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Three‐dimensional (3D) nanoporous CuAg (NPCuAg) alloy catalysts with various Cu/Ag ratios are prepared by electrochemical dealloying of metallic glassy (MG) precursors. All dealloyed samples exhibit homogenous nanoporous structure and element composition distribution. After systematically evaluating their electrocatalytic performance toward MOR, it was found that the catalytic activity of the NPCuAg catalysts is enhanced along with the increase of Cu/Ag ratio, which may be attributed to the more exposed active reaction sites derived from high surface area of nanoporous structure and the optimal synergistic effect. Thus, the NPCu1.75Ag alloy catalyst presents the best methanol electro‐oxidation properties, including a high current density of 397.2 mA cm−2 and good operation stability that retaining 84.5 % catalytic activity even after 7200 s. These results outperform most reported copper‐based MOR catalysts in alkaline methanol solution. Considering these advantages, the designed electrodes are expected to be promising catalysts for alkaline DMFCs applications. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

Short-term traffic prediction (STTP) is one of the most critical capabilities in Intelligent Transportation Systems (ITS), which can be used to support driving decisions, alleviate traffic congestion and improve transportation efficiency. However, STTP of large-scale road networks remains challenging due to the difficulties of effectively modeling the diverse traffic patterns by high-dimensional time series. Therefore, this paper proposes a framework that involves a deep clustering method for STTP in large-scale road networks. The deep clustering method is employed to supervise the representation learning in a visualized way from the large unlabeled dataset. More specifically, to fully exploit the traffic periodicity, the raw series is first divided into a number of sub-series for triplet generation. The convolutional neural networks (CNNs) with triplet loss are utilized to extract the features of shape by transforming the series into visual images. The shape-based representations are then used to cluster road segments into groups. Thereafter, a model sharing strategy is further proposed to build recurrent NNs-based predictions through group-based models (GBMs). GBM is built for a type of traffic patterns, instead of one road segment exclusively or all road segments uniformly. Our framework can not only significantly reduce the number of prediction models, but also improve their generalization by virtue of being trained on more diverse examples. Furthermore, the proposed framework over a selected road network in Beijing is evaluated. Experiment results show that the deep clustering method can effectively cluster the road segments and GBM can achieve comparable prediction accuracy against the IBM with less number of prediction models. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) nanoporous CuAg (NPCuAg) alloy catalysts with various Cu/Ag ratios are prepared by electrochemical dealloying of metallic glassy (MG) precursors. All dealloyed samples exhibit homogenous nanoporous structure and element composition distribution. After systematically evaluating their electrocatalytic performance toward MOR, it was found that the catalytic activity of the NPCuAg catalysts is enhanced along with the increase of Cu/Ag ratio, which may be attributed to the more exposed active reaction sites derived from high surface area of nanoporous structure and the optimal synergistic effect. Thus, the NPCu 1.75 Ag alloy catalyst presents the best methanol electro-oxidation properties, including a high current density of 397.2 mA cm -2 and good operation stability that retaining 84.5 % catalytic activity even after 7200 s. These results outperform most reported copper-based MOR catalysts in alkaline methanol solution. Considering these advantages, the designed electrodes are expected to be promising catalysts for alkaline DMFCs applications., (© 2023 Wiley-VCH GmbH.)

Methanol oxidation reaction (MOR) in anodes is one of the significant aspects of direct methanol fuel cells (DMFCs), which also plays a critical role in achieving a carbon-neutral economy. Designing and developing efficient, cost-effective, and durable non-Pt group metal-based methanol oxidation catalysts are highly desired, but a gap still remains. Herein, we report well-defined hierarchical NiZn x @CuO nanoarray architectures as active electrocatalysts for MOR, synthesized by combining thermal oxidation treatment and magnetron sputtering deposition through a brass mesh precursor. After systematically evaluating the electrocatalytic performance of NiZn x @CuO nanoarray catalysts with different preparation conditions, we found that the NiZn 1000 @CuO (thermally oxidized at 500 °C for 2 h, nominal thickness of the NiZn alloy film is 1000 nm) electrode delivers a high current density of 449.3 mA cm -2 at 0.8 V for MOR in alkaline media as well as excellent operation stability (92% retention after 12 h). These outstanding MOR performances can be attributed to the hierarchical well-defined structure that can not only render abundant active sites and a synergistic effect to enhance the electrocatalytic activity but also can effectively facilitate mass and electron transport. More importantly, we found that partial Zn atoms could leach from the NiZn alloy, resulting in rough surface nanorods, which would further increase the specific surface area. These results indicate that the NiZn 1000 @CuO nanoarray architecture could be a promising Pt group metal alternative as an efficient, cost-effective, and durable anode catalyst for DMFCs.

The bandwidth shortage has motivated the exploration of the millimeter wave (mmWave) frequency spectrum for future communication networks. To compensate for the severe propagation attenuation in the mmWave band, massive antenna arrays can be adopted at both the transmitter and receiver to provide large array gains via directional beamforming. To achieve such array gains, channel estimation (CE) with high resolution and low latency is of great importance for mmWave communications. However, classic super-resolution subspace CE methods such as multiple signal classification (MUSIC) and estimation of signal parameters via rotation invariant technique (ESPRIT) cannot be applied here due to RF chain constraints. In this paper, an enhanced CE algorithm is developed for the off-grid problem when quantizing the angles of mmWave channel in the spatial domain where off-grid problem refers to the scenario that angles do not lie on the quantization grids with high probability, and it results in power leakage and severe reduction of the CE performance. A new model is first proposed to formulate the off-grid problem. The new model divides the continuously-distributed angle into a quantized discrete grid part, referred to as the integral grid angle, and an offset part, termed fractional off-grid angle. Accordingly, an iterative off-grid turbo CE (IOTCE) algorithm is proposed to renew and upgrade the CE between the integral grid part and the fractional off-grid part under the Turbo principle. By fully exploiting the sparse structure of mmWave channels, the integral grid part is estimated by a soft-decoding based compressed sensing (CS) method called improved turbo compressed channel sensing (ITCCS). It iteratively updates the soft information between the linear minimum mean square error (LMMSE) estimator and the sparsity combiner. Monte Carlo simulations are presented to evaluate the performance of the proposed method, and the results show that it enhances the angle detection resolution greatly., Competing Interests: The authors declare no conflicts of interest.