Experimental study of subcooled flow boiling heat transfer on micro-pin-finned surfaces in short-term microgravity

The flow boiling heat transfer of subcooled air-dissolved FC-72 on micro-pin-finned surfaces was studied in microgravity by utilizing the drop tower facility in Beijing. The micro-pin-fins with the dimension of 30 x 30 x 60 mu m(3) (width x thickness x height), named PF30-60, were fabricated on a silicon chip by using the dry etching technique. For comparison, experiments of flow boiling heat transfer in terrestrial gravity were also conducted. The effects of inlet velocity on both flow boiling heat transfer and bubble behavior were explored. It was found that gravity has nearly no effect on flow boiling heat transfer for the departure of the inertial-force dominant bubbles in the low and moderate heat fluxes regions. In contrast, in the high-heat-flux region, the flow boiling heat transfer deteriorates and the critical heat flux (CHF) decreases due to the bubble accumulation in the channel. For PF30-60 at V = 0.5 m/s, the CHF point can be inferred to be between 20.8 and 24.5 W/cm(2), which is 63.0-74.2% of that in normal gravity. Regarding PF30-60 at V = 1.0 m/s, the CHF point can be inferred to be between 25.4 and 31.6 W/cm(2), which is 67.6-84.0% of that in normal gravity. The impact of gravity on CHF is closely linked to the channel geometry parameter and surface modification. The dimensionless numbers, Ch (Channel number) and Sf (Surface number), were proposed to describe the effect of the channel geometry and surface modification on the ratio of CHF in microgravity to that in normal gravity (CHF mu g/CHF1g). An empirical correlation based on We (Weber number), Ch and Sf was proposed to predict the value of CHF mu g/CHF1g ratio in good agreement with the experimental data. This study provides a new perspective to determine the threshold inlet velocity of inertial-force-dominant flow boiling under different experimental conditions at different gravity levels

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure

Improving the effectiveness of energy savings measures at companies by means of a new baseline adjustment strategy

This paper discusses a strategy for establishing an energy consumption baseline for the effects of defining and applying new strategies to improve the effectiveness of energy savings measures. Through this analysis, the energy baseline is adjusted to the dynamics of a typical operation, reducing uncertainty about operating data when it is not possible to determine that a given energy consumption level is typical. The strategy enables focusing efforts on the points in the operation with greatest impact on energy efficiency as a function of frequency of operation

PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data

Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein-protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.published_or_final_versio

Surface plasmon-enhanced electroluminescence in organic light-emitting diodes incorporating Au nanoparticles

Surface plasmon-enhanced electroluminescence (EL) in an organic light-emitting diode is demonstrated by incorporating the synthesized Au nanoparticles (NPs) in the hole injection layer of poly(3,4-ethylene dioxythiophene):polystyrene sulfonic acid. An increase of ∼25% in the EL intensity and efficiency are achieved for devices with Au NPs, whereas the spectral and electrical properties remain almost identical to the control device. Time-resolved photoluminescence spectroscopy reveals that the EL enhancement is ascribed to the increase in spontaneous emission rate due to the plasmonic near-field effect induced by Au NPs. © 2012 American Institute of Physics

Boundaries of Disk-like Self-affine Tiles

Let $T:= T(A, {\mathcal D})$ be a disk-like self-affine tile generated by an integral expanding matrix $A$ and a consecutive collinear digit set ${\mathcal D}$, and let $f(x)=x^{2}+px+q$ be the characteristic polynomial of $A$. In the paper, we identify the boundary $\partial T$ with a sofic system by constructing a neighbor graph and derive equivalent conditions for the pair $(A,{\mathcal D})$ to be a number system. Moreover, by using the graph-directed construction and a device of pseudo-norm $\omega$, we find the generalized Hausdorff dimension $\dim_H^{\omega} (\partial T)=2\log \rho(M)/\log |q|$ where $\rho(M)$ is the spectral radius of certain contact matrix $M$. Especially, when $A$ is a similarity, we obtain the standard Hausdorff dimension $\dim_H (\partial T)=2\log \rho/\log |q|$ where $\rho$ is the largest positive zero of the cubic polynomial $x^{3}-(|p|-1)x^{2}-(|q|-|p|)x-|q|$, which is simpler than the known result.Comment: 26 pages, 11 figure

Application advances of artificial intelligence algorithms in dynamics simulation of railway vehicle

The application examples and domestic and foreign literatures using artificial intelligence algorithm for railway vehicle system dynamics simulation were reviewed. The machine learning and deep learning algorithms commonly used in railway vehicle dynamics simulation were summarized, and the application classifications of the 2 algorithms in railway vehicle system dynamics modelling and simulation were concluded and interpreted. According to railway vehicle system dynamics modelling, dynamics performance prediction and dynamics performance optimization, the advantages and limitations of applying artificial intelligence algorithms in force-elements modelling and simulation, track irregularity prediction, running stability prediction, noise prediction, crosswind safety prediction, running safety prediction, suspension optimization, wheel-rail matching optimization, structure optimization, and active and semi-active control were discussed in detail. The problems of applications of artificial intelligence algorithms in railway dynamics simulation were lack of training samples, generalization ability and interpretability. The development directions and key research contents of the interdisciplinary research between artificial intelligence and vehicle system dynamics were given. Research result shows that the hybrid modelling theory combining classical mechanics and artificial intelligence algorithms can be as a key research direction in the future. There is great potential to use the artificial intelligence algorithms to solve the random uncertainty in stochastic dynamics and improve the performance of stochastic dynamics. The artificial intelligence algorithms combinated with optimization algorithms can exploit their advantages in the dynamics performance optimization.

Fabrication and operation of a two-dimensional ion-trap lattice on a high-voltage microchip

Microfabricated ion traps are a major advancement towards scalable quantum computing with trapped ions. The development of more versatile ion-trap designs, in which tailored arrays of ions are positioned in two dimensions above a microfabricated surface, will lead to applications in fields as varied as quantum simulation, metrology and atom–ion interactions. Current surface ion traps often have low trap depths and high heating rates, because of the size of the voltages that can be applied to them, limiting the fidelity of quantum gates. Here we report on a fabrication process that allows for the application of very high voltages to microfabricated devices in general and use this advance to fabricate a two-dimensional ion-trap lattice on a microchip. Our microfabricated architecture allows for reliable trapping of two-dimensional ion lattices, long ion lifetimes, rudimentary shuttling between lattice sites and the ability to deterministically introduce defects into the ion lattice

Modulation of the virus-receptor interaction by mutations in the V5 loop of feline immunodeficiency virus (FIV) following in vivo escape from neutralising antibody

<b>BACKGROUND:</b> In the acute phase of infection with feline immunodeficiency virus (FIV), the virus targets activated CD4+ T cells by utilising CD134 (OX40) as a primary attachment receptor and CXCR4 as a co-receptor. The nature of the virus-receptor interaction varies between isolates; strains such as GL8 and CPGammer recognise a "complex" determinant on CD134 formed by cysteine-rich domains (CRDs) 1 and 2 of the molecule while strains such as PPR and B2542 require a more "simple" determinant comprising CRD1 only for infection. These differences in receptor recognition manifest as variations in sensitivity to receptor antagonists. In this study, we ask whether the nature of the virus-receptor interaction evolves in vivo.<p></p> <b>RESULTS:</b> Following infection with a homogeneous viral population derived from a pathogenic molecular clone, a quasispecies emerged comprising variants with distinct sensitivities to neutralising antibody and displaying evidence of conversion from a "complex" to a "simple" interaction with CD134. Escape from neutralising antibody was mediated primarily by length and sequence polymorphisms in the V5 region of Env, and these alterations in V5 modulated the virus-receptor interaction as indicated by altered sensitivities to antagonism by both anti-CD134 antibody and soluble CD134.<p></p> <b>CONCLUSIONS:</b> The FIV-receptor interaction evolves under the selective pressure of the host humoral immune response, and the V5 loop contributes to the virus-receptor interaction. Our data are consistent with a model whereby viruses with distinct biological properties are present in early versus late infection and with a shift from a "complex" to a "simple" interaction with CD134 with time post-infection.<p></p&gt