Shroud heat transfer measurements from a rotating cavity with an axial throughflow of air

The paper discusses measurements of heat transfer obtained from the inside surface of the peripheral shroud. The experiments were carried out on a rotating cavity, comprising two 0.985-m-dia disks, separated by an axial gap of 0.065 m and bounded at the circumference by a carbon fiber shroud. Tests were conducted with a heated shroud and either unheated or heated disks. When heated, the disks had the same temperature level and surface temperature distribution. Two different temperature distributions were tested; the surface temperature either increased, or decreased with radius. The effects of disk, shroud, and air temperature levels were also studied. Tests were carried out for the range of axial throughflow rates and speeds: 0.0025 ≤ m ≤ 0.2 kg/s and 12.5 ≤ Ω ≤ 125 rad/s, respectively. Measurements were also made of the temperature of the air inside the cavity. The shroud Nusselt numbers are found to depend on a Grashof number, which is defined using the centripetal acceleration. Providing the correct reference temperature is used, the measured Nusselt numbers also show similarity to those predicted by an established correlation for a horizontal plate in air. The heat transfer from the shroud is only weakly affected by the disk surface temperature distribution and temperature level. The heat transfer from the shroud appears to be affected by the Rossby number. A significant enhancement to the rotationally induced free convection occurs in the regions 2 ≤ Ro ≤ 4 and Ro ≥ 20. The first of these corresponds to a region where vortex breakdown has been observed. In the second region, the Rossby number may be sufficiently large for the central throughflow to affect the shroud heat transfer directly. Heating the shroud does not appear to affect the heat transfer from the disks significantly.

Multi-walled carbon nanotube conductivity enhancement and band gap widening via rapid pulsed thermal annealing

Herein we report on the transport characteristics of rapid pulsed vacuum-arc thermally annealed, individual and network multi-walled carbon nanotubes. Substantially reduced defect densities (by at least an order of magnitude), measured by micro-Raman spectroscopy, and were achieved by partial reconstruction of the bamboo-type defects during thermal pulsing compared with more traditional single-pulse thermal annealing. Rapid pulsed annealed processed networks and individual multi-walled nanotubes showed a consistent increase in conductivity (of over a factor of five at room temperature), attributed to the reduced number density of resistive axial interfaces and, in the case of network samples, the possible formation of structural bonds between crossed nanotubes. Compared to the highly defective as-grown nanotubes, the pulsed annealed samples exhibited reduced temperature sensitivity in their transport characteristics signifying the dominance of scattering events from structural defects. Transport measurements in the annealed multi-walled nanotubes deviated from linear Ohmic, typically metallic, behavior to an increasingly semiconducting-like behavior attributed to thermally induced axial strains. Rapid pulsed annealed networks had an estimated band gap of 11.26 meV (as-grown; 6.17 meV), and this observed band gap enhancement was inherently more pronounced for individual nanotubes compared with the networks most likely attributed to mechanical pinning effect of the probing electrodes which possibly amplifies the strain induced band gap. In all instances the estimated room temperature band gaps increased by a factor of two. The gating performance of back-gated thin-film transistor structures verified that the observed weak semiconductivity (p-type) inferred from the transport characteristic at room temperature. © 2014 Copyright Taylor & Francis Group, LLC.

Optical response of high-order band gap photonic crystal applying in-plane in one-dimensional integration

A new broadband filter, based on the high-order band gap in one-dimensional photonic crystal (PCs) of the form Si vertical bar air vertical bar Si vertical bar air vertical bar Si vertical bar air vertical bar Si vertical bar air vertical bar Si vertical bar air vertical bar Si, has been designed by the plane wave expansion method (PWEM) and transfer matrix method (TMM) and fabricated by lithography. The optical response of this filter to normal-incident and oblique-incident light proves that utilizing the high-order band gaps of PCs is an efficient method to lower the difficulties of fabricating PCs, increase the etching depth of semiconductor materials, and reduce the coupling loss at the interface between optical fibers and PC device. (c) 2007 Elsevier B.V. All rights reserved.

Determining of the delay time for a heating ventilating and air-conditioning plant using multi-weights neurons approach

This paper presents an multi weights neurons approach to determine the delay time for a Heating ventilating and air-conditioning (HVAC) plan to respond to control actions. The multi weights neurons is a fully connected four-layer network. An acceleration technique was used to improve the general delta rule for the learning process. Experimental data for heating and cooling modes were used with both the multi weights neurons and a traditional mathematical method to determine the delay time. The results show that multi weights neurons can be used effectively determining the delay time for HVAC systems.

Gap states of hydrogenated amorphous silicon near and above the threshold of microcrystallinity with subtle boron compensation

The effects of hydrogen dilution, subtle boron compensation, and light-soaking on the gap states of hydrogenated amorphous silicon films (a-Si:H) near and above the threshold of microcrystallinity have been investigated in detail by the constant photocurrent method and the improved phase-shift analysis of modulated photocurrent technique. It is shown that high hydrogen dilution near the threshold of microcrystallinity leads to a more ordered network structure and to the redistribution of gap states; it gives rise to a small peak at about 0.55 eV and a shoulder at about 1.2 eV below the conduction band edge, which are associated with the formation of microcrystallites embedded in the amorphous silicon host matrix. A concurrent subtle boron compensation is demonstrated to prevent excessive formation of microcrystallinity, and to help promote the growth of the ordered regions and reduce the density of gap defect states, particularly those associated with microcrystallites. Hydrogen-diluted and appropriately boron-compensated a-Si:H films deposited near the threshold of microcrystallinity show the lowest density of the defects in both the annealed and light-soaked states, and hence, the highest performance and stability. (C) 2001 American Institute of Physics.

An ATP-dependent mechanism mediates intercellular calcium signaling in bone cell network under single cell nanoindentation

To investigate the roles of intercellular gap junctions and extracellular ATP diffusion in bone cell calcium signaling propagation in bone tissue, in vitro bone cell networks were constructed by using microcontact printing and self-assembled monolayer technologies. In the network, neighboring cells were interconnected through functional gap junctions. A single cell at the center of the network was mechanically stimulated by using an AFM nanoindenter. Intracellular calcium ([Ca2+](i)) responses of the bone cell network were recorded and analyzed. In the untreated groups, calcium propagation from the stimulated cell to neighboring cells was observed in 40% of the tests. No significant difference was observed in this percentage when the intercellular gap junctions were blocked. This number, however, decreased to 10% in the extracellular ATP-pathway-blocked group. When both the gap junction and ATP pathways were blocked, intercellular calcium waves were abolished. When the intracellular calcium store in ER was depleted, the indented cell can generate calcium transients, but no [Ca2+](i) signal can be propagated to the neighboring cells. No [Ca2+](i) response was detected in the cell network when the extracellular calcium source was removed. These findings identified the biochemical pathways involved in the calcium signaling propagation in bone cell networks. Published by Elsevier Ltd.

Robustness and dissipation of mitogen-activated protein kinases signal transduction network: Underlying funneled landscape against stochastic fluctuations

We uncovered the underlying energy landscape of the mitogen-activated protein kinases signal transduction cellular network by exploring the statistical natures of the Brownian dynamical trajectories. We introduce a dimensionless quantity: The robustness ratio of energy gap versus local roughness to measure the global topography of the underlying landscape. A high robustness ratio implies funneled landscape. The landscape is quite robust against environmental fluctuations and variants of the intrinsic chemical reaction rates.

Quantifying robustness and dissipation cost of yeast cell cycle network: The funneled energy landscape perspectives

We study the origin of robustness of yeast cell cycle cellular network through uncovering its underlying energy landscape. This is realized from the information of the steady-state probabilities by solving a discrete set of kinetic master equations for the network. We discovered that the potential landscape of yeast cell cycle network is funneled toward the global minimum, G1 state. The ratio of the energy gap between G1 and average versus roughness of the landscape termed as robustness ratio ( RR) becomes a quantitative measure of the robustness and stability for the network. The funneled landscape is quite robust against random perturbations from the inherent wiring or connections of the network. There exists a global phase transition between the more sensitive response or less self-degradation phase leading to underlying funneled global landscape with large RR, and insensitive response or more self-degradation phase leading to shallower underlying landscape of the network with small RR. Furthermore, we show that the more robust landscape also leads to less dissipation cost of the network. Least dissipation and robust landscape might be a realization of Darwinian principle of natural selection at cellular network level. It may provide an optimal criterion for network wiring connections and design.

Preparation of divalent rare earth ions in air by aliovalent substitution and spectroscopic properties of Ln(2+)

When alkaline earth ions in borates, phosphates or borophosphates [SrB4O7, SrB6O10, BaB8O13, MBPO5 (M=Ca,Sr)] are substituted partially and aliovalently by trivalent rare earth ions such as Sm3+, Eu3+, these rare earth ions can be reduced to divalent state by the produced negative charge vacancy V-M". The matrices must have appropriate structure containing a rigid three-dimensional network of tetragonal AO(4) groups (A=B,P). These groups can surround and isolate the produced divalent RE2+ ions from the reaction with oxygen. Therefore, this reduction reaction can be carried out even in air at high temperature. The produced divalent rare earth ions can be detected by luminescence and XANES methods and their spectroscopic properties are discussed.

Neural network retrieval of ocean surface parameters from SSM/I data

A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.

Extracting Salient Curves from Images: An Analysis of the Saliency Network

The Saliency Network proposed by Shashua and Ullman is a well-known approach to the problem of extracting salient curves from images while performing gap completion. This paper analyzes the Saliency Network. The Saliency Network is attractive for several reasons. First, the network generally prefers long and smooth curves over short or wiggly ones. While computing saliencies, the network also fills in gaps with smooth completions and tolerates noise. Finally, the network is locally connected, and its size is proportional to the size of the image. Nevertheless, our analysis reveals certain weaknesses with the method. In particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. Furthermore, in some cases the saliency measure changes its preferences when curves are scaled uniformly. Also, we show that for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. In addition, we analyze the time complexity required by the method. We show that the number of steps required for convergence in serial implementations is quadratic in the size of the network, and in parallel implementations is linear in the size of the network. We discuss problems due to coarse sampling of the range of possible orientations. We show that with proper sampling the complexity of the network becomes cubic in the size of the network. Finally, we consider the possibility of using the Saliency Network for grouping. We show that the Saliency Network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one.

AIRSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.

Neural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistance

This article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

A Self-Organizing Neural Network for Learning a Body-Centered Invariant Representation of 3-D Target Position

This paper describes a self-organizing neural network that rapidly learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets (Bullock, Grossberg, and Guenther, 1993).