Functionality of selected strains of moulds and yeasts from Vietnamese rice wine starters

The role of starch-degrading mycelial fungi, and the alcohol production and ethanol tolerance of the yeasts isolated from selected Vietnamese traditional rice wine starters were examined, and optimum conditions for these essential steps in rice wine fermentation were determined. Of pure isolates from Vietnamese rice wine starters, mould strains identified as Amylomyces rouxii, Amylomyces aff. rouxii, Rhizopus oligosporus and Rhizopus oryzae, were superior in starch degradation, glucose production and amyloglucosidase activity during the saccharification of purple glutinous rice. A. rouxii was able to produce up to 25%w/w glucose with an amyloglucosidase activity up to 0.6 Ug(-1) of fermented moulded mass. Five yeast isolates identified as Saccharomyces cerevisiae were selected for their superior alcohol productivity. They were able to deplete a relatively high initial percentage of glucose (20% w/v), forming 8.8% w/v ethanol. The ethanol tolerance of S. cerevisiae in challenge tests was 9-10% w/v, and 13.4% w/v as measured in fed-batch fermentations. Optimum conditions for the saccharification were: incubation for 2 d at 34 degrees C, of steamed rice inoculated with 5 log cfu g(-1); for the alcoholic fermentation 4 d at 28.3 degrees C, of saccharified rice liquid inoculated with 5.5 log cfu mL(-1).     

Reconstructing surfaces by volumetric regularization using radial basis functions

We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to vision-based data sets. Our method constructs a 3D implicit surface, formulated as a sum of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data, (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data, and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness.     

Efficient duration and hierarchical modeling for human activity recognition

A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.     

A printed circuit board capacitive sensor for air bubble inside fluidic flow detection

This paper presents a three-electrode capacitive fluidic sensor for detecting an air bubble inside a fluidic channel such as blood vessels, oil or medical liquid channels. The capacitor is designed and fabricated based on a printed circuit board (PCB). The electrodes are fabricated by using copper via structure through top to bottom surface of the PCB. A plastic pipe is layout through the capacitive sensor and perpendicular to the PCB surface. Capacitance of sensor changes when an air bubble inside fluidic flow cross the sensor. The capacitance change can be monitored by using a differential capacitive amplifier, a lock-in amplifier, filter and an NI acquisition card. Signal is processed and calculated on a computer. Air bubble inside the liquid flow are detected by monitor the unbalance signal between the three electrode potential voltages. Output voltage depends on the volume of the air bubble due to dielectric change between capacitor’s electrodes. Output voltage is up to 53 mV when an 2.28 mm³ air bubble crosses the sensing channel. Air bubble velocity can be estimated based on the output pulse signal. This proposed fluidic sensor can be used for void fraction detection in medical devices and systems; fluidic characterization; and water–gas, oil–water and oil–water–gas multiphase flows in petroleum technology. That structure also can apply to the micro-size for detecting in microfluidic to monitor and control changes in microfluidic channels.     

Identification of depigmenting components from <Emphasis Type=Italic>Nigella glandulifera</Emphasis> Freyn

To develop a novel skin depigmenting agent from natural sources, the inhibition of melanogenesis by Chinese plants, N. glandulifrea, was evaluated. The methanol extract of this plant showed significantly down-regulated melanin synthesis in a dose-dependent manner at a non-toxic concentration in cultured B16F10 mouse melanoma cells. This extract was further fractionated by using solvent-solvent partition and silica open column chromatography to identify the active components. From GC-MS data, oleic acid methyl ester was found as one of the depigmenting agents. In conclusion, we suggest that this fraction may be a safe and effective depigmentation agent.     

Synthesis, characterization and electrochemical properties of 4.8 V LiNi0.5Mn1.5O4 cathode material in lithium-ion batteries

In this work the synthesis of a nickel doped cubic manganese spinel has been studied for application as cathode material in secondary lithium batteries. Six different experimental approaches have been tested in order to carry out a screening of the various possible synthetic routes. The used synthetic strategies were wet chemistry (WC), solid state (SS), combustion synthesis (CS), cellulose-based sol-gel synthesis (SG-C), ascorbic acid-based sol-gel synthesis (SG-AA) and resorcinol/formaldehyde-based sol-gel synthesis (SG-RF). The goal of our study is to obtain insights about how the synthesis conditions can be modified in order to achieve a material with improved electrochemical performances in such devices, especially in high current operating regimes. The synthesized materials have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), atomic absorption, inductively coupled plasma (ICP-MS) atomic emission spectroscopy, surface area measurements and tested as high voltage cathodes in Li-ion electrochemical devices.     

Simulations of passivation phenomena based on discrete lattice gas automata

We present simulation results for a simple lattice gas cellular automata model of passivation. The lattice sites representing the corrosion product are produced at the corroding surface and diffuse executing a random walk. Asymmetric simple exclusion rules of the random walk account for an attractive potential between the corrosion product particles. The particles can aggregate and when sufficiently numerous form a compact phase on the corroding surface. The model predicts a transition from the active to passive state when increasing the reactivity of the surface. The transition is characterized by a sudden increase in the surface coverage of the corrosion product interpreted as a passive layer formation. The layer blocks contact of the metal surface with the environment and reduces the corrosion rate. The model reproduces the known paradox of passivity—the surface must be reactive enough for the layer to form. A further increase in the bare reactivity reduces largely the observed reaction rate. The simulations yield information on the morphological changes of the surface layer before and after the transition. In terms of the corrosion current, the active state is described by the current increase with the polarizing potential according to the Tafel law while in the passive state the current is independent of the anodic potential. Our simple model reproduces principal features of passivation.