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- Institut für Strategische Innovation und Technologiemanagement - IST (29)
- Institut für Systemdynamik - ISD (64)
- Institut für Werkstoffsystemtechnik Konstanz - WIK (5)
Ziegelsplittbetone der Nachkriegsjahre und moderne RC-Betone - Nachhaltigkeit an Objektbeispielen
(2018)
Das Bauwesen gehört zu den größten Verbrauchern an natürlichen Ressourcen und Energie in der deutschen Wirtschaft. Das ist in vielen Fällen trotzdem ökologisch und ökonomisch vertretbar, weil die Bauteile und Bauwerke verglichen mit anderen "Produkten" eine deutlich längere technische Lebensdauer haben - im Fall des Betons zwischen ca. 25 und 100 Jahren - und wenn nach dem Rückbau hohe Recyclingquoten erzielt werden. In Bezug auf Einsparmöglichkeiten spielt der Massenbaustoff Beton eine ganz zentrale Rolle. Neben der Einsparung des sehr energieintensiven Zements und der Entwicklung von Substitutionsbindemitteln, stehen auch die Gesteinskörnungen im Fokus, die den größten Anteil am Beton ausmachen. Im Artikel werden auf der Basis eigener Ergebnisse aus einem DBU?geförderten Forschungsprojekt zu RC?Betonen die Nachhaltigkeit von Beton an Objektbeispielen vorgestellt und über den Sachstand zur aktuellen Nutzung von R-Betonen in Deutschland informiert. Der Fokus liegt dabei auf der Wertigkeit mineralischer Baustoffe aus vergleichsweise wenigen, überwiegend natürlichen Komponenten wie beim Beton, dessen Instandsetzungsmöglichkeiten und bessere Voraussetzungen für späteres Recycling gegenüber vielen modernen, kunststoffhaltigen Verbundbaustoffen.
Wirkungen gestalten
(2016)
Durch den Einsatz von mobilen Endgeräten (z.B. Tablets, Smartphones) erschließen sich immer mehr Möglichkeiten, die Ausführung von Geschäftsprozessen zu unterstützen. Beispielsweise können Geschäftsprozessaktivitäten (z.B. Genehmigung eines Angebots) ortsunabhängig bearbeitet werden, wodurch die Durchlaufzeit signifikant reduziert wird. Die Nutzung von mobilen Apps beschränkt sich hierbei meist nur auf die Unterstützung von effizienter und flexibler Interaktion zwischen den verschiedenen ausführenden Rollen. Dieser Artikel beschreibt, wie mobile Apps nicht nur die Ausführung, sondern auch die Optimierung von Geschäftsprozessen unterstützen können. Hierzu werden vordefinierte Qualitätskriterien kontextabhängig während der Ausführung von Aktivitäten erfasst. Die durch traditionelle Methoden erfassten Daten (z.B. Messung von Kennzahlen) werden somit durch in Echtzeit gesammeltes User Feedback ergänzt. Der Ansatz wird am Beispiel einer eigens entwickelten mobilen App demonstriert und evaluiert.
Nowadays established companies use Corporate Entrepreneurship (CE) as a means to create discontinuous innovations. Many companies thereby even implement multiple CE units that typically involve several entrepreneurial activities. This explorative study aimed to identify the reasons why established companies implement multiple CE units concurrently. In conducting a comparative case study with eight companies from different industries, valuable insights for science and practice were gained. We provide an overview of different 11 reasons for implementing multiple CE units. This shows that the combination of CE units used by companies differs depending on the reason. It further allowed to derive general approaches of established companies to the implementation of CE units. Last, we identify the concept of co-specialization to be a central driver explaining the creation of the need to set up multiple units. We conclude by indicating implications and subjects for future research.
The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel Hidden Markov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.