Mean Percent Bias Research Articles

Identification of small-scale hydropower potential sites using geographic information system and hydrologic modeling technique: Awata river, Genale Dawa basin, Ethiopia

Small-scale hydropower is a significant source of renewable and inexpensive energy. This study was carried out to identify suitable potential sites for small-scale hydropower for the generation of electricity along the Awata River in Ethiopia. Primary and secondary data collected from various sources were used in the present study to achieve its objective. After the consistency of rainfall was checked and areal rainfall was estimated using the Theissen polygon method, the rainfall–runoff​ relationship was simulated using HEC-HMS. The head of each identified site was estimated by using GIS to overlay the DEM and stream network starting at the outlet. To estimate the discharge, the flow duration curve was developed. HEC-HMS performance was evaluated using Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE), Percent Bias (PBIAS), and coefficient of determination (R2). During calibration, the values were 0.82, 4.39, −2.93, and 0.83, and during validation, the values were 0.76, 5.35, 6.25, and 0.76. The identified sites were ranked using an analytical hierarchy process based on a ratio of five criteria. There are twenty-eight sites identified, with estimated power output and energy output of 45.26 MW and 198.79 GWh at 50% and 6.74 MW and 53.23 GWh at 90%, respectively. The overall rank indicated that the selected site code 29 is ranked first and can be used for the implementation of a small hydropower plant. As a result, the design of all the components of small hydropower plants will be required to implement and generate energy for rural areas in future development.

Estimativa de dados faltantes de precipitação mensal no sudoeste da Colômbia: comparação de diferentes métodos

ABSTRACT Historical rainfall records are relevant in hydrometeorological studies because they provide information on the spatial features, frequency, and amount of precipitated water in a specific place, therefore, it is essential to make an adequate estimation of missing data. This study evaluated four methods for estimating missing monthly rainfall data at 46-gauge stations in southwestern Colombia covering 1983-2019. The performance of the Normal Ratio (NR), Principal Components Regression (PCR), Principal Least Square Regression (PLSR), and Artificial Neural Networks (ANN) methods were compared using three standardized error metrics: Root Mean Square Error (RMSE), Percent BIAS (PBIAS), and Mean Absolute Error (MAE). The results generally showed a better performance of the nonlinear ANN method. Regarding the linear methods, the best performance was registered by the PLSR, followed by the PCR. The results suggest the applicability of the ANN method in regions with a low density of stations and a high percentage of missing data, such as southwestern Colombia.

A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction

In this work, we have proposed a novel methodology for greenhouse tomato yield prediction, which is based on a hybrid of an explanatory biophysical model—the Tomgro model, and a machine learning model called CNN-RNN. The Tomgro and CNN-RNN models are calibrated/trained for predicting tomato yields while different fusion approaches (linear, Bayesian, neural network, random forest and gradient boosting) are exploited for fusing the prediction result of individual models for obtaining the final prediction results. The experimental results have shown that the model fusion approach achieves more accurate prediction results than the explanatory biophysical model or the machine learning model. Moreover, out of different model fusion approaches, the neural network one produced the most accurate tomato prediction results, with means and standard deviations of root mean square error (RMSE), r2-coefficient, Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) being 17.69 ± 3.47 g/m2, 0.9995 ± 0.0002, 0.9989 ± 0.0004 and 0.1791 ± 0.6837, respectively.

A fuzzy logic framework to handle uncertainty in remote sensing‐based hydrological data for water budget improvement across mid‐ and small‐scale basins

AbstractIn recent years, the efficacy of remote sensing (RS) products for water budget (WB) analysis has been widely tested and implemented in global and regional basins. Although RS products provide high temporal and spatial resolution images with near‐global coverage, uncertainty is still a significant problem. Fuzzy logic is a powerful technique for dealing with uncertainty in different engineering problems. In this study, the annual residual error () in the WB equation arising from the uncertainties of the RS products was minimized by applying fuzzy correction coefficients to each WB component. For analysis, three different fuzzy linear regression (FLR) models with 14 different sub‐models were used in the two basins having different spatial characteristics, namely Sakarya and Cyprus basins. Although FLR sub‐models produce similar findings in the Sakarya basin, they generated more complex results in the Cyprus basin. This is mainly due to the higher uncertainty of the RS products in the Cyprus basin. The Cyprus basin is too small for some low‐resolution RS‐based products to resolve, and it has a higher leakage error due to across ocean/land boundary. In addition, the general performance of sub‐models is better in the Sakarya basin than that in the Cyprus basin. The best fuzzy sub‐models reduced the error up to 68% and 52% in terms of mean absolute error compared with the non‐fuzzy model in the Sakarya and Cyprus basins, respectively. Further evaluations showed that the best sub‐model precipitation well captured the temporal patterns of gauge observations in both basins. Moreover, they have the best consistency with gauge observations in terms of root mean square error, Kling‐Gupta efficiency, and percent bias in both basins. The results proved that this study will provide valuable insights into WB analysis in ungauged basins by incorporating the fuzzy logic approach into hydrological RS products.

A Global Implementation of Single‐ and Dual‐Source Surface Energy Balance Models for Estimating Actual Evapotranspiration at 30‐m Resolution Using Google Earth Engine

AbstractEvapotranspiration (ET) provides a robust connection between hydrological cycles and surface energy balance. Accurate and near‐daily ET estimation has utility in water resources, agricultural management applications, crop yields and drought monitoring. This study describes the implementation of an ET modeling system based on a Priestley‐Taylor version of the Two‐Source (soil and vegetation) Energy Balance Model (TSEB‐PT) within Google Earth Engine environment. TSEB‐PT performance was compared with the simpler single‐source HSEB (Hybrid Surface Energy Balance) ET model to assess relative advantages and disadvantages for operational application. Results were evaluated across multiple biomes and climatic zones across the US, Europe, and Australia in comparison with eddy covariance data from 30 flux tower sites. Both models produced similar results when considering all biomes at daily, weekly, and monthly timescales. Daily ET metrics for all sites combined yielded comparable results for both models, with a slightly lower root‐mean‐square error for TSEB‐PT (HSEB) of 1.2 (1.3) mm/d and a higher correlation (r) of 0.83 (0.80), but a larger mean percent bias error (MPBE = −9%) than HSEB (MPBE = 1%). TSEB‐PT performance was lowest for sites in warm summer humid continental and hot semi‐arid climates and in evergreen broadleaf forest cover, while HSEB showed lowest performance in tropical savanna hot semi‐arid climates and in savanna covers. Model performance was improved for both cropland and non‐cropland sites when TSEB‐PT and HSEB ET estimates were combined through simple averaging due to cancellation of opposing errors, showing a promise as potential tools for water resource management on a global scale.

AgSAT: A Smart Irrigation Application for Field-Scale Daily Crop ET and Water Requirements Using Satellite Imagery

With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman–Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts.

Full-length versus intact PTH concentrations in pseudohypoparathyroidism type 1 and primary hyperparathyroidism: clinical evaluation of immunoassays in individuals from China.

PurposeThe application of the third-generation parathyroid hormone (PTH) assay [PTH(1–84) assay] for evaluating PTH levels in patients with pseudohypoparathyroidism type-1 (PHP1) is less popular than the second-generation assay. Therefore, we aimed at examining the conformity between the PTH(1–84) assay and the intact PTH (iPTH) assay, specifically examining their performance in individuals with PHP1 versus individuals with primary hyperparathyroidism (PHPT), compared to healthy controls.MethodsPTH(1–84) and iPTH assay were performed in patients with PHP1, patients with PHPT, and healthy volunteers. ∆PTH%, PTH(1–84)/iPTH (3rd/2nd ratio), iPTH/upper limit of normal (ULN), and PTH (1–84)/ULN of each group were calculated for comparison. Linear regression, Kappa conformity test, and Bland–Altman analysis of ∆PTH/mean of iPTH and PTH(1–84) (percent bias) plotted against the mean of iPTH and PTH(1–84) were performed to determine the conformance of PTH(1–84) assay with iPTH assay.ResultsA total of 54 patients with PHP1, 127 patients with PHPT, and 65 healthy volunteers were enrolled in this study. All the three groups showed strong linear relationship between iPTH and PTH (1–84) (r2 = 0.9661, 0.7733, and 0.9575, respectively). No significant differences were noted in 3rd/2nd ratio (median 0.76 vs. 0.72) between the PHP1 and PHPT groups (p > 0.05). Conformity examination showed the Kappa value was 0.778 and 0.395 for PHP1 and PHPT groups respectively. No difference in the Kappa values was found between PHP1A and PHP1B subgroups. Bland–Altman plot demonstrated that the proportion of data points that were plotted within mean ± 1.96 SD in PHP1, PHPT and normal control groups were 96.3%, 93.7%, and 98.5%, respectively. The mean percent bias of the three groups were 26.1%, 31.2%, and 17.0%, respectively. The range of mean ± 1.96 SD of percent bias of the three groups were 2.2%–50.0%, −14.3%–76.6%, and 6.7%–27.2%, respectively.ConclusionAlthough iPTH and PTH(1–84) values were both lower in the present PHP1 cohort than in the PHPT cohort, there appear to be differences in the relative agreement between both immunoassays, and in the relationship between the two values, especially in comparison to healthy controls. Whether these differences are due to differential accumulation of C-terminal fragments or other factors requires further study.

Numerical Model to Simulate Soil Wetting Pattern under Drip Irrigation System

Background: A properly designed drip irrigation system increases the efficiency of irrigation water and its design is based on the soil wetting pattern. Methods: In this study, a numerical model has been developed using one-dimensional Richard’s Equation to simulate soil wetting pattern under drip irrigation. A fully implicit finite-difference scheme has been used to solve the governing equations with suitable initial and boundary conditions. An experiment was also conducted to analyse the wetting pattern in sandy loam soil. Wetting front computed by numerical model has been compared with the experimental wetting front at different time interval of drip irrigation system. The present model is also validated with the results available in the literature. Result: Statistical indices - root mean square error, percentage bias and coefficient of determination have been used to evaluate the model’s performance. The computed results are closely matched with the and experimental results with RMSE is less than 3, PBIAS less than 6% and R2 greater than 0.96 and also with the earlier models with RMSE is less than 0.01, PBIAS less than 4% and R2 greater than 0.98, It shows that the present model can simulate soil wetting pattern in the drip irrigation system.

Using SIMEX and regression calibration to correct for mixtures of classical and Berkson measurement error

BACKGROUND AND AIM Measurement error in modelled air pollution data is complex consisting of classical and Berkson error components. As part of the MELONS project, we investigated the adequacy of regression calibration and SIMEX in correcting health effect estimates when measurement error contains both components and may also be spatially correlated. METHODS In a simulation study of 5,000 subjects, we considered scenarios based on the amount of total measurement error (either 20% or 40% of the variance in the true annual average pollutant concentrations); the proportion of error that was classical (0%,20%,40%,60%, 80% or 100%); and whether total measurement error was homoscedastic or heteroscedastic. Within each simulation, we obtained a dataset of true, and error prone annual average pollutant concentrations and outcome data linked to the true by a hazard ratio of 1.041 per 10µg/m³. The error prone pollutant concentrations were then used as the exposure variable in a Cox regression, and for error correction, a 5% hypothetical validation sub-sample of the data were randomly selected and used to estimate the classical and Berkson error variances. Based on 1,600 simulations per scenario we investigated mean percent bias in the estimated log hazard ratio and the ability of regression calibration and SIMEX to correct for this bias. RESULTS Mean percent bias in the uncorrected log hazard ratio was typically negative (i.e., towards the null) ranging from -27.4% to +2.0% across scenarios. Mean percent bias in the corrected estimates ranged from -4.0% to +5.7% for regression calibration and -6.0% to +4.2% for SIMEX. CONCLUSIONS Across a range of scenarios varying the mix of classical and Berkson error and allowing total measurement error to vary spatially, SIMEX and regression calibration produced corrected log hazard ratios with relatively small mean percent bias. KEYWORDS Measurement Error, SIMEX, Regression Calibration

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) ( i.e ., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) ( i.e ., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random: 70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE = 0.75 cm, MAE = 0.59 cm, NSE = 0.98), while the SCS model had the lowest performance (RMSE = 4.77 cm, MAE = 2.64 cm, NSE = 0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.

Regional thermal analysis approach: A management tool for predicting water temperature metrics relevant for thermal fish habitat

The field of water resource management, including fisheries, is facing new challenges associated with climate change. This study sheds light on the modeling of water temperature indices (metrics) that describe critical thermal maxima of the Atlantic salmon (salmo salar). These thermal metrics include MaxWaterTmax (interannual mean of maximum summer temperature), MaxNumDay (interannual mean of the number of consecutive days with maximum water temperature > 25 °C and minimum water temperature > 20 °C). The latter is an important indicator to evaluate thermal variability. Three other parameters of a Gaussian function fitted to the interannual daily mean temperatures characterizing the thermal regime of 146 stations located in Eastern Canada were estimated. These three parameters are Gaussian_a (maximum of interannual daily mean temperature), Gaussian_b (mean duration of the warm period), and Gaussian_c (date of occurrence of the interannual maximum temperature). The classical Multiple linear regression model (MLR) and the non-linear Generalized additive model (GAM) were tested and compared to estimate the five thermal metrics. The regression-based approaches involve the identification of thermally homogeneous regions based on three approaches: hierarchical clustering analysis (HCA), regions of influence (ROI) as well as canonical correlation analysis (CCA). Then, the regional MLR and GAM models were applied within the delineated homogenous regions. Also, the regional models were compared to models encompassing all stations (i.e., one region). For each regional estimation model and each thermal metric, a set of optimal explanatory variables were selected using a forward stepwise procedure. The database consisted of 22 environmental predictors related to physiography, topography, climate, land cover and surface deposits. To assess performance of the models, the following statistical metrics were used: coefficient of determination R2, root mean square error (RMSE), bias, relative root mean square error (RRMSE) and percent bias (PBias). The results demonstrate that the non-linear GAM model was consistently better than the simpler MLR model for estimating the five thermal metrics. Results also show that the best practice consists in delineating homogeneous regions before applying the regional GAM model. According to all performance criteria, delineation of regions with the HCA approach is considered to be more flexible and to lead to better performances than neighborhood-based approaches (CCA and ROI).

11C-methionine PET/MRI in postoperative patients after craniotomy: zero echo time and head atlas versus CT-based attenuation correction.

Attenuation correction (AC) is an important topic in PET/MRI and particularly challenging after brain tumor surgery, near metal implants, adjacent bone and burr holes. In this study, we evaluated the performance of two MR-driven AC methods, zero-echo-time AC (ZTE-AC) and atlas-AC, in comparison to reference standard CT-AC in patients with surgically treated brain tumors at 11C-methionine PET/MRI. This retrospective study investigated seven postoperative patients with neuropathologically confirmed brain tumor at 11C-methionine PET/MRI. Three AC maps - ZTE-AC, atlas-AC and reference standard CT-AC - were generated for each patient. Standardized uptake values (SUV) were obtained at the metal implant, adjacent bone and burr hole. Standard uptake ratio (SUR) SURmetal/mirror, SURbone/mirror and SURburrhole/mirror were then calculated and analyzed with Bland-Altman, Pearson correlation and intraclass correlation reliability. Smaller mean percent bias range (Bland-Altman) was found for ZTE-AC than atlas-AC in all analyses (metal ZTE -0.46 to -0.02, metal atlas -3.57 to -3.26; bone ZTE -4.60 to -2.16, bone atlas -5.25 to -3.81; burr hole ZTE -0.95 to -0.52, burr hole atlas 7.86 to 8.87). Percent SD range (Bland-Altman) was large for both methods in all analyses, with lower absolute values for ZTE-AC (ZTE 7.02-8.49; atlas 11.47-14.83). A very strong correlation (Pearson correlation) was demonstrated for both methods compared to CT-AC (ZTE ρ 0.97-0.99, P<0.001; atlas ρ 0.88-0.91, P≤0.009) with higher absolute values for ZTE. An excellent intraclass correlation coefficient was found across all analyses for ZTE, atlas and CT maps (ICC ≥0.88). ZTE for MR-driven PET attenuation correction presented a more comparable performance to reference standard CT-AC at the postoperative site. ZTE-AC may serve as a useful diagnostic tool for MR-driven AC in patients with surgically treated brain tumors.

Validating Restricted Mean Survival Time Estimates From Reconstructed Kaplan-Meier Data Against Original Trial Individual Patient Data From Trials Conducted by the Canadian Cancer Trials Group

ObjectivesThe development of novel cancer therapies, including immuno-oncology agents, has increased interest in reconstructed individual patient data (IPD) based restricted mean survival time (RMST) analyses. Additionally, reconstructed IPD–based RMST is recommended in cost-effectiveness analyses when original trial IPD are not available. Nevertheless, recently concerns regarding potential bias of reconstructed-IPD RMST have been presented, because reconstructed-IPD RMSTs have not been validated and previous validation endpoints may not capture the entire Kaplan-Meier (KM) curve, especially the “tail.” Our study aims to validate the recommended method of IPD reconstruction by comparing reconstructed IPD– and original trial IPD–based RMST. MethodsCanadian Cancer Trials Group trials from 1990 to 2017 were included. Overall survival and progression-free survival IPD were reconstructed based on published KM curves using the Guyot method. Analysts were blinded to original trial IPD. RMST was calculated at 1 year and over the entire KM curve. Reconstructed-IPD and original trial–IPD (gold-standard) RMSTs were compared for accuracy and predictive error via mean deviation, mean absolute error (MAE), mean percentage bias, and Bland-Altman plots and across KM curve quality (vector traced or bitmapped). ResultsWe identified 39 trials. The mean deviation, MAE, and mean percentage bias of RMST between the reconstructed IPD and original trial IPD were small. In particular, the mean deviation was −0.01 months and −0.04 months, MAE was 0.19 months and 0.24 months, and mean percentage bias was 0.82% and 0.84% in overall survival KM curves in control and experimental arms, respectively. Accuracy was generally not associated with KM curve quality. ConclusionsRMST derived from reconstructed IPD displayed excellent accuracy and predictive error compared with the gold standard. Reconstructed IPD could be used to calculate RMST in lieu of original trial IPD, to facilitate decision making for clinicians, researchers, and policy makers.

Effects of Road Traffic on the Accuracy and Bias of Low-Cost Particulate Matter Sensor Measurements in Houston, Texas.

Although PM2.5 measurements of low-cost particulate matter sensors (LCPMS) generally show moderate and strong correlations with those from research-grade air monitors, the data quality of LCPMS has not been fully assessed in urban environments with different road traffic conditions. We examined the linear relationships between PM2.5 measurements taken by an LCPMS (Dylos DC1700) and two research grade monitors, a personal environmental monitor (PEM) and the GRIMM 11R, in three different urban environments, and compared the accuracy (slope) and bias of these environments. PM2.5 measurements were carried out at three locations in Houston, Texas (Clinton Drive largely with diesel trucks, US-59 mostly with gasoline vehicles, and a residential home with no major sources of traffic emissions nearby). The slopes of the regressions of the PEM on Dylos and Grimm measurements varied by location (e.g., PEM/Dylos slope at Clinton Drive = 0.98 (R2 = 0.77), at US-59 = 0.63 (R2 = 0.42), and at the residence = 0.29 (R2 = 0.31)). Although the regression slopes and coefficients differed across the three urban environments, the mean percent bias was not significantly different. Using the correct slope for LCPMS measurements is key for accurately estimating ambient PM2.5 mass in urban environments.

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the complete multivariate series where the controls are available by taking the attribute of interest as a response variable. IBFI is tested against other frequently used imputation methods, namely mean, median, mode, predictive mean matching (PMM), and hot-deck procedures. The performance of the different imputation methods was assessed using root mean squared error (RMSE), mean squared log error (MSLE), mean absolute percentage error (MAPE), percent bias (PB), and mean squared error (MSE) statistics. The imputation process requires more attention when multiple variables are missing in different samples, resulting in challenges to machine learning methods because some controls are missing. IBFI appears to have an advantage in such circumstances. For testing IBFI, Radon Time Series Data (RTS) has been used and data was collected from 1st March 2017 to the 11th of May 2018, including 4 seismic activities that have taken place during the data collection time.

Anomalies Prediction in Radon Time Series for Earthquake Likelihood Using Machine Learning-Based Ensemble Model

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range [1262.864, 1409.616] which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.

Derivation of a Modified Friedewald's equation for LDL cholesterol estimation in Sub-Himalayan population: A comparative study.

ABSTRACTBackground:Despite the availability of alternative homogenous assays for LDL-C measurement, most of the laboratories still use Friedewald Equation (FE). However, various novel equations have shown better performance than FE specific to a particular population. Besides, no equation has been devised for use in Sub-Himalayan population.Methods:A cross-sectional laboratory data-based study was conducted by recruiting lipid profiles of 1851 samples to validate 10 different equations for calculating LDL and to devise a novel Modified Friedewald Equation (MFE) specific for Sub-Himalayan population.Results:The novel MFE is presented as: LDL-C = -2.421 + (0.752 × TC) - (0.047 × TG) - (0.350 × HDL). A significant difference was observed between direct LDL-C (118.84 ± 40.39 mg/dL) and all other equations except MFE (118.84 ± 37.96 mg/dl, P > 0.999) and Puavilai Equation (117.99 ± 49.05 mg/dL, P = 0.138). Additionally, MFE showed lowest mean percentage bias of 0.14% with 95% limits of agreement within ± 2SD on Bland-Altman analysis. On ROC analysis at cut-offs of clinical decision limits of 100 mg/dl, 130 mg/dl, 160 mg/dl, and 190 mg/dl, MFE outperformed all other equations with highest AUC (0.974, 0.978, 0.982, and 0.995) respectively with specificity >95% at higher levels. MFE also showed highest correlation (r = 0.954, P < 0.001) and least rMSE (13.8) with direct LDL although all the equations showed significant positive correlation with direct LDL (p < 0.001).Conclusion:MFE derived in this study showed a better diagnostic performance as compared to other 10 equations taking Direct LDL-C as gold standard for Sub-Himalayan Population and may be used as a substitute for FE in the study population.

Comparison of soil dielectric mixing models for soil moisture retrieval using SMAP brightness temperature over croplands in India

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R2) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 m3/m3), followed by Wang & Schmugge model (RMSE = 0.08 m3/m3), Hallikainen model (RMSE = 0.09 m3/m3), Dobson model (RMSE = 0.10 m3/m3) and original SMAP radiometer SM (RMSE = 0.12 m3/m3). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

Recent Trends in Creatinine Assays in Korea: Long-Term Accuracy-Based Proficiency Testing Survey Data by the Korean Association of External Quality Assessment Service (2011-2019).

BackgroundAccurate serum creatinine (Cr) concentration measurement is essential for evaluating kidney function. In 2011, the Korean Association of External Quality Assessment Service (KEQAS) launched an accuracy-based Cr proficiency testing (ABCr PT) survey. We analyzed long-term data of the KEQAS ABCr PT survey collected between 2011 and 2019 to assess recent trends in Cr assays in Korea.MethodsThe ABCr PT survey including three commutable fresh-frozen serum samples was performed twice a year. The target Cr concentration was assigned using isotope-dilution mass spectrometry. We analyzed data obtained from the participating laboratories, calculated the yearly bias, and evaluated bias trends for the major reagents and instruments. Outliers were excluded from all analysis.ResultsThe mean percentage bias based on the total data of all participating laboratories was 10.8% in the 2011-A survey and 0.2% in 2019-B survey. Bias for the major reagents and instruments differed depending on the manufacturer. Enzymatic assays generally showed desirable bias ranging from –3.9% to 3.2% at all Cr concentrations and lower interlaboratory variability than non-enzymatic assays (enzymatic vs. non-enzymatic, 3.3%–7.2% vs. 6.3%–9.1%).ConclusionsAlthough the mean percentage bias of Cr assays tends to decrease over time, it is necessary to continuously strive to improve Cr assay accuracy, especially at low concentrations.

Prognostic Analysis of High-Speed Cylindrical Roller Bearing Using Weibull Distribution and k-Nearest Neighbor

Abstract Bearing remnant operational life can be determined by implementing a data-driven prognostics method. In this work, the bearing run-to-failure data from experimentation on test rig is used to extract time-domain features. The sudden change in time-domain information signifies the fault inception which led to failure stage promptly. The monotonicity metric is utilized to select the optimal feature set that best represents bearing degradation. Principal component analysis (PCA) is used for dimension reduction and fusion, and a unidimensional health indicator (HI) is constructed. Fluctuations of HI are smoothed by fitting it with a Weibull failure rate function (WFRF) and the corresponding parameters are estimated using nonlinear least-squares method. By inverting the model, the predicted time values are calculated, and hence remnant operational life of bearing is evaluated and compared with the actual life from experimental data. The performance assessment metrics utilized are mean absolute percentage error (MAPE), mean-square error (MSE), root-mean-square error (RMSE), and bias. Besides this, an online degradation state classification method using the k-nearest neighbor (KNN) classifier is implemented. The KNN model performance is assessed by constructing receiver operating characteristics (ROC) curve, which indicates the value of area under the curve (AUC) equal to 0.94, representing high accuracy of the KNN. The remaining useful life (RUL) is predicted within 95% confidence limits, and the predicted RUL almost follows the actual one with some fluctuations. The model performance is found promising and can be implemented to evaluate the remaining useful life of bearing.