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AbstractLichtablenkungseffekte können bei der interferometrischen Beobachtung von Grenzschichten zu großen Fehlern bei der Ableitung der Konzentrationsprofile führen, sofern nicht Korrekturen bei der Interpretation vorgenommen werden.

Abstract. The impact of multiphase reactions involving nitrogen dioxide (NO2) and aromatic compounds was simulated in this study. A mechanism (CAPRAM 2.4, MODAC Mechanism) was applied for the aqueous phase reactions, whereas RACM was applied for the gas phase chemistry. Liquid droplets were considered as monodispersed with a mean radius of 0.1 µm and a liquid content (LC) of 50 µg m-3. The multiphase mechanism has been further extended to the chemistry of aromatics, i.e. reactions involving benzene, toluene, xylene, phenol and cresol have been added. In addition, reaction of NO2 with dissociated hydroxyl substituted aromatic compounds has also been implemented. These reactions proceed through charge exchange leading to nitrite ions and therefore to nitrous acid formation. The strength of this source was explored under urban polluted conditions. It was shown that it may increase gas phase HONO levels under some conditions and that the extent of this effect is strongly pH dependent. Especially under moderate acidic conditions (i.e. pH above 4) this source may represent more than 75% of the total HONO/NO2 - production rate, but this contribution drops down close to zero in acidic droplets (as those often encountered in urban environments).

This report summarizes the experimental work carried out at the Lawrence Livermore National Laboratory on the electronic structure and reactivity of uranium thin films on Pd, Pt, Si, graphite, Cu, and Au substrates from 1990 to 1993. The U-Pd system was studied in the most detail because it was the first to be chosen right after the completion of the experimental equipment. We first studied and characterized clean U overlayers and the possible surface reactions between this metal and the substrates studied. We then subjected these systems to reactive conditions such as heating and adsorbing corrosive gases (O{sub 2}, CO, CO{sub 2}, and C{sub 2}H{sub 4}). Finally we investigated the diffusion of U metal and some of its compounds into the substrates. A new technique was developed, based on Auger Electron Spectroscopy, to follow in real time the diffusion of U overlayers into the substrate. The temperature of the sample is ramped linearly up to 900{degrees}C while following the Auger peak intensities of the two components for a given system. Diffusion rates are obtained by differentiating the measured intensity curves, then peaks result corresponding to diffusion processes with different activation energies. This technique bears a strong similarity to thermal desorption spectroscopy (TDS), where the sample is heated linearly and the rate of desorption is measured as a function of temperature and heating rate.

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.