A Combined Fit on the Annihilation Corrections in Bu,d,sB_{u,d,s} →\to PPPP Decays Within QCDF

Motivated by the possible large annihilation contributions implied by recent CDF and LHCb measurements on nonleptonic annihilation B meson decays, and the refined experimental measurements on hadronic B meson decays, we study the strength of annihilation contributions within QCD factorization (QCDF) in this paper. With the available measurements of two-body B_{u,d,s} -> pi pi, pi K, K K decays, a comprehensive fit on the phenomenological parameters X_A^{i,f} (or rho_A^{i,f} and phi_A^{i,f}) which are used to parameterize the endpoint singularity in annihilation amplitudes is performed with the statistical chi^2 approach. It is found that (1) flavor symmetry breaking effects are hardly to be distinguished between X_{A,s}^i and X_{A,d}^i due to the large experimental errors and theoretical uncertainties, where X_{A,s}^i and X_{A,d}^i are related to the nonfactorization annihilation contributions in B_s and B_{u,d} decays, respectively. So X_{A,s}^i = X_{A,d}^i is a good approximation by now. (2) In principle, parameter X_{A}^f which is related to the factorization annihilation contributions and independent of the initial state can be regarded as the same variable for B_{u,d,s} decays. (3) Numerically, two solutions are found, one is (rho_A^i, phi_A^i) = (2.98^+1.12_-0.86,-105^+34_-24) and (rho_A^f, phi_A^f) = (1.18^+0.20_-0.23,-40^+11_-8), the other is (rho_A^i, phi_A^i) = (2.97^+1.19_-0.90,-105^+32_-24) and (rho_A^f, phi_A^f) = (2.80^+0.25_-0.21,165^+4_-3). Obviously, nonfactorization annihilation parameter X_A^i is generally unequal to factorization annihilation parameter X_A^f, which differ from the traditional treatment. With the fitted parameters, all results for observables of B_{u,d,s} ->pi pi, pi K, K K decays are in good agreement with experimental data.Comment: 12 pages, version accepted by PL

A New Method on Software Reliability Prediction

As we all know, relevant data during software life cycle can be used to analyze and predict software reliability. Firstly, the major disadvantages of the current software reliability models are discussed. And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model are proposed, and the improved model is established. Lastly, simulation results show that compared with classic models, the improved model has better prediction precision, better generalization ability, and lower dependence on the number of samples, which is more applicable for software reliability prediction

The 2016 Planned Giving Study

Charitable bequests and other planned gifts have historically played a significant role in the funding of higher education institutions. Prominent institutions such as Harvard University, Johns Hopkins University, and the Julliard School have been established as a direct result of bequests, and these gifts continue to have a profound impact today. The field of planned giving has become more sophisticated over time. However, the complexity of various planned giving vehicles and the comparatively long time period required for planned gifts to be formalized make it difficult for researchers to systematically track and examine planned giving behavior. Existing studies, therefore, heavily rely on self-reported survey data or tax returns. This study is one of the first efforts that seek to understand the changing landscape of planned giving and to explore donor life-cycle trajectories at higher education institutions. This whitepaper is the first in what is hoped to be a series of reports based upon data on planned gifts and donors in the field of higher education. The whitepaper discusses findings from five case-study universities located across the U.S. As the study expands the sample to include more universities and colleges in the next phase, this report series will offer richer data and insights into more underexplored, yet important, questions in planned giving

Dissimilar thermal transport properties in κ\kappa-Ga2_2O3_3 and β\beta-Ga2_2O3_3 revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations

The lattice thermal conductivity (LTC) of Ga$_2$O$_3$ is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neuroevolution potentials for single-crystalline $\beta$-Ga$_2$O$_3$ and $\kappa$-Ga$_2$O$_3$, and apply them to perform homogeneous nonequilibrium molecular dynamics simulations to predict their LTCs. The LTC of $\beta$-Ga$_2$O$_3$ was determined to be 10.3 $\pm$ 0.2 W/(m K), 19.9 $\pm$ 0.2 W/(m K), and 12.6 $\pm$ 0.2 W/(m K) along [100], [010], and [001], respectively, aligning with previous experimental measurements. For the first time, we predict the LTC of $\kappa$-Ga$_2$O$_3$ along [100], [010], and [001] to be 4.5 $\pm$ 0.0 W/(m K), 3.9 $\pm$ 0.0 W/(m K), and 4.0 $\pm$ 0.1 W/(m K), respectively, showing a nearly isotropic thermal transport property. The reduced LTC of $\kappa$-Ga$_2$O$_3$ versus $\beta$-Ga$_2$O$_3$ stems from its restricted low-frequency phonons up to 5 THz. Furthermore, we find that the $\beta$ phase exhibits a typical temperature dependence slightly stronger than $\sim T^{-1}$, whereas the $\kappa$ phase shows a weaker temperature dependence, ranging from $\sim T^{-0.5}$ to $\sim T^{-0.7}$.Comment: 8 pages, 7 figure