We used airborne light detection and ranging (LiDAR) data to reevaluate the singleevent offsets of the 1920 Haiyuan Ms 8.5 earthquake and the cumulative offsets along the western and middle segments of the coseismic surface rupture zone. Our LiDAR data indicate that the offset observations along both the western and middle segments fall into groups. The group with the minimum slip amount is associated with the 1920 Haiyuan Ms 8.5 earthquake, which ruptured both the western and middle segments. Our research highlights two new interpretations: First, the previously reported maximum displacement of the 1920 earthquake was likely due to at least two earthquakes; second, our results reveal that the cumulative offset probability density (COPD) peaks of the same offset amounts on the western and middle segments do not correspond to one another one-to-one. We suggest that any discussion of the rupture pattern of a certain fault based on the offset data should also consider fault segmentation and paleoseismological data. Therefore, the COPD peaks should be computed and analyzed on fault subsections and not entire fault zones to study the number of paleoearthquakes and their rupture patterns.

For developing lithium-sulfur (Li-S) batteries, it is critical to design advanced cathode materials with high sulfur loading/utilization ratios and strong binding interactions with sulfur species to prevent the dissolution of intermediate polysulfides. Here we report an effective sulfur host material prepared by implanting cerium oxide (CeO) nanocrystals homogeneously into well-designed bimodal micromesoporous nitrogen-rich carbon (MMNC) nanospheres. With the high conductivity and abundant hierarchical pore structures, MMNC nanospheres can effectively store and entrap sulfur species. Moreover, the inserted polar and electrocatalytically active CeO nanocrystals and high nitrogen content of MMNC can synergistically solve the hurdle of the polysulfide dissolution and furthermore significantly promote stable redox activity. By combining these advantages, CeO/MMNC-S cathodes with 1.4 mg cm sulfur exhibit high reversible capacities (1066 mAh g at 0.2 C after 200 cycles and 836 mAh g at 1.0 C after 500 cycles), good rate capability (737 mAh g at 2.0 C), and high cycle stability (721 mAh g at 2.0 C after 1000 cycles with a low capacity decay of 0.024% per cycle). Furthermore, a high and stable reversible capacity of 611 mAh g is achieved after cycling for 200 cycles with higher sulfur loading of 3.4 mg cm.

This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (ANPs) automatically discovered from the tags of web photos, and can be utilized as effective statistical cues for detecting emotions depicted in the images. Nearly one million Flickr images tagged with these ANPs are downloaded to train the classifiers of the concepts. We adopt the popular model of deep convolutional neural networks which recently shows great performance improvement on classifying largescale web-based image dataset such as ImageNet. Our deep CNNs model is trained based on Caffe, a newly developed deep learning framework. To deal with the biased training data which only contains images with strong sentiment and to prevent overfitting, we initialize the model with the model weights trained from ImageNet. Performance evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called DeepSentiBank) is significantly improved in both annotation accuracy and retrieval performance, compared to its predecessors which mainly use binary SVM classification models.

This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM? data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha -1 ya -1 in 2005, and the area of 47.5 km 2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.

Au catalysts with layered double hydroxide (LDH) as support were fabricated and the crystal faces feature of LDH platelets was revealed to have a crucial effect on the location and particle size of gold nanoparticles (AuNPs). A preferential deposition of AuNPs with a narrow size distribution was formed on the lateral {101̅0} faces of LDH platelets. Catalytic property evaluation of the resulting Au/LDH samples showed that the conversion of styrene with tert-butyl hydroperoxide (TBHP) to styrene oxide (SO) occurred on the AuNPs with particle size of 2−3 nm, mainly deposited on the lateral faces of LDH support.

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Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has suffered widespread and significant landslide events in recent years. In our study, an object-oriented landslide mapping (OOLM) framework was proposed for reliable and accurate LM from 'ZY-3' high spatial resolution (HSR) satellite images. The framework was based on random forests (RF) and mathematical morphology (MM). RF was first applied as an object feature information reduction tool to identify the significant features for describing landslides, and it was then combined with MM to map the landslides. Three object-feature domains were extracted from the 'ZY-3' HSR data: layer information, texture, and geometric features. A total group of 124 features and 24 landslides were used as inputs to determine the landslide boundaries and evaluate the landslide classification accuracy. The results showed that: (1) the feature selection (FS) method had a positive influence on effective landslide mapping; (2) by dividing the data into two sets, training sets which consisted of 20% of the landslide objects (O LS ) and non-landslide objects (O NLS ), and test sets which consisted of the remaining 80% of the O LS and O NLS , the selected feature subsets were combined for training to obtain an overall classification accuracy of 93.3% ± 0.12% of the test sets; (3) four MM operations based on closing and opening were used to improve the performance of the RF classification. Seven accuracy evaluation indices were used to compare the accuracies of these landslide mapping methods. Finally, the landslide inventory maps were obtained. Based on its efficiency and accuracy, the proposed approach can be employed for rapid response to natural hazards in the Three Gorges area.

High-resolution topographic or imagery data effectively reveal geomorphic offsets along faults that can be used to deduce slipper event of recurrent rupture events. Documentation of patterns of geomorphic offsets is scarce on faults that undergo both creep and coseismic rupture. In this paper, we used newly acquired high-resolution light detection and ranging (LiDAR) data to compile geomorphic offsets along the Laohu Shan section of the Haiyuan fault, in the northern Tibetan Plateau, where interferometric synthetic aperture radar (InSAR) data suggest creep presently occurs over a 35-km-long stretch at a rate comparable to the long-term geological slip rate, despite evidence for past coseismic fault rupture. Numerous offset gullies identified using the LiDAR data yield a range of offsets from less than 2 m up to 50 m. These offsets have well-separated probability density peaks at 2-3 m, ~7 m, and ~14 m, with increments of 2-3 m, 4-6 m, and 5-7 m. The sequence of paleoseismic events along the Laohu Shan section indicates that the gullies with offsets of 2-3 m are likely related to surface rupture of the historical 1888 Jingtai earthquake, plus subsequent creep. Offset increments of 4-6 m and 5-7 m may represent coseismic slip in past paleoseismic events plus creep during the interseismic period. The creeping Laohu Shan section preserves numerous discrete cumulative offsets, with an offset clustering pattern indistinguishable from that on a locked fault with recurrent earthquake ruptures. Association of offset increments with known paleoseismic events yields a slip rate of 3-5 mm/yr during the past 200 years, roughly similar to the ~5 mm/yr creep rate. If the ratio of surface creep rate to the total fault slip rate has been continuous, then seismic moment release by brittle ruptures, and thus seismic hazard, would be much reduced on the Laohu Shan section of the Haiyuan fault. Alternatively, the current high creep rate may be a transient phenomenon, perhaps after slip following the 2000 Jingtai Mw 5.6 earthquake or in response to the adjacent 1920 M ~8 Haiyuan earthquake rupture that terminated immediately to the east.