Role of two-electron processes in the excitation-ionization of lithium atoms by fast ion impact

We study excitation and ionization in the 1.5 MeV/amu O$^{8+}$-Li collision system, which was the subject of a recent reaction-microscope-type experiment [Fischer \textit{et al.}, Phys. Rev. Lett. \textbf{109}, 113202 (2012)]. Starting from an independent-electron model based on determinantal wave functions and using single-electron basis generator method and continuum distorted-wave with eikonal initial-state calculations we show that pure single ionization of a lithium $K$-shell electron is too weak a process to explain the measured single differential cross section. Rather, our analysis suggests that two-electron excitation-ionization processes occur and have to be taken into account when comparing with the data. Good agreement is obtained only if we replace the independent-electron calculation by an independent-event model for one of the excitation-ionization processes and also take a shake-off process into account

Online data fusion using incremental tensor learning

© Springer Nature Switzerland AG 2019. Despite the advances in Structural Health Monitoring (SHM) which provides actionable information on the current and future states of infrastructures, it is still challenging to fuse data properly from heterogeneous sources for robust damage identification. To address this challenge, the sensor data fusion in SHM is formulated as an incremental tensor learning problem in this paper. A novel method for online data fusion from heterogeneous sources based on incrementally-coupled tensor learning has been proposed. When new data are available, decomposed component matrices from multiple tensors are updated collectively and incrementally. A case study in SHM has been developed for sensor data fusion and online damage identification, where the SHM data are formed as multiple tensors to which the proposed data fusion method is applied, followed by a one-class support vector machine for damage detection. The effectiveness of the proposed method has been validated through experiments using synthetic data and data obtained from a real-life bridge. The results have demonstrated that the proposed fusion method is more robust to noise, and able to detect, assess and localize damage better than the use of individual data sources

The Solution Structures of Two Prophage Homologues of the Bacteriophage λ Ea8.5 Protein Reveal a Newly Discovered Hybrid Homeodomain/Zinc-Finger Fold

A cluster of genes in the <i>exoxis</i> region of bacteriophage λ are capable of inhibiting the initiation of DNA synthesis in <i>Escherichia coli</i>. The most indispensible gene in this region is <i>ea8.5</i>. Here, we report the nuclear magnetic resonance structures of two <i>ea8.5</i> orthologs from enteropathogenic <i>E. coli</i> and <i>Pseudomonas putida</i> prophages. Both proteins are characterized by a fused homeodomain/zinc-finger fold that escaped detection by primary sequence search methods. While these folds are both associated with a nucleic acid binding function, the amino acid composition suggests otherwise, leading to the possibility that Ea8.5 associates with other viral and host proteins