Silver (Ag) nanoparticles were added into (Cu_0.5Tl_0.5)Ba₂Ca₂Cu₃O_10−δ (CuTl-1223) high T_c superconducting matrix to get (Ag)_x/CuTl-1223, x = 0, 0.5, 1.0, 2.0, and 4 wt. %, nano-superconductor composites. The activation energy {U (eV)} and zero resistivity critical temperature {T_c (0)} were increased with increasing contents of Ag nanoparticles in (CuTl-1223) phase up to x = 2.0 wt. %. The increase of activation energy is most probably due to interaction of carriers with the metallic Ag nanoparticles present at grain boundaries of the host CuTl-1223 superconducting matrix. The systematic increase in T_c (0) and gradual decrease in normal state resistivity {ρ_300 K (Ω cm)} may be due to improved inter-grains coupling by filling up the voids and pores with the inclusion of metallic Ag nanoparticles at the grain-boundaries. There are two possible mechanisms associated with the inclusion of Ag nanoparticles, one is the formation of non-superconducting regions causing the increase of activation energy and other (dominating) is the improved inter-grains connectivity promoting T_c (0). The microscopic parameters (i.e., zero temperature coherence length along c-axis {ξ_c (0)}, inter-layer coupling (J), inter-grain coupling (α), etc.) deduced from the fluctuation induced conductivity analysis reasonably explained the experimental findings.

After the Fukushima accident, it is necessary to develop some technique that can monitor the progression of severe accidents in nuclear power plants (NPPs). It is therefore very important for an operator to monitor safety related parameters for the diagnosis of severe accidents and to manage it properly. So to monitor and to check the availability of plant instrumentation during severe accidents, this paper presents quantitative and qualitative analyses of safety parameters by using online risk monitor system (ORMS). An ORMS considers the increasing potential for failure for a working component due to aging, which appears in the form of component's performance degradation. ORMS therefore requires a continuous feedback regarding performance and failure probabilities of components, which directly or indirectly contributes to the failure of a system. ORMS has been designed to automatically update the online risk models and reliability parameters of equipment. A case study of emergency diesel generator (EDG) of Daya Bay NPP has been performed, and operational failure rate and demand failure probability of EDG have been calculated with the help of ORMS. The results of ORMS are well matched with data obtained from Daya Bay NPP. ORMS can support in decision-making process for operators and managers at NPPs. Copyright © 2013 John Wiley & Sons, Ltd.