The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.
Microbial communities play a crucial role in various biogeochemical processes in aquatic ecosystems. However, existing knowledge on microbial communities in the waters of wetlands is still very scant. The objective of the present study was to investigate the bacterioplankton community in the Luoshijiang Wetland, a high-altitude freshwater wetland in the Yunnan-Kweichow Plateau. Water samples were collected from different sites. The bacterioplankton community was characterized using 16S rRNA gene clone library analysis. A spatial variation of the diversity and composition of the bacterioplankton community was observed. Verrucomicrobia and Proteobacteria were the most abundant components. Proteobacteria might play an important role in water self-purification, but the significance of Verrucomicrobia remained unclear. Moreover, Pearson's correlation analysis showed that Actinobacteria and Gemmatimonadetes were positively correlated with nitrite nitrogen in waters, while Alphaproteobacteria with dissolved phosphorous.
An inhomogeneous mixing of reactants causes a reduction of their chemical removal compared to the homogeneously mixed case in turbulent atmospheric flows. This can be described by the intensity of segregation I-S being the covariance of the mixing ratios of two species divided by the product of their means. Both terms appear in the balance equation of the mixing ratio and are discussed for the reaction between isoprene and OH for data of the field study ECHO 2003 above a deciduous forest. For most of these data, I-S is negatively correlated with the fraction of mean OH mixing ratio reacting with isoprene. I-S is also negatively correlated with the isoprene standard deviation. Both findings agree with model results discussed by Patton et al. (2001) and others. The correlation coefficient between OH and isoprene and, therefore, I-S increases with increasing mean reaction rate. In addition, the balance equation of the covariance between isoprene and OH is applied as the theoretical framework for the analysis of the same field data. The storage term is small, and, therefore, a diagnostic equation for this covariance can be derived. The chemical reaction term R-ij is dominated by the variance of isoprene times the quotient of mixing ratios of OH and isoprene. Based on these findings a new diagnostic equation for I-S is formulated. Comparing different terms of this equation, I-S and R-ij show a relation also to the normalised isoprene standard deviation. It is shown that not only chemistry but also turbulent and convective mixing and advection - considered in a residual term - influence I-S. Despite this finding, a detection of the influence of coherent eddy transport above the forest according to Katul et al. (1997) on I-S fails, but a relation to the turbulent and advective transport of isoprene variance is determined. The largest values of I-S are found for most unstable conditions with increasing buoyant production, confirming qualitatively model predictions by Ouwersloot et al. (2011).
Bioinformatics is a fast-growing interdisciplinary field in which the demand for quality education exceeds the supply, especially in developing regions and countries. A massive open online course (MOOC) is a new model for education that delivers videotaped lectures and other course materials over the Internet for all interested persons around the globe to learn for free. Here we present our MOOC “Bioinformatics: Introduction and Methods,” which is the second bioinformatics MOOC in the world and one of the first batch of seven MOOCs from China. In the first two runs of this bilingual MOOC, more than 30,000 students with diverse backgrounds registered from 110 countries and regions. In this manuscript, we present the content design of the MOOC, the demographic profiles and learning patterns of the students, the requirement for English support, and feedback from on-campus students. We offer a few suggestions to other scientists who may be interested in creating a MOOC. We also remember the S* course, a successful open online bioinformatics course that ran from 2001 to 2007, long before the current wave of MOOCs. We believe that MOOC education has great potential to enhance global bioinformatics education.
Ambient volatile organic compounds (VOCs) were measured intensively using an online gas chromatography–mass spectrometry/flame ionization detector (GC–MS/FID) at Ziyang in the Chengdu–Chongqing Region (CCR) from 6 December 2012 to 4 January 2013. Alkanes contributed the most (59%) to mixing ratios of measured non-methane hydrocarbons (NMHCs), while aromatics contributed the least (7%). Methanol was the most abundant oxygenated VOC (OVOC), contributing 42% to the total amount of OVOCs. Significantly elevated VOC levels occurred during three pollution events, but the chemical composition of VOCs did not differ between polluted and clean days. The OH loss rates of VOCs were calculated to estimate their chemical reactivity. Alkenes played a predominant role in VOC reactivity, among which ethylene and propene were the largest contributors; the contributions of formaldehyde and acetaldehyde were also considerable. Biomass burning had a significant influence on ambient VOCs during our study. We chose acetonitrile as a tracer and used enhancement ratio to estimate the contribution of biomass burning to ambient VOCs. Biomass burning contributed 9.4%–36.8% to the mixing ratios of selected VOC species, and contributed most (>30% each) to aromatics, formaldehyde, and acetaldehyde.
Black carbon (BC) mass emission factors (EFBC; g BC (kg fuel)(-1)) from a variety of ocean-going vessels have been determined from measurements of BC and carbon dioxide (CO2) concentrations in ship plumes intercepted by the R/V Atlantis during the 2010 California Nexus (CalNex) campaign. The ships encountered were all operating within 24 nautical miles of the California coast and were utilizing relatively low sulphur fuels (average fuel sulphur content of 0.4%, 0.09% and 0.03% for vessels operating slow-speed, medium-speed and high-speed diesel engines, respectively). Black carbon concentrations within the plumes, from which EFBC values are determined, were measured using four independent instruments: a photoacoustic spectrometer and a particle soot absorption photometer, which measure light absorption, and a single particle soot photometer and soot particle aerosol mass spectrometer, which measure the mass concentration of refractory BC directly. These measurements have been used to assess the level of agreement between these different techniques for the determination of BC emission factors from ship plumes. Also, these measurements greatly expand upon the number of individual ships for which BC emission factors have been determined during real-world operation. The measured EFBC's have been divided into vessel type categories and engine type categories, from which averages have been determined. The geometric average EFBC (excluding outliers) determined from over 71 vessels and 135 plumes encountered was 0.31 +/- 0.31 gBC (kg fuel)(-1), where the standard deviation represents the variability between individual vessels. The most frequent engine type encountered was the slow-speed diesel (SSD), and the most frequent SSD vessel type was the cargo ship sub-category. Average and median EFBC values from the SSD category are compared with previous observations from the Texas Air Quality Study (TexAQS) in 2006, during which the ships encountered were predominately operating on high-sulphur fuels (average fuel sulphur content of 1.6 %). There is a statistically significant difference between the EFBC values from CalNex and TexAQS for SSD vessels and for the cargo and tanker ship types within this engine category. The CalNex EFBC values are lower than those from TexAQS, suggesting that operation on lower sulphur fuels is associated with smaller EFBC values.
