Tau Physics from B Factories

Some recent $\tau$-physics results are presented from the BaBar and Belle experiments at the SLAC and KEK B factories, which produce copious numbers of $\tau$-lepton pairs. Measurements of the tau mass and lifetime allow to test lepton universality and CPT invariance, while searches for lepton-flavour violation in tau decays are powerful ways to look for physics beyond the Standard Model. In semihadronic, non-strange tau decays, the vector hadronic final state is particularly important in helping determine the hadronic corrections to the anomalous magnetic moment of the muon, while studies of strange final states are the best available ways to measure the CKM matrix element $V_{\rm us}$ and the mass of the strange quark.Comment: Presented at Charm 2006, International Workshop on Tau-Charm Physics, June 05-07 2006, Beijing, Chin

Performance and selection of winter durum wheat genotypes in different European conventional and organic fields

Sustainability is a key factor for the future of agriculture. Productivity in agriculture has more than tripled in developed countries since the 1950s. Beyond the success of plant breeding, the increased use of inorganic fertilizers, application of pesticides, and spread of irrigation also contributed to this success. However, impressive yield increases started to decline in the 1980s because of the lack of sustainability. One of the most beneficial ways to increase sustainability is organic agriculture. In such agro-ecosystem-based holistic production systems the prerequisite of successful farming is the availability of crop genotypes that perform well. However, selection of winter durum wheat for sub-optimal growing conditions is still mainly neglected, and the organic seed market also lacks of information on credibly tested winter durum varieties suitable for organic agriculture

Lambda Polarization in Polarized Proton-Proton Collisions at RHIC

We discuss Lambda polarization in semi-inclusive proton-proton collisions, with one of the protons longitudinally polarized. The hyperfine interaction responsible for the $\Delta$-$N$ and $\Sigma$-$\Lambda$ mass splittings gives rise to flavor asymmetric fragmentation functions and to sizable polarized non-strange fragmentation functions. We predict large positive Lambda polarization in polarized proton-proton collisions at large rapidities of the produced Lambda, while other models, based on SU(3) flavor symmetric fragmentation functions, predict zero or negative Lambda polarization. The effect of $\Sigma^0$ and $\Sigma^*$ decays is also discussed. Forthcoming experiments at RHIC will be able to differentiate between these predictions.Comment: 18 pages, 5 figure

How and When Socially Entrepreneurial Nonprofit Organizations Benefit From Adopting Social Alliance Management Routines to Manage Social Alliances?

Social alliance is defined as the collaboration between for-profit and nonprofit organizations. Building on the insights derived from the resource-based theory, we develop a conceptual framework to explain how socially entrepreneurial nonprofit organizations (SENPOs) can improve their social alliance performance by adopting strategic alliance management routines. We test our framework using the data collected from 203 UK-based SENPOs in the context of cause-related marketing campaign-derived social alliances. Our results confirm a positive relationship between social alliance management routines and social alliance performance. We also find that relational mechanisms, such as mutual trust, relational embeddedness, and relational commitment, mediate the relationship between social alliance management routines and social alliance performance. Moreover, our findings suggest that different types of social alliance motivation can influence the impact of social alliance management routines on different types of the relational mechanisms. In general, we demonstrate that SENPOs can benefit from adopting social alliance management routines and, in addition, highlight how and when the social alliance management routines–social alliance performance relationship might be shaped. Our study offers important academic and managerial implications, and points out future research directions

Magnetostrictive materials for aerospace applications

Structural health monitoring of composite structures to detect barely visible damage is vitally important for the aerospace industry. This research has investigated amorphous magnetostrictive wires (Fe77.5Si7.5B15 and Co72.5Si12.5B15), as a possible solution to monitoring aerospace composites. The different amorphous wires were either embedded into the composite or epoxied on to the surface. How the wires effected the structure of the composite along with ultimate tensile strength was studied. Inductance measurements were used to study the strain within the composite, which provided a non-intrusive method of monitoring the composite

Halting a Runaway Train: Reforming Teacher Pensions for the 21st Century

When it comes to public-sector pensions, writes lead author Michael B. Lafferty in this report, "A major public-policy (and public-finance) problem has been defined and measured, debated and deliberated, but not yet solved. Except where it has been." As recounted in "Halting a Runaway Train: Reforming Teacher Pensions for the 21st Century", these exceptions turn out to be revealing -- and encouraging

A Spectral Line Survey of Selected 3 mm Bands Toward Sagittarius B2(N-LMH) Using the NRAO 12 Meter Radio Telescope and the BIMA Array I. The Observational Data

We have initiated a spectral line survey, at a wavelength of 3 millimeters, toward the hot molecular core Sagittarius B2(N-LMH). This is the first spectral line survey of the Sgr B2(N) region utilizing data from both an interferometer (BIMA Array) and a single-element radio telescope (NRAO 12 meter). In this survey, covering 3.6 GHz in bandwidth, we detected 218 lines (97 identified molecular transitions, 1 recombination line, and 120 unidentified transitions). This yields a spectral line density (lines per 100 MHz) of 6.06, which is much larger than any previous 3 mm line survey. We also present maps from the BIMA Array that indicate that most highly saturated species (3 or more H atoms) are products of grain chemistry or warm gas phase chemistry. Due to the nature of this survey we are able to probe each spectral line on multiple spatial scales, yielding information that could not be obtained by either instrument alone.Comment: 35 pages, 15 figures, to be published in The Astrophysical Journa

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

A molecular insight into algal-oomycete warfare : cDNA analysis of Ectocarpus siliculosus infected with the basal oomycete Eurychasma dicksonii

Peer reviewedPublisher PD

Hybridization in parasites: consequences for adaptive evolution, pathogenesis and public health in a changing world

[No abstract available