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Satellite-Based Augmentation Systems (SBASs) are designed to enhance the performance of Global Navigation Satellite Systems (GNSSs) for civil aviation users. As SBAS Geostationary Earth Orbit (GEO) satellite signals are received in a wide area, far more non-aeronautical GNSS users utilize SBAS to improve navigation performance. Since ionospheric delay is the principal error source of GNSS and a leading correction aspect of SBAS, the GEO-broadcast ionospheric data quality of six SBASs over the past three years is assessed in this article. Results show that European Geostationary Navigation Overlay Service (EGNOS), System for Differential Correction and Monitoring (SDCM), and Wide-Area Augmentation System (WAAS) have the highest Grid Ionospheric Vertical Delay (GIVD) accuracy judged by the residual Standard Deviation (STD), then followed by BeiDou SBAS (BDSBAS) and Multi-functional Satellite Augmentation System (MSAS), while GPS-Aided GEO Augmented Navigation (GAGAN) is the worst. The 6 m Grid Ionospheric Vertical Error (GIVE) availability is over 90 percent of the time for most Ionospheric Grid Points (IGPs) of all tested SBASs. However, GAGAN and WAAS have a significant availability decline at 3 m GIVE. In addition, the integrity limits of MSAS and WAAS are fairly conservative. In contrast, the confidence bounds of BDSBAS and SDCM are excessively radical that fail to meet the integrity requirement. To improve the ionospheric data quality received from SBAS, a Multi-Layer Perceptron (MLP)-based user-applicable GIVD calibration method is proposed in the article. A well-trained MLP learned temporal and spatial correlations of the IGP delays and extracted transfer functions between broadcast and reference GIVDs to eliminate error. The SBAS processing errors in Differential Code Bias (DCB) compensation, GIVD estimation modeling, and GEO message quantization are rectified by the MLP trained by abundant historical data with Center for Orbit Determination in Europe (CODE) final Global Ionospheric Maps (GIMs) as reference. Test results on real-time data show that over 85 percent IGPs have STD decline after calibration. The STD declination rates of six SBASs except for SDCM are over 15 percent, and the Mean Absolute Error (MAE) and Root Mean Square Error (RMS) calibration effects are dramatic for all systems. Finally, the average GIVEs are reduced by more than 50 percent for EGNOS, GAGAN, MSAS, and WAAS with a 99.9 percent integrity level maintained. Despite no promotion on availability for BDSBAS and SDCM, the proposed method compensates for the integrity issues of the systems. [ABSTRACT FROM AUTHOR]

Due to recent legal cases highlighting a lack of cross-ethnicity validity research using correctional risk assessment tools, we evaluated psychometric properties of Dynamic Risk Assessment for Offender Re-entry (DRAOR) scores across Māori (n = 1,812) and New Zealand (NZ) European samples (n = 1,211) in Aotearoa NZ. Using routine administrative data, our analyses suggested scoring properties were invariant across ethnicity for 15 of 19 items. Discrimination properties were also equivalent, but we observed a higher recidivism base rate among Māori participants, consistent with official statistics. Consequently, calibration analyses using a fixed follow-up (N = 372) demonstrated higher predicted recidivism rates for Māori participants at each DRAOR score. This suggests that Māori participants with similar levels of DRAOR-assessed need factors as NZ European participants experienced relatively greater continued justice contact. DRAOR users should prioritize delivering quality case management to clients, recognizing that both case-specific and systemic factors may underlie differential base rates. [ABSTRACT FROM AUTHOR]

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

• Most Radiotherapy Departments in Europe have HDR-PDR brachytherapy equipment with Ir-192 or Co-60. • The present GEC-ESTRO ACROP Recommendations provide guidance on the calibration of such sources. • Practical aspects and issues not specifically accounted for in well-accepted societal recommendations are included. • The aim is to provide a European-wide standard in HDR-PDR BT source calibration at the hospital level. The vast majority of radiotherapy departments in Europe using brachytherapy (BT) perform temporary implants of high- or pulsed-dose rate (HDR-PDR) sources with photon energies higher than 50 keV. Such techniques are successfully applied to diverse pathologies and clinical scenarios. These recommendations are the result of Working Package 21 (WP-21) initiated within the BRAchytherapy PHYsics Quality Assurance System (BRAPHYQS) GEC-ESTRO working group with a focus on HDR-PDR source calibration. They provide guidance on the calibration of such sources, including practical aspects and issues not specifically accounted for in well-accepted societal recommendations, complementing the BRAPHYQS WP-18 Report dedicated to low energy BT photon emitting sources (seeds). The aim of this report is to provide a European-wide standard in HDR-PDR BT source calibration at the hospital level to maintain high quality patient treatments. [ABSTRACT FROM AUTHOR]

Global-scale canopy height mapping is an important tool for ecosystem monitoring and sustainable forest management. Various studies have demonstrated the ability to estimate canopy height from a single spaceborne multispectral image using end-to-end learning techniques. In addition to texture information of a single-shot image, our study exploits multitemporal information of image sequences to improve estimation accuracy. We adopt a convolutional variant of a long short-term memory (LSTM) model for canopy height estimation from multitemporal instances of Sentinel-2 products. Furthermore, we utilize the deep ensembles technique for meaningful uncertainty estimation on the predictions and postprocessing isotonic regression model for calibrating them. Our lightweight model (${\sim }320{\mathrm {k}}$ trainable parameters) achieves the mean absolute error (MAE) of $1.29 ~{\mathrm {m}}$ in a European test area of $79 ~{\mathrm {km}}^{2}$. It outperforms the state-of-the-art methods based on single-shot spaceborne images as well as costly airborne images while providing additional confidence maps that are shown to be well calibrated. Moreover, the trained model is shown to be transferable in a different country of Europe using a fine-tuning area of as low as ${\sim }2 ~{\mathrm {km}}^{2}$ with ${\mathrm {MAE}}=1.94 {\mathrm {m}}$. [ABSTRACT FROM AUTHOR]

Objectives: First, to validate a previously developed model for screening for pre-eclampsia (PE) by maternal characteristics and medical history in twin pregnancies; second, to compare the distributions of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum placental growth factor (PlGF) and serum pregnancy-associated plasma protein-A (PAPP-A) in twin pregnancies that delivered with PE to those in singleton pregnancies and to develop new models based on these results; and, third, to examine the predictive performance of these models in screening for PE with delivery at < 32 and < 37 weeks' gestation.Methods: Two datasets of prospective non-intervention multicenter screening studies for PE in twin pregnancies at 11 + 0 to 13 + 6 weeks' gestation were used. The first dataset was from the EVENTS (Early vaginal progesterone for the preVention of spontaneous prEterm birth iN TwinS) trial and the second was from a previously reported study that examined the distributions of biomarkers in twin pregnancies. Maternal demographic characteristics and medical history from the EVENTS-trial dataset were used to assess the validity of risks from our previously developed model. The combined data from the first and second datasets were used to compare the distributional properties of log10 multiples of the median (MoM) values of UtA-PI, MAP, PlGF and PAPP-A in twin pregnancies that delivered with PE to those in singleton pregnancies and develop new models based on these results. The competing-risks model was used to estimate the individual patient-specific risks of delivery with PE at < 32 and < 37 weeks' gestation. Screening performance was measured by detection rates (DR) and areas under the receiver-operating-characteristics curve.Results: The EVENTS-trial dataset comprised 1798 pregnancies, including 168 (9.3%) that developed PE. In the validation of the prior model based on maternal characteristics and medical history, calibration plots demonstrated very good agreement between the predicted risks and the observed incidence of PE (calibration slope and intercept for PE < 32 weeks were 0.827 and 0.009, respectively, and for PE < 37 weeks they were 0.942 and -0.207, respectively). In the combined data, there were 3938 pregnancies, including 339 (8.6%) that developed PE and 253 (6.4%) that delivered with PE at < 37 weeks' gestation. In twin pregnancies that delivered with PE, MAP, UtA-PI and PlGF were, at earlier gestational ages, more discriminative than in singleton pregnancies and at later gestational ages they were less so. For PAPP-A, there was little difference between PE and unaffected pregnancies. The best performance of screening for PE was achieved by a combination of maternal factors, MAP, UtA-PI and PlGF. In screening by maternal factors alone, the DR, at a 10% false-positive rate, was 30.6% for delivery with PE at < 32 weeks' gestation and this increased to 86.4% when screening by the combined test; the respective values for PE < 37 weeks were 24.9% and 41.1%.Conclusions: In the assessment of risk for PE in twin pregnancy, we can use the same prior model based on maternal characteristics and medical history as reported previously, but in the calculation of posterior risks it is necessary to use the new distributions of log10 MoM values of UtA-PI, MAP and PlGF according to gestational age at delivery with PE. © 2020 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

The effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection. [ABSTRACT FROM AUTHOR]

This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an "imperfect model" approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes. [ABSTRACT FROM AUTHOR]

This study examines methods of calibrating projections of future regional climate using large single model ensembles (the CESM Large Ensemble and MPI Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920 -2016). The calibration methods were tested and verified using an imperfect model approach using the historical/RCP 8.5 simulations from the CMIP5 archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely Homogeneous Gaussian Regression, is used for the subsequent analysis. An extension to this method - applying it to dynamically decomposed data (in which the underlying data is separated into dynamical and residual components) - is also tested. The verification indicates that this calibration method produces more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes. [ABSTRACT FROM AUTHOR]

Background: The Simplified Mortality Score for the Intensive Care Unit (SMS-ICU) is a clinical prediction model, which estimates the risk of 90-day mortality in acutely ill adult ICU patients using 7 readily available variables. We aimed to externally validate the SMS-ICU and compare its discrimination with existing prediction models used with 90-day mortality as the outcome.Methods: We externally validated the SMS-ICU using data from 3282 patients included in the Stress Ulcer Prophylaxis in the Intensive Care Unit trial, which randomised acutely ill adult ICU patients with risk factors for gastrointestinal bleeding to prophylactic pantoprazole or placebo in 33 ICUs in Europe. We assessed discrimination, calibration and overall performance of the SMS-ICU and compared discrimination with the commonly used and more complex SAPS II and SOFA scores.Results: Mortality at day 90 was 30.7%. The discrimination (area under the receiver operating characteristic curve) for the SMS-ICU was 0.67 (95% CI: 0.65-0.69), as compared with 0.68 (95% CI: 0.66-0.70, P = 0.35) for SAPS II and 0.63 (95% CI: 0.61-0.65, P < 0.001) for the SOFA score. Calibration (intercept and slope) was 0.001 and 0.786, respectively, and Nagelkerke's R2 (overall performance) was 0.06. The proportions of missing data for the SMS-ICU, SAPS II and SOFA scores were 0.2%, 8.5% and 6.8%, respectively.Conclusions: Discrimination for 90-day mortality of the SMS-ICU in this cohort was poor, but similar to SAPS II and better than that of the SOFA score with markedly less missing data. [ABSTRACT FROM AUTHOR]

Context. Fireball networks are developing over the whole planet, with the aim of recovering meteorites and at the same time determining their orbits. The ultimate goal of such networks is to identify the parent bodies of meteorite families to achieve this, orbit accuracy is critical. Yet, the determination of an orbit relies on a long and complex reduction process including: (1) astrometry, with heavy distortion for fish-eye lenses, (2) estimation of the external bias on the observation, (3) fit of the trajectory, (4) deceleration model, and (5) actual orbit computation. Aims. Our goal is to compute accurate trajectories with an estimate of internal and external errors as realistic as possible, taking advantage of the dense observation network FRIPON (Fireball Recovery and InterPlanetary Observation Network), which comprises more than 100 cameras in France and Europe. In particular, we pay special attention to the distortion of images due to fish-eye lenses. In the present paper, we describe the analytical protocol that allows us to compute trajectories and their uncertainties. Methods. We developed a general distortion model to be used on the FRIPON fish-eye cameras. Such a model needs to be accurate even at low elevation, as most fireball observations are performed low on the horizon. The radial distortion is modelled by a nine-degree odd polynomial, hence by five parameters. In addition, we used three parameters to describe the geometry of the camera and two for non-symmetrical distortion. Lastly, we used a new statistical method taking systematic errors into account, which allows us to compute realistic confidence intervals. We tested our method on a fireball that fell on 2017-08-94 UT 00:06. Results. The accuracy of our astrometrical model for each camera is 2 arcmin (1σ), but the internal error on the fireball of 2017-08-94 UT 00:06 measurement is 0.7 arcmin (better than 1/10 pixel). We developed a method to estimate the external error considering that each station is independent and found it equal to 0.8 arcmin. Real residuals are coherent with our estimation of internal and external error for each camera, which confirms the internal consistency of our method. We discuss the advantages and disadvantages of this protocol. [ABSTRACT FROM AUTHOR]

Deformation bands (DB) are ubiquitous structural elements that can be found in Miocene pre- and syn-rift sediments of the extensional Pannonian Basin, central Europe. Across the field sites we see examples of disaggregation to cataclastic DB sets and evidence of frequent reactivation by discrete faulting. Thin section analysis, cross-cutting relationships and well-defined time constraints of analysed brittle structures demonstrate that with increased burial depth, DB deformation mechanisms progressed from granular flow to cataclasis. The DB sets were classified into 10 deformation phases based on formerly published independent fault-slip analysis and structural mapping. Subsidence curves were constructed for each stratigraphic level involved in deformation and were used along with the intersection of deformation episodes to calculate the depth intervals of DB generations and subsequent structural elements. The DB formation depths obtained were transferred to the depth range of the related mechanisms. This combined methodology permits a more quantitative approach to determine the changes in the deformation mechanisms with depth. Our results show that granular flow (disaggregation bands) dominates down to 100–150 m as the earliest deformation structure, followed by weak then moderate cataclasis. The transition between weak and moderate cataclasis is at approximately 300 ± 100 m for host rock rich in feldspar or fragile tuffitic components and from around 900 ± 100 m in quartz-rich sediments. In addition, deformation by frictional sliding concentrates on discrete fault planes at the margin of cataclastic bands or on new fracture planes from ∼500 ± 100 m in volcanoclastic or feldspar rich host rocks, and 1000 ± 100 m in quartz-rich host rocks. We suggest that burial-induced diagenetic processes is dependent on subsidence history, and partly in connection with regional fluid migration path, control the transition from moderate or advanced cataclasis to discrete fault slip. All these changes affect the pore structure and porosity that contribute to rheological changes, and hence change in deformation mechanism of coeval fracturing events. • Comparative study of distinct type of brittle structures contributes to understanding of deformation mechanisms during burial. • Fault-slip and subsidence curve analyses are undertaken to understand the temporal evolution of deformation mechanisms. • The finer depth calibration of deformation mechanism permits to predict the role of distinct types of DBs on fluid migration. [ABSTRACT FROM AUTHOR]

Background: The CRB-65 score is recommended as a decision support tool to help identify patients with community-acquired pneumonia (CAP) who can safely be treated as outpatients.Objective: To perform an updated meta-analysis of the accuracy, discrimination, and calibration of the CRB-65 score using a novel approach to calculation of stratum-specific likelihood ratios.Design: Meta-analysis of accuracy, discrimination, and calibration.Methods: We searched PubMed, Google, previous systematic reviews, and reference lists of included studies. Data was abstracted and quality assessed in parallel by two investigators. The quality assessment used an adaptation of the TRIPOD and PROBAST criteria. Measures of discrimination, calibration, and stratum-specific likelihood ratios are reported.Key Results: Twenty-nine studies met our inclusion criteria and provided usable data. Most studies were set in Europe, none in North America, and 12 were judged to be at low risk of bias. The pooled estimate of area under the receiver operating characteristic curve was 0.74 (95% CI 0.71-0.77) for all studies. Calibration was good although there was significant heterogeneity; the pooled estimate of the ratio of observed to expected mortality for all studies was 1.04 (95% CI 0.91-1.19). The corresponding values for studies at low risk of bias where patients could be treated as outpatients or inpatients were 0.76 (0.70-0.81) and 0.88 (0.69-1.13). Summary estimates of stratum-specific likelihood ratios for all studies were 0.19 for the low-risk group, 1.1 for the moderate-risk group, and 4.5 for the high-risk group, and 0.13, 1.3, and 5.6 for studies at low risk of bias where patients could be treated as outpatients or inpatients.Conclusions: The CRB-65 is useful for identifying low-risk patients for outpatient therapy. Given a 4% overall mortality risk, patients classified as low risk by the CRB-65 had an outpatient mortality risk of no more than 0.5%. [ABSTRACT FROM AUTHOR]

• Recommendations to assure harmonized and high-quality seed calibration in European clinics. • To provide a European wide high standard in LE-LDR source calibration at end-user level. • Level of agreement between the measured source strength by the user and the vendor value, actions to undertake when the difference exceeds a certain level. • Recalibration of ionization chambers to maintain the long-term stability of their calibration factors. • Knowledge on multi-seed inserts and their relationship to a single (calibration) seed setup. Prostate brachytherapy treatment using permanent implantation of low-energy (LE) low-dose rate (LDR) sources is successfully and widely applied in Europe. In addition, seeds are used in other tumour sites, such as ophthalmic tumours, implanted temporarily. The calibration issues for LE-LDR photon emitting sources are specific and different from other sources used in brachytherapy. In this report, the BRAPHYQS (BRAchytherapy PHYsics Quality assurance System) working group of GEC-ESTRO, has developed the present recommendations to assure harmonized and high-quality seed calibration in European clinics. There are practical aspects for which a clarification/procedure is needed, including aspects not specifically accounted for in currently existing AAPM and ESTRO societal recommendations. The aim of this report has been to provide a European wide standard in LE-LDR source calibration at end-user level, in order to keep brachytherapy treatments with high safety and quality levels. The recommendations herein reflect the guidance to the ESTRO brachytherapy users and describe the procedures in a clinic or hospital to ensure the correct calibration of LE-LDR seeds. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81‐87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30‐day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30‐day‐mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was.80, and the Brier score was.18. At a cut point of 10 or higher (75% of all patients), the model predicts 30‐day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30‐day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision‐making capacity. [ABSTRACT FROM AUTHOR]

This study gives a summary of lessons learned during the absolute calibration of the airborne, high-power Ka-band cloud radar HAMP MIRA on board the German research aircraft HALO. The first part covers the internal calibration of the instrument where individual instrument components are characterized in the laboratory. In the second part, the internal calibration is validated with external reference sources like the ocean surface backscatter and different air- and spaceborne cloud radar instruments. A key component of this work was the characterization of the spectral response and the transfer function of the receiver. In a wide dynamic range of 70 dB , the receiver response turned out to be very linear (residual 0.05 dB). Using different attenuator settings, it covers a wide input range from -105 to -5 dBm. This characterization gave valuable new insights into the receiver sensitivity and additional attenuations which led to a major improvement of the absolute calibration. The comparison of the measured and the previously estimated total receiver noise power (-95.3 vs. -98.2 dBm) revealed an underestimation of 2.9 dB. This underestimation could be traced back to a larger receiver noise bandwidth of 7.5 MHz (instead of 5 MHz) and a slightly higher noise figure (1.1 dB). Measurements confirmed the previously assumed antenna gain (50.0 dBi) with no obvious asymmetries or increased side lobes. The calibration used for previous campaigns, however, did not account for a 1.5 dB two-way attenuation by additional waveguides in the airplane installation. Laboratory measurements also revealed a 2 dB higher two-way attenuation by the belly pod caused by small deviations during manufacturing. In total, effective reflectivities measured during previous campaigns had to be corrected by +7.6 dB. To validate this internal calibration, the well-defined ocean surface backscatter was used as a calibration reference. With the new absolute calibration, the ocean surface backscatter measured by HAMP MIRA agrees very well (<1 dB) with modeled values and values measured by the GPM satellite. As a further cross-check, flight experiments over Europe and the tropical North Atlantic were conducted. To that end, a joint flight of HALO and the French Falcon 20 aircraft, which was equipped with the RASTA cloud radar at 94 GHz and an underflight of the spaceborne CloudSat at 94 GHz were performed. The intercomparison revealed lower reflectivities (-1.4 dB) for RASTA but slightly higher reflectivities (+1.0 dB) for CloudSat. With effective reflectivities between RASTA and CloudSat and the good agreement with GPM, the accuracy of the absolute calibration is estimated to be around 1 dB. [ABSTRACT FROM AUTHOR]

Objective: To examine timing of eating across ten European countries.Design: Cross-sectional analysis of the European Prospective Investigation into Cancer and Nutrition (EPIC) calibration study using standardized 24 h diet recalls collected during 1995-2000. Eleven predefined food consumption occasions were assessed during the recall interview. We present time of consumption of meals and snacks as well as the later:earlier energy intake ratio, with earlier and later intakes defined as 06.00-14.00 and 15.00-24.00 hours, respectively. Type III tests were used to examine associations of sociodemographic, lifestyle and health variables with timing of energy intake.Setting: Ten Western European countries.Subjects: In total, 22 985 women and 13 035 men aged 35-74 years (n 36 020).Results: A south-north gradient was observed for timing of eating, with later consumption of meals and snacks in Mediterranean countries compared with Central and Northern European countries. However, the energy load was reversed, with the later:earlier energy intake ratio ranging from 0·68 (France) to 1·39 (Norway) among women, and from 0·71 (Greece) to 1·35 (the Netherlands) among men. Among women, country, age, education, marital status, smoking, day of recall and season were all independently associated with timing of energy intake (all P<0·05). Among men, the corresponding variables were country, age, education, smoking, physical activity, BMI and day of recall (all P<0·05).Conclusions: We found pronounced differences in timing of eating across Europe, with later meal timetables but greater energy load earlier during the day in Mediterranean countries compared with Central and Northern European countries. [ABSTRACT FROM AUTHOR]

Introduction: Availability of a rapid and reliable platelet activation assay avoiding limitations of current techniques would be valuable to diagnose heparin‐induced thrombocytopenia and platelet secretion disorders. Objectives: The first aim was to develop and validate an ultra‐performance liquid chromatography‐tandem mass spectrometry (UHPLC‐MS/MS) method to quantify in a single run TxB2 synthesized and serotonin released from platelets. The second aim was to use our method in association with light transmission aggregometry (LTA) to select good platelet responders for the diagnosis of HIT. Methods: Electrospray ionization and chromatographic separation were optimized for the simultaneous dosage of serotonin and TxB2. The method was validated according to the European Medicines Agency (EMA) guideline for bioanalytical method validation. LTA was performed with monoclonal anti‐CD9 (clone ALB6) as platelet activator to select good responders. Results: Detection was performed using a tandem mass spectrometer with alternated positive and negative electrospray ionization. The total run time was 6 minutes. The method was validated for calibration curves, precision, accuracy, lower limit of quantification, carry‐over, selectivity, and matrix effect. Platelet response to ALB6 was highly variable among donors. Conclusion: We developed and validated a UHPLC‐MS/MS method for the simultaneous quantification of TxB2 and serotonin. [ABSTRACT FROM AUTHOR]

Background To optimise measurement precision, relevance to patients and flexibility, patient-reported outcome measures (PROMs) should ideally be adapted to the individual patient/study while retaining direct comparability of scores across patients/studies. This is achievable using item banks and computerised adaptive tests (CATs). The European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 (QLQ-C30) is one of the most widely used PROMs in cancer research and clinical practice. Here we provide an overview of the research program to develop CAT versions of the QLQ-C30's 14 functional and symptom domains. Methods The EORTC Quality of Life Group's strategy for developing CAT item banks consists of: literature search to identify potential candidate items; formulation of new items compatible with the QLQ-C30 item style; expert evaluations and patient interviews; field-testing and psychometric analyses, including factor analysis, item response theory calibration and simulation of measurement properties. In addition, software for setting up, running and scoring CAT has been developed. Results Across eight rounds of data collections, 9782 patients were recruited from 12 countries for the field-testing. The four phases of development resulted in a total of 260 unique items across the 14 domains. Each item bank consists of 7–34 items. Psychometric evaluations indicated higher measurement precision and increased statistical power of the CAT measures compared to the QLQ-C30 scales. Using CAT, sample size requirements may be reduced by approximately 20–35% on average without loss of power. Conclusions The EORTC CAT Core represents a more precise, powerful and flexible measurement system than the QLQ-C30. It is currently being validated in a large independent, international sample of cancer patients. [ABSTRACT FROM AUTHOR]

The Time Transfer by Laser Link (T2L2) project allows for the synchronization of remote ultrastable clocks over intercontinental distances. The principle is derived from the satellite laser ranging technology with a dedicated space equipment designed to record arrival times of laser pulses at the satellite. The space segment was launched in 2008 as a passenger instrument on the ocean altimetry satellite Jason 2. For the first time, we have conducted by the end of 2016 a dedicated time transfer comparison campaign between Global Positioning System and T2L2 over intercontinental distances. The campaign was carried out between two laboratories in Europe and two in China. The campaign has demonstrated a consistency of the time transfer techniques at the 1-ns level, together with the confirmation of a subnanosecond level for continental distances. [ABSTRACT FROM AUTHOR]

Summary: Feasibility evaluation of early detection of osteoporosis in oncologic patients by bone mineral density (BMD) on abdominal computed tomography (CT) scans performed for other clinical indications, by using dual-energy X-ray absorptiometry (DXA) as reference. Abdominal CT images can identify patients with osteoporosis BMD without additional radiation exposure or cost. Introduction: The purpose of the study is to evaluate the feasibility of early detection of osteoporosis by bone mineral density (BMD) on abdominal computed tomography (CT) scans performed in oncologic patients, comparing calibrated and uncalibrated measurements by using dual-energy X-ray absorptiometry (DXA) as reference. We also performed an external validation of a threshold of 160 Hounsfield units (HU), proposed as highly sensitive. Methods: Cohort comprised CT-DXA pairs within a 6-month period performed for any indication on 326 consecutive adults, aged 62.4 ± 12.38 years (mean ± standard deviation). CT attenuation of trabecular bone in HU was measured at the axial cross sections of L1, L2, L3, and L4 vertebrae. Vertebral compression fractures were assessed by sagittal reconstruction view. Diagnostic performance measures and the area under the receiver operator characteristic curve (AUC) for diagnosing osteoporosis were calculated. Results: BMD values were statistical significantly lower at any vertebral level from L1 to L4 for patients with osteoporosis defined by DXA ( p < 0.001). Calibrated and uncalibrated BMD values were significantly correlated ( R = 0.833, p < 0.01). An uncalibrated L1 CT attenuation threshold of 160 HU was more than 90 % sensitive, and a threshold of 73 HU was more than 90 % specific for distinguishing osteoporosis BMD. Fifty-nine percent of patients with vertebral compression fracture had non-osteoporotic DXA T-scores. Conclusions: Abdominal CT images obtained for other reasons can identify patients with osteoporosis BMD without additional radiation exposure or cost. Uncalibrated values at L1 can detect more osteoporosis patients with spinal compression fractures than DXA in oncologic patients. [ABSTRACT FROM AUTHOR]

The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scenario development which, consists of three components - (i) multi-area, multi-period calibration, (ii) growth mode clustering, and (iii) cross-application. The framework finds clusters of parameters, referred to as growth modes: within the clusters, parameters represent similar spatial development trajectories; between the clusters, parameters represent substantially different spatial development trajectories. The framework is tested with a stochastic dynamic urban growth model across European functional urban areas over multiple time periods, estimated using a Bayesian method on an open global urban settlement dataset covering the period 1975–2014. The results confirm a lack of transferability, with reduced confidence in the model over the validation period, compared to the calibration period. Over the calibration period the probability that parameters estimated specifically for an area outperforms those for other areas is 96%. However, over an independent validation period, this probability drops to 72%. Four growth modes are identified along a gradient from compact to dispersed spatial developments. For most training areas, spatial development in the later period is better characterized by one of the four modes than their own historical parameters. The results provide strong support for using identified parameter clusters as a tool for data-driven and quantitative scenario development, to reflect part of the uncertainty of future spatial development trajectories. A promising further application is to use the growth modes to characterize past spatial development patterns. A trend of increasingly dispersed patterns could be identified over the studied functional urban areas which calls for more detailed explorations. [Display omitted] • Extrapolate urban spatial development processes from multi-area, multiperiod historical data. • Establish data-driven urban spatial development scenarios. • Confirm low transferability from the calibration period to the projection period. • Manage uncertainty as most areas are better represented by different established scenarios than their historic trajectory. • Detect a dispersed spatial development trend across studied urban areas in Europe. [ABSTRACT FROM AUTHOR]

An important source of model uncertainty in climate models arises from unconfined model parameters in physical parameterizations. These parameters are commonly estimated on the basis of manual adjustments (expert tuning), which carries the risk of overtuning the parameters for a specific climate region or time period. This issue is particularly germane in the case of regional climate models (RCMs), which are often developed and used in one or a few geographical regions only. This study addresses the role of objective parameter calibration in this context. Using a previously developed objective calibration methodology, an RCM is calibrated over two regions (Europe and North America) and is used to investigate the transferability of the results. A total of eight different model parameters are calibrated, using a metamodel to account for parameter interactions. The study demonstrates that the calibration is effective in reducing model biases in both domains. For Europe, this concerns in particular a pronounced reduction of the summer warm bias and the associated overestimation of interannual temperature variability that have persisted through previous expert tuning efforts and are common in many global and regional climate models. The key process responsible for this improvement is an increased hydraulic conductivity. Higher hydraulic conductivity increases the water availability at the land surface and leads to increased evaporative cooling, stronger low cloud formation, and associated reduced incoming shortwave radiation. The calibrated parameter values are found to be almost identical for both domains; that is, the parameter calibration is transferable between the two regions. This is a promising result and indicates that models may be more universal than previously considered. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper focuses on rapid and accurate energy-economic performance prediction. • A novel Energy-Flow based Ensemble Calibration model was proposed. • Few studies addressed deep energy retrofit performance in single-family houses. • The coupling effects in deep energy retrofit combinations were analysed. • Real energy consumption of the retrofitted case study was used for validation. In Europe, 68% of residential building stocks are single-family houses, of which roughly 54% are constructed before the 1970 s with poor energy efficiency. However, few studies have addressed the issue of 'Rapid and accurate energy-economic performance prediction of deep energy retrofit in single-family houses' over the past decades. Recently, this issue has been discussed in limited research via Black- or White-box based building energy models to predict post-retrofit energy-economic performance. Applying the Black-box models, low accuracy occurs due to inadequate data, neglect of coupling effects in deep energy retrofit combinations and lack of complex heat transfer phenomena in single-family houses. Conversely, the White-box models with time-consuming and numerous dynamic simulations are very hard to be implemented at a building cluster or urban scale. This paper proposes a novel Energy-Flow based Ensemble Calibration model to tackle this issue, with a maximum discrepancy of 6% compared to other building performance simulation results and 0.72 s computing time on a single combination. This model consists of three layers 1) input neurons, 2) complex heat and electrical transfer models, and 3) output neurons. By replacing the layer of hidden neurons with complex heat and electrical transfer models in conventional Black-box models, the coupling effects in deep energy retrofit combinations and complex heat transfer phenomena are thoroughly considered in this research. Besides, the computational efforts are substantially reduced with only a few building performance simulations required for baseline building and identified individual retrofit measures. A semi-detached 1960 s Italian single-family house is selected as a case study to validate the calculation results from this model against real energy consumption of baseline and post-retrofit scenario, with a discrepancy of −0.66% and 1.03%, respectively. [ABSTRACT FROM AUTHOR]

Background: The Charlson and Elixhauser comorbidity measures are commonly used methods to account for patient comorbidities in hospital-level comparisons of clinical quality using administrative data. Both have been validated in North America, but there is less evidence of their performance in Europe and in pooled cross-country data, which are features of the European Collaboration for Healthcare Optimization (ECHO) project. This study compares the performance of the Charlson/Deyo and Elixhauser comorbidity measures in predicting in-hospital mortality using data from five European countries in three inpatient groups. Methods: Administrative data is used from five countries in 2008–2009 for three indicators commonly used in hospital quality comparisons: mortality rates following acute myocardial infarction, coronary artery bypass graft surgery and stroke. Logistic regression models are constructed to predict mortality controlling for age, gender and the relevant comorbidity measure. Model discrimination is evaluated using c-statistics. Model calibration is evaluated using calibration slopes. Overall goodness-of-fit is evaluated using Nagelkerke’s R2 and the Akaike information criterion. All models are validated internally by using bootstrapping and externally by using the 2009 model parameters to predict mortality in 2008. Results: The Elixhauser measure has better overall predictive ability in terms of discrimination and goodness-of-fit than the Charlson/Deyo measure or the age–sex only model. There is no clear difference in model calibration. These findings are robust to the choice of country, to pooling all five countries and to internal and external validation. Conclusions: The Elixhauser list contains more comorbidities, which may enable it to achieve better discrimination than the Charlson measure. Both measures achieve similar calibration, so for the purpose of ECHO we judged the Elixhauser measure to be preferable. [ABSTRACT FROM AUTHOR]

Budburst models have mainly been developed to capture the processes of individual trees, and vary in their complexity and plant physiological realism. We evaluated how well eleven models capture the variation in budburst of birch and Norway spruce in Germany, Austria, the United Kingdom and Finland. The comparison was based on the models performance in relation to their underlying physiological assumptions with four different calibration schemes. The models were not able to accurately simulate the timing of budburst. In general the models overestimated the temperature effect, thereby the timing of budburst was simulated too early in the United Kingdom and too late in Finland. Among the better performing models were three models based on the growing degree day concept, with or without day length or chilling, and an empirical model based on spring temperatures. These models were also the models least influenced by the calibration data. For birch the best calibration scheme was based on multiple sites in either Germany or Europe, and for Norway spruce the best scheme included multiple sites in Germany or cold years of all sites. Most model and calibration combinations indicated greater bias with higher spring temperatures, mostly simulating earlier than observed budburst. [ABSTRACT FROM AUTHOR]

Eleven European countries participated in an exercise to harmonise diatom-based methods used for status assessment in lakes. Lakes were divided into low, medium and high alkalinity types for this exercise. However, it was not possible to perform a full intercalibration on low alkalinity lakes due to the short gradient and confounding factors. Values of the Trophie Index were computed for all samples in order that national datasets could all be expressed on a common scale. Not all participants had reference sites against which national methods could be standardised and, therefore, a Generalised Linear Modelling approach was used to control the effect of national differences in datasets. This enabled the high/good and good/moderate status boundaries to be expressed on a common scale and for deviations beyond ±0.25 class widths to be identified. Those countries which had relaxed boundaries were required to adjust these to within ±0.25 class widths whilst the intercalibration rules allowed those countries with more stringent boundaries to retain these. Despite biogeographical and typological differences between countries, there was broad agreement on the characteristics of high, good and moderate status diatom assemblages, and the exercise has ensured consistent application of Water Framework Directive assessments around Europe. [ABSTRACT FROM AUTHOR]

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study. [ABSTRACT FROM AUTHOR]

Over the medium temperature range (from $$156\,^{\circ }\mathrm{C}$$ to $$1000\,^{\circ }\mathrm{C}$$ ), radiation thermometry is usually established within a national metrology institute (NMI) by means of variable temperature blackbody radiation sources, whose temperature is determined using a platinum resistance thermometer or thermocouple, calibrated in terms of the International Temperature Scale of 1990 (ITS-90), positioned in close proximity to the back radiating surface of the blackbody. It is also reasonably common to establish a scale using a suitable radiation thermometer, such as an indium gallium arsenide (InGaAs) detector-based narrow band radiation thermometer, calibrated using a number of fixed-point blackbody sources from the indium (In) to silver (Ag) (or copper (Cu)) points, with the calibration results fitted using a parameterized Planckian interpolation function. During 2007 and 2008, two InGaAs-based radiation thermometers were circulated around seven NMIs within the European Association of National Metrology Institutes (EURAMET) region in order to undertake a comparison of parameters necessary for radiation thermometry over the medium temperature range. Measurements were made of the size-of-source effect and gain (range) ratios of the two thermometers along with an assessment of the effect of changes in the ambient temperature and humidity on the thermometer output. The thermometers were also calibrated using fixed-point and/or variable temperature blackbody sources at each institute. A brief overview of the results obtained by this project is presented in this paper. [ABSTRACT FROM AUTHOR]

In this letter, we propose the calibration procedure for a Snow Water Equivalent (SWE) forecasting model, using Moderate-Resolution Imaging Spectroradiometer (MODIS) multi-temporal snow cover maps andin situmeasurements. The presented study refers to one of the largest artificial lakes in the Western Europe – the Serre-Ponçon reservoir, on the Durance river, in the region of the French Alps. The SWE model, an integral part of the MORDOR (MOdèle à Réservoirs de Détermination Objective du Ruissellement) hydrological model, provides SWE as a function of local precipitation and temperature, as well as of accumulation and melting correction coefficients. The principal motivation for the proposed calibration method comes from the significant model sensitivity with respect to these two coefficients, which, given that they account for the influences of topology and mountain winds, ought to vary spatially. Three different optimization procedures are compared using the set ofin situmeasurements acquired by the EDF (Eléctricité de france) cosmic-ray snow sensors for 4 out of 36 ground stations in the regions of interest. The appropriate optimization method is selected and the corresponding representative optimal coefficients are derived for these four stations. Further, by combining the selected optimization algorithm and the continuous activation function, we propose a new method for deriving the spatially varying coefficients characterizing the entire region, using multi-temporal MODIS snow cover binary maps. When analysed with respect to the mean square error (MSE) criterion, the SWE model, calibrated in this manner, appears to be significantly more accurate than the original version (usinga prioriestimated, spatially fixed coefficients). Furthermore, the calibration procedure based on MODIS data is comparable and, for some ground stations, exhibits even better performances than the one based on thein situmeasurements. [ABSTRACT FROM AUTHOR]

Highlights: [•] Three solar radiation models were calibrated using Meteosat Second Generation data. [•] Accuracy of daily solar radiation retrieval improved significantly for two models. [•] Spatial interpolation resulted in maps of model coefficients for Europe. [ABSTRACT FROM AUTHOR]

Mobile mapping systems that detect and geo‐reference road markings almost always consist of a stereo camera system integrated with a global positioning system/inertial navigation system. The data acquired by this navigational system allows features detected in the stereo images to be assigned global co‐ordinates. An essential step in this process is the calibration of the cameras, as it relates the pose of the two cameras to each other and a world co‐ordinate system. In Europe, road markings must be evaluated from a 35 m range, so the cameras are required to have a wide field of view. Traditional calibration methods supposedly require a calibration object that would fill most of the calibration images. This large field of view would require a calibration object of substantial size that would be impractical for the purposes of this portable system. This study explores the theory of camera calibration and then details two camera calibration techniques (using portable 3D and 2D calibration objects). The accuracy of these methods is then evaluated using a ground‐truth experiment. [ABSTRACT FROM AUTHOR]

Abstract: Process-based crop simulation models are increasingly used in regional climate change impact studies, but little is known about the implications of different calibration strategies on simulated yields. This study aims to assess the importance of region-specific calibration of five important field crops (winter wheat, winter barley, potato, sugar beet and maize) across 25 member countries of the European Union (EU25). We examine three calibration strategies and their implications on spatial and temporal yield variability in response to climate change: (i) calculation of phenology parameters only, (ii) consideration of both phenology calibration and a yield correction factor and (iii) calibration of phenology and selected growth processes. The analysis is conducted for 533 climate zones, considering 24 years of observed yield data (1983–2006). The best performing strategy is used to estimate the impacts of climate change, increasing CO2 concentration and technology development on yields for the five crops across EU25, using seven climate change scenarios for the period 2041–2064. Simulations and calibrations are performed with the crop model LINTUL2 combined with a calibration routine implemented in the modelling interface LINTUL-FAST. The results show that yield simulations improve if growth parameters are considered in the calibration for individual regions (strategy 3); e.g. RMSE values for simulated winter wheat yield are 2.36, 1.10 and 0.70Mgha−1 for calibration strategies 1, 2 and 3, respectively. The calibration strategy did not only affect the model simulations under reference climate but also the extent of the simulated climate change impacts. Applying the calibrated model for impact assessment revealed that climatic change alone will reduce crop yields. Consideration of the effects of increasing CO2 concentration and technology development resulted in yield increases for all crops except maize (i.e. the negative effects of climate change were outbalanced by the positive effects of CO2 and technology change), with considerable differences between scenarios and regions. Our simulations also suggest some increase in yield variability due to climate change which, however, is less pronounced than the differences among scenarios which are particularly large when the effects of CO2 concentration and technology development are considered. Our results stress the need for region-specific calibration of crop models used for Europe-wide assessments. Limitations of the considered strategies are discussed. We recommend that future work should focus on obtaining more comprehensive, high quality data with a finer resolution allowing application of improved strategies for model calibration that better account for spatial differences and changes over time in the growth and development parameters used in crop models. [Copyright &y& Elsevier]

Abstract: Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections. [Copyright &y& Elsevier]

A methodology of intercalibration of C-band spaceborne scatterometers is developed and applied to European Remote Sensing satellite-1 (ERS-1), European Remote Sensing satellite-2 (ERS-2), and Meteorological Operational satellite (METOP) scatterometer data. Assuming that the differences between the instruments can be represented by an incidence-angle-dependent bias, this paper presents and discusses four methods, providing an estimate of that bias and of its standard deviation. Model-based methods performed more accurately than a direct comparison of \sigma0. The latter provides a systematic larger positive bias. The methodology is applied to ERS-1 and ERS-2 data acquired during the tandem mission in 1996. The same methodology is applied to ERS-2 and Advanced Scatterometer (ASCAT) data acquired in December 2008. Generally, the bias between the ERS-1 and ERS-2 scatterometers is smaller than 0.2 dB over most incidence angles, and the four methods provide relatively consistent results. The bias between ERS-2 and ASCAT is slightly higher, reaching 0.4 dB at certain incidence angles. These results suggest that these scatterometers need to be intercalibrated to achieve a consistent backscatter data. [ABSTRACT FROM PUBLISHER]

A framework for the verification of ensemble forecasts of near-surface wind speed is described. It is based on existing scores and diagnostic tools, though considering observations from synoptic stations as reference instead of the analysis. This approach is motivated by the idea of having a user-oriented view of verification, for instance with the wind power applications in mind. The verification framework is specifically applied to the case of ECMWF ensemble forecasts and over Europe. Dynamic climatologies are derived at the various stations, serving as a benchmark. The impact of observational uncertainty on scores and diagnostic tools is also considered. The interest of this framework is demonstrated from its application to the routine evaluation of ensemble forecasts and to the assessment of the quality improvements brought in by the recent change in horizontal resolution of the ECMWF ensemble prediction system. The most important conclusions cover (1) the generally high skill of these ensemble forecasts of near-surface wind speed when evaluated at synoptic stations, (2) the noteworthy improvement of scores brought by the change of horizontal resolution, and, (3) the scope for further improvements of reliability and skill of wind speed ensemble forecasts by appropriate post-processing. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: The Early and Middle Neolithic in Northern Central Europe and Southern Scandinavia is characterised by substantial changes in economic technology as well as in material culture in different periods. One of the main drivers for social development, but also for anthropogenic changes in the environment, is surely shifts in population density. To evaluate group sizes and population density we need archaeological proxies. Similar to other studies, we use 14C dates to reconstruct the intensity of prehistoric activities. A comparison of the human impact from pollen data with a quantification based on 14C dates proves a correlation which supports our appreciation of the value of sum-calibrated probabilities of radiometric measurements as a proxy for demographic developments. The large amount of usable dates in this study not only enables us to draw general conclusions on a supraregional level, but also makes it possible for us to compare the character of different areas on a regional scale. As a result, we reconstruct a significant rise in population between 4100 and 3500 cal BC and a degression around 3350–3100 cal BC, followed by a reiterated increase for the Funnel Beaker West and North Groups. On the Danish Isles, as well as in the Funnel Beaker North Eastern Group, different tendencies are observable. [Copyright &y& Elsevier]

This paper analyzes changes of maximum temperatures in Europe, which are evaluated using two state-of-the-art regional climate models from the EU ENSEMBLES project. Extremes are expressed in terms of return values using a time-dependent generalized extreme value (GEV) model fitted to monthly maxima. Unlike the standard GEV method, this approach allows analyzing return periods at different time scales (monthly, seasonal, annual, etc). The study focuses on the end of the 20th century (1961-2000), used as a calibration/validation period, and assesses the changes projected for the period 2061-2100 considering the A1B emission scenario. The performance of the regional models is evaluated for each season of the calibration period against the high-resolution gridded E-OBS dataset, showing a similar South-North gradient with larger values over the Mediterranean basin. The inter-RCM changes in the bias pattern with respect to the E-OBS are larger than the bias resulting from a change in the boundary conditions from ERA-40 to ECHAM5 20c3m. The maximum temperature response to increased green house gases, as projected by the A1B scenario, is consistent for both RCMs. Under that scenario, results indicate that the increments for extremes (e.g. 40-year return values) will be two or three times higher than those for the mean seasonal temperatures, particularly during Spring and Summer in Southern Europe. [ABSTRACT FROM AUTHOR]

This work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoring. [ABSTRACT FROM AUTHOR]

We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of technical indicators and design a new set of trading rules. We tested this approach with high-frequency data of the Dow Jones EURO STOXX 50 Index Futures (FESX) and the DAX Futures (FDAX) for March 2009. Our method was implemented with different learning algorithms and outperformed a combination of the same group of technical analysis indicators using the parameters typically recommended by practitioners. We incorporated this method of model calibration in a trading agent that relies on a layered structure consisting of the machine learning algorithm described above, an online learning utility, a trading strategy, and a risk management overlay. The online learning layer combines the output of several experts and suggests a short or long position. If the expected position is positive (negative), the trading agent sends a buy (sell) limit order at prices slightly lower (higher) than the bid price at the top of the buy (sell) order book less (plus) transaction costs. If the order is not 100% filled within a fixed period (i.e. 1 minute) of being issued, the existent limit orders are cancelled, and limit orders are reissued according to the new experts' forecast. As part of its risk management capability, the trading agent eliminates any weak trading signal. The trading agent algorithm generated positive returns for the two major European index futures (FESX and FDAX) and outperformed a buy-and-hold strategy. [ABSTRACT FROM AUTHOR]

Summary: Direct current meter measurements are rarely available for extreme flash floods. Corresponding discharges are generally estimated using so-called “indirect” techniques such as the slope – area method. These methods are based on empirical hydraulic formulae that typically use Manning’s equation, and have been calibrated and also widely tested for flow conditions that differ significantly from those encountered during flash floods. Recent work conducted in Europe, as part of the HYDRATE research project and other studies, has shown that the use of these formulae and their associated tabulated roughness values available in current guidance documents, without further verification, can lead to over-estimates of peak discharges in the case of flash floods. After having discussed the limitations of indirect methods based on Manning’s formula, the paper illustrates how the uncertainty in indirect discharge estimates can be reduced through the analysis of various types of data that can be collected during post-event surveys and through consistency checks. Based on a review of current literature and on recent flash flood studies, this paper proposes simple guidelines to assist practitioners in estimating extreme discharges during post-event surveys. [ABSTRACT FROM AUTHOR]

The author present a tactics and statistical method for modelling permafrost distribution of entire mountain regions as well as provide the results of statistical analyses and model calibration for the European Alps. The prediction of the debris model is biased towards and overestimation of the permafrost distribution using intact and relict rock glaciers as calibration data. The steps offered by the authors that are needed to use the approach for Alpine-wide model application are discussed.

Abstract: A 274-lake calibration data-set for chironomid-based temperature reconstruction is presented which is based on the taxonomic amalgamation of a 117-lake data-set from Switzerland and a 157-lake data-set from Norway and Svalbard. Taxonomic consistency of the two data-sets was ensured by joint microscope sessions by the two involved analysts, re-identifying chironomid assemblages in the Swiss data-set to reach an identical taxonomic resolution as in the Norwegian data, and by double-checking selected samples of the Norwegian calibration data-set. The combined Swiss-Norwegian calibration data-set contains information on the distribution of 154 chironomid taxa over a July air temperature range of 3.5–18.4 °C, a pH range of 4.7–8.8, and an altitudinal range of 5–2815 masl from lakes in temperate, subarctic, arctic, and alpine environments. Inference models developed based on this data-set using weighted averaging-partial least squares (WA-PLS) regression outperformed inference models based on maximum likelihood regression. After outlier deletion WA-PLS regression predicted July air temperature with a bootstrapped (cross-validated) root mean squared error of prediction (RMSEP) of 1.40 °C. Inference models developed from the separate regional data-sets have an RMSEP of 1.16 and 1.43 °C for the Norwegian and the Swiss calibration data-set, respectively. The WA-PLS inference model based on the Norwegian data-set adequately predicted July air temperature based on chironomid assemblages in the Swiss data-set (RMSEP 2.05 °C; r 2 0.74). In contrast, the WA-PLS model based on the Swiss chironomid assemblages performed poorly in inferring July air temperatures based on the Norwegian chironomid assemblages (RMSEP 2.70 °C; r 2 0.39). We attribute this discrepancy to the large proportion of chironomid taxa in Norwegian samples not represented in the Swiss data-set, the larger range of both pH and lake types included in the Norwegian calibration data-set, and the lack of some chironomid taxa with an arctic and boreal distribution in the Swiss data-set. The WA-PLS inference model based on the combined Swiss-Norwegian calibration data-set is the largest taxonomically consistent chironomid-based inference model available to date and outperforms other existing models if the r 2 and RMSEP relative to the overall temperature gradient are examined. We demonstrate that the combined model can reconstruct a larger range of temperatures when applied to fossil assemblages than models based on the Norwegian or Swiss calibration data-sets only. The combined data-set includes the majority of chironomid taxa expected in late Pleistocene and Holocene sediments of lakes from Northern, Eastern, and Central Europe as well as from southern European mountain lakes. The newly developed WA-PLS inference model is therefore well suited for developing continental-scale reconstructions of late Quaternary temperature change based on chironomid records from different parts of Europe. [Copyright &y& Elsevier]

Recently, the introduction of GM maize in agricultural production in the EU and elsewhere has raised the issue of adventitious presence of GM seeds in conventional seed lots. Adventitious presence may occur in all arable farming, and at any step in the production of seeds or grain, or in processing of harvested product in the food/feed chain. As of today, there are no official thresholds governing the adventitious presence of GM seeds in conventional seed lots in Europe. However, it is assumed that GM admixture in seed lots could have a considerable influence on the level of adventitious presence in the non-GM harvested product. The experiments highlighted in this paper aim at the consequences of adventitious presence of GM maize seeds in conventional seed lots. It is shown for varieties belonging to the same maturity group that the final GM rate (% seeds) in the harvest product is nearly same as the initial seed admixture (% seeds). This corresponds to Hardy-Weinberg expectations. The variation depends mainly on the flowering coincidence, the site and climatic conditions. In cases where the admixed seeds are of different maturity group, the level of cross-pollination in the harvest product is reduced. Furthermore a comparison between the visual GM seed detection and real-time PCR detection was done. It is evident that the result of the real-time PCR detection method has a more variable uncertainty associated with its results than the visual seed testing method. The accuracy of prediction from % GM seed to % GM DNA depends on the reference material used for calibration curves. [ABSTRACT FROM AUTHOR]

An international intercomparison has been carried out with a commercial dry block calibrator as a transfer standard by the Danish Technological Institute (DTI). The intercomparison involved 16 participating laboratories from five European countries. The intercomparison comprised five measurement points in the range from −20 °C to +150 °C. The purposes of the intercomparison were twofold: to compare the results of the participating laboratories during calibration of a dry block calibrator and to establish the dry block calibrators' reproducibility and suitability both as a transfer standard and as a working measurement standard for disseminating the ITS-90 traceability in industry. The characterization and performance of state- of-the-art multi-zone dry block calibrators and the results of the intercomparison are presented. [ABSTRACT FROM AUTHOR]

Abstract: We present first results of using the European Global Navigation Satellite System (GNSS) Galileo for determining the Total Electron Content (TEC). Furthermore, we describe a calibration technique which can be used to determine GNSS inter-frequency and inter-system biases along with calibrated TEC. This technique is demonstrated by analysing two sets of data. The first data set covers Europe using GPS and GLONASS data provided by IGS ground stations, and GPS and GIOVE (Galileo In Orbit Validation Element) data collected by GIOVE sensor stations (GESS). The second data set uses GPS and GIOVE data from a global network of GESS. We find that the inter-frequency and inter-system biases are stable within the accuracy of the reconstruction and that the TEC values reconstructed from GIOVE data are compatible with TEC values obtained from GPS and GLONASS data. The RMS of the GIOVE-A/B inter-satellite biases, determined over a period of one month, is found to be below 1TECU. [Copyright &y& Elsevier]

Abstract: This paper presents a comprehensive assessment of the suspended sediment load for the period (1998–2008) in the lower river Ebro (NE Iberian Peninsula), one of the most regulated large rivers in Europe. Sediment load estimates are based on continuous discharge and turbidity records, calibrated with direct suspended sediment samples covering a wide range of river''s hydraulic conditions. The temporal variability of the sediment load and the role of the effective discharge at different temporal scales are specifically examined. At the log-term, the study period can be considered as dry. Mean annual load is estimated at around 0.092×106 t, a value that represents less than the 1% of what was transported at the beginning of the twentieth century in the absence of dams and under different land uses. In general, sediment concentrations observed between 1998 and 2008 are low (mean was 9mgl−1 and maximum was 240mgl−1). Specific sediment yield was in the order of 1t km2 y−1, also much lower than values reported in other western European rivers of the same scale and impact. At the annual scale, large floods were responsible for most of the transported suspended sediment (up to 80%). Nevertheless, when the effective discharge is calculated for the whole study period, floods of moderate magnitude and high frequency are responsible for the largest amount of sediment transport. The temporal scale used to calculate the effective discharge thus determines the final results. Load estimates derived from continuous suspended sediment and discharge data and from the flow duration method yielded similar values. This suggests that the latter method is also appropriate when sediment samples cover a large proportion of the observed discharges and statistical significant relations can be obtained and applied to long periods of record. This study provides further insights into the sedimentary status of one of the largest rivers in Europe. Results corroborate previous studies highlighting sediment deficit downstream from large dams. This type of information constitutes a key element to inform and improve current management and restoration actions. [Copyright &y& Elsevier]

The Atacama Large Millimeter/submillimeter (ALMA) Array Front End (FE) system is the first element in a complex chain of signal receiving, conversion, processing and recording. 70 Front Ends will be required for the project. The Front End is designed to receive signals in ten different frequency bands. In the initial phase of operations, the antennas will be fully equipped with six bands. These are Band 3 (84-116 GHz), Band 4 (125-163 GHz), Band 6 (211-275 GHz), Band 7 (275-373 GHz), Band 8 (385-500 GHz) and Band 9 (602-720 GHz). It is planned to equip the antennas with the missing bands at a later stage of ALMA operations, with a few Band 5 (163-211 GHz) and Band 10 (787-950 GHz) receivers in use before the end of the construction project. The ALMA Front End is far superior to any existing receiver systems; spin-offs of the ALMA prototypes are leading to improved sensitivities in existing millimeter and submillimeter observatories. The Front End units are comprised of numerous elements, produced at different locations in Europe, North America and East Asia and are integrated at several Front End integration centers (FEIC) to insure timely delivery of all the units to Chile. The North American FEIC (NA FEIC) is at the National Radio Astronomy Observatory facility in Charlottesville, Virginia, USA. This paper describes the design and performance of the test set used at the NA FEIC to check the performance of the Front Ends, following integration and prior to shipment to Chile. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To verify the accuracy and precision of the logistic European system for cardiac operative risk evaluation (EuroSCORE) in high-risk cardiac surgery patients and to develop and externally validate a new system of recalibration. Methods: The development series included 4279 high-risk patients who had undergone cardiac operations at the IRCCS Policlinico S. Donato. Performance, accuracy, and precision of the logistic EuroSCORE were assessed in this series, using a deciles-based comparison between expected and observed mortality rates, a receiver operating characteristic analysis, and a Hosmer–Lemeshow test for calibration. Differences between predicted and observed mortality rates were mathematically evaluated to develop an adjusted logistic EuroSCORE. This adjusted risk score was subsequently validated with the same approach on an external series of 1459 high-risk patients who had undergone cardiac operations at the Siena hospital. Results: The adjusted logistic EuroSCORE was based on five different correction factors applied to the crude logistic EuroSCORE depending on its value. At the external validation, this model provided a good performance, with observed mortality rates not significantly different from the expected in 8 out of 10 deciles of risk distribution. The adjusted EuroSCORE had the same moderate balanced accuracy of the crude logistic EuroSCORE (area under the curve: 0.695), with a better precision (Hosmer–Lemeshow calibration test: χ 2: 3.6, p =0.891). Conclusions: Recalibration of the logistic EuroSCORE in high-risk patients is needed due to its tendency to overestimate the mortality risk. The application of a variable correction factor results in a better performance, increased precision, with unaltered balanced accuracy. [Copyright &y& Elsevier]

Abstract: Comprehensive data sets for understanding the global distribution of black carbon (BC) in soil are lacking because the currently available methods are too time consuming for routine monitoring of BC in the environment. Here we report that reliable screening of soil BC can be achieved using mid-infrared spectroscopy (MIRS) and multivariate data analysis. Calibration models were built employing 309 samples from different soil depths and land use systems in America, Asia and Europe, characterized for soil organic carbon (SOC) and benzene polycarboxylic acids (BPCAs) as specific BC markers. About 99% of total SOC variability was explained by local calibrations with an error of prediction < 0.1g SOCkg−1 soil. In addition, BPCA carbon was assessable. The precision was lower (R 2 >0.8), partly reflecting different BC quality. A measure of the latter is the mellitic acid carbon percentage. This also correlated with MIRS patterns (R 2 ⩾0.6), which therefore even allowed classification of BC according to its degree of aromatic condensation. [Copyright &y& Elsevier]