This thesis aims to contribute to the understanding of quantum materials by employing a combination of experimental techniques, such as angle-resolved photoemission spectroscopy and magnetic and transport measurements. Further collaborative support in the form of first-principles calculations is included and discussed in tandem. In this thesis, a lanthanide-based semimetal of the ZrSiS type, is investigated. Multiple nodal lines which remain gapless are observed along the X-R direction of the Brillouin zone. We also present a nodal line that is observed further below the Fermi level and aligned in the G-M direction; this nodal line appears to be sensitive to light source polarization. A surface state at the X point is also observed. Finally, this thesis includes some discussion on the development of a sample growth laboratory, along with the presentation and characterization of grown Bi2Se3 samples. With potential applications in the fields of spintronics and novel microelectronic devices, the experimental realization and understanding of quantum materials is key to a deeper understanding of materials physics.

The diagnosis of genetic illnesses has undergone a revolution with advancements in sequencing technology. Next-generation sequencing (NGS) has become a standard practice in genetic diagnostics, enabling the identification of various genetic variations. However, distinguishing causative variants from a vast number of benign background variants presents a significant challenge. This study focuses on improving the rank score model used in genetic rare-disease diagnostics at a clinical genomics facility in Stockholm. The objective is to develop a more effective and optimized model through the utilization of exploratory data analysis techniques and machine learning methods, investigating the strengths and weaknesses of various existing annotation scores to identify suitable features and enhance the model's classification performance. The research methodology involved analyzing publicly available ClinVar data, utilizing statistical methods such as principal component analysis (PCA), heatmap, Welch's t-test, and Chi-Square test to evaluate the correlation, patterns, and classification abilities of different variant types. In addition, the study employed a machine learning approach that combines allele frequency filtering and logistic regression trained on both public and in-house datasets to prioritize single nucleotide variants (SNVs) and insertions/deletions (InDels). The resulting model assigns binary class labels (benign or pathogenic) and provides scores for variant classification. Promising performance was observed in both the ClinVar dataset and the unique patient datasets, demonstrating the model's potential for clinical application. The findings of this study hold the potential to enhance genetic rare-disease diagnostics and contribute to advancements in rare disease research.

The yields of xenon and krypton from the neutron- induced fission of Am241 and Am242 have been measured with a mass spectrometer. This was accomplished by irradiating samples of Am241 for different lengths of time so that the effect of the growth of highly fissionable Am242 could be determined. These studies reveal that both the degree of fine structure in the mass yield curve and the fission-product charge distribution are dependent on the energy of the incident neutrons. This has not been previously observed for any fissioning nuclide. These studies also reveal effects of the 50-neutron shell and of the neutron-proton ratio of the fissioning nuclide on the mass yield curve. / Thesis / Doctor of Philosophy (PhD)

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