Density Model Research Articles

A Stochastic-Geometrical Framework for Object Pose Estimation Based on Mixture Models Avoiding the Correspondence Problem

Pose estimation of rigid objects is a practical challenge in optical metrology and computer vision. This paper presents a novel stochastic-geometrical modeling framework for object pose estimation based on observing multiple feature points. This framework utilizes mixture models for feature point densities in object space and for interpreting real measurements. Advantages are the avoidance to resolve individual feature correspondences and to incorporate correct stochastic dependencies in multi-view applications. First, the general modeling framework is presented, second, a general algorithm for pose estimation is derived, and third, two example models (camera and lateration setup) are presented. Numerical experiments show the effectiveness of this modeling and general algorithm by presenting four simulation scenarios for three observation systems, including the dependence on measurement resolution, object deformations and measurement noise. Probabilistic modeling utilizing mixture models shows the potential for accurate and robust pose estimations while avoiding the correspondence problem.

Escape Velocity Mass of A1063

We measure the radius–velocity phase-space edge profile of A1063 using galaxy redshifts from Karman et al. and Mercurio et al. Combined with a cosmological model and after accounting for interlopers and sampling effects, we infer the escape velocity profile. Using the Poisson equation, we then directly constrain the gravitational potential profile and find excellent agreement between three different density models. For the Navarro–Frenk–White profile, we find log10(M 200,crit) = 15.40−0.12+0.06 M ⊙, consistent to within 1σ of six recently published lensing masses. We argue that this consistency is due to the fact that the escape technique shares no common systematics with lensing other than radial binning. These masses are 2–4σ lower than estimates made using X-ray data, in addition to the Gómezet al. velocity dispersion estimate. We measure the 1D velocity dispersion within r 200 to be σv=1477−99+87 km s−1, which combined with our escape velocity mass, brings the dispersion for A1063 in-line with hydrodynamic cosmological simulations for the first time.

Demonstrating a Hybrid Machine Learning Approach for Snow Characteristic Estimation Throughout the Western United States

AbstractSnow is a critical component of global climate and provides water resources to over 1 billion people worldwide. Yet current measurement methods and modeling techniques lack the ability to fully capture snow characteristics such as snow water equivalent (SWE) and density across variable landscapes. In recent years, physics‐informed machine learning (ML) methods have demonstrated promise for combining data‐driven learning and physical information. However, this capability has not been widely explored within snow hydrology. Here, we develop a “hybrid” model that applies ML informed by outputs from a physical model and assess whether it provides more accurate estimations of SWE and snow density. We trained and evaluated models at 49 SNOw TELemetry locations spanning a range of snow climates in the western US using 9 years of daily data. The research addressed two questions. In the first, the performance of the hybrid model was compared against a plain neural network (long short‐term memory, Long‐Short Term Memory), a high‐quality physical model, and a statistical snow density model. The second question focused on how regionally trained hybrid models compared to a westwide model as well as their transferability between multiple snow regions. The results showed that combining physical information and ML reduced SWE Root Mean Square Error by 35% compared to a physical model and 51% compared to a neural network. Additionally, regional training only provided minimal benefits compared with a westwide model. These findings indicate that a hybrid approach can yield more accurate snowpack characterization than either physical snow models or ML alone.

Application of the Bagged Trees Technique on Retrieving the Nighttime Ionospheric Peak Density From OI‐135.6 nm Airglow

AbstractThe NASA global‐scale observations of the limb and disk (GOLD) mission is a measurement opportunity to scan the far ultraviolet airglow at ∼134–162 nm over the American Hemisphere since October 2018. The FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission has provided thousands of daily radio occultation soundings in the low‐ and mid‐latitude regions since July 2019. The nighttime OI–135.6 nm emission is mainly through radiative recombination, and the radiance is used to derive the peak electron density. Comparison with corresponding F7/C2 observations demonstrates good correlation in low‐latitudes, while is overestimated near mid‐latitudes in winter, induced by the photoelectrons emanating from magnetically conjugate Hemisphere. The machine learning technique Bagged Trees is implemented to develop an intensity to peak density model training from GOLD and F7/C2 observations. The validation demonstrates that Bagged Trees peak‐density has less influence from conjugate photoelectrons and indicates the power of machine learning techniques for geophysics data processing.

3D large-scale forward modeling of gravitational fields using triangular spherical prisms with polynomial densities in depth

To take the sphericity of the Earth into account, tesseroids are often utilized as grid elements in large-scale gravitational forward modeling. However, such elements in a latitude–longitude mesh suffer from degenerating into poorly shaped triangles near poles. Moreover, tesseroids have limited flexibility in describing laterally variable density distributions with irregular boundaries and also face difficulties in achieving completely equivalent division over a spherical surface that may be desired in a gravity inversion. We develop a new method based on triangular spherical prisms (TSPs) for 3D gravitational modeling in spherical coordinates. A TSP is defined by two spherical surfaces of triangular shape, with one of which being the radial projection of the other. Due to the spherical triangular shapes of the upper and lower surfaces, TSPs enjoy more advantages over tesseroids in describing mass density with different lateral resolutions. In addition, such an element also allows subdivisions with nearly equal weights in spherical coordinates. To calculate the gravitational effects of a TSP, we assume the density in each element to be polynomial along radial direction so as to accommodate a complex density environment. Then, we solve the Newton’s volume integral using a mixed Gaussian quadrature method, in which the surface integral over the spherical triangle is calculated using a triangle-based Gaussian quadrature rule via a radial projection that transforms the spherical triangles into linear ones. A 2D adaptive discretization strategy and an extension technique are also combined to improve the accuracy at observation points near the mass sources. The numerical experiments based on spherical shell models show that the proposed method achieves good accuracy from near surface to a satellite height in the case of TSPs with various dimensions and density variations. In comparison with the classical tesseroid-based method, the proposed algorithm enjoys better accuracy and much higher flexibility for density models with laterally irregular shapes. It shows that to achieve the same accuracy, the number of elements required by the proposed method is much less than that of the tesseroid-based method, which substantially speeds up the calculation by more than 2 orders. The application to the tessellated LITHO1.0 model further demonstrates its capability and practicability in realistic situations. The new method offers an attractive tool for gravity forward and inverse problems where the irregular grids are involved.

Prediction of separation density of pulsed fluidized bed based on bidisperse particle characteristics

ABSTRACT Fluidized bed technology plays a vital role in petroleum, chemical, and coal separation. The dry separation of coarse coals with a particle size of >6 mm has been efficiently achieved using the fluidized bed. However, for fine coal with a particle size of 6–1 mm, ordinary fluidized bed technology is unable to effectively separate it. In light of this issue, the present study incorporates airflow vibration energy into conventional fluidized beds, with a specific focus on the separation process of fine coal. The investigation explores the variation pattern of separation density and delves deep into the influencing factors affecting coal preparation through bed density. A theoretical model for separation density is established based on the characteristics of bidisperse particles, which is subsequently validated and calibrated using dry separation experiments conducted on fine coal. The error is controlled within 10% while achieving a possible deviation E as low as 0.095 g/cm3.

A Comprehensive Analysis of the Habitability of TRAPPIST-1 Exoplanets

TRAPPIST-1 is the most studied star system other than our Solar System. The announcement of the discovery of the system fostered international attention for its potential to harbor extraterrestrial life, and maybe one day even human life. This paper aims to evaluate the validity of this excitement by interpreting and compiling all available information regarding factors that could potentially affect habitability. To do this, we look at all data regarding the Habitable Zone and Water Presence, Tidal Forces, and Atmospheric Composition resulting in Extreme UV (EUV) Flux. Specifically, we compare Transmission spectra of the four planets in the habitable zone with synthetic, potentially habitable atmospheres, we compare composition and density of the planets with Earth and other density models to determine water composition, and we consider both high and low stellar activity to determine EUV flux in an anoxic, Earth-like and very thin atmosphere. This paper shows that on net, due to the lack of a thick atmosphere, various issues arise that lead to the conclusion that the planetary system is uninhabitable for even the most resistant terrestrial organisms. This set of considerations could be used in studying other planetary systems and evaluating their habitability as well.

Population status of the range-restricted Red Lark Calendulauda burra in a conservation area stronghold

Many desert birds are vulnerable to anthropogenic global warming and landscape transformation, particularly in the case of range-restricted species. We quantified suitable habitat and the current population of Red Larks Calendulauda burra (family Alaudidae), a range-restricted southern African species, in a conservation area thought to be one of the species’ strongholds — the Black Mountain Mine Conservation Area (BMMCA) in the Northern Cape, South Africa. We modelled habitat suitability and estimated home range sizes using telemetry data and kernel density estimates. Home range sizes did not differ between males (42.4 ± 28.3 ha; n = 5) and females (41.9 ± 31.3 ha; n = 5). Transect data and home range density models indicated densities of 0.086–0.127 individuals·ha-1 for adults in suitable habitat. An estimated 33.2% (30.2 km2) of the BMMCA is suitable habitat for Red Larks, suggesting a population of 232–382 individuals. This population estimate is equivalent to <50% of the population estimated for the BMMCA in 1989. Our findings here, combined with a recent analysis showing that much of the species’ range will become thermally unsuitable for its persistence by the end of the 21st century, suggest that urgent conservation action is needed to ensure the Red Lark’s future in coming decades.

Emle-engine: A Flexible Electrostatic Machine Learning Embedding Package for Multiscale Molecular Dynamics Simulations.

We present in this work the emle-engine package (https://github.com/chemle/emle-engine)─the implementation of a new machine learning embedding scheme for hybrid machine learning potential/molecular-mechanics (ML/MM) dynamics simulations. The package is based on an embedding scheme that uses a physics-based model of the electronic density and induction with a handful of tunable parameters derived from in vacuo properties of the subsystem to be embedded. This scheme is completely independent of the in vacuo potential and requires only the positions of the atoms of the machine learning subsystem and the positions and partial charges of the molecular mechanics environment. These characteristics allow emle-engine to be employed in existing QM/MM software. We demonstrate that the implemented electrostatic machine learning embedding scheme (named EMLE) is stable in enhanced sampling molecular dynamics simulations. Through the calculation of free energy surfaces of alanine dipeptide in water with two different ML options for the in vacuo potential and three embedding models, we test the performance of EMLE. When compared to the reference DFT/MM surface, the EMLE embedding is clearly superior to the MM one based on fixed partial charges. The configurational dependence of the electronic density and the inclusion of the induction energy introduced by the EMLE model leads to a systematic reduction in the average error of the free energy surface when compared to MM embedding. By enabling the usage of EMLE embedding in practical ML/MM simulations, emle-engine will make it possible to accurately model systems and processes that feature significant variations in the charge distribution of the ML subsystem and/or the interacting environment.

Monodisperse and polydisperse packings of equiaxed concave particles: Effects of angularity and roughness

In this study, the monodisperse and polydisperse packings with intentionally designed angularity index (AI) and roughness (RG) distributions were constructed via DEM simulations. The corresponding packing properties, such as packing density, coordination number, connectivity number and anisotropy of branch vectors were comprehensively examined. The isolated effects of AI and RG on packing properties were investigated based on simulation results of monodisperse packings. The study also explored how the polydispersity of AI and RG influences packing properties, with insights drawn from the simulation results of polydisperse packings. By analyzing variations in pore volume, the study proposed predictive models that account for the contributions of AI and RG for packing density of monodisperse packings. These models were further used as the foundation for constructing predictive models for packing density of polydisperse packings.

Equivalent power densities model for multi-pulse ultrashort pulsed laser processing

Equivalent power densities model for multi-pulse ultrashort pulsed laser processing

Migration of Fast Magnetosonic Waves in the Magnetosphere With a Plasmaspheric Plume

AbstractPlasmaspheric density structures are considered to control the propagation trajectories of fast magnetosonic (MS) waves in the inner magnetosphere. However, whether the plasmaspheric plume can effectively alter the propagation of MS waves remains unknown. Based on the analytical model of plasma density, ray tracing simulations are performed to investigate the propagation of exactly perpendicular MS waves in the equatorial plane in the magnetosphere containing a plasmaspheric plume. We find that plasmatrough and plume MS waves propagating toward the plasmaspheric plume can be reflected into the plasmaspheric core by the plume, then potentially migrating globally and thus quasi‐trapped inside the plasmaspheric core. The simulations also indicate that lower‐frequency MS waves approaching the plasmaspheric plume are more easily reflected and quasi‐trapped inside the plasmaspheric core. Our findings illustrate a previously unexplored way that plasmatrough MS waves could access and be trapped inside the plasmaspheric core via azimuthal plasmaspheric density structures.

A comparative approach for estimating microstructural characteristics of BaTi1−xZrxO3 (0.0 ≤ x ≤ 0.3) nanoparticles via X-ray diffraction patterns

Barium titanate materials are currently a special topic for scientific research due to their effective technological applications. The tetragonal BaTi1-xZrxO3 (0.0 ≤ x ≤ 0.3) nanoparticles (NPs) were synthesized using a modified citrate technique. The current work provides a comparative approach for the calculation of crystallite size, stress, strain, and elastic characteristics based on X-ray diffraction (XRD) patterns. Various models have been developed to analyze XRD data; these models differ in their assumptions, mathematical approaches, and the type of information they provide. The Scherrer model ignores lattice micro-structures that develop in nanostructures, such as intrinsic strain. To overcome such drawbacks, three Williamson-Hall models, (the uniform deformation model (UDM)), the uniform stress deformation model (USDM), and the uniform deformation energy density model (UDEDM) have been discussed. According to the USDM model, with increasing Zr ion concentrations, interplanar space increases, causing a drop in Young’s modulus. All the previous approaches take into account the diffraction angle (2θ)-dependent peak broadening, which is thought to represent a combination of size and strain-driven induced broadening.Graphical

A simple model for density anomaly and excess thermodynamic properties of alcohol-like in water-like mixture

ABSTRACT Molecular dynamics simulations were employed to gain insights into the density anomaly and excess thermodynamic properties of a binary mixture composed of a solvent resembling water and a solute resembling alcohol. In our model, both fluids were represented by spherical particles interacting through effective potentials: a two-length scale potential for the solvent–solvent interaction while purely repulsive potentials were used for the solute–solute and solute–solvent interactions. The temperature of maximum density (TMD) present in the pure solvent system is also observed in our mixture. The TMD exhibits a positive deviation in the mixture when compared to the pure solvent fluid under high-pressure conditions, while it shows a negative deviation under low-pressure conditions what is consistent with experiments. The excess volume, enthalpy and isobaric heat capacity show extreme values at x ≈ 0.4 . Using the radial distribution function and the translational order parameter we suggest that this behaviour can be explained in terms of solute particles caged in solvent network.

Assessment of eco‐environmental changes along roads in the Qinghai‐Tibetan Plateau based on remote sensing data: A case study of the Sino‐Nepalese Transport Corridor

AbstractUnderstanding the evolutionary patterns of ecological quality in transport corridor areas and their driving mechanisms is essential for maintaining regional sustainable development. However, an integrated assessment of the ecology of plateau transport corridors is still lacking. This study focuses on the Sino‐Nepalese Transport Corridor (SNTC) and comprehensively evaluates the spatio‐temporal patterns and evolution of eco‐environmental quality from 2000 to 2021 based on remote sensing data and quantifies and explores the potential driving factors. The results indicate significant spatial heterogeneity in the eco‐environmental quality of the SNTC, with an overall bipolar “northeast‐southwest” distribution pattern. The area of poor and worse quality is the largest (32,471.75 km2), accounting for about 47.20% of the total area of the SNTC, distributed in the plateau bare land and the Yarlung Tsangpo River valley. From 2000 to 2021, the eco‐environmental quality showed an initial decline and then an increase, with a relatively gentle overall trend. The increase in the remote sensing ecological index in Region (I) from 0.314 in the first 10 years to 0.334 in the second 10 years, an increase of 6.37%, was mainly attributed to afforestation projects in the “One River and two tributaries Nature Reserve” region. The eco‐environmental quality of the SNTC is influenced by multiple factors including human activities, climate, and topography. Grazing intensity, population density, and digital elevation model are the main negative disturbance factors, while temperature, precipitation, gross domestic product, and afforestation areas promote eco‐environmental quality. In particular, afforestation projects partially offset the negative impact of human activities. These results will provide useful information for understanding the complex effects of human activities and natural factors on the eco‐environmental quality in the plateau transport corridor region and for formulating ecological conservation policies.

Differential responses in water-use strategies of evergreen (Ligustrum lucidum) and deciduous (Robinia pseudoacacia) trees to tunnel excavation in a subtropical karst trough valley

Tunnel(s) excavation could decrease groundwater levels and soil water storage and consequently change trees growth conditions in karst areas. However, the adaptation mechanisms of different tree species to altered hydrological conditions caused by tunnel(s) construction in karst areas remain unknown. Here, we investigated the water-use strategies of two different tree species (the evergreen Ligustrum lucidum and deciduous Robinia pseudoacacia) in response to tunnel excavation in a typical subtropical karst trough valley by using the sap flow density, stable isotopes (δ13C, δ2H, and δ18O) and MixSIAR models. Tunnel excavation resulted in a 3.7–6.3 % reduction in soil moisture content (SMC) in Zhongliang Mountain Karst Trough (ZMKT) Valley. Both tree species demonstrated reduced transpiration in tunnel-affected valley (TAV) compared to tunnel-unaffected valley (TUAV) (transpiration in TAV was only 77.6 % of TUAV), with evergreen trees exhibiting a smaller decline in transpiration compared to deciduous trees. Furthermore, the proportions of karst groundwater utilized by evergreen and deciduous tree species in TAV were 8.3 % and 5.0 % higher than in TUAV, respectively, with evergreen tree species exhibiting greater absorption of karst groundwater than deciduous tree species. Meanwhile, the average leaf δ13C values and WUEi (intrinsic water-use efficiency) of both tree species in TAV were 0.9 ‰ and 15.7 μmol/mol higher than those in TUAV, respectively, with evergreen trees exhibiting a smaller increase in WUEi compared to deciduous trees. These results indicate that tunneling-induced water stress events may prompt trees to adopt more conservative water-use strategies. Evergreen tree species rely on more karst groundwater absorption to sustain relatively higher transpiration rates, and deciduous trees adapt to severe water stress by increasing their WUEi or shedding leaves to minimize transpiration. Our finding contributes to the evaluation of eco-hydrological processes in karst areas affected by tunnel(s) excavation.

Supplemental structured surveys and pre-existing detection models improve fine-scale density and population estimation with opportunistic community science data

Density and population estimates aid in conservation and stakeholder communication. While free and broadly available community science data can effectively inform species distribution models, they often lack the information necessary to estimate imperfect detection and area sampled, thus limiting their use in fine-scale density modeling. We used structured distance-sampling surveys to model detection probability and calculate survey-specific detection offsets in community science models. We estimated density and population for 16 songbird species under three frameworks: (1) a fixed framework that assumes perfect detection within a specified survey radius, (2) an independent framework that calculates offsets from an independent source, and (3) a calibration framework that calculates offsets from supplemental surveys. Within the calibration framework, we examined the effects of calibration dataset size and data pooling. Estimates of density and population size were consistently biased low in the fixed framework. The independent and calibration frameworks produced reliable estimates for some species, but biased estimates for others, indicating discrepancies in detection probability between structured and community science surveys. The calibration framework produced reliable population estimates with as few as 10 calibration surveys with positive detections. Data pooling dramatically decreased bias. This study provides conservationists and managers with a cost-effective method of estimating density and population.

The electron density at the midpoint of the plasmapause

The high density plasmasphere in the magnetosphere is often separated from a lower density region outside of the plasmasphere, called the plasmatrough, by a sharp gradient in electron density called the plasmapause. Here we use plasmapause events identified from electron density data from the ISEE, CRRES, Polar, and IMAGE missions, and the nonlinear genetic algorithm TuringBot, to find models for the electron density at the midpoint of the plasmapause, ne,pp. A good model for ne,pp should include dependence on L, which is the strongest dependence. But models can be improved by including weaker dependencies on the magnetic local time, MLT, the solar EUV index F10.7, and geomagnetic activity as indicated by averages of Kp and AE. The most complicated model that we present predicts ne,pp within a factor of 1.64, and is within the range of observed plasmapause densities for about 96% of our events. These models can be useful for separating plasma populations into plasmasphere-like and plasmatrough-like populations. We also make available our database of electron density measurements categorized into various populations.

Molecular dynamics investigations on the thermophysical property of fatty acid ethyl esters

To develop innovative fuel additives or substitutes and design spray-assisted internal combustion engines, it is important to have a comprehensive understanding of the thermophysical characteristics of biodiesels. Rooted in statistical mechanics, the molecular simulation method can predict the thermophysical properties over a wide range of temperatures and pressures, which overcomes the challenges of experimental measurements and facility restrictions. This work focused on the evaluation of three classical force fields (FFs) in terms of their ability to predict the thermophysical properties of ethyl-caprylate (EtC8:0), ethyl-caprate (EtC10:0), ethyl-laurate (EtC12:0), ethyl-myristate (EtC14:0), ethyl-palmitate (EtC16:0), and ethyl-stearate (EtC18:0) within a temperature range of (303.15 to 403.15) K and pressures up to 100 MPa. The Optimized Potential for Liquid Simulations–All atom and its modification (OPLS and L–OPLS) and Transferable Potential for Phase Equilibria–United Atom (TraPPE–UA) FFs were evaluated. These findings demonstrated that the L–OPLS FF, when applied in the isobaric-isothermal (NPT) and canonical ensembles (NVT), effectively replicated the experimental data and semi-empirical models for density, viscosity, diffusivity coefficient, and interfacial behavior at elevated temperatures and pressures. Moreover, the utilization of the radial distribution function, radius of gyration, and end-to-end distance distribution in the analysis of liquid conformations allowed for a comprehensive investigation of the microscopic structure. This work provides the theoretical methodologies and empirical evidence for the advancement of biodiesel and other petrochemicals.

Prediction model for low bone mass mineral density in type 2 diabetes: an observational cross-sectional study.

Considering the prevalence of type 2 diabetes (T2D), osteoporosis should be considered a serious complication. However, an effective tool for the assessment of low bone mass mineral density (BMD) in T2D patients is not currently available. Therefore, the aim of our study was to establish a simple-to-use risk assessment tool by exploring risk factors for low BMD in T2D patients. This study included 436 patients with a low BMD and 381 patients with a normal BMD. Multiple logistic regression analysis was performed to evaluate risk factors for low BMD in T2D patients. A nomogram was then developed from these results. A receiver operating characteristic (ROC) curve, calibration plot, and goodness-of-fit test were used to validate the nomogram. The clinical utility of the nomogram was also assessed. Multivariate logistic regression indicated that age, sex, education, body mass index (BMI), fasting C-peptide, high-density cholesterol (HDL), alkaline phosphatase (ALP), estimated glomerular filtration rate (eGFR), and type I collagen carboxy terminal peptide (S-CTX) were independent predictors for low BMD in T2D patients. The nomogram was developed from these variables using both the unadjusted area under the curve (AUC) and the bootstrap-corrected AUC (0.828). Calibration plots and the goodness-of-fit test demonstrated that the nomogram was well calibrated. The nomogram-illustrated model can be used by clinicians to easily predict the risk of low BMD in T2D patients. Our study also revealed that common factors are independent predictors of low BMD risk. Our results provide a new strategy for the prediction, investigation, and facilitation of low BMD in T2D patients.