Smoothed Particle Hydrodynamics for Computational Fluid Dynamics

Smoothed particle hydrodynamics (SPH) is a simple and effective numerical method that can be used to solve a variety of challenging problems in computational mechanics. It is a Lagrangian mesh-free method ideal for solving deformation problems. In the SPH method, the state of a system is represented by a set of particles, which possesses individual material properties and interact with each other within a specific range defined as a support domain by a weight function or smoothing function. SPH features flexibility in handling complex flow fields and in including physical effects. In theory, the basic concept of the SPH method is introduced in this paper. Some detailed numerical aspects are discussed including the kernel approximation in continuous form and particle approximation in discrete form, the properties for the smoothing functions and some of the most frequently used ones in the SPH literature, the concept of support and interface domain, SPH formulations for Navier-Stokes equation, time integration, boundary treatment, particle interaction, artificial viscosity, laminar viscosity, shifting algorithm, and so on. In applications, this paper presents an improved SPH method for modeling the diffusion process of a microneedle and using smoothed particle hydrodynamics (SPH) method to simulate the 25% cross-section stenosis blood vessel model and the 75% crosssection stenosis blood vessel model. The obtained numerical results are in close agreement with available theoretical and experimental results in the literature. As an emerging transdermal drug delivery device, microneedles demonstrate some superior potential and advantages over traditional metallic needles-on-syringes in skin injection and vaccine [1]. However, very few research papers are available. This project uses a high order continuous method, the spectral element method (SEM), and a low order discrete method, the Smoothed Particle Hydrodynamics (SPH), to investigate this new drug delivery system. The incompressible Navier-Stokes equations were solved with SEM under appropriate initial and slip boundary conditions for the transport of medicine inside microneedles of rectangular and circular cross-sections. In addition, Darcy-Brinkman equations and a concentration equation were solved with SEM under appropriate initial and boundary conditions for the infiltration of medicine solution through porous media of the dermis tissue once a microneedle enters the skin. Meanwhile, the Lagrangian form of the Navier-Stokes equations were solved with the weighted interpolation approach via numerical integrations without inverting any matrices. Results from the mesh-based SEM and the mesh-free SPH simulations revealed technical details about the processes of delivery of medicine particles through microneedles and diffusion in the skin tissue, and the medicine concentration changes with space and time. The overall effect of medicine delivery under initial concentration and conditions were simulated and the effect of drug delivery were assessed. The formation of thrombus is a complicated process. The existing literature rarely has a model for high-fidelity simulation of the effects and hazards of blood clots on blood flow. In this model, high-fidelity simulations are performed for complex human internal environments. The result of this simulation indicates high pressure area in blood vessel wall which matches the real condition of the vessel experiment

Deep Variational Luenberger-type Observer for Stochastic Video Prediction

Considering the inherent stochasticity and uncertainty, predicting future video frames is exceptionally challenging. In this work, we study the problem of video prediction by combining interpretability of stochastic state space models and representation learning of deep neural networks. Our model builds upon an variational encoder which transforms the input video into a latent feature space and a Luenberger-type observer which captures the dynamic evolution of the latent features. This enables the decomposition of videos into static features and dynamics in an unsupervised manner. By deriving the stability theory of the nonlinear Luenberger-type observer, the hidden states in the feature space become insensitive with respect to the initial values, which improves the robustness of the overall model. Furthermore, the variational lower bound on the data log-likelihood can be derived to obtain the tractable posterior prediction distribution based on the variational principle. Finally, the experiments such as the Bouncing Balls dataset and the Pendulum dataset are provided to demonstrate the proposed model outperforms concurrent works

M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data

Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S

LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA

True-Triaxial Drained Test of Tengger Desert Sand

For the lack of accurate test results in design and maintenance of desert-crossing highways in the Tengger Desert of western China, the GDS true-triaxial system was used to conduct the drained test on dense sand. Under the condition of different intermediate principal stress ratio b-value, the results showed that the stress-strain relationships in three orthogonal directions had significant differences, presenting significant anisotropy. The peak of the generalized shear stress increased with the increase of b-value. Except under the condition of b = 0, the specimen contracted firstly and then dilated, while the others dilated. The results of the different confining pressures showed that the stress-strain relationships appeared as a hardening type at low confining pressures, and as the confining pressure increased, the stress-strain relationships exhibit hardening, peaking, softening, and stable deformation characteristics. At low confining pressure, the contractive behaviors were not obvious, mainly as dilatancy, and as the confining pressure increased, the dilatancy increased gradually. The specimen transformed contract to dilatancy, and when the confining pressure reached 800 kPa, the specimen exhibited contractive behavior. The test results will provide data for subgrade design and construction in desert area

Electrical Characterizations of 35-kV Semi-Insulating Gallium Arsenide Photoconductive Switch

In this paper, a three-layer GaAs photoconductive semiconductor switch (GaAs PCSS) is designed to withstand high voltage from 20 to 35 kV. The maximum avalanche gain and minimum on-state resistance of GaAs PCSS are 1385 and 0.58 Ω, respectively, which are the highest values reported to date. Finally, the influence of the bias voltage on the avalanche stability is analyzed. The stability of the GaAs PCSS is evaluated and calculated. The results show that the jitter values at the bias voltages of 30 kV and 35 kV are 164.3 ps and 106.9 ps, respectively. This work provides guidance for the design of semiconductor switches with high voltage and high gain

Enhancement of conversion from bio-syngas to higher alcohols fuels over K-promoted Cu-Fe bimodal pore catalysts

A novel K-promoted Cu-Fe bimodal derived catalyst was designed to optimize the catalytic activity and higher alcohols selectivity in higher alcohols synthesis (HAS). The characterization results indicated that the Cu-Fe bimodal derived catalyst presented the bimodal pore structures. The adding of K promoter increased the BET surface area and promoted the dispersion of Cu and Fe species in the bimodal pores without destroying the bimodal structure, whereas the excessive adding of potassium resulted in easily the aggregation of bimetal active species. Incorporation of moderate K content enhanced the reduction of Cu and Fe species and promoted the formation of active bimetal species for HAS, while the bimodal derived catalyst with excessive K content restrained the reduction of bimetal partides, decreasing the catalytic activity for higher alcohols synthesis. In addition, the gradual increasing of K content in the Cu-Fe bimodal derived catalyst strengthened the interaction of K and bimetal active species, which was combined with the ''confinement effect'' of bimodal pore structures, shifting product distribution towards C2+OH. (C) 2017 Elsevier B.V. All rights reserved

In Vitro Antimetastatic Effect of Phosphatidylinositol 3-Kinase Inhibitor ZSTK474 on Prostate Cancer PC3 Cells

Abstract: Tumor metastasis is the main cause of lethality of prostate cancer, because conventional therapies like surgery and hormone treatment rarely work at this stage. Tumor cell migration, invasion and adhesion are necessary processes for metastasis. By providing nutrition and an escape route from the primary site, angiogenesis is also required for tumor metastasis. Phosphatidylinositol 3-kinases (PI3Ks) are well known to play important roles in tumorigenesis as well as metastasis. ZSTK474 is a specific PI3K inhibitor developed for solid tumor therapy. In the present report, antimetastatic activities of ZSTK474 were investigated in vitro by determining the effects on the main metastatic processes. ZSTK474 exhibited inhibitory effects on migration, invasion and adhesive ability of prostate cancer PC3 cells. Furthermore, ZSTK474 inhibited phosphorylation of Akt substrate-Girdin, and the secretion of matrix metalloproteinase (MMP), both of which were reported to be closely involved in migration and invasion. On the other hand, ZSTK474 inhibited theInt. J. Mol. Sci. 2013, 14 1357