Suchergebnisse

2011 2nd International Conference on Advances in Energy Engineering, ICAEE 2011, Bangkok, 27-28 December 2011 ; 201812 bcrc ; Version of Record ; Published

Abstract. Pattern Informatics (PI) algorithm uses earthquake catalogues for estimating the increase of the probability of strong earthquakes. The main measure in the algorithm is the number of earthquakes above a threshold magnitude. Since aftershocks occupy a significant proportion of the total number of earthquakes, whether de-clustering affects the performance of the forecast is one of the concerns in the application of this algorithm. This problem is of special interest after a great earthquake, when aftershocks become predominant in regional seismic activity. To investigate this problem, the PI forecasts are systematically analyzed for the Sichuan-Yunnan region of southwest China. In this region there have occurred some earthquakes larger than MS 7.0, including the 2008 Wenchuan earthquake. In the analysis, the epidemic-type aftershock sequences (ETAS) model was used for de-clustering. The PI algorithm was revised to consider de-clustering, by replacing the number of earthquakes by the sum of the ETAS-assessed probability for an event to be a "background event" or a "clustering event". Case studies indicate that when an intense aftershock sequence is included in the "sliding time window", the hotspot picture may vary, and the variation lasts for about one year. PI forecasts seem to be affected by the aftershock sequence included in the "anomaly identifying window", and the PI forecast using "background events" seems to have a better performance.

Abstract. The b-value in the Gutenberg-Richter frequency-magnitude distribution, which is assumed to be related to stress heterogeneity or asperities, was mapped along the Longmenshan fault zone which accommodated the 12 May 2008, Wenchuan, MS 8.0 earthquake. Spatial distributions of b-value before and after the Wenchuan earthquake, respectively, were compared with the slip distribution of the mainshock. It is shown that the mainshock rupture nucleated near to, but not within, the high-stress (low b-value) asperity in the south part of the Longmenshan fault, propagating north-eastward to the relatively low stress (high b-value) region. Due to the significant difference between the rupture process results from different sources, the comparison between slip distribution and pre-seismic b-value distribution leads to only conclusion of the rule-of-thumb. The temporal change of b-value before the mainshock shows a weak trend of decreasing, being hard to be used as an indicator of the approaching of the mainshock. Distribution of b-values for the aftershocks relates the termination of the mainshock rupture to the harder patch along the Longmenshan fault to the north.

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Abstract
Practices for reducing the impacts of floods are becoming more and more advanced, centered on communities and reaching out to vulnerable populations. Vulnerable individuals are characterized by social and economic attributes and by societal dynamics rooted in each community. These indicators can magnify the negative impacts of disasters together with the capacity of each individual to cope with these events. The Social Vulnerability Index (SoVI) provides an empirical basis to compare social differences in various spatial scenarios and for specific environmental hazards. This research shows the application of the SoVI to the floodplain of northern Italy, based on the use of 15 census variables. The chosen study area is of particular interest for the high occurrence of flood events coupled with a high level of human activity, landscape transformations, and an elevated concentration of assets and people. The analysis identified a positive spatial autocorrelation across the floodplain that translates into the spatial detection of vulnerable groups, those that are likely to suffer the most from floods. In a second stage, the output of the index was superimposed on the flood hazard map of the study area to analyze the resulting risk. The Piemonte and Veneto regions contain the main areas prone to flood "social" risk, highlighting the need for a cohesive management approach at all levels to recognize local capacities and increase communication, awareness, and preparedness to mitigate the undesirable effects of such events.

To explore the effect of tea polysaccharides on the gut microbiota and their short-chain fatty acid (SCFA) metabolic products, we used the faecal microbiota to simulate the gut microbiota in vitro, and cultured them to obtain a preculture solution. Ultrapure water, tea polysaccharides, and glucose were added to the precultured solution for anaerobic fermentation. Samples of each group were harvested at 0, 6, 12, and 24th hour during fermentation to test the contents of the SCFAs. In addition, high-throughput 16S rRNA sequencing was performed to analyse the microbiota in the fermentation medium. Results showed that the faecal microbiota used tea polysaccharides to generate SCFAs. When compared with the fermentation group with the addition of ultrapure water, the group with the addition of tea polysaccharides increased the relative abundance of Firmicutes, and decreased the relative abundance of Bacteroidetes at the phylum level. The relative abundances of Butyricimonas, Roseburia, Eubacterium rectale group, Ruminococcus 1, Lachnospira, and Parasutterella increased significantly at the genus level. Based on the LEfSe analysis of key microbiota at the genus level, significant differences between the groups were observed. It was clear that tea polysaccharides selectively enriched the microbiota to produce SCFAs, and the correlation between the SCFAs and faecal microbiota was confirmed.

16 páginas, 3 figuras, 5 tablas. ; Global mean temperature is predicted to increase by 2–7 1C and precipitation to change across the globe by the end of this century. To quantify climate effects on ecosystem processes, a number of climate change experiments have been established around the world in various ecosystems. Despite these efforts, general responses of terrestrial ecosystems to changes in temperature and precipitation, and especially to their combined effects, remain unclear. We used metaanalysis to synthesize ecosystem-level responses to warming, altered precipitation, and their combination. We focused on plant growth and ecosystem carbon (C) balance, including biomass, net primary production (NPP), respiration, net ecosystem exchange (NEE), and ecosystem photosynthesis, synthesizing results from 85 studies. We found that experimental warming and increased precipitation generally stimulated plant growth and ecosystem C fluxes, whereas decreased precipitation had the opposite effects. For example, warming significantly stimulated total NPP, increased ecosystem photosynthesis, and ecosystem respiration. Experimentally reduced precipitation suppressed aboveground NPP (ANPP) and NEE, whereas supplemental precipitation enhanced ANPP and NEE. Plant productivity and ecosystem C fluxes generally showed higher sensitivities to increased precipitation than to decreased precipitation. Interactive effects of warming and altered precipitation tended to be smaller than expected from additive, single-factor effects, though low statistical power limits the strength of these conclusions. New experiments with combined temperature and precipitation manipulations are needed to conclusively determine the importance of temperature–precipitation interactions on the C balance of terrestrial ecosystems under future climate conditions. ; This work was supported by the National Institute of Climatic Change Research, the US National Science Foundation (DEB-0092642), the government of Catalunya (AGAUR 2005PIV00123, Grant SGR2009-458), the Spanish Government (Grants CGL2006- 04025/BOS and Consolider Ingenio Montes CSD2008-00040), and Science Foundation Arizona (GRF 0001-07). ; Peer reviewed