One of the most challenging difficulties for incumbent organisations, especially small- and medium-sized enterprises (SMEs), is to manage digital transformation driven by technological change. Incumbent organisations' responses to digital transformation have been extensively studied in the current literature. However, most research neglects digital transformation in SMEs. There are hardly any valid developed measures for the maturity of digital transformation. We present a holistic digital transformation maturity model based on an extensive literature review, qualitative computer-assisted data analysis, and empirical findings. The digital transformation maturity model focuses on small- and medium-sized enterprises' unique features and characteristics. We proved the practical applicability and relevance of the digital transformation maturity model in an extensive study involving various organisations, particularly German SMEs (n = 310). Organisations can use this model to assess themselves initially and, through this process, gain a comprehensive understanding of the multiple forms of digital transformation.

Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda.

Objective: Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs. Materials and Methods: We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A "train and validation") using cross-validation, and then applied the models to a second dataset (dataset B "test") to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance. Results: With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the "train and validation" dataset A and 0.94 in the "test" dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension. Conclusion: These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.

Virtual Reality (VR) leads to the highest level of immersion if presented using a 1:1 mapping of virtual space to physical space—also known as real walking. The advent of inexpensive consumer virtual reality (VR) headsets, all capable of running inside-out position tracking, has brought VR to the home. However, many VR applications do not feature full real walking, but instead, feature a less immersive space-saving technique known as instant teleportation. Given that only 0.3% of home users run their VR experiences in spaces more than 4m2, the most likely explanation is the lack of the physical space required for meaningful use of real walking. In this thesis, we investigate how to overcome this hurdle. We demonstrate how to run 1:1-mapped VR experiences in small physical spaces and we explore the trade-off between space and immersion. (1) We start with a space limit of 15cm. We present DualPanto, a device that allows (blind) VR users to experience the virtual world from a 1:1 mapped bird’s eye perspective—by leveraging haptics. (2) We then relax our space constraints to 50cm, which is what seated users (e.g., on an airplane or train ride) have at their disposal. We leverage the space to represent a standing user in 1:1 mapping, while only compressing the user’s arm movement. We demonstrate our 4 prototype VirtualArms at the example of VR experiences limited to arm movement, such as boxing. (3) Finally, we relax our space constraints further to 3m2 of walkable space, which is what 75% of home users have access to. As well- established in the literature, we implement real walking with the help of portals, also known as “impossible spaces”. While impossible spaces on such dramatic space constraints tend to degenerate into incomprehensible mazes (as demonstrated, for example, by “TraVRsal”), we propose plausibleSpaces: presenting meaningful virtual worlds by adapting various visual elements to impossible spaces. Our techniques push the boundary of spatially meaningful VR interaction in various small spaces. We see further future challenges for new design approaches to immersive VR experiences for the smallest physical spaces in our daily life.