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Background: Between 2004 and 2006, 1,170 Swedish soldiers were deployed to Liberia. They were prescribed mefloquine or atovaquone/proguanil as malaria chemoprophylaxis. Our study aims were to estimate the chemoprophylaxis effectiveness and adverse events. Methods: Cases or malaria were routinely reported during and after the mission. After return to Sweden. the soldiers filled in a questionnaire concerning type of accommodation, use of prophylaxis, and adverse events. Results: No cases of Plasmodium falciparum malaria were recorded during a total of -7,000 person-months, Adverse events (AE) were reported by 57% in the mefloquine group and 34% in the atovaquone/proguanil group. In the mefloquine group, the soldiers reported more neuropsychological AE. Conclusions: Both drugs were sate and 100% effective as long-term prophylaxis far prevention of P. falciparum malaria, Atovaquone/ proguanil was better tolerated with respect to serf-reported AE. [ABSTRACT FROM AUTHOR]

This article is about timetable-free schools, the latest "innovation' in Swedish educational policy, and is based on findings from an ongoing research project. In autumn 2000, the Swedish government started a 5-year trial period where a limited number of municipalities and schools were allowed to abandon the current restrictions in the national timetable for comprehensive schools. The research primarily focuses on the effects of abandoning the timetable on the inner life of the schools. Two categories of schools are followed: (two) schools with the national timetable and (four) schools without. Primary findings indicate that the use of time in school is a complex sphere of operation. In many aspects, the differences within the category schools without the national timetable are more notable than differences between the two categories of schools. How time is spent in schools is related to a wide range of interlinked factors on different levels, and the national timetable is only one of them. It seems that when the new so-called freedom increases, the instruments of individual control also increase. This may be an indication that disciplining and selection still are fundamental tasks for schools to fulfil. [ABSTRACT FROM AUTHOR]

The aim of this project has been to find suitable methods for flow measurement and characterization with passive accelerometers. The objectives were twofold. Firstly, the process industry could make use of such a sensor for process surveillance. Secondly, the water utilities of today lack simple and cost-efficient alternatives to equip their ageing infrastructures with online flow meters. These kinds of efforts are necessary for the realization of smart maintenance and for the decrease of the currently increasing amount of maintenance needs water utilities of today are experiencing. Liquid flowing in a pipe generates vibrations, detectable with accelerometers fitted along the pipe exterior. The correlated sound from synchronized accelerometers experience a lag which is dependent on the flow rate. Also, if the acquired sound is further processed, there exist a possibility to extract enough features to estimate some additional characteristics, in this case temperature. Experiments were performed at two nominal temperatures, 20 °C and 40 °C. A deep neural network was constructed for non-linear regression purposes to predict flow velocities based on lag and mean frequencies of the vibrations. Further, a proof of concept for this methodology was shown which reached a root mean square deviation from 100.8 L/min to 171.1 L/min for a nominal flow range of 0 to 1500 L/min. In addition, we train a k-nearest neighbour classifier to predict the nominal temperature of our validation dataset with 83 percent accuracy. The work was performed at RISE Research Institutes of Sweden, serving as Sweden's national metrology institute for liquid flow and acoustics. • Passive accelerometers might be the cure for ageing water utilities. • Time-lagged cross-correlation used for flow estimation. • Frequency content important for flow characterization. • Temperature classification with the k-Nearest Neighbour algorithm. [ABSTRACT FROM AUTHOR]