shengzhou. Nowadays, I am a PhD student of Computer Science in University of Tsukuba. My research topic is "Data-Driven and Machine Learning Based Material Science Research" under the supervision of Pro. Nakata Ayako from NIMS and Pro. Sakurai Tetsuya from University of Tsukuba.
+
+## Interests
+
+<img src="{{ site.baseurl }}/assets/icons/kvm.webp" alt="KVM" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/docker.webp" alt="Docker" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/php.svg" alt="PHP" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/python.svg" alt="Python" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/nodejs.svg" alt="Nodejs" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/linux.svg" alt="Linux" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/R.svg" alt="R" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/mysql.png" alt="Mysql" class="interest">
+<img src="{{ site.baseurl }}/assets/icons/photoshop.svg" alt="Photoshop" class="interest">
+
+## Educations
+
+- Shanghai University (China), School of Computer Engineering and Science, Bachelor degree. (2012/09~2016/06)
+- Shanghai University (China), School of Computer Engineering and Science, Master degree. (2016/09~2019/04)
+- Northeast Normal University (China), Learning Japanese. (2019/10~2020/08)
+- University of Tsukuba (Japan), Graduate School of Science and Technology, Degree Programs in Systems and Information Engineering, Doctoral Program in Computer Science. (2020/10~Now) (MEXT Scholarship)
+
+## Publications
+
+- **S Li**, H Zhang, D Dai, G Ding, X Wei, Y Guo. Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning[J]. *Journal of Alloys and Compounds*, 2019, 782: 110-118.[[DOI]](https://doi.org/10.1016/j.jallcom.2018.12.136)
+- H Zhang, X Liu, G Zhang, Y Zhu, **S Li**, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys. *Computational Materials Science*, 2023, 228:112349.[[DOI]](https://doi.org/10.1016/j.commatsci.2023.112349)
+- H Zhang, R Hu, X Liu, **S Li**, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys. *Transactions of Nonferrous Metals Society of China*, 2022, [[DOI]](https://oversea.cnki.net/kcms/detail/43.1239.TG.20220908.1626.028.html)
+- W Zheng , H Zhang, H Hu, Y Liu, **S Li**, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(04): 803-809.[[DOI]](http://www.ysxbcn.com/down/2019/04_cn/17-P0803-37307.pdf)(Chinese)
+- Y Liu, H Zhang, Y Xu, **S Li**, D Dai, C Li, G Ding, W Shen, Q Qian. Prediction of Superconducting Transition Temperature Using A Machine-Learning Method[J]. *Materiali in tehnologije*, 2018, 52(5): 639-643.[[DOI]](https://doi.org/10.17222/mit.2018.043)
+- H Zhang, G Zhou, **S Li**, X Fan, Z Guo, T Xu, Y Xu, X Chen, D Dai, Q Qian. Application of fuzzy learning in the research of binary alloys: Revisit and validation[J]. *Computational Materials Science*, 2020, 172: 109350.[[DOI]](https://doi.org/10.1016/j.commatsci.2019.109350)
+- D Dai, T Xu, H Hu, Z Guo, Q Liu, **S Li**, Q Qian, Y Xu, H Zhang. A New Method to Characterize Limited Material Datasets of High-Entropy Alloys Based on the Feature Engineering and Machine Learning[J]. *Available at SSRN 3442010*.[[DOI]](https://dx.doi.org/10.2139/ssrn.3442010)
+
+## Contact
+
+Email: zhonger[at]live.cn (Please replace [at] with @.)
diff --git a/pages/index.md b/pages/index.md
@@ -3,45 +3,11 @@ layout: page
 home-title: Welcome to zhonger's blog!
 description: Writing, writing, writing ...
 permalink: /
+langs: ["zh-Hans", "en"]
+lang: "zh-Hans"
 ---
 
-## About me
-
-&emsp;&emsp;I'm Li Shengzhou. Nowadays, I am a PhD student of Computer Science in University of Tsukuba. My research topic is "Data-Driven and Machine Learning Based Material Science Research" under the supervision of Pro. Nakata Ayako from NIMS and Pro. Sakurai Tetsuya from University of Tsukuba.
-
-## Interests
-
-<img src="{{ site.baseurl }}/assets/icons/kvm.webp" alt="KVM" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/docker.webp" alt="Docker" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/php.svg" alt="PHP" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/python.svg" alt="Python" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/nodejs.svg" alt="Nodejs" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/linux.svg" alt="Linux" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/R.svg" alt="R" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/mysql.png" alt="Mysql" class="interest">
-<img src="{{ site.baseurl }}/assets/icons/photoshop.svg" alt="Photoshop" class="interest">
-
-## Educations
-
-- Shanghai University (China), School of Computer Engineering and Science, Bachelor degree. (2012/09~2016/06)
-- Shanghai University (China), School of Computer Engineering and Science, Master degree. (2016/09~2019/04)
-- Northeast Normal University (China), Learning Japanese. (2019/10~2020/08)
-- University of Tsukuba (Japan), Graduate School of Science and Technology, Degree Programs in Systems and Information Engineering, Doctoral Program in Computer Science. (2020/10~Now) (MEXT Scholarship)
-
-## Publications
-
-- **S Li**, H Zhang, D Dai, G Ding, X Wei, Y Guo. Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning[J]. *Journal of Alloys and Compounds*, 2019, 782: 110-118.[[DOI]](https://doi.org/10.1016/j.jallcom.2018.12.136)
-- H Zhang, X Liu, G Zhang, Y Zhu, **S Li**, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys. *Computational Materials Science*, 2023, 228:112349.[[DOI]](https://doi.org/10.1016/j.commatsci.2023.112349)
-- H Zhang, R Hu, X Liu, **S Li**, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys. *Transactions of Nonferrous Metals Society of China*, 2022, [[DOI]](https://oversea.cnki.net/kcms/detail/43.1239.TG.20220908.1626.028.html)
-- W Zheng , H Zhang, H Hu, Y Liu, **S Li**, G Ding, J Zhang. Performance prediction of perovskite materials based on different machine learning algorithms[J]. The Chinese Journal of Nonferrous Metals, 2019, 29(04): 803-809.[[DOI]](http://www.ysxbcn.com/down/2019/04_cn/17-P0803-37307.pdf)(Chinese)
-- Y Liu, H Zhang, Y Xu, **S Li**, D Dai, C Li, G Ding, W Shen, Q Qian. Prediction of Superconducting Transition Temperature Using A Machine-Learning Method[J]. *Materiali in tehnologije*, 2018, 52(5): 639-643.[[DOI]](https://doi.org/10.17222/mit.2018.043)
-- H Zhang, G Zhou, **S Li**, X Fan, Z Guo, T Xu, Y Xu, X Chen, D Dai, Q Qian. Application of fuzzy learning in the research of binary alloys: Revisit and validation[J]. *Computational Materials Science*, 2020, 172: 109350.[[DOI]](https://doi.org/10.1016/j.commatsci.2019.109350)
-- D Dai, T Xu, H Hu, Z Guo, Q Liu, **S Li**, Q Qian, Y Xu, H Zhang. A New Method to Characterize Limited Material Datasets of High-Entropy Alloys Based on the Feature Engineering and Machine Learning[J]. *Available at SSRN 3442010*.[[DOI]](https://dx.doi.org/10.2139/ssrn.3442010)
-
-## Contact
-
-Email: zhonger[at]live.cn (Please replace [at] with @.)
-
+# 个人简介
 
 ## 关于我