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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil-fuel combustion and cement production (EFF) are based on energy statistics, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2 and land cover change (some including nitrogen–carbon interactions). All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2003–2012), EFF was 8.6 ± 0.4 GtC yr−1, ELUC 0.9 ± 0.5 GtC yr−1, GATM 4.3 ± 0.1 GtC yr−1, SOCEAN 2.5 ± 0.5 GtC yr−1, and SLAND 2.8 ± 0.8 GtC yr−1. For year 2012 alone, EFF grew to 9.7 ± 0.5 GtC yr−1, 2.2% above 2011, reflecting a continued growing trend in these emissions, GATM was 5.1 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and assuming an ELUC of 1.0 ± 0.5 GtC yr−1 (based on the 2001–2010 average), SLAND was 2.7 ± 0.9 GtC yr−1. GATM was high in 2012 compared to the 2003–2012 average, almost entirely reflecting the high EFF. The global atmospheric CO2 concentration reached 392.52 ± 0.10 ppm averaged over 2012. We estimate that EFF will increase by 2.1% (1.1–3.1%) to 9.9 ± 0.5 GtC in 2013, 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy. With this projection, cumulative emissions of CO2 will reach about 535 ± 55 GtC for 1870–2013, about 70% from EFF (390 ± 20 GtC) and 30% from ELUC (145 ± 50 GtC).

Contains fulltext : 69403.pdf (Publisher’s version ) (Open Access) INTRODUCTION: Mammographic density is known to be a strong risk factor for breast cancer. A particularly strong association with risk has been observed when density is measured using interactive threshold software. This, however, is a labour-intensive process for large-scale studies. METHODS: Our aim was to determine the performance of visually assessed percent breast density as an indicator of breast cancer risk. We compared the effect on risk of density as measured with the mediolateral oblique view only versus that estimated as the average density from the mediolateral oblique view and the craniocaudal view. Density was assessed using a visual analogue scale in 10,048 screening mammograms, including 311 breast cancer cases diagnosed at that screening episode or within the following 6 years. RESULTS: Where only the mediolateral oblique view was available, there was a modest effect of breast density on risk with an odds ratio for the 76% to 100% density relative to 0% to 25% of 1.51 (95% confidence interval 0.71 to 3.18). When two views were available, there was a considerably stronger association, with the corresponding odds ratio being 6.77 (95% confidence interval 2.75 to 16.67). CONCLUSION: This indicates that a substantial amount of information on risk from percentage breast density is contained in the second view. It also suggests that visually assessed breast density has predictive potential for breast cancer risk comparable to that of density measured using the interactive threshold software when two views are available. This observation needs to be confirmed by studies applying the different measurement methods to the same individuals.

Accurate face registration is of vital importance to the performance of a face recognition algorithm. We propose a face registration method which searches for the optimal alignment by maximizing the score of a face recognition algorithm. In this paper we investigate the practical usability of our face registration method. Experiments show that our registration method achieves better results in face verification than the landmark based registration method. We even obtain face verification results which are similar to results obtained using landmark based registration with manually located eyes, nose and mouth as landmarks. The performance of the method is tested on the FRGCv1 database using images taken under both controlled and uncontrolled conditions.

To evaluate the benthic diversity of River Gomti in relation to the prevailing environmental conditions, four stations, viz., Maa Chandrika Devi, Daliganj, Ambedkar Park and Aquaduct, were identified from upstream to downstream along the course of the river in Lucknow. Dissolved oxygen was low on many occasions at all the stations except Maa Chandrika Devi and chemical oxygen demand values were high. There was a gradual increase in mean nitrite and phosphate values from up to downstream. Benthic fauna was dominated by oligochaetes and chironomus larvae. Other groups reported were leeches, nematodes, sponges, crustaceans, pelycypodes, gastropods and fish fry. Population density was exceptionally high at Daliganj (20,135 m sub(-1) ) followed by Ambedkar Park (5,199 m sub(-1)) and Aquaduct (3,287 m sub(-1)), and low at Maa Chandrika Devi (264 m sub(-1)). Oligochaete genera common at all the four stations were Lumbricillus, Limnodrillus, Branchiura, Chaetogaster, Nais and Tubifex. Odonates were reported only from Maa Chandrika Devi while sponges were encountered at Daliganj and Aquaduct. On some occasions, fish fry were also found at Ambedkar Park and Aquaduct. Seasonally, maxima for population density were observed during pre-monsoon and minima during monsoon. The organic pollution indicator benthic species reported were tubificids, chironomids, culicoid larvae, Lamellidens sp., Corbicula sp., Lymnaea sp. and leech. Branchiurans, Tubifex sp. and Chironomus larvae were reported at all the stations. Filthy condition with foul smell throughout the length of the river coupled with poor water quality and appearance of indicator organisms at all the stations indicate that the river is under severe pollution stress due to anthropogenic discharges and it has reached an alarming stage.

This paper demonstrates the feasibility of a new method of hand-geometry recognition based on parameters derived from the contour of the hand. The contour is completely determined by the black-and-white image of the hand and can be derived from it by means of simple image-processing techniques. It can be modelled by parameters, or features, that can capture more details of the shape of the hand than what is possible with the standard geometrical features used in hand-geometry recognition. The set of features considered in this paper consists of the spatial coordinates of certain landmarks on the contour. The feature set and the recognition method used are discussed in detail. The usefulness of the proposed feature set is evaluated experimentally in a verification context. The verification performance obtained with contour-based features is compared with the verification performance of other methods described in the literature.