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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. 

Ecological research increasingly considers integrative relationships among phenomena at broad spatial and temporal domains. However, such large-scale inferences are commonly confounded by changing properties in the processes that govern phenomena (termed nonstationarity), which can violate assumptions underlying standard analytical methods. Changing conditions are funda-mental and pervasive features in ecology, but their influence on ecological inference and prediction increases with larger spatial and temporal domains for a host of factors. Fortunately, tools for identifying and accommodating potentially confounding spatial or temporal trends are available, and new methods are being rapidly developed. Here, we provide guidance for gaining a better understanding of nonstationarity, its causes, and how it can be addressed. Acknowledging and addressing non-constant trends in ecological patterns and processes is key to conducting large-scale research and effectively translating findings to local policies and practices.