Social Network Effect on Bidding Strategy Adoption in Online P2P Lending Market

Bidding strategy in online auctions, as a sort of strategic behavior, can help bidders to get what they want more efficiently and effectively. It receives much attention in many researches. However, the determinants of bidding strategy adoption still remain unclear. In this study, we investigate the role of social network in bidding strategy adoption using real transaction data from an online P2P lending market. The analyses reveal that 1) bidding strategy tends to be homogeneous in different online social networks. 2) Joining an online social network does not change the bidding strategy adoption behavior significantly. 3) The size of social network will affect bidding strategy adoption and smaller ones are more homogeneous than bigger ones. 4) In a social network, bidders with different roles have different preferences on bidding strategies. Our findings can be considered as important empirical evidences for theories about social influence and human behavior

Heterogeneous silver-polyaniline nanocomposites with tunable morphology and controllable catalytic properties

National Natural Science Foundation of China [51173153, U1205113]; Special Program for Key Research of Chinese National Basic Research Program [2011CB612303]; Xiamen Science and Technology Committee [3502Z20121021, 3502Z20120015]This paper introduces not only a simple hydrothermal route to silver-polyaniline (Ag-PANI) nanocomposites with controllable morphology, but also a type of catalyst possessing tunable and switchable catalytic capability. Ag-PANI Janus nanoparticles (NPs) and Ag@PANI core-shell NPs have been constructed successfully at different hydrothermal temperatures. The diameter of both Ag and PANI hemispheres of Janus NPs, as well as the PANI shell thickness of core-shell NPs, was finely tuned via adjustment of the feed ratio. We also gained a deeper insight into the functionalities of PANI components in the catalytic capability of the heterogeneous catalysts, choosing catalytic reductions of nitrobenzene (NB) and 4-nitrophenol (4-NP) as model reactions. Our results showed that the catalytic capability of the nanocomposites was dependent on the PANI morphology and hydrophobicity. The PANI shell coating on Ag NPs can concentrate the lipophilic NB, thus leading to an enhanced catalytic capability of Ag@PANI core-shell NPs. However, this enhanced catalytic capability was not observed for Ag-PANI Janus NPs when catalytically reducing NB. More importantly, the catalytic capability of the core-shell NPs in the reduction of hydrophilic 4-NP is switchable by varying the PANI shell from an undoped to a doped state

Optimal Supersaturated Design for Finite Sample Populations

Numerous construction methodologies have been developed in literature to construct balanced supersaturated designs (SSD) from the unconstrained $2^p$ sample population. The first part of the thesis introduces introduces a row exchange algorithm construction methodology to construct optimal SSDs efficiently from a finite sample population using UE(s2) as the evaluation criteria. Computational results show the algorithm consistently produces optimal SSDs in a computationally efficient manner. The second part of the thesis introduces introduce an optimality criteria for supersaturated designs that is based on an underlying inference methodology, the asymptotically optimal confidence regions for high-dimensional data1, which has been shown to have good theoretical properties, as well as expected coverage on simulated data. The criteria is defined for confidence intervals of single components from a supersaturated design. Supersaturated designs with better criteria should produce shorter desparsified lasso confidence intervals while maintaining coverage. An optimal condition for single component confidence intervals is also derived