Course schedule | Fall 2021

http://dufetea.edufe.com.cn/

 

weekday time course classroom
Thursday 08.35-11.00 

旅游研究方法(phd)

shixuezhai 212
Thursday 13.30-15.00  tourism research methods(master) online
Thursday 15.45-18.20  tourism research methods(phd) online

 

 
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
1 08/29 08/30 08/31 09/01 09/02 09/03 09/04
2 09/05 09/06 09/07 09/08 09/09 09/10 09/11
3
09/12
09/13
09/14
09/15
09/16
09/17
09/18
4
09/19
09/20
09/21
09/22
09/23
09/24
09/25
5
09/26
09/27
09/28
09/29
09/30
10/01
10/02
6
10/03
10/04
10/05
10/06
10/07
10/08
10/09
7
10/10
10/11
10/12
10/13
10/14
10/15
10/16
8
10/17
10/18
10/19
10/20
10/21
10/22
10/23
9
10/24
10/25
10/26
10/27
10/28
10/29
10/30
10
10/31
11/01
11/02
11/03
11/04
11/05
11/06
11
11/07
11/08
11/09
11/10
11/11
11/12
11/13
12
11/14
11/15
11/16
11/17
11/18
11/19
11/20
13
11/21
11/22
11/23
11/24
11/25
11/26
11/27
14
11/28
11/29
11/30
12/01
12/02
12/03
12/04
15
12/05
12/06
12/07
12/08
12/09
12/10
12/11
16
12/12
12/13
12/14
12/15
12/16
12/17
12/18
17
12/19
12/20
12/21
12/22
12/23
12/24
12/25
18
12/26
12/27
12/28
12/29
12/30
12/31
01/01
19
01/02
01/03
01/04
01/05
01/06
01/07
01/08
20
01/09
01/10
01/11
01/12
01/13
01/14
01/15

 

  

 


kaiwu2021fall EN

 


1. personal course site (moodle website)

http://kaiwu.city/course/

 moodle logo

 

 

2.chaoxing

logo chaoxing

 
course
language
level
weblink
research methods in tourism (master)
English
master
research methods in tourism (phd)
English
phd
research methods in tourism Chinese undergraduate http://mooc1-1.chaoxing.com/course/214234739.html
quantitative research methods in tourism Chinese undergraduate http://mooc1-1.chaoxing.com/course/216392997.html
research methods in tourism(master) Chinese master    https://mooc1-1.chaoxing.com/course/207856222.html
research methods in tourism(phd) Chinese phd https://mooc1-1.chaoxing.com/course/219455049.html
research methods for business students(SII)
English
undergraduate
tourism planning(SII)
English undergraduate
service management (SII)
English undergraduate
Data-driven Business Intelligence in Hospitality Chinese
master     
information management in hospitality
Chinese undergraduate

  

 

 

  

课表 | 2021年秋季

http://dufetea.edufe.com.cn/

 

工作日 时间 课程 地点
星期四 08.35-11.00 

旅游研究方法(phd)

师学斋212
星期四 13.30-15.00 

tourism research methods(master)

online
星期四 15.45-18.20  tourism research methods(phd) online

 

 
星期一
星期二
星期三
星期四
星期五
星期六
星期日
1
08/30
08/31
09/01
09/02
09/03
09/04
09/05
2
09/06
09/07
09/08
09/09
09/10
09/11
09/12
3
09/13
09/14
09/15
09/16
09/17
09/18
09/19
4
09/20
09/21
09/22
09/23
09/24
09/25
09/26
5
09/27
09/28
09/29
09/30
10/01
10/02
10/03
6
10/04
10/05
10/06
10/07
10/08
10/09
10/10
7
10/11
10/12
10/13
10/14
10/15
10/16
10/17
8
10/18
10/19
10/20
10/21
10/22
10/23
10/24
9
10/25
10/26
10/27
10/28
10/29
10/30
10/31
10
11/01
11/02
11/03
11/04
11/05
11/06
11/07
11
11/08
11/09
11/10
11/11
11/12
11/13
11/14
12
11/15
11/16
11/17
11/18
11/19
11/20
11/21
13
11/22
11/23
11/24
11/25
11/26
11/27
11/28
14
11/29
11/30
12/01
12/02
12/03
12/04
12/05
15
12/06
12/07
12/08
12/09
12/10
12/11
12/12
16
12/13
12/14
12/15
12/16
12/17
12/18
12/19
17
12/20
12/21
12/22
12/23
12/24
12/25
12/26
18
12/27
12/28
12/29
12/30
12/31
01/01
01/02
19
01/03
01/04
01/05
01/06
01/07
01/08
01/09
20
01/10
01/11
01/12
01/13
01/14
01/15
01/16

 

  

 


kaiwu2021fall CN

 


1. 个人课程资源网站 (基于moodle)

http://kaiwu.city/course/

 moodle logo

 

 

2. 超星学习通平台

logo chaoxing

 
课程 语言
学生类别
网址链接
research methods in tourism (master)
English
master
research methods in tourism (phd)
English
phd

旅游研究方法

Chinese undergraduate http://mooc1-1.chaoxing.com/course/214234739.html
旅游数量研究方法 Chinese undergraduate http://mooc1-1.chaoxing.com/course/216392997.html
旅游研究方法(master) Chinese master    https://mooc1-1.chaoxing.com/course/207856222.html
旅游研究方法(phd) Chinese phd https://mooc1-1.chaoxing.com/course/219455049.html
research methods for business students(SII)
English
undergraduate
tourism planning(SII)
English undergraduate
service management (SII)
English undergraduate
数据驱动的接待业商务智能 Chinese
master     
酒店信息管理
Chinese undergraduate

  

 

 

听了得到app,卓克.科技参考   259 | 老生常谈的口罩问题
 
 卓克的总结是:
1在病毒浓度较高的环境里,通过普通外科口罩被吸入的病毒就足以导致感染;在病毒浓度较低的环境里,戴口罩的人就安全很多。
2在防止病毒传染给其他人方面,N95口罩和外科口罩不相上下,而想防止被感染,最好还是佩戴N95口罩。
3要想从科上严谨地证实一个观念的是非常困难的,既要有数学模型、实测结果,还要与现实高度吻合。
 

Cheng, Y., Ma, N., Witt, C., Rapp, S., Wild, P. S., Andreae, M. O., Pöschl, U., & Su, H. (2021). Face Masks Effectively Limit the Probability of SARS-CoV-2 Transmission. Science, 372(6549), 1439–1443. https://doi.org/10.1126/science.abg6296
 

Masking out air sharing

The effectiveness of masks in preventing the transmission of severe acute respiratory syndrome coronavirus 2 has been debated since the beginning of the COVID-19 pandemic. One important question is whether masks are effective despite the forceful expulsion of respiratory matter during coughing and sneezing. Cheng et al. convincingly show that most people live in conditions in which the airborne virus load is low. The probability of infection changes nonlinearly with the amount of respiratory matter to which a person is exposed. If most people in the wider community wear even simple surgical masks, then the probability of an encounter with a virus particle is even further limited. In indoor settings, it is impossible to avoid breathing in air that someone else has exhaled, and in hospital situations where the virus concentration is the highest, even the best-performing masks used without other protective gear such as hazmat suits will not provide adequate protection.

Science, abg6296, this issue p. 1439

Abstract

Airborne transmission by droplets and aerosols is important for the spread of viruses. Face masks are a well-established preventive measure, but their effectiveness for mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance and are related to population-average infection probability and reproduction number. For SARS-CoV-2, the viral load of infectious individuals can vary by orders of magnitude. We find that most environments and contacts are under conditions of low virus abundance (virus-limited), where surgical masks are effective at preventing virus spread. More-advanced masks and other protective equipment are required in potentially virus-rich indoor environments, including medical centers and hospitals. Masks are particularly effective in combination with other preventive measures like ventilation and distancing.

 
F1.large 2
 
 
F4.large
 
 
 
The two pillars of science are logic and observation. A scientific understanding of the world must (1) make sense and (2) correspond with what we observe. Both elements are essential to science and relate to three major aspects of the overall scientific enterprise: theory, data collection, and data analysis.
 
Babbie, E. (2020). The Practice of Social Research (15th). Cengage Learning. p8
 
 
Cheng, Y., Ma, N., Witt, C., Rapp, S., Wild, P. S., Andreae, M. O., Pöschl, U., & Su, H. (2021). Face Masks Effectively Limit the Probability of SARS-CoV-2 Transmission. Science, 372(6549), 1439–1443. https://doi.org/10.1126/science.abg6296
 
 
 
altmetric
 

Masking out air sharing

The effectiveness of masks in preventing the transmission of severe acute respiratory syndrome coronavirus 2 has been debated since the beginning of the COVID-19 pandemic. One important question is whether masks are effective despite the forceful expulsion of respiratory matter during coughing and sneezing. Cheng et al. convincingly show that most people live in conditions in which the airborne virus load is low. The probability of infection changes nonlinearly with the amount of respiratory matter to which a person is exposed. If most people in the wider community wear even simple surgical masks, then the probability of an encounter with a virus particle is even further limited. In indoor settings, it is impossible to avoid breathing in air that someone else has exhaled, and in hospital situations where the virus concentration is the highest, even the best-performing masks used without other protective gear such as hazmat suits will not provide adequate protection.

Science, abg6296, this issue p. 1439

Abstract

Airborne transmission by droplets and aerosols is important for the spread of viruses. Face masks are a well-established preventive measure, but their effectiveness for mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance and are related to population-average infection probability and reproduction number. For SARS-CoV-2, the viral load of infectious individuals can vary by orders of magnitude. We find that most environments and contacts are under conditions of low virus abundance (virus-limited), where surgical masks are effective at preventing virus spread. More-advanced masks and other protective equipment are required in potentially virus-rich indoor environments, including medical centers and hospitals. Masks are particularly effective in combination with other preventive measures like ventilation and distancing.

 
F1.large 2
 
 
F4.large
 
 
 
 
 
听了得到app,卓克.科技参考   258 | 蛋白质数据库公开的意义
 
在2021年7月22日,DeepMind欧洲分子生物学实验室(European Bioinformatics Institute)联合宣布,两家合作,用自己开发的人工智能分析工具AlphaFold2预测了36.5万种蛋白质的结构,然后把这些结果做成数据库,免费给全球所有科研人员使用。
 
 alphafold Q8W3K0
 
 
这不仅会推动生物学相关的研究,也在改变科学研究的面貌——人工智能、数据科学可能带来科学研究领域划时代的变革。

1.  论文全文
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly Accurate Protein Structure Prediction with Alphafold. Nature, 1–11. https://doi.org/10.1038/s41586-021-03819-2

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
PDF格式论文全文
 
 deep相关博客文章
 
 

Callaway, E. (2020). ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature, 588(7837), 203–204. https://doi.org/10.1038/d41586-020-03348-4
alpha fold2

2. alphafold 开源内容
 
2.1 alphafold protein structure database
 
alphafold protein structure database
 
 
 
 
2.2 alphafold open source code
 
github alphafold
 

 
3. alpha fold项目介绍
 
 
 about alphafold