Radiative Corrections to Democratic Lepton Mixing

A new ansatz of democratic lepton mixing is proposed at the GUT scale and the radiative corrections to its phenomenological consequences are calculated at the electroweak scale. We demonstrate that it is possible to obtain the experimentally favored results for both neutrino masses and lepton flavor mixing angles from this ansatz, provided the neutrino Yukawa coupling matrix takes a specific nontrivial pattern. The seesaw threshold effects play a significant role in the running of relevant physical quantities.Comment: 10 pages (1 table, 2 figures). More discussions added. Phys. Lett. B in pres

Regional variation of the minimum wages in China

PowerPoint presentationPart of the project “Labour Market Regulations in China: Minimum Wage Policy,” this presentation reviews some results of implementation of minimum wage standards in 2004. Minimum wages vary across regions, where local governments play a dominant role in the minimum wages adjustment process. The provincial government usually applies minimum wages of several levels according to the economic development level of different regions. This presentation analyzes variations over time and different regions while attempting to account for them. There is evidence suggesting that competition between regions plays a role in determining minimum wage disparity

Intrinsic Deviation from the Tri-bimaximal Neutrino Mixing in a Class of A_4 Flavor Models

It is well known that the tri-bimaximal neutrino mixing pattern V_0 can be derived from a class of flavor models with the non-Abelian A_4 symmetry. We point out that small corrections to V_0, which are inherent in the A_4 models and arise from both the charged-lepton and neutrino sectors, have been omitted in the previous works. We show that such corrections may lead the 3 \times 3 neutrino mixing matrix V to a non-unitary deviation from V_0, but they cannot result in a nonzero value of \theta_13 or any new CP-violating phases. Current experimental constraints on the unitarity of V allow us to constrain the model parameters to some extent.Comment: 11 pages, no figures; a reference added, accepted for publication in Phys. Lett.

Modeling and Experimental Study of Soft Error Propagation Based on Cellular Automaton

Aiming to estimate SEE soft error performance of complex electronic systems, a soft error propagation model based on cellular automaton is proposed and an estimation methodology based on circuit partitioning and error propagation is presented. Simulations indicate that different fault grade jamming and different coupling factors between cells are the main parameters influencing the vulnerability of the system. Accelerated radiation experiments have been developed to determine the main parameters for raw soft error vulnerability of the module and coupling factors. Results indicate that the proposed method is feasible

Study on Spinnability of PP/PU Blends and Preparation of PP/PU Bi-component Melt Blown Nonwovens

Melt blown polymer blends offers a good way to combine two polymers in the same fiber generating nonwovens with new and novel properties. In this study, polypropylene (PP) and polyurethane (PU) were blended to prepare PP/PU bicomponent melt blown nonwovens. The spinnability of PP/PU composites was investigated and PP/PU bi-component nonwovens with compositions of 95/5, 90/10, 80/20 and 70/30 were prepared by using the melt blowing technique. The melt blown fibers exhibited a ‘sea-island’ structure with PP as the continuous phase and PU as the dispersed phase. When the content of PU in the blend was above 40 %, PP/PU melt blown nonwovens could not be produced due to fiber breaking. For PP/PU (90/10) nonwovens, it was found that the average fiber diameter decreased with increasing die to collector (DCD) and elevated hot air pressure

A hybrid motion planning framework for autonomous driving in mixed traffic flow

As a core part of an autonomous driving system, motion planning plays an important role in safe driving. However, traditional model- and rule-based methods lack the ability to learn interactively with the environment, and learning-based methods still have problems in terms of reliability. To overcome these problems, a hybrid motion planning framework (HMPF) is proposed to improve the performance of motion planning, which is composed of learning-based behavior planning and optimization-based trajectory planning. The behavior planning module adopts a deep reinforcement learning (DRL) algorithm, which can learn from the interaction between the ego vehicle (EV) and other human-driven vehicles (HDVs), and generate behavior decision commands based on environmental perception information. In particular, the intelligent driver model (IDM) calibrated based on real driving data is used to drive HDVs to imitate human driving behavior and interactive response, so as to simulate the bidirectional interaction between EV and HDVs. Meanwhile, trajectory planning module adopts the optimization method based on road Frenet coordinates, which can generate safe and comfortable desired trajectory while reducing the solution dimension of the problem. In addition, trajectory planning also exists as a safety hard constraint of behavior planning to ensure the feasibility of decision instruction. The experimental results demonstrate the effectiveness and feasibility of the proposed HMPF for autonomous driving motion planning in urban mixed traffic flow scenarios