Linköping University

About: Linköping University is a education organization based out in Linköping, Sweden. It is known for research contribution in the topics: Population & Thin film. The organization has 15671 authors who have published 50013 publications receiving 1542189 citations.

Frequency of suicide on Gotland after systematic postgraduate education of general practitioners.

Abstract: In 1983-1984 the Swedish Committee for Prevention and Treatment of Depression (PTD Committee) introduced an educational program for all general practitioners (GPs) on the Swedish island of Gotland The primary goal was to increase knowledge about diagnosis and treatment of patients with affective disorders The effects of the educational programs were evaluated in detail; GPs identified more patients with depressive disorders and treated them more accurately The suicide rate on Gotland was followed, primarily to ensure that the new treatment strategies did not include a risk for the individual patients However, it was also hoped that increased awareness of patients with affective disorders and better treatment routines could reduce the suicide rate The suicide rate dropped the year after the educational programs were introduced This was a statistically significant deviation both from the long-term trend on Gotland and from the trends in Sweden as a whole Programs aiming at giving GPs increased capacity and responsibility to treat patients with affective disorders do not increase the frequency of suicide Better primary treatment of patients with depressive disorders may reduce the suicide rate in a given area

Long-term outcome of primary percutaneous coronary intervention vs prehospital and in-hospital thrombolysis for patients with ST-elevation myocardial infarction

Abstract: Context: Whether the superior results of percutaneous coronary intervention (PCI) reported in clinical trials in which patients with ST-segment elevation myocardial infarction (STEMI) received repe ...

Algorithms for the regularization of ill-conditioned least squares problems

Abstract: Two regularization methods for ill-conditioned least squares problems are studied from the point of view of numerical efficiency. The regularization methods are formulated as quadratically constrained least squares problems, and it is shown that if they are transformed into a certain standard form, very efficient algorithms can be used for their solution. New algorithms are given, both for the transformation and for the regularization methods in standard form. A comparison to previous algorithms is made and it is shown that the overall efficiency (in terms of the number of arithmetic operations) of the new algorithms is better.

Crowdsourcing the creation of image segmentation algorithms for connectomics

Abstract: To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with ground truth from human experts. The winning team had no prior experience with EM images, and employed a convolutional network. This ``deep learning'' approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

Stress constrained topology optimization

Abstract: This paper develops and evaluates a method for handling stress constraints in topology optimization. The stress constraints are used together with an objective function that minimizes mass or maximizes stiffness, and in addition, the traditional stiffness based formulation is discussed for comparison. We use a clustering technique, where stresses for several stress evaluation points are clustered into groups using a modified P-norm to decrease the number of stress constraints and thus the computational cost. We give a detailed description of the formulations and the sensitivity analysis. This is done in a general manner, so that different element types and 2D as well as 3D structures can be treated. However, we restrict the numerical examples to 2D structures with bilinear quadrilateral elements. The three formulations and different approaches to stress constraints are compared using two well known test examples in topology optimization: the L-shaped beam and the MBB-beam. In contrast to some other papers on stress constrained topology optimization, we find that our formulation gives topologies that are significantly different from traditionally optimized designs, in that it actually manage to avoid stress concentrations. It can therefore be used to generate conceptual designs for industrial applications.