Good Calibration Results Research Articles

Modeling the effects of land use/land cover changes on river runoff using SWAT models: A case study of the Danjiang River source area, China

Land use/land cover (LULC) is a crucial factor that directly influences the hydrology and water resources of a watershed. In order to assess the impacts of LULC changes on river runoff in the Danjiang River source area, we analyzed the characteristics of LULC data for three time periods (2000, 2010, and 2020). The LULC changes during these periods were quantified, and three Soil and Water Assessment Tool (SWAT) models were established and combined with eight LULC scenarios to quantitatively analyze the effects of LULC changes on river runoff. The results revealed a decrease in the cropland area and an increase in the forest, grassland, and urban land areas from 2000 to 2020. Grassland, forest, and cropland collectively accounted for over 94% of the total area, and conversions among these land types were frequent. The SWAT models constructed based on the LULC data demonstrated good calibration and validation results. Based on the LULC data in three periods, the area of each LULC type changed slightly, so the simulation results were not significantly different. In the subsequent LULC scenarios, we found that the expansion of cropland, grassland, and urban areas was associated with increased river runoff, while an increase in forest area led to a decrease in river runoff. Among the various LULC types, urban land exerted the greatest influence on changes in river runoff. This study establishes three SWAT models and combines multiple LULC scenarios, which is novel and innovative. It can provide scientific basis for the rational allocation of water resources and the optimization of LULC structure in the Danjiang River source area.

Application of Near-Infrared Spectroscopy to Detect Modification of the Cation Exchange Properties of Soils from European Beech and Silver Fir Forest Stands in Poland.

This study investigated changes in the composition of the cation exchange capacity of soil samples caused by the acid leaching of soil cations under laboratory conditions. Furthermore, near-infrared (NIR) spectroscopy was used to evaluate the properties of forest soils. The potential influence of the species composition of stands (beech and fir) was also investigated. Eighty soil samples from the topsoil of plots located in central Poland were analyzed. Soil samples were leached 0 (non-leached), 5, 10, and 15 times and then analyzed to determine the contents of cations (Al3+, Ca2+, K+, and Mg2+), the total carbon content, and the pH. From NIR spectra obtained by scanning 54 samples and measurement results for soil sample properties, a calibration model was developed. The model was validated using 26 independent samples. The results showed that acid leaching decreased the pH of soil solutions and the carbon content. The amounts of Al3+, Ca2+, K+, and Mg2+ decreased with an increasing number of leaching treatments, but most leaching had occurred after five treatments. Data analysis showed that leaching with hydrochloric acid depleted alkaline cations and Al3+ in the soil, which reduced the stability of organic matter, causing its release. Modification of ion exchange properties is observable based on the analysis of the NIR spectra. Good calibration results were achieved for all tested parameters (R2C ≥ 0.89). The best validation results were obtained for Al3+ and C contents under fir stands, and for the pH and Al3+ content of soils under beech stands (R2V > 0.8). However, the differences between the measured and estimated mean values of the investigated soil were relatively small (no significant difference, p > 0.05). The species composition of stands (beech and fir) had no impact on the developed mathematical models. Soil assessment using NIR spectroscopy allowed calibration models to be obtained, which were successfully used to calculate soil properties at a much lower cost and in a much shorter time compared with other laboratory methods. The results of the paper affirmed that using a relatively small number of samples (3-4) to calculate an average of soil content properties provided satisfactory results.

Fourier-transform infrared spectroscopy for characterization of liquid protein solutions: A comparison of two sampling techniques

Whereas industrial laboratory systems for Fourier-Transform Infrared (FTIR) spectroscopic characterization have been available for decades, dedicated FTIR solutions for in-line process control or for industrial at-line analysis of protein quality are still scarce. Thus, the present study aimed to compare two industrially viable sampling techniques, namely attenuated total reflectance (ATR) and dry film FTIR spectroscopy, for qualitative and quantitative analysis of liquid protein solutions. For this purpose, two sample sets were acquired: set 1 consisted of 95 protein hydrolysate samples produced in the laboratory from salmon processing by-products, and set 2 consisted of 133 protein hydrolysate samples obtained from an industrial processing plant of poultry by-products. Average molecular weights (AMW) and Brix measurements of protein hydrolysates were used as reference values for obtaining regression models. AMW was predicted with higher accuracy and lower estimation errors using dry film FTIR compared to ATR measurements for both sample types. The difference in predictive performance was higher in poultry hydrolysates because the protein and tissue complexities are higher than in salmon hydrolysates, and information from the amide I region in the FTIR spectra is needed to provide good calibration results. ATR, on the other hand, was more reliable for the prediction of Brix values of protein hydrolysates. The study also showed that FTIR spectroscopy can be used to predict protein quality features of industrially produced protein hydrolysates with a sensitivity of high industrial relevance. Therefore, developing industrially viable portable instruments for at-line process analysis in the food, feed, and biotech industries is a natural next development stage.

A Predictive Model of Early Readmission for Patients with Heart Failure

Background: Readmission within 30 days of discharge for heart failure (HF) has become a challenging public health issue. Predicting the risk of 30-day readmission may assist clinicians in making individualized treatment plans for HF patients. Methods: A total of 2254 patients were enrolled in this study. The risk predictors associated with 30-day readmission were selected using the least absolute shrinkage and the selection operator regression model. The performance of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow (HL) test, and decision curve analysis (DCA). Results: The 30-day all-cause readmission rate was 7.1%. Thirteen clinical parameters were identified as the risk predictors, including age, cystatin C, albumin, red cell distribution width coefficient variation, neutrophils, N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponin T, myoglobin, sex, dyslipidaemia, left ventricular ejection fraction, left ventricular end-diastolic dimension, and atrial fibrillation. The nomogram showed good discrimination, with an area under the ROC curve of 0.653 (95% confidence interval: 0.608–0.698) and good calibration results (HL test p = 0.328). The DCA showed that the nomogram would have good clinical utility. Conclusions: This predictive model based on clinical data makes it simple for clinicians to assess the 30-day HF readmission risk.

Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?

Recently the application of spectral reflection data for the prediction of crop parameters for applications in precision agriculture, such as green area index (GAI), total aboveground dry matter (DM), and total aboveground nitrogen content (N content) increases. However, the usability of vegetation indices (VI) for the prediction of crop parameters is strongly limited by the fact that most VI calibrations are only valid for specific crops and growth periods. The results of the presented study based on the differentiation of primary (main driver of the reflectance signal) and secondary (not directly related to reflectance signal) crop parameters. For GAI prediction, a universal (without crop-specific parametrization) simple ratio vegetation index (SR) provided good calibration (R2 adj. = 0.90, MAE = 0.32, rMAE = 22%) and evaluation results (MAE = 0.33, rMAE = 18%). The disentanglement of primary and secondary traits allowed the development of a functional two-step model for the estimation of the N content during vegetative growth (MAE = 19.2 g N m−1, rMAE = 44%). This model was based on fundamental, crop-specific relationships between the crop parameters GAI and N content. Additionally, an advanced functional approach was tested enabling the whole-season prediction of DM and confirming a reliable GAI estimation throughout the whole growing season (R2 = 0.89–0.93).

Microwave method based on curve fitting method for high-precision collision frequency diagnosis

In this work, the results of plasma microwave transmission diagnosis were analyzed. According to the attenuation and phase shift of the electromagnetic wave propagating in the plasma, the electron density and collision frequency of the plasma can be diagnosed. Since part of the electromagnetic wave is reflected or diffracted when propagating in the plasma, and is not absorbed by the plasma, and this part of the attenuation is still included in the measured attenuation, the attenuation is distorted. Therefore, a curve fitting method is proposed to remove the attenuation caused by the plasma reflection, thereby improving the accuracy of the diagnosis of the collision frequency. The calibration effect of this method on plasmas with different electron densities and collision frequencies is analyzed, and a diagnostic frequency band with good calibration results is given. The curve fitting method is verified by experiment and simulation. After adopting the newly proposed method, the diagnosis accuracy of collision frequency can be increased by 30%. This method can be widely used in various types of plasma diagnosis and provides a new idea for plasma diagnosis.

Homogeneous synthesis of hydroxyethyl acrylate catalyzed by organochromium(III) complexes: Kinetics and ligand effect

The ring-opening reaction of ethylene oxide with acrylic acid catalyzed by organochromium(III) complexes is the most promising route for the industrial synthesis of 2-hydroxyethyl acrylate. However, the heterogeneous batch process with highly explosive gaseous ethylene oxide possesses difficulties on the process optimization and safety. To resolve these problems, homogeneous synthesis process is developed by pressurization and in-line mixing of ethylene oxide and acrylic acid. Stop-flow microreactor is applied to investigate the homogeneous reaction characteristics with high efficiency. A reduced kinetic model on the basis of the simplified reaction network is proposed with good calibration results. The catalytic process of ring-opening reaction is distinguished from the non-catalytic one according to the kinetic model parameters. After recognizing the activation process of catalyst during reaction and fulfilling it before reaction, the ligand effect is quantitatively studied by carefully comparing the catalytic features of four types of organochromium catalysts, chromium(III) acetate, chromium(III) 2-ethylhexanoate, chromium(III) picolinate and chromium(III) acetylacetonate, indicating a high efficiency catalyst might contain such ligand (or support) that possesses weak interaction with Cr metal center as well as considerable steric hindrance.

New method for calibrating optical dissolved oxygen sensors in seawater based on an intelligent learning algorithm.

Oxygen sensors based on luminescence quenching are the most commonly used instruments for in situ measurement in seawater due to their accuracy and long-term stability. The calibration method of the sensor is crucial for their accuracy. Conventional methods exhibit some defects, such as strict control of calibration conditions and cumbersome and time-consuming operation. To improve calibration operation and obtain good calibration results, a new calibration method was proposed for the optical dissolved oxygen sensor in seawater based on an intelligent learning algorithm. The sensor to be calibrated and the reference sensor were deployed in the water for synchronous measurements. The calibration system consisted of a temperature-regulated device and a sampling method to improve calibration operation. An intelligent learning algorithm was used to train the calibration data and model the oxygen response of the sensor. Calibration and test results in both laboratory and field showed that the new calibration method is feasible and efficient. It is highly significant for sensor development and in situ measurement in seawater.

Nomogram to predict survival outcome of patients with veno-arterial extracorporeal membrane oxygenation after refractory cardiogenic shock

ABSTRACT Objective : This study aims to develop a nomogram model to predict the survival of refractory cardiogenic shock (RCS) patients that received veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Methods A total of 235 and 209 RCS patients were supported with VA-ECMO from January 2018 to December 2019 in Guangdong Provincial People’s Hospital, and from January 2020 to December 2020 in four third-grade and class-A hospitals were a development cohort (DC) and validation cohort (VC), respectively. Finally, 137 and 98 patients were included in the DC and VC. Multivariate logistic regression analysis was used to identify variables, and only these independent risk factors were used to establish the nomogram model. The receiver operating characteristic curve (ROC), calibration plot, decision curve, and clinical impact curves were used to evaluate the nomogram’s discriminative ability, predictive accuracy, and clinical application value. Results Pre-ECMO cardiogenic arrest (pre-ECA), lactate (Lac), inotropic score (IS), and modified nutrition risk in the critically ill score (mNUTRIC score) were incorporated into the nomogram. This showed good discrimination in the DC, with an area under ROC (AUROC) and a 95% confidence interval (CI) of 0.959 (0.911–0.986). The AUROC (95% CI) of the VC was 0.928 (0.858–0.971). The calibration plots of the DC and VC presented good calibration results. The decision curve and clinical impact curve of the nomogram provided improved benefits for RCS patients. Conclusions This study established a prediction nomogram composed of pre-ECA, Lac, IS, and mNUTRIC scores that could help clinicians to predict the survival probability at hospital discharge precisely and rapidly for RCS patients that received VA-ECMO.

Comparative Non-Darcian Modeling of Subsurface Preferential Flow Experimental Observations in a Riparian Buffer

HighlightsHigh ecohydrological activity drives macropore prevalence in riparian buffers.An abundance of macropore flow (MF) was confirmed in a field riparian buffer in Kenya.Source-response (SR) and multilayer kinematic diffusive wave (MKDW) MF models are compared.A novel MKDW modeling framework efficiently identifies and predicts preferential flow in riparian buffers.Abstract. The significant ecohydrological activity typical of riparian buffers makes them potential hotspots of macropores, i.e., structured preferential flow pathways, through the soil vadose zone. The prevalence of these preferential pathways can allow transported contaminants to bypass the soil matrix and quickly reach a seasonal shallow water table and the adjacent surface waterbody. This quick transport can ultimately limit the role of riparian buffers for runoff pollution control. Currently, there are no management tools that incorporate macropore flow (MF) when assessing riparian buffer performance. The objective of this study was to experimentally quantify and mathematically simulate macropore flow and arrival time in a riparian buffer under field conditions. Three infiltration experiments were conducted with a grid of 20 time-domain transmission (TDT) dielectric soil moisture sensors along a field riparian buffer transect in Kenya to quantify the presence of macropore flow and to test two non-Darcian soil MF models, including the source-responsive (SR) model and the modified kinematic-dispersive wave (MKDW) model developed in this study, by adding a user-defined multilayer convection scheme and a new hysteresis function between water flux and content. The abundance of MF in the riparian buffer was corroborated experimentally. Modeling results showed that the MKDW model was an efficient (average NSE of 0.937 and 0.721 for calibration and testing, respectively), flexible, and robust method to identify and represent non-linear and non-sequential MF signals at any soil depth and antecedent conditions. The SR model was computationally inexpensive and provided good calibration results (NSE = 0.867) but required piecemeal recalibration of the travel time and maximum water content at each layer and yielded lower performance in testing. The Akaike (AIC) and Bayesian (BIC) information criteria showed that MKDW outperformed SR when accounting for the trade-off between model complexity and efficiency. The results support further research focused on independent characterization of model parameters at the field scale, and the inclusion of MKDW in holistic riparian buffer management and decision-support tools such as VFSmod. Keywords: Kinematic-dispersive wave, Macropore flow, Numerical modeling, Preferential flow, Riparian vadose zone.

Determining ammonia nitrogen decay rate of Malaysian river water in a laboratory flume

There is lack of information regarding ammonia nitrogen (AN), (i.e. NH3-N) decay rate of river water in tropical regions like Malaysia. AN decay rate is a very important kinetic parameter to estimate NH3-N, nitrate nitrogen (NO3-N) and dissolved oxygen concentrations of river water by using computer models. This study presents determination of ammonia nitrogen decay rate of river water in the tropical environment of Malaysia. A laboratory flume was used to conduct twelve experiments. The flume was used to represent the turbulent condition of a typical river. Ammonia nitrogen decay rate for the tropical environment of Malaysia was observed to be between 0.194 and 0.554 per day. Median value of AN decay rate was 0.26 per day, which is slightly lower than the global median value of 0.295 per day. To check the accuracy of flume experiments, the AN decay rate of Pusu River obtained from the flume experiment was used to calibrate and validate ammonia nitrogen concentration of the river by using water quality analysis and simulation program (WASP). Very good calibration and validation results were achieved, which substantiated the accuracy of the flume experiments.

Cross-ratio–based line scan camera calibration using a planar pattern

A flexible new technique is proposed to calibrate the geometric model of line scan cameras. In this technique, the line scan camera is rigidly coupled to a calibrated frame camera to establish a pair of stereo cameras. The linear displacements and rotation angles between the two cameras are fixed but unknown. This technique only requires the pair of stereo cameras to observe a specially designed planar pattern shown at a few (at least two) different orientations. At each orientation, a stereo pair is obtained including a linear array image and a frame image. Radial distortion of the line scan camera is modeled. The calibration scheme includes two stages. First, point correspondences are established from the pattern geometry and the projective invariance of cross-ratio. Second, with a two-step calibration procedure, the intrinsic parameters of the line scan camera are recovered from several stereo pairs together with the rigid transform parameters between the pair of stereo cameras. Both computer simulation and real data experiments are conducted to test the precision and robustness of the calibration algorithm, and very good calibration results have been obtained. Compared with classical techniques which use three-dimensional calibration objects or controllable moving platforms, our technique is affordable and flexible in close-range photogrammetric applications.

A systematic MEMS sensor calibration framework

Abstract. In this paper we present a systematic method to determine sets of close to optimal sensor calibration points for a polynomial approximation. For each set of calibration points a polynomial is used to fit the nonlinear sensor response to the calibration reference. The polynomial parameters are calculated using ordinary least square fit. To determine the quality of each calibration, reference sensor data is measured at discrete test conditions. As an error indicator for the quality of a calibration the root mean square deviation between the calibration polynomial and the reference measurement is calculated. The calibration polynomials and the error indicators are calculated for all possible calibration point sets. To find close to optimal calibration point sets, the worst 99% of the calibration options are dismissed. This results in a multi-dimensional probability distribution of the probably best calibration point sets. In an experiment, barometric MEMS (micro-electromechanical systems) pressure sensors are calibrated using the proposed calibration method at several temperatures and pressures. The framework is applied to a batch of six of each of the following sensor types: Bosch BMP085, Bosch BMP180, and EPCOS T5400. Results indicate which set of calibration points should be chosen to achieve good calibration results.

Option calibration of exponential Lévy models: confidence intervals and empirical results

Observing prices of European put and call options, we calibrate exponential L\'evy models nonparametrically. We discuss the efficient implementation of the spectral estimation procedures for L\'evy models of finite jump activity as well as for self-decomposable L\'evy models. Based on finite sample variances, confidence intervals are constructed for the volatility, for the drift and, pointwise, for the jump density. As demonstrated by simulations, these intervals perform well in terms of size and coverage probabilities. We compare the performance of the procedures for finite and infinite jump activity based on options on the German DAX index and find that both methods achieve good calibration results. The stability of the finite activity model is studied when the option prices are observed in a sequence of trading days.

An Analysis of the Heston Stochastic Volatility Model: Implementation and Calibration Using Matlab

This paper analyses the implementation and calibration of the Heston Stochastic Volatility Model. We first explain how characteristic functions can be used to estimate option prices. Then we consider the implementation of the Heston model, showing that relatively simple solutions can lead to fast and accurate vanilla option prices. We also perform several calibration tests, using both local and global optimization. Our analyses show that straightforward setups deliver good calibration results. All calculations are carried out in Matlab and numerical examples are included in the paper to facilitate the understanding of mathematical concepts.

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

Abstract. The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011–2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49–0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors.

Maximum likelihood calibration of a magnetometer using inertial sensors

Magnetometers and inertial sensors (accelerometers and gyroscopes) are widely used to estimate 3D orientation. For the orientation estimates to be accurate, the sensor axes need to be aligned and the magnetometer needs to be calibrated for sensor errors and for the presence of magnetic disturbances. In this work we use a grey-box system identification approach to compute maximum likelihood estimates of the calibration parameters. An experiment where the magnetometer data is highly disturbed shows that the algorithm works well on real data, providing good calibration results and improved heading estimates. We also provide an identifiability analysis to understand how much rotation is needed to be able to solve the calibration problem.

Update and external validation of a head and neck cancer prognostic model

The purpose of this study was to update and external validation of a prognostic model that is able to predict the survival probability of newly diagnosed patients with head and neck cancer. Our original prognostic model is based on historical data of 1371 patients with primary head and neck cancer, diagnosed and treated in the Leiden University Medical Center, between 1981 and 1999. The model contains the predictors age, sex, tumor site, TNM-classification, prior tumors, and comorbidity. We updated the model with follow-up data until January 2010. The updated model was then externally validated in 598 patients with head and neck cancer from the Siteman Cancer Center/Barnes-Jewish Hospital, St. Louis, Missouri. Median follow-up was 5.5 years (range, 0-25.5). Only 2.5% of patients were lost to follow-up. During follow-up 1099 patients (80.2%) passed away. Discrimination of the updated prognostic model was good, with a C-index of 0.73 after internal validation. The discrimination was slightly lower in the external validation set (C-index, 0.69). The predicted 2-year and 5-year survival rates correlated satisfactorily with some slight deviations from the perfect calibration line. We used recent follow-up information to update the Leiden prognostic model for newly diagnosed patients with head and neck cancer. The model showed acceptably good calibration and discrimination results in internal and external validation procedures.

Calibration of Hydraulic Model in Rea-Life Water Distribution System

The most uncertain input parameters that often considered for calibration in water distribution system hydraulic model are pipe roughness coefficients and nodal demands. Both pipe roughness coefficients and nodal demands are considered to be calibrated in the calibration process, which works alternately. The calibration model was formulated as a constrained optimization problem. The entire head losses under different loading conditions are introduced in the objective function to guide the calibration direction, which can make consistent calibration effects on different loading conditions. The calibration model uses real-coded genetic algorithm along with a general network solver (EPANET 2.0) to adjust pipe roughness coefficients and nodal demands multipliers until the preset criteria are meet. The approach was applied in the calibration of a real-life water distribution system hydraulic model in China, which takes three loading conditions (max, min and average hour) into consider. The results show that the approach works well in achieving good calibration results, which match field observation in a reasonable level and meet engineering requirements.

Calibrating parametric exponential Lévy models to option market data by incorporating statistical moments priors

We investigate a parametric method for calibrating European option pricing using the state-of-art exponential Lévy models. We propose a derivative-free calibration method constrained by four observable statistical moments (mean, variance, skewness and kurtosis) from underlying time series to conquer the ill-posed inverse problem and to incorporate priors on observable statistical moments. We present a numerical implementation scheme for calibrating the exponential Lévy models and show that it can resolve the instability of the inverse problems empirically and can produce good calibration results. In particular, we apply our approach to real market data sets of S&P 500 call options with significantly better performance.