30 resultados para Mean-square slope (mss)

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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We describe the recovery of three daily meteorological records for the southern Alps (Domodossola, Riva del Garda, and Rovereto), all starting in the second half of the nineteenth century. We use these new data, along with additional records, to study regional changes in the mean temperature and extreme indices of heat waves and cold spells frequency and duration over the period 1874–2015. The records are homogenized using subdaily cloud cover observations as a constraint for the statistical model, an approach that has never been applied before in the literature. A case study based on a record of parallel observations between a traditional meteorological window and a modern screen shows that the use of cloud cover can reduce the root-mean-square error of the homogenization by up to 30% in comparison to an unaided statistical correction. We find that mean temperature in the southern Alps has increased by 1.4°C per century over the analyzed period, with larger increases in daily minimum temperatures than maximum temperatures. The number of hot days in summer has more than tripled, and a similar increase is observed in duration of heat waves. Cold days in winter have dropped at a similar rate. These trends are mainly caused by climate change over the last few decades.

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A new overground body-weight support system called ZeroG has been developed that allows patients with severe gait impairments to practice gait and balance activities in a safe, controlled manner. The unloading system is capable of providing up to 300 lb of static support and 150 lb of dynamic (or constant force) support using a custom-series elastic actuator. The unloading system is mounted to a driven trolley, which rides along an overhead rail. We evaluated the performance of ZeroG's unloading system, as well as the trolley tracking system, using benchtop and human-subject testing. Average root-mean-square and peak errors in unloading were 2.2 and 7.2 percent, respectively, over the range of forces tested while trolley tracking errors were less than 3 degrees, indicating the system was able to maintain its position above the subject. We believe training with ZeroG will allow patients to practice activities that are critical to achieving functional independence at home and in the community.

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The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the Meteorological Operational Satellite (MetOp) polar orbiting satellites is the only instrument offering more than 25 years of satellite data to analyse aerosols on a daily basis. The present study assessed a modified AVHRR aerosol optical depth τa retrieval over land for Europe. The algorithm might also be applied to other parts of the world with similar surface characteristics like Europe, only the aerosol properties would have to be adapted to a new region. The initial approach used a relationship between Sun photometer measurements from the Aerosol Robotic Network (AERONET) and the satellite data to post-process the retrieved τa. Herein a quasi-stand-alone procedure, which is more suitable for the pre-AERONET era, is presented. In addition, the estimation of surface reflectance, the aerosol model, and other processing steps have been adapted. The method's cross-platform applicability was tested by validating τa from NOAA-17 and NOAA-18 AVHRR at 15 AERONET sites in Central Europe (40.5° N–50° N, 0° E–17° E) from August 2005 to December 2007. Furthermore, the accuracy of the AVHRR retrieval was related to products from two newer instruments, the Medium Resolution Imaging Spectrometer (MERIS) on board the Environmental Satellite (ENVISAT) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua/Terra. Considering the linear correlation coefficient R, the AVHRR results were similar to those of MERIS with even lower root mean square error RMSE. Not surprisingly, MODIS, with its high spectral coverage, gave the highest R and lowest RMSE. Regarding monthly averaged τa, the results were ambiguous. Focusing on small-scale structures, R was reduced for all sensors, whereas the RMSE solely for MERIS substantially increased. Regarding larger areas like Central Europe, the error statistics were similar to the individual match-ups. This was mainly explained with sampling issues. With the successful validation of AVHRR we are now able to concentrate on our large data archive dating back to 1985. This is a unique opportunity for both climate and air pollution studies over land surfaces.

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In this study, the effect of time derivatives of flow rate and rotational speed was investigated on the mathematical modeling of a rotary blood pump (RBP). The basic model estimates the pressure head of the pump as a dependent variable using measured flow and speed as predictive variables. Performance of the model was evaluated by adding time derivative terms for flow and speed. First, to create a realistic working condition, the Levitronix CentriMag RBP was implanted in a sheep. All parameters from the model were physically measured and digitally acquired over a wide range of conditions, including pulsatile speed. Second, a statistical analysis of the different variables (flow, speed, and their time derivatives) based on multiple regression analysis was performed to determine the significant variables for pressure head estimation. Finally, different mathematical models were used to show the effect of time derivative terms on the performance of the models. In order to evaluate how well the estimated pressure head using different models fits the measured pressure head, root mean square error and correlation coefficient were used. The results indicate that inclusion of time derivatives of flow and speed can improve model accuracy, but only minimally.

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We examined the impact of physical activity (PA) on surrogate markers of cardiovascular health in adolescents. 52 healthy students (28 females, mean age 14.5 ± 0.7 years) were investigated. Microvascular endothelial function was assessed by peripheral arterial tonometry to determine reactive hyperemic index (RHI). Vagal activity was measured using 24 h analysis of heart rate variability [root mean square of successive normal-to-normal intervals (rMSSD)]. Exercise testing was performed to determine peak oxygen uptake ([Formula: see text]) and maximum power output. PA was assessed by accelerometry. Linear regression models were performed and adjusted for age, sex, skinfolds, and pubertal status. The cohort was dichotomized into two equally sized activity groups (low vs. high) based on the daily time spent in moderate-to-vigorous PA (MVPA, 3,000-5,200 counts(.)min(-1), model 1) and vigorous PA (VPA, >5,200 counts(.)min(-1), model 2). MVPA was an independent predictor for rMSSD (β = 0.448, P = 0.010), and VPA was associated with maximum power output (β = 0.248, P = 0.016). In model 1, the high MVPA group exhibited a higher vagal tone (rMSSD 49.2 ± 13.6 vs. 38.1 ± 11.7 ms, P = 0.006) and a lower systolic blood pressure (107.3 ± 9.9 vs. 112.9 ± 8.1 mmHg, P = 0.046). In model 2, the high VPA group had higher maximum power output values (3.9 ± 0.5 vs. 3.4 ± 0.5 W kg(-1), P = 0.012). In both models, no significant differences were observed for RHI and [Formula: see text]. In conclusion, in healthy adolescents, PA was associated with beneficial intensity-dependent effects on vagal tone, systolic blood pressure, and exercise capacity, but not on microvascular endothelial function.

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The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

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OBJECTIVE: The objective of this study was to evaluate the feasibility and reproducibility of high-resolution magnetic resonance imaging (MRI) and quantitative T2 mapping of the talocrural cartilage within a clinically applicable scan time using a new dedicated ankle coil and high-field MRI. MATERIALS AND METHODS: Ten healthy volunteers (mean age 32.4 years) underwent MRI of the ankle. As morphological sequences, proton density fat-suppressed turbo spin echo (PD-FS-TSE), as a reference, was compared with 3D true fast imaging with steady-state precession (TrueFISP). Furthermore, biochemical quantitative T2 imaging was prepared using a multi-echo spin-echo T2 approach. Data analysis was performed three times each by three different observers on sagittal slices, planned on the isotropic 3D-TrueFISP; as a morphological parameter, cartilage thickness was assessed and for T2 relaxation times, region-of-interest (ROI) evaluation was done. Reproducibility was determined as a coefficient of variation (CV) for each volunteer; averaged as root mean square (RMSA) given as a percentage; statistical evaluation was done using analysis of variance. RESULTS: Cartilage thickness of the talocrural joint showed significantly higher values for the 3D-TrueFISP (ranging from 1.07 to 1.14 mm) compared with the PD-FS-TSE (ranging from 0.74 to 0.99 mm); however, both morphological sequences showed comparable good results with RMSA of 7.1 to 8.5%. Regarding quantitative T2 mapping, measurements showed T2 relaxation times of about 54 ms with an excellent reproducibility (RMSA) ranging from 3.2 to 4.7%. CONCLUSION: In our study the assessment of cartilage thickness and T2 relaxation times could be performed with high reproducibility in a clinically realizable scan time, demonstrating new possibilities for further investigations into patient groups.

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Using a convenient and fast HPLC procedure we determined serum concentrations of the fungistatic agent 5-fluorocytosine (5-FC) in 375 samples from 60 patients treated with this drug. The mean trough concentration (n = 127) was 64.3 mg/l (range: 11.8-208.0 mg/l), the mean peak concentration (n = 122) was 99.9 mg/l (range: 25.6-263.8 mg/l), the mean nonpeak/nontrough concentration (n = 126) was 80.1 mg/l (range: 10.5-268.0 mg/l). Totally 134 (35.7%) samples were outside the therapeutic range (25-100 mg/l), 108 (28.8%) being too high, 26 (6.9%) being too low. Forty-four (73%) patients showed 5-FC serum concentrations outside the therapeutic range at least once during the treatment course. In a prospective study we performed 65 dosage predictions on 30 patients by use of a 3-point method previously developed for aminoglycoside dosage adaptation. The mean absolute prediction error of the dosage adaptation was +0.7 mg/l (range: -26.0 to +28.0 mg/l). The root mean square prediction error was 10.7 mg/l. The mean predicted concentration (65.3 mg/l) agreed very well with the mean measured concentration (64.6 mg/l). The frequency distribution of 5-FC serum concentrations indicates that 5-FC monitoring is important. The applied pharmacokinetic method allows individual adaptations of 5-FC dosage with a clinically acceptable prediction error.

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BACKGROUND: In this paper we present a landmark-based augmented reality (AR) endoscope system for endoscopic paranasal and transnasal surgeries along with fast and automatic calibration and registration procedures for the endoscope. METHODS: Preoperatively the surgeon selects natural landmarks or can define new landmarks in CT volume. These landmarks are overlaid, after proper registration of preoperative CT to the patient, on the endoscopic video stream. The specified name of the landmark, along with selected colour and its distance from the endoscope tip, is also augmented. The endoscope optics are calibrated and registered by fast and automatic methods. Accuracy of the system is evaluated in a metallic grid and cadaver set-up. RESULTS: Root mean square (RMS) error of the system is 0.8 mm in a controlled laboratory set-up (metallic grid) and was 2.25 mm during cadaver studies. CONCLUSIONS: A novel landmark-based AR endoscope system is implemented and its accuracy is evaluated. Augmented landmarks will help the surgeon to orientate and navigate the surgical field. Studies prove the capability of the system for the proposed application. Further clinical studies are planned in near future.

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BACKGROUND: Elevated plasma fibrinogen levels have prospectively been associated with an increased risk of coronary artery disease in different populations. Plasma fibrinogen is a measure of systemic inflammation crucially involved in atherosclerosis. The vagus nerve curtails inflammation via a cholinergic antiinflammatory pathway. We hypothesized that lower vagal control of the heart relates to higher plasma fibrinogen levels. METHODS: Study participants were 559 employees (age 17-63 years; 89% men) of an airplane manufacturing plant in southern Germany. All subjects underwent medical examination, blood sampling, and 24-hour ambulatory heart rate recording while kept on their work routine. The root mean square of successive differences in RR intervals during the night period (nighttime RMSSD) was computed as the heart rate variability index of vagal function. RESULTS: After controlling for demographic, lifestyle, and medical factors, nighttime RMSSD explained 1.7% (P = 0.001), 0.8% (P = 0.033), and 7.8% (P = 0.007), respectively, of the variance in fibrinogen levels in all subjects, men, and women. Nighttime RMSSD and fibrinogen levels were stronger correlated in women than in men. In all workers, men, and women, respectively, there was a mean +/- SEM increase of 0.41 +/- 0.13 mg/dL, 0.28 +/- 0.13 mg/dL, and 1.16 +/- 0.41 mg/dL fibrinogen for each millisecond decrease in nighttime RMSSD. CONCLUSIONS: Reduced vagal outflow to the heart correlated with elevated plasma fibrinogen levels independent of the established cardiovascular risk factors. This relationship seemed comparably stronger in women than men. Such an autonomic mechanism might contribute to the atherosclerotic process and its thrombotic complications.

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Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used.The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9),Maybar (84. 0.57, 2.5),Megech (85, 0.15, 2.6), andWondoGenet (86, 0.52, 2.7) indicating that themodels were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

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In this paper, we show statistical analyses of several types of traffic sources in a 3G network, namely voice, video and data sources. For each traffic source type, measurements were collected in order to, on the one hand, gain better understanding of the statistical characteristics of the sources and, on the other hand, enable forecasting traffic behaviour in the network. The latter can be used to estimate service times and quality of service parameters. The probability density function, mean, variance, mean square deviation, skewness and kurtosis of the interarrival times are estimated by Wolfram Mathematica and Crystal Ball statistical tools. Based on evaluation of packet interarrival times, we show how the gamma distribution can be used in network simulations and in evaluation of available capacity in opportunistic systems. As a result, from our analyses, shape and scale parameters of gamma distribution are generated. Data can be applied also in dynamic network configuration in order to avoid potential network congestions or overflows. Copyright © 2013 John Wiley & Sons, Ltd.

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Abstract. A number of studies have shown that Fourier transform infrared spectroscopy (FTIRS) can be applied to quantitatively assess lacustrine sediment constituents. In this study, we developed calibration models based on FTIRS for the quantitative determination of biogenic silica (BSi; n = 420; gradient: 0.9–56.5 %), total organic carbon (TOC; n = 309; gradient: 0–2.9 %), and total inorganic carbon (TIC; n = 152; gradient: 0–0.4 %) in a 318 m-long sediment record with a basal age of 3.6 million years from Lake El’gygytgyn, Far East Russian Arctic. The developed partial least squares (PLS) regression models yield high cross-validated (CV) R2 CV = 0.86–0.91 and low root mean square error of crossvalidation (RMSECV) (3.1–7.0% of the gradient for the different properties). By applying these models to 6771 samples from the entire sediment record, we obtained detailed insight into bioproductivity variations in Lake El’gygytgyn throughout the middle to late Pliocene and Quaternary. High accumulation rates of BSi indicate a productivity maximum during the middle Pliocene (3.6–3.3 Ma), followed by gradually decreasing rates during the late Pliocene and Quaternary. The average BSi accumulation during the middle Pliocene was �3 times higher than maximum accumulation rates during the past 1.5 million years. The indicated progressive deterioration of environmental and climatic conditions in the Siberian Arctic starting at ca. 3.3 Ma is consistent with the first occurrence of glacial periods and the finally complete establishment of glacial–interglacial cycles during the Quaternary.

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Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.