We investigate field quantization in high-curvature geometries. The models and calculations can help with understanding the elastic and inelastic scattering of photons and electrons in nanostructures and probe-like metallic domains. The results find important applications in high-resolution photonic and electronic modalities of scanning probe microscopy, nano-optics, plasmonics, and quantum sensing. Quasistatic formulation, leading to nonretarded quantities, is employed and justified on the basis of the nanoscale, here subwavelength, dimensions of the considered domains of interest. Within the quasistatic framework, we represent the nanostructure material domains with frequency-dependent dielectric functions. Quantities associated with the normal modes of the electronic systems, the nonretarded plasmon dispersion relations, eigenmodes, and fields are then calculated for several geometric entities of use in nanoscience and nanotechnology. From the classical energy of the charge density oscillations in the modeled nanoparticle, we then derive the Hamiltonian of the system, which is used for quantization. The quantized plasmon field is obtained and, employing an interaction Hamiltonian derived from the first-order perturbation theory within the hydrodynamic model of an electron gas, we obtain an analytical expression for the radiative decay rate of the plasmons. The established treatment is applied to multiple geometries to investigate the quantized charge density oscillations on their bounding surfaces. Specifically, using one sheet of a two-sheeted hyperboloid of revolution, paraboloid of revolution, and cylindrical domains, all with one infinite dimension, and the finite spheroidal and toroidal domains are treated. In addition to a comparison of the paraboloidal and hyperboloidal results, interesting similarities are observed for the paraboloidal domains with respect to the surface modes and radiation patterns of a prolate spheroid, a finite geometric domain highly suitable for modeling of nanoparticles such as quantum dots. The prolate and oblate spheroidal calculations are validated by comparison to the spherical case, which is obtained as a special case of a spheroid. In addition to calculating the potential and field distributions, and dispersion relations, we study the angular intensity and the relation between the emission angle with the rate of radiative decay. The various morphologies are compared for their plasmon dispersion properties, field distributions, and radiative decay rates, which are shown to be consistent. For the specific case of a nanoring, modeled in the toroidal geometry, significant complexity arises due to an inherent coupling among the various modes. Within reasonable approximations to decouple the modes, we study the radiative decay channel for a vacuum bounded single solid nanoring by quantizing the fields associated with charge density oscillations on the nanoring surface. Further suggestions are made for future studies. The obtained results are relevant to other material domains that model a nanostructure such as a probe tip, quantum dot, or nanoantenna.

The eIF4E is an important eukaryotic translation initiation factor, because of its ability to bind cap at 5'end of mRNA. There are three members of this protein family found in humans: eIF4E1, eIF4E2 and eIF4E3. eIF4E1 also plays role in in export of some mRNA from nucleus to cytoplasm. This protein is mostly regulated by mTOR signaling pathway and malfunctions in regulation leads to increased cell proliferation and thus tumorogenesis. eIF4E2 plays a role in regulating of translation during embryogenesis and it is known to mediate translation in terms of hypoxia. Role of eIF4E3 is so far shrouded in mystery. Some studies suggest it might be able to suppress tumor growth, but no studies have been done on human eIF4E3. Big potential of our work is, that all proteins we work with, are human. Based on our results, the endogenous amount of eIF4E3 protein is higher than it was thought. This is one of the reasons, why this protein should not escape our attention. In my diploma thesis, I have studied physiological characteristics of cell cultures overexpressing eIF4E proteins after mTOR inhibition treatment. I have realized that the most efficient inhibitor in all tested cell cultures is PP-242, which binds directly into active site of mTOR kinase. I have cloned 3xC FLAG tagged eIF4Es constructs and used...

Liquid Argon Time Projection Chambers (LArTPCs) represent one of the most widely utilized neutrino detection techniques in neutrino experiments, for instance, in the Short Baseline Neutrino (SBN) program and the future large-scale LArTPC: Deep Underground Neutrino Experiment (DUNE). The high-end technique, facilitating excellent spatial and calorimetric reconstruction resolution, also enables testing exotic Beyond Standard Model (BSM) theories, such as baryon number violation (BNV) processes (e.g., proton-decay, neutron-antineutron oscillation). At the same time, Machine Learning (ML) techniques have demonstrated their ubiquitous use in recent decades; ML techniques have also become some of the most powerful tools in high-energy physics (HEP) analyses. Furthermore, the development of algorithms to cater to the needs of problems in HEP (i.e., triggering, reconstruction, improving sensitivity, etc.) has also become an active area of research. By developing a combined approach using Convolutional Neural Network (CNN) and Boosted Decision Tree (BDT) techniques, the sensitivity of neutron-antineutron oscillation in DUNE is evaluated for a projected exposure of 400kton⋅ years. Additionally, to meet the triggering requirement to select such rare events in DUNE, such a search is only supported with highly efficient self-triggering algorithms. An ML-based self-triggering scheme for large-scale LArTPCs, such as DUNE, is also developed with the intention of implementation on field-programmable gate arrays (FPGAs). The ML-based approach for searching for neutron-antineutron oscillation can be demonstrated and validated on the current LArTPC MicroBooNE. The analysis in MicroBooNE represents the first-ever search for neutron-antineutron oscillation in a LArTPC. DUNE's projected 90％ C.L. sensitivity to the neutron antineutron oscillation lifetime is 6.45✕10³² years, assuming 1.327✕10³⁵ neutron⋅ years, equivalent to 10 years of DUNE far detector exposure (400kton⋅ years). For MicroBooNE, assuming 372 seconds of exposure (equivalent to 3.13✕10³⁶ neutron⋅ years), the 90% C.L. lifetime sensitivity is found at 3.07✕10²⁵ yrs, after accounting for Monte-Carlo statistical uncertainty and systematic uncertainty from detector effects.

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The intention of the project is to create an environment where the viewer can explore and form a personal narrative in the process of organic interaction with the work. At first glance, the scale of the installation will attract the viewer to the exhibition, however, upon close investigation he will discover there is deterioration, decay, and mutation. The ideas of time, beauty, decay, mortality, and interdependence will be discussed in this paper. The visitors are invited to interact with the work. I hope through spontaneous interaction the arrangements from the viewer will result in evolution of the work. The balance and tension between patterns and evolution, between creativity and predictability will evolve naturally. I hope the viewer can build a meaningful experience based on his or her cultural, intellectual, and social background through interacting with the installation.

The intention of the project is to create an environment where the viewer can explore and form a personal narrative in the process of organic interaction with the work. At first glance, the scale of the installation will attract the viewer to the exhibition, however, upon close investigation he will discover there is deterioration, decay, and mutation. The ideas of time, beauty, decay, mortality, and interdependence will be discussed in this paper. The visitors are invited to interact with the work. I hope through spontaneous interaction the arrangements from the viewer will result in evolution of the work. The balance and tension between patterns and evolution, between creativity and predictability will evolve naturally. I hope the viewer can build a meaningful experience based on his or her cultural, intellectual, and social background through interacting with the installation.

Nuclear Magnetic Resonance is used for the determination of the properties of porous media in Geophysics and oil exploration. As it stands, there is a challenge in understanding the connection between the times measured in Free Induction Decay Nuclear Magnetic Resonance experiments and the shape of samples. In this work, suspensions and watersaturated densely-packed porous media with the volume fraction of the glass solid phase in the range from 10–4 to ∼1 are found to exhibit FID decay rates proportional to the square root of the volume fraction of the solid phase of the samples. A model of spheres in liquid is proposed for the description of such behavior.

The question of the absolute mass scale of neutrinos is of particular interest for particle physics, astrophysics, and cosmology. The KATRIN experiment (KArlsruhe TRItium Neutrino experiment) aims to address the effective electron antineutrino mass from the shape of the tritium β-spectrum with an unprecedented sensitivity of 0.2 eV/c2 . One of the major systematic effects concerns the experimental energy scale, which has to be stable at the level of only a few parts in a million. For its calibration and monitoring the monoener- getic electrons emitted in the internal conversion of γ-transition of the metastable isotope 83m Kr will be extensively applied. The aim of this thesis is to address the problem of KA- TRIN energy scale distortions and its monitoring in detail. The source of electrons based on 83m Kr embedded in a solid as well as the source based on gaseous 83m Kr are studied. Based on the experimental results an approach for the continuous stability monitoring is proposed. 1

Purpose – Leveraged exchange traded funds (ETFs) have become increasingly popular since their introduction in 2006. In recent years, options on leveraged ETFs have been promoted as a means of enhancing returns and reducing risk. The purpose of this paper is to examine the interchangeability of S&P 500 ETF options with leveraged S&P 500 ETF options and to what extent these options allow investors to manage their risk exposure. Design/methodology/approach – With increasing liquidity for these fund’s options, simple option strategies such as covered calls and protective puts can be implemented. This study derives call-call and put-put parity between options on the underlying index and the associated leveraged ETFs. The paper examines comparative measures of return and risk on the underlying indices, along with covered call and protective put positions. Findings – Using the formulations derived, this study shows options on non-leveraged ETFs or on the underlying index can be substituted for leveraged ETF options. Empirical results suggest substituting options on leveraged ETFs with options on the underlying index or index ETF give comparable results, but can differ as the realized leverage ratio over time differs from projected values. Originality/value – This study is the first to the authors’ knowledge that investigates option strategies on leveraged and inverse ETFs of equity indices. It is also the first to derive call-call and put-put parity relations between options on ETFs and related leveraged and inverse ETFs. The results contribute to securities issuance, investment strategies, and option parity relations.

Neutrinoless Double Beta Decay(0𝜈𝛽𝛽) is one of the major research interests in neutrino physics. The discovery of 0𝜈𝛽𝛽 would answer persistent puzzles in the Standard Model of Elementary Particles. KamLAND-Zen is one of the leading efforts in the search of 0𝛽𝛽 and has acquired data from 745 kg of ^{136}Xe over 224 live-days. This data is analyzed using a Bayesian approach consisting of a Markov Chain Monte Carlo (MCMC) algorithm. The implementation of the Bayesian analysis, which is the focal point of this dissertation, yields a 90\% Credible Interval at T^{0𝜈}_{1/2} = 7.03 × 10^{25} years. Finally, a machine learning event classification algorithm, based on a spherical convolutional neural network (spherical CNN) was developed to increase the T^{0𝜈}_{1/2} sensitivity. The classification power of this algorithm was demonstrated on a Monte Carlo detector simulation, and a data driven classifier was trained to reject crucial backgrounds in the 0𝜈𝛽𝛽 analysis. After implementing the spherical CNN, an increase in T^{0𝜈}_{1/2} sensitivity of 11.0% is predicted. These early studies pave the way for substantial improvements in future 0𝜈𝛽𝛽 analyses.