Mean Percent Bias Research Articles

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.

Multi sources hydrological assessment over Vu Gia Thu Bon Basin, Vietnam

ABSTRACT The study aims to evaluate the long-term accuracy of global precipitation (Climate Prediction Center (CPC) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR)) along with raingauge datasets at multiple temporal scales in the Vu Gia Thu Bon basin, Vietnam. Since there are few rainfall stations in this basin, it is important to validate multisource data for multiple purposes. This is the first time that a lumped hydrological model (i.e. Probability Distributed Moisture (PDM)) has been used for this basin. Various statistical indicators, including the correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), percent bias (BIAS) and Taylor diagram, were used to evaluate the applicability of the global precipitation data and the PDM model. The precipitation datasets showed a good correlation with the raingauge rainfall data. In contrast, CPC underestimates while PERSIANN-CDR overestimates the raingauge rainfall. In general, PERSIANN-CDR performed slightly better than CPC. The daily streamflow simulation driven by PDM and all data sources underestimates the actual flow.

Validating the use of stereo-video cameras to conduct remote measurements of sea turtles.

Point 1: Stereo‐video camera systems (SVCSs) are a promising tool to remotely measure body size of wild animals without the need for animal handling. Here, we assessed the accuracy of SVCSs for measuring straight carapace length (SCL) of sea turtles.Point 2: To achieve this, we hand captured and measured 63 juvenile, subadult, and adult sea turtles across three species: greens, Chelonia mydas (n = 52); loggerheads, Caretta caretta (n = 8); and Kemp's ridley, Lepidochelys kempii (n = 3) in the waters off Eleuthera, The Bahamas and Crystal River, Florida, USA, between May and November 2019. Upon release, we filmed these individuals with the SVCS. We performed photogrammetric analysis to extract stereo SCL measurements (eSCL), which were then compared to the (manual) capture measurements (mSCL).Point 3: mSCL ranged from 25.9 to 89.2 cm, while eSCL ranged from 24.7 to 91.4 cm. Mean percent bias of eSCL ranged from −0.61% (±0.11 SE) to −4.46% (±0.31 SE) across all species and locations. We statistically analyzed potential drivers of measurement error, including distance of the turtle to the SVCS, turtle angle, image quality, turtle size, capture location, and species.Point 4: Using a linear mixed effects model, we found that the distance between the turtle and the SVCS was the primary factor influencing measurement error. Our research suggests that stereo‐video technology enables high‐quality measurements of sea turtle body size collected in situ without the need for hand‐capturing individuals. This study contributes to the growing knowledge base that SVCS are accurate for body size measurements independent of taxonomic clade.

Comparison of two immunoassays for the measurement of serum HE4 for ovarian cancer

IntroductionThe use of Human Epididymis Protein 4 (HE4) as a biomarker for ovarian cancer is gaining traction, providing the impetus for development of a high throughput automated HE4 assay that is comparable to the conventional manual enzyme immunometric-assay (EIA). The aim of this study was to compare two immunoassay methods for the measurement of serum HE4. Materials and methods1348 serum samples were analysed for serum HE4 using both the EIA and the automated chemiluminescent immunoassay (CLEIA) methods. HE4 values were compared using a Passing-Bablok regression and agreement assessed using Lin's concordance correlation coefficient (CCC). The absolute and percentage bias of the CLEIA compared to EIA was determined. ResultsThere was moderate agreement between the two methods (CCC 0.929, 95%CI 0.923-0.936). Passing-Bablok regression demonstrated an overestimation of the CLEIA [constant 4.44 (95%CI 2.96-5.68), slope 1.04 (95%CI 1.02-1.07)]. The CLEIA method had a mean percentage bias of 16.25% compared to the EIA method. ConclusionThe CLEIA significantly overestimated serum HE4 values compared to the EIA, which could impact clinical interpretation and patient management. Further studies are required to develop an appropriate cut-off depending on the population being investigated and the analytic method being used.

Verification of Harmonization of Serum Total and Free Prostate-Specific Antigen (PSA) Measurements and Implications for Medical Decisions.

Previous studies have shown that the harmonization of prostate-specific antigen (PSA) assays remained limited even after the introduction of WHO International Standards. This information needs updating for current measuring systems (MS) and reevaluation according to established analytical performance specifications (APS) and the characteristics of antibodies used. Total (tPSA) and free (fPSA) PSA were measured in 135 and 137 native serum samples, respectively, by Abbott Alinity i, Beckman Access Dxl, Roche Cobas e801, and Siemens Atellica IM MSs. Passing-Bablok regression and difference plots were used to compare results from each MS to the all-method median values. Agreement among methods was evaluated against APS for bias derived from biological variation of the 2 measurands. The median interassay CV for tPSA MSs (11.5%; 25-75th percentiles, 9.2-13.4) fulfilled the minimum APS goal for intermethod bias (15.9%), while the interassay CV for fPSA did not [20.4% (25-75th percentiles, 18.4-22.7) vs goal 17.6%]. Considering the all-method median value of each sample as reference, all tPSA MSs exhibited a mean percentage bias within the minimum goal. On the other hand, Alinity (+21.3%) and Access (-24.2%) were out of the minimum bias goal for fPSA, the disagreement explained only in minimal part by the heterogeneity of employed antibodies. The harmonization among tPSA MSs is acceptable only when minimum APS are applied and necessitates further improvement. The marked disagreement among fPSA MSs questions the use of fPSA as a second-level test for biopsy referral.

Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research: A Large-scale, Real-world Data Study.

Due to the non-randomized nature of real-world data, prognostic factors need to be balanced, which is often done by propensity scores (PSs). This study aimed to investigate whether autoencoders, which are unsupervised deep learning architectures, might be leveraged to compute PS. We selected patient-level data of 128,368 first-line treated cancer patients from the Flatiron Health EHR-derived de-identified database. We trained an autoencoder architecture to learn a lower-dimensional patient representation, which we used to compute PS. To compare the performance of an autoencoder-based PS with established methods, we performed a simulation study. We assessed the balancing and adjustment performance using standardized mean differences, root mean square errors (RMSE), percent bias, and confidence interval coverage. To illustrate the application of the autoencoder-based PS, we emulated the PRONOUNCE trial by applying the trial's protocol elements within an observational database setting, comparing two chemotherapy regimens. All methods but the manual variable selection approach led to well-balanced cohorts with average standardized mean differences <0.1. LASSO yielded on average the lowest deviation of resulting estimates (RMSE 0.0205) followed by the autoencoder approach (RMSE 0.0248). Altering the hyperparameter setup in sensitivity analysis, the autoencoder approach led to similar results as LASSO (RMSE 0.0203 and 0.0205, respectively). In the case study, all methods provided a similar conclusion with point estimates clustered around the null (e.g., HRautoencoder 1.01 [95% confidence interval = 0.80, 1.27] vs. HRPRONOUNCE 1.07 [0.83, 1.36]). Autoencoder-based PS computation was a feasible approach to control for confounding but did not perform better than some established approaches like LASSO.

Geographic information system and process-based modeling of soil erosion and sediment yield in agricultural watershed

BACKGROUND AND OBJECTIVES: The study explored the capability of the geographic information system interface for the water erosion prediction project, a process-based model, to predict and visualize the specific location of soil erosion and sediment yield from the agricultural watershed of Taganibong.METHODS: The method involved the preparation of the four input files corresponding to climate, slope, land management, and soil properties. Climate file processing was through the use of a breakpoint climate data generator. The team had calibrated and validated the model using the observed data from the three monitoring sites.FINDINGS: Model evaluation showed a statistically acceptable performance with coefficient of determination values of 0.64 (probability value = 0.042), 0.85 (probability value = 0.000), and 0.69 (probability value = 0.001) at 95% level, for monitoring sites 1, 2, and 3, respectively. A further test revealed a statistically satisfactory model performance with root mean square error-observations standard deviation ratio, Nash-Sutcliffe efficiency, and percent bias of 0.62, 0.61, and 44.30, respectively, for monitoring site 1; 0.65, 0.56, and 25.60, respectively, for monitoring site 2; and 0.60, 0.65, and 27.90, respectively, for monitoring site 3. At a watershed scale, the model predicted the erosion and sediment yield at 89 tons per hectare per year and 22 tons per hectare per year, respectively, which are far beyond the erosion tolerance of 10 tons per hectare per year. The sediment delivery ratio of 0.20 accounts for a total of 126,390 tons of sediments that accumulated downstream in a year.CONCLUSION: The model generated maps that visualize a site-specific hillslope, which is the source of erosion and sedimentation. The study enables the researchers to provide information helpful in the formulation of a sound policy statement for sustainable soil management in the agricultural watershed of Taganibong.

Verification of Low-Density Lipoprotein Cholesterol Levels Measured by Anion-Exchange High-Performance Liquid Chromatography in Comparison with Beta Quantification Reference Measurement Procedure.

A new lipoprotein testing method based on anion-exchange HPLC (AEX-HPLC) was recently established. We verified the accuracy of LDL-C levels, a primary therapeutic target for the prevention of cardiovascular disease (CVD), measured by AEX-HPLC comparing with LDL-C levels measured by beta quantification-reference measurement procedure (BQ-RMP), homogenous assays, and calculation methods. We compared LDL-C levels measured by AEX-HPLC (adLDL-Ch: LDL-Ch and IDL-Ch) and BQ-RMP using blood samples from 52 volunteers. AdLDL-Ch levels were also compared with those measurements by homogeneous assays and calculation methods (Friedewald equation, Martin equation, and Sampson equation) using blood samples from 411 participants with dyslipidemia and/or type 2 diabetes. The precision and accuracy of adLDL-Ch were verified by BQ-RMP. The mean percentage bias [bias (%)] for LDL-C was 1.2%, and the correlation was y = 0.990x + 3.361 (r = 0.990). These results met the acceptable range of accuracy prescribed by the National Cholesterol Education Program. Additionally, adLDL-Ch levels were correlated with LDL-C levels measured by the 2 homogeneous assays (r > 0.967) and the calculation methods (r > 0.939), in serum samples from patients with hypertriglyceridemia. AEX-HPLC is a reliable method for measuring LDL-C levels for CVD risk in daily clinical laboratory analyses.

Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms

The infiltration process during irrigation is an essential variable for better water management and hence there is a need to develop an accurate model to estimate the amount infiltration water during irrigation. However, the fact that the infiltration process is a highly non-linear procedure and hence required special modeling approach to accurately mimic the infiltration procedure. Therefore, the ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated water during irrigation in the furrow for sustainable management is proposed. The main innovation of current research is the first attempt to employ the ANFIS model for predicating infiltration rates, in addition, integrate the ANFIS model with three new optimization algorithms. Three optimizing algorithms, viz. Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) were used to tune the ANFIS-parameters. Experimental data from six different studies in different countries have been used in this study to validate the proposed model. The inflow rate, furrow length, infiltration opportunity time, cross-sectional area, and waterfront advance time have been utilized as the input parameters. The results indicated that the ANFIS-SCA could provide a better estimation for the infiltration rate compared to ANFIS-PSO. The Mean Absolute Error (MAE) and Percent Bias (PBIAS) errors computed for the ANIFS-SCA (0.007 m3/m and 0.12) was significantly better than those achieved from the ANFIS-FFA and the ANFIS-PSO In addition to that, ANIFS-SCA model outperformed ANFIS-FFA with high level of accuracy. The proposed Hybrid ANFIS-SCA showed outstanding performance over the other optimizer algorithms in estimating the infiltration rate and could be applied in different irrigation systems for better sustainable irrigation management.

Evaluation of 32 Simple Equations against the Penman–Monteith Method to Estimate the Reference Evapotranspiration in the Hexi Corridor, Northwest China

Evapotranspiration plays an inevitable role in various fields of hydrology and agriculture. Reference evapotranspiration (ET0) is mostly applied in irrigation planning and monitoring. An accurate estimation of ET0 contributes to decision and policymaking processes governing water resource management, efficiency, and productivity. Direct measurements of ET0, however, are difficult to achieve, often requiring empirical methods. The Penman–Monteith FAO56 (PM-FAO56) method, for example, is still considered to be the best way of estimating ET0 in most regions of the globe. However, it requires a large number of meteorological variables, often restricting its applicability in regions with poor or missing meteorological observations. Furthermore, the objectivity of some elements of the empirical equations often used can be highly variable from region to region. The result is a need to find an alternative, objective method that can more accurately estimate ET0 in regions of interest. This study was conducted in the Hexi corridor, Northwest China. In it we aimed to evaluate the applicability of 32 simple empirical ET0 models designed under different climatic conditions with different data inputs requirements. The models evaluated in this study are classified into three types of methods based on temperature, solar radiation, and mass transfer. The performance of 32 simple equations compared to the PM-FAO56 model is evaluated based on model evaluation techniques including root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE). The results show that the World Meteorological Organization (WMO) and the Mahringer (MAHR) models perform well and are ranked as the best alternative methods to estimate daily and monthly ET0 in the Hexi corridor. The WMO and MAHR performed well with monthly mean RMSE = 0.46 mm and 0.56 mm, PBIAS = 12.1% and −11.0%, and NSE = 0.93 and 0.93, before calibration, respectively. After calibration, both models showed significant improvements with approximately equal PBIAS of −2.5%, NSE = 0.99, and RMSE of 0.24 m. Calibration also significantly reduced the PBIAS of the Romanenko (ROM) method by 82.12% and increased the NSE by 16.7%.

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

The importance of impact-based forecasting services, which can support decision-making, is being emphasized to reduce the damage of meteorological disasters, centered around the World Meteorological Organization. The Korea Meteorological Administration (KMA) began developing impact-based forecasting technology and warning services in 2018. This paper proposes statistical downscaling and bias correction methods for acquiring high-resolution meteorological data for the heat-wave impact forecast system operated by KMA. Hence, digital forecast data from KMA, with 5 km spatial resolution, were downscaled and corrected to a spatial resolution of 1 km using statistical interpolation methods. Cross-validation indicated the superior performance of the Gaussian process regression model (GPRM) technique with low root mean square error and percent bias values and high CC value. The GPRM technology had the lowest forecast error, especially during the hottest period in Korea. In addition, temperatures for land-use areas with low elevations and high activity, such as the urban, road, and agricultural areas, were high. It is essential to provide accurate heat exposure information at the screen level with high human activity. Spatiotemporally accurate heat exposure information can be used more realistically for risk management in agriculture, livestock and fishery, and for adjusting the working hours of outdoor workers in construction and shipbuilding.

A New Framework for Evaluation of Rainfall Temporal Variability through Principal Component Analysis, Hybrid Adaptive Neuro-Fuzzy Inference System, and Innovative Trend Analysis Methodology

In this research, a new framework has been introduced for rainfall temporal variability evaluation by using combination of monthly rainfall data sets in three synoptic stations, Principal Component Analysis (PCA), Adaptive Neuro Fuzzy Inference System (ANFIS), Grasshopper Optimization Algorithm (GOA), and Innovative Trend Analysis (ITA) methodology. The first five components were chosen as inputs of the soft-computing models, based on PCA. The GOA was used for training the ANFIS model, in order to estimate the monthly rainfall. The outputs of the ANFIS-GOA were compared to the rainfall estimates by ANFIS-Particle Swarm Optimization (ANFIS-PSO) and ANFIS-Genetic Algorithm (ANFIS-GA). Moreover, various statistical indices, such as mean absolute error (MAE), percent bias (PBIAS) and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the soft-computing models’ performance. Results indicated that ANFIS-GOA had higher accuracy in estimating the rainfall (values of MAE, NSE and PBIAS were 0.21, 0.92 and 0.16 for Mehrabad station, 0.16, 0.94 and 0.14 for Semnan station and 0.24, 0.91 and 0.17 for Noshahr station, respectively) in the testing phase. These values showed significant improvements (67.8%, 21% and 40% for Mehrabad station, 69.2%, 17.5% and 33.3% for Semnan station and 57.1%, 21.3% and 37% for Noshahr station) versus indices related to standalone ANFIS model, which reflected the supremacy and higher accuracy of ANFIS-GOA model in rainfall prediction for different climatic conditions. It was also concluded that the ANFIS-GOA, ANFIS-PSO, and ANFIS-GA models performed superior to the standalone ANFIS-based model, respectively. Furthermore, possible trends in monthly rainfall have been detected by ITA, which is a new graphical model. Results showed significant decreasing trends in January and July for all the rainfall values in Mehrabad station. By comparison of the results obtained from ANFIS and the hybrid models with observed data, it was also concluded that the trends of observed data were close to the ANFIS-GOA predictions.

A Comparison Of Step Tests To Predict Maximal Oxygen Consumption

INTRODUCTION: Of the many methods to estimate maximal oxygen consumption (VO2max), step tests provide a quick estimation of VO2max without expensive equipment. Few studies have investigated varied step height and cadence speed across a broad sample population. The purpose of this study was to generate and validate models to predict VO2max using three different step tests. METHODS: A sample of 162 adults, aged 18-79 years, completed a graded exercise test to assess VO2max. All three step tests were completed on a later day. The step tests differed in duration, number of stages, and step height. Test 1 (T1) was a two-stage, six-minute test, and had a six-inch step height, Test 2 (T2) was a three-stage, nine-minute test, and had a six-inch step height, and Test 3 (T3) was a three-stage, nine-minute test, and had an eight-inch step height. Stepping cadence increased with each stage and was prescribed based on age group (<40 years, 40-60 years, and >60 years). Recovery heart rate (HRrec) was measured every 30-seconds for two-minutes after each test. Model equations were obtained using hierarchal multiple regression analysis. Covariates for each model included age, sex, body mass, stepping cadence, and HRrec at all measured time intervals. The baseline model for each test consisted of age, sex, and body mass. Stepping cadence was entered in step two but was only significant for T3. Thirty-second HRrec was entered in the final step and was significant for all three tests. Validity was assessed using a Jackknife cross-validation method, allowing for root mean square error (RMSE) and percent bias to be calculated. RESULTS: The baseline model accounted for ~72% of the variance for predicting VO2max. Each model accounted for ~83% of VO2max variance and had an error ~4.15 ml.kg-1.min-1. The results of the jackknife analysis found that bias for all three tests was minimal (<0.001%) and the resulting bias-adjusted R2 was 0.825, 0.826, and 0.834 for T1, T2, and T3, respectively. Adjusted RMSE was 4.259, 4.223, and 4.102 ml.kg-1.min-1 for T1, T2, and T3, respectively. DISCUSSION: All three step tests account for ~83% of the variance of VO2max with an error around 4.2 ml.kg-1.min-1. As there is a minimal difference between tests, there is flexibility in the utilization of these step tests based on the need of the administrator and ability of the client.

Role of Regression Models in Bridge Pier Scour Prediction

Scour monitoring is an important concern in the design of any hydraulic structure. This study introduces the application of regression models in the prediction of scour depth around a bridge pier. Feedforward Neural Network (FFNN) and Multivariate Adaptive Regression Spline (MARS) models have been developed using different flow parameters. The flow parameters taken into consideration are the flow depth, flow velocity, pier diameter, and Froude's number. The FFNN models with different combinations of input parameters along with a simultaneous variation in the number of hidden neurons were developed to further increase the prediction accuracy. The best combination of hidden neurons and input parameters was selected and compared with the developed MARS model. Further, these models were compared with the selected empirical models to find out the best possible model for bridge pier scour prediction. All the developed regression models and selected empirical models were compared using standard statistical performance evaluation measures such as Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Percentage BIAS (PBIAS). The FFNN model developed with 4-input parameters performed better compared with other combinations of input parameters. The performance indices of all developed models show that as the input parameter increases, prediction accuracy also increases. A superior prediction accuracy was observed with FFNN model with 4-input parameters compared to MARS model and other selected empirical models.

Adapting the APEX Model to Simulate Detasseling in Inbred Corn for Hybrid Seed Production

HighlightsThe APEX model was adapted to simulate detasseling in inbred corn for hybrid seed production.The adapted model satisfactorily predicted detasseling effects on LAI, grain yield, and N content.An inbred corn model could be applied to evaluate best management practices for inbred corns.Abstract. Hybrid seed corn production comprises approximately 10% of the entire corn acreage in the U.S. Because of seed corn’s high economic value, and to maximize yields, seed corn growers often over-irrigate or apply nitrogen (N) fertilizers equal to or in excess of those recommended for commercial hybrid corn. Detasseling female corn inbred lines during hybrid corn seed production is critical to ensure the purity of seeds. In addition to the removal of tassels, detasseling also results in the removal of several leaves, which may lead to reduced seed yields. The objective of this study was to adapt the Agricultural Policy/Environmental eXtender (APEX) model to simulate the detasseling of female inbred corns in hybrid seed production. An APEX inbred corn model was developed to simulate the effects of detasseling and leaf removals on the development of inbred corn, leaf area index (LAI), grain yield, and grain N content. Growth characteristics of inbred corn were parameterized in APEX using data from a field study conducted in Nebraska. Overall, the APEX inbred corn model successfully predicted the effects of detasseling on LAI, grain yield, and grain N content under the conditions of the field experiment. There was a significant correlation between simulated and measured LAI (Pearson r = 0.86 and R2 = 0.74 at p = 0.05). The computed paired t-test and permutation test p-values indicated no significant differences between measured and simulated LAI. The mean simulation percent difference and percent bias (PBIAS) were respectively 4.2% and 4.7%, while measured and simulated LAI values had an average root mean square error (RMSE) of 0.14. The APEX model predicted grain yield with RMSE of 120 kg ha-1, mean simulation percent difference of 0.48%, and PBIAS of 0.26%. Like LAI, predicted grain yields exhibited significant correlation with field data (Pearson r = 0.99 and R2 = 0.97 at p = 0.05). Similarly, computed paired t-test and permutation test p-values indicated no significant differences between measured and simulated grain yields. Grain N content was predicted with RMSE of 6.75 kg N ha-1, mean simulation percent difference of 1.46%, and PBIAS of 2.45%. Predicted and measured grain N content values were correlated (Pearson r = 0.81 and R2 = 0.65 at p = 0.05), while the t-test and permutation test p-values indicated no significant differences between measured and predicted grain N content. Overall, detasseling effects were better predicted for grain yield than for LAI and grain N content as indicated by a Nash-Sutcliffe efficiency (NSE) of 0.92 compared to NSE values of 0.47 for LAI and 0.43 for grain N content. In conclusion, the hybrid seed corn industry could benefit from the application of inbred corn models that could allow growers to evaluate and identify optimal irrigation and N management practices for inbred corn, similar to the benefits that have been obtained with model simulation for commercial hybrid corn grain production systems. Keywords: APEX parameterization, Detasseling, Inbred corn, Leaf area index.

Hydrological Modeling of Upper OumErRabia Basin (Morocco), Comparative Study of the Event-Based and Continuous-Process HEC-HMS Model Methods

Human population growth and land-use changes raise demand and competition for water resources. The Upper OumErRabia River Basin is experiencing high rangeland and matorral conversion to irrigated agricultural land expansion. Given Morocco’s per capita water availability, River-basin hydrologic modelling could potentially bring together agricultural, water resources and conservation objectives. However, not everywhere have hydrological models considered events and continuous assessment of climatic data. In this study, HEC-HMS modelling approach is used to explore the event-based and continuous-process simulation of land-use and land cover change (LULCC) impact on water balance. The use of HEC-GeoHMS facilitated the digital data processing for coupling with the model. The basin’s physical characteristics and the hydro-climatic data helped to generate a geospatial database for HEC-HMS model. We analyzed baseline and future scenario changes for the 1980-2016 period using the SCS Curve-Number and the Soil Moisture Accounting (SMA) loss methods. SMA was coupled with the Hargreaves evapotranspiration method. Model calibration focused on reproducing observed basin runoff hydrograph. To evaluate the model performance for both calibration and validation, the Coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RSR) and Percent Bias (PBIAS) criteria were exploited. The average calibration NSE values were 0.740 and 0.585 for event-based (daily) and continuous-process (annual) respectively. The R2, RSR and PBIAS values were 0.624, 0.634 and +16.7 respectively. This is rated as good performance besides the validation simulations were satisfactory for subsequent hydrologic analyses. We conclude that the basin’s hydrologic response to positive and negative LULCC scenarios is significant both positive and negative scenarios. The study findings provide useful information for key stakeholders/decision-makers in water resources.

The accuracy of a point of care measurement of activated partial thromboplastin time in intensive care patients

Point of care (POC) devices are increasingly being used in intensive care units to obtain faster results. Data are limited on the performance of these devices in critically ill patients, especially those on heparin infusion. The objective of this study was to assess the agreement between POC activated partial thromboplastin time (APTT) and laboratory APTT results in patients on heparin infusion and to determine its impact on the clinical decisions regarding heparin dosage. We screened all patients admitted to the intensive care unit (ICU) at St George Hospital, Sydney, over a 7-month period and enrolled those who were receiving intravenous heparin infusion. We measured APTT by two methods: bedside POC test (Hemochron Junior Signature Plus) and central laboratory method (STA analyser). We used the Bland-Altman method to test the statistical agreement between the two measurements and Cohen's kappa statistic to test the clinical agreement regarding heparin dosing decision. A total of 176 paired samples from 44 patients (mean age 63 years, 64% males, mean APACHE 18) were analysed. The mean turnaround time for the point of care APTT result was significantly shorter than the central laboratory result (5.0±0.2 min vs 64.6±2.7 min, p<0.0001). Despite the statistically significant correlation, the overall agreement tested by the Bland-Altman method was poor. The 95% limits of agreement were widest (-27.266 to 64.791) and mean percentage bias was highest (24%) for the comparison between POC APTT using citrated blood and laboratory APTT. When POC APTT results of less than 90 seconds using whole blood were compared to laboratory APTT results, the limits of the agreement became narrower (-23.243 to 28.419), and the mean percentage bias decreased to 5%. The agreement between clinical decisions regarding heparin dosage based on the two methods was poor for plain and citrated blood (kappa 0.35 and 0.11, respectively). The POC APTT results were not sufficiently accurate for use in patients on heparin infusion compared to laboratory APTT assay.

The effects of climate change on groundwater recharge for different soil types of the west shore of Lake Urmia—Iran

Global warming and climate change are the most important phenomena of the century with an impact on the water resources throughout the world. Increases in temperature led to changes in amount and time of precipitation, as well as evaporation across the world; as a result, variation in quality and quantity of the surface water and groundwater resources emerged. In the present research, two models of water general circulation, namely, CanESM2 and HadCM3, were used for 30-year period (1990–1961). For assessing the models, the mean absolute error and ​Percent Bias (PBIAS) index. PBIAS index were employed and CanESM2 model was used for determining the required factors of Visual-Hydrologic Evaluation of Landfill Performance (HELP) in order to investigate the recharge rate of groundwater resources between 2011 and 2099 for five soil types in the west shore of Lake Urmia, Iran. Generally, the climatic change led to increase in recharge rate of groundwater for all soil types selected from Urmia Plain. The predicted values for recharge rate by the end of 2080 period for clay, gravel, sand, sandy clay, and silt clay are 15%, 9.3%, 13.9%, 13.2%, and 10.8% higher than that of recharge during the observational period for each soil.

Thrombin generation in different commercial sodium citrate blood tubes.

This study aimed to verify whether blood drawn into six different commercial coagulation tubes generated comparable results of thrombin generation. Blood was sequentially collected from 20 healthy subjects into different brand and draw volume 3.2% sodium citrate tubes (4.3 mL Sarstedt, 3.0 mL Greiner, 2.7 mL Becton Dickinson, 2.0 mL Kima, 1.8 mL Sarstedt and 1.0 mL Greiner). Thrombin generation was measured in plasma with the fully-automated ST Genesia analyzer using the weakest trigger (STG-BleedScreen). Different values of lag time (LT), time to reach thrombin peak (TP), thrombin peak height (PH) and endogenous thrombin potential (ETP) were commonly found in different tubes. Thrombin generation was the lowest in 4.3 mL Sarstedt tubes and the highest in 1.0 mL Greiner tubes. Other tubes displayed intermediate values. In multiple comparisons, LT was significantly different in 6/15 cases (40%), whilst PH, TP and ETP were significantly different in 14/15 (93%), 13/15 (87%) and 13/15 (87%) cases. The mean percent bias of LT, PH, TP and ETP ranged between -6% and +1%, -27% and +116%, -22% and +8%, and between -18% and +65%. The intra-assay imprecision of LT, PH, TP and ETP was exceeded in 0/15 (0%), 13/15 (87%), 6/15 (40%) and 13/15 (87%) comparisons. The correlation of LT, PH, TP and ETP values in different tubes ranged between 0.718-0.971, 0.570-0.966, 0.725-0.977 and 0.101-0.904. Blood collection for thrombin generation assays requires local standardization using identical tubes for brand and draw volume, and reference ranges calculated according to type of tubes.

Continuous monitoring of membrane lung carbon dioxide removal during ECMO: experimental testing of a new volumetric capnometer.

Extracorporeal membrane oxygenation constitutes a complex support modality, and accurate monitoring is required. An ideal monitoring system should promptly detect ECMO malfunctions and provide real-time information to optimize the patient-machine interactions. We tested a new volumetric capnometer which enables continuous monitoring of membrane lung carbon dioxide removal (V'CO2ML), to help in estimating the oxygenator performance, in terms of CO2 removal and oxygenator dead space (VDsML). This study was conducted on nine pigs undergoing veno-arterial ECMO due to cardiogenic shock after induced acute myocardial infarction. The accuracy and reliability of the prototype of the volumetric capnometer (CO2RESET™, by Eurosets srl, Medolla, Italy) device was evaluated for V'CO2ML and VDsML measurements by comparing the obtained measurements from the new device to a control capnometer with the sweep gas values. Measurements were taken at five different levels of gas flow/blood flow ratio (0.5-1.5). Agreement between the corresponding measurements was taken with the two methods. We expected that 95% of differences were between d - 1.96s and d + 1.96s. In all, 120 coupled measurements from each device were obtained for the V'CO2ML calculation and 40 for the VDsML. The new capnometer mean percentage bias (95% confidence interval limits of agreement) was 3.86% (12.07-4.35%) for V'CO2ML and 2.62% (8.96-14.20%) for VDsML. A negative proportional bias for V'CO2ML estimation with the new device was observed with a mean of 3.86% (12.07-4.35%). No correlations were found between differences in the coupled V'CO2ML and VDsML measurements and the gas flow/blood flow ratio or temperature. Coupled measurements for V'CO2ML showed strong correlation (rs = 0.991; p = 0.0005), as did VDsML calculations (rs = 0.973; p = 0.0005). The volumetric capnometer is reliable for continuous monitoring of CO2 removal by membrane lung and VDsML calculations. Further studies are necessary to confirm these data.