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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.
Der Abschlussbericht fasst die im Wintersemester 2023/2024 erzielten Ergebnisse des Forschungs- und Entwicklungsprojektes im Studiengang Master of Science Wirtschaftsinformatik an der FH Münster zusammen.
Das Forschungsprojekt befasste sich mit der Fragestellung, wie sich hochperformanter Code in systemfernen Programmiersprachen wie Java oder JavaScript integrieren lässt, um die vorhandene Hardwareleistung moderner CPUs und GPUs besser ausnutzen zu können. Derzeitig wird hierzu sowohl im Umfeld der Java-Plattform als auch in einer Working Group des World Wide Web Consortiums an Vorschlägen zur verbesserten SIMD-Integration gearbeitet. Im Forschungprojekt wurden diese Vorschläge aufgegriffen und hinichtlich des resultierenden Programmieraufwandes und der erzielbaren Leistungssteigerung qualitativ und quantitativ bewertet. Für JavaScript-basierte Browseranwendungen standen die Schnittstellen WebGPU und WebGL im Mittelpunkt, im Java-Umfeld wurden die drei Schnittstellen Foreign Functions & Memory API, Java Vector API und Java Native Interface (JNI) miteinander verglichen und bewertet.
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.
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.
There are many challenges in identifying and managing a disruptive innovation stemming from the limited knowledge on how it unfolds over time. Researchers have identified early signals and ex ante conditions that indicate its occurrence. However, an analysis from a process view acknowledging the underlying dynamics is yet to be done. By taking a process view within a systematic literature review, we analyse the scattered findings on the process of disruptive innovation to identify events and actions leading to a disruptive effect over time. We challenge the understanding of disruptive innovation as an outcome and the linearity of the process by proposing that disruptive innovation can be understood as occurring through emergent dynamics. These dynamics are constituted by: (a) the timing of entry and underlying processes that influences (b) the synchronization of events and actions and is shaped by (c) the adaptability of strategic actions. Thus, we complexify the concept of disruptive innovation to support the understanding of its unfolding and advance its manageability.