Quality Electric Motor Repair: A Guidebook for Electric Utilities

This guidebook provides utilities with a resource for better understanding and developing their roles in relation to electric motor repair shops and the industrial and commercial utility customers that use them. The guidebook includes information and tools that utilities can use to raise the quality of electric motor repair practices in their service territories

Industrial motor repair in the United States

This report characterizes the motor repair industry in the United States; summarizes current motor repair and testing practice; and identifies barriers to energy motor repair practice and recommends strategies for overcoming those barriers

Status of the AGS Polarized H- Source

Development of a polarized H/sup -/ source for the AGS polarized proton project, initially begun at Argonne National Laboratory in 1979, has been continuing at Brookhaven National Laboratory since early 1982. A description of the source is given and the status of the project is reviewed

Efficiency of silicon thin-film photovoltaic modules with a front coloured glass

Photovoltaic electricity has already proven its ability to compete with other well established technologies for energy production. The abundant and non-toxic raw materials, the yearly increasing efficiency as well as the production cost of silicon thin-film solar cells getting lower and lower make this technology always more interesting for a wide spread use. Beside the functional features, the size, colour and glass texture of a PV module determine its appearance and aesthetics. In order to be more compliant with the built environment, photovoltaic installations have to be improved in terms of visual rendering, matching of colour of the existing roof-tops and parasitic reflections. The crystalline technology already offers various types of systems with a large choice of shapes, textures and colours as well as “semi-transparent” modules more easily integrated in the roof-tops or facade. By changing the anti-reflective coating (ARC) of a crystalline solar cell, it is possible to modify their colour [1]. However, for thin-film silicon technology the challenge is completely different, and up to now, the only way to modify the module colour is to reduce the thickness of the active layer and consequently its efficiency. Therefore new ways to enhance the visual rendering of the thin-film modules have to be explored. A study led in the frame of the ArchinSolar project [2] has shown that architects are ready to integrate PV modules with enhanced aesthetic aspect, even though there was a 10 % loss in efficiency. The present study shows how new coloured filters can be used to enhance PV modules’ appearance while minimizing power loss, to achieve a better integration in the traditional urban or rural environment

Measurement of the CKM Matrix Element ∣Vcb∣|V_{cb}| from B0→D∗−ℓ+νℓB^{0} \to D^{*-} \ell^+ \nu_\ell at Belle

We present a new measurement of the CKM matrix element $|V_{cb}|$ from $B^{0} \to D^{*-} \ell^+ \nu_\ell$ decays, reconstructed with the full Belle data set of $711 \, \rm fb^{-1}$ integrated luminosity. Two form factor parameterizations, originally conceived by the Caprini-Lellouch-Neubert (CLN) and the Boyd, Grinstein and Lebed (BGL) groups, are used to extract the product $\mathcal{F}(1)\eta_{\rm EW}|V_{cb}|$ and the decay form factors, where $\mathcal{F}(1)$ is the normalization factor and $\eta_{\rm EW}$ is a small electroweak correction. In the CLN parameterization we find $\mathcal{F}(1)\eta_{\rm EW}|V_{cb}| = (35.06 \pm 0.15 \pm 0.56) \times 10^{-3}$, $\rho^{2}=1.106 \pm 0.031 \pm 0.007$, $R_{1}(1)=1.229 \pm 0.028 \pm 0.009$, $R_{2}(1)=0.852 \pm 0.021 \pm 0.006$. For the BGL parameterization we obtain $\mathcal{F}(1)\eta_{\rm EW}|V_{cb}|= (34.93 \pm 0.23 \pm 0.59)\times 10^{-3}$, which is consistent with the World Average when correcting for $\mathcal{F}(1)\eta_{\rm EW}$. The branching fraction of $B^{0} \to D^{*-} \ell^+ \nu_\ell$ is measured to be $\mathcal{B}(B^{0}\rightarrow D^{*-}\ell^{+}\nu_{\ell}) = (4.90 \pm 0.02 \pm 0.16)\%$. We also present a new test of lepton flavor universality violation in semileptonic $B$ decays, $\frac{{\cal B }(B^0 \to D^{*-} e^+ \nu)}{{\cal B }(B^0 \to D^{*-} \mu^+ \nu)} = 1.01 \pm 0.01 \pm 0.03~$. The errors correspond to the statistical and systematic uncertainties respectively. This is the most precise measurement of $\mathcal{F}(1)\eta_{\rm EW}|V_{cb}|$ and form factors to date and the first experimental study of the BGL form factor parameterization in an experimental measurement

Rapid generation of long synthetic tandem repeats and its application for analysis in human artificial chromosome formation

Human artificial chromosomes (HACs) provide a unique opportunity to study kinetochore formation and to develop a new generation of vectors with potential in gene therapy. An investigation into the structural and the functional relationship in centromeric tandem repeats in HACs requires the ability to manipulate repeat substructure efficiently. We describe here a new method to rapidly amplify human alphoid tandem repeats of a few hundred base pairs into long DNA arrays up to 120 kb. The method includes rolling-circle amplification (RCA) of repeats in vitro and assembly of the RCA products by in vivo recombination in yeast. The synthetic arrays are competent in HAC formation when transformed into human cells. As short multimers can be easily modified before amplification, this new technique can identify repeat monomer regions critical for kinetochore seeding. The method may have more general application in elucidating the role of other tandem repeats in chromosome organization and dynamics

Main assumptions for energy pathways

© The Author(s) 2019. The aim of this chapter is to make the scenario calculations fully transparent and comprehensible to the scientific community. It provides the scenario narratives for the reference case (5.0 °C) as well as for the 2.0 °C and 1.5 °C on a global and regional basis. Cost projections for all fossil fuels and renewable energy technologies until 2050 are provided. Explanations are given for all relevant base year data for the modelling and the main input parameters such as GDP, population, renewable energy potentials and technology parameters

A mathematical and computational review of Hartree-Fock SCF methods in Quantum Chemistry

We present here a review of the fundamental topics of Hartree-Fock theory in Quantum Chemistry. From the molecular Hamiltonian, using and discussing the Born-Oppenheimer approximation, we arrive to the Hartree and Hartree-Fock equations for the electronic problem. Special emphasis is placed in the most relevant mathematical aspects of the theoretical derivation of the final equations, as well as in the results regarding the existence and uniqueness of their solutions. All Hartree-Fock versions with different spin restrictions are systematically extracted from the general case, thus providing a unifying framework. Then, the discretization of the one-electron orbitals space is reviewed and the Roothaan-Hall formalism introduced. This leads to a exposition of the basic underlying concepts related to the construction and selection of Gaussian basis sets, focusing in algorithmic efficiency issues. Finally, we close the review with a section in which the most relevant modern developments (specially those related to the design of linear-scaling methods) are commented and linked to the issues discussed. The whole work is intentionally introductory and rather self-contained, so that it may be useful for non experts that aim to use quantum chemical methods in interdisciplinary applications. Moreover, much material that is found scattered in the literature has been put together here to facilitate comprehension and to serve as a handy reference.Comment: 64 pages, 3 figures, tMPH2e.cls style file, doublesp, mathbbol and subeqn package

PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

BACKGROUND: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design. METHODS: Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose PepDist: a novel approach for predicting binding affinity. Our approach is based on learning peptide-peptide distance functions. Moreover, we suggest to learn a single peptide-peptide distance function over an entire family of proteins (e.g. MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a semi-supervised learning problem with partial information in the form of equivalence constraints. Specifically, we propose to use DistBoost [1,2], which is a semi-supervised distance learning algorithm. RESULTS: We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors. One of the major advantages of our novel approach is that it can also learn an affinity function over proteins for which only small amounts of labeled peptides exist. In these cases, our method's performance gain, when compared to other computational methods, is even more pronounced. We have recently uploaded the PepDist webserver which provides binding prediction of peptides to 35 different MHC class I alleles. The webserver which can be found at is powered by a prediction engine which was trained using the framework presented in this paper. CONCLUSION: The results obtained suggest that learning a single distance function over an entire family of proteins achieves higher prediction accuracy than learning a set of binary classifiers for each of the proteins separately. We also show the importance of obtaining information on experimentally determined non-binders. Learning with real non-binders generalizes better than learning with randomly generated peptides that are assumed to be non-binders. This suggests that information about non-binding peptides should also be published and made publicly available