Personlighetstyper och upplevelse av ett nytt arbetsstöd : undersökning gjord på Trafikverkets Intranät och anställda

In order to get a better understanding of the interaction between employees and their technical work tools one needs to know what factors influence the interaction. The purpose of this study was to examine if there is a correlation between the personality traits Extraversion (E) and Neuroticism (N), tested with Eysenck Personality Inventory (EPI), and experience of the new Intranet among employees at The Swedish National Transport Administration (SNTA), and also to gather information of employees’ opinions about the new Intranet. A survey, containing questions about the Intranet and a personality test (EPI), was posted on SNTA’s Intranet for eight workdays (N = 88, females = 53, males = 35). A Multiple Regression showed no significant correlations between personality traits (E/N) and experience of the new Intranet. Considering the study’s low Power (.34) one cannot draw any conclusions of the statistical tests. A majority of the participants did not think that the new Intranet is better than the old one, and thought it was difficult to find necessary information on the Intranet at first. However, they did not think this had a negative effect on the time it took to accomplish their work tasks. For upcoming studies more participants are required (preferable more than 200) and the survey should not only be available via computers in order to reach people who is not frequent users of computers.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.

Running economy and blood lactate accumulation in elite football players with high and low maximal aerobic power

The purpose was to determine running economy and lactate threshold among a selection of male elite football players with high and low aerobic power. Forty male elite football players from the highest Swedish division (“Allsvenskan”) participated in the study. In a test of running economy (RE) and blood lactate accumulation the participants ran four minutes each at 10, 12, 14, and 16 km•h-1 at horizontal level with one minute rest in between each four minutes interval. After the last sub-maximal speed level the participants got two minutes of rest before test of maximal oxygen uptake (VO2max). Players that had a maximal oxygen uptake lower than the average for the total population of 57.0 mL O2•kg-1•minute-1 were assigned to the low aerobic power group (LAP) (n=17). The players that had a VO2max equal to or higher than 57.0 mL O2•kg-1•minute-1 were selected for the high aerobic power group (HAP) (n=23). The VO2max was significantly different between the HAP and LAP group. The average RE, measured as oxygen uptake at 12, 14 and 16km•h-1 was significantly lower but the blood lactate concentration was significantly higher at 14 and 16 km•h-1 for theLAP group compared with the HAP group.