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A survey on pre-training requirements for deep learning models to detect obstructive sleep apnea events
No full textThe development of automatic solutions for the detection of physiological events of interest is booming. Improvements in the collection and storage of large amounts of healthcare data allow access to these data faster and more efficiently. This fact means that the development of artificial intelligence models for the detection and monitoring of a large number of pathologies is becoming increasingly common in the medical field. In particular, developing deep learning models for detecting obstructive apnea (OSA) events is at the forefront. Numerous scientific studies focus on the architecture of the models and the results that these models can provide in terms of OSA classification and Apnea-Hypopnea-Index (AHI) calculation. However, little focus is put on other aspects of great relevance that are crucial for the training and performance of the models. Among these aspects can be found the set of physiological signals used and the preprocessing tasks prior to model training. This paper covers the essential requirements that must be considered before training the deep learning model for obstructive sleep apnea detection, in addition to covering solutions that currently exist in the scientific literature by analyzing the preprocessing tasks prior to training
Software Scripts for Sensor Data Extraction in Rasberry Pi
No full textThis paper compares two popular scripting implementations for hardware prototyping: Python scripts exe- cut from User-Space and C-based Linux-Driver processes executed from Kernel-Space, which can provide information to researchers when considering one or another in their implementations. Conclusions exhibit that deploying software scripts in the kernel space makes it possible to grant a certain quality of sensor information using a Raspberry Pi without the need for advanced real-time operational systems
Influence of gender and age distinction on patient data for sleep apnea detection using artificial intelligence models
No full textThe massive use of patient data for the training of artificial intelligence algorithms is common nowadays in medicine. In this scientific work, a statistical analysis of one of the most used datasets for the training of artificial intelligence models for the detection of sleep disorders is performed: sleep health heart study 2. This study focuses on determining whether the gender and age of the patients have a relevant influence to consider working with differentiated datasets based on these variables for the training of artificial intelligence models
Exploring the Parallel Use of Multiple Corporate Entrepreneurship Units: An Empirical Investigation of the German Business Landscape
No full textCorporate Entrepreneurship (CE) units have become an increasingly important part of established companies’ development activities enabling them to also create more discontinuous innovations. As a result, companies have developed and implemented different forms of CE units, such as corporate accelerators, incubators, startup supplier programs, and corporate venture capital. Driven by the need to innovate, companies have even begun to use multiple CE units simultaneously. However, this has not been empirically investigated yet. Thus, with this study, we aim to shed some light on this by investigating the parallel use of multiple CE units in the German business landscape. We conducted an extensive desk research, combining, coding, and analyzing different sources. We found that 55 out of 165 large established companies have multiple CE units, which allowed us to characterize the parallel use and identify differences and similarities, e.g., in terms of industry, company size, and CE forms implemented. We conclude by presenting different implications for both practice and research and by pointing out directions for future research
Faktor Mensch im Fokus der digitalen Transformation von klein- und mittelständischen Unternehmen (KMUs) am Bau
Get PDFDeep transformation models for functional outcome prediction after acute ischemic stroke
No full text5 Minuten fürs Klima
No full text5 Minuten fürs Klima ist ein angewandtes Forschungsprojekt zu innovativer Klimakommunikation von drei Professor:innen der Hochschulen für angwandte Wissenschaft in Augsburg, Konstanz und Landshut. Wir laden Sie herzlich ein, unsere Videos in Ihre Vorlesung einzubetten.
Unsere Idee:
In einem wiederkehrenden 5-Minuten-Fenster (in jeder Vorlesung) lässt sich über das Semester (und unabhängig vom Fach) zu Lebensstiländerungen inspirieren, zu beruflichem Wirken ermuntern, für die Akzeptanz des Wandels werben und zur Unterstützung politischer Prozesse ermutigen
Optimale Verteilung knapper Güter
No full textVortrag von Prof. Dr. Frank Best (HTWG Konstanz) mit einer Vorführung des Impro Theaters Konstanz im Rahmen des Konstanzer Wissenschaftsforums „Klima im Wandel – von Kipppunkten, Korallen und Klimakonferenzen“ am 11. und 12. November 2022.
Die Klimakrise fordert nicht nur Politik und Gesellschaft auf eine bisher nicht dagewesene Art und Weise heraus. Auch die Wissenschaft muss sich – über alle Disziplinen hinweg – drängenden Aufgaben und Fragen stellen.
In einer neuen interaktiven Ausgabe der beliebten Reihe „Konstanzer Wissenschaftsforum“ erklären Wissenschaftler*innen Zusammenhänge, beziehen Expert*innen Stellung und geben Praktiker*innen Einblicke in ihr Arbeitsumfeld rund um den Klimawandel
Jahresbericht 2022
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Berichtszeitraum: 1.9.2021 - 31.8.202