Optimal Calibration Research Articles

Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation.

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a between ±0.03 °C with standard deviation () less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37–38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.

Subkilometer scale ionospheric studies at the SKA-Low site, using MWA extended baselines

The ambitious scientific goals of SKA require a matching capability for calibration of instrumental and atmospheric propagation contributions as functions of time, frequency and position. The development of novel calibration algorithms to meet these requirements is an active field of research. In this work {we aim to characterize} these, focusing on the spatial and temporal structure scales of the ionospheric effects; ultimately, these provide the guidelines for designing the optimum calibration strategy. We used empirical ionospheric measurements at the site where the SKA-Low will be built, using MWA Phase-2 Extended baseline observations and the station-based Low-frequency Excision of Atmosphere in Parallel (LEAP) calibration algorithm. We have done this via direct regression analysis of the ionospheric screens and by forming the full and detrended structure functions. We found that 50% of the screens show significant non-linear structures at scales >0.6km that dominate at >2km, and 1% show significant sub-minute temporal changes, providing that there is sufficient sensitivity. Even at the moderate sensitivity and baseline lengths of MWA, non-linear corrections are required at 88 MHz during moderate-weather and at 154 MHz during poor weather, or for high SNR measurements. Therefore we predict that improvements will come from correcting for higher-order defocusing effects in observations with MWA Phase-2, and further with new developments in MWA Phase-3. Because of the giant leap in sensitivity, the correction for complex ionospheric structures will be mandatory on SKA-Low, for both imaging and tied-array beam formation.

Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control.

The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration.

Optimal calibration of a tuned liquid column damper (TLCD) for rotating wind turbine blades

Blade edgewise vibration is a growing concern for modern megawatt wind turbines, as it may significantly shorten the blade lifetime. In this paper, optimal calibration (tuning) of a blade-mounted tuned liquid column damper (TLCD) using closed-form formulas is investigated. A 2-DOF model for the blade-TLCD system is first established, acting as the basis for analytical derivation of the optimal tuning formulas. Two different frequency-tuning criteria are employed, one based on the equal dynamic amplification at two neutral frequencies and the other one based on the equal modal damping ratio of the two free vibration modes. Two optimal frequency-tuning (frequency ratio) formulas having similar expressions are derived based on these two criteria, and both formulas are dependent on the rotational speed, the mounting position, the blade edgewise eigenfrequency and the TLCD prescribed parameters. Next, an explicit expression for the optimal equivalent TLCD damping ratio is obtained by equalizing the dynamic amplification at three excitation frequencies, and it also exhibits dependence on the rotational speed and the edgewise eigenfrequency. A closed-form formula for the optimal TLCD head loss coefficient is then proposed, providing a straightforward method to determine the optimal value without resorting to iteration. Comparisons of the two optimal frequency-tuning formulas are conducted in terms of the dynamic amplification, the root loci of the 2-DOF system, and the parametric sensitivity, using data from the NREL 5MW reference wind turbine. The results indicate that these two formulas lead to very similar optimal design of the blade-mounted TLCD as well as the damper performance, although the 1st formula only guarantees the equal dynamic amplification while the 2nd one only guarantees the equal modal damping ratio. The set of closed-form formulas developed in this study facilitates a convenient and robust design of blade-mounted-TLCD for suppressing edgewise vibrations, and forms the basis for developing semi-active control algorithms.

Multicenter External Validation of a Nomogram for Predicting Positive Prostate-specific Membrane Antigen/Positron Emission Tomography Scan in Patients with Prostate Cancer Recurrence

BackgroundA nomogram has recently been developed to predict 68Ga-labeled prostate-specific membrane antigen (PSMA)-11 positron emission tomography (PET)/computed tomography (PSMA-PET) results in recurrent prostate cancer (PCa) patients. ObjectiveTo perform external validation of the original nomogram in a multicentric setting. Design, setting, and participantsA total of 1639 patients who underwent PSMA-PET for prostate-specific antigen (PSA) relapse after radical therapy were retrospectively included from six high-volume PET centers. The external cohort was stratified according to clinical setting categories: group 1: first-time biochemical recurrence (n = 774); group 2: PSA relapse after salvage therapy (n = 499); group-3: biochemical persistence after radical prostatectomy (n = 210); and group-4: advanced-stage PCa before second-line systemic therapies (n = 124). InterventionPSMA-PET in recurrent PCa. Outcome measurements and statistical analysisPSMA-PET detection rate was assessed in the overall population and in each subgroup. A multivariable logistic regression model was produced to evaluate the predictors of a positive scan. The performance characteristics of the model were assessed by quantifying the predictive accuracy (PA) according to model calibration. The Youden’s index was used to find the best nomogram’s cutoff. Decision curve analysis (DCA) was implemented to quantify the nomogram’s clinical net benefit. Results and limitationsIn the external cohort, the overall detection rate was 53.8% versus 51.2% in the original population. At multivariate analysis, International Society of Urological Pathology grade group, PSA, PSA doubling time, and clinical setting were independent predictors of a positive scan (all p ≤ 0.02). The PA of the nomogram was identical to the original model (82.0%); the model showed an optimal calibration curve. The best nomogram’s cutoff was 55%. In the DCA, the nomogram revealed clinical net benefit when the threshold nomogram probabilities were ≥20%. The retrospective design is a major limitation. ConclusionsThe original nomogram exhibited excellent characteristics on external validation. The incidence of a false negative scan can be reduced if PSMA-PET is performed when the predicted probability is ≥20%. Patient summaryA nomogram has been developed to predict prostate-specific membrane antigen/positron emission tomography (PSMA-PET) results for recurrent prostate cancer (PCa). The nomogram represents an easy tool in the decision-making process of recurrent PCa.

Automation of solving problems of optimization of parameters of metrological maintenance system of measuring equipment

Automation of the metrological service of the enterprise allows to reduce the use of labor to perform tasks of maintenance of measuring equipment and reduce staff errors. At the same time, the process of creating appropriate automated systems requires solving a number of problems related to the need to optimize the parameters of the designed system to ensure its highest efficiency. This article attempts to solve the problem of covering various models in the synthesis and study of simulation computer models that actively use the database of measuring instruments of enterprises. Developed and software implemented a model that allows interactive mode, taking into account the characteristics coming from the calibration laboratories of the service, and given economic and metrological criteria, to determine the values of optimal calibration and repair intervals, the optimal number of inspections in the repair interval and other parameters. The dependence of FTA reliability indicators on the quality of metrological maintenance and repair was investigated by simulation. The influence of inter - repair and inter - calibration intervals on economic efficiency of metrological service is investigated. As a result, the dependences are obtained, which also allow to investigate the influence of service quality parameters on complex reliability indicators. The use of research results allows in the dialog mode of the automated workplace of the metrologist to solve problems of optimization of parameters of metrological service on eco-nomic criteria and restrictions of concrete production on indicators of metrological reliability of measuring instruments, to carry out diagnostics and to forecast metrological reliability of measuring instruments. measurement accuracy and reduce the likelihood of hidden failures.

A Decoupled Calibration Method Based on the Multi-Output Support Vector Regression Algorithm for Three-Dimensional Electric-Field Sensors.

Aiming at the problem that the measured accuracy of the electric field intensity which is affected by the coupling interference by sensor output signal from the component of a three dimensional electric field, the causes of the coupling error was analyzed, and a decoupled calibration method based on support vector regression algorithm for three-dimensional electric field sensor is proposed. The solution of the decoupled calibration matrix was regarded as a multi-objective optimization process, and the optimal decoupling calibration matrix was obtained by the ν-SVR algorithm. The complex inverse calculation of the matrix was avoided, and the calculation error was reduced. A rotary calibration device was designed to accurately measure the angle between the induction electrode of the sensor and the electric-field vector, and an accurate calculation model of the theoretical electric field was established. The experimental results showed that the error between the calculated and theoretical values of the electric-field components obtained by the proposed method were smaller than those obtained by the traditional inverse matrix calibration method, the accuracy of the calibration was improved, the rationality of the calibration method was proven, and the accuracy of the three-dimensional electric-field intensity measurements was further improved.

A Clinical Predictive Nomogram for Traumatic Brain Parenchyma Hematoma Progression.

IntroductionAcute traumatic intraparenchymal hematoma (tICH) expansion is a major cause of clinical deterioration after brain contusion. Here, an accurate prediction tool for acute tICH expansion is proposed.MethodsA multicenter hospital-based study for multivariable prediction model was conducted among patients (889 patients in a development dataset and 264 individuals in an external validation dataset) with initial and follow-up computed tomography (CT) imaging for tICH volume evaluation. Semi-automated software was employed to assess tICH expansion. Two multivariate predictive models for acute tICH expansion were developed and externally validated.ResultsA total of 198 (22.27%) individuals had remarkable acute tICH expansion. The novel Traumatic Parenchymatous Hematoma Expansion Aid (TPHEA) model retained several variables, including age, coagulopathy, baseline tICH volume, time to baseline CT time, subdural hemorrhage, a novel imaging marker of multihematoma fuzzy sign, and an inflammatory index of monocyte-to-lymphocyte ratio. Compared with multihematoma fuzzy sign, monocyte-to-lymphocyte ratio, and the basic model, the TPHEA model exhibited optimal discrimination, calibration, and clinical net benefits for patients with acute tICH expansion. A TPHEA nomogram was subsequently introduced from this model to facilitate clinical application. In an external dataset, this device showed good predicting performance for acute tICH expansion.ConclusionsThe main predictive factors in the TPHEA nomogram are the monocyte-to-lymphocyte ratio, baseline tICH volume, and multihematoma fuzzy sign. This user-friendly tool can estimate acute tICH expansion and optimize personalized treatments for individuals with brain contusion.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40120-021-00306-8.

Optimal Shrinkage Estimation of Predictive Densities Under α-Divergences

We consider the problem of estimating the predictive density in a heteroskedastic Gaussian model under general divergence loss. Based on a conjugate hierarchical set-up, we consider generic classes of shrinkage predictive densities that are governed by location and scale hyper-parameters. For any α-divergence loss, we propose a risk-estimation based methodology for tuning these shrinkage hyper-parameters. Our proposed predictive density estimators enjoy optimal asymptotic risk properties that are in concordance with the optimal shrinkage calibration point estimation results established by Xie, Kou, and Brown (2012) for heteroskedastic hierarchical models. These α-divergence risk optimality properties of our proposed predictors are not shared by empirical Bayes predictive density estimators that are calibrated by traditional methods such as maximum likelihood and method of moments. We conduct several numerical studies to compare the non-asymptotic performance of our proposed predictive density estimators with other competing methods and obtain encouraging results.

A hybrid deep learning framework for urban air quality forecasting

Deep learning models address air quality forecasting problems far more effectively and efficiently than the traditional machine learning models. Specifically, Long Short-Term Memory networks (LSTMs) constitute a significant breakthrough in understanding the complex sequential behavioral dependencies of the time series. Further, LSTM models justify well with the speed–accuracy tradeoff, among other deep learning models. However, there are several limitations of such deep learning models. Firstly, the addition of multiple hidden layers, on the one hand, improves the performance but, on the other hand, requires extensive hardware and computation capabilities. Secondly, most of the previous works that utilized LSTMs for air quality forecasting do not consider the issue of optimal hyperparameter calibration. While deciding the gradient, network learning parameters should be so fixed such that the model does not underfit or overfit. To address these issues, a stochastic optimization algorithm, mimicking the pattern of flocking birds, is utilized to find the most fitting solution in the parameter search space. Particle swarm optimization setup primarily models varying particles representing parameters to reach an optimum state. Furthermore, the Spatio-temporal instabilities of LSTM models are addressed in this work using preprocessing, segmentation and feature engineering to understand seasonal and trend characteristics along with the Spatio-temporal correlation of the time series. The proposed model is employed on the air quality dataset of 15 locations in India. A variety of experiments are performed to prove the superiority of the proposed method. Firstly, a comparison with traditional sequential models and deep learning models is done. Secondly, results are further evaluated over several existing benchmark dataset samples. Results suggest that the proposed method outperforms existing forecasting models when evaluated over a variety of performance metrics.

Development and validation of a nomogram for predicting the probability of new vertebral compression fractures after vertebral augmentation of osteoporotic vertebral compression fractures

IntroductionNew vertebral compression fractures (NVCFs) are adverse events after vertebral augmentation of osteoporotic vertebral compression fractures (OVCFs). Predicting the risk of vertebral compression fractures (VCFs) accurately after surgery is still a significant challenge for spinal surgeons. The aim of our study was to identify risk factors of NCVFs after vertebral augmentation of OVCFs and develop a nomogram.MethodsWe retrospectively reviewed the medical records of patients with OVCFs who underwent percutaneous vertebroplasty (PVP) or percutaneous kyphoplasty (PKP). Patients were divided into the NVCFs group and control group, base on the patients with or without NVCFs within 2 years follow-up period after surgery. A training cohort of 403 patients diagnosed in our hospital from June 2014 to December 2016 was used for model development. The independent predictive factors of postoperative VCFs were determined by least absolute shrinkage and selection operator (LASSO) logistic regression, univariate analysis and multivariate logistic regression analysis. We provided a nomogram for predicting the risk of NVCFs based on independent predictive factors and used the receiver operating characteristic curve (ROC), calibration curve, and decision curve analyses (DCA) to evaluated the prognostic performance. After internal validation, the nomogram was further evaluated in a validation cohort of 159 patients included between January 2017 and June 2018.ResultsOf the 403 patients in the training cohort, 49(12.16%) were NVCFs at an average of 16.7 (1 to 23) months within the 2 years follow-up period. Of the 159 patients in the validation cohort, 17(10.69%) were NVCFs at an average of 8.7 (1 to 15) months within the 2 years follow-up period. In the training cohort, the proportions of elderly patients older than 80 years were 32.65 and 13.56% in the NVCFs and control group, respectively (p = 0.003). The percentages of patients with previous fracture history were 26.53 and 12.71% in the NVCFs and control group, respectively (p = 0.010). The volume of bone cement were 4.43 ± 0.88 mL and 4.02 ± 1.13 mL in the NVCFs and Control group, respectively (p = 0.014). The differences have statistical significance in the bone cement leakage, bone cement dispersion, contact with endplate, anti-osteoporotic treatment, post-op Cobb angle and Cobb angle restoration characteristics between the two groups. The model was established by multivariate logistic regression analysis to obtain independent predictors. In the training and validation cohort, the AUC of the nomogram were 0.882 (95% confidence interval (CI), 0.824-0.940) and 0.869 (95% CI: 0.811-0.927), respectively. The C index of the nomogram was 0.886 in the training cohort and 0.893 in the validation cohort, demonstrating good discrimination. In the training and validation cohort, the optimal calibration curves demonstrated the coincidence between prediction and actual status, and the decision curve analysis demonstrated that the full model had the highest clinical net benefit across the entire range of threshold probabilities.ConclusionA nomogram for predicting NVCFs after vertebral augmentation was established and validated. For patients evaluated by this model with predictive high risk of developing postoperative VCFs, postoperative management strategies such as enhance osteoporosis-related health education and management should be considered.

Optimizing calibration settings for accurate water equivalent path length assessment using flat panel proton radiography

Objective: Proton range uncertainties can compromise the effectiveness of proton therapy treatments. Water equivalent path length (WEPL) assessment by flat panel detector proton radiography (FP-PR) can provide means of range uncertainty detection. Since WEPL accuracy intrinsically relies on the FP-PR calibration parameters, the purpose of this study is to establish an optimal calibration procedure that ensures high accuracy of WEPL measurements. To that end, several calibration settings were investigated. Approach: FP-PR calibration datasets were obtained simulating PR fields with different proton energies, directed towards water-equivalent material slabs of increasing thickness. The parameters investigated were the spacing between energy layers (ΔE) and the increment in thickness of the water-equivalent material slabs (ΔX) used for calibration. 30 calibrations were simulated, as a result of combining ΔE = 9, 7, 5, 3, 1 MeV and ΔX = 10, 8, 5, 3, 2, 1 mm. FP-PRs through a CIRS electron density phantom were simulated, and WEPL images corresponding to each calibration were obtained. Ground truth WEPL values were provided by range probing multi-layer ionization chamber simulations on each insert of the phantom. Relative WEPL errors between FP-PR simulations and ground truth were calculated for each insert. Mean relative WEPL errors and standard deviations across all inserts were computed for WEPL images obtained with each calibration. Main results: Large mean and standard deviations were found in WEPL images obtained with large ΔE values (ΔE = 9 or 7 MeV), for any ΔX. WEPL images obtained with ΔE ≤ 5 MeV and ΔX ≤ 5 mm resulted in a WEPL accuracy with mean values within ±0.5% and standard deviations around 1%. Significance: An optimal FP calibration in the framework of this study was established, characterized by 3 MeV ≤ ΔE ≤ 5 MeV and 2 mm ≤ ΔX ≤ 5 mm. Within these boundaries, highly accurate WEPL acquisitions using FP-PR are feasible and practical, holding the potential to assist future online range verification quality control procedures.

Sensitivity Analysis of Calibration Methods and Factors Effecting the Statistical Nature of Radiation Measurement

The scintillator detectors are recalibrated against the datasheet given by the manufacturer. Optimal and mutual dependent values of (a) high voltage at PMT (Photomultiplier Tube), (b) amplifier gain, (c) average time to count the radiation particles (set by operator), and (d) number of instances/sample number are estimated. Total 5: two versions of Central Limit Theorem (CLT), (3) industry preferred Pulse Width Saturation, (4) calibration based on MPPC coupled Gamma-ray detector, and (5) gross method are used. It is shown that the CLT method is the most optimal method to calibrate the detector and its respective electronics couple. An inverse modeling-based Computerized Tomography method is used for verification. It is shown that statistically averaging results are more accurate and precise data than mode and median if the data is not skewed and a random number of samples are used during the calibration process. It is also shown that the average time to count the radiation particle is the most important parameter affecting the optimal calibration setting for precision and accurate measurements of gamma radiation.

Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis.

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM

AbstractParameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require model runs and are sensitive to internal variability noise, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only model runs and can accommodate internal climate variability. The approach consists of three stages: (a) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (b) an emulation stage emulates the parameter‐to‐data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (c) a sampling stage approximates the Bayesian posterior distributions by sampling the GP emulator with MCMC. We demonstrate the feasibility and computational efficiency of this calibrate‐emulate‐sample (CES) approach in a perfect‐model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from synthetic data generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties.

A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models

This article develops a frequentist solution to the functional calibration problem, where the value of a calibration parameter in a computer model is allowed to vary with the value of control variables in the physical system. The need of functional calibration is motivated by engineering applications where using a constant calibration parameter results in a significant mismatch between outputs from the computer model and the physical experiment. Reproducing kernel Hilbert spaces (RKHS) are used to model the optimal calibration function, defined as the functional relationship between the calibration parameter and control variables that gives the best prediction. This optimal calibration function is estimated through penalized least squares with an RKHS-norm penalty and using physical data. An uncertainty quantification procedure is also developed for such estimates. Theoretical guarantees of the proposed method are provided in terms of prediction consistency and consitency of estimating the optimal calibration function. The proposed method is tested using both real and synthetic data and exhibits more robust performance in prediction and uncertainty quantification than the existing parametric functional calibration method and a state-of-art Bayesian method.

Quantitative Detection of Components in Polymer-Bonded Explosives through Near-Infrared Spectroscopy with Partial Least Square Regression.

The components in polymer-bonded explosive X, including cyclotetramethylene-tetranitramine, paraffin, and polytetrafluoroethylene, were determined using near-infrared (NIR) spectroscopy. Using partial least squares as the multivariate calibration method, quantitative calibration models for components in X were verified internally and externally. The possible combinations of eight general spectral pretreatment methods and different bands of the scanning spectral region (12,500–4000 cm–1) were established. The models were analyzed, evaluated, and optimized via the fitting effect. The data were combined with the mathematical meaning of the model spectral pretreatment methods and the chemical significance of the modeling spectral bands. Prediction performance offered optimal quantitative calibration models. Paired bilateral Student’s t tests show that there is no significant difference between the values obtained by NIR and chemical analysis methods, and the NIR method has good accuracy. Moreover, the precision of the NIR method is better than that of the chemical method, and the analysis time is reduced from 2 days to a few minutes.

Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy

In this study, a classification model was established based on near-infrared spectroscopy and random forest method to accurately distinguish three samples of Schisandra chinensis from different habitats. At the same time, the feasibility of fast and effective prediction of polysaccharide contents in Schisandra chinensis by near-infrared spectroscopy combined with chemometrics was evaluated. In this paper, phenol sulfuric acid method was used to determine the content of total polysaccharides in samples, and partial least squares regression algorithm was used to link the spectral information with the reference value. Different spectral pretreatment methods were used to optimize the model to improve its predictability and stability. The results showed that random forest could distinguish these samples accurately, with an accuracy of 97.47%. In the established prediction model, the RMSEC of the optimal model calibration set is 0.0012, and the coefficient of determination R is 0.9976. The RMSEP of prediction set is 0.0024, the coefficient of determination R is 0.9922, and the RPD is 11.36. In general, the method has good stability and applicability, which provides a new analytical method for the identification of Schisandra chinensis origin and quality evaluation.

Tolerance to geometrical inaccuracies in CBCT systems: A comprehensive study.

The last decades have seen the consolidation of the cone-beam CT (CBCT) technology, which is nowadays widely used for different applications such as micro-CT for small animals, mammography, dentistry, or surgical procedures. Some CBCT systems may suffer mechanical strains due to the heavy load of the x-ray tube. This fact, together with tolerances in the manufacturing process, lead to different types of undesirable effects in the reconstructed image unless they are properly accounted for during the reconstruction. To obtain good quality images, it is necessary to have a complete characterization of the system geometry including the angular position of the gantry, the source-object and detector-object distances, and the position and pose of the detector. These parameters can be obtained through a calibration process done periodically, depending on the stability of the system geometry. To the best of our knowledge, there are no comprehensive works studying the effect of inaccuracies in the geometrical calibration of CBCT systems in a systematic and quantitative way. In this work, we describe the effects of detector misalignments (linear shifts, rotation, and inclinations) on the image and define their tolerance as the maximum error that keeps the image free from artifacts. We used simulations of four phantoms including systematic and random misalignments. Reconstructions of these data with and without errors were compared to identify the artifacts introduced in the reconstructed image and the tolerance to miscalibration deemed to provide acceptable image quality. Visual assessment provided an easy guideline to identify the sources of error by visual inspection of the artifactual images. Systematic errors result in blurring, shape distortion and/or reduction of the axial field of view while random errors produce streaks and blurring in all cases, with a tolerance which is more than twice that of systematic errors. The tolerance corresponding to errors in position of the detector along the tangential direction, that is, skew (<0.2°) and horizontal shift (<0.4mm), is tighter than the tolerance to those errors affecting the position along the longitudinal direction or the magnification, that is, vertical shift (<2mm), roll (<1.5°), tilt (<2°), and SDD (<3mm). We present a comprehensive study, based on realistic simulations, of the effects on the reconstructed image quality of errors in the geometrical characterization of a CBCT system and define their tolerance. These results could be used to guide the design of new systems, establishing the mechanical precision that must be achieved, and to help in the definition of an optimal geometrical calibration process. Also, the thorough visual assessment may be valuable to identify the most predominant sources of error based on the effects shown in the reconstructed image.

Selecting optimal calibration samples using proximal sensing EM induction and γ-ray spectrometry data: An application to managing lime and magnesium in sugarcane growing soil

Calcium (Ca) and magnesium (Mg) are essential for growth of sugarcane leaves and roots, as well as respiration and nitrogen metabolism, respectively. To assist farmers decide suitable application rates of lime and Mg fertiliser, respectively, the Australian sugarcane industry established the Six-Easy-Steps nutrient management guidelines based on topsoil (0–0.3 m) Ca (cmol(+) kg−1) and Mg (cmol(+) kg−1). Given the heterogeneous nature of soil, digital soil mapping (DSM) methods can be employed to allow for the precise application rate to be determined. In this study, we examine statistical models (i.e., ordinary kriging [OK], linear mixed model [LMM], quantile regression forests [QRF], support vector machine [SVM], and Cubist regression kriging [CubistRK]) to predict topsoil and subsoil (0.6–0.9) Ca and Mg, employing digital data in combination (i.e., proximal sensing electromagnetic induction (EMI) [e.g., 1mPcon, 1mHcon, etc.], gamma-ray [γ-ray] spectrometry [i.e., TC, K, U and Th] and digital elevation model [DEM] derivatives). We also investigate various sampling designs (i.e., spatial coverage [SCS], feature space coverage [FSCS], conditioned Latin hypercube [cLHS] and simple random sampling [SRS]) and calibration sample size (i.e., n = 180, 150, 120, 90, 60 and 30). The predictions are assessed using Lin's concordance correlation coefficient (LCCC) and ratio of performance to interquartile distance (RPIQ) with an independent validation dataset (i.e., n = 40). The best results were for prediction of subsoil Mg, utilising CubistRK (LCCC = 0.82) with the largest calibration sample size (n = 180), followed by LMM (0.79), SVM (0.76), QRF (0.70) and OK (0.65). This was generally the case for topsoil and subsoil Ca. We also conclude that no single sampling design was universally better, and 180 samples were necessary for predicting topsoil Ca and Mg with moderate agreement (0.65 < LCCC < 0.80). However, with FSCS, a minimum of 120 samples were enough to calibrate a CubistRK model and achieve substantial (LCCC > 0.80) agreement for predicting subsoil Ca and Mg. With respect to soil use and management according to the Six-Easy-Steps, the sandy soil in the north and south require large application rate of lime (3.5 t/ha) and Mg (125 kg/ha), respectively. Conversely, varying amounts of fertiliser rates of lime (2.0, 1.5 and 1 t/ha) and Mg (50 kg/ha) were recommended where Vertosols were previously mapped.