6,621 research outputs found

    A Synthesis of Global Coastal Ocean Greenhouse Gas Fluxes

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    International audienceThe coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO 2) and releasing nitrous oxide (N 2 O) and methane (CH 4). In this second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP2), we quantify global coastal ocean fluxes of CO 2 , N 2 O and CH 4 using an ensemble of global gap-filled observation-based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO 2 in both observational products and models, but the magnitude of the median net global coastal uptake is ∌60% larger in models (-0.72 vs.-0.44 PgC year-1 , 1998-2018, coastal ocean extending to 300 km offshore or 1,000 m isobath with area of 77 million km 2). We attribute most of this model-product difference to the seasonality in sea surface CO 2 partial pressure at mid-and high-latitudes, where models simulate stronger winter CO 2 uptake. The coastal ocean CO 2 sink has increased in the past decades but the available time-resolving observation-based products and models show large discrepancies in the magnitude of this increase. The global coastal ocean is a major source of N 2 O (+0.70 PgCO 2-e year-1 in observational product and +0.54 PgCO 2-e year-1 in model median) and CH 4 (+0.21 PgCO 2-e year-1 in observational product), which offsets a substantial proportion of the coastal CO 2 uptake in the net radiative balance (30%-60% in CO 2-equivalents), highlighting the importance of considering the three greenhouse gases when examining the influence of the coastal ocean on climate

    Carbonates from Ryugu: Evidence of Parent Body Aqueous Alteration in Sample A0112

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    The Japanese Hayabusa2 sample return mission retrieved fragments of the near-Earth C-type car- bonaceous asteroid Ryugu 162173, that exhibit similarities in composition to the Ivuna meteorite. The high carbon content and presence of volatile-rich materials, suggests it could potentially be categorized as a primitive CI-type asteroid. [1] Among the collected samples, the piece A0112 was sent to Planetary Spectroscopy Laboratories (PSL) at the German Aerospace Center (DLR) for spectral analysis. Micro infrared spectroscopy and Raman microspectros- copy measurements were carried out on sample A0112, when the grain was still sealed within its sample holder. The analysis of the sample, allowed the determination of its bulk composition and its mineralogy under glass, without it being affected by terrestrial alteration. Carbonates could be identified, which provided significant evidence of the aqueous alteration processes that occurred in the parent body of Ryugu

    Seasonal variability of the surface ocean carbon cycle: A synthesis

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    Abstract The seasonal cycle is the dominant mode of variability in the air‐sea CO 2 flux in most regions of the global ocean, yet discrepancies between different seasonality estimates are rather large. As part of the Regional Carbon Cycle Assessment and Processes phase 2 project (RECCAP2), we synthesize surface ocean p CO 2 and air‐sea CO 2 flux seasonality from models and observation‐based estimates, focusing on both a present‐day climatology and decadal changes between the 1980s and 2010s. Four main findings emerge: First, global ocean biogeochemistry models (GOBMs) and observation‐based estimates ( p CO 2 products) of surface p CO 2 seasonality disagree in amplitude and phase, primarily due to discrepancies in the seasonal variability in surface DIC. Second, the seasonal cycle in p CO 2 has increased in amplitude over the last three decades in both p CO 2 products and GOBMs. Third, decadal increases in p CO 2 seasonal cycle amplitudes in subtropical biomes for both p CO 2 products and GOBMs are driven by increasing DIC concentrations stemming from the uptake of anthropogenic CO 2 (C ant ). In subpolar and Southern Ocean biomes, however, the seasonality change for GOBMs is dominated by C ant invasion, whereas for p CO 2 products an indeterminate combination of C ant invasion and climate change modulates the changes. Fourth, biome‐aggregated decadal changes in the amplitude of p CO 2 seasonal variability are largely detectable against both mapping uncertainty (reducible) and natural variability uncertainty (irreducible), but not at the gridpoint scale over much of the northern subpolar oceans and over the Southern Ocean, underscoring the importance of sustained high‐quality seasonally‐resolved measurements over these regions

    Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

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    Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a novel approach to improve the prediction of passenger numbers that enhances a day-ahead prediction with real-time data. We first train a baseline predictor on historical automatic passenger counting data. Next, we train a real-time model on the deviations between baseline prediction and observed values, thus capturing events not addressed by the baseline. For the forecast, we attempt to detect emerging patterns in real time and adjust the baseline prediction with deviations from the patterns. Our experiments with data from Germany show that the proposed model improves the forecast of the baseline model and is only outperformed by artificial neural networks in some instances. If the training sets only cover a limited period of up to four months, our approach outperforms competing methods. For larger training sets, there are mixed results in the sense that for some test cases, certain types of neural networks yield slightly better results, but our method still performs well with less training effort, is explainable along the whole prediction process and can be applied to existing prediction methods

    Prognostic value of different anthropometric indices over different measurement intervals to predict mortality in 6-59-month-old children

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    Objective: To compare the prognostic value of mid-upper arm circumference (MUAC), weight-for-height z-score (WHZ) and weight-for-age z-score (WAZ) for predicting death over periods of one, three and six months follow-up in children. Design: Pooled analysis of 12 prospective studies examining survival after anthropometric assessment. Sensitivity and false-positive ratios to predict death within one, three and six months were compared for three individual anthropometric indices and their combinations. Setting: Community-based, prospective studies from 12 countries in Africa and Asia Participants: Children aged 6-59 months living in the study areas Results: For all anthropometric indices, the receiver operating characteristic curves were higher for shorter than for longer durations of follow-up. Sensitivity was higher for death with one month follow-up compared to six months by 49% (95% CI: 30-69%) for MUAC <115 mm (p<0.001), 48% (95%CI: 9.4-87%) for WHZ <-3 (p<0.01) and 28% (95%CI: 7.6-42%) for WAZ <-3 (p<0.005). This was accompanied by an increase in false-positives of only 3% or less. For all durations of follow-up, WAZ <-3 identified more children who died and were not identified by WHZ <-3 or by MUAC <115 mm, 120 mm or 125 mm but the use of WAZ <-3 led to an increased false-positive ratio up to 16.4% (95%CI: 12.0-20.9%) compared to 3.5% (0.4-6.5%) for MUAC <115 mm alone. Conclusions: Frequent anthropometric measurements significantly improve the identification of malnourished children with a high risk of death without markedly increasing false-positives. Combining two indices increases sensitivity but also increases false-positives among children meeting case definitions.publishedVersionPeer reviewe

    The reliability and predictive ability of the Test of Infant Motor Performance (TIMP) in a community-based study in Bhaktapur, Nepal

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    Aim: In a Nepalese setting, to measure the reliability of the Test of Infant Motor Performance (TIMP) and its ability to predict development scores at 6 months. Methods: Nepalese infants (n = 705) were assessed by the TIMP when they were 8-12 weeks old and the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) at 6 months. Inter-rater agreement was expressed by intraclass correlation coefficients (ICCs), the internal consistency by Cronbach's alphas and Pearson correlation coefficients. Predictive ability was estimated in linear regression models. Results: Inter-rater agreement was excellent (ICCs > 0.93). Alphas for the TIMP total scores were 0.76 for infants born to term and 0.72 in those born preterm. Correlation coefficients between TIMP total and Bayley-III subscale-scores ranged from 0.05 to 0.28 for term infants and from 0.15 to 0.43 for preterm infants. Using American norms, 56.3 % had TIMP scores within average and 43.7 % below average range. Bayley-III subscale scores were lower in children with TIMP scores below the average range, with the strongest estimates for Gross motor and Socio-emotional development. Interpretation: The reliability of the TIMP was acceptable, and the TIMP could be a feasible tool to monitor infant motor development in low-resource settings. Properties of the TIMP differed according to gestational age. Keywords: Infant motor development; Low-resource setting; Nepal; Psychometric properties; TIMP.The reliability and predictive ability of the Test of Infant Motor Performance (TIMP) in a community-based study in Bhaktapur, NepalpublishedVersio

    Data-Driven, Short-Term Prediction of Charging Station Occupation

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    Enhancing electric vehicle infrastructure by forecasting the availability of charging stations can boost the attractiveness of electric vehicles. The transportation sector plays a crucial role in battling climate change. The majority of available prediction algorithms either achieve poor accuracy or predict the availability at certain points in time in the future. Both of these situations are not ideal and may potentially hinder the model’s applicability to real-world situations. This paper provides a new model for estimating the charging duration of charging events in real time, which may be used to estimate the waiting time of users at fully occupied charging stations. First, the prediction is made using the random forest regressor (RF), and then the prediction is enhanced utilizing the findings of the RF model and real-time information of the currently occurring charging events. We compare the proposed method with the RF model, which is the approach’s foundational model, and the best-performing prediction model of the light gradient boosting machine (LightGBM). Here, we make use of historical information of charging events gathered from 2079 charging stations across Germany’s 4602 fast-charging connectors. To reduce data bias, we specifically simulate prediction requests for 30% of the charging events with various characteristics that were not trained with the model. Overall, the suggested method performs better than both the RF and the LightGBM. In addition, the model’s structure is adaptable and can incorporate real-time information on charging events
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