Implications of new data in charmless B decays

Based on the latest experimental data of $B \to \pi\pi$ and $\pi K$ modes, a model-independent analytical analysis is presented. The CP-averaged branching ratio difference $\Delta R = R_c - R_n$ in $B\to \pi K$ decays with $R_c = 2Br(\pi^0K^-)/Br(\pi^-\bar{K}^0)$ and $R_n =Br(\pi^+K^-)/2Br(\pi^0\bar{K}^0)$ is reduced though it remains larger than the prediction from the standard model(SM) as both measured $R_n$ and $R_c$ are enhanced, which indicates that a room for new physics becomes smaller. The present data of $\pi\pi$ decay reduce the ratio $|C/T|$ from the previous value of $|C/T|\simeq 0.8$ to $|C/T| \simeq 0.65$, which is still larger than the theoretical estimations based on QCD factorization and pQCD. Within SM and flavor SU(3) symmetry, the current $\pi K$ data also diminish the ratio $|C'/T'|$ from the previous value $|C'/T'| \simeq 2$ to $|C'/T'| \simeq 1.16$ with a large strong phase $\delta_{C'} \simeq -2.65$, while its value remains much larger than the one extracted from the $\pi \pi$ modes. The direct CP violation $A_{CP}(\pi^0\bar{K}^0)$ is predicted to be $A_{CP}(\pi^0\bar{K}^0) = -0.15\pm0.03$, which is consistent with the present data. Two kinds of new effects in both strong and weak phases of the electroweak penguin diagram are considered. It is found that both cases can reduce the ratio to $|C'/T'| = 0.40\sim 0.80$ and lead to roughly the same predictions for CP violation in $\pi^0 K^0$.Comment: 13 pages, 4 figure

Analysis of some mixed elements for the Stokes problem

AbstractIn this paper we discuss some mixed finite element methods related to the reduced integration penalty method for solving the Stokes problem. We prove optimal order error estimates for bilinear-constant and biquadratic-bilinear velocity-pressure finite element solutions. The result for the biquadratic-bilinear element is new, while that for the bilinear-constant element improves the convergence analysis of Johnson and Pitkäranta (1982). In the degenerate case when the penalty parameter is set to be zero, our results reduce to some related known results proved in by Brezzi and Fortin (1991) for the bilinear-constant element, and Bercovier and Pironneau (1979) for the biquadratic-bilinear element. Our theoretical results are consistent with the numerical results reported by Carey and Krishnan (1982) and Oden et al. (1982)

Advanced model-based risk reasoning on automatic railway level crossings

Safety is a core issue in the railway operation. In particular, as witnessed by accident/incident statistics, railway level crossing (LX) safety is one of the most critical points in railways. In the present paper, a Bayesian network (BN) based framework for causal reasoning related to risk analysis is proposed. It consists of a set of integrated stages, namely risk scenario definition, real field data collection and processing, BN model establishment and model performance validation. In particular, causal structural constraints are introduced to the framework forthe purpose of combining empirical knowledge with automatic learning approaches, thus to identify effective causalities and avoid inappropriate structural connections. Then, the proposed framework is applied to risk analysis of LX accidents in France. In details, the BN risk model is established on the basis of real field data and the model performance is validated. Moreover, forward and reverse inferences based on the BN risk model are performed to predict LX accident occurrence and quantify the contribution degree of various impacting factors respectively, so as to identify the riskiest factors. Besides, influence strength and sensitivity analyses are further carried out to scrutinize the influence strength of various causal factors on the LX accident occurrence likelihood and determine which factors the LX accident occurrence is most sensitive to. The main outputs of our study attest that the proposed framework is sound and effective in terms of risk reasoning analysis and offers significant insights on exploring practical recommendations to prevent LX accidents

Affecting Fundamental Transformation in Future Construction Work Through Replication of the Master-Apprentice Learning Model in Human-Robot Worker Teams

Construction robots continue to be increasingly deployed on construction sites to assist human workers in various tasks to improve safety, efficiency, and productivity. Due to the recent and ongoing growth in robot capabilities and functionalities, humans and robots are now able to work side-by-side and share workspaces. However, due to inherent safety and trust-related concerns, human-robot collaboration is subject to strict safety standards that require robot motion and forces to be sensitive to proximate human workers. In addition, construction robots are required to perform construction tasks in unstructured and cluttered environments. The tasks are quasi-repetitive, and robots need to handle unexpected circumstances arising from loose tolerances and discrepancies between as-designed and as-built work. It is therefore impractical to pre-program construction robots or apply optimization methods to determine robot motion trajectories for the performance of typical construction work. This research first proposes a new taxonomy for human-robot collaboration on construction sites, which includes five levels: Pre-Programming, Adaptive Manipulation, Imitation Learning, Improvisatory Control, and Full Autonomy, and identifies the gaps existing in knowledge transfer between humans and assisting robots. In an attempt to address the identified gaps, this research focuses on three key studies: enabling construction robots to estimate their pose ubiquitously within the workspace (Pose Estimation), robots learning to perform construction tasks from human workers (Learning from Demonstration), and robots synchronizing their work plans with human collaborators in real-time (Digital Twin). First, this dissertation investigates the use of cameras as a novel sensor system for estimating the pose of large-scale robotic manipulators relative to the job sites. A deep convolutional network human pose estimation algorithm was adapted and fused with sensor-based poses to provide real-time uninterrupted 6-DOF pose estimates of the manipulator’s components. The network was trained with image datasets collected from a robotic excavator in the laboratory and conventional excavators on construction sites. The proposed system yielded an uninterrupted and centimeter-level accuracy pose estimation system for articulated construction robots. Second, this dissertation investigated Robot Learning from Demonstration (LfD) methods to teach robots how to perform quasi-repetitive construction tasks, such as the ceiling tile installation process. LfD methods have the potential to be used in teaching robots specific tasks through human demonstration, such that the robots can then perform the same tasks under different conditions. A visual LfD and a trajectory LfD methods are developed to incorporate the context translation model, Reinforcement Learning method, and generalized cylinders with orientation approach to generate the control policy for the robot to perform the subsequent tasks. The evaluated results in the Gazebo robotics simulator confirm the promise and applicability of the LfD method in teaching robot apprentices to perform quasi-repetitive tasks on construction sites. Third, this dissertation explores a safe working environment for human workers and robots. Robot simulations in online Digital Twins can be used to extend designed construction models, such as BIM (Building Information Models), to the construction phase for real-time monitoring of robot motion planning and control. A bi-directional communication system was developed to bridge robot simulations and physical robots in construction and digital fabrication. Through empirical studies, the high accuracy of the pose synchronization between physical and virtual robots demonstrated the potential for ensuring safety during proximate human-robot co-work.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169666/1/cjliang_1.pd

QCD Factorization Based on Six-Quark Operator Effective Hamiltonian from Perturbative QCD and Charmless Bottom Meson Decays B(s)→ππ,πK,KKB_{(s)}\to \pi\pi,\pi K, KK

The charmless bottom meson decays are systematically investigated based on an approximate six quark operator effective Hamiltonian from perturbative QCD. It is shown that within this framework the naive QCD factorization method provides a simple way to evaluate the hadronic matrix elements of two body mesonic decays. The singularities caused by on mass-shell quark propagator and gluon exchanging interaction are appropriately treated. Such a simple framework allows us to make theoretical predictions for the decay amplitudes with reasonable input parameters. The resulting theoretical predictions for all the branching ratios and CP asymmetries in the charmless $B^0, B^+, B_s\to \pi\pi, \pi K, KK$ decays are found to be consistent with the current experimental data except for a few decay modes. The observed large branching ratio in $B\to \pi^0\pi^0$ decay remains a puzzle though the predicted branching ratio may be significantly improved by considering the large vertex corrections in the effective Wilson coefficients. More precise measurements of charmless bottom meson decays, especially on CP-violations in $B\to K K$ and $B_s\to \pi\pi, \pi K, KK$ decay modes, will provide a useful test and guide us to a better understanding on perturbative and nonperturbative QCD.Comment: 36 pages, 5 figures, typos correcte

Accident Prediction Modeling Approaches for European Railway Level Crossing Safety

Safety is a core concern in the railway operation. Particularly, in Europe, level crossing (LX) safety is one of the most critical issues for railways. LX accidents often lead to fatalities and weighted injuries and seriously hamper railway safety reputation. Moreover, according to statistics, collisions between trains and motorized vehicles contribute most to LX accidents. With this in mind, we will elaborate on accident prediction modeling for train-vehicle collisions at LXs in this chapter. The methods and findings discussed in this chapter will offer an in-depth insight for interpreting significant aspects underlying collision occurrence and facilitate identifying technical countermeasures to improve LX safety