Verbesserung der Wiederholbarkeit bei Verbrauchmessunger durch Normierung

The method involves recording input variables such as accelerator position, as fuel consumption during a test, and forming a neural network with the recorded variables, where the network specifies an output variable such as fuel consumption, after a training phase. The output variable is simulated by the network trained at a real vehicle using a standardized target curve and/or a speed curve. The simulated output variable corresponds to the real vehicle such that the vehicle accurately follows the target curve.

Dots versus antidots : computational exploration of structure, magnetism, and half-metallicity in boron-nitride nanostructures

Triangle-shaped nanohole, nanodot, and lattice antidot structures in hexagonal boron-nitride (h-BN) monolayer sheets are characterized with density functional theory calculations utilizing the local spin density approximation. We find that such structures may exhibit very large magnetic moments and associated spin splitting. N-terminated nanodots and antidots show strong spin anisotropy around the Fermi level, that is, half-metallicity. While B-terminated nanodots are shown to lack magnetism due to edge reconstruction, B-terminated nanoholes can retain magnetic character due to the enhanced structural stability of the surrounding two-dimensional matrix. In spite of significant lattice contraction due to the presence of multiple holes, antidot super lattices are predicted to be stable, exhibiting amplified magnetism as well as greatly enhanced half-metallicity. Collectively, the results indicate new opportunities for designing h-BN-based nanoscale devices with potential applications in the areas of spintronics, light emission, and photocatalysis. © 2009 American Chemical Society.

Evolution of cooperation in reputation system by group-based scheme

Reputation systems are very useful in large online communities in which users may frequently have the opportunity to interact with users with whom they have no prior experience. Recently, how to enhance the cooperative behaviors in the reputation system has become to one of the key open issues. Emerging schemes focused on developing efficient reward and punishment mechanisms or capturing the social or economic properties of participants. However, whether this kind of method can work widely or not has been hard to prove until now. Research in evolutionary game theory shows that group selection (or multilevel selection) can favor the cooperative behavior in the finite population. Furthermore, some recent works give fundamental conditions for the evolution of cooperation by group selection. In the paper, we extend the original group selection theory and propose a group-based scheme to enhance cooperation for online reputation systems. Related concepts are defined to capture the social structure and ties among participants in reputation system, e.g., group, assortativity, etc. Also, we use a Fermi distribution function to reflect the bounded rationality of participants and the existence of stochastic factors in evolutionary process. Extended simulations show that our scheme can enhance cooperation and improve the average performance of participants (e.g. payoff) in reputation system.

Dielectric screening in atomically thin boron nitride nanosheets

Two-dimensional (2D) hexagonal boron nitride (BN) nanosheets are excellent dielectric substrate for graphene, molybdenum disulfide, and many other 2D nanomaterial-based electronic and photonic devices. To optimize the performance of these 2D devices, it is essential to understand the dielectric screening properties of BN nanosheets as a function of the thickness. Here, electric force microscopy along with theoretical calculations based on both state-of-the-art first-principles calculations with van der Waals interactions under consideration, and nonlinear Thomas-Fermi theory models are used to investigate the dielectric screening in high-quality BN nanosheets of different thicknesses. It is found that atomically thin BN nanosheets are less effective in electric field screening, but the screening capability of BN shows a relatively weak dependence on the layer thickness.

Decomposition properties of PVA/graphene composites during melting-crystallization

Abstract The thermal decomposition of PVA and PVA composites during the melting-crystallization process is still unclear due to indistinct changes in chemical compositions. Using graphene as a model, the decomposition properties of PVA and PVA-graphene composites were systematically analyzed under multiple melting-crystallization cycles. And a series of isothermal decomposition experiments around the melting-crystallization temperature were carried out to simulate the corresponding decomposition kinetics. Based on multiple cycle melting-crystallization, the weight loss of PVA and PVA/graphene composites was successfully quantified. Further morphology investigation and chemical structure analysis indicated that the decomposition was non-uniformly distributed, rendering the possibility of crystallization for PVA and PVA/graphene composites after multiple heating-cooling cycles. In addition, isothermal decomposition analysis based on reduced time plot approach and model-free iso-conversional method indicated that Avrami-Eroffev model could well match the decomposition process of the neat PVA and PG-0.3 composite, while the Avrami-Eroffev and first order models could precisely forecast the decomposition of PG-0.9 composite. Both analyses during multiple cycle melting-crystallization and isothermal decomposition demonstrated that graphene served as decomposition accelerator in the whole thermal decomposition process, and particularly the decomposition of neat PVA and PVA/graphene composites was highly related to the band area ratios of C-H and O-H vibrations in Fourier transform infrared (FTIR) spectrum.