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Wang, Wen-Cheng Vincent, Shih-Chun Candice Lung, and Chun-Hu Liu. "Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network." Sensors 20, no. 17 (September 3, 2020): 5002. http://dx.doi.org/10.3390/s20175002.
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Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM2.5 levels were compared with those of GRIMM-calibrated PM2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 μg/m3, reduced from 18.4 ± 6.5 μg/m3 before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM2.5 sensor networks.
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van Walraven, Carl. "Improved Correction of Misclassification Bias With Bootstrap Imputation." Medical Care 56, no. 7 (July 2018): e39-e45. http://dx.doi.org/10.1097/mlr.0000000000000787.
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Bohn, Theodore J., Mergia Y. Sonessa, and Dennis P. Lettenmaier. "Seasonal Hydrologic Forecasting: Do Multimodel Ensemble Averages Always Yield Improvements in Forecast Skill?" Journal of Hydrometeorology 11, no. 6 (December 1, 2010): 1358–72. http://dx.doi.org/10.1175/2010jhm1267.1.
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Abstract Multimodel techniques have proven useful in improving forecast skill in many applications, including hydrology. Seasonal hydrologic forecasting in large basins represents a special case of hydrologic modeling, in which postprocessing techniques such as temporal aggregation and time-varying bias correction are often employed to improve forecast skill. To investigate the effects that these techniques have on the performance of multimodel averaging, the performance of three hydrological models [Variable Infiltration Capacity, Sacramento/Snow-17, and the Noah land surface model] and two multimodel averages [simple model average (SMA) and multiple linear regression (MLR) with monthly varying model weights] are examined in three snowmelt-dominated basins in the western United States. These evaluations were performed for both simulating and forecasting [using the Ensemble Streamflow Prediction (ESP) method] monthly discharge, with and without monthly bias corrections. The single best bias-corrected model outperformed the multimodel averages of raw models in both retrospective simulations and ensemble mean forecasts in terms of RMSE. Forming an MLR multimodel average from bias-corrected models added only slight improvements over the best bias-corrected model. Differences in performance among all bias-corrected models and multimodel averages were small. For ESP forecasts, both bias correction and multimodel averaging generally reduced the RMSE of the ESP ensemble means at lead times of up to 6 months in months when flow is dominated by snowmelt, with the reduction increasing as lead time decreased. The primary reason for this is that aggregating simulated streamflows from daily to monthly time scales increases model cross correlation, which in turn reduces the effectiveness of multimodel averaging in reducing those components of model error that bias correction cannot address. This effect may be stronger in snowmelt-dominated basins because the interannual variability of winter precipitation is a common input to all models. It was also found that both bias correcting and multimodel averaging using monthly varying parameters yielded much greater error reductions than methods using time-invariant parameters.
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Catasus, Miguel, Wayne Branagh, and Eric D. Salin. "Improved Calibration for Inductively Coupled Plasma-Atomic Emission Spectrometry Using Generalized Regression Neural Networks." Applied Spectroscopy 49, no. 6 (June 1995): 798–807. http://dx.doi.org/10.1366/0003702953964444.
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Artificial neural networks have been recently used in different fields of science in applications ranging from pattern recognition to semi-quantitative analysis. In this work, two types of neural networks were applied to the problems of spectral interferences, matrix effects, and the measurement drift in ICP-AES. Their performance was compared to that of the more conventional technique of multiple linear regressions (MLR). The two types of neural networks examined were “traditional” multilayer perceptron neural networks and generalized regression neural networks (GRNNs). The GRNN is comparable to, or better than, MLR for modeling spectral interferences and matrix effects covering several orders of magnitude. In the case of an Fe spectral interference on Zn, the GRNN reduced the error from 81% to 24%, while MLR reduced the average error to only 49%. For matrix effects caused by large backgrounds of Mg (0–10,000 ppm) on Zn, average error was reduced to 55% from 67%. In the case of combinations of spectral overlaps and matrix effects, the GRNN reduced average error by approximately 10%. MLR performed poorly on systems involving matrix effects. GRNN is also a very promising tool for the correction of drift caused by fluctuations in power levels, reducing drift over a two-hour period from 2.3% to 0.6%. GRNNs, both by themselves and in multinetwork combinations, seem to be highly promising for the correction of nonlinear matrix effects and long-term signal drift in ICP-AES.
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Yang, Ming, Alceste Z. Bonanos, Bi-Wei Jiang, Jian Gao, Panagiotis Gavras, Grigoris Maravelias, Shu Wang, et al. "Evolved massive stars at low metallicity." Astronomy & Astrophysics 639 (July 2020): A116. http://dx.doi.org/10.1051/0004-6361/201937168.
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We present the most comprehensive red supergiant (RSG) sample for the Small Magellanic Cloud (SMC) to date, including 1239 RSG candidates. The initial sample was derived based on a source catalog for the SMC with conservative ranking. Additional spectroscopic RSGs were retrieved from the literature, and RSG candidates were selected based on the inspection of Gaia and 2MASS color-magnitude diagrams (CMDs). We estimate that there are in total ∼1800 or more RSGs in the SMC. We purify the sample by studying the infrared CMDs and the variability of the objects, though there is still an ambiguity between asymptotic giant branch stars (AGBs) and RSGs at the red end of our sample. One heavily obscured target was identified based on multiple near-IR and mid-IR (MIR) CMDs. The investigation of color-color diagrams shows that there are fewer RSGs candidates (∼4%) showing PAH emission features compared to the Milky Way and LMC (∼15%). The MIR variability of RSG sample increases with luminosity. We separate the RSG sample into two subsamples (risky and safe), and identify one M5e AGB star in the risky subsample based on simultaneous inspection of variabilities, luminosities, and colors. The degeneracy of mass loss rate (MLR), variability, and luminosity of the RSG sample is discussed, indicating that most of the targets with high variability are also the bright ones with high MLR. Some targets show excessive dust emission, which may be related to previous episodic mass loss events. We also roughly estimate the total gas and dust budget produced by entire RSG population as ∼1.9−1.1+2.4 × 10−6 M⊙ yr−1 in the most conservative case, according to the derived MLR from IRAC1–IRAC4 color. Based on the MIST models, we derive a linear relation between Teff and observed J − KS color with reddening correction for the RSG sample. By using a constant bolometric correction and this relation, the Geneva evolutionary model is compared with our RSG sample, showing a good agreement and a lower initial mass limit of ∼7 M⊙ for the RSG population. Finally, we compare the RSG sample in the SMC and the LMC. Despite the incompleteness of LMC sample in the faint end, the result indicates that the LMC sample always shows redder color (except for the IRAC1–IRAC2 and WISE1–WISE2 colors due to CO absorption) and higher variability than the SMC sample, which is likely due to a positive relation between MLR, variability and the metallicity.
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Yu, Chen, Jianchun Zheng, Deyong Hu, Yufei Di, Xiuhua Zhang, and Manqing Liu. "Evaluation and Correction of IMERG Late Run Precipitation Product in Rainstorm over the Southern Basin of China." Water 13, no. 2 (January 19, 2021): 231. http://dx.doi.org/10.3390/w13020231.
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Satellite precipitation products play an essential role in providing effective global or regional precipitation. However, there are still many uncertainties in the performance of satellite precipitation products, especially in extreme precipitation analysis. In this study, a Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) late run (LR) product was used to evaluate the rainstorms in the southern basin of China from 2015 to 2018. Three correction methods, multiple linear regression (MLR), artificial neural network (ANN), and geographically weighted regression (GWR), were used to get correction products to improve the precipitation performance. This study found that IMERG LR’s ability to characterize rainstorm events was limited, and there was a significant underestimation. The observation error and detection ability of IMERG LR decrease gradually from the southeast coast to the northwest inland. The error test shows that in the eastern coastal area (zone I and II), the central area (zone III), and the western inland area (zone IV and V), the optimal correction method is MLR, ANN, and GWR, respectively. The performance of three correction products is slightly better compared with the original product IMERG LR. From zone I to V, correlation coefficient (CC) and root mean square error (RMSE) show a decreasing trend. Zone II has the highest relative bias (RB), and the deviation is relatively large. The categorical indices of inland area performed better than coastal area. The correction product’s precipitation is slightly lower than the observed value from April to November with a mean error of 8.03%. The correction product’s precipitation was slightly higher than the observed values in other months, with an average error of 12.27%. The greater the observed precipitation, the higher the uncertainty of corrected precipitation result. The coefficient of variation showed that zone II had the highest uncertainty, and zone V had the lowest uncertainty. MLR had a high uncertainty with an average of 9.72%. The mean coefficient of variation of ANN and GWR is 7.74% and 7.29%, respectively. This study aims to generate a set of precipitation products with good accuracy through the IMERG LR evaluation and correction to support regional extreme precipitation research.
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Van Altena, William F., Terrence M. Girard, and John T. Lee. "Calibration of the Mass-Luminosity Relation Using Trigonometric Parallaxes. I." International Astronomical Union Colloquium 135 (1992): 276–83. http://dx.doi.org/10.1017/s0252921100006564.
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AbstractThe calibration of luminosities using trigonometric parallaxes introduces well-known errors that are a function of the ratio of the parallax error to the parallax, when the sample is chosen from stars with measured parallaxes larger than some minimum parallax. In this paper it is shown that similar errors are also introduced into the mass axis of the mass-luminosity relation (MLR) and can result in a biased MLR. The bias is shown to be related to the Lutz-Kelker correction to the absolute magnitude, as extended by Hanson for the case of selection effects in the data sample. The size of the correction in the mass axis is substantial and for the case of a uniform distribution in space, it can amount to a multiplicative factor of 1.17 in the mass for a ten sigma (σ/π = 0.10) parallax and a factor of 1.62 for a five sigma (σ/π = 0.20) parallax.
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Wang, Wei, Jun Yao, Yang Li, and Aimin Lv. "Research on carbonate reservoir interwell connectivity based on a modified diffusivity filter model." Open Physics 15, no. 1 (May 6, 2017): 306–12. http://dx.doi.org/10.1515/phys-2017-0034.
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AbstractAccording to the solution of dual-porosity model, a diffusivity filter model of carbonate reservoir was established, which can effectively illustrate the injection signal attenuation and lag characteristic. The interwell dynamic connectivity inversion model combines a multivariate linear regression (MLR) analysis with a correction coefficient to eliminate the effect of fluctuating bottom-hole pressure (BHP). The modified MLR model was validated by synthetic field with fluctuating BHP. The method was applied to Tahe oilfield which showed that the inversion result was reliable. The interwell dynamic connectivity coefficients could reflect the real interwell connectivity of reservoir. The method is easy to use and proved to be effective in field applications.
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Eker, Z., F. Soydugan, S. Bilir, and V. Bakış. "Standard stellar luminosities: what are typical and limiting accuracies in the era after Gaia?" Monthly Notices of the Royal Astronomical Society 507, no. 3 (September 8, 2021): 3583–92. http://dx.doi.org/10.1093/mnras/stab2302.
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ABSTRACT Methods of obtaining stellar luminosities (L) have been revised and a new concept, standard stellar luminosity, has been defined. In this paper, we study three methods: (i) a direct method from radii and effective temperatures; (ii) a method using a mass–luminosity relation (MLR); and (iii) a method requiring a bolometric correction. If the unique bolometric correction (BC) of a star extracted from a flux ratio (fV/fBol) obtained from the observed spectrum with sufficient spectral coverage and resolution are used, the third method is estimated to provide an uncertainty (ΔL/L) typically at a low percentage, which could be as accurate as 1 per cent, perhaps more. The typical and limiting uncertainties of the predicted L of the three methods were compared. The secondary methods, which require either a pre-determined non-unique BC or MLR, were found to provide less accurate luminosities than the direct method, which could provide stellar luminosities with a typical accuracy of 8.2–12.2 per cent while its estimated limiting accuracy is 2.5 per cent.
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Zhan, Chesheng, Jian Han, Shi Hu, Liangmeizi Liu, and Yuxuan Dong. "Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area." Advances in Meteorology 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/1506017.
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As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.
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Inversetti, Annalisa, Luca Mandia, Massimo Candiani, Irene Cetin, Alessandro Larcher, Valeria Savasi, Enrico Papaleo, and Paolo Cavoretto. "Uterine artery Doppler pulsatility index at 11–38 weeks in ICSI pregnancies with egg donation." Journal of Perinatal Medicine 46, no. 1 (January 26, 2018): 21–27. http://dx.doi.org/10.1515/jpm-2016-0180.
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AbstractBackground:Uterine artery Doppler pulsatility index (UtA-PI) may be different in pregnancies with egg donation (ICSI-ED) as compared to conceptions with autologous intra-cytoplasmatic sperm injection (autologous ICSI) and to spontaneous conceptions (SC).Methods:One hundred and ninety-four pregnant women with different modes of conception (MC) were prospectively evaluated: 53 ICSI-ED, 36 autologous ICSI and 105 SC. To evaluate the effects of different MC on PI, multivariable linear regression (MLR) models predicting UtA-PI were fitted after adjustment for maternal age, body mass index, race, parity, smoking status and gestational age.Results:In the first trimester, at MLR, autologous ICSI was not associated with a significantly different UtA-PI [estimate (EST) 0.01; 95% confidence interval (CI) −0.19, 0.2; P=0.9] when compared to SC. Conversely, MC by ICSI-ED was associated with lower first trimester UtA-PI (EST −0.32; CI −0.55, −0.08; P=0.01) when compared to SC. At MLR, MC by autologous ICSI and by ICSI-ED were not associated with significant differences in the second and third trimester UtA-PI when compared to SC.Conclusion:ICSI-ED conception presented lower UtA-PI when compared to SC at 11+0–13+6weeks but not at later assessments. Correction of UtA-PI measurement specifying the origin of oocyte may be useful in first trimester screening.
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Roberts, Keith J., Brian A. Colle, Nickitas Georgas, and Stephan B. Munch. "A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast." Journal of Applied Meteorology and Climatology 54, no. 8 (August 2015): 1773–91. http://dx.doi.org/10.1175/jamc-d-14-0314.1.
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AbstractA multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October–31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York–New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%–38.0%. A bias-correction technique reduces the average mean absolute error by 10%–15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010–14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model’s [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and bias-corrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.
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Iyer, Sridhar, and Shree Prakash Singh. "Spectral and Power-Efficiency Trade-off in Fixed-Grid Optical Networks." International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems 6, no. 3 (September 26, 2017): 97. http://dx.doi.org/10.11601/ijates.v6i3.236.
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The improvement of spectral efficiency in the MLR networks can be obtained by the reduction of sub-band spacing, or by minimizing the spacing of the sub-bands that operate at varied data rates. However, due to the presence of physical layer impairments, minimization in sub-band spacing leads to adverse effects on the channel(s) transmission reach. As a result there occurs an increase in the consumed power due to the requirement of increase in regeneration of the signal. In the current work we propose an improved DWDM grating in view of obtaining higher spectral efficiency. For a system, with and without Forward Error Correction capabilities (i) for various SLR solutions, we find and compare power consumption values of the components with respect to the total traffic, and (ii) for different MLR and SLR solutions, for a fixed QoT, we evaluate the minimum values of the sub-band and the channel spacing, and also evaluate and compare the power-efficiency with the distance of transmission.
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&NA;. "Correction: Do Variations in Disease Prevalence Limit the Usefulness of Population-Based Hospitalization Rates for Studying Variations in Hospital Admissions?" Medical Care 43, no. 12 (December 2005): 1265. http://dx.doi.org/10.1097/01.mlr.0000193814.78032.34.
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Oliveira, R. A., R. Näsi, O. Niemeläinen, L. Nyholm, K. Alhonoja, J. Kaivosoja, N. Viljanen, et al. "ASSESSMENT OF RGB AND HYPERSPECTRAL UAV REMOTE SENSING FOR GRASS QUANTITY AND QUALITY ESTIMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 489–94. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-489-2019.
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<p><strong>Abstract.</strong> The information on the grass quantity and quality is needed for several times in a growing season for making optimal decisions about the harvesting time and the fertiliser rate, especially in northern countries, where grass swards quality declines and yield increases rapidly in the primary growth. We studied the potential of UAV-based photogrammetry and spectral imaging in grass quality and quantity estimation. To study this, a trial site with large variation in the quantity and quality parameters was established by using different nitrogen fertilizer application rates and harvesting dates. UAV-based remote sensing datasets were captured four times during the primary growth season in June 2017 and agricultural reference measurements including dry biomass and quality parameters, such as the digestibility (D-value) were collected simultaneously. The datasets were captured using a flying height of 50&thinsp;m which provided a GSD of 0.7&thinsp;cm for the photogrammetric imagery and 5&thinsp;cm for the hyperspectral imagery. A rigorous photogrammetric workflow was carried out for all data sets aiming to determine the image exterior orientation parameters, camera interior orientation parameters, 3D point clouds and orthomosaics. The quantitative radiometric calibration included sensor corrections, atmospheric correction, and correction for the radiometric non-uniformities caused by illumination variations, BRDF correction and the absolute reflectance transformation. Random forest (RF) and multilinear regression (MLR) estimators were trained using spectral bands, vegetation indices and 3D features, extracted from the remote sensing datasets, and insitu reference measurements. From the FPI hyperspectral data, the 35 spectral bands and 11 spectral indices were used. The 3D features were extracted from the canopy height model (CHM) generated using RGB data. The most accurate results were obtained in the second measurement day (15th June) which was near to the optimal harvesting time and generally RF outperformed MLR slightly. When assessed with the leave-one-out-estimation, the best root mean squared error (RMSE%) were 8.9% for the dry biomass using 3D features. The best D-value estimation using RF algorithm (RMSE%&thinsp;=&thinsp;0.87%) was obtained using spectral features. Using the estimators, we then calculated grass quality and quantity maps covering the entire test site to compare different techniques and to evaluate the variability in the field. The results showed that the low-cost drone remote sensing gave excellent precision both for biomass and quality parameter estimation if accurately calibrated, offering an excellent tool for efficient and accurate management of silage grass production.</p>
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Vekshina, V. N. "The development of digital models of the soil cover in the western part of Bol’shezemel’skaya tundra." Dokuchaev Soil Bulletin, no. 99 (December 9, 2019): 21–46. http://dx.doi.org/10.19047/0136-1694-2019-99-21-46.
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The methods of digital mapping are promising for creating soil maps on difficultly accessible territories. This study was aimed at searching of optimal approaches for digital mapping of the soil cover in poorly studied western part of the Bol’shezemel’skaya tundra on different scales. Medium-scale (1 : 200 000) and small-scale (1 : 1 M) soil maps served as the source of initial information about soils of this region; actual information of the state of the territory was obtained from remote sensing data (Landsat 8 scenes, Aug. 14, 2013) and digital elevation model ASTER GDEM v.2. After extraction of information and the choice of predictors, the analysis of digital soil cover models obtained with the use of different algorithms – Random Forest (RF), Multinomial Logistic Regression (MLR) and Linear Discriminant Analysis (LDA) – was performed. The coefficient of agreement between the newly developed digital models and the initial paper-based soil maps (kappa) was calculated. This test demonstrated that the RF algorithm ensures the best results, so the final digital maps were obtained using it. Averaged kappa values for the compared small- and medium-scale models were as follows: RF – 0.39 and 0.36; MLR – 0.31 and 0.31; and LDA – 0.28 and 0.18, respectively. After the preliminary correction of the initial medium-scale map, the kappa values somewhat increased (RF – 0.39, MLR – 0.35, LDA – 0.30). At the stage of evaluation of digital soil maps obtained with the use of RF algorithm, these maps and the initial soil maps were compared with independent point-size terrain data. The degree of agreement between these data and the new digital soil maps proved to be no less than that for the initial maps. For the initial and digital small-scale maps, it reached 24 and 26 %, respectively; for the initial and digital medium-scale maps, 54 and 43 %, respectively. After the preliminary correction of the initial medium-scale map, the degree of agreement between the digital model and terrain data improved considerably and reached 61 %. This method of digital soil mapping on the basis of analogous data seems to be optimal.
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Chen, Chuanfa, Shuai Yang, and Yanyan Li. "Accuracy Assessment and Correction of SRTM DEM Using ICESat/GLAS Data under Data Coregistration." Remote Sensing 12, no. 20 (October 19, 2020): 3435. http://dx.doi.org/10.3390/rs12203435.
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Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) inherently suffers from various errors. Many previous works employed Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data to assess and enhance SRTM DEM accuracy. Nevertheless, data coregistration between the two datasets was commonly neglected in their studies. In this paper, an automated and simple three dimensional (3D) coregistration method (3CM) was introduced to align the 3-arc-second SRTM (SRTM3) DEM and ICESat/GLAS data over Jiangxi province, China. Then, accuracy evaluation of the SRTM3 DEM using ICESat/GLAS data with and without data coregistration was performed on different classes of terrain factors and different land uses, with the purpose of evaluating the importance of data coregistration. Results show that after data coregistration, the root mean square error (RMSE) and mean bias of the SRTM3 DEM are reduced by 14.4% and 97.1%, respectively. Without data coregistration, terrain aspects with a sine-like shape are strongly related to SRTM3 DEM errors; nevertheless, this relationship disappears after data coregistration. Among the six land uses, SRTM3 DEM produces the lowest accuracy in forest areas. Finally, by incorporating land uses, terrain factors and ICESat/GLAS data into the correction models, the SRTM3 DEM was enhanced using multiple linear regression (MLR), back propagation neural network (BPNN), generalized regression NN (GRNN), and random forest (RF), respectively. Results exhibit that the four enhancement models with data coregistration obviously outperform themselves without the coregistration. Among the four models, RF produces the best result, and its RMSE is about 3.1%, 2.7% and 11.3% lower than those of MLR, BPNN, and GRNN, respectively. Moreover, 146 Global Navigation Satellite System (GNSS) points over Ganzhou city of Jiangxi province were used to assess the accuracy of the RF-derived SRTM3 DEM. It is found that the DEM quality is improved and has a similar error magnitude to that relative to the ICESat/GLASS data.
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Song, Peibing, Weifeng Liu, Jiahui Sun, Chao Wang, Lingzhong Kong, Zhenxue Nong, Xiaohui Lei, and Hao Wang. "Annual Runoff Forecasting Based on Multi-Model Information Fusion and Residual Error Correction in the Ganjiang River Basin." Water 12, no. 8 (July 23, 2020): 2086. http://dx.doi.org/10.3390/w12082086.
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Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.
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Chen, Jie Yu, Chie Iyo, Fuminori Terada, and Sumio Kawano. "Effect of Multiplicative Scatter Correction on Wavelength Selection for near Infrared Calibration to Determine Fat Content in Raw Milk." Journal of Near Infrared Spectroscopy 10, no. 4 (October 2002): 301–7. http://dx.doi.org/10.1255/jnirs.346.
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The effect of multiplicative scatter correction (MSC) on wavelength selection for near infrared (NIR) calibration to determine fat content in raw milk was investigated. Short-wave NIR spectra (700–1100 nm) of raw milk samples were measured. The calibration equations for fat content were performed by multiple linear regression (MLR) using original, second derivative and MSC-treated spectra. It was found that first wavelength selection from the fat absorption band for a calibration equation was generally effective in all cases of original, second derivative and MSC-treated spectra. However, correlation plots did not always work well because of the multiplicative scatter effect presented in the samples. Whereas, correlation plots were still useful in the case of MSC-treated spectra and normalised second derivative spectra, even when the original spectra exhibited a multiplicative scatter effect.
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Li, Gui Feng. "Nondestructive Measurement Model of Apple Internal Browning Based on FT-NIR Spectroscopy." Advanced Materials Research 304 (July 2011): 316–21. http://dx.doi.org/10.4028/www.scientific.net/amr.304.316.
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A rapid and nondestructive method for measuring internal browning of apple was put forward based on FT-NIR spectroscopy and the relationship between NIR spectroscopy nondestructive measurement and internal browning was developed. The NIR spectroscopies were acquired from 512 apples. Cluster analysis algorithm based on Euclidean distance was applied to selection of representative samples. The multivariable analysis concluding partial least squares (PLS) and multiple linear regression (MLR) were applied to build the regression models. The excellent model with high R2 (0.871) was obtained by PLS based on 3 wavelength ranges (950–1440 nm, 1480–1890 nm, 1960–2300 nm) and with multiplicative scatter correction (MSC) pretreatment. These suggest the model of apple internal browning was reliable with good predict ability and can meet the requirement to quick determination of internal browning of apples.
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Wang, Lei, Shibo Fang, Zhifang Pei, Yongchao Zhu, Dao Nguyen Khoi, and Wei Han. "Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture." Remote Sensing 12, no. 6 (March 24, 2020): 1038. http://dx.doi.org/10.3390/rs12061038.
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Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.
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Ardon-Dryer, Karin, Yuval Dryer, Jake N. Williams, and Nastaran Moghimi. "Measurements of PM<sub>2.5</sub> with PurpleAir under atmospheric conditions." Atmospheric Measurement Techniques 13, no. 10 (October 13, 2020): 5441–58. http://dx.doi.org/10.5194/amt-13-5441-2020.
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Abstract. The PurpleAir PA-II unit is a low-cost sensor for monitoring changes in the concentrations of particulate matter (PM) of various sizes. There are currently more than 10 000 PA-II units in use worldwide; some of the units are located in areas where no other reference air monitoring system is present. Previous studies have examined the performance of these PA-II units (or the sensors within them) in comparison to a co-located reference air monitoring system. However, because PA-II units are installed by PurpleAir customers, most of the PA-II units are not co-located with a reference air monitoring system and, in many cases, are not near one. This study aims to examine how each PA-II unit performs under atmospheric conditions when exposed to a variety of pollutants and PM2.5 concentrations (PM with an aerodynamic diameter smaller than 2.5 µm), when at a distance from the reference sensor. We examine how PA-II units perform in comparison to other PA-II units and Environmental Protection Agency (EPA) Air Quality Monitoring Stations (AQMSs) that are not co-located with them. For this study, we selected four different regions, each containing multiple PA-II units (minimum of seven per region). In addition, each region needed to have at least one AQMS unit that was co-located with at least one PA-II unit, all units needed to be at a distance of up to 5 km from an AQMS unit and up to 10 km between each other. Correction of PM2.5 values of the co-located PA-II units was implemented by multivariate linear regression (MLR), taking into account changes of temperature and relative humidity. The fit coefficients, received from the MLR, were then used to correct the PM2.5 values in all the remaining PA-II units in the region. Hourly PM2.5 measurements from each PA-II unit were compared to those from the AQMSs and other PA-II units in its region. The correction of the PM2.5 values improved the R-squared value (R2), root-mean-square error (RMSE), and mean absolute error (MAE) and slope values between all units. In most cases, the AQMSs and the PA-II units were found to be in good agreement (75 % of the comparisons had a R2>0.8); they measured similar values and followed similar trends; that is, when the PM2.5 values measured by the AQMSs increased or decreased, so did those of the PA-II units. In some high-pollution events, the corrected PA-II had slightly higher PM2.5 values compared to those measured by the AQMS. Distance between the units did not impact the comparison between units. Overall, the PA-II unit, after corrections of PM2.5 values, seems to be a promising tool for identifying relative changes in PM2.5 concentration with the potential to complement sparsely distributed monitoring stations and to aid in assessing and minimizing the public exposure to PM.
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Dardenne, Pierre, George Sinnaeve, and Vincent Baeten. "Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method?" Journal of Near Infrared Spectroscopy 8, no. 4 (October 2000): 229–37. http://dx.doi.org/10.1255/jnirs.283.
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The four most important regression methods are evaluated on very large data sets: Multiple Linear Regression (MLR), Partial Least Squares (PLS), Artificial Neural Network (ANN) and a new concept called “LOCAL” (PLS with selection of a calibration sample subset of the closest neighbours for each sample to predict). The Standard Errors of Prediction ( SEPs) are statistically tested and the results show that the regression methods are almost equal and that the data matrices are more important than the fitting methods themselves. The types of pre-treatments (Multiplicative Scatter Correction, Detrend, Standard Normal Variate, derivative etc.) of the spectra are too numerous to be able to test all the combinations. For each test, the pre-treatment found as the best with the PLS method is fixed for the other ones. The second part of the paper emphasises the importance of the number of samples. If any agricultural commodity, and probably any kind of product measured by an NIR instrument, can be considered as a mixture of several constituents, the databases built by collecting actual samples bringing new information can reach hundreds, if not thousands, of samples.
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Crawford, William, Sergey Frolov, Justin McLay, Carolyn A. Reynolds, Neil Barton, Benjamin Ruston, and Craig H. Bishop. "Using Analysis Corrections to Address Model Error in Atmospheric Forecasts." Monthly Weather Review 148, no. 9 (August 25, 2020): 3729–45. http://dx.doi.org/10.1175/mwr-d-20-0008.1.
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Abstract This paper illustrates that analysis corrections, when applied as a model tendency term, can be used to improve nonlinear model forecasts and are consistent with the hypothesis that they represent an additive 6-h accumulation of model error. Comparison of mean analysis corrections with observational estimates of bias further illustrates the fidelity with which mean analysis corrections capture the model bias. While most previous implementations have explored the use of analysis corrections to correct forecast biases in short-range forecasts, this is the first implementation of the correction method using both a seasonal mean and random analysis correction for medium-range forecasts (out to 10 days). Overall, the analysis correction–based perturbations are able to improve forecast skill in ensemble and deterministic systems, especially in the first 5 days of the forecast where bias and RMSE in both lower-tropospheric temperature and 500 hPa geopotential height are significantly improved across all experiments. However, while the method does provide some significant improvement to forecast skill, some degradation in bias can occur at later lead times when the average bias at analysis time trends toward zero over the length of the forecast, leading to an overcorrection by the analysis correction–based additive inflation (ACAI) method. Additionally, it is shown that both the mean and random component of the ACAI perturbations play a role in adjusting the model bias, and that the two components can have a complicated and sometimes nonlinear interaction.
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Leslie, S. K., E. Schinnerer, B. Groves, M. T. Sargent, G. Zamorani, P. Lang, and E. Vardoulaki. "Probing star formation and ISM properties using galaxy disk inclination." Astronomy & Astrophysics 616 (August 2018): A157. http://dx.doi.org/10.1051/0004-6361/201833114.
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We evaluate dust-corrected far-ultraviolet (FUV) star formation rates (SFRs) for samples of star-forming galaxies at z ~ 0 and z ~ 0.7 and find significant differences between values obtained through corrections based on UV colour, from a hybrid mid-infrared (MIR) plus FUV relation, and from a radiative transfer based attenuation correction method. The performances of the attenuation correction methods are assessed by their ability to remove the dependency of the corrected SFR on inclination, as well as returning, on average, the expected population mean SFR. We find that combining MIR (rest-frame ~ 13 μm) and FUV luminosities gives the most inclination-independent SFRs and reduces the intrinsic SFR scatter of the methods we tested. However, applying the radiative transfer based method also gives corrections to the FUV SFR that are inclination independent and in agreement with the expected SFRs at both z ~ 0 and z ~ 0.7. SFR corrections based on the UV-slope perform worse than the other two methods we tested. For our local sample, the UV-slope method works on average, but does not remove inclination biases. At z ~ 0.7, we find that the UV-slope correction we used locally flattens the inclination dependence compared to the raw FUV measurements, but was not sufficient to correct for the large attenuation observed at z ~ 0.7.
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Li, Yuan, Gao, and Tan. "High-Accuracy Correction of a Microlens Array for Plenoptic Imaging Sensors." Sensors 19, no. 18 (September 11, 2019): 3922. http://dx.doi.org/10.3390/s19183922.
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Microlens array (MLA) errors in plenoptic cameras can cause the confusion or mismatching of 4D spatio-angular information in the image space, significantly affecting the accuracy and efficiency of target reconstruction. In this paper, we present a high-accuracy correction method for light fields distorted by MLA errors. Subpixel feature points are extracted from the microlens subimages of a raw image to obtain correction matrices and perform registration of the corresponding subimages at a subpixel level. The proposed method is applied for correcting MLA errors of two different categories in light-field images, namely form errors and orientation errors. Experimental results show that the proposed method can rectify the geometric and intensity distortions of raw images accurately and improve the quality of light-field refocusing. Qualitative and quantitative comparisons between images before and after correction verify the performance of our method in terms of accuracy, stability, and adaptability.
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Zhu, Yanqiu, John C. Derber, R. James Purser, Bradley A. Ballish, and Jeffrey Whiting. "Variational Correction of Aircraft Temperature Bias in the NCEP’s GSI Analysis System." Monthly Weather Review 143, no. 9 (August 31, 2015): 3774–803. http://dx.doi.org/10.1175/mwr-d-14-00235.1.
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Abstract Various studies have noted that aircraft temperature data have a generally warm bias relative to radiosonde data around 200 hPa. In this study, variational aircraft temperature bias correction is incorporated in the Gridpoint Statistical Interpolation analysis system at the National Centers for Environmental Prediction. Several bias models, some of which include information about aircraft ascent/descent rate, are investigated. The results show that the aircraft temperature bias correction cools down the atmosphere analysis around 200 hPa, and improves the analysis and forecast fits to the radiosonde data. Overall, the quadratic aircraft ascent/descent rate bias model performs better than other bias models tested here, followed closely by the aircraft ascent/descent rate bias model. Two other issues, undocumented in previous studies, are also discussed in this paper. One is the bias correction of aircraft report (AIREP) data. Unlike Aircraft Meteorological Data Relay (AMDAR) data, where unique corrections are applied for each aircraft, bias correction is applied indiscriminately (without regard to tail numbers) to all AIREP data. The second issue is the problem of too many aircraft not reporting time in seconds, or too infrequently, to be able to determine accurate vertical displacement rates. In addition to the finite-difference method employed to estimate aircraft ascent/descent rate, a tensioned-splines method is tested to obtain more continuously smooth aircraft ascent/descent rates and mitigate the missing time information.
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Allen, Douglas R., Craig H. Bishop, Sergey Frolov, Karl W. Hoppel, David D. Kuhl, and Gerald E. Nedoluha. "Hybrid 4DVAR with a Local Ensemble Tangent Linear Model: Application to the Shallow-Water Model." Monthly Weather Review 145, no. 1 (December 16, 2016): 97–116. http://dx.doi.org/10.1175/mwr-d-16-0184.1.
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Abstract An ensemble-based tangent linear model (TLM) is described and tested in data assimilation experiments using a global shallow-water model (SWM). A hybrid variational data assimilation system was developed with a 4D variational (4DVAR) solver that could be run either with a conventional TLM or a local ensemble TLM (LETLM) that propagates analysis corrections using only ensemble statistics. An offline ensemble Kalman filter (EnKF) is used to generate and maintain the ensemble. The LETLM uses data within a local influence volume, similar to the local ensemble transform Kalman filter, to linearly propagate the state variables at the central grid point. After tuning the LETLM with offline 6-h forecasts of analysis corrections, cycling experiments were performed that assimilated randomly located SWM height observations, based on a truth run with forced bottom topography. The performance using the LETLM is similar to that of the conventional TLM, suggesting that a well-constructed LETLM could free 4D variational methods from dependence on conventional TLMs. This is a first demonstration of the LETLM application within a context of a hybrid-4DVAR system applied to a complex two-dimensional fluid dynamics problem. Sensitivity tests are included that examine LETLM dependence on several factors including length of cycling window, size of analysis correction, spread of initial ensemble perturbations, ensemble size, and model error. LETLM errors are shown to increase linearly with correction size in the linear regime, while TLM errors increase quadratically. As nonlinearity (or forecast model error) increases, the two schemes asymptote to the same solution.
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Yu, Chun-Yen, Shih-Wen Wan, Yih-Chyang Weng, and Ching-Han Hsu. "Three corrections for overshoot effect improved the dose for step-and-shoot intensity-modulated radiation therapy." PLOS ONE 16, no. 4 (April 23, 2021): e0250243. http://dx.doi.org/10.1371/journal.pone.0250243.
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We measured the overshoot effect in a linac and reduced it using block correction, reverse-sequence correction, and index correction. A StarTrack detector was used on a Varian iX. Five segments, 1 × 10 cm2 in area, were designed; the centers were at −4, −2, 0, 2, and 4 cm on the x axis for measuring the overshoot effect on a 10 × 10 cm2 collimator setting. Block correction was applied to two segments. The first was on the new first segment at −6 cm, and the other was on the new last segment at 6 cm. Both two new segments were obtained from the 10 × 10 cm2 collimator setting. The order of segments was reversed in reverse-sequence correction. Reverse-sequence correction averages the dose at every segment after two irradiations. When we used MLC Shaper, index correction reduced the first segment’s index (cumulative radiation occupation) by 60% and increased the last segment’s radiation occupation by 60% in a new MLC.log file. As for relative dose, the first segment had an overdose of 52.4% and the last segment had an underdose of 48.6%, when irradiated at 1 MU at 600 MU/min. The relative doses at the first segment, irradiated at 1 MU, after block correction, reverse-sequence correction, and index correction were applied decreased from 152.5% to 95.1%, 104.8%, and 100.1%, respectively. The relative doses at the last segment, irradiated at 1 MU, after block correction, reverse-sequence correction, and index correction were applied increased from 48.6% to 97.3%, 91.1%, and 95.9%, respectively. The overshoot effect depended on the speed of irradiation. High irradiation speeds resulted in notable overdosing and underdosing at the first and last segments, respectively. The three corrections mitigated the overshoot effect on dose. To save time and effort, the MLC.log file should be edited with a program in the future.
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Srivastava, Satyam, Abhai Tiwari, Pankaj Kumar, and Shashikant Sadistap. "A Multispectral Spectroscopic Based Sensing System for Quality Parameters Measurement in Raw Milk Samples." Sensor Letters 18, no. 4 (April 1, 2020): 311–21. http://dx.doi.org/10.1166/sl.2020.4222.
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Lactometer is used to monitor milk quality at various dairy centers but this may lead towards incorrect results because it requires human intervention and exact temperature correction as well as overall process is time-consuming. Presented work proposes the multispectral based spectroscopic approach along with the comparative study of different chemometric and artificial neural network (ANN) based techniques to measure different milk quality parameters. A multispectral spectroscopic sensing module has been designed using off the shelf components and further interfaced with 8-bit microcontroller based embedded system to produce three different spectrums of transmittance and scattering at +90 degree and –90 degree over the wavelength range of 340–1030 nm. Data acquisition process has been performed for 150 milk samples (cow, buffalo, and mix) collected from the bulk milk cooling center (BMC), Jaipur. Different statistical modeling techniques such as principle component regression (PCR), multiple linear regression (MLR) and partial least square regression (PLSR) have been implemented to develop correlation models between extracted features and target milk parameters. Implemented techniques have been compared based on the accuracy of their prediction models and it has been observed that PLSR shows better results compared to other two techniques. ANN-based modeling approach also has been explored to improve the accuracy of results. Five different artificial neural networks (ANN) based modeling techniques (LevenbergMarquardt, Bayesian regulation, scaled conjugate gradient, gradient descent and resilient) have been used to predict targeted milk quality parameters. Out of them, Gradient descent modeling technique performs better to predict fat content of the milk (R2 = 0.96198), Bayesian regulation performs better to predict lactose content (R2 = 0.90594) and others (solid non-fat (SNF), protein) are just satisfactory (R2 = 0.76077 for SNF using scaled conjugate gradient, R2 = 0.41935 for protein using Levenberg Marquardt). Produced results are validated with the MilkoScan FT1 system installed at Rajasthan Corporation of Dairy Federation (RCDF), Jaipur and it has been observed that results presented higher order of coefficient of determination as mentioned above (except protein, S.N.F.). A smartphone-based android application also has been developed to acquire data from the embedded system using Bluetooth protocol and transfer to cloud with the location information for further analysis.
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Necker, Tobias, Martin Weissmann, Yvonne Ruckstuhl, Jeffrey Anderson, and Takemasa Miyoshi. "Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble." Monthly Weather Review 148, no. 3 (March 1, 2019): 1229–49. http://dx.doi.org/10.1175/mwr-d-19-0154.1.
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Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.
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Navratilova, Zdenka, Stanislav Losse, Pavla Petrova, Katerina Sikorova, Alzbeta Chabronova, and Martin Petrek. "The Effect of Tobacco Smoking and Smoking Cessation on Urinal miRNAs in a Pilot Study." Life 10, no. 9 (September 10, 2020): 191. http://dx.doi.org/10.3390/life10090191.
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The diseases associated with tobacco smoking affect miRNAs and small single-stranded non-coding RNAs. However, there are no data on urinal miRNAs in healthy smokers. We searched for the possible effect of smoking and smoking cessation on miRNA urine expression. For screening, Affymetrix miRNA 4.0 arrays were used in 33 urine samples obtained from six never smokers and from current smokers in three time-points before smoking cessation (n = 10), after short time abstinence (3–8 weeks), and after long-term abstinence (1 year). For validation, a quantitative (q) polymerase chain reaction (PCR) method was used in 93 urine samples obtained from 18 never smokers and 25 current smokers in three time-points before smoking cessation, after short time abstinence (3–8 weeks), and after long-term abstinence (1 year). In screening analysis, 5 miRNAs (hsa-miR-3620-5p, hsa-miR-3613-5p, hsa-miR-3921, hsa-miR-5094, and hsa-miR-337-3p) were dysregulated in current vs. never smokers after multiple testing corrections. Smoking cessation was accompanied by miRNA dysregulation that did not reach a significant level after a multiple testing correction. In validation analysis, three miRNAs correlated with cotinine, but they were affected neither after smoking cessation nor between current and never smokers. Our whole-genome screening of 2.578 miRNAs and validation suggest that tobacco smoking has no or only a small effect on urinal miRNAs.
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Osbahr, Inga, Joachim Krause, Kai Bachmann, and Jens Gutzmer. "Efficient and Accurate Identification of Platinum-Group Minerals by a Combination of Mineral Liberation and Electron Probe Microanalysis with a New Approach to the Offline Overlap Correction of Platinum-Group Element Concentrations." Microscopy and Microanalysis 21, no. 5 (July 21, 2015): 1080–95. http://dx.doi.org/10.1017/s1431927615000719.
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AbstractIdentification and accurate characterization of platinum-group minerals (PGMs) is usually a very cumbersome procedure due to their small grain size (typically below 10 µm) and inconspicuous appearance under reflected light. A novel strategy for finding PGMs and quantifying their composition was developed. It combines a mineral liberation analyzer (MLA), a point logging system, and electron probe microanalysis (EPMA).As a first step, the PGMs are identified using the MLA. Grains identified as PGMs are then marked and coordinates recorded and transferred to the EPMA. Case studies illustrate that the combination of MLA, point logging, and EPMA results in the identification of a significantly higher number of PGM grains than reflected light microscopy. Analysis of PGMs by EPMA requires considerable effort due to the often significant overlaps between the X-ray spectra of almost all platinum-group and associated elements. X-ray lines suitable for quantitative analysis need to be carefully selected. As peak overlaps cannot be avoided completely, an offline overlap correction based on weight proportions has been developed. Results obtained with the procedure proposed in this study attain acceptable totals and atomic proportions, indicating that the applied corrections are appropriate.
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Nyhan, Brendan, Jason Reifler, and Peter A. Ubel. "The Hazards of Correcting Myths About Health Care Reform." Medical Care 51, no. 2 (February 2013): 127–32. http://dx.doi.org/10.1097/mlr.0b013e318279486b.
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Dorozhovets, Mykhaylo. "Effectiveness of Automatic Correction of Systematic Effects in Measuring Chains." Measurement Science Review 19, no. 4 (August 1, 2019): 132–43. http://dx.doi.org/10.2478/msr-2019-0019.
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Abstract The uncertainty of measurements associated with the following correction methods: advanced correction of additive linear drift, correction of additive and multiplicative effects, as well as joint correction of a linear drift and systematic additive and multiplicative effects is analyzed in the present article. For each correction method sensitivity coefficients and amplitude responses according to which noise and internal and external interferences influence the corrected measurement result have been determined. Besides uncertainty of reference quantities, the main factors which limit the efficiency of correction are: non-linearity of measurement function including non-linearity of ADC, no idealities of the switching systems and external and internal noises and periodic interferences. The efficiency of correction of systematic additive and multiplicative effects was studied for the multifunction 16 bit PCI DAQ of family NI 6250.
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Bauer, Peter, Gábor Radnóti, Sean Healy, and Carla Cardinali. "GNSS Radio Occultation Constellation Observing System Experiments." Monthly Weather Review 142, no. 2 (January 24, 2014): 555–72. http://dx.doi.org/10.1175/mwr-d-13-00130.1.
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Abstract Observing system experiments within the operational ECMWF data assimilation framework have been performed for summer 2008 when the largest recorded number of Global Navigation Satellite System (GNSS) radio occultation observations from both operational and experimental satellites were available. Constellations with 0%, 5%, 33%, 67%, and 100% data volume were assimilated to quantify the sensitivity of analysis and forecast quality to radio occultation data volume. These observations mostly constrain upper-tropospheric and stratospheric temperatures and correct an apparent model bias that changes sign across the upper-troposphere–lower-stratosphere boundary. This correction effect does not saturate with increasing data volume, even if more data are assimilated than available in today’s analyses. Another important function of radio occultation data, namely, the anchoring of variational radiance bias corrections, is demonstrated in this study. This effect also does not saturate with increasing data volume. In the stratosphere, the anchoring by radio occultation data is stronger than provided by radiosonde and aircraft observations.
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Li, Xiang Gang, Yue Jun Liu, Yi Chen, Xiao Yuan Zhou, Hui Tan, and Long Mao. "Size Design of Capillary and Barrel of Multi-Function and All-Electric Rheometer." Advanced Materials Research 853 (December 2013): 536–40. http://dx.doi.org/10.4028/www.scientific.net/amr.853.536.
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Multi-function and all-electric rheometer (MAR) was designed by the authors to study the nonlinear viscoelasticity of polymer melts at high shear rate. It is important to design suitable size of capillary and barrel because it is the calculation basis for some other important parts and determines the shear rate range of MAR. Considering the shear rate range, the entrance pressure correction and the wall slip correction, the length and diameter of capillary and barrel of MAR were designed through particular analysis and precise calculation.
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Raupach, R., T. Beyer, and K. P. Schäfers. "Combined 18F-FDG-PET/CT imaging of the head and neck." Nuklearmedizin 45, no. 05 (2006): 219–22. http://dx.doi.org/10.1055/s-0038-1625223.
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Summary:PET/CT imaging is particularly promising for head/neck malignancies, but dental implants lead to biased CT attenuation and PET activity values following CT-based attenuation correction (CT-AC). Objective: Here, we implement a metal artifact correction procedure (MAR) as part of the CT-AC for PET/CT imaging. Results: Phantom studies indicate a maximum quantitative bias in CT and PET of 1000 HU and 30 %, which is reduced to 230 HU and 6 %, respectively following MAR. These results were verified in selected patients. Conclusion: Artifacts and biases from dental implants can be reduced in PET/CT imaging by applying a simple MAR as part of the CT-AC.
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Kamal, Muhammad, Faaris H. Muhammad, and Shifa A. Mahardhika. "Effect of image radiometric correction levels of Landsat images to the land cover maps resulted from maximum likelihood classification." E3S Web of Conferences 153 (2020): 02004. http://dx.doi.org/10.1051/e3sconf/202015302004.
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Radiometric correction of remote sensing images is required to improve the quality of image pixel values and provide a measurable physical unit of each pixel. Selection of the appropriate image radiometric and atmospheric correction level defines the success of any remote sensing-based mapping applications. This study aims to assess the effects of radiometric correction levels applied to Landsat 8 (Operational Land Imager, OLI) image acquired in 2018 to the results of the land cover classification using the Maximum Likelihood Classifier (MLC). The image was corrected into four levels of radiometric and atmospheric correction; no correction (digital number), at-sensor radiance, at-sensor reflectance (top of atmosphere, ToA), and at-surface reflectance (bottom of atmosphere, BoA). A set of classification training sample covering five land cover classes (mangroves, inland vegetation, exposed soil, built-up area, and water body) was selected from the image. To ensure fair class comparison, the number of training sample were set to be proportional to the area of targeted classes. The results of this study show that there is no difference in the classification results of each level of correction, both in the area and distribution of the classes. This finding indicates that MLC result is invariable of image correction level.
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Aijaz, Saima, Jeffrey D. Kepert, Hua Ye, Zhendong Huang, and Alister Hawksford. "Bias Correction of Tropical Cyclone Parameters in the ECMWF Ensemble Prediction System in Australia." Monthly Weather Review 147, no. 11 (November 1, 2019): 4261–85. http://dx.doi.org/10.1175/mwr-d-18-0377.1.
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Abstract Global ensemble prediction systems have considerable ability to predict tropical cyclone (TC) formation and subsequent evolution. However, because of their relatively coarse resolution, their predictions of intensity and structure are biased. The biases arise mainly from underestimated intensities and enlarged radii, in particular the radius of maximum winds. This paper describes a method to reduce this limitation by bias correcting TCs in the ECMWF Ensemble Prediction System (ECMWF-EPS) for a region northwest of Australia. A bias-corrected TC system will provide more accurate forecasts of TC-generated wind and waves to the oil and gas industry, which operates a large number of offshore facilities in the region. It will also enable improvements in response decisions for weather sensitive operations that affect downtime and safety risks. The bias-correction technique uses a multivariate linear regression method to bias correct storm intensity and structure. Special strategies are used to maintain ensemble spread after bias correction and to predict the radius of maximum winds using a climatological relationship based on wind intensity and storm latitude. The system was trained on the Australian best track TC data and the ECMWF-EPS TC data from two cyclone seasons. The system inserts corrected vortices into the original surface wind and pressure fields, which are then used to estimate wind exceedance probabilities, and to drive a wave model. The bias-corrected system has shown an overall skill improvement over the uncorrected ECMWF-EPS for all TC intensity and structure parameters with the most significant gains for the maximum wind speed prediction. The system has been operational at the Australian Bureau of Meteorology since November 2016.
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Lianfu, Han, Fu Changfeng, Wang Jun, and Tang Wenyan. "Outlier Detection and Correction for the Deviations of Tooth Profiles of Gears." Measurement Science Review 13, no. 2 (April 1, 2013): 56–62. http://dx.doi.org/10.2478/msr-2013-0013.
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To decrease the influence of outlier on the measurement of tooth profiles, this paper proposes a method of outlier detection and correction based on the grey system theory. After studying the characteristics of outliers from the deviations of tooth profiles, this paper proposes a preprocessing method for the modeling data which include abnormal value, and establishes an outlier detection and correction model for the deviations of tooth profiles. Simulation results show that the precision of ONDGM(1,1)(one order and one variable non-homogenous discrete grey model whose outlier is processed by the preprocessing method proposed in this paper) is higher than that of NDGM(1,1)(one order and one variable non-homogenous discrete grey model), and the ONDGM(1,1) is more suitable than the NDGM(1,1) for dealing with the outliers from the deviations of tooth profiles. The experiment results show that the outlier detection and correction model detects and corrects the outliers from the deviations of tooth profiles, and the correction value of the outlier is basically in accordance with the actual deviation. Therefore, the method of outlier detection and correction decreases the influence of outlier and improves the precision in the measurement of tooth profiles.
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Iroegbu, Chukwuemeka D., Zhou Zhongxi, Tan Sheng, B. O. Jiang, and Liu Jingsong. "Pectus excavactum-insights into its diagnosis and the current treatment options along with its clinical outcomes: two case reports." International Surgery Journal 5, no. 2 (January 25, 2018): 756. http://dx.doi.org/10.18203/2349-2902.isj20180391.
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Pectus excavatum (PE) is a congenital sternal depression, one of the most frequent major congenital malformations of the chest wall. Generally, the malformation is not associated with functional disorders and often constitutes an aesthetic alteration with significant psychological distress. Nevertheless, the surgical repair of PE in childhood has been a well-established procedure with modified Ravitch repair (MRR) and minimally invasive repair (MIR) by Nuss been the two most popular methods of corrections. As a means of concealing the ugly skin scars caused by the MRR technique, the procedure was however highly modified with the use of bilateral inframammarian separated skin incisions. However, MIR has been a preferable technique due to its shorter operative time and minimal blood loss, but its postoperative complications have so far seemed to be its limiting factor whereas, extensive and combined deformities of the ventral chest wall are classically corrected using either MIR by Nuss and the MRR technique. Notwithstanding, Conservative treatment using alloplastic implants or vacuum bell to elevate the sternum in patients with mild PE defect is becoming a potential alternative and a means of preventing unnecessary surgical procedures mostly in mild funnel chest. Presented here is a case of PE surgical correction in a 12-year-old boy and an 11-year-old girl with pectus bar dislodgment. This article analyses the chain of events between both patients, reviews the literature on the subject and other currently available treatment options.
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Roj, J. "Neural Network Based Real-time Correction of Transducer Dynamic Errors." Measurement Science Review 13, no. 6 (December 1, 2013): 286–91. http://dx.doi.org/10.2478/msr-2013-0042.
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Abstract In order to carry out real-time dynamic error correction of transducers described by a linear differential equation, a novel recurrent neural network was developed. The network structure is based on solving this equation with respect to the input quantity when using the state variables. It is shown that such a real-time correction can be carried out using simple linear perceptrons. Due to the use of a neural technique, knowledge of the dynamic parameters of the transducer is not necessary. Theoretical considerations are illustrated by the results of simulation studies performed for the modeled second order transducer. The most important properties of the neural dynamic error correction, when emphasizing the fundamental advantages and disadvantages, are discussed.
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Lee, Jeong Yong, Jung Oh Kim, Han Sung Park, Chang Soo Ryu, Ji Hyang Kim, Young Ran Kim, Woo Sik Lee, Jung Ryeol Lee, and Nam Keun Kim. "Correction: Kim, N.K., et al. Study of the Association between microRNA (miR-25T>C, miR-32C>A, miR-125C>T, and miR-222G>T) Polymorphisms and the Risk of Recurrent Pregnancy Loss in Korean Women. Genes 2020, 11, 354." Genes 11, no. 8 (August 17, 2020): 948. http://dx.doi.org/10.3390/genes11080948.
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Lampens, P., J. Kovalevsky, M. Froeschlé, and G. Ruymaekers. "On The Mass-Luminosity Relation." Highlights of Astronomy 11, no. 1 (1998): 568. http://dx.doi.org/10.1017/s1539299600022267.
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The new Hipparcos parallaxes and photometry are used to determine individual masses and absolute (bolometric) magnitudes for the components of nearby visual binaries with good to very good orbits. The impact on the mass-luminosity relation (MLR) in the range 0 < MBol< +7.5 mag is then evaluated.We selected 335 visual binaries within 50 pc (σπ/π < 10%) for a full error analysis of their orbits by computing the covariance matrix of the orbital elements with Pourbaix’(1994) algorithm. Using ΔHp and π, we estimated fractional and component masses as well as absolute magnitudes with theirrespective errors: 52 binary systems have relative mass errors smaller than 15%. Lutz and Kelker (1973) corrections have been applied to both datatypes. A new relation BC(Hp) as a function of Teff was obtained for the conversion to bolometric magnitudes. A doubly weighted linear regression model was applied next (Babu and Feigelson, 1996): we derived a ”mean” slope 3.82 ± 0.07 and zero point 4.94 ± 0.03 for the MLR, assuming a linear relationship.Conclusions: a) the improvement of the data on masses by Hipparcos is largely quantitative; b) not all systems agree: small fluctuations from a ”mean” MLR are found as expected from evolutionary or abundance effects; c) the break in the slope of the MLR near MBol = +7 cannot be assessed due to a lack of low-mass binaries in our sample.
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Harris, William Thomas, and Farruk Kabir. "2058 miRNA manipulation to improve CFTR correction in cystic fibrosis." Journal of Clinical and Translational Science 2, S1 (June 2018): 20. http://dx.doi.org/10.1017/cts.2018.96.
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OBJECTIVES/SPECIFIC AIMS: CFTR is the mutant protein that causes cystic fibrosis (CF), a fatal respiratory diseases affecting 1 in 3500 children. CFTR modulators are small molecules that directly address mutant CFTR function. Improving correction of the F508del CFTR mutation (affecting 90% of CF patients) is one of the most pressing unmet needs in CF. Currently available F508del therapeutics only marginally improve CF, In vitro, we have identified a miRNA that impairs utility of CFTR directed therapies. miR-145 is upregulated by TGF-β (a genetic modifier of CF lung disease) with a direct binding site on the 3’-untranslated region of CFTR mRNA. Binding of miR-145 to CFTR destabilizes mRNA transcript and impedes protein translation. Overexpression of miR-145 abolishes benefit of F508del CFTR correction. Antagonists to miR-145 block TGF-β suppression of CFTR function and augment response to CFTR correction. This project evaluate in vivo impact of TGF-beta and miRNA manipulation on CFTR functional readouts including nasal potential difference (NPD) and short circuit current (Isc) across tracheal explants in addition to standard biochemical measures. METHODS/STUDY POPULATION: Wild-type Sprague-Dawley rats were inoculated with an adenoviral vector containing bioactive TGF-beta or sham at 1×109 pfu/animal placed in the left nares. Seven days post-inoculation, functional, and biochemical measures were conducted. NPD was measured with a microelectrode placed in the left nare and grounded the tail. The nare was sequentially perfused with standard Ringer’s solution, amiloride (to block the ENaC sodium channel), low chloride Ringer’s (to stimulate chloride efflux), forskolin (to open the CFTR channel) and CFTRinh-172 (to block the CFTR channel. Tracheal explants were harvested, microdissected, and placed on modified Ussing chambers. RESULTS/ANTICIPATED RESULTS: We have inoculated WT rats with bioactive TGF-β Versus sham delivered by intranasal inoculation of an adenoviral vector. Functional readout of CFTR function is by Isc across tracheal epithelia and NPD. Lung homogenates are analyzed for TGF-β signaling, miRNA expression, and CFTR transcripts. Both tracheal explants and NPD indicate TGF-β stimulation diminishes CFTR function in vivo. In tracheal explants, TGF-β exposure diminishes CFTR response to forskolin-stimulation by 75%. Loss of current after CFTR inhibition (CFTRinh-172) is halved. By nasal PD, TGF-β inoculation similarly halves the bioelectric response to low chloride and forskolin stimulation. Evaluation by qPCR reveals a strong increase in TGF-β signaling demarcated by PAI-1, prompting a reduction in CFTR mRNA. miR-145 is expressed highly in rat pulmonary tissue, but no change in overall miR-145 levels was detected between TGF-β and sham exposed rats. This finding reflects what we have observed in human lungs, with a localized increased miR-145 expression in CF epithelia, but similarly high levels of miR-145 in both CF and non-CF whole lung homogenates. Although expressed at lower levels than miR-145, we did find increased expression in TGF-β relevant miR-101, miR-494, and miR-144 that have a predicted binding site on rat 3’-UTR in TGF-β exposed Versus sham lungs. DISCUSSION/SIGNIFICANCE OF IMPACT: Our data indicate the relevance of TGF-β stimulation to suppress CFTR synthesis and function in vivo. Future work will evaluate whether these additional miRNA with CFTR binding sites may mediate TGF-β suppression of CFTR in the rat model, and the utility of miRNA manipulation to augment F508del CFTR correction.
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Stinis, Panos, Huan Lei, Jing Li, and Hui Wan. "Improving Solution Accuracy and Convergence for Stochastic Physics Parameterizations with Colored Noise." Monthly Weather Review 148, no. 6 (May 6, 2020): 2251–63. http://dx.doi.org/10.1175/mwr-d-19-0178.1.
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Abstract Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. In previous studies, it has been demonstrated that a correction term, known in stochastic analysis as the Itô correction, can help improve solution accuracy for various deterministic numerical schemes and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. The formulation is applied to a test problem described by an advection–diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and, hence, helps to reduce the computational cost of the numerical simulation.
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Aparicio, Josep M., and Stéphane Laroche. "Estimation of the Added Value of the Absolute Calibration of GPS Radio Occultation Data for Numerical Weather Prediction." Monthly Weather Review 143, no. 4 (March 31, 2015): 1259–74. http://dx.doi.org/10.1175/mwr-d-14-00153.1.
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Abstract An analysis of the impact of GPS radio occultation observations on Environment Canada’s global deterministic weather prediction system is presented. Radio occultation data, as any other source of weather observations, have a direct impact on the analyses. Since they are assimilated assuming that they are well calibrated, they also impact the bias correction scheme employed for other data, such as satellite radiances. The authors estimate the relative impact of occultation data obtained from, first, their assimilation as atmospheric measurements and, second, their influence on the bias correction for radiance data. This assessment is performed using several implementations of the thermodynamic relationships involved, and also allowing or blocking this influence to the radiance bias correction scheme. The current implementation of occultation operators at Environment Canada is presented, collecting upgrades that have been detailed elsewhere, such as the equation of state of air and the expression of refractivity. The performance of the system with and without assimilation of occultations is reviewed under conditions representative of current operations. Several denial runs are prepared, withdrawing only the occultation data from the assimilation, but keeping their influence on the radiance bias correction, or assimilating occultations but denying their impact on the bias correction procedure, and a complete denial. It is shown that the impact of occultations on the analysis is significant through both paths—assimilation and radiance bias correction—albeit the first is larger. The authors conclude that the traceability link of the ensemble of occultations has an added value, beyond the value of each datum as an atmospheric measurement.
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Jiang, Lina, Yizhou Jiang, Xiaohui Ji, Jiangtao Li, and Xiaomei Zhai. "Correction: MiR-132 enhances proliferation and migration of HaCaT cells by targeting TIMP3." RSC Advances 10, no. 43 (2020): 25888. http://dx.doi.org/10.1039/d0ra90075c.
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Tan, Chen, Fan Jia, Peng Zhang, Xinghuai Sun, Yunsheng Qiao, Xueli Chen, Youxiang Wang, Junyi Chen, and Yuan Lei. "Correction: A miRNA stabilizing polydopamine nano-platform for intraocular delivery of miR-21-5p in glaucoma therapy." Journal of Materials Chemistry B 9, no. 16 (2021): 3595. http://dx.doi.org/10.1039/d1tb90052h.
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