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Despite the emergence of artificial intelligence (AI) and machine learning (ML) in medicine and the resultant interest in predictive analytics in surgery, there remains a paucity of research on the actual impact of prediction models and their effect on surgeons’ risk assessment of post-surgical complications. This research evaluated how spinal surgeons predict post-surgical complications with and without additional information generated by a ML predictive model. The study was conducted in two stages. In the preliminary stage an ML prediction model for post-surgical complications in spine surgery was developed. In the second stage, a survey instrument was developed, using patient vignettes, to determine how providing ML model support affected surgeons’ predictions of post-surgical complications. Results show that support provided by a ML prediction model improved surgeons’ accuracy to correctly predict the presence or absence of a complication in patients undergoing spinal surgery from 49.1% to 54.8% (p=0.024). It is clear that predicting post-surgical complications in patients undergoing spinal surgery is difficult, for models and experienced surgeons, but it is not surprising that additional information provided by the ML model prediction was beneficial overall. This is the first study in the spine surgery literature that has evaluated the impact of a ML prediction model on surgeon prediction accuracy of post-surgical complications.

This diploma thesis deals with the problem of portfolio optimization in relation to the mean vector and the variance matrix of yields. The emphasis is put on Mar- kowitz model. In the thesis there are explored some possibilities of robustification based on the used parametric set. Beside the classic formulation of the task our focus is also devoted to the cases in which short sales are not allowed. The core of the thesis constitutes of a simulation study that models the impact of errors in the estimation of the input parameters of Markowitz model. It takes into account different types of risk aversions and different approaches to modelling parameter perturbations . Therefore it specifies the hypothesis of the dominating influence of the mean vector estimate which is valid only for a risk lover. 1

Passive safety is a well-known term. This term can be further categorized into different topics of the car passive safety, restraint systems, safety assistants (ABS, ESP, ASR, etc.). One of these topics is passive safety of the fuel system. Safety and tightness of the fuel system must be guaranteed even under non-standard conditions, for example a collision against a fixed obstacle. This issue is not often mentioned in the field of car safety. It is considered a standard. Passive safety of the fuel system is often ensured using various interesting technical solutions and devices, usually patented ones. The development of these solutions is supported by numerical simulations in different stages of development process. The doctoral thesis deals with impact loading of the plastic components of the fuel system, in particular Fuel Supply Module (FSM), which is mounted inside the fuel tank. The flange is the most important part of the fuel supply module from the car safety point of view. The flange closes FSM on the external side of the fuel tank. The thesis focuses on the finite element analysis of the complete or partial FSM, and the flange itself during impact loading. The main objective of this thesis are numerical material models, taking into account important aspects of the mechanical behavior of polymer materials during impact loading. There are a lot of ad hoc invented or standardized experiments described in this thesis. These experiments are used for estimation of the material parameters or comparison of numerical analysis vs real conditions, or tests. The solver LS-DYNA was mainly used for numerical simulations. The final results of this thesis brings new quantified knowledge about behavior of the Typical Semi-Crystal Polymer (TSCP), not only for impact loading. The practical part of this thesis defines new methodology for the numerical simulation approach of impact loading for FSM. This methodology is directly usable for new product development. A lot of numerical material models were developed and tested. The best results were achieved using numerical material model *MAT_24 with combination of *MAT_ADD_EROSION card. The limits and parameters for this numerical material model was estimated empirically during conducting experiments. The numerical material model SAMP-1 was partly solved in this doctoral thesis, but more detail study will be given in future works.

Additive manufacturing is described in literature as a disruptive technology for spare parts supply chains and shows the potential to impact business models for spare parts production through various advantages over conventional manufacturing. Understanding changes in business models is important for companies to adopt any technology and explore business opportunities around it. This research aims to fill the gap in literature on how business models change when additive manufacturing is used for spare parts production in the automotive industry. To investigate those changes a qualitative research design with semi-guided interviews with experts in automotive companies was chosen. The findings showed that the adoption of additive manufacturing in the automotive industry is generally slow and only a few spare parts are specifically developed for additive manufacturing. Especially for niche low-volume and high-value applications AM is already used. At the same time identifying new business cases is needed for broader adoption. Contrasting opinions in literature that additive manufacturing is a disruptive technology, the adopters referred to it rather as a tool. Moreover, the findings showed that the potential of additive manufacturing described in literature is still there but cannot be utilized yet due to various identified barriers at the current stage. The main identified barriers are lack of knowledge, suitable manufacturing data, and resources to explore additive manufacturing.