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Two different porous copper metal–organic frameworks (Cu-MOFs) named as Cu3(BTC)2 and Cu(BDC) were synthesized and applied as heterogeneous catalysts for the catalytic wet peroxide oxidation (CWPO) of simulated phenol wastewater (100 mg L−1).

Abstract. In 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of global coverage, mostly due to cloud covers. In this paper we introduce the updates of AW3D30 filling the voids with other open-access DSMs such as Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., through inter-comparisons among these datasets.

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Metal cluster-based compounds have difficulty finishing the photocatalytic carbon dioxide reduction reaction (CO 2 RR) and water oxidation reaction (WOR) simultaneously because of the big challenge in realizing the coexistence of independently and synergistically reductive and oxidative active sites in one compound. Herein, we elaborately designed and synthesized one kind of crystalline reduction–oxidation ( RO ) cluster-based catalysts connecting reductive { M 3 L 8 (H 2 O) 2 } (M = Zn, Co, and Ni for RO-1 , 2 , 3 respectively) cluster and oxidative {PMo 9 V 7 O 44 } cluster through a single oxygen atom bridge to achieve artificial photosynthesis successfully. These clusters can all photocatalyze CO 2 -to-CO and H 2 O-to-O 2 reactions simultaneously, of which the CO yield of RO-1 is 13.8 μmol/g·h, and the selectivity is nearly 100%. Density functional theory calculations reveal that the concomitantly catalytically reductive and oxidative active sites (for CO 2 RR and WOR, respectively) and the effective electron transfer between the sites in these RO photocatalysts are the key factors to complete the overall photosynthesis.

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