Multiple Superconducting Gaps, Anisotropic Spin Fluctuations and Spin-Orbit Coupling in Iron-Pnictides

This article reviews the NMR and NQR studies on iron-based high-temperature superconductors by the IOP/Okayama group. It was found that the electron pairs in the superconducting state are in the spin-singlet state with multiple fully-opened energy gaps. The antiferromagnetic spin fluctuations in the normal state are found to be closely correlated with the superconductivity. Also the antiferromagnetic spin fluctuations are anisotropic in the spin space, which is different from the case in copper oxide superconductors. This anisotropy originates from the spin-orbit coupling and is an important reflection of the multiple-bands nature of this new class of superconductors.Comment: 20 pages, 16 figure

(E,E)-2,5-Bis(5-chloro-2-methoxyphenyl)-3,4-diazahexa-2,4-diene

The title compound, C18H18Cl2N2O2, was synthesized by the reaction of 1-(5-chloro-2-methoxy­phen­yl)ethanone with hydrazine hydrate. The mol­ecule lies on a crystallographic twofold axis passing through the mid-point of the N—N bond with one half-mol­ecule in the asymmetric unit. The dihedral angle between the two aromatic rings is 44.33 (4)°. In the crystal, inter­molecular C—H⋯O inter­actions link the mol­ecules into columns along the c axi

A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems

Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach