Particle Representation Research Articles

Mean–variance portfolio selection under partial information with drift uncertainty

ABSTRACT In this paper, we study the mean–variance portfolio selection problem under partial information with drift uncertainty. First we show that the market model is complete even in this case while the information is not complete and the drift is uncertain. Then, the optimal strategy based on partial information is derived, which reduces to solving a related backward stochastic differential equation (BSDE). Finally, we propose an efficient numerical scheme to approximate the optimal portfolio that is the solution of the BSDE mentioned above. Malliavin calculus and the particle representation play important roles in this scheme.

Higher-order particle representation for particle-in-cell simulations

In this paper we present an alternative approach to the representation of simulation particles for unstructured electrostatic and electromagnetic PIC simulations. In our modified PIC algorithm we represent particles as having a smooth shape function limited by some specified finite radius, r0. A unique feature of our approach is the representation of this shape by surrounding simulation particles with a set of virtual particles with delta shape, with fixed offsets and weights derived from Gaussian quadrature rules and the value of r0. As the virtual particles are purely computational, they provide the additional benefit of increasing the arithmetic intensity of traditionally memory bound particle kernels. The modified algorithm is implemented within Sandia National Laboratories' unstructured EMPIRE-PIC code, for electrostatic and electromagnetic simulations, using periodic boundary conditions. We show results for a representative set of benchmark problems, including electron orbit, a transverse electromagnetic wave propagating through a plasma, numerical heating, and a plasma slab expansion. Good error reduction across all of the chosen problems is achieved as the particles are made progressively smoother, with the optimal particle radius appearing to be problem-dependent.

Multisphere Representation of Convex Polyhedral Particles for DEM Simulation

The representation of particles of complex shapes is one of the key challenges of numerical simulations based on the discrete element method (DEM). A novel algorithm has been developed by the authors to accurately represent 2D arbitrary particles for DEM modelling. In this paper, the algorithm is extended from 2D to 3D to model convex polyhedral particles based on multisphere methods, which includes three steps: the placement of spheres at the corners, along the edges, and on the facets in sequence. To give a good representation of a polyhedral particle, the spheres are placed tangent to the particle surface in each step. All spheres placed in the three steps are clumped together into a clump in DEM. In addition, the mass properties of the clump are determined based on the corresponding polyhedral particle to obtain accurate simulation results. Finally, an example is used to validate the robust and automatic performance of the algorithm in generating a sphere clump model for an assembly of polyhedral particles. A current FORTRAN version of the algorithm is available by contacting the authors.

Lattice Boltzmann Method for Fluid Flow

We present an overview of the lattice Boltzmann method (LBM), a parallel and efficient algorithm for simulating single-phase and multiphase fluid flows and for incorporating additional physical complexities. The LBM is especially useful for modeling complicated boundary conditions and multiphase interfaces. Recent extensions of this method are described, including simulations of fluid turbulence, suspension flows, and reaction diffusion systems. Lattice Boltzmann method (LBM) is a methodology based on the microscopic particle models and mesoscopic kinetic equations. According to Kadanoff (1986), it has been found that macroscopic behavior of a fluid system is generally not very sensitive to the underlying microscopic particle behavior if only collective macroscopic flow behavior is of interest. The fundamental idea behind the LBM is to construct simplified kinetic models that incorporate only the essential physics of microscopic or mesoscopic processes so that the macroscopic averaged properties obey the desired macroscopic equations. This subsequently avoids the use of the full Boltzmann equation, and one also avoids following each particle as in molecular dynamics simulations. LBM is based on a particle representation, the principal focus remains in the averaged macroscopic behavior. The kinetic nature of the LBM introduces three important features that distinguish this methodology from other numerical methods. Firstly, the convection operator of the LBM in the velocity phase is linear. The inherent simple convection when combined with the collision operator allows the recovery of the nonlinear macroscopic advection through multiscale expansions. Secondly, the incompressible Navier–Stokes equations can be obtained in the nearly incompressible limit of the LBM. The pressure is calculated directly from the equation of state in contrast to satisfying Poisson's equation with velocity strains acting as sources. Thirdly, the LBM utilizes the minimum set of velocities in the phase space. Because only one or two speeds and a few moving directions are required, the transformation relating the microscopic distribution function and macroscopic quantities is greatly simplified and consists of simple arithmetic calculations.

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we have also proposed a binary encoding scheme for the particle representation to alleviate this problem. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has also been compared to multiple classical and new clustering solutions and two other automatic clustering techniques in continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion in most datasets. Moreover, the application of the proposed framework to the correlated binary dataset is also reported as a case study. The presence of correlation in a dataset results from the similarity between data points in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. Simplicity, customizability, and flexibility in adding extra conditions to the proposed solution and having a dynamic number of clusters are the advantages of the proposed framework.

The effect of decay and lexical uncertainty on processing long-distance dependencies in reading

To make sense of a sentence, a reader must keep track of dependent relationships between words, such as between a verb and its particle (e.g. turn the music down). In languages such as German, verb-particle dependencies often span long distances, with the particle only appearing at the end of the clause. This means that it may be necessary to process a large amount of intervening sentence material before the full verb of the sentence is known. To facilitate processing, previous studies have shown that readers can preactivate the lexical information of neighbouring upcoming words, but less is known about whether such preactivation can be sustained over longer distances. We asked the question, do readers preactivate lexical information about long-distance verb particles? In one self-paced reading and one eye tracking experiment, we delayed the appearance of an obligatory verb particle that varied only in the predictability of its lexical identity. We additionally manipulated the length of the delay in order to test two contrasting accounts of dependency processing: that increased distance between dependent elements may sharpen expectation of the distant word and facilitate its processing (an antilocality effect), or that it may slow processing via temporal activation decay (a locality effect). We isolated decay by delaying the particle with a neutral noun modifier containing no information about the identity of the upcoming particle, and no known sources of interference or working memory load. Under the assumption that readers would preactivate the lexical representations of plausible verb particles, we hypothesised that a smaller number of plausible particles would lead to stronger preactivation of each particle, and thus higher predictability of the target. This in turn should have made predictable target particles more resistant to the effects of decay than less predictable target particles. The eye tracking experiment provided evidence that higher predictability did facilitate reading times, but found evidence against any effect of decay or its interaction with predictability. The self-paced reading study provided evidence against any effect of predictability or temporal decay, or their interaction. In sum, we provide evidence from eye movements that readers preactivate long-distance lexical content and that adding neutral sentence information does not induce detectable decay of this activation. The findings are consistent with accounts suggesting that delaying dependency resolution may only affect processing if the intervening information either confirms expectations or adds to working memory load, and that temporal activation decay alone may not be a major predictor of processing time.

Modeling of non-spherical particle flows: Movement and orientation behavior

The fast advancement of existing and new functional material systems, where particulate morphology differs from traditional systems (e.g. fibers, flakes,), demands technical know-how on the processing of non-spherical particles in liquid media. There are few to no technical relevant tools available for the prediction of such particulate systems, specifically when particle-particle interactions and particle orientation are relevant, making development of such processes time consuming and expensive. Therefore, a tool is needed to support process design and further understand of non-spherical systems. On this work, a novel approach for the calculation of drag forces and drag induced torques for non-spherical particles in the context of CFD-DEM coupling is presented. The approach is inspired by Stokesian Dynamics for the computation of drag forces and torques when using the multi-sphere method for the representation of non-spherical particles.

Mass Extinction Efficiency Approximation for Polydispersed Aerosol Using Harmonic Mean-Type Approximation

Among many parameters characterizing atmospheric aerosols, aerosol mass extinction efficiency (MEE) is important for understanding the optical properties of aerosols. MEE is expressed as a function of the refractive indices (i.e., composition) and size distributions of aerosol particles. Aerosol MEE is often considered as a size-independent constant that depends only on the chemical composition of aerosol particles. The famous Malm’s reconstruction equation and subsequent revised methods express the extinction coefficient as a function of aerosol mass concentration and MEE. However, the used constant MEE does not take into account the effect of the size distribution of polydispersed chemical composition. Thus, a simplified expression of size-dependent MEE is required for accurate and conventional calculations of the aerosol extinction coefficient and also other optical properties. In this study, a simple parameterization of MEE of polydispersed aerosol particles was developed. The geometric volume–mean diameters of up to 10 µm with lognormal size distributions and varying geometric standard deviations were used to represent the sizes of various aerosol particles (i.e., ammonium sulfate and nitrate, elemental carbon, and sea salt). Integrating representations of separate small mode and large mode particles using a harmonic mean-type approximation generated the flexible and convenient parameterizations of MEE that can be readily used to process in situ observations and adopted in large-scale numerical models. The calculated MEE and the simple forcing efficiency using the method developed in this study showed high correlations with those calculated using the Mie theory without losing accuracy.

Resolving aerosol mixing state increases accuracy of black carbon respiratory deposition estimates

Summary Black carbon (BC) particles, also known as soot, deteriorate air quality and threaten public health. BC is emitted from the incomplete combustion of fossil fuel, which is a major energy source in the world today. Therefore, evaluating the impact of BC on global public health is vital, especially in the context of growing global population and energy demand. Since the atmospheric lifetime and deposition efficiency of BC after inhalation are governed by the size and chemical composition, realistic representation of those properties in models is required for such evaluation, yet is currently lacking. Here, we show that a simple but commonly found representation of size and chemical composition of BC particles in global models results in globally prevalent underestimation of the respiratory deposited BC mass concentration. The results emphasize that realistic representation of BC particles is indispensable to the evaluation and hence the mitigation of BC impacts on global public health.

A Triple-Moment Representation of Ice in the Predicted Particle Properties (P3) Microphysics Scheme

AbstractIn the original Predicted Particle Properties (P3) bulk microphysics scheme, all ice-phase hydrometeors are represented by one or more “free” ice categories, where the physical properties evolve smoothly through changes to the four prognostic variables (per category), and with a two-moment representation of the particle size distribution. As such, the spectral dispersion cannot evolve independently, which thus results in limitations in representation of ice—in particular, hail—due to necessary constraints in the scheme to prevent excessive gravitational size sorting. To overcome this, P3 has been modified to include a three-moment representation of the size distribution of each ice category through the addition of a fifth prognostic variable, the sixth moment of the size distribution. The details of the three-moment ice parameterization in P3 are provided. The behavior of the modified scheme, with the single-ice-category configuration, is illustrated through simulations in a simple 1D kinematic model framework as well as with near large-eddy-resolving (250-m grid spacing) 3D simulations of a hail-producing supercell. It is shown that the three-moment ice configuration controls size sorting in a physically based way and leads to an improved capacity to simulate large, heavily rimed ice (hail), including mean and maximum sizes and reflectivity, and thus an overall improvement in the representation of ice-phase particles in the P3 scheme.

Neutron-pair structure in the continuum spectrum of O26

The structure of $^{26}$O is currently being investigated on both theoretical and experimental fronts. It is well established that it is unbound and the resonance parameters are fairly well-known. The theoretical analysis may involved two- and three-body interactions, as well as correlations with the continuum spectrum of energy. In order to properly assess the structure of the ground and excited states, it is imperative to include a large single particle representation with the right asymptotic behavior. The purpose of this work is to provide details of the single particle continuum configurations of the ground and excited $0^+$ states. We use a large complex energy single particle basis, formed by resonances and complex energy scattering states, the so called Berggren basis, and a separable interaction, which is convenient to solve in a large model space. Three $0^+$ states were found in the complex energy plane. Changes of the resonant parameters, i.e. energy and width, were analyzed as a function of strength of the residual interaction. It is shown how a subtle difference in the interaction could change the unbound character of $^{26}$O into a Borromean nucleus. Only one of the two excited states can be considered as a candidate for a physical meaningful resonance. The calculated occupation probabilities are in agreement with other theoretical approaches although the calculated half live is three-order of magnitude smaller than the experimental one.

ENHANCING STUDENTS’ HIGH-ORDER COGNITIVE SKILLS FOR HIERARCHICAL DESIGNS IN MICRO AND SYMBOLIC PARTICULATE NATURE OF MATTER

To be familiar with micro and symbolic performances, students could work out more effective approaches of innovated techniques known as five hierarchical designs in chemistry equilibrium. However, the most frequently reported problem in students’ assessment of chemistry study is attributed to their poor skill recognizing basic concepts. The aim of this research was to construct the 6 test items to help undergraduate students assess their high-order and low-order cognitive skills as a deeper framework of particle representations. It takes a dynamic appeal for students to identify their profound understanding of cognitive skills in accordance with their potential performances of particle representations. In this research, students activate the test instrument to sustain their development of content validity and inter-rater reliability. The research results mark a different responsive engagement of both high-order and low-order cognitive skills in students’ total performances with the skill ratio score of 1:6 for conceptual recognition and analyses of micro and symbolic performances. All students’ micro and symbolic performances offer an indication for understanding their advanced cognitive skills in the particulate nature of matter. It is beneficial to improve new perspectives in the discussions and activity hierarchical designs in their chemistry equilibrium classrooms. Keywords: high-order cognitive skills, hierarchical designs, micro, symbolic performances, particulate nature of matter

Transmission spectra of bistable systems: From the ultraquantum to the classical regime

We present an analytical and numerical study of the fluorescence spectra of a bistable driven system by means of Keldysh diagram technique in pseudo--particle representation. The spectra exhibit smooth transition between ultra-quantum and the quasiclassical limits and indicate the threshold value of the external field when changing of the most probable stable state occurs. The analysis of the fluorescence spectra also allows to determine the most probable stable state of the system. It was also shown that at integer and half-integer detuning-nonlinearity ratio multiphoton resonance leads to abrupt changes in fluorescence spectra. It was also revealed that the fluorescence spectra are symmetric in the limit of zero environment temperature. The predicted features of the spectra could be observed in experiments with ultra-high quality resonators in either microwave or optical domain.

Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54

Abstract. It was hypothesized that using mineral dust emission climatologies in global chemistry climate models (GCCMs), i.e. prescribed monthly-mean dust emissions representative of a specific year, may lead to misrepresentations of strong dust burst events. This could result in a negative bias of model dust concentrations compared to observations for these episodes. Here, we apply the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) as part of the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model. We employ two different representations of mineral dust emissions for our model simulations: (i) a prescribed monthly-mean climatology of dust emissions representative of the year 2000 and (ii) an online dust parametrization which calculates wind-driven mineral dust emissions at every model time step. We evaluate model results for these two dust representations by comparison with observations of aerosol optical depth from ground-based station data. The model results show a better agreement with the observations for strong dust burst events when using the online dust representation compared to the prescribed dust emissions setup. Furthermore, we analyse the effect of increasing the vertical and horizontal model resolution on the mineral dust properties in our model. We compare results from simulations with T42L31 and T63L31 model resolution (2.8∘×2.8∘ and 1.9∘×1.9∘ in latitude and longitude, respectively; 31 vertical levels) with the reference setup (T42L19). The different model versions are evaluated against airborne in situ measurements performed during the SALTRACE mineral dust campaign (Saharan Aerosol Long-range Transport and Aerosol-Cloud Interaction Experiment, June–July 2013), i.e. observations of dust transported from the Sahara to the Caribbean. Results show that an increased horizontal and vertical model resolution is able to better represent the spatial distribution of airborne mineral dust, especially in the upper troposphere (above 400 hPa). Additionally, we analyse the effect of varying assumptions for the size distribution of emitted dust but find only a weak sensitivity concerning these changes. The results of this study will help to identify the model setup best suited for future studies and to further improve the representation of mineral dust particles in EMAC-MADE3.

Thermodynamic collapse in a lattice-gas model for a two-component system of penetrable particles.

We study a lattice-gas model of penetrable particles on a square-lattice substrate with same-site and nearest-neighbor interactions. Penetrability implies that the number of particles occupying a single lattice site is unlimited and the model itself is intended as a simple representation of penetrable particles encountered in realistic soft-matter systems. Our specific focus is on a binary mixture, where particles of the same species repel and those of the opposite species attract each other. As a consequence of penetrability and the unlimited occupation of each site, the system exhibits thermodynamic collapse, which in simulations is manifested by an emergence of extremely dense clusters scattered throughout the system with energy of a cluster E∝-n^{2}, where n is the number of particles in a cluster. After transforming a particle system into a spin system, in the large density limit the Hamiltonian recovers a simple harmonic form, resulting in the discrete Gaussian model used in the past to model the roughening transition of interfaces. For finite densities, due to the presence of a nonharmonic term, the system is approximated using a variational Gaussian model.

Appearances Can Be Deceiving - Viral-like Inclusions in COVID-19 Negative Renal Biopsies by Electron Microscopy.

Since the discovery of the causative agent for the novel severe acute respiratory syndrome (SARS)–like pneumonia syndrome pandemic that started in China in 2019 (1,2), a coronavirus named SARS coronavirus 2 (SARS-CoV-2), electron microscopy images have populated the medical literature (2) and media outlets alike displaying the characteristic 60–140 nm round particles surrounded by a “corona” of 9–12 nm distinctive spikes (2). Although many of these images were obtained after “ in vitro ” infection of cultured cells with SARS-CoV-2 (2) and are thus likely a true representation of viral particles, we have observed morphologically indistinguishable inclusions within podocytes and tubular epithelial cells both in patients negative for coronavirus disease 2019 (COVID-19) as well as in renal biopsies from the pre–COVID-19 era. Although direct infection of the kidney is theoretically possible, given the presence of angiotensin-converting enzyme 2 (the receptor used by SARS-CoV-2 to gain access to cells) within proximal tubular epithelium (3) and podocytes (4), the virus has not been detected by real-time RT-PCR in urine samples from patients with COVID-19 (5⇓–7). Additionally, for the virus to have access to kidney parenchyma, viremia should occur, and this has only been detected in a minority of patients (6⇓–8). We would, therefore, like to issue a note of caution for inferring viral tissue infection by morphology alone using electron microscopy images from tissues obtained from biopsies or autopsy material in patients with COVID-19. Moreover, caution should be used when interpreting immunohistochemical results, especially within proximal tubules, which are prone to nonspecific staining by a variety of antibodies due to their intense reabsorptive capacity. Additionally, more specific techniques such as immunoelectron microscopy using specific viral antigens (9), or in situ hybridization for viral RNA, are likely necessary to undoubtedly confirm tissue infection in …

Sedimentation of general shaped particles using a multigrid fictitious boundary method

In this paper, a direct numerical simulation technique, the Finite Element Fictitious Boundary Method (FBM), is used to simulate fluid–solid two-phase flows of different general shaped particles. The momentum interactions between solid and fluid phases are handled by using the FBM. The continuity and momentum equations are solved on a fixed Eulerian grid that is independent of flow features by using a discrete projection scheme inside a multi-grid finite element approach. A detailed description is presented for the geometric representation and modeling of two-dimensional particles of different general shapes, i.e., circular, elliptical, square, rectangular, triangular, and pentagonal shapes inside the fluid. We discussed the effects of particle shapes and the influences on the settling behavior of the particles. A comparison of the settling trajectories of the particles of the same mass but with different shapes is presented. Moreover, depending upon the particle’s shape, some interesting facts are discovered, which have a great influence on the particles’ trajectory and settling velocity. Some very important correlations between the drag force coefficient and particle’s Reynolds numbers with different density ratios of particles are obtained. Furthermore, we also studied the settling behavior of elliptical and rectangular particles with different axis ratios and a boomerang particle with different concave angles. The authors of the article agree to the retraction of the article effective AUGUST 20, 2021.

Particle representation for the kaonic NNK¯ system

The kaonic system $NN\overline{K}$ is studied based on the configuration space Faddeev equations. We consider two models associated with isospin ``natural'' basis and isospin ``charge'' basis. One basis is related to another by a unitary transformation. We show that the ``particle representation'' for $NN\overline{K}({s}_{NN}=0)$ system motivated by the charge basis does not describe the system in terms of coupled particle channels $pp{K}^{\ensuremath{-}}/pn{\overline{K}}^{0}$. The coupling is associated with the nondiagonal elements of the matrix representation for the $N\overline{K}$ potential in the charge basis. The matrix can be diagonalized by a simple unitary transformation. With this relation, the Kyoto potential is discussed. The particle configurations of the $NN\overline{K}$ system may be classified by the presence or absence of the Coulomb interaction according to an analogy with the $NNN$ system. The $NN\overline{K}({s}_{NN}=0)$ system is represented by four particle configurations: $pp{K}^{\ensuremath{-}}$, $np{K}^{\ensuremath{-}}$, $np{\overline{K}}^{0}$, and $nn{\overline{K}}^{0}$.

Manufacturing Impact on Electrochemical and Mechanical Performance of NMC Cathodes Via Mesoscale Simulation

Lithium-ion battery performance is determined by numerous coupled multi-physics effects which can be tuned through various manufacturing methods. Electrode calendering pressure can compress local mesostructures, shortening conductive pathways and introducing contact points where mechanical stresses manifest. The addition of a secondary conductive binder phase alters ionic and electronic conduction pathways, limits available surface area for reaction, and buffers mechanical contacts. Coupled physics simulation can visualize localized behaviors that arise from altering these macroscale conditions.Here, we use a mesoscale simulation approach to explore the influence of manufacturing conditions on the performance of an NMC333 half-cell. Image-based particle representations are combined with the Conformal Decomposition Finite Element Mesh (CDFEM) algorithm to provide a conformal, solvable mesh for coupled electrochemical/mechanical simulations. Resolution at this particle scale allows analysis of localized phenomena dependent on particle size, shape, and local mesostructure. Our discussion will detail the limiting physics and variations across domains of four different calendering pressures (0, 300, 600, 2000 bar) and conductive binder fraction (90, 92, 94, 96wt% active material). Per-particle analyses will quantify differences between mesostructures and discuss how local variations influence macroscale properties like cell pressure, effective conductivity, and the discharge curve. Furthermore, we will discuss the statistical effects of domain sample selection on electrode-scale behaviors. This mesoscale analysis allows us to isolate the most significant limiting factors for discharge performance, providing suggestions for future battery design.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Classification of all 3 particle S-matrices quadratic in photons or gravitons

We explicitly construct every kinematically allowed three particle graviton- graviton-P and photon-photon-P S-matrix in every dimension and for every choice of the little group representation of the massive particle P. We also explicitly construct the spacetime Lagrangian that generates each of these couplings. In the case of gravitons we demonstrate that this Lagrangian always involves (derivatives of ) two factors of the Riemann tensor, and so is always of fourth or higher order in derivatives. This result verifies one of the assumptions made in the recent preprint [1] while attempting to establish the rigidity of the Einstein tree level S-matrix within the space of local classical theories coupled to a collection of particles of bounded spin.