Encounter, dialogue, and knowledge: Italy as a special case of religious engagement in foreign policy

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Foreign policy and religious engagement: the special case of Italy

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Inclusive production of a Higgs or Z boson in association with heavy quarks

We calculate the cross section for the production of a Z boson in association with heavy quarks. We suggest that this cross section can be measured using an inclusive heavy-quark tagging technique. This could be used as a feasibility study for the search for a Higgs boson produced in association with bottom quarks. We argue that the best formalism for calculating that cross section is based on the leading-order process b b -> h, and that it is valid for all Higgs masses of interest at both the Fermilab Tevatron and the CERN Large Hadron Collider.Comment: 14 page

Choosing the Factorization Scale in Perturbative QCD

We define the collinear factorization scheme, which absorbs only the collinear physics into the parton distribution functions. In order to isolate the collinear physics, we introduce a procedure to combine real and virtual corrections, canceling infrared singularities prior to integration. In the collinear scheme, the factorization scale $\mu$ has a simple physical interpretation as a collinear cutoff. We present a method for choosing the factorization scale and apply it to the Drell-Yan process; we find $\mu \approx Q/2$, where $Q$ is the vector-boson invariant mass. We show that, for a wide variety of collision energies and $Q$, the radiative corrections are small in the collinear scheme for this choice of factorization scale.Comment: 25 pages, 18 figure

The early B-type star Rho Oph A is an X-ray lighthouse

We present the results of a 140 ks XMM-Newton observation of the B2 star $\rho$ Ophiuchi A. The star has exhibited strong X-ray variability: a cusp-shaped increase of rate, similar to that which we partially observed in 2013, and a bright flare. These events are separated in time by about 104 ks, which likely corresponds to the rotational period of the star (1.2 days). Time resolved spectroscopy of the X-ray spectra shows that the first event is caused by an increase of the plasma emission measure, while the second increase of rate is a major flare with temperatures in excess of 60 MK ($kT\sim5$ keV). From the analysis of its rise, we infer a magnetic field of $\ge300$ G and a size of the flaring region of $\sim1.4-1.9\times10^{11}$ cm, which corresponds to $\sim25\%-30\%$ of the stellar radius. We speculate that either an intrinsic magnetism that produces a hot spot on its surface or an unknown low mass companion are the source of such X-rays and variability. A hot spot of magnetic origin should be a stable structure over a time span of $\ge$2.5 years, and suggests an overall large scale dipolar magnetic field that produces an extended feature on the stellar surface. In the second scenario, a low mass unknown companion is the emitter of X-rays and it should orbit extremely close to the surface of the primary in a locked spin-orbit configuration, almost on the verge of collapsing onto the primary. As such, the X-ray activity of the secondary star would be enhanced by its young age, and the tight orbit as in RS Cvn systems and $\rho$ Ophiuchi would constitute an extreme system that is worthy of further investigation.Comment: 10 pages, 7 figures, 2 tables, A&A accepted, this is the version after the language editor correction

CROP INSURANCE VALUATION UNDER ALTERNATIVE YIELD DISTRIBUTIONS

Considerable disagreement exists about the most appropriate characterization of farm-level yield distributions. Yet, the economic importance of alternate yield distribution specifications on insurance valuation, product designs and farm-level risk management has not been investigated or documented. The results of this study demonstrate that large differences in expected payments from popular crop insurance products can arise solely from the parameterization chosen to represent yields. The results suggest that the frequently unexamined yield distribution specification may lead to incorrect conclusions in important areas of insurance and risk management research such as policy rating, and assessment of expected payments from policies.Risk and Uncertainty,

ESTIMATING FARM-LEVEL YIELD DISTRIBUTIONS FOR CORN AND SOYBEANS IN ILLINOIS

Many yield modeling approaches have been developed in attempts to provide accurate characterizations of farm-level yield distributions due to the importance of yield uncertainty in crop insurance design and rating, and for managing farm-level risk. Competing existing models of crop yields accommodate varying skewness, kurtosis, and other departures from normality including features such as multiple modes. Recently, the received view of crop yield modeling has been challenged by Just and Weninger who indicate that there is insufficient evidence to reject normality given data limitations and potential methodological shortcomings in controlling for deterministic components (trend) in yields. They point out that past empirical efforts to estimate and validate specific-farm distributional characterizations have been severely hampered by data limitations. As a result, they argue in favor of normality as an appropriate parameterization of crop yields. This paper investigates alternate representations of farm-level crop yield distributions using a unique data set from the University of Illinois Endowment farms, containing same-site yield observations for a relatively long period of time, and under conditions that closely mirror actual farm conditions in Illinois. Results from alternate econometric model specifications controlling for trend effects suggest that a linear trend provides an adequate representation of crop yields at the farm level during the period covered by the estimations. Specification tests based on a linear-trend model suggest significant heteroskedasticity is present in only a few farms, and that the residuals are white noise. With these data, Jarque-Bera normality test results indicate that normality of detrended yield residuals is rejected by a far greater number of fields than would be explained due to randomness. Thus, to further clarify the issue of yield distribution characterizations, more complete goodness-of-fit measures are compared across a larger set of candidate distributions. The results indicate that the Weibull distribution consistently ranks better than the normal distribution both in fields where normality is rejected and in fields where normality is not rejected. The results highlight the fact that failing to reject normality is not the same as identifying normality as a "best" parameterization, and provide guidance for progressing toward better representations of farm-level crop yields.Productivity Analysis, Research Methods/ Statistical Methods, Teaching/Communication/Extension/Profession,