The Standard Model scalar potential contains a minimum at the Electroweak scale, responsible for the masses of the weak gauge bosons through the Higgs mechanism. However, if the Electroweak minimum is only a local minimum, and there exists a global minimum at a higher energy in the Higgs potential, then in a su ciently old universe we would expect the vacuum expectation value to be at the global minimum. The absence of a global minimum at higher energy is related to the condition that the Higgs self coupling is greater than or equal to zero for all energies. For any model that fails this, we expect new physics to enter before the energy at which the coupling becomes negative. We developed tools to automate the derivation of beta functions for renormalisable gauge theories, and used these to carry out evolution of the renormalisation group equations for the Standard Model and three extensions to the Standard Model | the Standard Model with a fourth generation, the Standard Model with right-handed neutrinos and a Left-Right Symmetric Model. We conclude that of these four models, the Standard Model is the only one in which all the couplings remain perturbative, and in which the Electroweak minimum is a global minimum.

No description available.

Quantum chemical calculations have been used to model dye-sensitized nanostructured titanium dioxide systems that can be used in solar cells for solar energy to electricity conversion. Structural, electronic and spectral properties of isolated dyes and both bare and dye-sensitized TiO2 have been calculated with density functional theory, providing detailed information about both the separate parts and the dye-TiO2 interface. The connection between the geometry, the ligand field splitting and the lifetime of the triplet metal-to-ligand charge transfer (MLCT) excited state has been explored for a series of ruthenium polypyridyl dyes. Moreover, the relative energetics of MLCT and metal centered triplet excited states have been studied for a number of such systems. It was found that small alterations of the polypyridyl ligands can result in significant changes in ligand field splitting and in the energetics of the triplet states. Attachment of the dyes to the TiO2 surface is achieved via anchor and spacer groups. The influence of such groups on various properties of the dye and their ability to act as mediators of photo-induced surface electron transfer has been studied. Delocalization of the lowest unoccupied dye orbital onto the spacer and/or anchor group indicates that certain unsaturated groups can mediate electron transfer. With a combination of methods that enables efficient computations and a scheme for construction of metal oxide clusters, chemical models for bare TiO2 nanocrystals in the 1-2 nm size range have been developed. The electronic structures show well-developed band structures with essentially no electronic band gap defect states. Atomistic models of the interface between TiO2 nanocrystals and Ru(II)-bis-terpyridine dyes, the so-called N3 dye as well as perylene dyes are reported. Electronic coupling strengths, which provide estimates for the electron injection times, are extracted from the interfacial electronic structure and the lowest electronic excitations are calculated.

One of the main challenges in wireless power transfer (WPT) devices is performance degradation when the receiver’s position and characteristics vary. The variations in the system parameters such as load impedance and coupling strength in WPT devices affect performance characteristics such as output voltage and power. When the system parameters are different from the optimal operating conditions, the performances are degraded. Therefore, the load impedance and coupling strength must be monitored to do the necessary optimization and control. However, such control approaches require additional sensing circuits and a data communication link between transmitter- and receiver-sides. This study proposes a new machine learning (ML) assisted WPT system that predicts the power delivered to the receiver by only using measurements at the transmitter-side. In addition, a method is also proposed to estimate load impedance and coupling coefficient using machine learning approach. We study what parameters measurable at the transmitter-side can be used to predict the output power delivered to receivers at variable load impedance and coupling strengths. In the proposed method, the output power of an inductor-capacitor-capacitor (LCC)-Series tuned WPT system is successfully predicted only using the measured root-mean-square (RMS) of the input current. Random forest algorithm has shown best accuracy to estimate the output power based on transmitter-side parameters only. The proposed approach is experimentally validated using a laboratory prototype. Harmonic components of the input current are used to assess the load impedance and coupling coefficient successfully. Multi-output regression has the highest accuracy for estimating the load impedance and coupling coefficient. The proposed ML algorithm is also used to classify the turn-on and -off regimes to ensure high-efficient operation. / En av de viktigaste utmaningarna med trådlösa kraftöverföring enheter är degraderingen av prestandan när mottagarens position och egenskaper varierar. Variationerna av systemets parametrar, såsom belastningsmotstånd och kopplings styrka i WPT-anordning, påverkar prestanda egenskaperna såsom spänning och effekt. När system parametrarna skiljer sig från de optimala drifts förhållandena, försämras prestandan. Därför måste luftmotståndet och kopplings styrkan övervakas, för att göra nödvändig optimering och kontroll. Sådana styrmetoder kräver emellertid ytterligare avkännings kretsar, och en data kommunikationslänk mellan sändar- och mottagarsidan. Denna studie föreslår ett nytt maskininlärning assisterat WPT-system, som förutsäger kraften som levereras till mottagaren genom att endast använda mätningar på sändarsidan. Dessutom föreslås en metod för att detektera belastningsimpedans och kopplings koefficient med användning av maskin inlärningsmetoder. Vi studerar vilka parametrar som är mätbara på sändarsidan och som kan användas för att förutsäga utgången effekten som levereras till mottagare vid varierande belastningsmotstånd och kopplings nivåer. I den föreslagna metoden förutses framgångs effekten för ett induktor-kondensator-kondensator LCCserie avstämt WPT-system endast framgångsrikt med hjälp av det uppmätta effektivvärdet för ingångs strömmen. Slumpmässig skogsalgoritm har visat exceptionell noggrannhet för att uppskatta uteffekten endast baserat på sändarsidans parametrar. Den föreslagna metoden valideras experimentellt med användning av en laboratorium prototyp. Harmoniska komponenter i ingångs strömmen används för att framgångsrikt bedöma last motståndet och kopplings koefficienten. Multi-utgångsregression har verkat vara mycket exakt för att uppskatta belastningsimpedans och kopplingskoefficient. Den föreslagna maskininlärning algoritmen används också för att klassificera start-och-off-regimer för att säkerställa hög effektiv drift.