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Institute
- Fachbereich Elektrotechnik und Informationstechnik (1150) (remove)
Harmful effects of electromagnetic fields (EMF) on cognitive and behavioural features of humans and rodents have been controversially discussed and raised persistent concern about adverse effects of EMF on general brain functions. In the present study we applied radio-frequency (RF) signals of the Universal Mobile Telecommunications System (UMTS) to full brain exposed male Wistar rats in order to elaborate putative influences on stress hormone release (corticosteron; CORT and adrenocorticotropic hormone; ACTH) and on hippocampal derived synaptic long-term plasticity (LTP) and depression (LTD) as electrophysiological hallmarks for memory storage and memory consolidation. Exposure was computer controlled providing blind conditions. Nominal brain-averaged specific absorption rates (SAR) as a measure of applied mass-related dissipated RF power were 0, 2, and 10 W/kg over a period of 120 min. Comparison of cage exposed animals revealed, regardless of EMF exposure, significantly increased CORT and ACTH levels which corresponded with generally decreased field potential slopes and amplitudes in hippocampal LTP and LTD. Animals following SAR exposure of 2 W/kg (averaged over the whole brain of 2.3 g tissue mass) did not differ from the sham-exposed group in LTP and LTD experiments. In contrast, a significant reduction in LTP and LTD was observed at the high power rate of SAR (10 W/kg). The results demonstrate that a rate of 2 W/kg displays no adverse impact on LTP and LTD, while 10 W/kg leads to significant effects on the electrophysiological parameters, which can be clearly distinguished from the stress derived background. Our findings suggest that UMTS exposure with SAR in the range of 2 W/kg is not harmful to critical markers for memory storage and memory consolidation, however, an influence of UMTS at high energy absorption rates (10 W/kg) cannot be excluded.
Das anhaltende Wachstum wissenschaftlicher Veröffentlichungen wirft die Fragestellung auf, wie Literaturana-lysen im Rahmen von Forschungsprozessen digitalisiert und somit produktiver realisiert werden können. Insbesondere in informationstechnischen Fachgebieten ist die Forschungspraxis durch ein rasant wachsendes Publikationsaufkommen gekennzeichnet. Infolgedessen bietet sich der Einsatz von Methoden der Textanalyse (Text Analytics) an, die Textdaten automatisch vorbereiten und verarbeiten können. Erkenntnisse entstehen dabei aus Analysen von Wortarten und Subgruppen, Korrelations- sowie Zeitreihenanalysen. Dieser Beitrag stellt die Konzeption und Realisierung eines Prototypen vor, mit dem Anwender bibliographische Daten aus der etablierten Literaturdatenbank EBSCO Discovery Service mithilfe textanalytischer Methoden erschließen können. Der Prototyp basiert auf dem Analysesystem IBM Watson Explorer, das Hochschulen lizenzkostenfrei zur Verfügung steht. Potenzielle Adressaten des Prototypen sind Forschungseinrichtungen, Beratungsunternehmen sowie Entscheidungsträger in Politik und Unternehmenspraxis.
Einfluß der Kontoführungsmodelle auf den Effektivzins und die Restschuld bei Annuitätendarlehen
(1989)
Einfluss von Künstlicher Intelligenz auf Customer Journeys am Beispiel von intelligentem Parken
(2021)
Im Konsumentenmarkt entstehen vermehrt neue Anwendungen von Künstlicher
Intelligenz (KI). Zunehmend drängen auch Geräte und Dienste in den Markt, die
eigenständig über das Internet kommunizieren. Dadurch können diese Geräte und
Dienste mit neuartigen KI-basierten Diensten verbessert werden. Solche Dienste
können die Art und Weise beeinflussen, wie Kunden kommerzielle Entscheidungen
treffen und somit das Kundenerlebnis maßgeblich verändern. Der Einfluss von KI
auf kommerzielle Interaktionen wurde bisher noch nicht umfassend untersucht.
Basierend auf einem Framework, welches einen ersten Überblick über die Effekte
von KI auf kommerzielle Interaktionen gibt, wird in diesem Kapitel der Einfluss von KI auf Customer Journeys am konkreten Anwendungsfall des intelligenten Parkens analysiert. Die daraus gewonnenen Erkenntnisse können in der Praxis als Grundlage
genutzt werden, um das Potenzial von KI zu verstehen und bei der Gestaltung eigener Customer Journeys umzusetzen.