Wink screensnapshot software

Wink (http://www.debugmode.com/wink/) is a free software to catch screen-shots.

Main features:

1. three modi: 1) take single screen shots 2) take shots at each mouse OR key action 3) take at real time. You can change between these modi in a session.
2. You can choose to record a specific window only (for example slices) or the whole screen.
3. You can edit the final screenshots: add voice, add text elements.
4. You can interleave different sessions.
5. The final formal is an adobe Flash swf-file, which is started via an html file. Any user is able to download Flash from Adobe.

Two commerical alternatives are camtasisa (microsoft http://www.techsmith.com/camtasia.asp about 300 dollar) and captivate (adobe http://www.adobe.com/products/captivate/ about 600 dollar).

Captive seems to be more like wink, as it allows to take individual snapshots. It has the addtitional freature to allow interative learning facilitites as quizzies etc.

See for a comparison of both:

http://www.streamingmedia.com/article.asp?id=9393

Ulrich

ICA (independent component analysis)

Following the Epital-presentation in April, I surfed the web a bit about ICA (independent component analysis). It has - at least on the surface - a strong resemblance with factor analysis. Some web-links are:

http://en.wikipedia.org/wiki/Independent_Component_Analysis
http://www.sccn.ucsd.edu/~arno/indexica.html ("ICA for dummies")
http://www.cnl.salk.edu/~tewon/ica_cnl.html
http://www.cis.hut.fi/projects/ica/ (where additional links can be found)
A book on the topic is mentioned at http://www.cis.hut.fi/projects/ica/book

There are two R packages related to ICA: fastICA and mlica, see also http://cran.r-project.org/src/contrib/Views/Multivariate.html

R for SAS and SPSS users

I came across this document - RforSAS&SPSSusers.pdf - which gives a nice R-introduction to people familiar with other statistical packages. It might be of use in connection with various courses.

Lameness of dairy cattle - early identification and consequences for behaviour and production

The purpose is to develop and optimize a novel tool for early identification of dairy cattle with lameness in order to increase animal welfare, product quality and producer profit. Moreover, the consequences of lameness for animal welfare and production will be illuminated.

Development of the Virtual Slaughter

The purpose of the project is to develop 3-D models of the body of slaughter pigs based on CT images for

 

1. Product optimization: characterization of slaughter quality is related to marked demands.
2. Product development: new dissections of the body performed via the virtual model can be analyzed with respect to slaughter quality and economical consequences.
3. Predictor finding: Identification of measurable characteristics of the slaughter body related to slaughter quality that can be used for online measurements and consultation for farmers under production.

Our group is responsible for the identification of measurable predictors (part 3). An experiment is conducted by Pia Nissen (Department of Food Science, DJF) to gain information about the development of the pig body and to test the robustness of predictors developed on already scanned slaughter pigs.

The project is lead by Eli Vibeke Olsen(Danish Meat Association) in collaboration with DTU and DJF.

 

Project repsonsible (part 3): Ulrich Halekoh

ulrich.halekoh@agrsci.dk

Project period: 2006-2008 (months 8)

AUREGAB - Automated registration of animal behaviour

The AUREGAB project aims at developing new tools for monitoring and for early identification of dairy cows with reduced appetite, with lameness problems and cows in heat. In the project we use Bluetooth technology for determining the positions and lying behaviour of the animals.

Project period: 2006-2008

Project participants from our group include Frede Aakman Tøgersen and Søren Højsgaard (project leader)

ILSORM - Integrated risk management

The ILSORM project is about integrated risk management in dairy production using information technology and other modern technologies. Some keywords for our activities in the project are: sensor measurements, dynamic models, decision making, optimal decisions.

Project period: 2007-2009.

Project participants from our group include Lars Relund Nielsen, Asger Roer Pedersen and Søren Højsgaard.

Statistical Reviewers Improve Reporting in Biomedical Articles: A Randomized Trial

Det er et stykke tid siden, der har været indlæg på bloggen, så hvorfor ikke dette link til en artikel i tidsskriftet PLoS One:

Statistical Reviewers Improve Reporting in Biomedical Articles: A Randomized Trial

Og jeg ved godt at det ikke er alle, der vil betragte artiklens konklusioner som en nyhed.

Biosens II

The Biosens II project deals with improved monitoring and management of dairy production and milk quality based on on-farm biosensors.

In the work package which we are primily involved with we focus on developing self-adjusting dynamic statistical models to predict oestrus and mastitis from inline measurements and Markov decision models to find optimal decision strategies.

Project period: 2007-2011

Project participants from our group include Lars Relund Nielsen, Erik Jørgensen and Søren Højsgaard

Data and program examples from SAS for Mixed Models

In our current mixed model course we use the book SAS for Mixed Models, Second Edition by Ramon C. Littell, George A. Milliken, Walter W. Stroup, Russell D. Wolfinger, and Oliver Schabenberger

This is the first time we use the 2nd edition of the book. The changes in the second edition includes extensive use of the output delivery system (ODS) and examples using e.g. PROC NLMIXED and PROC GLIMMIX rather than the glimmix macro used in the 1st edition.

Participants in our previous courses might be interested in updating the file with program and data examples. It can be downloaded from the companion page above. The link to the file is a little different to find on the page, so you may just follow this link to program examples and data