Mass Estimation Method Research Articles

Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision

The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation method, which can accurately estimate the mass of free-swimming tilapia under non-contact conditions. First, image enhancement is performed on the original image, and the depth image is obtained by correcting and stereo matching the enhanced image using binocular stereo vision technology. And the fish body is segmented by an SAM model. Then, the segmented fish body is labeled with key points, thus realizing the 3D reconstruction of tilapia. Five mass estimation models are established based on the relationship between the body length and the mass of tilapia, so as to realize the mass estimation of tilapia. The results showed that the average relative errors of the method models were 5.34%~7.25%. The coefficient of determination of the final tilapia mass estimation with manual measurement was 0.99, and the average relative error was 5.90%. The improvement over existing deep learning methods is about 1.54%. This study will provide key technical support for the non-destructive measurement of tilapia, which is of great significance to the information management of aquaculture, the assessment of fish growth condition, and baiting control.

A novel hybrid-view technique for accurate mass estimation of kimchi cabbage using computer vision

This study addresses the accurate estimation of kimchi cabbage mass, as cabbage leaves exhibit size variability and complex leaf structures. Conventional mass estimation methods, which rely solely on external imaging, often overlook leaf gaps. To improve accuracy, we propose an innovative computer vision system utilizing hybrid-view images and detailed saturation analysis. Our system quantifies the impact of leaf gaps on mass using features from the saturation channel of images of bisected cabbage. Our proposed method can be easily integrated into existing workflows and has the potential to improve labor efficiency. Our approach outperforms the conventional method (R2 of 0.66 and relative error of 8.68%), achieving a 0.92 R2 value and lowering the relative error to 4.22%. This advancement offers a robust solution for the mass estimation of kimchi cabbage and suggests potential applications for other foods and crops with internal voids.

A novel microwave Doppler method for mass estimation of machine-picked seed cotton

Obtaining information on cotton yields and creating a spatial map of cotton production is the starting point for implementing precision agriculture. Microwave technology has been used for yield estimation in China for crops such as corn and soybeans and has shown strong applicability. However, research on cotton yield estimation using microwave technology is weak. Therefore, this paper proposes a method for seed cotton mass estimation based on the microwave Doppler method and designs bench tests to prove the feasibility of the method. First, a seed cotton speed estimation model was established based on the microwave Doppler effect and fast Fourier transform. On this basis, a seed cotton velocity estimation experiment was designed to study the effects of sensor installation angle and position, pipe collision friction, air resistance, and the interaction force between the seed cotton on the seed cotton movement velocity. The power spectral density (PSD) of the acquired microwave data is then estimated using the modified periodogram method. The least squares method was used to establish a linear regression model between seed cotton mass and the corresponding PSD, and finally the free-fall mass experiment and pneumatic conveying mass experiment yielded that the estimation errors of seed cotton mass were 7.30% and 6.17%, respectively. The experimental results show that the estimation error is small when using microwave technology for seed cotton mass estimation, which meets the requirements of actual operation.

Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging.

This initial study aimed at testing whether fat-containing agents can be used for the fat mass estimation methods using magnetic resonance imaging (MRI). As an example for clinical application, fat-containing agents (based on soybean oil, 10% and 20%), 100% soybean oil, and saline as reference substances were placed outside the proximal femurs obtained from 14 participants and analyzed by 0.3T MRI. Fat content was the estimated fat fraction (FF) based on signal intensity (SIeFF, %). The SIeFF values of the femoral bone marrow, including the femoral head, neck, shaft, and trochanter area, were measured. MRI data were compared in terms of bone mineral content (BMC) and bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) in the proximal femur. Twelve pig femurs were also used to confirm the correlation between FF by the DIXON method and SIeFF. According to Pearson's correlation coefficient, the SIeFF and total BMC and BMD data revealed strong and moderate negative correlations in the femoral head (r < - 0.74) and other sites (r = - 0.66 to - 0.45). FF and SIeFF showed a strong correlation (r = 0.96). This study was an initial investigation of a method for estimating fat mass with fat-containing agents and showed the potential for use in MRI. SIeFF and FF showed a strong correlation, and SIeFF and BMD and BMC showed correlation; however, further studies are needed to use SIeFF as a substitute for DXA.

Galaxy stellar and total mass estimation using machine learning

ABSTRACT Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning (ML), which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions for the distributions of stellar and dark matter. In this work, we use a general sample of galaxies from the TNG100 simulation to investigate the ability of multibranch convolutional neural network (CNN) based ML methods to predict the central (i.e. within 1−2 effective radii) stellar and total masses, and the stellar mass-to-light ratio (M*/L). These models take galaxy images and spatially resolved mean velocity and velocity dispersion maps as inputs. Such CNN-based models can, in general, break the degeneracy between baryonic and dark matter in the sense that the model can make reliable predictions on the individual contributions of each component. For example, with r-band images and two galaxy kinematic maps as inputs, our model predicting M*/L has a prediction uncertainty of 0.04 dex. Moreover, to investigate which (global) features significantly contribute to the correct predictions of the properties above, we utilize a gradient-boosting machine. We find that galaxy luminosity dominates the prediction of all masses in the central regions, with stellar velocity dispersion coming next. We also investigate the main contributing features when predicting stellar and dark matter mass fractions (f*, fDM) and the dark matter mass MDM, and discuss the underlying astrophysics.

Development and Validation of a Method of Body Volume and Fat Mass Estimation Using Three-Dimensional Image Processing with a Mexican Sample.

Body composition assessment using instruments such as dual X-ray densitometry (DXA) can be complex and their use is often limited to research. This cross-sectional study aimed to develop and validate a densitometric method for fat mass (FM) estimation using 3D cameras. Using two such cameras, stereographic images, and a mesh reconstruction algorithm, 3D models were obtained. The FM estimations were compared using DXA as a reference. In total, 28 adults, with a mean BMI of 24.5 (±3.7) kg/m2 and mean FM (by DXA) of 19.6 (±5.8) kg, were enrolled. The intraclass correlation coefficient (ICC) for body volume (BV) was 0.98-0.99 (95% CI, 0.97-0.99) for intra-observer and 0.98 (95% CI, 0.96-0.99) for inter-observer reliability. The coefficient of variation for kinetic BV was 0.20 and the mean difference (bias) for BV (liter) between Bod Pod and Kinect was 0.16 (95% CI, -1.2 to 1.6), while the limits of agreement (LoA) were 7.1 to -7.5 L. The mean bias for FM (kg) between DXA and Kinect was -0.29 (95% CI, -2.7 to 2.1), and the LoA was 12.1 to -12.7 kg. The adjusted R2 obtained using an FM regression model was 0.86. The measurements of this 3D camera-based system aligned with the reference measurements, showing the system's feasibility as a simpler, more economical screening tool than current systems.

Using magnetic resonance imaging to measure head muscles: An innovative method to opportunistically determine muscle mass and detect sarcopenia.

Sarcopenia is associated with multiple adverse outcomes. Traditional methods to determine low muscle mass for the diagnosis of sarcopenia are mainly based on dual-energy X-ray absorptiometry (DXA), whole-body magnetic resonance imaging (MRI) and bioelectrical impedance analysis. These tests are not always available and are rather time consuming and expensive. However, many brain and head diseases require a head MRI. In this study, we aim to provide a more accessible way to detect sarcopenia by comparing the traditional method of DXA lean mass estimation versus the tongue and masseter muscle mass assessed in a standard brain MRI. The H70 study is a longitudinal study of older people living in Gothenburg, Sweden. In this cross-sectional analysis, from 1203 participants aged 70years at baseline, we included 495 with clinical data and MRI images available. We used the appendicular lean soft tissue index (ALSTI) in DXA images as our reference measure of lean mass. Images from the masseter and tongue were analysed and segmented using 3D Slicer. For the statistical analysis, the Spearman correlation coefficient was used, and concordance was estimated with the Kappa coefficient. The final sample consisted of 495 participants, of which 52.3% were females. We found a significant correlation coefficient between both tongue (0.26) and masseter (0.33) with ALSTI (P<0.001). The sarcopenia prevalence confirmed using the alternative muscle measure in MRI was calculated using the ALSTI (tongue=2.0%, masseter=2.2%, ALSTI=2.4%). Concordance between sarcopenia with masseter and tongue versus sarcopenia with ALSTI as reference has a Kappa of 0.989 (P<0.001) for masseter and a Kappa of 1 for the tongue muscle (P<0.001). Comorbidities evaluated with the Cumulative Illness Rating Scale were significantly associated with all the muscle measurements: ALSTI (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.07-1.26, P<0.001), masseter (OR 1.16, 95% CI 1.07-1.26, P<0.001) and tongue (OR 1.13, 95% CI 1.04-1.22, P=0.002); the higher the comorbidities, the higher the probability of having abnormal muscle mass. ALSTI was significantly correlated with tongue and masseter muscle mass. When performing the sarcopenia diagnostic algorithm, the prevalence of sarcopenia calculated with head muscles did not differ from sarcopenia calculated using DXA, and almost all participants were correctly classified using both methods.

Identification of an optimal method for echocardiographic right ventricular mass estimation in a swine model of pulmonary hypertension

Abstract Background Right ventricular (RV) mass and Fulton Index (FI) - the ratio of right to left ventricular mass - are useful markers of RV remodeling and predictors of mortality in pulmonary hypertension (PH). Cardiac magnetic resonance imaging provides an accurate estimation of RV mass but cost, availability and patient contraindications limit broad application. While echocardiography is universally available and offers feasible longitudinal assessments, current guidelines lack a standardized method for RV mass determination. Purpose We compared the performance of four previously described echocardiographic models of RV mass to estimate actual RV weight and FI measured post-mortem in a swine model of PH. Methods Echocardiography was performed in a Yorkshire pig model of post-capillary PH induced by pulmonary vein banding and healthy animals using a Philips iE-33 ultrasound system (n=70). RV mass was measured using the following models: trapezoid method (1), exact radius method (2), longitudinal plane method (3), and an algorithm (4) proposed by Kochav's group. Figure 1 depicts pertinent echocardiographic views and parameters. A two-dimensional area-length method was applied to estimate left ventricular mass and FI was calculated. On the day of echocardiography, pigs were euthanized, hearts were explanted, and ventricular weights were recorded. Pearson’s correlation, linear regression and Bland-Altman analysis were employed to evaluate different methods compared to recorded weights and Fulton Index. Results PH animals presented with significant RV remodeling and higher RV weights (42.4g, range 18.4-93.6g) compared to control pigs (30.5g, range 11.1-46.0g), allowing for examination of echocardiographic methods across a wide span of RV weights. FI ranged from 0.29 to 3.60 with a mean of 0.70. All methods demonstrated a strong linear relationship with post-mortem RV weight (r(1) = 0.88, r(2) = 0.88, r(3) = 0.84 r(4) = 0.82, p&amp;lt;0.0001). Bland-Altmann analysis revealed good agreement with recorded RV mass for trapezoid, exact radius and longitudinal plane method (bias(1) = 4.5±7.5g, bias(2) = -5.9±6.6g, bias(3) = -2.7±8.4g) while Kochav’s algorithm tended to underestimate RV weights (bias(4) = -18.5±8.7g). Figure 2 illustrates the performance of exact radius and longitudinal plane methods. Measured left ventricular mass correlated strongly with post-mortem left ventricular weight and reached good agreement (r = 0.91, p&amp;lt;0.0001; bias = -4.0±9.8g). FI was best approximated by exact radius (bias = -0.04±0.12) and longitudinal plane (bias = 0.01±0.18) methods. The trapezoid model tended to overestimate FI (bias = 0.10±0.11) and Kochav’s algorithm underestimated (bias = -0.23±0.12). Conclusion RV mass and FI can be successfully approximated using echocardiography in a clinically relevant porcine PH model. Longitudinal plane and exact radius methods performed best and can be suggested for evaluating RV mass in clinical PH patients.Figure 1Figure 2

Galaxy And Mass Assembly (GAMA): The group H i mass as a function of halo mass

ABSTRACT We determine the atomic hydrogen (H i) to halo mass relation (HIHM) using Arecibo Legacy Fast ALFA survey H i data at the location of optically selected groups from the Galaxy And Mass Assembly (GAMA) survey. We make direct H i detections for 37 GAMA groups. Using H i group spectral stacking of 345 groups, we study the group H i content as a function of halo mass across a halo mass range of 1011–1014.7 M⊙. We also correct our results for Eddington bias. We find that the group H i mass generally rises as a function of halo mass from 1.3% of the halo mass at $10^{11.6} \, \text{M}_\odot$ to 0.4% at $10^{13.7} \, \text{M}_\odot$ with some indication of flattening towards the high-mass end. Despite the differences in optical survey limits, group catalogues, and halo mass estimation methods, our results are consistent with previous group H i-stacking studies. Our results are also consistent with mock observations from shark and IllustrisTNG.

Center of Mass Estimation Using a Force Platform and Inertial Sensors for Balance Evaluation in Quiet Standing

Accurate estimation of the center of mass is necessary for evaluating balance control during quiet standing. However, no practical center of mass estimation method exists because of problems with estimation accuracy and theoretical validity in previous studies that used force platforms or inertial sensors. This study aimed to develop a method for estimating the center of mass displacement and velocity based on equations of motion describing the standing human body. This method uses a force platform under the feet and an inertial sensor on the head and is applicable when the support surface moves horizontally. We compared the center of mass estimation accuracy of the proposed method with those of other methods in previous studies using estimates from the optical motion capture system as the true value. The results indicate that the present method has high accuracy in quiet standing, ankle motion, hip motion, and support surface swaying in anteroposterior and mediolateral directions. The present method could help researchers and clinicians to develop more accurate and effective balance evaluation methods.

The evolution of maximum terrestrial body mass in sauropod dinosaurs

The long-necked dinosaurs, sauropods, are famous for their extreme body sizes, evolving body masses several times greater than the next-heaviest terrestrial animals, elephant-like and rhinoceros- like mammals and 'duck-billed' dinosaurs. The pace of sauropod discovery has been exponential in recent decades, resulting in the recognition of sauropods as a global, ecologically diverse group of herbivorous dinosaurs comprising over 250 known species1. However, limitations due to missing data from their patchy fossil record have so far limited studies of sauropod body-size evolution to less than half their known diversity1. Here, I present models to confidently predict unknown limb-bone measurements in sauropods, resulting in a dataset 50% larger than previously assembled. Leveraging the emerging consilience among body mass estimation methods for fossil tetrapods, I then map sauropod body mass evolution through time in a phylogenetic context. Likelihood-based ancestral state reconstruction reveals that sauropods convergently surpassed maximum terrestrial mammalian body mass at least three dozen times over the course of 100 million years, on at least six landmasses and in at least five ecomorphologically disparate clades. Sauropod maximum body mass rapidly increased early in their evolutionary history from under 5,000 kg before levelling off around 40,000 kg (with notable exceptions)2, in a pattern similar to that observed in terrestrial mammals3.

Blade mass imbalance identification and estimation for three-bladed wind turbine rotor based on modal analysis

Early detection of rotor imbalance is one promising approach to address wind turbine durability. This work proposes a novel blade mass imbalance identification and estimation method for the wind turbine rotor. The method is capable of identifying the presence of a wind turbine rotor mass imbalance. In addition, if a mass imbalance exists in the wind turbine rotor, this method can identify which blade is imbalanced, and then quantify the imbalance severity. The remediation to balance the wind turbine rotor is also provided. The advantages of this method include that only a handful of data is required as input variables (which are the frequencies and modes shapes of all first-order modes) and the method can be applied to a stationary (non-rotating) rotor prior to the first operation. The decision-making process of this method is fast and reliable. The performance of the method is illustrated with the help of both numerical simulation and experimental tests. Model to test correlation and direct blade physical measurements are also conducted to validate the ability of the method to both identify mass imbalance and quantify the degree of mass imbalance. This method was demonstrated to be a high-fidelity tool for identifying the imbalance of the wind turbine rotor and it is also applicable to the wind turbines operating in the field. In addition, this method also shows strong potential to be extended to other wind turbine configurations and even other rotating machinery such as propellors and symmetric, repeated structures.

A Pig Mass Estimation Model Based on Deep Learning without Constraint.

The body mass of pigs is an essential indicator of their growth and health. Lately, contactless pig body mass estimation methods based on computer vision technology have gained attention thanks to their potential to improve animal welfare and ensure breeders' safety. Nonetheless, current methods require pigs to be restrained in a confinement pen, and no study has been conducted in an unconstrained environment. In this study, we develop a pig mass estimation model based on deep learning, capable of estimating body mass without constraints. Our model comprises a Mask R-CNN-based pig instance segmentation algorithm, a Keypoint R-CNN-based pig keypoint detection algorithm and an improved ResNet-based pig mass estimation algorithm that includes multi-branch convolution, depthwise convolution, and an inverted bottleneck to improve accuracy. We constructed a dataset for this study using images and body mass data from 117 pigs. Our model achieved an RMSE of 3.52 kg on the test set, which is lower than that of the pig body mass estimation algorithm with ResNet and ConvNeXt as the backbone network, and the average estimation speed was 0.339 s·frame-1 Our model can evaluate the body quality of pigs in real-time to provide data support for grading and adjusting breeding plans, and has broad application prospects.

Dielectric Constant-Based Grain Mass Estimation Using Radio Frequencies Sensing Technology

HighlightsRadio frequency sensing technology was used to estimate clean grain mass based on grain moisture content and grain properties.Multiple variable regression analysis was used to develop grain mass estimation model.A grain mass estimation model with high R2 was developed by introducing dielectric properties and phase angle.Parameter of dielectric constant e' indicated the domination of moisture content in grain mass estimation model.Abstract. Grain mass estimation is critical in many precision agriculture applications, especially in yield monitoring during harvest procedures. A new clean grain mass estimation method using Radio Frequency (RF) sensing technology is discussed in this paper. RF sensing technology is sensitive to moisture content and grain properties. In this study, a vector network analyzer (VNA) and a pair of horn antennas were used to collect phase shift and attenuation data from 1 to 18 GHz of grain samples (soybean, canola, and corn) on a static testbed in an anechoic chamber. Using multiple variable linear regression analysis, a comprehensive clean grain mass estimation model was developed based on the dielectric properties of the grain samples derived from the S-Parameters at 13 GHz. Dielectric (e') constant/properties and phase shift were introduced into the regression models and generated a grain mass estimation result with R2 values of 0.976, 0.977, and 0.989 for soybean, canola, and corn samples, respectively. The results indicate that RF sensing technology can reveal how grain attributes interact with electromagnetic fields at a certain frequency and has the potential to provide more accurate sensing methods for estimating grain mass in multiple precision agricultural applications. Keywords: Keywords., Dielectric properties, Grain mass estimation, Microwave frequency, Phase shifts, Radio frequency sensing.

Fissile mass estimation using β-delayed γ-rays and neutrons from fast and epithermal induced fissions

A potential method for mass estimation of shielded special nuclear materials (SNM) is proposed. The method uses fast and epithermal neutrons, generated by an external pulsed neutron source, to induce fission in SNM. The subsequent β-delayed γ-rays and neutrons, emitted by the fission products, are measured simultaneously by two 3He slab detectors and a coaxial high purity germanium (HPGe) detector, located around the tested material. Measuring simultaneously both, β-delayed γ-rays and neutrons measurement, offers improved sensitivity for fissile materials detecting.Laboratory examination of the method was performed in the Pulsed Neutron Interrogation Test Assembly (PUNITA), using CBNM (Central Bureau for Nuclear Measurements) samples of Uranium.

Calculation model for water mass entrained by the water exit of a particle using two projected images captured from orthogonal directions

Water exit is an extremely important phenomenon because it contributes to the development of physics and biology and its application in engineering. In particular, it is necessary to consider the spatiotemporal change in the water mass entrained around an object to correctly understand the scenario of water mass. In this study, a solid particle is launched vertically upward in water toward the surface. The volume of the water mass is estimated using images captured from two orthogonal directions while considering the spatiotemporal development of the water mass. In this study, two water mass estimation methods (Models 1 and 2) are constructed and applied to experimental results of the water exit reported in a previous study by the authors Takamure and Uchiyama (2020a). Model 1 considers the spatiotemporal development of the water mass and estimates the vertical motion of the particle under the influence of water mass with high accuracy when the water mass barely develops around the particle (i.e., submergence depth is small). However, when a large water mass entrains around the particle with an increase in the submergence depth, the estimation accuracy of the volume of the water mass decreases owing to the multilayer structure of the water mass. Model 2 improves the estimation accuracy of the volume of the water mass when the particle covers a large amount of water mass by considering the existence of the multilayer structure of the water mass. For the largest submergence depth in the experiment in this study on the water exit of the particle, the estimation accuracy of the volume of the water mass for Model 1 was approximately 50%, while that for Model 2 was over 80%. Further, the motion of the particle can be estimated with high accuracy even if the water mass is relatively complex and has a multilayer structure by incorporating the effects of the multilayer structure of the water mass into Model 1. This method enables us to estimate the water mass quantity easily and accurately by photographing an object from two orthogonal directions using two synchronized high-speed cameras. This water mass estimation technique is useful for understanding physical phenomena and for engineering applications.

Chandra follow-up of the Hectospec Cluster Survey: Comparison of caustic and hydrostatic masses and constraints on the hydrostatic bias

Context. Clusters of galaxies are powerful probes with which to study cosmology and astrophysics. However, for many applications, an accurate measurement of a cluster’s mass is essential. A systematic underestimate of hydrostatic masses from X-ray observations (the so-called hydrostatic bias) may be responsible for tension between the results of different cosmological measurements. Aims. We compare X-ray hydrostatic masses with masses estimated using the caustic method (based on galaxy velocities) in order to explore the systematic uncertainties of both methods and place new constraints on the level of hydrostatic bias. Methods. Hydrostatic and caustic mass profiles were determined independently for a sample of 44 clusters based on Chandra observations of clusters from the Hectospec Cluster Survey. This is the largest systematic comparison of its kind. Masses were compared at a standardised radius (R500) using a model that includes possible bias and scatter in both mass estimates. The systematics affecting both mass determination methods were explored in detail. Results. The hydrostatic masses were found to be systematically higher than caustic masses on average, and we found evidence that the caustic method increasingly underestimates the mass when fewer galaxies are used to measure the caustics. We limit our analysis to the 14 clusters with the best-sampled caustics where this bias is minimised (≥210 galaxies), and find that the average ratio of hydrostatic-to-caustic mass at R500 is M500,X/M500,C = 1.12−0.10+0.11. Conclusions. We interpret this result as a constraint on the level of hydrostatic bias, favouring small or zero levels of hydrostatic bias (less than 20% at the 3σ level). However, we find that systematic uncertainties associated with both mass estimation methods remain at the 10 − 15% level, which would permit significantly larger levels of hydrostatic bias.

XMM-Newton study of six massive, X-ray luminous galaxy cluster systems in the redshift range z = 0.25–0.5

Massive galaxy clusters are interesting astrophysical and cosmological objects to study, but they are relatively rare. In the redshift range z = 0.25–0.5 which is, for example, a favourable region for gravitational lensing studies, about 100 such systems are known. Most of them have been studied in X-rays. In this paper we study the six remaining massive clusters in this redshift interval in the highly complete CLASSIX (Cosmic Large-Scale Structure in X-rays) survey which have so far not been observed with sufficiently deep exposures in X-rays. With data from our new XMM-Newton observations we characterise their structures, derive X-ray properties such as the X-ray luminosity and intracluster medium temperature, and estimate their gas and total masses. We find that one cluster, RXCJ1230.7+3439, is dynamically young with three distinct substructures in the cluster outskirts and RXCJ1310.9+2157/RXCJ1310.4+2151 is a double cluster system. Mass determination is difficult in the systems with substructure. We therefore discuss several methods of mass estimation including scaling relations. In summary, we find that five of the six targets of study are indeed massive clusters as expected, while the last cluster RXCJ2116.2−0309 is a close projection of a distant and a nearby cluster which has led to a previous overestimation of its mass. In the XMM-Newton observation fields, we also find three low redshift clusters close to the targets which are also analysed and described here. In the field of RXCJ2116.2−0309, we discover serendipitously a highly variable X-ray source which has decreased its flux within a year by more than a factor of eight. This source is most probably an active galactic nucleus (AGN).

Testing a generalized leaf mass estimation method for diverse tree species and climates of the continental United States.

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available.

The Fundamental Planes of black hole activity for radio-loud and radio-quiet quasars

ABSTRACT We examine the Fundamental Plane of black hole activity for correlations with redshift and radio loudness in both radio-loud and radio-quiet quasar populations. Sources are compiled from archival data of both radio-loud and radio-quiet quasars over redshifts 0.1 &amp;lt; z &amp;lt; 5.0 to produce a sample of 353 sources with known X-ray, radio, and black hole mass measurements. A Fundamental Plane of accretion activity is fit to a sample of radio-loud and radio-quiet quasars, and we find a dichotomy between radio-loud and radio-quiet sources. The set of best-fitting equations that best describe the two samples are log LR = (1.12 ± 0.06)log LX − (0.20 ± 0.07)log M − (5.64 ± 2.99) for our radio-loud sample and log LR = (0.48 ± 0.06)log LX + (0.50 ± 0.08)log M + (15.26 ± 2.66) for our radio-quiet sample. Our results suggest that the average radio-quiet quasar emission is consistent with advection-dominated accretion, while a combination of jet and disc emission dominates in radio-loud quasars. We additionally examine redshift trends amongst the radio-loud and radio-quiet samples, and we observe a redshift dependence for the Fundamental Plane of radio-loud quasars. Lastly, we utilize the Fundamental Plane as a black hole mass estimation method and determine it useful in studying systems where standard spectral modelling techniques are not viable.