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Anion exchange membrane water electrolysis (AEMWE) with low cost and high electrolysis efficiency is an emerging technology for large-scale hydrogen production. High-performance anion exchange membranes (AEMs) are crucial for the commercialization of AEMWE. In this work, four hydrophilic and hydrophobic di-piperidine crosslinked membranes were prepared and tested. The combination of cationic side chains and hydrophilic oxygenated cross-linkers facilitates the construction of efficient and continuous ion transport channels, reducing resistance and enhancing the alkaline stability of AEMs. With cationic side chains and hydrophilic flexible di-piperidine crosslinkers, the PTP-N-QABIEP membrane exhibits high ionic conductivity (151.7 mS cm−1 at 80 °C), good dimensional stability (WU 118.4% and SR 20.2% at 80 °C) and excellent alkaline stability (2 M NaOH > 2300 h at 80 °C). Meanwhile, satisfactory AEMWE performance was demonstrated in 1 M KOH (2.14 A cm−2 and 2.2 V at 60 °C) and pure water (0.600 A cm−2 and 2.2 V at 60 °C), respectively, while there was also a low voltage decay (0.542 mV h−1) after 240 h of operation in 1 M KOH at 500 mA cm−2 and 60 °C. Therefore, the PTP-N-QABIEP membrane with relatively excellent comprehensive performance shows favorable prospects for AEMWE applications in 1 M KOH or pure water. [ABSTRACT FROM AUTHOR]

The roofs of heritage buildings are subject to long-term degradation, resulting in poor heat insulation, heat regulation, and water leakage prevention. Researchers have predominantly employed feature-based traditional machine learning methods or individual deep learning techniques for the detection of natural deterioration and human-made damage on the surfaces of heritage building roofs for preservation. Despite their success, balancing accuracy, efficiency, timeliness, and cost remains a challenge, hindering practical application. The paper proposes an integrated method that employs a convolutional autoencoder, thresholding techniques, and a residual network to automatically detect anomalies on heritage roof surfaces. Firstly, unmanned aerial vehicles (UAVs) were employed to collect the image data of the heritage building roofs. Subsequently, an artificial intelligence (AI)-based system was developed to detect, extract, and classify anomalies on heritage roof surfaces by integrating a convolutional autoencoder, threshold techniques, and residual networks (ResNets). A heritage building project was selected as a case study. The experiments demonstrate that the proposed approach improved the detection accuracy and efficiency when compared with a single detection method. The proposed method addresses certain limitations of existing approaches, especially the reliance on extensive data labeling. It is anticipated that this approach will provide a basis for the formulation of repair schemes and timely maintenance for preventive conservation, enhancing the actual benefits of heritage building restoration. [ABSTRACT FROM AUTHOR]

With the extensive application of deep learning in the field of human rehabilitation, skeleton based rehabilitation recognition is becoming more and more concerned with large-scale bone data sets. The key factor of this task is the two intra frame representations of the combined co-and the inter-frame. In this paper, an inter frame representation method based on RNN is proposed. Pointtion of each joint is joint-coded they are assembled into semantic both spatial and temporal domains.we introduce a global spatial aggregation which is able to learn superior joint co features over local aggregation.

In order to study operating performance of six-phase induction motor under control of trapezoidal wave phase current, six-phase trapezoidal wave phase current was decomposed into fundamental current and a series of harmonic current by Fourier decomposition, and then different harmonic magnetic produced by each subharmonic current was calculated. Finite element analysis results show that the six-phase induction motor eliminates the most obvious torque ripple in general motor generated by the fifth and seventh harmonic current.

In order to analyze the effect of curtain cloth moisture content on the deposition performance of fine particles in air, the deposition loss rate of particles with different particle sizes under different moisture content of curtain samples were obtained by the test in a specific designed closed chamber. The experimental results show that the particle deposition loss rate curves are V-shaped with the increase of particle size, and the deposition loss rate of particles decrease with the increase of moisture content. Under the same moisture content, the deposition loss rate of particles with different particle sizes on the surface of hemp samples are greater than those of polyester samples. [ABSTRACT FROM AUTHOR]