Online English Conversation Course for PhD Students
Wednesday, 22 April 2015
Monday, 20 April 2015
Week 2 (20-26.04): Technological innovation and patents
One way of finding promising technology developments is to analyze new patents. Researchers have found a way to measure the rate at which a technology is improving by analyzing relevant patent metrics. The results can be used to predict which technologies are most likely to succeed and provide a good investment opportunity.
Among the 28 domains analyzed by the study, the ones that are growing at the fastest pace included: optical and wireless communications, 3D printing, and MRI technology. Other domains showed slower rates of progress. Some of them were to be expected (e.g. combustion engines which already have a long history), but interestingly the technology destined to replace them - batteries (for electric cars) also seem to have a slow rate of progress.
To find out more about this topic, please read the following article:
http://www.sciencedaily.com/releases/2015/04/150415155329.htm
Source:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121635
Christopher L. Benson , Christopher L. Magee: "Quantitative Determination of Technological Improvement from Patent Data"
Questions:
1. Are you more inclined to believe that patents stifle or spur technological innovation?
2. Do you think it is a good idea to create a technology rating system based on patents? Will patent trolls find a way to abuse this method?
3. Should software patents be awarded? What safeguards or conditions would you propose for software patents?
4. Tesla Motors company, which is arguably at the forefront of battery technology innovation, decided to open up to competitors all the electric car related patents that made their product successful. Why do you think they did that? Was it a smart move? What effect could it have on the ranking from the study?
5. What should never be allowed to be patented?
Among the 28 domains analyzed by the study, the ones that are growing at the fastest pace included: optical and wireless communications, 3D printing, and MRI technology. Other domains showed slower rates of progress. Some of them were to be expected (e.g. combustion engines which already have a long history), but interestingly the technology destined to replace them - batteries (for electric cars) also seem to have a slow rate of progress.
To find out more about this topic, please read the following article:
http://www.sciencedaily.com/releases/2015/04/150415155329.htm
Source:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121635
Christopher L. Benson , Christopher L. Magee: "Quantitative Determination of Technological Improvement from Patent Data"
Questions:
1. Are you more inclined to believe that patents stifle or spur technological innovation?
2. Do you think it is a good idea to create a technology rating system based on patents? Will patent trolls find a way to abuse this method?
3. Should software patents be awarded? What safeguards or conditions would you propose for software patents?
4. Tesla Motors company, which is arguably at the forefront of battery technology innovation, decided to open up to competitors all the electric car related patents that made their product successful. Why do you think they did that? Was it a smart move? What effect could it have on the ranking from the study?
5. What should never be allowed to be patented?
Sunday, 12 April 2015
Week 1 (13-19.04.2015): Social Media in Hazards Monitoring
Social Media popularity has found its application in hazards monitoring. Please, read the paper published at >>location<<. If you suffer from lack of time there is a very short description of the paper. Your task is to shortly answer the questions:
Q1. Do you think that application of LBSN in hazards monitoring make sense?
Give your opinion (3 sentences only).
Q2. Do you think that Twitter provides accurate and useful spatio-temporal information?
Q3. What are the scientific areas of IT application in social media monitoring?
Paper description
The Internet phenomenon finds a new application every day. Since the social media appeared people still wonders how to use it in a new, an original way. We can share photos, movies through the Facebook or just a short text through the Twitter, etc. The paper shows haw the Location Based Social Networks can be used in hazards monitoring. The article consist of 4 main parts.
Introduction:
The most important aspect of the chapter is that authors characterised a few notations. The firs one is VGI (Volunteered Geographic Information) that means "user generated contents having a geographical dimension". Location Based Social Network (LBSN) are a very important part of VGI as it has a location functionalities combined with SM functionalities, so it plays an important role in SDIs (Spatial Data Infrastructure) as well.
Previous works review:
In the second part Twitter`s main features (messages syntax - hash tags) as a representative of "micro-blogging" Social Network Services are described. Moreover, literature review on Twitter, its application in crisis management, IT tools (API: Application Programming Interface, PHP Data Mining Web Crawling Scripts) that helped in harvesting spatio-temporal information are undermentioned too.
Case Study - the Marseille Fire:
Spatio - temporal aspects of the forest fire (date, time, location) were described in here. Authors underlined, that they had selected Marseille case study because of the low Twitter usage, the fire took place near the area with thousands of citizens - so it was expected a large number of tweets. Four hypothesis for verification were set. 346 tweets were selected through the API - 1 hour before fire started and 5 after it was put out.
Results and discussion:
In the aspect of temporal dynamics "Chronology of the Marseille Fire, number of related tweets per hour and selected tweets’ contents" were prepared (analysis 1).
In the aspect of spatial dynamics "Hectares of burnt area reported in tweets over time"
and "Location, frequency and time of the first citation of place names cited in tweets, and estimated total burnt area" were compiled (analysis 2).
Q1. Do you think that application of LBSN in hazards monitoring make sense?
Give your opinion (3 sentences only).
Q2. Do you think that Twitter provides accurate and useful spatio-temporal information?
Q3. What are the scientific areas of IT application in social media monitoring?
Paper description
The Internet phenomenon finds a new application every day. Since the social media appeared people still wonders how to use it in a new, an original way. We can share photos, movies through the Facebook or just a short text through the Twitter, etc. The paper shows haw the Location Based Social Networks can be used in hazards monitoring. The article consist of 4 main parts.
Introduction:
The most important aspect of the chapter is that authors characterised a few notations. The firs one is VGI (Volunteered Geographic Information) that means "user generated contents having a geographical dimension". Location Based Social Network (LBSN) are a very important part of VGI as it has a location functionalities combined with SM functionalities, so it plays an important role in SDIs (Spatial Data Infrastructure) as well.
Previous works review:
In the second part Twitter`s main features (messages syntax - hash tags) as a representative of "micro-blogging" Social Network Services are described. Moreover, literature review on Twitter, its application in crisis management, IT tools (API: Application Programming Interface, PHP Data Mining Web Crawling Scripts) that helped in harvesting spatio-temporal information are undermentioned too.
Case Study - the Marseille Fire:
Spatio - temporal aspects of the forest fire (date, time, location) were described in here. Authors underlined, that they had selected Marseille case study because of the low Twitter usage, the fire took place near the area with thousands of citizens - so it was expected a large number of tweets. Four hypothesis for verification were set. 346 tweets were selected through the API - 1 hour before fire started and 5 after it was put out.
Results and discussion:
In the aspect of temporal dynamics "Chronology of the Marseille Fire, number of related tweets per hour and selected tweets’ contents" were prepared (analysis 1).
In the aspect of spatial dynamics "Hectares of burnt area reported in tweets over time"
and "Location, frequency and time of the first citation of place names cited in tweets, and estimated total burnt area" were compiled (analysis 2).
Week 1 (13-19.04.2015): Video Games Can Actually Be Good For You
Read the presentation Video Games Can Actually Be Good For You published at
http://konwersatorium1-ms-pjwstk.blogspot.com/2015/01/week-9-1901-250115-video-games-can.html
and present/discuss scientific opinions on the topic (quote research, experiments). Share with us your personal opinion/experience as well.
http://konwersatorium1-ms-pjwstk.blogspot.com/2015/01/week-9-1901-250115-video-games-can.html
and present/discuss scientific opinions on the topic (quote research, experiments). Share with us your personal opinion/experience as well.
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