Information processing with optoelectronic devices provides an alternative way to efficiently process hybrid optical and electronic signals. Ferroelectric field‐effect transistors (FeFETs) can effectively respond to external optical and electrical stimuli by modulating their polarization states. Here, a 2D FeFET is demonstrated by the epitaxial growth of high‐quality 2D bismuth layered oxyselenide (Bi2O2Se) films on PMN‐PT(001) ferroelectric single‐crystal substrates. Upon switching the polarization direction of PMN‐PT, the authors realize in situ, reversible, and nonvolatile manipulation of the resistance of Bi2O2Se thin film (≈877%). The device simultaneously exhibits a polarization‐dependent photoresponse through visible light (λ = 405 nm) and infrared light (IR, λ = 980 nm) illumination. Combining optical stimuli with ferroelectric gating, it is demonstrated that the devices not only show nonvolatile memory and optoelectronic responses, but also show coincidence detection of visible and IR light. This work holds great potential in constructing new multiresponse and multifunction 2D‐FeFETs.

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.

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Water is a fundamental natural and strategic economic resource that plays a vital role in promoting economic and social development. With the accelerated urbanization and industrialization in China, the potential demand for water resources will be enormous. Therefore, accurate prediction of water resources demand is important for the formulation of industrial and agricultural policies, development of economic plans, and many other aspects. In this study, we develop a model based on principal component analysis (PCA) and back propagation (BP) neural network to predict water resources demand in Taiyuan, Shanxi Province, a city with severe water shortage in China. The prediction accuracy is then compared with PCA-ANN, ARIMA, NARX, Grey–Markov, serial regression, and LSTM models, and the results showed that the PCA-BP model outperformed other models in many evaluation factors. The proposed PCA-BP model reduces the dimensionality of high-dimensional variables by PCA and transformed them into uncorrelated composite data, which can make them easier to compute. More importantly, BP and weight threshold adjustment in model training further improve the prediction accuracy of the model. The model analysis will provide an important reference for water demand assessment and optimal water allocation in other regions.

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