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Abstract Two different types of monoclinic HfO 2 nanocrystals were employed in this work to study the effect of nanocrystal shape and crystallinity on the structural defects in the YBa2Cu3O7−δ (YBCO) matrix as it leads to an enhancement of pinning performances of solution-derived YBCO nanocomposite films. In this work the nanorod-like HfO 2 nanocrystals obtained from surfactant-controlled synthesis led to short intergrowths surrounding the particles, while spherical HfO 2 nanocrystals from the solvent-controlled synthesis led to the formation of long stacking faults in the YBCO matrix. It means that the small difference in crystallinity, lattice parameters, nanocrystal structures, core diameter of preformed nanocrystals in colloidal solutions have a strong influence on the formation of the structural defects around the particles in the YBCO matrix, leading to different pinning performances.

We examine the capacity of the Large Hadron Collider to determine the mean proper lifetime of long-lived particles assuming different decay final states. We mostly concentrate on the high luminosity runs of the LHC, and therefore, develop our discussion in light of the high amount of pile-up and the various upgrades for the HL-LHC runs. We employ model-dependent and model-independent methods in order to reconstruct the proper lifetime of neutral long- lived particles decaying into displaced leptons, potentially accompanied by missing energy, as well as charged long- lived particles decaying ihnto leptons and missing energy. We also present a discussion for lifetime estimation of neu- tral long-lived particles decaying into displaced jets, along with the challenges in the high PU environment of HL-LHC. After a general discussion, we illustrate and discuss these methods using several new physics models. We conclude that the lifetime can indeed be reconstructed in many concrete cases. Finally, we discuss to which extent including timing information, which is an important addition in the Phase-II upgrade of CMS, can improve such an analysis.

The successful deployment of autonomous real-time systems is contingent on their ability to recover from performance degradation of sensors, actuators, and other electro-mechanical subsystems with low latency. In this article, we introduce ALERA, a novel framework for real-time control law adaptation in nonlinear control systems assisted by system state encodings that generate an error signal when the code properties are violated in the presence of failures. The fundamental contributions of this methodology are twofold—first, we show that the time-domain error signal contains perturbed system parameters’ diagnostic information that can be used for quick control law adaptation to failure conditions and second, this quick adaptation is performed via reinforcement learning algorithms that relearn the control law of the perturbed system from a starting condition dictated by the diagnostic information, thus achieving significantly faster recovery. The fast (up to 80X faster than traditional reinforcement learning paradigms) performance recovery enabled by ALERA is demonstrated on an inverted pendulum balancing problem, a brake-by-wire system, and a self-balancing robot.