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This study investigates the association between income and unmet need for personal assistance with daily activities and introduces four policy options for financial support for the unmet need, particularly the public long-term care insurance (LTCI) system. Using the logit model and random forest algorithm, we find that household income is a significant contributing factor to unmet need. Moreover, we find that older adults in the low-income group have a higher level of unmet needs. China's policy experimentation shows that financial support can be provided in the policy arrangements of LTCI financing with low individual contributions and benefits with a high reimbursement rate.

The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures.

Ferroelectric tunnel junctions (FTJs) are very promising as a new type of nonvolatile memory devices due to the tunneling electroresistance (TER) effect. In recent years, with the rise of two-dimensional (2D) materials, 2D ferroelectrics and their application in FTJs have attracted intensive attention, with the advantage of greatly reducing the FTJ based memory device sizes, as demanded by the ongoing device minituriazation in modern electronic circuits. However, all present schemes for realizing giant TER ratio with 2D FTJs are based on the polarization reversal of the whole ferroelectric layer upon an electrical field. In this work, we explore the quantum transport properties of the 2D FTJs with the partial reversal of polarization, namely, the formation of domain walls (DWs) by constructing two kinds of FTJs. One is in a uniform-polarization state and the other one is a state with domain walls. Structural relaxation confirms the stability of the domain-wall state. By quantum transport calculation, we obtain a TER ratio as high as 2.75 × 10 4%. Further analysis of the electronic structure shows that there is charge accumulation or charge depletion at the two DWs. Such asymmetric interface polarization charges result in a built-in electrical field and thus affect the distribution of the effective potential along the transport direction. This leads to partial metal-insulator transition around the DWs and finally the giant TER ratio. Our results indicate that DWs may greatly affect the quantum transport and provide a new mechanism for realizing giant TER effect in 2D FTJs.

Within the context of corporate acquisition decisions of China's Publicly Listed Corporations (PLCs), this paper investigates the monitoring and coordination behaviour of institutional shareholders. Institutional shareholders are divided into four groups: large outsider, large insider, small outsider and small insider. The outsider and insider categorization, respectively, represents the absence of significant business links with relevant PLCs. In China, institutional shareholders tend to either monitor the acquisitions decisions of PLCs or coordinate their response with the controlling shareholders (coordination in the present context amounts to cooperation). Using micro data from China's stock market over the 2003–2008 period, we find that (through ex-ante coordination with the controlling shareholders) the insider institutional investment tends to increase the likelihood of PLC acquisitions. However, through ex-ante monitoring of the PLC acquisition offers, the outsider institutional investment tends to decrease the acquisition likelihood. We find that, through ex-post monitoring of PLC acquisitions, institutional investors with large shareholdings can help improve the post-acquisition performance of Chinese PLCs. On the other hand, institutional investors with small shareholdings tend to coordinate their actions with the controlling shareholders. Accordingly, small institutional shareholders cannot affect the post-acquisition performance of China's PLCs. Finally, we find that the so-called ‘cherry-picking effect’ exists only in the case of large outsider institutional investors. © 2018 Elsevier Inc.

Accuracy of interpolation coefficients fitting to the auto-calibrating signal data is crucial for k-space-based parallel reconstruction. Both conventional generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction that utilizes linear interpolation function and nonlinear GRAPPA (NLGRAPPA) reconstruction with polynomial kernel function are sensitive to interpolation window and often cannot consistently produce good results for overall acceleration factors. In this study, sparse multi-kernel learning is conducted within the framework of least squares support vector regression to fit interpolation coefficients as well as to reconstruct images robustly under different subsampling patterns and coil datasets. The kernel combination weights and interpolation coefficients are adaptively determined by efficient semi-infinite linear programming techniques. Experimental results on phantom and in vivo data indicate that the proposed method can automatically achieve an optimized compromise between noise suppression and residual artifacts for various sampling schemes. Compared with NLGRAPPA, our method is significantly less sensitive to the interpolation window and kernel parameters.