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The purpose of this paper is to consider the effect of heat treatment on alloying element distribution and the electrochemical properties of Al‐5Zn‐0.03In anodes.

The Al‐5Zn‐0.03In alloy anodes are treated at 510°C for 10 h, then cooled in water. Electron probe microanalysis of JXA‐8800 and EDAX quantitative energy dispersive X‐ray analysis is used to examine the microstructure of the anodes before and after heat treatment, and the electrochemical properties of the anodes are tested.

By heat treatment, the solubility of Zn in aluminum is increased while the solubilities of Fe and Si are changed only slightly. The quantity of the Al‐Zn intermetallic compounds is evidently decreased and the Al‐Fe‐Si intermetallic compound is preserved. Strip segregation along grain boundaries is changed to spherical particulates. The attack of aluminum anodes initiates and propagates in grain boundaries and interdendritic zones, which are enriched in In and Zn, so the current efficiency of the aluminum anodes is related to the degree of corrosion taking place at grain boundaries and the extent of exfoliation of grains. The greater the extent of Al‐Zn metallic compounds that are present at grain boundaries, the more sensitive to grain boundary corrosion is the alloy and the greater the degree of desquamation of grains, the lower is the current efficiency of the aluminum anode.

The results of this paper clarify the role of water‐quenching affect on the microstructure and electrochemical properties of Al‐Zn‐In anodes.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

This article examines the impact of different policy instruments on livestock farmers' willingness to recycle manure. The results shed light on the optimal policy combination.

A game theoretical framework is constructed to illustrate farmers' optimal strategies under different policies. Theoretical results are empirically tested by survey data from beef cattle farmers in Central China.

Empirical results show that penalties work better than subsidies if each type of policy is implemented separately. The authors also find a positive interaction between subsidy and penalty policies, suggesting that a combination of subsidy and penalty policies produces the best outcome in incentivizing livestock farmers to recycle manure. Furthermore, planting and breeding simultaneously have the strongest effect on increasing livestock farmers' willingness to recycle manure, suggesting that the combination of planting and breeding can be an optimal strategy for manure management.

This study is based on firsthand survey data and provides new evidence on the effectiveness of alternative environmental policies on manure recycling.