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The rational design of economic and high‐performance electrocatalytic water‐splitting systems is of great significance for energy and environmental sustainability. Developing a sustainable energy conversion‐assisted electrocatalytic process provides a promising novel approach to effectively boost its performance. Herein, a self‐sustained water‐splitting system originated from the heterostructure of perovskite oxide with 2D Ti3C2Tx MXene on Ni foam (La1‐xSrxCoO3/Ti3C2Tx MXene/Ni) that shows high activity for solar‐powered water evaporation and simultaneous electrocatalytic water splitting is presented. The all‐in‐one interfacial electrocatalyst exhibits highly improved oxygen evolution reaction (OER) performance with a low overpotential of 279 mV at 10 mA cm−2 and a small Tafel slope of 74.3 mV dec−1, superior to previously reported perovskite oxide‐based electrocatalysts. Density functional theory calculations reveal that the integration of La0.9Sr0.1CoO3 with Ti3C2Tx MXene can lower the energy barrier for the electron transfer and decrease the OER overpotential, while COMSOL simulations unveil that interfacial solar evaporation could induce OH− enrichment near the catalyst surfaces and enhance the convection flow above the catalysts to remove the generated gas, remarkably accelerating the kinetics of electrocatalytic water splitting.

In this review, we focus on metallocenes (e.g., ferrocene, ruthenocene, cobaltocene/cobaltocenium, etc.), a class of prototypical organometallic compounds, as emerging stress-responsive building blocks for materials. In particular, they are used...

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

The spline filter is a standard linear profile filter recommended by ISO/TS 16610–22 (2006). The main advantage of the spline filter is that no end-effects occur as a result of the filter. The ISO standard also provides the tension parameter to make the transmission characteristic of the spline filter approximately similar to the Gaussian filter. However, when the tension parameter β is not zero, end-effects appear. To resolve this problem, we analyze 14 different combinations of boundary conditions of the spline filter and propose a set of new boundary conditions in this paper. The new boundary conditions can provide satisfactory end portions of the output form without end-effects for the spline filter while still maintaining the value of .

This paper deals with the application of the spline filter as an areal filter for surface metrology. A profile (2D) filter is often applied in orthogonal directions to yield an areal filter for a three-dimensional (3D) measurement. Unlike the Gaussian filter, the spline filter presents an anisotropic characteristic when used as an areal filter. This disadvantage hampers the wide application of spline filters for evaluation and analysis of areal surface topography. An approximation method is proposed in this paper to overcome the problem. In this method, a profile high-order spline filter serial is constructed to approximate the filtering characteristic of the Gaussian filter. Then an areal filter with isotropic characteristic is composed by implementing the profile spline filter in the orthogonal directions. It is demonstrated that the constructed areal filter has two important features for surface metrology: an isotropic amplitude characteristic and no end effects. Some examples of applying this method on simulated and practical surfaces are analyzed.

Advanced process modeling methods have been used for prediction and monitoring of key quality indices in wastewater treatment processes. However, single conventional models usually have limited precision accuracy when predicting the effluent indices in papermaking wastewater treatment processes. To achieve a better prediction accuracy and robustness, we propose a stacking ensemble learning (SEL) method which utilizes the advantages of the internal base-learning models. The method combines base-learning algorithms including partial least squares, support vector regression, and artificial neural networks with a meta-learning algorithm, which is a multiple-response linear regression in this work. To evaluate the model performance in practical applications, both real wastewater data and simulation wastewater data are used for modeling. The predicted effluent indices include effluent suspended solid (SS eff ), effluent chemical oxygen demand (COD eff ), effluent ammonia concentration (S NHeff ), and effluent nitrate concentration (S NOeff ). Compared with base-learning algorithms and other ensemble learning methods, the results demonstrate that SEL significantly improves the prediction accuracy and reduces the prediction errors, which provides a new way to achieve real-time monitoring of wastewater treatment processes.INDEX TERMS Stacking ensemble learning, papermaking process modeling, effluent indices, prediction accuracy, wastewater treatment processes.