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The standard model of particle physics currently provides our best description of fundamental particles and their interactions. The theory predicts that the different charged leptons, the electron, muon and tau, have identical electroweak interaction strengths. Previous measurements have shown that a wide range of particle decays are consistent with this principle of lepton universality. This article presents evidence for the breaking of lepton universality in beauty-quark decays, with a significance of 3.1 standard deviations, based on proton–proton collision data collected with the LHCb detector at CERN’s Large Hadron Collider. The measurements are of processes in which a beauty meson transforms into a strange meson with the emission of either an electron and a positron, or a muon and an antimuon. If confirmed by future measurements, this violation of lepton universality would imply physics beyond the standard model, such as a new fundamental interaction between quarks and leptons.

The development of highly active and durable inexpensive electrocatalysts for hydrogen evolution reaction (HER) is still a formidable challenge. Herein, an ordered hexagonal-closed-packed (hcp)-Ru nanocrystal coated with a thin layer of N-doped carbon (hcp-Ru@NC) was fabricated through the thermal annealing of polydopamine (PDA)-coated Ru nanoparticle (RuNP@PDA). As an alternative to Pt/C catalyst, the hcp-Ru@NC nanocatalyst exhibited the small overpotential of 27.5 mV at a current density of 10 mA cm −2 , as well as long-term stability for HER in acid media. Interestingly, the HER performance of hcp-Ru is highly dependent on its crystallinity. The calculation from density functional theory (DFT) revealed that the difference in HER activity over various exposed surface causes the crystallinity-dependent property of hcp-Ru. The results provided clues to guide the design of Ru-based inexpensive HER electrocatalyst.

A facile surfactant-free process is introduced to prepare multifunctional polypropylene (PP) nanocomposites filled with highly dispersed Fe@Fe2O3 core@shell nanoparticles (NPs). Transmission electron microscopy (TEM) observations confirm the formation of uniform NPs in the PP matrix and the particle size increases with increasing the particle loading. The melt rheology measurements show an obvious change in the frequency dependent storage modulus (G′), loss modulus (G′′) and complex viscosity (η * ) particularly at low frequencies. These changes are often related to the filler “percolation threshold”, which has also been verified in the sharp change of electrical resistance and dielectric permittivity of these nanocomposites in higher particle loadings. The continuous decrease in the resistivity with increasing filler loading from 5 wt % to 20 wt % demonstrates the structural transition of the nanocomposites. The monotonic increase in the dielectric permittivity with increasing particle loadings combined with the direct evidence from the TEM observations indicate that the NPs are well separated and uniformly dispersed in the polymer matrix. Thermal gravimetric analysis (TGA) results reveal a surprisingly high enhancement of the thermal stability by ∼120 °C in air due to the oxygen trapping effect of the NPs and the polymer–particle interfacial interaction. The differential scanning calorimetry (DSC) results show that the crystalline temperature (T c ) of the nanocomposites is reduced by 16–18 °C as compared to that of PP, while the melting temperature (T m ) almost maintains the same. The nanocomposites is found to be soft ferromagnetic at room temperature.

As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer Artificial Neural Networks (ANN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm. Firstly, the highlevel architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feedforward neural network (FFNN) is introduced. Based on the basic concept of backpropagation, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

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