Density Model Research Articles

Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China

The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.

Binary aqueous solutions of choline salts: Determination and modelling of liquid density (298.15 or 313.15 K) and Vapour Pressure Osmometry (313.15 K)

Salts composed of the cholinium cation (such as choline hydroxide and choline chloride) have become one of the main reactants for the synthesis of greener ionic liquids, but, for a proper description of the aqueous solutions of these strong electrolytes by thermodynamic modelling, reliable data on liquid density (ρ) and vapour pressure (p) are needed, which are often unavailable. So, in this work, the liquid density (ρ) of binary aqueous solutions of choline bicarbonate ([Ch][Bic]), choline (2R,3R)-bitartrate ([Ch][Bit]), choline chloride ([Ch]Cl), choline dihydrogen citrate ([Ch][H2Cit]) and choline hydroxide ([Ch]OH) were measured at 298.15 or 313.15 K and 0.1 MPa and correlated using second-degree polynomials with salt molality (m), obtaining determination coefficients (R2) higher than 0.9974. Furthermore, the osmotic coefficients (ϕ) of these binaries were determined using vapour pressure osmometry (VPO), at 313.15 K and 0.1 MPa, being satisfactorily described by the Extended Pitzer Model of Archer, which yielded low values of standard deviation (3.58<σϕ·103<12.86). Then, the mean molal activity coefficients (γ±) and excess Gibbs free energies (GE/RT) were calculated, following the order [Ch][H2Cit] > [Ch][Bit] > [Ch][Bic] > [Ch]OH > [Ch]Cl, which corresponds to the decreasing polarity of the choline salts and agrees with the empirical law of matching water affinities (LMWA).

Prediction model for the quantity and density of first-order branches of Larix kaempferi in eastern area of Liaoning Province, China.

As an important branch characteristic factor, the quantity of branches could influence crown structure, tree growth, and wood quality. Taking Larix kaempferi plantation in Dagujia Forest Farm, Qingyuan County, Liao-ning Province as the research object, we developed a mixed effect prediction model of the first-order branches quantity of L. kaempferi including sprouting branches based on the negative binomial distribution model, and a mixed effect prediction model of the first-order branches density of L. kaempferi including sprouting branches based on the negative exponential model. The results showed that the mixed effect model considering sample level as the random effect effectively decreased the heteroscedasticity and autocorrelation. The fitting goodness was better than the traditional model. The quantity of the first-order branches increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches quantity as the random effect parameter was determined as the optimal model, with Ra2=0.552 and the RMSE=7.242. As for the density of the first-order branches, the heteroscedasticity and autocorrelation were also reduced when the random effect was added. The density of the first-order increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches density model and branch depth as random effects was determined as the optimal model, with Ra2=0.792 and the RMSE=4.447. The model for branch quantity and density of L. kaempferi constructed would lay an important foundation for making scientific forest management plans and improving wood quality.

CryoDRGN-ET: deep reconstructing generative networks for visualizing dynamic biomolecules inside cells.

Advances in cryo-electron tomography (cryo-ET) have produced new opportunities to visualize the structures of dynamic macromolecules in native cellular environments. While cryo-ET can reveal structures at molecular resolution, image processing algorithms remain a bottleneck in resolving the heterogeneity of biomolecular structures in situ. Here, we introduce cryoDRGN-ET for heterogeneous reconstruction of cryo-ET subtomograms. CryoDRGN-ET learns a deep generative model of three-dimensional density maps directly from subtomogram tilt-series images and can capture states diverse in both composition and conformation. We validate this approach by recovering the known translational states in Mycoplasma pneumoniae ribosomes in situ. We then perform cryo-ET on cryogenic focused ion beam-milled Saccharomyces cerevisiae cells. CryoDRGN-ET reveals the structural landscape of S. cerevisiae ribosomes during translation and captures continuous motions of fatty acid synthase complexes inside cells. This method is openly available in the cryoDRGN software.

Statistics and Models of the Electron Plasma Density From the Van Allen Probes

AbstractWe use the full NASA Van Allen Probes mission (2012–2019) to extract the electron plasma density from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) and Electric Field and Waves (EFW) instruments and discuss the evolution of the plasmasphere. We generate new statistics including mean and standard deviations of the plasma density with respect to L‐shell, magnetic local time (MLT), and various geomagnetic indices. These statistics are generated to be applied in radiation belt physics and space weather codes (with fits provided). The mean plasmasphere is circular around Earth with respect to MLT for Kp &lt; 1. The mean 100 cm−3 level line is above L = 5 and mean 10 cm−3 level expands above the Van Allen Probes apogee for Kp &lt; 1. The outer electron belt lies within the plasmasphere for 60% of all times. As activity increases (Kp &gt; 2), a gradual MLT asymmetry forms with higher mean density in the afternoon sector due to plumes expanding outward. Conversely, the mean density decreases on the dawn and night sectors. The mean density is between ∼500 and ∼50 cm−3 between L ∼ 4 and L ∼ 6 during quiet and moderately active times (Kp &lt; 3), representing ∼80% of all times. Statistics in regions of high density below L = 2 are underdefined for intense activity. The highest standard deviation of density represents a factor 2.5 to 3 times the mean above L = 5 and for active times. We find the percent difference between the EFW and EMFISIS densities is bounded by ±20% for quiet and moderate activity (Kp &lt; 5) and goes up to ±100% for extreme activity.

Inversion of Gravity Data Constrained by a Magnetotelluric Resistivity Model: Application to the Asal Rift, Djibouti

AbstractBefore exploiting a geothermal resource in a volcanic setting such as the Asal rift, it is necessary to acquire a better knowledge of the subsoil, with the objective of locating the geothermal reservoir and evaluating the resource characteristic (permeability, temperature, etc.). For this type of resource, geophysical exploration methods are essential (such as gravimetry, magnetotellurics, etc.). However, a particular data type does not necessarily have the resolution and sensitivity. Furthermore, individual inversions of these geophysical data face the ambiguity of the non‐uniqueness of the inverse solution. In this paper, we present a new linear approach of gravity data using the constraint of a MT resistivity model. We coupled the resistivity and density using inversion cross‐gradients and the linear correlations. The approach was tested and validated on synthetic data and applied to gravity and MT data in the Asal Rift. Multiple inversions with different levels of coupling provided a series of density models. We applied the principal component analysis (PCA) technique to assess these models. We were able to define two dominant processes acting differently on the density and resistivity distribution at depth, namely the geothermal activity of the rift and the structural control of active tectonics.

The Implications of ‘Oumuamua on Panspermia

Panspermia is the hypothesis that life originated on Earth from the bombardment of foreign interstellar ejecta harboring polyextremophile microorganisms. Since the 2017 discovery of the interstellar body ‘Oumuamua (1I/2017 U1) by the Pan-STARRS telescope, various studies have reexamined panspermia based on updated number density models that accommodate for ‘Oumuamua’s properties. By utilizing ‘Oumuamua’s properties as an anchor, we estimate the mass and number density of ejecta in the interstellar medium (ρ m [units of kg au−3] and ρ n [units of au−3]). We build upon prior work by first accounting for the minimum ejecta size to shield microbes from supernova radiation. Second, we estimate the total number of impact events C n on Earth after its formation and prior to the emergence of life (≈0.8 Gyr). We derive a conditional probability relation for the likelihood of panspermia for Earth specifically of <10−5, given a number of factors including f B, the fraction of ejecta harboring extremophiles and other factors that are poorly constrained. However, we find that panspermia is a plausible potential life-seeding mechanism for (optimistically) potentially up to ∼105 of the ∼109 Earth-sized habitable zone worlds in our Galaxy.

Fuel property modeling by high-speed gas chromatography coupled with partial least squares data analysis

Partial least squares (PLS) regression is a valuable chemometric tool for property prediction when coupled with gas chromatography (GC). Since the separation run time and stationary phase selection are crucial for effective PLS modeling, we study these GC parameters on the prediction of viscosity, density and hydrogen content for 50 aerospace fuels. Due to the diversity of compounds in the fuels (primarily alkanes, cycloalkanes, and aromatics), we explore both polar and non-polar stationary phase columns. The robustness for the PLS models was evaluated by their normalized root mean square error of cross-validation (NRMSECV). PLS models built for viscosity across 1-min, 3-min, 7-min, and 10-min time window (TW) high-speed GC separations produced nearly the same NRMSECV with the polar column data with an average (standard deviation) of 4.41 % (0.34 %) versus the non-polar column data of 4.69 % (0.15 %). In contrast, while the NRMSECV of density modeling with the polar column data varied more than the viscosity models, averaging 7.54 % (0.67 %), the non-polar column data produced a significantly higher average NRMSECV of 10.06 % (0.35 %). Similarly, for hydrogen content, the NRMSECV with the polar column data averaged 9.50 % (0.87 %), which was significantly lower than the NRMSECV with the non-polar column data averaging 12.10 % (0.88 %). We also investigated the impact of smoothing the GC data on the corresponding PLS models. By applying varying degrees of smoothing, we can effectively obtain similar chromatographic peak patterns in a shorter TW. For example, a 10-min smoothed chromatogram appears like the 1-min separation with no smoothing but resulted in nearly the same NRMSECV. Overall, the fast separation with a 1-min TW produced robust PLS models for viscosity with either stationary phase column, whereas for density and hydrogen content the polar stationary phase column produced superior PLS models, thus with proper stationary phase selection, a fast separation run time could be readily applied with optimal PLS property modeling results.

Determining the lifetime distribution using fractional moments with maximum entropy

Here we propose a model-free, non-parametric method to solve an ill-posed inverse problem arising in several fields. It consists of determining a probability density of the lifetime, or the probability of survival of an individual, from the knowledge of the fractional moments of its probability distribution. The two problems are related, but they are different because of the natural normalization condition in each case. We provide a maximum entropy based approach to solve both problems. This problem provides a concrete framework to analyze an interesting problem in the theory of exponential models for probability densities. The central issue that comes up concerns the choice of the fractional moments and their number. We find that there are many possible choices that lead to solutions compatible with the data but in all of them, no more than four moments are necessary. The fact that a given data set can be accurately described by different exponential families poses a challenging problem for the model builder when attaching theoretical meaning to the resulting exponential density.

Hirshfeld atom refinement and dynamical refinement of hexagonal ice structure from electron diffraction data.

Reaching beyond the commonly used spherical atomic electron density model allows one to greatly improve the accuracy of hydrogen atom structural parameters derived from X-ray data. However, the effects of atomic asphericity are less explored for electron diffraction data. In this work, Hirshfeld atom refinement (HAR), a method that uses an accurate description of electron density by quantum mechanical calculation for a system of interest, was applied for the first time to the kinematical refinement of electron diffraction data. This approach was applied here to derive the structure of ordinary hexagonal ice (Ih). The effect of introducing HAR is much less noticeable than in the case of X-ray refinement and it is largely overshadowed by dynamical scattering effects. It led to only a slight change in the O-H bond lengths (shortening by 0.01 Å) compared with the independent atom model (IAM). The average absolute differences in O-H bond lengths between the kinematical refinements and the reference neutron structure were much larger: 0.044 for IAM and 0.046 Å for HAR. The refinement results changed considerably when dynamical scattering effects were modelled - with extinction correction or with dynamical refinement. The latter led to an improvement of the O-H bond length accuracy to 0.021 Å on average (with IAM refinement). Though there is a potential for deriving more accurate structures using HAR for electron diffraction, modelling of dynamical scattering effects seems to be a necessary step to achieve this. However, at present there is no software to support both HAR and dynamical refinement.

Impact of the UNB topographical density model on precise geoid determination in the high mountainous region

A precise gravimetric geoid model is computed utilising Stokes’s formula, supposing an absence of topography above the geoid. Subsequently, the geoid model undergoes a simple correction for topographic masses, the constant density is taken as 2670 kg/m3. Notably, the true density of topographical mass deviates by approximately ±20% from this constant value. Recently, the University of New Brunswick in Canada released a global topographical density model at a 30 arc-second resolution. This paper investigates the impact of incorporating this model on the precision of the gravimetric geoid within a mountainous region in the Colorado test region. Numerical findings reveal that variations in geoid undulation attributable to this model can extend to a few decimetres, a discrepancy that cannot be neglected in geoid modelling with one-centimetre precision. It is therefore recommended that the considerable impact of topographic density fluctuations on geoid determination be taken into account, particularly in mountainous regions.

Non-stationary modeling and simulation of strong winds

Wind velocity is usually assumed to obey a stationary stochastic process in wind engineering, and this may cause significant bias in describing extremely severe strong wind such as typhoons and thunderstorms. To take into account the non-stationary characteristics of extreme wind, a novel evolutionary power spectral density (EPSD) model is proposed, and the spectral representation method (SRM) is introduced to simulate the whole process of strong winds. Firstly, the wavelet transform (WT) method is adopted to capture the three-dimensional time-varying properties of the low-frequency mean winds, and the associated turbulence features, including turbulent intensity, gust factor, probability density function, and power spectrum, are analyzed in depth. Secondly, the measured horizontal EPSD of strong winds are estimated. Thirdly, the performance of the proposed EPSD model is validated. Finally, the whole process of non-stationary strong winds are simulated and discussed. The results show that the proposed EPSD models are in good agreement with the measured EPSD, and the time-frequency features of the power spectrum of the simulated winds are well reproduced, which provides a powerful tool for large eddy simulation and wind engineering studies under non-stationary extreme wind climate.

Laser wavelength and sample conditioning effects on biochemical monitoring of SARS-CoV-2 VLP production upstream stage by Raman spectroscopy

This work assessed the impact of laser wavelength and sample conditioning on offline monitoring (viable cell density, cell viability, virus titer, glucose, lactate, glutamine, glutamate, and ammonium) of SARS-CoV-2 virus-like particles production upstream stage by Raman spectroscopy. The evaluated chemometrics techniques were Partial Least Squares (PLS) and Artificial Neural Networks (ANN), and different spectral filtering approaches were also considered. ANN showed better prediction capacity for most of the parameters, but ammonium and lactate, better predicted by PLS, and glutamine, no difference between modeling techniques was detected. For cell growth parameters and virus titer, samples without cells measured at 785 nm Raman laser wavelength originated better-adjusted models. This laser wavelength was also more appropriate for the remaining monitored experimental parameters except for glucose, in which the best model came from the spectral database acquired at 1064 nm wavelength. Cell remotion significantly increased the accuracy of viable cell density, cell viability, glutamate, and virus titer models. However, glucose, lactate, and ammonium models showed better prediction capacity for samples containing cells. Thus, it was demonstrated that laser wavelength, sample conditioning, spectral preprocessing, and chemometric modeling techniques need to be tailored for each experimental parameter to improve accuracy.

Gravity and magnetic joint imaging based on Gramian constraints

Gravity and magnetic imaging produce numerical images proportional to the density and magnetization source distribution through the upward continuation of potential field data. However, the inherent ambiguity and nonuniqueness in gravity or magnetic imaging restrict the reliability of the imaging model. We develop a joint iterative imaging framework for gravity and magnetic data based on Gramian constraints. The imaging fields of gravity and magnetic fields are initially calculated by the depth from extreme points imaging method. With the recovered magnetization and density distribution, the gradient directions of the Gramian function for these model parameters are calculated and used to regularize the joint imaging directions for gravity and magnetic fields. The introduction of Gramian constraints achieves mutual complementarity of the gravity and magnetic data and generates density and magnetization models with more distinct boundaries and improved structural coherence. Our approach is tested with two synthetic examples and applied to field data from the Galinge iron deposit in Qinghai, China. The real case results are verified by information from drillholes and physical properties measurements of ore and rock samples. Joint imaging provides an alternative to joint inversion with improved resolution and reliability of imaging models compared with separate imaging through the complementarity of gravity and magnetic data.

Interface area density model for Large-Eddy Simulation of assisted atomization in fiber regime

In the context of simulating liquid rocket engines (LREs), accurately reproducing the complex combustion chamber environment poses a significant challenge. Recent works have shown that Direct Numerical Simulation has made good progress in achieving the simulation of the whole range of temporal and spatial two-phase flow scales involved. However, due to the computational costs, it is currently out of reach in an industrial context. Large-Eddy Simulations (LES) considerably reduce the computational cost but require modeling the two-phase flow at sub-grid level. A promising approach for such modeling is the interface area density (IAD) approach, which was developed initially for LES of flame fronts and subsequently adapted to Diesel jet atomization to recover spray droplet size distributions. In this method, a subgrid density of interface area is transported with an advection equation, while opportune source terms simulate its growth by breakup or its reduction by coalescence effects. In the context of the LREs assisted atomization, the complex mechanism of the assisted atomization in fiber regime must be taken into account by the subgrid model to recover the correct spray size distribution. The present work proposes a new interface area density evolution modeling for two-phase LES of coaxial assisted atomization in fiber regime. The proposed model focuses on recovering a realistic liquid-gas relative velocity at all the scales, enabling accurate modeling of sub-grid scales by using the IAD as a measure of liquid structure sizes.

A Quantitative Study of How Effective the Transformed Gravity Gradient Tensor is for Subway Tunnel Imaging

We investigate the efficacy of spatial frequency filtering in improving gravity inversion when gravity gradient data are unavailable. The technique simulates gravity gradient data from gravity readings by extracting distinct frequency components. Previous research shows that the joint inversion of gravity and gradient data derives accurate density models. Given the lack of gradient instruments, the potential of frequency filtering to enhance inversions is explored. A series of inversion experiments are conducted using synthetic and field-measured gravity data. The research aims to determine if filtered gradient data, transformed from gravity data, can enhance inversion results compared to using gravity alone. Synthetic models assess the filter, showing that low-pass filtering at an optimal cutoff enhances the correlation between transformed and forward-modeled gradient components. Inversions using transformed gradients consistently outperform those with only gravity. Component combinations and cutoff wavelengths must be optimized to achieve the best reconstruction. Applying the method to Beijing subway tunnel gravity readings, gravity and transformed Uzz gradient data are combined to detail underground double tunnels. The information agrees with engineering data, validating the approach for improving inversions, especially for shallow, large targets. This work establishes the practicality of frequency filtering when direct gradients cannot be measured, contributing valuable insights into better characterizing geology through enhanced inversion. Findings imply improved interpretation; particularly where gradient acquisition proves difficult.

In-house validation of a visible and near infrared spectroscopy non-targeted method to support panel test of virgin olive oils

In this study, an in-house validation of Visible and Near Infrared Spectroscopy was performed to distinguish between extra virgin olive oil (EVOO) and virgin olive oil (VOO). A total of 161 samples of olive oil of three different categories (EVOO, VOO and lampante (LOO)) were analysed by transflectance using a monochromator instrument. One-class models were initially developed using Partial Least Squares (PLS) Density Modelling to characterize EVOO and VOO category. Once the LOO samples were discriminated, linear and non-linear discriminant models were built to classify EVOO and VOO. Different data pre-treatments and variable selection algorithms were evaluated to establish the best models in terms of Correct Classification Rate (CCR). The best model, obtained after variable selection using PLS Discriminant Analysis, yielded CCR values of 82.35 % for EVOO and 66.67 % for VOO in external validation. These results confirmed that VIS + NIRS technology may be used to provide rapid, non-destructive preliminary screening of olive oil samples for categorization; suspect samples may then be analysed by official analytical methods.

Development of an adaptive 4-D water vapour density model for the vertical constraints in GNSS tropospheric tomography

Development of an adaptive 4-D water vapour density model for the vertical constraints in GNSS tropospheric tomography

Analytical modeling of threshold voltage and on-resistance in multi-barrier E-mode MISHEMT with gate-recess and field-plates

This work presents an analytical model for threshold voltage and On-resistance of multi barrier Metal–Insulator–Semiconductor High-Electron-Mobility-Transistor (MISHEMT) with gate-recess and field-plates. The device featuring a high two-dimensional electron-gas (2DEG) density in the channel region. The primary objectives of this device are to achieve a high threshold voltage (Vth) and enhance electron mobility with specific low ON-resistance (Ron_sp) by mitigating the degradation effects arising from scattering and interface-charges. Also, a physics based analytical model for Vth and 2DEG charge density at upper and lower channels is presented. This model is validated by comparing with TCAD numerical simulations and are well matched. The proposed MISHEMT demonstrates improved electron mobility in the lower channel of 1260 cm2/V.s, Vth of ∼2.6 V and Ron_sp is minimized by 33 % in contrast with a conventional MISHEMT. Additionally, the proposed MISHEMT becomes a promising device for achieving both high threshold voltage and mobility which are required for power semiconductor devices.

Mapping of Orogenic Gold Mineralization Potential in the Kushaka Schist Belt, Northcentral Nigeria: Insights from Point Pattern, Kernel Density, Staged-Factor, and Fuzzy AHP Modeling Techniques

Mapping of Orogenic Gold Mineralization Potential in the Kushaka Schist Belt, Northcentral Nigeria: Insights from Point Pattern, Kernel Density, Staged-Factor, and Fuzzy AHP Modeling Techniques