Heterogeneous Mixture Model Research Articles

Numerical study of a CO2 swirl ejector with lubricating oil

At present, most simulations of ejectors ignore the presence of lubricating oil, which may affect the accuracy of the results. Additionally, there has been less research conducted on the influence of motive nozzles with swirl generators on the performance of ejectors. In this study, a heterogeneous mixture model was developed for a three-phase swirl ejector equipped with a motive nozzle featuring a swirl generator. This article examines the performance of swirl ejectors under supercritical conditions using three-dimensional numerical simulations conducted in ANSYS. The study explores swirl ejectors with varying helical angles of 77°, 64°, 58°, 50°, 37° . The study also takes into account the influence of lubricating oil to enhance the practicality of the research. Firstly, lubricating oil with a volume fraction of 2 % was introduced into both the motive and suction streams, followed by conducting an ejection analysis. The results indicate that the swirl enhances the entrainment ratio, and the swirl strength can be modified by changing the helical angle. A smaller helical angle results in greater swirl strength and a higher entrainment ratio. When the helical angle is 37° and the helical pitch is 20 mm, a swirl number of 0.42, an entrainment ratio of 0.998, and an effective entrainment ratio of 0.863 is achieved. However, when the oil circulation ratio in the suction stream is increased from 2 % to 10 % while keeping the oil circulation ratio of the motive stream constant, the entrainment ratio decreases.

Transient numerical study of CO2 two-phase flow in a needle adjustable ejector

Steady-state numerical modeling is mostly used for predicting the flow and transport characteristics in an ejector, but it may not be able to reflect the actual performances of an ejector when operating condition varies. Moreover effects of needle movement on the performances of an ejector is not very clear. In this study, a transient heterogeneous mixture model was developed for a two-phase CO2 needle adjustable ejector with motive nozzle inlet in supercritical, critical or sub-critical regions. The dynamic mesh method was used to control the needle movement, and the unsteady coupling solution between the needle movement and the flow field is solved for the adjustable two-phase CO2 ejector. Modeling results show that the transient modeling can predict dynamic performances of CO2 ejector more accurately than the steady-state modeling. Based on the transient modeling, it is found that as the needle moves further into the motive nozzle throat, effective nozzle throat area decreases, expansion angle of motive flow increases then decreases, the velocity at the motive nozzle outlet decreases then increases, and entrainment ratio decreases, then increases sharply, then decreases, finally become negative. High pressure at the motive nozzle inlet leads to high entrainment ratio owning to high velocity at the motive nozzle throat outlet. A sudden increase of pressure at the motive nozzle inlet in different thermodynamic state regions (supercritical, sub-critical) leads to different dynamic performance responses and oscillation times of ejector. The mechanisms of boundary layer separation and backflow vortices as well as shock waves were explored to explain the suction flow chocking phenomena. This study is helpful to understand flow mechanisms inside the CO2 ejector, and improve optimal design and control of an adjustable two-phase CO2 ejector in variable operating conditions in practice.

Characterization of Three Complete Mitogenomes of Flatidae (Hemiptera: Fulgoroidea) and Compositional Heterogeneity Analysis in the Planthoppers' Mitochondrial Phylogenomics.

Although sequences of mitogenomes have been widely used for investigating phylogenetic relationship, population genetics, and biogeography in many members of Fulgoroidea, only one complete mitogenome of a member of Flatidae has been sequenced. Here, the complete mitogenomes of Cerynia lineola, Cromna sinensis, and Zecheuna tonkinensis are sequenced. The gene arrangements of the three new mitogenomes are consistent with ancestral insect mitogenomes. The strategy of using mitogenomes in phylogenetics remains in dispute due to the heterogeneity in base composition and the possible variation in evolutionary rates. In this study, we found compositional heterogeneity and variable evolutionary rates among planthopper mitogenomes. Phylogenetic analysis based on site-homogeneous models showed that the families (Delphacidae and Derbidae) with high values of Ka/Ks and A + T content tended to fall together at a basal position on the trees. Using a site-heterogeneous mixture CAT + GTR model implemented in PhyloBayes yielded almost the same topology. Our results recovered the monophyly of Fulgoroidea. In this study, we apply the heterogeneous mixture model to the planthoppers’ phylogenetic analysis for the first time. Our study is based on a large sample and provides a methodological reference for future phylogenetic studies of Fulgoroidea.

Electrical and Magnetic Properties of the Binary Heterogeneous Mixture Model

Electrical and Magnetic Properties of the Binary Heterogeneous Mixture Model

Signal detection via Phi-divergences for general mixtures

The family of goodness-of-fit tests based on $\Phi$-divergences is known to be optimal for detecting signals hidden in high-dimensional noise data when the heterogeneous normal mixture model is underlying. This test family includes Tukey’s popular higher criticism test and the famous Berk–Jones test. In this paper we address the open question whether the tests’ optimality is still present beyond the prime normal mixture model. On the one hand, we transfer the known optimality of the higher criticism test for different models, for example, for the heteroscedastic normal, general Gaussian and exponential-$\chi^{2}$-mixture models, to the whole test family. On the other hand, we discuss the optimality for new model classes based on exponential families including the scale exponential, the scale Fréchet and the location Gumbel models. For all these examples we apply a general machinery which might be used to show the tests’ optimality for further models/model classes in future.

H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called “cell astronomy” systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers.

MotivationMammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Coupled with the typically small numbers of training examples, semi-supervised approaches for heterogeneous data are needed; however, current enhancer prediction methods are not designed to handle heterogeneous data in the semi-supervised paradigm.ResultsWe implemented a Dirichlet Process Heterogeneous Mixture model that infers Gaussian, Bernoulli and Poisson distributions over features. We derived a novel variational inference algorithm to handle semi-supervised learning tasks where certain observations are forced to cluster together. We applied this model to enhancer candidates in mouse heart tissues based on heterogeneous features. We constrained a small number of known active enhancers to appear in the same cluster, and 47 additional regions clustered with them. Many of these are located near heart-specific genes. The model also predicted 1176 active promoters, suggesting that it can discover new enhancers and promoters.Availability and implementationWe created the ‘dphmix’ Python package: https://pypi.org/project/dphmix/.Supplementary information Supplementary data are available at Bioinformatics online.

On the mixture of wind speed distribution in a Nordic region

The assessment of wind energy potential at sites of interest requires reliable estimates of the statistical characteristics of wind speed. A probability density function (pdf) is usually fitted to short-term observed local wind speed data. It is common for wind speed data to present bimodal distributions for which conventional one-component pdfs are not appropriate. Mixture distributions represent an appropriate alternative to model such wind speed data. Homogeneous mixture distributions remain rarely used in the field of wind energy assessment while heterogeneous mixture models have only been developed recently. The present work aims to investigate the potential of homogeneous and heterogeneous mixture distributions to model wind speed data in a northern environment. A total of ten two-component mixture models including mixtures of gamma, Weibull, Gumbel and truncated normal are evaluated in the present study. The estimation of the parameter of the mixture models are obtained with the least-squares (LS) and the maximum likelihood (ML) methods. The optimization of the objective functions related to these estimation methods is carried out with a genetic algorithm that is more adapted to mixture distributions. The case study of the province of Québec (Canada), a Northern region with an enormous potential for wind energy production, is investigated in the present work. A total of 83 stations with long data records and providing a good coverage of the territory of the province are selected. To identify the most appropriate one-component distribution for the selected stations, the newly proposed method of L-moment ratio diagram (MRD) is used. The advantages of this approach are that it is simple to apply and it allows an easy comparison of the fit of several pdfs for several stations on a single diagram. One-component distributions are compared with the selected mixture distributions based on model selection criteria. Results show that mixture distributions often provide better fit than conventional one-component distributions for the study area. It was also observed that the ML method outperforms the LS method and that the mixture model combining two Gumbel distributions using ML is the overall best model.

A two-phase model for aluminized explosives on the ballistic and brisance performance

The performance of aluminized high explosives is considered by varying the aluminum (Al) mass fraction in a heterogeneous mixture model. Since the time scales of the characteristic induction and combustion of high explosives and Al particles differ, the process of energy release behind the leading detonation wave front occurs over an extended period of time. For simulating the performance of aluminized explosives with varying Al mass fraction, HMX (1,3,5,7-tetrahexmine-1,3,5,7-tetrazocane) is considered as a base explosive when formulating the multiphase conservation laws of mass, momentum, and energy exchanges between the HMX product gases and Al particles. In the current study, a two-phase model is utilized in order to determine the effects of the Al mass fraction in a condensed phase explosive. First, two types of confined rate stick tests are considered to investigate the detonation velocity and the acceleration ability, which refers to the radial expansion velocity of the confinement shell. The simulation results of the confined rate stick test are compared with the experimental data for the Al mass fraction range of 0%–25%, and the optimal Al mass fraction is provided, which is consistent with the experimental observations. Additionally, a series of plate dent test simulations are conducted, the results of which show the same tendency as those of the experimental tests with varying Al mass fractions.

The mitogenome phylogeny of Adephaga (Coleoptera)

The beetle suborder Adephaga consists of several aquatic (‘Hydradephaga’) and terrestrial (‘Geadephaga’) families whose relationships remain poorly known. In particular, the position of Cicindelidae (tiger beetles) appears problematic, as recent studies have found them either within the Hydradephaga based on mitogenomes, or together with several unlikely relatives in Geadephaga based on 18S rRNA genes. We newly sequenced nine mitogenomes of representatives of Cicindelidae and three ground beetles (Carabidae), and conducted phylogenetic analyses together with 29 existing mitogenomes of Adephaga. Our results support a basal split of Geadephaga and Hydradephaga, and reveal Cicindelidae, together with Trachypachidae, as sister to all other Geadephaga, supporting their status as Family. We show that alternative arrangements of basal adephagan relationships coincide with increased rates of evolutionary change and with nucleotide compositional bias, but these confounding factors were overcome by the CAT-Poisson model of PhyloBayes. The mitogenome + 18S rRNA combined matrix supports the same topology only after removal of the hypervariable expansion segments. Densely sampled mitogenomes, analyzed with site heterogeneous mixture models, support a plausible hypothesis of basal relationships in the Adephaga.

Effects of nanoparticle migration and asymmetric heating on magnetohydrodynamic forced convection of alumina/water nanofluid in microchannels

The present paper is a theoretical investigation on effects of nanoparticle migration and asymmetric heating on forced convective heat transfer of alumina/water nanofluid in microchannels in presence of a uniform magnetic field. Walls are subjected to different heat fluxes; qt″ for top wall and qb″ for bottom wall, and because of non-adherence of the fluid–solid interface due to the microscopic roughness in microchannels, Navier’s slip boundary condition is considered at the surfaces. A two-component ​heterogeneous mixture model is used for nanofluid with the hypothesis that Brownian motion and thermophoretic diffusivities are the only significant slip mechanisms between solid and liquid phases. Assuming a fully developed flow and heat transfer, the basic partial differential equations including continuity, momentum, and energy equations have been reduced to two-point ordinary boundary value differential equations and solved numerically. It is revealed that nanoparticles eject themselves from heated walls, construct a depleted region, and accumulate in the core region, but more likely to accumulate near the wall with lower heat flux. Also, the non-uniform distribution of nanoparticles causes velocities to move toward the wall with a higher heat flux and enhances heat transfer rate there. In addition, inclusion of nanoparticles in a very strong magnetic field and slip velocity at the walls has a negative effect on performance.

An integrated phylogenomic approach toward pinpointing the origin of mitochondria.

Overwhelming evidence supports the endosymbiosis theory that mitochondria originated once from the Alphaproteobacteria. However, its exact position in the tree of life remains highly debated. This is because systematic errors, including biased taxonomic sampling, high evolutionary rates and sequence composition bias have long plagued the mitochondrial phylogenetics. In this study, we address this issue by 1) increasing the taxonomic representation of alphaproteobacterial genomes by sequencing 18 phylogenetically novel species. They include 5 Rickettsiales and 4 Rhodospirillales, two orders that have shown close affiliations with mitochondria previously, 2) using a set of 29 slowly evolving mitochondria-derived nuclear genes that are less biased than mitochondria-encoded genes as the alternative “well behaved” markers for phylogenetic analysis, 3) applying site heterogeneous mixture models that account for the sequence composition bias. With the integrated phylogenomic approach, we are able to for the first time place mitochondria unequivocally within the Rickettsiales order, as a sister clade to the Rickettsiaceae and Anaplasmataceae families, all subtended by the Holosporaceae family. Our results suggest that mitochondria most likely originated from a Rickettsiales endosymbiont already residing in the host, but not from the distantly related free-living Pelagibacter and Rhodospirillales.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

Identification of Objectionable Audio Segments Based on Pseudo and Heterogeneous Mixture Models

In this paper, we generalize the Gaussian Mixture Model (GMM) in two ways: a) by introducing novel distance measures between two vectors based on nonlinear maps to give more general mixture models; b) by building mixture models based on multiple different kinds of distributions. These two generalizations cope with different problems arisen in feature modeling. Mixture model obtained by first method is called pseudo Gaussian Mixture Model (pseudo GMM). Compared to the traditional GMM, pseudo GMM with nonlinear maps have better performance on nonlinear problems, while the computational complexity is almost the same as the Expectation-Maximization (EM) algorithm for traditional GMM according to the iteration procedures. The second generalization considers that in practice the practical learning problem often involves multiple, heterogeneous data sources, while classical mixture models are based on a single kind of distribution. In this work, we consider heterogeneous mixture models (hetMM) based on multiple different kinds of distributions. Different types of distributions in hetMM may have quite different properties and may capture different features of the data. Component classifiers including pseudo and hetMM based classifiers are employed in our task of erotic audio recognition. Experimental results with classifiers built based on pseudo GMM and hetMM for erotic audio recognition demonstrate the effectiveness of the proposed model. Online and off-line experiments show that the proposed approach is highly effective for erotic audio recognition.

Water-in-Crude Oil Emulsions from the Norwegian Continental Shelf

Properties of water-in-crude oil emulsions and model systems stabilized by interfacially active crude oil fractions or tetraoxyethylene nonylphenyl ether were analyzed using time domain dielectric spectroscopy. The amount of dispersed aqueous phase containing 5% NaCl varied from 10 to 50% by volume. The dielectric spectra were fitted to a Debye function. It was found that models based on heterogeneous mixture models could not explain the static permittivities found without a slight flocculation. In order to interpret the whole dielectric spectrum the Boned-Peyrelasse equation was numerically solved for 80 frequencies between 20 MHz and 5 GHz. A Debye function was fitted to these theoretical spectra and corresponding relaxation times were solved. The shape factors (A) obtained show the necessity of a flocculation (axial ratios 1:10) at lower water concentrations while other more symmetrical structures occur at higher water contents.