Original Calibration Research Articles

One year stability of Cernox™ and DT-670-SD silicon diode cryogenic temperature sensors operated at 77 K

Cryogenic temperature sensors are commonly used in space applications where long-term operation, stability, and reliability/survivability are critical. For temperature sensors, these criteria are most often evaluated by either performing a high number of thermal shocks between temperature extremes or performing an accelerated life test at an elevated temperature. Neither of these tests addresses the situation of continued operation at a cold temperature. This work examines the stability of cryogenic temperature sensors stored and monitored in an open liquid nitrogen dewar for a period of one year. Three models were tested in this work, Cernox™ resistance temperature sensors models CX-1080-SD and CX-1070-SD, and the silicon diode temperature sensor model DT-670-SD. All three models are manufactured by Lake Shore Cryotronics, Inc. and are commonly used in aerospace applications due to their robustness. Both Cernox™ models were stable at a level with a standard deviation varying from 5 mK to 19 mK and overall range of offsets varying from ±10 mK to ±54 mK. The model DT-670-SDs were stable at a level with a standard deviation of 10 mK and overall range of offsets of ±24 mK. Recalibration comparisons show the CX-1080-SDs to be within ±10 mK of the original calibration from 20 K to 325 K, the model CX-1070-SDs to be within ±5 mK below 10 K and within ±25 mK up to 325 K of the original calibration, and the model DT-670-SDs to be within ±20 mK of the original calibration from 2 K to 325 K. Experimental details and lessons learned are detailed.

Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra platform, which was launched in 1999, has three separate subsystems: a visible and near-infrared (VNIR) radiometer, a shortwave-infrared radiometer, and a thermal-infrared radiometer. The ASTER VNIR bands have been radiometrically corrected for approximately 14 years by the sensor degradation curves estimated from the onboard calibrator according to the original calibration plan. However, this calibration by the onboard calibrator encountered a problem; specifically, it is inconsistent with the results of vicarious calibration and cross calibration. Therefore, the ASTER VNIR processing was applied by the radiometric degradation curves calculated from the results of three calibration approaches, i.e., the onboard calibrator, the vicarious calibration, and the cross calibration since February 2014. Even though the current degradation curves were revised, the inter-band and lunar calibrations show some inconsistencies owing to the different traceability in the bands by different calibration approaches. In this study, the current degradation curves and their problems are explained, and the new curves that are derived from the vicarious calibration with lunar calibration are discussed. The new degradation curves that have the same traceability in the bands will be used for future ASTER VNIR processing.

Developing an Item Bank for Progress Tests and Application of Computerized Adaptive Testing by Simulation in Medical Education

Progress Test (PT) is a form of assessment that simultaneously measures ability levels of all students in a certain educational program and their progress over time by providing them with same questions and repeating the process at regular intervals with parallel tests. Our objective was to generate an item bank for the PT and to examine the possible fit of CAT for PT application. This study is a descriptive study. 1206 medical students participated. During the analysis of the psychometric properties of PT item bank, “the Rasch model for dichotomous items was used”. Several CAT simulations were performed by applying various stopping rules of different standard errors. CAT simulation estimates were compared with the estimates generated from the original calibration of the Rasch model where all items were included. After Rasch analysis, a unidimensional PT item bank consisting of 103 items was obtained. The item bank reliability was calculated as 0.77 with Person Separation Index (PSI) and Kuder-Richardson Formula 20 (KR-20). A high correlation between θ estimations obtained from paper-and-pencil (θRM) and CAT applications (θCAT) was detected for simulation conditions ([N(0,1)] and [N(0,3)]) at the end of our analysis. In CAT, estimation can be made with an average of 14 questions (reduced 86,4%) and 17 questions (reduced 83,4%) [for N(0,1) and [N(0,3) respectively] with reliability of 0,75. This study reveals that it is possible to develop an appropriate item bank for the PT, and the difficulty of administering large number of items in PT can be scaled down by incorporating CAT application.

Optimization of a diesel engine calibration for operating with a residual glycerol-derived biofuel

The new European directive for the promotion of renewable energy mandates an increase in the share of advanced and waste-based biofuels in the transport sector. In this study, an advanced glycerol-derived biofuel was used as a component of a ternary blend, denoted as o·bio®. This blend included 27.4 %v/v of fatty acid glycerol formal ester, 69.6 %v/v of fatty acid methyl ester and 3 %v/v of acetals obtained as a by-product of the fatty acid glycerol formal ester production process (which were proved to improve cold-flow properties). Finally, o·bio® was blended with diesel fuel at a content of 20 %v/v. Two operating conditions based on usual driving modes were selected, where the engine calibration could be re-optimized after the change of fuel, corresponding to vehicle velocities of 50 and 70 km/h. Since the main effect of the blend used is to reduce particulate matter emissions, exhaust gas recirculation was increased and injection was delayed, so that the initial benefits in particulate matter emissions could be re-distributed into benefits in both particulate matter and nitrogen oxides (NOx) emissions. From a combined analysis of the particulate matter–NOx trade-off and trying to limit the negative effect of delaying injection on fuel consumption, the final proposal was to set an additional 6% exhaust gas recirculation at 50 km/h and an additional 3% exhaust gas recirculation at 70 km/h, while delaying injection 2 °CA after top dead center at both vehicle operating conditions with respect to the original calibration. The use of the blend along with the optimization of the engine calibration is expected to reduce particulate matter and NOx emissions by around 50% with a vehicle speed condition of 50 km/h and to reduce particulate matter and NOx emissions by around 30% and 40% at 70 km/h with respect to diesel fuel emissions.

Uncertainty in the R2 Calibration Curve for High Iron Concentration

Introduction: MRI assessment of liver iron concentration (LIC) has become the standard of care for monitoring iron chelation strategies. The Ferriscan R2 quantification of LIC has been validated against 338 biopsies in two large cohorts, demonstrates good interstudy reproducibility, and has strong quality control practices. However, its cost remains a challenge for many institutions, making iron measurements by R2* acquisitions more attractive. Several studies comparing LIC by R2* and by Ferriscan R2 have identified substantial bias between R2* and R2 LIC estimates. We postulated that the original Ferriscan R2 calibration overestimates LIC at high iron concentrations, causing the disagreement between the two techniques. Methods: We identified three studies comparing single echo R2 to liver biopsy (362 biopsies). We used a publicly available digital capture program(www.arizona-software.ch/graphclick) to capture the values of R2 for each liver iron concentration level. We fit the data to the existing FDA-approved calibration and compared Bland Altman agreement to liver biopsy against two other calibration curves. The first was data-derived from the aggregate liver biopsy results using a linear fit in log transformed LIC and R2 coordinates (resulting in a Power Law fit). The second was derived from a spline fit to data generated from previously published Monte Carlo simulation model from our laboratory. This model generates "synthetic" R2-iron pairs over the entire physiological range of iron overload, using ideal mathematical approximations to the MRI imaging physics. Results: Panel A demonstrates R2-LIC pairs for the aggregate liver biopsy data and the three evaluated R2-iron calibration curves. Using Bland-Altman analysis, both the Power Law (0.5 ± 4.9 mg/g) and Simulation (1.7 ± 5.1 mg/g) calibrations had 17-22% lower variance than the Ferriscan calibration (-1.1 ± 6.2 mg/g), p<0.0001 by two sample variance test. More importantly, the predicted LIC values diverge dramatically for R2 values greater than 155 Hz (roughly 14 mg/g) (Panel B). For example, a R2 value of 300 Hz will yield an estimated LIC of 48 mg/g by Ferriscan but only 34 mg/g and 32.5 mg/g by the Power Law and Simulation calibrations respectively. Discussion: These data suggest that Ferriscan overestimates true liver iron concentration for R2 values exceeding 155 Hz, with the differences growing geometrically. The calibration error is sufficient to completely explain the differences between LIC by R2* and Ferriscan R2 described in previously studies. Importantly, any future attempt to "calibrate" R2* or other MRI methods against Ferriscan need to account for this bias. Fortunately, the clinical impact of the observed calibration bias is manageable. Patients with LIC below 14 mg/g are being accurately risk stratified. Patients with LIC greater than 14 mg/g in their liver are universally at high risk and should be treated aggressively, regardless. Serial trends in LIC-R2 values also lessen the impact of calibration bias. Both Ferriscan and R2* LIC estimates and provide accurate estimates of chelator efficiency on an annual basis. Further, both Ferriscan and liver R2* LIC estimates are more accurate than liver biopsy in tracking changes in total body iron concentration. However, hematologists should treat Ferriscan predicted LIC values of more than 14 mg/g with appropriate caution and integrate the predicted LIC values with the patient's entire clinical picture to avoid over-reacting to large changes in predicted LIC. ACKNOWLEDGEMENTS This work supported by the National Institutes of Health, Diabetes, Digestive and Kidney Diseases (1R01DK097115-01A1). DISCLOSURES Dr. Wood serves as a consultant to Apopharma, Biomedinformatics, Bluebirdbio, Celgene, Ionis Pharmaceuticals, Imago Biosciences, Silence Therapeutics, and World Care Clinical. Figure Disclosures Wood: National Institutes of Health: Research Funding; Celgene: Consultancy; Apopharma: Consultancy; WorldcareClinical: Consultancy; BluebirdBio: Consultancy; Imago Biosciences: Consultancy; BiomedInformatics: Consultancy; Philips Healthcare: Research Funding. Coates:celgene: Consultancy, Honoraria, Other: steering committee of clinical study; vifor: Consultancy, Honoraria; apo pharma: Consultancy, Honoraria, Speakers Bureau; agios pharma: Consultancy, Honoraria.

Ridge regression combined with model complexity analysis for near infrared (NIR) spectroscopic model updating

Near infrared (NIR) calibration models can be used to predict those samples that fall into the calibration domain. However, unmodeled sources of variance within new samples, such as instrumental drift and sample variations, would result in unreliable predictions of product properties. In this case, the model updating approach will be very important. It involves the recalculation of model coefficients with the addition of a few new samples to the original calibration samples. Considering the cost of collecting new samples and their reference measurements, normally few samples are used for model updating. Therefore, it is necessary to balance the mutual importance of old and new samples by weighting the new samples. Compared with the weight of new samples, the model parameter in the regression method has much more influence on the performance of an updated model. The bias/variance tradeoff (L curve) has been applied to the selection of the model parameter. However, this approach contains a degree of subjectivity and does not always obtain satisfactory models. To solve the model selection problem, a new method named model complexity analysis (MCA) was proposed in this work. According to MCA, the 2-norm of the regression coefficients vector of an updated model (||β∗||2) should be smaller than that of the original model (||β||2). The ratio of ||β∗||2 over ||β||2 was defined as ε, which should be in the range of 0–1. For a given value of ε, the model parameter can be uniquely determined by the following equation:min(abs(||β∗||2−ε*||β||2)).The influence of the number of new samples and their representativity on the selection of ε was studied. In this work, ridge regression (RR) was used for model updating, because it is a regression method based on 2-norm constraint. Results show that the proposed method based on MCA could select a reliable RR parameter. RR-MCA shows excellent performance on three NIR datasets used in this work.

Relative Humidity on Mars: New Results From the Phoenix TECP Sensor.

In situ measurements of relative humidity (RH) on Mars have only been performed by the Phoenix (PHX) and Mars Science Laboratory (MSL) missions. Here we present results of our recalibration of the PHX thermal and electrical conductivity probe (TECP) RH sensor. This recalibration was conducted using a TECP engineering model subjected to the full range of environmental conditions at the PHX landing site in the Michigan Mars Environmental Chamber. The experiments focused on the warmest and driest conditions (daytime) because they were not covered in the original calibration (Zent et al., 2010, https://doi.org/10.1029/2009JE003420) and previous recalibration (Zent et al., 2016, https://doi.org/10.1002/2015JE004933). In nighttime conditions, our results are in excellent agreement with the previous 2016 recalibration, while in daytime conditions, our results show larger water vapor pressure values. We obtain vapor pressure values in the range ~0.005–1.4 Pa, while Zent et al. (2016, https://doi.org/10.1002/2015JE004933) obtain values in the range ~0.004–0.4 Pa. Our higher daytime values are in better agreement with independent estimates from the ground by the PHX Surface Stereo Imager instrument and from orbit by Compact Reconnaissance Imaging Spectrometer for Mars. Our results imply larger day‐to‐night ratios of water vapor pressure at PHX compared to MSL, suggesting a stronger atmosphere‐regolith interchange in the Martian arctic than at lower latitudes. Further, they indicate that brine formation at the PHX landing site via deliquescence can be achieved only temporarily between midnight and 6 a.m. on a few sols. The results from our recalibration are important because they shed light on the near‐surface humidity environment on Mars.

A new calibration model transferring strategy maintaining the predictive abilities of NIR multivariate calibration model applied in different batches process of extraction

Extraction process plays an important role in the pretreatment of Flos Lonicerae Japonicae for Qingkailing oral liquid and is also one of the most popular preparation processes of traditional Chinese medicine (TCM). In order to monitor the content of API in the extraction solution, a PLS model was developed based on NIR spectroscopy. However, due to the raw materials’ natural variability introduced into the extraction process of TCM or variations in spectral measurement conditions, it was unable to foresee the changes in the spectral response for new source batches, which could render a calibration model invalid. The development of a reliable multivariate calibration model for each new source batch is usually time-consuming and costly. In this work, a new calibration model transfer strategy which combined direct orthogonal signal correction with slope and bias correction (DOSC-SBC) was proposed and applied to maintain the predictive abilities of the original calibration model for new source batches. Performance of this model transfer strategy has been validated by two new individual batches which raw materials were from different sources, and the predictive relative errors of the samples from the above new batches decreased from 15.67% and 19.82% to 8.35% and 6.80% respectively after using DOSC-SBC strategy, demonstrating the good performance of the proposed model transfer strategy.

Improving the Evaluation of Adult Mental Disorders in the Criminal Justice System With Computerized Adaptive Testing.

The authors sought to develop and validate a suite of dimensional measures of psychiatric syndromes for use in a criminal justice population. The previously validated Computerized Adaptive Test-Mental Health (CAT-MH) was administered to a sample of 475 defendants in the Cook County Bond Court. Item-level data were used to determine which test items exhibited differential item functioning in this population compared with the population used for the original calibration. After removal of nine items that exhibited differential item functioning from the CAT-MH, correlations between scores based on the original calibration from a nonjustice-involved population and the newly computed scores based on a sample of bond court defendants showed a correlation coefficient of r=0.96 to r=0.99. With a slight modification of the original CAT-MH, the tool was successfully used to measure severity of depression, anxiety, mania and/or hypomania, suicidality, and substance use disorder in an English- and Spanish-speaking criminal justice population.

Response to Comment on “Earth and Moon impact flux increased at the end of the Paleozoic”

Hergarten et al . interpret our results in terms of erosion and uncertain calibration, rather than requiring an increase in impact flux. Geologic constraints indicate low long-term erosion rates on stable cratons where most craters with diameters of ≥20 kilometers occur. We statistically test their proposed recalibration of the lunar crater ages and find that it is disfavored relative to our original calibration.

Predicting ocean waves along the US east coast during energetic winter storms: sensitivity to whitecapping parameterizations

Abstract. The performance of two methods for quantifying whitecapping dissipation incorporated in the Simulating Waves Nearshore (SWAN) wave model is evaluated for waves generated along and off the US east coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use “Komen” and “Westhuysen” to denote these two approaches). Observations of wave parameters and frequency spectra at four National Data Buoy Center (NDBC) buoys are used to evaluate simulation results. Model–data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower. Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the east coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the “slanting fetch” effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by available source terms (wind input, whitecapping, and quadruplet) in models and their interaction. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation.

Interstellar dust in the solar system: model versus in situ spacecraft data

Context. In the early 1990s, contemporary interstellar dust penetrating deep into the heliosphere was identified with the in situ dust detector on board the Ulysses spacecraft. Later on, interstellar dust was also identified in the data sets measured with dust instruments on board Galileo, Cassini, and Helios. Ulysses monitored the interstellar dust stream at high ecliptic latitudes for about 16 yr. The three other spacecraft data sets were obtained in the ecliptic plane and cover much shorter time intervals. Aims. To test the reliability of the model predictions, we compare previously published in situ interstellar dust measurements, obtained with these four spacecraft, with predictions of an advanced model for the dynamics of interstellar dust in the inner solar system (Interplanetary Meteoroid environment for EXploration; IMEX). Methods. Micrometer and sub-micrometer-sized dust particles are subject to solar gravity, radiation pressure and the Lorentz force on a charged dust particle moving through the interplanetary magnetic field. These forces lead to a complex size-dependent flow pattern of interstellar dust in the planetary system. The IMEX model was calibrated with the Ulysses interstellar dust measurements and includes these relevant forces. We study the time-resolved flux and mass distribution of interstellar dust in the solar system. Results. The IMEX model agrees with the spacecraft measurements within a factor of 2–3, including time intervals and spatial regions not covered by the original model calibration with the Ulysses data set. The model usually underestimates the dust fluxes measured by the space missions which were not used for the model calibration, i.e. Galileo, Cassini, and Helios. Conclusions. A unique time-dependent model, IMEX is designed to predict the interstellar dust fluxes and mass distributions for the inner and outer solar system. The model is suited to study dust detection conditions for past and future space missions.

Calibration of Granier-Type (TDP) Sap Flow Probes by a High Precision Electronic Potometer.

Thermal dissipation probe (TDP) method (Granier, 1985) is widely used to estimate tree transpiration (i.e., the water evaporated from the leaves) because it is simple to build, easy to install, and relatively inexpensive. However, the universality of the original calibration has been questioned and, in many cases, proved to be inaccurate. Thus, when the TDP is used in a new species, specific tests should be carried out. Our aim was to propose a new method for improving the accuracy of TDP on trees in the field. Small hazelnut trees (diameter at breast height 5 cm) were used for the experiment. The response of TDP sensors was compared with a reference water uptake measured with an electronic potometer system provided with a high precision liquid flow meter. We equipped three stems where we measured the sap flow density, the sapwood area (by using fuchsine), the total tree water uptake (reference), and the main meteorological parameters during summer 2018. Results confirmed that the original Granier’s calibration underestimated the effective tree transpiration (relative error about −60%). We proposed a new equation for improving the measurement accuracy within an error of about 4%. The system proposed appeared an easier solution compared to potted trees and particularly suitable for orchards, thus contributing to improve the irrigation management worldwide.

Quantifying the Impact of Uncertainty within the Longitudinal Dispersion Coefficient on Concentration Dynamics and Regulatory Compliance in Rivers

AbstractThe one‐dimensional advection dispersion equation (1D ADE) is commonly used in practice to simulate pollutant transport processes for assessment and improvement of water quality conditions in rivers. Various studies have shown that the longitudinal dispersion coefficient used within the 1D ADE is influenced by a range of hydraulic and geomorphological conditions. This study aims to quantify the impact and importance of the parameter uncertainty associated with the longitudinal dispersion coefficient on modeled pollutant time‐concentration profiles and its implications for meeting compliance with water quality regulations. Six regression equations for estimating longitudinal dispersion coefficients are evaluated, and commonly used evaluation criteria were assessed for their suitability. A statistical evaluation of the regression equations based on their original calibration data sets resulted in percent bias (PBIAS) values between −47.01% and 20.78%. For a case study, uncertainty associated with the longitudinal dispersion coefficient was propagated to time‐concentration profiles using 1D ADE and Monte Carlo simulations, and 75% confidence interval bands of the pollutant concentration versus time profiles were derived. For two studied equations, the measured peak concentration values were above the simulated 87.5th percentile, and for the other four equations it was close to the 87.5th percentile. Subsequent uncertainty propagation analysis of four diverse rivers show the potential considerable impact on concentration‐duration‐frequency‐based water quality studies, with 1D ADE modeling producing predictions of quality standard compliance which varied over hundreds of kilometers.

Experimental investigation of a variable compression ratio system applied to a gasoline passenger car engine

Gasoline spark-ignited internal combustion engines for passenger cars are still a major part of the worldwide market share. With legislation pushing towards a lower carbon footprint, many technological alternatives are under scrutiny. Among them, one of the most promising is a variable compression ratio (VCR). This paper aims to experimentally prove the potential of this technology when applied to a state-of-the-art 3-cylinder, direct injection, turbocharged engine fuelled with regular RON 95 gasoline. A 2-stage variable compression ratio (VCR) system was applied to a series engine: The VCR prototype engine features compression ratio stages of 9.56:1 and 12.11:1. The engine has undergone only minor modifications, remaining substantially original. Dynamometer tests with in-cylinder pressure analysis were performed. The engine was tested over an operational map up to 4000 rpm and up to 26 bar BMEP, with stoichiometric operation and original ECU calibration. A fuel consumption reduction of up to 4% was found at part-load, where combustion phasing tended to be optimum. Penalties in fuel consumption at high loads were present due to knock-limited spark-timing. Results show that an even higher compression ratio variation range would further reduce of fuel consumption in both low and high load.

Re-evaluation of the New Oxalic Acid standard with AMS

The New Oxalic Acid standard (NOX) was introduced in 1980 to replace the primarily Oxalic Acid (OX) standard, which was calibrated against OX utilizing decay counting measurements. However today, most radiocarbon measurements are performed with accelerator mass spectrometry that is more powerful as significantly less material is required and the measurement time is reduced. Here we give an update on the calibration of the NOX against OX. Over 500 radiocarbon measurements on either OX or NOX were compiled to an exact calibration, that outperforms the original calibration in precision. Our measurements are in agreement with the first measurements performed in the 80’s, demonstrating that AMS is capable of reproducing highest precision radiocarbon measurements on a routine basis.

Applied spatial modelling for retail planning in tourist resorts

Purpose The purpose of this paper is to demonstrate that applied spatial modelling can inform the planning, delivery and evaluation of retail services, offering improvements over traditional retail impact assessment (RIA), especially within localities which experience seasonal fluctuations in demand. Design/methodology/approach The paper first describes a new theoretically informed tourist-based spatial interaction model (SIM) which has been custom-built and calibrated to capture the dynamics of the grocery sector in Cornwall, UK. It tests the power of the model to predict store performance for stores not used in the original calibration process, using client data for a new store development. The model is operationalised for the evaluation of various retail development schemes, demonstrating its contribution across a full suite of location decision making application areas. Findings The paper demonstrates that this highly disaggregate modelling framework can provide considerable insight into the local economic and social impacts of new store developments, rarely addressed in the retail location modelling literature. Practical implications Whilst SIMs have been widely used in retail location research by the private sector, the paper shows that such a model can have considerable value for public sector retail planning, a sector which seemed to have abandoned such models from the 1980s onwards, replacing them with often very limited and crude RIA. Originality/value The ability to review the forecasting capabilities of a model (termed post-investment review) are very rare in academic research. This paper offers new evidence that SIMs can support the RIA process.

Successful validation of the CAT-MH Scales in a sample of Latin American migrants in the United States and Spain.

We examined cultural differences in the item characteristic functions of self-reported of symptoms of depression, anxiety, and mania-hypomania in a Latino population taking Computerized Adaptive Tests for Mental Health (CAT-MH) in Spanish versus a non-Latino sample taking the tests in English. We studied differential item functioning (DIF) of the most common adaptively administered symptom items out of a bank of 1,008 items between Latino (n = 1276) and non-Latino (n = 798) subjects. For depression, we identified 4 items with DIF that were good discriminators for non-Latinos but poor discriminators for Latinos. These items were related to cheerfulness, life satisfaction, concentration, and fatigue. The correlation between the original calibration and a Latino-only new calibration after eliminating these items was r = .990. For anxiety, no items with DIF were identified. The correlation between the original and new calibrations was r = .993. For mania-hypomania, we identified 4 items with differential item functioning that were good discriminators for non-Latinos but poor discriminators for Latinos. These items were related to risk-taking, self-assurance, and sexual activity. The correlation between the original and new calibration was r = .962. Once the identified items were removed, the correlation between the original calibration and a Latino-only calibration was r = .96 or greater. These findings reveal that the CAT-MH can be reliably used to measure depression, anxiety, and mania in Latinos taking these tests in Spanish. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A revised above-ground maximum biomass layer for the Australian continent

The carbon accounting model FullCAM is used in Australia’s National Greenhous Gas Inventory to provide estimates of carbon stock changes and emissions in response to deforestation and afforestation/reforestation. FullCAM-predicted above-ground woody biomass is heavily influenced by the parameter M, which defines the maximum upper limit to biomass accumulation for any location within the Australian continent. In this study we update FullCAM’s M spatial input layer through combining an extensive database of 5739 site-based records of above-ground biomass (AGB) with the Random Forest ensemble machine learning algorithm, with model predictions of AGB based on 23 environmental predictor covariates. A Monte-Carlo approach was used, allowing estimates of uncertainty to be calculated. Overall, the new biomass predictions for woodlands, with 20–50% canopy cover, were on average 49.5 ± 1.3 (s.d.) t DM ha−1, and very similar to existing model predictions of 48.5 t DM ha−1. This validates the original FullCAM model calibrations, which had a particular focus on accounting for greenhouse gas emissions in Australian woodlands. In contrast, the prediction of biomass of forests with a canopy cover >50% increased significantly, from 172.1 t DM ha−1, to 234.4 ± 5.1 t DM ha−1. The change in forest biomass was most pronounced at sub-continental scales, with the largest increases in the states of Tasmania (166 to 351 ± 22 t DM ha−1), Victoria (201 to 333 ± 14 t DM ha−1), New South Wales (210 to 287 ± 9 t DM ha−1), and Western Australia (103 to 264 ± 14 s.d. t DM ha−1). Testing of model predictions against independent data from the savanna woodlands of northern Australia, and from the high biomass Eucalyptus regnans forests of Victoria, provided confidence in the predictions across a wide range of forest types and standing biomass. When applied to the Australian Government’s National Inventory land clearing accounts there was an overall increase of 6% in continental emissions over the period 1970–2016. Greater changes were seen at sub-continental scales calculated within 6° × 4° analysis tiles, with differences in emissions varying from −21% to +35%. Further testing is required to assess the impacts on other land management activities covered by the National Inventory, such as reforestation; and at more local scales for sequestration projects that utilise FullCAM for determining abatement credits.

Medical physics 3.0 versus 1.0: A case study in digital radiography quality control.

PurposeThe study illustrates how a renewed approach to medical physics, Medical Physics 3.0 (MP3.0), can identify performance decrement of digital radiography (DR) systems when conventional Medical Physics 1.0 (MP1.0) methods fail.Methods MP1.0 tests included traditional annual tests plus the manufacturer's automated Quality Assurance Procedures (QAP) of a DR system before and after a radiologist's image quality (IQ) complaint repeated after service intervention. Further analysis was conducted using nontraditional MP3.0 tests including longitudinal review of QAP results from a 15‐yr database, exposure‐dependent signal‐to‐noise (SNR 2), clinical IQ, and correlation with the institutional service database. Clinical images were analyzed in terms of IQ metrics by the Duke University Clinical Imaging Physics Group using previously validated software.ResultsTraditional metrics did not indicate discrepant system performance at any time. QAP reported a decrease in contrast‐to‐noise ratio (CNR) after detector replacement, but remained above the manufacturer's action limit. Clinical images showed increased lung noise (Ln), mediastinum noise (Mn), and subdiaphragm‐lung contrast (SLc), and decreased lung gray level (Lgl) following detector replacement. After detector recalibration, QAP CNR improved, but did not return to previous levels. Lgl and SLc no longer significantly differed from before detector recalibration; however, Ln and Mn remained significantly different. Exposure‐dependent SNR 2 documented the detector operating within acceptable limits 9 yr previously but subsequently becoming miscalibrated sometime before four prior annual tests. Service records revealed catastrophic failure of the computer containing the original detector calibration from 11 yr prior. It is likely that the incorrect calibration backup file was uploaded at that time.Conclusions MP1.0 tests failed to detect substandard system performance, but MP3.0 methods determined the root cause of the problem. MP3.0 exploits the wealth of data with more sensitive performance indicators. Data analytics are powerful tools whose proper application could facilitate early intervention in degraded system performance.