scholarly journals A New Plagioclase-Liquid Hygrometer Specific to Trachytic Systems

Minerals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 375 ◽  
Author(s):  
Matteo Masotta ◽  
Silvio Mollo
Keyword(s):  
Test Data ◽  
Melt Inclusions ◽  
Data Set ◽  
Vulcano Island ◽  
Good Convergence ◽  
Crystallization Experiments ◽  
Model Reliability ◽  
System Differentiation ◽  
Trachytic Melts ◽  
Good Agreement

We present a new empirical plagioclase-liquid hygrometer for estimating the amount of H2O dissolved in trachytic magmas. The hygrometer is based on the exchange reaction of anorthite between plagioclase and liquid, and is calibrated using crystallization experiments where the concentration of H2O in quenched glasses has been accurately determined based on Fourier Transform Infrared Spectroscopy (FTIR) analysis. The multiple linear regression of plagioclase-liquid cation fractions and components from experimental data obtained at 150–202 MPa, 850–1020 °C, 1.17–7.57 wt. % H2O and ΔNNO + 2.5 buffer, yields to a highly accurate model with uncertainty of only ±0.29 wt. % H2O. The model reliability has been demonstrated using an independent test data set consisting of crystallization experiments from the literature and thermodynamically derived compositions. The fairly good convergence between our model calibration and the test data set excludes systematic H2O overestimates or underestimates caused by miscalibration and data overfitting. The plagioclase-liquid hygrometer from this study has been applied to trachyandesitic (latitic) and trachytic products erupted over the last 1000 years at the La Fossa cone of Vulcano Island (Aeolian Islands, Southern Italy). Results from calculations indicate that the concentration of H2O in the latitic and trachytic melts is comprised between ~2.5 and ~3.5 wt. %. These values are in good agreement with data from melt inclusions and, overall, testify to low-pressure, open-system differentiation of trachytic magmas under strong degassing conditions.

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

Agreement and reliability of magnetic, angular rate, and gravity (MARG) sensors to assess multiple body segment’s external loads during off-road running

2021 ◽  
pp. 175433712199698
Author(s):  
Daniel Rojas-Valverde ◽  
José Pino-Ortega ◽  
Rafael Timón ◽  
Randall Gutiérrez-Vargas ◽  
Braulio Sánchez-Ureña ◽  
...  
Keyword(s):  
Vastus Lateralis ◽  
Angular Rate ◽  
Wearable Sensors ◽  
Intraclass Correlation ◽  
Principal Component ◽  
Test Reliability ◽  
Data Set ◽  
Body Segments ◽  
External Loads ◽  
Good Agreement

The extensive use of wearable sensors in sport medicine, exercise medicine, and health has increased the interest in their study. That is why it is necessary to test these technologies’ efficiency, effectiveness, agreement, and reliability in different settings. Consequently, the purpose of this article was to analyze the magnetic, angular rate, and gravity (MARG) sensor’s test-retest agreement and reliability when assessing multiple body segments’ external loads during off-road running. A total of 18 off-road runners (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran two laps (1st and 2nd Lap) of a 12 km circuit wearing six MARG sensors. The sensors were attached to six different body segments: left (MPLeft) and right (MPRight) malleolus peroneus, left (VLLeft) and right (VLRight) vastus lateralis, lumbar (L1-L3), and thorax (T2-T4) using a special neoprene suit. After a principal component analysis (PCA) was performed, the total data set variance of all body segments was represented by 44.08%–70.64% for the 1st PCA factor considering two variables, Player LoadRT and Impacts, on L1-L3, respectively. These two variables were chosen among three total accelerometry-based external load indicators (ABELIs) to perform the agreement and reliability tests due to their relevance based on PCAs for each body segment. There were no significant differences between laps in the Player LoadRT or Impacts ( p > 0.05, trivial). The intraclass correlation and lineal correlation showed a substantial to almost perfect over-time test consistency assessed via reliability in both Player LoadRT and Impacts. Bias and t-test assessments showed good agreement between Laps. It can be concluded that MARGs sensors offer significant test re-test reliability and good agreement when assessing off-road kinematics in the six different body segments.

scholarly journals MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

2015 ◽  
Vol 8 (2) ◽  
pp. 941-963 ◽  
Author(s):  
T. Vlemmix ◽  
F. Hendrick ◽  
G. Pinardi ◽  
I. De Smedt ◽  
C. Fayt ◽  
...  
Keyword(s):  
Standard Deviation ◽  
A Priori ◽  
Systematic Bias ◽  
Aerosol Extinction ◽  
Data Set ◽  
Near Surface ◽  
Beijing Area ◽  
Least Squares Fit ◽  
Relative Differences ◽  
Good Agreement

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

Demographic Characterization of Anonymous Trace Travel Data

2015 ◽  
Vol 2526 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Joshua Auld ◽  
Abolfazl (Kouros) Mohammadian ◽  
Marcelo Simas Oliveira ◽  
Jean Wolf ◽  
William Bachman
Keyword(s):  
Test Data ◽  
Large Scale ◽  
Travel Demand ◽  
Null Model ◽  
Demographic Characteristics ◽  
Travel Pattern ◽  
Data Set ◽  
Trace Data ◽  
Demographic Models ◽  
Pattern Information

Research was undertaken to determine whether demographic characteristics of individual travelers could be derived from travel pattern information when no information about the individual was available. This question is relevant in the context of anonymously collected travel information, such as cell phone traces, when used for travel demand modeling. Determining the demographics of a traveler from such data could partially obviate the need for large-scale collection of travel survey data, depending on the purpose for which the data were to be used. This research complements methodologies used to identify activity stops, purposes, and mode types from raw trace data and presumes that such methods exist and are available. The paper documents the development of procedures for taking raw activity streams estimated from GPS trace data and converting these into activity travel pattern characteristics that are then combined with basic land use information and used to estimate various models of demographic characteristics. The work status, education level, age, and license possession of individuals and the presence of children in their households were all estimated successfully with substantial increases in performance versus null model expectations for both training and test data sets. The gender, household size, and number of vehicles proved more difficult to estimate, and performance was lower on the test data set; these aspects indicate overfitting in these models. Overall, the demographic models appear to have potential for characterizing anonymous data streams, which could extend the usability and applicability of such data sources to the travel demand context.

scholarly journals Global estimation of burned area using MODIS active fire observations

2006 ◽  
Vol 6 (4) ◽  
pp. 957-974 ◽  
Author(s):  
L. Giglio ◽  
G. R. van der Werf ◽  
J. T. Randerson ◽  
G. J. Collatz ◽  
P. Kasibhatla
Keyword(s):  
Vegetation Cover ◽  
Regression Trees ◽  
The United States ◽  
Tree Cover ◽  
Burned Area ◽  
Data Set ◽  
Active Fire ◽  
Terra Modis ◽  
Good Agreement ◽  
Area Data

Abstract. We present a method for estimating monthly burned area globally at 1° spatial resolution using Terra MODIS data and ancillary vegetation cover information. Using regression trees constructed for 14 different global regions, MODIS active fire observations were calibrated to burned area estimates derived from 500-m MODIS imagery based on the assumption that burned area is proportional to counts of fire pixels. Unlike earlier methods, we allow the constant of proportionality to vary as a function of tree and herbaceous vegetation cover, and the mean size of monthly cumulative fire-pixel clusters. In areas undergoing active deforestation, we implemented a subsequent correction based on tree cover information and a simple measure of fire persistence. Regions showing good agreement between predicted and observed burned area included Boreal Asia, Central Asia, Europe, and Temperate North America, where the estimates produced by the regression trees were relatively accurate and precise. Poorest agreement was found for southern-hemisphere South America, where predicted values of burned area are both inaccurate and imprecise; this is most likely a consequence of multiple factors that include extremely persistent cloud cover, and lower quality of the 500-m burned area maps used for calibration. Application of our approach to the nine remaining regions yielded comparatively accurate, but less precise, estimates of monthly burned area. We applied the regional regression trees to the entire archive of Terra MODIS fire data to produce a monthly global burned area data set spanning late 2000 through mid-2005. Annual totals derived from this approach showed good agreement with independent annual estimates available for nine Canadian provinces, the United States, and Russia. With our data set we estimate the global annual burned area for the years 2001-2004 to vary between 2.97 million and 3.74 million km2, with the maximum occurring in 2001. These coarse-resolution burned area estimates may serve as a useful interim product until long-term burned area data sets from multiple sensors and retrieval approaches become available.

Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project

2021 ◽  
Author(s):  
David Cotton ◽  
Keyword(s):  
Coastal Zone ◽  
Test Data ◽  
River Discharge ◽  
Altimeter Data ◽  
Inland Waters ◽  
Data Sets ◽  
Data Set ◽  
Discharge Data ◽  
Processing Algorithms ◽  
The Impact

<p><strong>Introduction</strong></p><p>HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.</p><p>New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.</p><p>A series of case studies will assess these products in terms of their scientific impacts.</p><p>All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided</p><p> </p><p><strong>Objectives</strong></p><p>The scientific objectives of HYDROCOASTAL are to enhance our understanding  of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes</p><p>The technical objectives are to develop and evaluate  new SAR  and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.</p><p><strong>Project  Outline</strong></p><p>There are four tasks to the project</p><ul><li>Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters</li> <li>Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets</li> <li>Impacts Assessment: The impact of these global products will be assess in a series of Case Studies</li> <li>Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.</li> </ul><p> </p><p><strong>Presentation</strong></p><p>The presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.</p><p> </p>

Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods

2021 ◽  
Vol 62 (4) ◽  
pp. 393-406
Author(s):  
Yanxiang Yu ◽  
◽  
Chicheng Xu ◽  
Siddharth Misra ◽  
Weichang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Short Term Memory ◽  
Rock Physics ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Blind Test ◽  
Data Set ◽  
Benchmark Model ◽  
Sonic Log

Compressional and shear sonic traveltime logs (DTC and DTS, respectively) are crucial for subsurface characterization and seismic-well tie. However, these two logs are often missing or incomplete in many oil and gas wells. Therefore, many petrophysical and geophysical workflows include sonic log synthetization or pseudo-log generation based on multivariate regression or rock physics relations. Started on March 1, 2020, and concluded on May 7, 2020, the SPWLA PDDA SIG hosted a contest aiming to predict the DTC and DTS logs from seven “easy-to-acquire” conventional logs using machine-learning methods (GitHub, 2020). In the contest, a total number of 20,525 data points with half-foot resolution from three wells was collected to train regression models using machine-learning techniques. Each data point had seven features, consisting of the conventional “easy-to-acquire” logs: caliper, neutron porosity, gamma ray (GR), deep resistivity, medium resistivity, photoelectric factor, and bulk density, respectively, as well as two sonic logs (DTC and DTS) as the target. The separate data set of 11,089 samples from a fourth well was then used as the blind test data set. The prediction performance of the model was evaluated using root mean square error (RMSE) as the metric, shown in the equation below: RMSE=sqrt(1/2*1/m* [∑_(i=1)^m▒〖(〖DTC〗_pred^i-〖DTC〗_true^i)〗^2 + 〖(〖DTS〗_pred^i-〖DTS〗_true^i)〗^2 ] In the benchmark model, (Yu et al., 2020), we used a Random Forest regressor and conducted minimal preprocessing to the training data set; an RMSE score of 17.93 was achieved on the test data set. The top five models from the contest, on average, beat the performance of our benchmark model by 27% in the RMSE score. In the paper, we will review these five solutions, including preprocess techniques and different machine-learning models, including neural network, long short-term memory (LSTM), and ensemble trees. We found that data cleaning and clustering were critical for improving the performance in all models.

scholarly journals Data Analysis With Shapley Values For Automatic Subject Selection in Alzheimer's Disease Data Sets Using Interpretable Machine Learning

2021 ◽  
Author(s):  
Louise Bloch ◽  
Christoph M. Friedrich
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Test Data ◽  
Noisy Data ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Model Interpretation ◽  
Percentage Points ◽  
Shapley Values

Abstract Background: The prediction of whether Mild Cognitive Impaired (MCI) subjects will prospectively develop Alzheimer's Disease (AD) is important for the recruitment and monitoring of subjects for therapy studies. Machine Learning (ML) is suitable to improve early AD prediction. The etiology of AD is heterogeneous, which leads to noisy data sets. Additional noise is introduced by multicentric study designs and varying acquisition protocols. This article examines whether an automatic and fair data valuation method based on Shapley values can identify subjects with noisy data. Methods: An ML-workow was developed and trained for a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The validation was executed for an independent ADNI test data set and for the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) cohort. The workow included volumetric Magnetic Resonance Imaging (MRI) feature extraction, subject sample selection using data Shapley, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) for model training and Kernel SHapley Additive exPlanations (SHAP) values for model interpretation. This model interpretation enables clinically relevant explanation of individual predictions. Results: The XGBoost models which excluded 116 of the 467 subjects from the training data set based on their Logistic Regression (LR) data Shapley values outperformed the models which were trained on the entire training data set and which reached a mean classification accuracy of 58.54 % by 14.13 % (8.27 percentage points) on the independent ADNI test data set. The XGBoost models, which were trained on the entire training data set reached a mean accuracy of 60.35 % for the AIBL data set. An improvement of 24.86 % (15.00 percentage points) could be reached for the XGBoost models if those 72 subjects with the smallest RF data Shapley values were excluded from the training data set. Conclusion: The data Shapley method was able to improve the classification accuracies for the test data sets. Noisy data was associated with the number of ApoEϵ4 alleles and volumetric MRI measurements. Kernel SHAP showed that the black-box models learned biologically plausible associations.

scholarly journals Tropical troposphere to stratosphere transport of carbon monoxide and long-lived trace species in the Chemical Lagrangian Model of the Stratosphere (CLaMS)

2014 ◽  
Vol 7 (4) ◽  
pp. 5087-5139 ◽  
Author(s):  
R. Pommrich ◽  
R. Müller ◽  
J.-U. Grooß ◽  
P. Konopka ◽  
F. Ploeger ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Large Scale ◽  
Lower Stratosphere ◽  
Lagrangian Model ◽  
Quasi Biennial Oscillation ◽  
Data Set ◽  
Model Version ◽  
The Tropics ◽  
The Impact ◽  
Good Agreement

Abstract. Variations in the mixing ratio of trace gases of tropospheric origin entering the stratosphere in the tropics are of interest for assessing both troposphere to stratosphere transport fluxes in the tropics and the impact of these transport fluxes on the composition of the tropical lower stratosphere. Anomaly patterns of carbon monoxide (CO) and long-lived tracers in the lower tropical stratosphere allow conclusions about the rate and the variability of tropical upwelling to be drawn. Here, we present a simplified chemistry scheme for the Chemical Lagrangian Model of the Stratosphere (CLaMS) for the simulation, at comparatively low numerical cost, of CO, ozone, and long-lived trace substances (CH4, N2O, CCl3F (CFC-11), CCl2F2 (CFC-12), and CO2) in the lower tropical stratosphere. For the long-lived trace substances, the boundary conditions at the surface are prescribed based on ground-based measurements in the lowest model level. The boundary condition for CO in the free troposphere is deduced from MOPITT measurements (at ≈ 700–200 hPa). Due to the lack of a specific representation of mixing and convective uplift in the troposphere in this model version, enhanced CO values, in particular those resulting from convective outflow are underestimated. However, in the tropical tropopause layer and the lower tropical stratosphere, there is relatively good agreement of simulated CO with in-situ measurements (with the exception of the TROCCINOX campaign, where CO in the simulation is biased low ≈ 10–20 ppbv). Further, the model results are of sufficient quality to describe large scale anomaly patterns of CO in the lower stratosphere. In particular, the zonally averaged tropical CO anomaly patterns (the so called "tape recorder" patterns) simulated by this model version of CLaMS are in good agreement with observations. The simulations show a too rapid upwelling compared to observations as a consequence of the overestimated vertical velocities in the ERA-interim reanalysis data set. Moreover, the simulated tropical anomaly patterns of N2O are in good agreement with observations. In the simulations, anomaly patterns for CH4 and CFC-11 were found to be consistent with those of N2O; for all long-lived tracers, positive anomalies are simulated because of the enhanced tropical upwelling in the easterly phase of the quasi-biennial oscillation.

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