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Abschlussbericht FEP 2022
(2022)
Der Abschlussbericht fasst die im Sommersemester 2022 erzielten Ergebnisse des Forschungs- und Entwicklungsprojektes im Studiengang Master of Science Wirtschaftsinformatik an der FH Münster zusammen.
Das Forschungsprojekt gliederte sich in drei thematische Blöcke. Der erste Block betrachtete den aktuell aufkommenden Ansatz, WebAssembly-Anwendungen in serverseitigen Umgebungen auszuführen. Die vielfältigen Leistungsfaktoren, z. B. mögliche Quellsprachen, Werkzeuge und Plattformen, wurde in systematischen Messungen hinsichtlich ihres Einflusses und der Leistungsfähigkeit miteinander verglichen. Im zweiten Themenblock standen jeweils zwei Programmiersprachen im Mittelpunkt. So wurden die Programmiersprachen Rust und Go hinsichtlich der Unterstützung von Nebenläufigkeit anhand des Beispiels eines prototypischen Webservers analysiert und gegenübergestellt. Weitere Leistungsdaten wurden für die Sprachen Rust und Python hinsichtlich der Verarbeitung von Graphalgorithmen erhoben und verglichen. Der dritte Themenblock befasste sich mit Kommunikationsaspekten in Service-Mesh-Architekturen. Hier wurden verschiedene Circuit-Breaker-Implementierungen sowie unterschiedliche Proxy-Ansätze zur Steuerung des Nachrichtenverkehrs gegenübergestellt und bewertet.
This report presents the findings related to the supporting mechanisms of university-business cooperation (UBC) that have been found to exist in Europe. These results derive from a fifteen and a half month study on the cooperation between HEIs1 and public and private organisations in Europe. The study was conducted by the Science-to-Business Marketing Research Centre, Germany (S2BMRC) for the DG Education and Culture at the European Commission (EC) during 2010 and 2011. The main components of the project are in-depth qualitative interviews with 11 recognised UBC experts as well as a major quantitative survey. The survey was translated into 22 languages and sent to all registered European HEIs (numbering over 3,000) in 33 countries during March 2011. Through this, a final sample population of 6,280 academics and HEI representatives was achieved making the study the largest study into cooperation between HEIs and business yet completed in Europe. Further, 30 good practice UBC case studies have been created to provide positive examples of European UBC.
This report presents the findings related to the factors affecting the extent of university-business cooperation that have been found to exist in Europe. These results derive from a fifteen and a half month study on the cooperation between higher education institutions1 (HEIs) and public and private organisations in Europe. The study was conducted by the Science-to-Business Marketing Research Centre, Germany (S2BMRC) for the DG Education and Culture at the European Commission (EC) during 2010 and 2011. The main components of the project are in-depth qualitative interviews with 11 recognised UBC experts as well as a major quantitative survey. The survey was translated into 22 languages and sent to all registered European HEIs (numbering over 3,000) in 33 countries during March 2011. Through this, a final sample population of 6,280 academics and HEI representatives was achieved making the study the largest study into cooperation between HEIs and business yet completed in Europe. Further, 30 good practice UBC case studies have been created to provide positive examples of European UBC.
This report presents the findings related to the barriers and drivers of university-business cooperation (UBC) that have been found to exist in Europe. These results derive from a fifteen and a half month study on the cooperation between higher education institutions1 (HEIs) and public and private organisations in Europe. The study was conducted by the Science-to-Business Marketing Research Centre, Germany (S2BMRC) for the DG Education and Culture at the European Commission (EC) during 2010 and 2011. The main components of the project are in-depth qualitative interviews with 11 recognised UBC experts as well as a major quantitative survey. The survey was translated into 22 languages and sent to all registered European HEIs (numbering over 3,000) in 33 countries during March 2011. Through this, a final sample population of 6,280 academics and HEI representatives was achieved, making the study the largest study into cooperation between HEIs and business yet completed in Europe. Further, 30 good practice UBC case studies have been created to provide positive examples of European UBC.
Background
Artificial intelligence (AI) has the capability to analyze vast amounts of data and has been applied in various healthcare sectors. However, its effectiveness in aiding pharmacotherapy decision-making remains uncertain due to the intricate, patient-specific, and dynamic nature of this field.
Objective
This study sought to investigate the potential of AI in guiding pharmacotherapy decisions using clinical data such as diagnoses, laboratory results, and vital signs obtained from routine patient care.
Methods
Data of a previous study on medication therapy optimization was updated and adapted for the purpose of this study. Analysis was conducted using R software along with the tidymodels extension packages. The dataset was split into 74% for training and 26% for testing. Decision trees were selected as the primary model due to their simplicity, transparency, and interpretability. To prevent overfitting, bootstrapping techniques were employed, and hyperparameters were fine-tuned. Performance metrics such as areas under the curve and accuracies were computed.
Results
The study cohort comprised 101 elderly patients with multiple diagnoses and complex medication regimens. The AI model demonstrated prediction accuracies ranging from 38% to 100% for various cardiovascular drug classes. Laboratory data and vital signs could not be interpreted, as the effect and dependence were unclear for the model. The study revealed that the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks.
Conclusion
In conclusion, the AI model exhibited promise in recommending appropriate medications for individual patients. While the study identified several obstacles during model development, most were successfully resolved. Future AI studies need to include the drug effect, not only the drug, if laboratory data is part of the decision. This could assist with interpreting their potential relationship. Human oversight and intervention remain essential for an AI-driven pharmacotherapy decision support system to ensure safe and effective patient care.
While the service sector is growing rapidly, the purchasing of services has not yet received significant attention in theory or practice. Service purchasers face serious challenges, and existing purchasing practices for services are often non-strategic. We choose an exploratory–qualitative research approach to investigate the purchasing of IT, logistics and Maintenance, Repair, and Overhaul (MRO) services. In particular, we focus on the role of visibility and analyze how service purchasers can benefit from extensive knowledge about their service networks. We determine that visibility indeed adds significant value to service purchasing and can help service purchasers to decrease costs, mitigate risks and maintain competitiveness.
This study investigates the role of individual differences in channel choice and switching behavior in a multichannel environment using latent class analysis on data from 1512 customers. Psychographic variables from five domains (risk attitudes, cognitive ability, motivation, personality, and decision-making style) serve as covariates for multichannel customer behavior. We identify six segments that differ significantly on six psychographic variables (readiness to take risks, need for cognition, autotelic and instrumental need for touch, and rational and intuitive decision-making styles). The results advance the theory-building of multichannel customer behavior and present insights for proactively managing customer journeys of distinct segments.