Traditional financial companies play a central role in ensuring financial stability and reliability. However, these companies face institutional problems due to inefficiencies, lack of transparency and limited innovation. For this reason, the use of distributed ledger technology, or blockchain, has been explored as an alternative and its integration into traditional finance towards a 'hybrid' approach. The Dutch mortgage market was chosen as the focus of this research and is further analyzed due to its high transaction costs, including the high cost of trust. This led to the research objective of designing a Hybrid Mortgage Lending System (HMLS) that reduces the cost of trust.

A design science research methodology was used for this research where two interwoven artefacts were developed. The first artefact was a contextualized trust model based on three trustworthiness factors: integrity, ability and benevolence. The second artefact was a demonstration for the HMLS using a technological model.

The trust model was validated through expert interviews and enriched with additional information. Based on the feedback from the expert interviews, nine meta-requirements for the technological model were developed. The proposed solution for the design of the HMLS is to automatically distribute fractional ownership of mortgages via Non-Fungible Tokens (NFTs) through an ecosystem with the traditional finance company as originator and public investors as lenders. The ownership of the NFTs is transparently tracked via the blockchain to monitor the fractional ownership of a home. These NFTs exist in mixed token pools and are controlled by the traditional finance companies. These pools are supplemented with other digital assets to create different risk profiles to suit different types of investors. A demonstration of these token pools is shown, with an overview of the architecture explaining the mortgage application and transaction processes through the different layers. Finally, a stakeholder map is shown and their roles within the ecosystem are explained. The research provided new insights by making the second model dependent on the first. The findings highlight new challenges for further research, such as prototyping or improving the versatility of the model.

High-Speed Atomic Force Microscopy is widely used for investigating biological architectures and the semiconductor industry. The main limitation comes from parachuting or the Wile E. Coyote effect. Parachuting is the phenomenon where the cantilever taps on the sample towards a steep decrease in height, but does not get adjusted for this decrease fast enough, resulting in poor imaging quality at higher scan speeds. A proof-of-concept has been modeled for an adaptive raster scanning methodology on the X and Y piezo actuators. There will be investigated how a variable scan speed can decrease the effect of parachuting and thereby improve the spatial resolution. A detection algorithm is designed to measure the parachuting and uphill events at high scanning speed as accurately and precisely as possible during the forward scan. This has been done by defining a mixed signal that multiplies the first and second derivatives of the filtered deflection signal. A variable scan speed will be applied for the backward scan. The detection algorithm turned out to have a very high repeatability of 97.1% for the uphill events, and 93.6% for the parachuting events. Its accuracy turned out to have a maximum deviation of one signal period, which has been accounted for within the controller. Implementing this for the adaptive controller results in an improvement in both resolution and time efficiency. The adaptive controller is up to 9.5 times more accurate and time efficient compared to conventional methods.