Skønhedssaloner: Et unyttigt men verdensåbnende dialogisk rum

I nærværende artikel beskrives et nyudviklet, dialogisk og fagpersonligt udviklende rum inden for sygeplejeprofessionen kaldet ’skønhedssaloner’. Ideen til- og praksisser omkring skønhedssalonerne er udviklet som en del af et igangværende fænomenologisk aktionsforskningsprojekt forankret i tre hospitalsafdelinger i Danmark. I artiklen beskrives skønhedssalonernes teoretiske samt praktiske forankring, og der gives eksempler på, hvorledes skønhedssaloner er blevet praktiseret inden for sygeplejen. Skønhedssalonerne præsenteres som et muligt supplement til - og samtidig forskellig fra – udvalgt kompetencearbejde og dertilhørende forståelser af kompetencer inden for sygepleje. Skønhedssalonerne beskrives i artiklen som et verdensåbnende og ikke-nytteorienteret dialogisk rum, hvor de deltagende sygeplejersker giver udtryk for at opleve sig ’løftet op’. Afslutningsvist diskuteres kort betydningen af og mulighederne for et sådant dialogisk rum i en sundhedsfaglig kontekst

Quantifying Noise Limitations of Neural Network Segmentations in High-Resolution Transmission Electron Microscopy

Motivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e. $e^-/A^{2}$) range for object detection and segmentation tasks with neural networks. The MSD-net architecture shows promising abilities over the industry standard U-net architecture in generalising to frame doses below the range included in the training set, for both simulated and experimental images. It also presents a heightened ability to learn from lower dose images. The MSD-net displays mild visibility of a Au nanoparticle at 20-30 $e^-/A^{2}$, and converges at 200 $e^-/A^{2}$ where a full segmentation of the nanoparticle is achieved. Between 30 and 200 $e^-/A^{2}$ object detection applications are still possible. This work also highlights the importance of modelling the modulation transfer function when training with simulated images for applications on images acquired with scintillator based detectors such as the Gatan Oneview camera. A parametric form of the modulation transfer function is applied with varying ranges of parameters, and the effects on low electron dose segmentation is presented.Comment: Revised version: Numerous clarifications and improvement

Beam induced heating in electron microscopy modeled with machine learning interatomic potentials

We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and eqivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces. This can be used both to improve the networks during the training phase, and to validate the performance when simulating systems too big to be described by DFT. The energy deposited into the nanoparticle by the electron beam is estimated by measuring the mean free path of the electrons and the average energy loss, both are done with Electron Energy Loss Spectroscopy (EELS) within the microscope. In combination, this allows us to predict the heating incurred by a nanoparticle as a function of its size, its shape, the support material, and the electron beam energy and intensity.Comment: 20 pages including supplementary online information (included in the PDF