Counting Uncertainties Research Articles

Compact, high-flow, water-based, turbulent-mixing, condensation aerosol concentrator for collection of spot samples.

A new high-flow, compact aerosol concentrator, using rapid, turbulent mixing to grow aerosol particles into droplets for dry spot sample collection, has been designed and tested. The "TCAC (Turbulent-mixing, Condensation Aerosol Concentrator)" is composed of a saturator for generating hot vapor, a mixing section where the hot vapor mixes with the cold aerosol flow, a growth tube where condensational droplet growth primarily occurs, and a converging nozzle that focuses the droplets into a beam. The prototype concentrator utilizes an aerosol sample flow rate of 4 L min-1. The TCAC was optimized by varying the operating conditions, such as relative humidity of the aerosol flow, mixing flow ratio, vapor temperature, and impaction characteristics. The results showed that particles with a diameter ≥ 25 nm can be grown to a droplet diameter > 1400 nm with near 100% efficiency. Complete activation and growth were observed at relative humidity ≥ 25% of the aerosol sample flow. A consistent spot sample with a diameter of (the diameter of a circle containing 90% of the deposited particles) was obtained regardless of the aerosol particle diameter ( ). For fiber counting applications using phase contrast microscopy, the TCAC can reduce the sampling time, or counting uncertainty, by two to three orders of magnitude, compared to the 25-mm-filter collection. The study shows that the proposed mixing-flow scheme enables a compact spot sample collector suitable for handheld or portable applications, while still allowing for high flow rates.

Consequences of Violating Conditions of Counting Statistics Are Not Severe when Measuring Radon Progeny Concentrations with the Thomas and Kusnetz Methods.

When analyzing samples of radon progeny using the Thomas or Kusnetz methods, we violate one of the conditions of counting statistics because we use counting times that are not short compared with the half-lives of the radionuclides. The result is that we overestimate the uncertainties of the counts if we use counting statistics without correction. In this work, I describe the method by which I adjusted the values of variance of the counts theoretically to values that are more accurate and calculated the amounts by which I overestimate the values of counting uncertainty by using counting statistics without correction. These values are surprisingly small: 4-5% for the Thomas method and 2-3% for the Kusnetz method. Now, I can correct uncertainty values of radon progeny measurements if it is appropriate to do so. The detailed calculations I present here may be used for determining corrections to the counting uncertainty for a method for measuring radon progeny concentration using different sampling and/or counting times than those described here. Further, they may be used for any sample, not necessarily radon progeny, that requires a long counting time to acquire a significant number of observed counts.

Bone Marrow Differential Cell Count Uncertainty In The Classification of Plasma Cell Neoplasms

Abstract Pathologic evaluation of bone marrow is diagnostically essential for many hematologic diseases, with quantification of cellular composition an important step in the pathologic work-up. Many diagnostic criteria for hematologic diseases contain precise cellular thresholds, for example 10% and 60% plasma cells in plasma cell neoplasms (PCN). Therefore, accurate cellular quantification is necessary, as it impacts treatment decisions. This process is typically performed manually and is subject to errors, including interoperator variability and heterogeneity of cellular composition within the aspirate smears. Rumke statistics were developed to predict errors in manual quantification of hematologic specimens, with resulting statistical confidence intervals. We set out to determine how statistical uncertainty of plasma cell percentages from manual differential counts could potentially affect the diagnosis and classification of PCN. Here, we extracted from the EMR a large data set containing pathologic diagnoses and laboratory data for 16,737 cases of PCN. Using the R programming language, we developed an automated method to extract plasma cell percentages from unstructured text containing manual bone marrow differential counts. Differential cell counts with plasma cell percentages were available for 4,523 cases, of which 2,445 were initial cases in the data set. Rumke statistics were calculated for each initial case using the Clopper-Pearson 95% confidence interval. Next, we identified cases where the plasma cell Rumke intervals encompassed the 10% or 60% threshold, thereby potentially changing diagnostic categorization. Of the initial cases, 228 (9.3%) encompassed a threshold (204 at 10% and 24 at 60%). To determine if diagnostic categorization would be affected, we screened for databased myeloma defining events (MDE) - hemoglobin (<10 g/dL), serum creatinine (>2 mg/dL), and total calcium (>11 mg/dL). At the 10% plasma cell threshold, 50 cases had one or more laboratory MDE and 127 had none. In cases with MDE, crossing the 10% threshold meets criteria for plasma cell myeloma (PCM) and below would likely lead to further work-up, including diagnostic imaging. In cases with no MDE, crossing the 10% threshold changes the diagnostic categorization from monoclonal gammopathy of undetermined significance to smoldering myeloma. In our data set, cases that straddled the 60% threshold all met criteria for PCM, whether they crossed the 60% threshold or not. These findings show that differential cell counts close to diagnostic thresholds fall within error ranges in a significant number of cases of PCN and potentially change diagnostic categorization. Such uncertainty must be appreciated and taken into consideration when categorizing a patient’s disease. This work also highlights the need to develop more accurate and representative methods to generate differential cell counts, such as automated methods from whole slide digital images. In addition, our methods highlight the utility of using R software to extract meaningful data from unstructured free text at large scale.

Improved counting statistics of an ultrafine differential mobility particle size spectrometer system

Abstract. Differential mobility particle size spectrometers (DMPSs) are widely used to measure the aerosol number size distribution. Especially during new particle formation (NPF), the dynamics of the ultrafine size distribution determine the significance of the newly formed particles within the atmospheric system. A precision quantification of the size distribution and derived quantities such as new particle formation and growth rates is therefore essential. However, size-distribution measurements in the sub-10 nm range suffer from high particle losses and are often derived from only a few counts in the DMPS system, making them subject to very high counting uncertainties. Here we show that a CPC (modified Airmodus A20) with a significantly higher aerosol optics flow rate compared to conventional ultrafine CPCs can greatly enhance the counting statistics in that size range. Using Monte Carlo uncertainty estimates, we show that the uncertainties of the derived formation and growth rates can be reduced from 10 %–20 % down to 1 % by deployment of the high statistics CPC on a strong NPF event day. For weaker events and hence lower number concentrations, the counting statistics can result in a complete breakdown of the growth rate estimate with relative uncertainties as high as 40 %, while the improved DMPS still provides reasonable results at 10 % relative accuracy. In addition, we show that other sources of uncertainty are present in CPC measurements, which might become more important when the uncertainty from the counting statistics is less dominant. Altogether, our study shows that the analysis of NPF events could be greatly improved by the availability of higher counting statistics in the used aerosol detector of DMPS systems.

Legacy radionuclides in cryoconite and proglacial sediment on Orwell Glacier, Signy Island, Antarctica

Cryoconite is a specific type of material found on the surface of glaciers and icesheets. Samples of cryoconite were collected from the Orwell Glacier and its moraines, together with suspended sediment from the proglacial stream on Signy Island, part of the South Orkney Islands, Antarctica. The activity concentrations of certain fallout radionuclides were determined in the cryoconite, moraine and suspended sediment, in addition to particle size composition and %C and %N. For cryoconite samples (n = 5), mean activity concentrations (±1SD) of 137Cs, 210Pbun and 241Am were 13.2 ± 20.9, 66.1 ± 94.0 and 0.32 ± 0.64 Bq kg−1, respectively. Equivalent values for the moraine samples (n = 7) were 2.56 ± 2.75, 14.78 ± 12.44 and <1.0 Bq kg−1, respectively. For the composite suspended sediment sample, collected over 3 weeks in the ablation season, the values (± counting uncertainty) for 137Cs, 210Pbun and 241Am were 2.64 ± 0.88, 49.2 ± 11.9 and <1.0 Bq kg−1, respectively. Thus, fallout radionuclide activity concentrations were elevated in cryoconite relative to moraine and suspended sediment. In the case of 40K, the highest value was for the suspended sediment (1423 ± 166 Bq kg−1). The fallout radionuclides in cryoconite were 1–2 orders of magnitude greater than values in soils collected from other locations in Antarctica. This work further demonstrates that cryoconite likely scavenges fallout radionuclides (dissolved and particulate) in glacial meltwater. In the case of 40K, the greater value in suspended sediment implies a subglacial source. These results are amongst the relatively few that demonstrate the presence of fallout radionuclides in cryoconites at remote locations in the Southern Hemisphere. This work adds to the growing contention that elevated activities of fallout radionuclides, and other contaminants, in cryoconites are a global phenomenon and may be a risk to downstream terrestrial and aquatic ecosystems.

Review Article : Applicable methods of evaluating peak area of gamma ray spectra

Gamma ray spectra have interesting information about energy and intensity of gamma photons. Normally, this information is available in the peak area and background region. A full energy peak sits on a background continuum and produced by full energy absorption of high energy photons, while background region is produced by Compton scattering of the photons. The most essential concern of errors is in the method of determining the events in both peak and background areas. The uncertainty is low when the background counts are small relative to the peak counts. However, it becomes high when the counts of the peak area are low with respect to that of background. The detection threshold for the peak is ultimately determined by the uncertainty in the background counts. This depends on the technique used and the form of the produced spectra. This paper reviews different methods of determining peak area and their associated uncertainties in terms of the principle and application of the techniques used.

A Bayesian approach to integrating radiometric dating and varve measurements in intermittently indistinct sediment

Abstract. Annually laminated lake sediment can track paleoenvironmental change at high resolution where alternative archives are often not available. However, information about the chronology is often affected by indistinct and intermittent laminations. Traditional chronology building struggles with these kinds of laminations, typically failing to adequately estimate uncertainty or discarding the information recorded in the laminations entirely, despite their potential to improve chronologies. We present an approach that overcomes the challenge of indistinct or intermediate laminations and other obstacles by using a quantitative lamination quality index combined with a multi-core, multi-observer Bayesian lamination sedimentation model that quantifies realistic under- and over-counting uncertainties while integrating information from radiometric measurements (210Pb, 137Cs, and 14C) into the chronology. We demonstrate this approach on sediment of indistinct and intermittently laminated sequences from alpine Columbine Lake, Colorado. The integrated model indicates 3137 (95 % highest probability density range: 2753–3375) varve years with a cumulative posterior distribution of counting uncertainties of −13 % to +7 %, indicative of systematic observer under-counting. Our novel approach provides a realistic constraint on sedimentation rates and quantifies uncertainty in the varve chronology by quantifying over- and under-counting uncertainties related to observer bias as well as the quality and variability of the sediment appearance. The approach permits the construction of a chronology and sedimentation rates for sites with intermittent or indistinct laminations, which are likely more prevalent than sequences with distinct laminations, especially when considering non-lacustrine sequences, and thus expands the possibilities of reconstructing past environmental change with high resolution.

Dual-Particle Imaging Performance of a Cs2LiYCl6:Ce (CLYC)-Based Rotational Modulation Collimator (RMC) System

A dual-particle imager based on a rotational modulation collimator (RMC) utilizing a Cs <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> LiYCl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> :Ce (CLYC) scintillator was developed and investigated for its dual-particle imaging performance. Design considerations and previous simulation work were verified by conducting experiments, and experimental results showing neutron and gamma-ray imaging capabilities were obtained. The imaging performance of the CLYC-based RMC system was further evaluated by investigating it in a mixed neutron and gamma-ray environment, and dual-particle imaging capability of the RMC system was demonstrated. Signal-to-noise ratio (SNR) values larger than 45 were achieved for reconstructed images of each neutron and gamma-ray source distribution, if modulation patterns were obtained with average fractional counting uncertainty of less than 3%. The structural similarity (SSIM) index of reconstructed images was calculated to be almost unity, indicating an artifact-free image.

Are modern pollen assemblages from soils and mosses the same? A comparison of natural pollen traps from subtropical China

This study investigated the pollen spectra from 46 pairs of soil and moss samples (pairs collected within a 1 m2 squared area). The samples were collected from six common subtropical vegetation communities in the Meiling Mountains, southeast China, with the vegetation proportions recorded at the collection point. Principal Component Analysis (PCA) was used to investigate the separation of the paired assemblages and to determine whether different plant communities produced distinctive pollen spectra. Paired soil and moss samples captured similar levels of variability in the pollen assemblages retained, but there are systematic differences in the mean values of key groups of taxa. Monte Carlo sampling shows that, in most cases, intra-pair differences are greater than could be explained by counting uncertainty alone.In this study, discriminant analysis of surface soil and moss found that 91.3% of the soil samples and 87% of the moss samples were correctly classified into their vegetation communities. However, the detailed pollen assemblages suggest that mosses provide a more accurate representation of the contemporary vegetation composition than soils.Pollen assemblages from moss samples seem to record local vegetation more accurately than those from soil samples. Higher vegetation diversity within an arboreal forest community leads to greater differences between moss-soil pairs. In bamboo forests, pollen assemblages in soils and moss show strong influence from the surrounding communities, which makes it hard to identify bamboo forest via surface sample pollen assemblages alone.

Measurement uncertainty evaluation and risk of false conformity assessment for microbial enumeration tests

Microbial enumeration tests are widely used to assess the microbiological quality of non-sterile pharmaceutical products. Despite of all efforts to guarantee the reliability of microbial enumeration tests, there will always be an uncertainty associated with the measured values, which can lead to false conformity/non-conformity decisions. In this work, we evaluated the measurement uncertainty using a bottom-up approach and estimate the consumer's or producer's risk due to the measurement uncertainty. Three main sources of uncertainty were identified and quantified: dilution factor, plated volume, and microbial plate counts. The contribution of these sources of uncertainty depends on the measured value of microbial load in pharmaceutical products. The contribution of dilution factor and plated volume uncertainties increase with an increase of measured value, while the contribution of microbial plate count uncertainty decreases with an increase of measured value. The overall uncertainty values were expressed as uncertainty factors, which provide an asymmetric 95% level confidence level of microbial load in pharmaceutical products. In addition, the risk of false conformity/non-conformity decisions due to measurement uncertainty was assess using Monte Carlo method. When the measured value is close to the upper specification limit and/or the measurement uncertainty is large, the risk of false conformity/non-conformity decisions may be significantly high. Thus, we conclude that the use of uncertainty factor in the conformity/non-conformity assessment is important to guarantee the reliability of microbial enumeration test results and to support decision-making.

The hidden side of diversity: Effects of imperfect detection on multiple dimensions of biodiversity.

Studies on ecological communities often address patterns of species distribution and abundance, but few consider uncertainty in counts of both species and individuals when computing diversity measures.We evaluated the extent to which imperfect detection may influence patterns of taxonomic, functional, and phylogenetic diversity in ecological communities.We estimated the true abundance of fruit‐feeding butterflies sampled in canopy and understory strata in a subtropical forest. We compared the diversity values calculated by observed and estimated abundance data through the hidden diversity framework. This framework evaluates the deviation of observed diversity when compared with diversities derived from estimated true abundances and whether such deviation represents a bias or a noise in the observed diversity pattern.The hidden diversity values differed between strata for all diversity measures, except for functional richness. The taxonomic measure was the only one where we observed an inversion of the most diverse stratum when imperfect detection was included. Regarding phylogenetic and functional measures, the strata showed distinct responses to imperfect detection, despite the tendency to overestimate observed diversity. While the understory showed noise for the phylogenetic measure, since the observed pattern was maintained, the canopy had biased diversity for the functional metric. This bias occurred since no significant differences were found between strata for observed diversity, but rather for estimated diversity, with the canopy being more clustered.We demonstrate that ignore imperfect detection may lead to unrealistic estimates of diversity and hence to erroneous interpretations of patterns and processes that structure biological communities. For fruit‐feeding butterflies, according to their phylogenetic position or functional traits, the undetected individuals triggered different responses in the relationship of the diversity measures to the environmental factor. This highlights the importance to evaluate and include the uncertainty in species detectability before calculating biodiversity measures to describe communities.

A Bayesian method for on-line evaluation of uncertainty in measurement of Coriolis flow meters

On-line evaluation of measurement uncertainty is crucial for process control and quality control in real applications. Traditional approaches to measurement uncertainty (MU) assume that measurands are repeated measurements collected in static laboratory conditions. On-line evaluation of MU, then, constitutes a challenging problem because the sensor data is collected under a variety of operating conditions. We propose a new method for the on-line evaluation of MU which consists of clustering time series data into groups with similar operational conditions and evaluating the MU using Bayesian inference. The mass count uncertainty measured using Coriolis mass flow meters on two hydrogen refueling stations is evaluated. The clustering of fueling events effectively reduces the process noise in on-line evaluation, and the Bayesian inference method identifies a much narrower uncertainty range than conventional methods. Therefore, our approach of using machine learning methods for on-line evaluation of MU is a promising practical approach.

DCMD: Distance-based classification using mixture distributions on microbiome data.

Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means classification and k-nearest neighbours framework. We develop two distance metrics that produce optimal results. The performance of the model is assessed using simulated and human microbiome study data, with results compared against a number of existing machine learning and distance-based classification approaches. The proposed method is competitive when compared to the other machine learning approaches, and shows a clear improvement over commonly used distance-based classifiers, underscoring the importance of modelling sparsity for achieving optimal results. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data. The source code is available at https://github.com/kshestop/DCMD for academic use.

On the origin of the gamma-ray emission from Omega Centauri: millisecond pulsars and dark matter annihilation

We explore two possible scenarios to explain the observed γ-ray emission associated with the atypical globular cluster ω-Centauri: emission from millisecond pulsars (MSP) and dark matter (DM) annihilation. In the first case we predict the total number of MSPs in the globular cluster (n=45+22−16) using a new Bayesian method that combines the observational uncertainty in γ-ray counts with the intrinsic variation in individual pulsar luminosities. A DM interpretation is motivated by the possibility of ω-Centauri being the remnant core of an ancient dwarf galaxy hosting a surviving DM component. At least two annihilation channels, light quarks and muons, can plausibly produce the observed γ-ray spectrum. We are able to constrain the DM particle mass despite substantial uncertainties in the shape of the density profile (10 GeV ≲ mχ ≲ 30 GeV for annihilation into light quarks and 5 GeV ≲ mχ ≲ 9 GeV for annihilation into muons). We translate upper limits on ω-Centauri's remnant dark matter content into lower limits on the annihilation cross section (⟨ σ v⟩ ≳ 10−29 cm3 s −1 and ⟨ σ v⟩ ≳ 10−28 cm3 s −1 for quarks and muons respectively), taking into account the spatial extension of the DM halo. Moreover, there is a relatively small range of DM density profiles which are consistent with ω-Centauri's kinematics and stellar mass and can explain its γ-ray spectrum while simultaneously evading CMB constraints on dark matter annihilation. Further analysis of ω-Centauri's internal kinematics, and/or additional information on the resident MSP population will yield much stronger constraints and shed light about the origin of this otherwise mysterious γ-ray source.

Corrigendum: Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1eV – 100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys

Cosmic ray neutron (CRN) sensing allows for non-invasive soil moisture measurements at the field scale and relies on the inverse correlation between aboveground measured epithermal neutron intensity (1 eV−100 keV) and environmental water content. The measurement uncertainty follows Poisson statistics and thus increases with decreasing neutron intensity, which corresponds to increasing soil moisture. In order to reduce measurement uncertainty, the neutron count rate is usually aggregated over 12 or 24 h time windows for stationary CRN probes. To obtain accurate soil moisture estimates with mobile CRN rover applications, the aggregation of neutron measurements is also necessary and should consider soil wetness and driving speed. To date, the optimization of spatial aggregation of mobile CRN observations in order to balance measurement accuracy and spatial resolution of soil moisture patterns has not been investigated in detail. In this work, we present and apply an easy-to-use method based on Gaussian error propagation theory for uncertainty quantification of soil moisture measurements obtained with CRN sensing. We used a 3rd order Taylor expansion for estimating the soil moisture uncertainty from uncertainty in neutron counts and compared the results to a Monte Carlo approach with excellent agreement. Furthermore, we applied our method with selected aggregation times to investigate how CRN rover survey design affects soil moisture estimation uncertainty. We anticipate that the new approach can be used to improve the strategic planning and evaluation of CRN rover surveys based on uncertainty requirements.

Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling

Adequate characterization of catchment storage dynamics is crucial in hydrological models, yet scale-representative storage measurements are rare. Recent developments in Cosmic Ray Neutron Sensor (CRNS) technology and monitoring networks provide a powerful source of more scale-appropriate soil moisture data for many modelling applications. However, the potential in rainfall-runoff modelling is undeveloped. Here we present the first application of CRNS data in conceptual rainfall-runoff modelling and explore this potential in the context of a mixed-agricultural landscape in Scotland. We deployed and calibrated a CRNS in a heterogeneous soil-land use footprint over a ∼3-year period. In this generally wet environment, the CRNS shallow sensing depth and relatively high neutron count uncertainty were identified as major challenges. However, given the better spatial coverage (up to 14 ha) and ease for maintenance, CRNS was thought to represent the simplest approach for long-term monitoring of managed mixed-agricultural sites. We used CRNS-derived, as well as single point-scale estimates, of near-surface soil storage (SNS) to explore their characterisation of storage dynamics at the catchment-scale. Inter-comparison using linear regression showed that SNS related well to catchment-scale storage dynamics, however this relationship was stronger for CRNS (R2 = 0.91) compared to point-scale derived estimates (R2 = 0.76). Based on this, we evaluated the effect of using the CRNS and point scale derived SNS data to constrain storage estimates controlling runoff generation in a common rainfall-runoff model (HBV-light). Including CRNS or point-scale field SNS data alone in model calibration was especially useful for intermediate and wet periods. A combined model calibration using discharge and either SNS storage estimates provided a better representation of catchment internal dynamics, additionally reducing uncertainty during low flows. In the context of mixed-agricultural landscapes in humid environments, this study showed the potential of using CRNS over point scale data (in terms of representativeness for single point data and practicality for point sensor networks) to characterise the catchment storage-discharge relationship and inform hydrological modelling.

On the (Lack of) Evolution of the Stellar Mass Function of Massive Galaxies from z = 1.5 to 0.4

Abstract We study the evolution in the number density of galaxies at the highest stellar masses over the past ≈9 Gyr ( ) using the Spitzer/HETDEX Exploratory Large-Area Survey (SHELA). SHELA includes complete imaging in eight photometric bands spanning 0.3–4.5 μm over 17.5 deg2 within the SDSS Stripe 82 field. The size of SHELA produces the lowest counting uncertainties and cosmic variance yet for massive galaxies at z ∼ 1.0. We study the evolution in the intrinsic stellar mass function (SMF) for galaxies with using a forward-modeling method that takes into full account the statistical and systematic uncertainties on stellar mass. From z = 0.4 to 1.5, the evolution in the massive end of the intrinsic SMF shows minimal change in its shape: the characteristic mass (M*) evolves by less than 0.1 dex (±0.05 dex); furthermore, the number density of galaxies with 11 stays roughly constant at (±0.05) from z = 1 to z = 0.4, consistent with no evolution, then declines to = −3.7 (±0.05) at z = 1.5. We discuss the uncertainties in the derived SMF, which are dominated by assumptions in the star formation history and details of stellar population synthesis models for stellar mass estimations. We also study the evolution in the SMF for samples of star-forming and quiescent galaxies selected by their specific star formation rate. For quiescent galaxies, the data are consistent with no (or slight) evolution (≲0.1 dex) in either the characteristic mass or number density from z ∼ 1.5 to the present even after accounting for the systematic uncertainty and the random error in the stellar mass measurement. The lack of number density evolution in the quiescent massive galaxy population means that any mass growth (presumably through “dry” mergers) must balance the rate of stellar mass losses owing to processes of late-stage stellar evolution and the formation of newly quiescent galaxies from the star-forming population. We provide an upper limit on this mass growth from z = 1.0 to 0.4 of ΔM */M * = 45% (i.e., ≃0.16 dex) for quiescent galaxies more massive than 1011 M ⊙.

Comparison between manta trawl and in situ pump filtration methods, and guidance for visual identification of microplastics in surface waters

Owing to the development and adoption of a variety of methods for sampling and identifying microplastics, there is now data showing the presence of microplastics in surface waters from all over the world. The difference between the methods, however, hampers comparisons, and to date, most studies are qualitative rather than quantitative. In order to allow for a quantitative comparison of microplastics abundance, it is crucial to understand the differences between sampling methods. Therefore, a manta trawl and an in situ filtering pump were compared during realistic, but controlled, field tests. Identical microplastic analyses of all replicates allowed the differences between the methods with respect to (1) precision, (2) concentrations, and (3) composition to be assessed. The results show that the pump gave higher accuracy with respect to volume than the trawl. The trawl, however, sampled higher concentrations, which appeared to be due to a more efficient sampling of particles on the sea surface microlayer, such as expanded polystyrene and air-filled microspheres. The trawl also sampled a higher volume, which decreased statistical counting uncertainties. A key finding in this study was that, regardless of sampling method, it is critical that a sufficiently high volume is sampled to provide enough particles for statistical evaluation. Due to the patchiness of this type of contaminant, our data indicate that a minimum of 26 particles per sample should be recorded to allow for concentration comparisons and to avoid false null values. The necessary amount of replicates to detect temporal or spatial differences is also discussed. For compositional differences and size distributions, even higher particle counts would be necessary. Quantitative measurements and comparisons would also require an unbiased approach towards both visual and spectroscopic identification. To facilitate the development of such methods, a visual protocol that can be further developed to fit different needs is introduced and discussed. Some of the challenges encountered while using FTIR microspectroscopic particle identification are also critically discussed in relation to specific compositions found.

Ability of non-destructive assay techniques to identify sophisticated material partial defects

This study explores the ability of non-destructive assay techniques to detect a partial material defect in which 100 g of plutonium are diverted from the center of a 1000 g can of PuO2 powder. Four safeguards measurements techniques: neutron multiplicity counting, calorimetry, gravimetry, and gamma ray spectroscopy are used in an attempt to detect the defect. Several materials are added to the partial defect PuO2 can to replicate signatures of the diverted material. 252Cf is used to compensate for the doubles neutron counts, 241Am is used to compensate for the decay heat, and aluminum is used to compensate for the weight. Although, the doubles and triples difference before and after diversion are statistically indistinguishable with the AWCC in fast and thermal mode, the difference in the singles counts are statistically detectable in both modes. The relatively short half-life of 252Cf leads to a decrease (three sigma uncertainty) in the doubles neutron counts after 161 days. Combining this with the precise quantity of 241Am needed (10.7 g) to mimic the heat signature and the extreme precision in 252Cf mass needed to defeat neutron multiplicity measurements gives reassurance in the International Atomic Energy Agency's ability to detect partial material defects.