Flocking Dynamics of the Inertial Spin Model with a Multiplicative Communication Weight

In this paper, we study a flocking dynamics of the deterministic inertial spin (IS) model. The IS model was introduced for the collective dynamics of active particles with an internal angular momentum, or spin. When the generalized moment of inertia becomes negligible compared to spin dissipation (overdamped limit) and mutual communication weight is a function of a relative distance between interacting particles, the deterministic inertial spin model formally reduces to the Cucker–Smale (CS) model with constant speed constraint whose emergent dynamics has been extensively studied in the previous literature. We present several sufficient frameworks leading to the asymptotic mono-cluster flocking, in which spins and relative velocities tend to zero asymptotically. We also provide several numerical simulations for the decoupled and coupled inertial spin models to see the effect of the C–S velocity flocking and compare them with our analytical results.

     

Pressure-driven flows of concentrated alumina suspensions depending on dispersion states of particles

The flow developments of 25 vol% alumina suspensions in slit channel were visualized and analyzed depending on the dispersion states. For the coagulated alumina suspension, the shear stress showed an N curve that included a region of stress decrease with an increase in shear rate followed by a monotonic increase. Depending on the region in the stress curve, the flow profile changed from a shear-banded profile to a plug-like flow profile similar to the Newtonian fluid. In addition, it was observed that the transient flow behavior over time at high shear rate in liquid state experienced all of the steady state flow profiles at lower shear rates in solid-liquid transition. During the solid-liquid transition, the flow profile was found to be shear banded, and the pressure profile did not reach a steady state but fluctuated with a characteristic time period. In contrast, the well-dispersed suspension showed only a monotonic increase of shear stress in the range of shear rates we could measure, indicating that the suspension was in liquid state. The flow profile was plug-like, and the pressure was fluctuating without any characteristic time period.     

Simple one-pot synthesis of Rh-Fe3O4 heterodimer nanocrystals and their applications to a magnetically recyclable catalyst for efficient and selective reduction of nitroarenes and alkenes

A simple synthesis of Rh-Fe(3)O(4) heterodimer nanocrystals was achieved by controlled one-pot thermolysis. The nanocrystals exhibited excellent activities for the selective reduction of nitroarenes and alkenes. Furthermore the nanocrystal catalyst could be easily separated by a magnet, and recycled eight times without losing the catalytic activity.     

In situ flow visualization of capillary flow of concentrated alumina suspensions

A new approach was taken to understand the flow behavior of concentrated particle suspensions in pressure-driven capillary flow. The flow of concentrated alumina suspensions in a slit channel was visualized and quantitatively analyzed with modified capillary rheometer. The suspensions showed complex flow behaviors; unique solid–liquid transition and shear banding. At low flow rates, 55 vol% alumina suspension showed a unique transient flow behavior; there was no flow at first and continuous change of flow profile was observed with time. At low shear rates in particular, the suspensions exhibited shear-banded flow profile which could be divided into three regions: the region with low flow rate near the wall, the region with rapid increase of flow velocity to maximum, and the region of velocity plateau. Based on both flow visualization and measurement of shear stress, it was found that the shear-banded flow profile in pressure-driven slit channel flow was strongly correlated with shear stress. The banding in pressure-driven flow was different from that in Couette flow. The banding of concentrated alumina suspensions was unique in that sluggish velocity profile was pronounced and two inflection points in velocity profile was exhibited. In this study, shear banding of concentrated alumina suspensions in slit channel flow was visualized and quantitatively analyzed. We expect that this approach can be an effective method to understand the flow behavior of particulate suspensions in the pressure-driven flow which is typical in industrial processing.     

Application of the Lagrangian CMC method in elliptic bluff body and swirl flames

The Lagrangian CMC method was implemented in the open source programme OpenFOAM and applied to turbulent nonpremixed bluff body and swirl flames. Lagrangian CMC is more efficient than Eulerian CMC with the number of Lagrangian flame groups much less than the number of computational cells for Eulerian CMC equations in general. It is based on the conditional flame structure depending on the residence time of the fuel of fixed Lagrangian identity from the nozzle. According to sensitivity study the injected fuel was divided into ten flame groups according to the injection sequence with the resulting conditional profiles between those by ISR and Eulerian CMC. Minor deviation from Eulerian CMC was attributed to the flame structure that is difficult to be characterised by the residence time only in elliptic recirculating flows of the bluff body and swirl flames. The Eulerian and Lagrangian CMC showed the same trend of deviation from measurements for conditional temperature, H₂O, OH, CO and H₂ mass fractions. The significant deviation of H₂ was due to uncertainty in the reaction chemistry, as observed in the previous works based on other reaction mechanisms for methane and methanol.     

On high-dimensional two sample mean testing statistics: a comparative study with a data adaptive choice of coefficient vector

The key issues involved in two sample tests in high dimensional problems arise due to large dimension of the mean vector for a relatively small sample size. Recently, Wang et al. (Stat Sin 23:667–690, 2013) proposed a jackknife empirical likelihood test that works under weak assumptions on the dimension of variables (p), and showed that the test statistic has a chi-square limit regardless of whether p is finite or diverges. The sufficient condition required for this statistic is still restrictive. In this paper we significantly relax the sufficient condition for the asymptotic chi-square limit with models allowing flexible dependence structures and derive simpler alternative statistics for testing the equality of two high dimensional means. The proposed statistics have a chi-squared distribution or the maximum of two independent chi-square statistics as their limiting distributions, and the asymptotic results hold for either finite or divergent p. We also propose a data-adaptive method to select the coefficient vector, and compare the various methods in simulation studies. The proposed choice of coefficient vector substantially increases power in the simulation.     

Large-scale synthesis of bioinert tantalum oxide nanoparticles for X-ray computed tomography imaging and bimodal image-guided sentinel lymph node mapping

Ever since Au nanoparticles were developed as X-ray contrast agents, researchers have actively sought alternative nanoparticle-based imaging probes that are not only inexpensive but also safe for clinical use. Herein, we demonstrate that bioinert tantalum oxide nanoparticles are suitable nanoprobes for high-performance X-ray computed tomography (CT) imaging while simultaneously being cost-effective and meeting the criteria as a biomedical platform. Uniformly sized tantalum oxide nanoparticles were prepared using a microemulsion method, and their surfaces were readily modified using various silane derivatives through simple in situ sol-gel reaction. The silane-modified surface enabled facile immobilization of functional moieties such as polyethylene glycol (PEG) and fluorescent dye. PEG was introduced to endow the nanoparticles with biocompatibility and antifouling activity, whereas immobilized fluorescent dye molecules enabled simultaneous fluorescence imaging as well as X-ray CT imaging. The resulting nanoparticles exhibited remarkable performances in the in vivo X-ray CT angiography and bimodal image-guided lymph node mapping. We also performed an extensive study on in vivo toxicity of tantalum oxide nanoparticles, revealing that the nanoparticles did not affect normal functioning of organs.     

Stochastic flocking dynamics of the inertial spin model with state-dependent noises

We study stochastic flocking dynamics of the inertial spin (IS) model with state-dependent noises. The IS model was considered to describe the collective behaviors of starling flocks moving with constant speed. Unlike mechanical flocking models extensively studied in the literature, this model incorporates an internal dynamic observable, namely spin (internal angular momentum) in addition to mechanical observables (position and velocity), and it describes how spin interacts with mechanical observables. In previous works, emergent dynamics of the deterministic counterparts for the IS model and its mean-field limit have been investigated under some specific setting in which network topology is multiplicatively separable. In this work, we present sufficient frameworks for stochastic flocking dynamics of the IS model, which state-dependent noises vanish at the equilibria of the deterministic IS model. The proposed frameworks are in terms of coupling strength, friction, and inertial coefficients, and our asymptotic convergence results for sample paths are given in both an almost sure and an expectation sense. We have also conducted several numerical experiments to verify our analytical results and to explore what can be studied further in future work     

Controlling the hydrophobicity of submicrometer silica spheres via surface modification for nanocomposite applications

We control the hydrophobicity of submicrometer silica spheres by modifying their surface with -CH3, -CH=CH2, -(CH2)(2)CH3, -CH2(CH2)(4)CH2-, -C(6)H(5), -(CH2)(7)CH3, and -(CH2)(11)CH3 groups through a modified one-step process. The scanning electron microscopy (SEM), quasi-elastic light scattering (QELS), UV-visible spectra, nitrogen sorption, and water vapor adsorption methods are used to characterize the particles. The SEM micrographs of the particles demonstrate that the modified particles are uniformly spherical, monodisperse, and well-shaped with the particle size ranging from 130 to 149 nm depending on the modified organic groups. In aqueous solution, the particles modified with phenyl groups have an obvious UV absorption peak at around 210 nm, whereas the other modified particles and unmodified particles do not have any UV-visible absorption peaks. There exist obvious differences in the amount of water vapor adsorbed depending on the type of surface functional groups of the modified particles. Compared with the unmodified particles, the modified particles have a lower water vapor adsorption because of the improved hydrophobicity of the particle surface. As a potential application, we prepared polystyrene/SiO2 nanocomposites by blending polystyrene with the synthesized particles. Water contact angle measurements show that the surface of the composite prepared with the modified particles are more hydrophobic. Confocal microscopy demonstrates that the particles are less agglomerated in the nanocomposite as the particles become more hydrophobic. These comprehensive experimental results demonstrate that the hydrophobicity of the particles can be easily controlled by surface modification with different organosilanes through a modified one-step process.     

Prediction interval for disease mapping using hierarchical likelihood

In disease mapping, the Bayesian approach is widely used for forming the prediction interval of relative risks. In this paper we propose a hierarchical-likelihood interval for disease mapping, which accounts for the inflation of standard error estimates caused by uncertainty in the estimation of the fixed parameters. Comparison is made with the Bayesian prediction intervals derived from penalized quasi-likelihood and fully Bayesian methods. Through simulation studies, we show that prediction intervals for random effects using hierarchical likelihood maintains the required level.