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Recent approaches in scaffold engineering for bone defects feature hybrid hydrogels made of a polymeric network (retains water and provides light and porous structures) and inorganic ceramics (add mechanical strength and improve cell-adhesion). Innovative scaffold materials should also induce bone tissue formation and incorporation of stem cells (osteogenic differentiation) and/or growth factors (inducing/supporting differentiation). Recently, purinergic P2X and P2Y receptors have been found to significantly influence the osteogenic differentiation process of human mesenchymal stem cells (hMSC). (1) Aim of this work is to develop polysaccharide (PS) composites to be used as scaffolds containing complementary receptor ligands to enable guided stem cell differentiation towards bone formation.
Temperature Dependency of Morphological Structure of Thermoplastic Polyurethane using WAXS and SAXS
(2016)
Polyurethanes achieved an exceptional position among the most important organic polymers due to their highly specific technological application areas. Polyurethanes represent a polyaddition product of isocyanate and diols. In terms of their enormous industrial importance, the chemistry of isocyanates has been extensively studied.
Möglichkeiten und Grenzen der Baustoffanalytik und anwendungstechnische Prüfungen an Objekten
(2018)
Untersuchungen zum Einfluss von chemischen Aktivatoren und Templaten auf die Zementhydratation
(2018)
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.
Cost efficient energy monitoring in existing large buildings demands for autonomous indoor sensors with low power consumption, high performance in multipath fading channels and economic implementation. Good performance in multipath fading channels can be achieved with noncoherent chaotic modulation schemes such as chaos on-off keying (COOK) or differential chaos shift keying (DCSK). While COOK stands out in the area of power consumption, DCSK excels when it comes to its performance in noisy and multipath fading channels. This paper evaluates a combination of both schemes for autonomous indoor sensors. The simulation results show 50% less power consumption than DCSK and more than 3dB SNR gain in Rayleigh fading channels at BER=10-3 as compared to COOK, making it a promising candidate for low power transmission in autonomous wireless indoor sensors. We further present an enhanced version of this scheme showing another 1 dB SNR improvement, but at 25% less power consumption than DCSK.