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Abstract Motivation: The steady growth of the number of available protein structures has constantly motivated the development of new algorithms for detecting structural correspondences in proteins. Detecting structural equivalences in two or more proteins is computationally demanding as it typically entails the exploration of the combinatorial space of all possible amino acid pairings in the parent proteins. The search is often aided by the introduction of various constraints such as considering protein fragments, rather than single amino acids, and/or seeking only sequential correspondences in the given proteins. An additional challenge is represented by the difficulty of associating to a given alignment, a reliable a priori measure of its statistical significance. Results: Here, we present and discuss MISTRAL (Multiple STRuctural ALignment), a novel strategy for multiple protein alignment based on the minimization of an energy function over the low-dimensional space of the relative rotations and translations of the molecules. The energy minimization avoids combinatorial searches and returns pairwise alignment scores for which a reliable a priori statistical significance can be given. Availability: MISTRAL is freely available for academic users as a standalone program and as a web service at http://ipht.cea.fr/protein.php. Contact: michelet@sissa.it Supplementary information: Supplementary data are available at Bioinformatics online.

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.

AbstractGold, silver, platinum and palladium typically crystallize with the face-centred cubic structure. Here we report the high-yield solution synthesis of gold nanoribbons in the 4H hexagonal polytype, a previously unreported metastable phase of gold. These gold nanoribbons undergo a phase transition from the original 4H hexagonal to face-centred cubic structure on ligand exchange under ambient conditions. Using monochromated electron energy-loss spectroscopy, the strong infrared plasmon absorption of single 4H gold nanoribbons is observed. Furthermore, the 4H hexagonal phases of silver, palladium and platinum can be readily stabilized through direct epitaxial growth of these metals on the 4H gold nanoribbon surface. Our findings may open up new strategies for the crystal phase-controlled synthesis of advanced noble metal nanomaterials.

The Petroleum Marketing Monthly (PMM) provides information and statistical data on a variety of crude oils and refined petroleum products. The publication presents statistics on crude oil costs and refined petroleum products sales for use by industry, government, private sector analysts, educational institutions, and consumers. Data on crude oil include the domestic first purchase price, the f.o.b. and landed cost of imported crude oil, and the refiners` acquisition cost of crude oil. Refined petroleum product sales data include motor gasoline, distillates, residuals, aviation fuels, kerosene, and propane. 7 figs., 50 tabs.

A specific role for the HTGR in a national energy strategy is examined. The issue is addressed in two ways. First, the role of the HTGR-GT Binary cycle plant is examined in a national energy strategy based on symbiosis between fast breeder and advanced converter reactors utilizing the thorium U233 fuel cycle. Second, the advantages of the HTGR-GT dry-cooled plant operating in arid regions is examined and compared with a dry-cooled LWR. An event tree analysis of potential benefits is applied.

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results.