Multiscale Technicolor and b→sγb \to s \gamma

Correction to the $b\rightarrow s\gamma$ branching ratio in the multiscale walking technicolor model (MWTCM) is examined. For the original MWTCM, the correction is too large to explain the recent CLEO data. We show that if topcolor is further introduced, the branching ratio in the topcolor assisted MWTCM can be in agreement with the CLEO data for a certain range of the parameters.Comment: 11 pages, Latex, no macros, 3 figures, hard copy is available upon request. to appear in Z. Phys.

Study on the rare radiative decay Bc→Ds∗γB_c \to D_s^*\gamma in the standard model and multiscale walking technicolor model

Applying the perturbative QCD ( PQCD ) method, we study the decay $B_c\rightarrow D_s^*\gamma$ in the standard model and multiscale walking technicolor model. In the SM, we find that the contribution of weak annihilation is more important than that of the electromagnetic penguin. The presence of Pseudo-Goldstone-Bosons in the MWTCM leads to a large enhancement in the rate of $B_c\rightarrow D_s^*\gamma$, but this model is in conflict with the branching ratio of $Z\rightarrow b\overline b$ ( $R_b$ ) and the CLEO data on the branching ratio BR ( $b\rightarrow s\gamma$ ). If topcolor is further introduced, the calculated results in the topcolor assisted MWTCM can be suppressed and be in agreement with the CLEO data for a certain range of the parameters.Comment: 16 pages, Latex, no macros, 1 figure(in Latex), hard copy is available upon request. to appear in Phys. Rev.

Micromechanical modeling of longitudinal compression behavior and failure mechanism of unidirectional carbon fiber reinforced aluminum composites involving initial fiber misalignment

A micromechanical model with realistic initial fiber misalignment (IFM) was developed to simulate the longitudinal compression behavior of unidirectional carbon fiber/aluminum composites. The matrix and fiber were modeled using ductile damage law and brittle fracture model, respectively. The interfacial properties were firstly determined by the single-fiber push-out and transverse tensile tests, and the cohesive zone model was adopted to capture the interfacial behavior. The calculated compressive response curve is in alignment with the experimental data. Compression failure can be attributed to fiber kinking, possibly triggered by the matrix shear damage. The increase of IFM angle makes the failure mode being transformed from fiber crushing to fiber kinking, along with a significant decrease in compressive strength. With the fiber content increasing, the compressive strength increases first and then decreases, while the compressive modulus increases monotonically. Increasing interfacial strength significantly improves the compressive strength, but this is limited by the matrix properties

Impact of Li addition in Al-rich alloys on hydrogen production in water

Abstract In this study, three types of aluminum alloys (Al-Li, Al-Ga-In-Sn and Al-Li-Ga-In-Sn alloys) were prepared via vacuum arc melting technology. The microstructures of the alloys were examined by x-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDX). The water discharge method was used to evaluate the water–aluminum reaction. The results show that the Al-Li alloy is inert in aqueous ambience, whereas the Al-Ga-In-Sn alloy and Al-Li-Ga-In-Sn alloy rapidly react with water. Meanwhile, the Li addition hinders the aluminum–water reaction mainly due to the formation of AlLi and Li₅Sn₂ intermetallic compounds, which causes a lower H₂ generation rate and a lower H₂ yield of the Al-Li-Ga-In-Sn alloy than those of the Al-Ga-In-Sn alloy

Adversarial Counterfactual Environment Model Learning

A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, recommender systems, and patients' treatment selection. We can take unlimited trials with such a model to identify the appropriate actions so that the costs of queries in the real world can be saved. It requires the model to handle unseen data correctly, also called counterfactual data. However, standard data fitting techniques do not automatically achieve such generalization ability and commonly result in unreliable models. In this work, we introduce counterfactual-query risk minimization (CQRM) in model learning for generalizing to a counterfactual dataset queried by a specific target policy. Since the target policies can be various and unknown in policy learning, we propose an adversarial CQRM objective in which the model learns on counterfactual data queried by adversarial policies, and finally derive a tractable solution GALILEO. We also discover that adversarial CQRM is closely related to the adversarial model learning, explaining the effectiveness of the latter. We apply GALILEO in synthetic tasks and a real-world application. The results show that GALILEO makes accurate predictions on counterfactual data and thus significantly improves policies in real-world testing