The test production of a simulated nuclear fuel, possible to use as a calibration standard was donethru dissolving the preferred dopant-metals in a nitric-salt into weak acid. This solution wasmixed with dissolved uranium-salt to aimed concentration. This was precipitated by rising pHforming metallic-hydroxide.The precipitated salt was dried in an reduction gas, pressed to pellets and sintered in oven. Thequality of the pellets and its content was evaluated thru comparing theoretical and measureddensity, ICP-MS for which ground elements and which concentration of these forming thepellets. XRD was used to evaluate formed associated ground-elements and the lattice parameters.Finally, SEM was used to evaluate how homogenous the distribution of the doped elementsturned out.There are still a variety of tests yet to perform in order to create a high quality calibrationstandard. Our preparation of dissolved metallic salts will hopefully be usable for any furtherexperimentalist in this field. The single-doped calibration-standards are aimed to be used byStudsvik to calibrate their Lazer Ablation Inductivity coupled Plasma Mass Spectrometryinstrument.

ECG is used clinically to detect a multitude of medical conditions, such as heart-problems like arrhythmias and heart failure, and to give a good general image of the function of the heart with a quick and harmless exam. In many clinical cases, normal ECG measurements cannot be taken, such as with fetuses where ECG signals from the mother’s own body hinder the measurement. This paper examines using machine learning algorithms to be able to simulate ECG graphs from ultrasound data alone. These algorithms are trained on ultrasound and ECG data acquired from the same patient simultaneously. The data used in the training of the algorithms is taken from samples acquired from 100 adult patients. The results found using this method to simulate an ECG indicate good possibilities for future usefulness, where machine learning to acquire simulated ECG can help facilitate clinicians in evaluating fetal heart function, as well as in other cases where ECG cannot be measured normally. / EKG används kliniskt för att upptäcka en mängd olika åkommor, så som hjärtsvikt och arytmier, men också för att ge en generell bild av hjärtfunktionen med en snabb och harmlös undersökning. I många kliniska fall kan dock inte normal EKG mätning ske, så som för foster då EKG signaler från moderns egna kropp hindrar EKG-mätningen. I detta papper undersöks användandet av maskininlärningsalgoritmer för att kunna simulera EKG grafer från enbart ultraljuds data. Dessa algoritmer är tränade på ultraljud och EKG data som simultant fåtts från samma undersökning av en patient. I detta papper har ultraljudsdatan som använts kommit från 100 mätningar från olika vuxna patienter. Resultaten funna från undersökningen av EKG simulerings metoden indikerar goda möjligheter för framtida användbarhet, då maskininlärningsalgoritmer för att simulera EKG kan underlätta när kliniker ska utvärdera hjärtfunktionen hos foster, eller i andra fall då EKG inte kan mätas normalt.