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This paper describes the realization of a novel neurocomputer which is based on the concepts of a coprocessor. In contrast to existing neurocomputers the main interest was the realization of a scalable, flexible system, which is capable of computing neural networks of arbitrary topology and scale, with full independence of special hardware from the software's point of view. On the other hand, computational power should be added, whenever needed and flexibly adapted to the requirements of the application. Hardware independence is achieved by a run time system which is capable of using all available computing power, including multiple host CPUs and an arbitrary number of neural coprocessors autonomously. The realization of arbitrary neural topologies is provided through the implementation of the elementary operations which can be found in most neural topologies.
Meitner-Auger-electron emitters have a promising potential for targeted radionuclide therapy of cancer because of their short range and the high linear energy transfer of Meitner-Auger-electrons (MAE). One promising MAE candidate is 197m/gHg with its half-life of 23.8 h and 64.1 h, respectively, and high MAE yield. Gold nanoparticles (AuNPs) that are labelled with 197m/gHg could be a helpful tool for radiation treatment of glioblastoma multiforme when infused into the surgical cavity after resection to prevent recurrence. To produce such AuNPs, 197m/gHg was embedded into pristine AuNPs. Two different syntheses were tested starting from irradiated gold containing trace amounts of 197m/gHg. When sodium citrate was used as reducing agent, no 197m/gHg labelled AuNPs were formed, but with tannic acid, 197m/gHg labeled AuNPs were produced. The method was optimized by neutralizing the pH (pH = 7) of the Au/197m/gHg solution, which led to labelled AuNPs with a size of 12.3 ± 2.0 nm as measured by transmission electron microscopy. The labelled AuNPs had a concentration of 50 μg (gold)/mL with an activity of 151 ± 93 kBq/mL (197gHg, time corrected to the end of bombardment).
The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.