DETERMINATION OF PARAMETERS OF FAST CHEMICAL REACTION OF ALIZARIN RED S BY SPECTROELECTROCHEMICAL METHOD WITH LOPTLC

The rate constant of very fast chemical reaction generally can be measured by electrochemical methods, but can not by the thin layer electrochemical methods because of the influence of diffusion effect. Long optical path length thin layer cell (LOPTLC) with large ratio of electrode area to solution volume can be used to monitor the fist chemical reaction in situ with high sensitivity and accuracy. It enable the adsorption spectra to be measured without the influence of diffusion effect. In the present paper, a fast chemical reaction of Alizarin Red S (ARS) with its oxidative state has been studied. The reaction equilibrium constant (K) under different potentials can be determined by single step potential-absorption spectra in LOPTLC. An equilibrium constant of 7.94 x 10(5) l.mol(-1) for the chemical reaction has been obtained from the plot of lgK vs. (E - E-1(0)'). Rate constant (k) under different potentials can be measured by single step potential-chronoabsorptiometry. A rate constant of 426.6 l.mol(-1).s(-1) for the chemical reaction has been obtained from the plot of lgK vs. (E - E-1(0)') with (E - E-1(0)') = 0.

Fast microzooplankton grazing on fast-growing, low-biomass phytoplankton: a case study in spring in Chesapeake Bay, Delaware Inland Bays and Delaware Bay

Dilution experiments were performed to examine the growth and grazing mortality rates of picophytoplankton (< 2 mu m), nanophytoplankton (2-20 mu m), and microphytoplankton (> 20 mu m) at stations in the Chesapeake Bay (CB), the Delaware Inland Bays (DIB) and the Delaware Bay (DB), in early spring 2005. At station CB microphytoplankton, including chain-forming diatoms were dominant, and the microzooplankton assemblage was mainly composed of the tintinnid Tintinnopsis beroidea. At station DIB, the dominant species were microphytoplanktonic dinoflagellates, while the microzooplankton community was mainly composed of copepod nauplii and the oligotrich ciliate Strombidium sp. At station DB, nanophytoplankton were dominant components, and Strombidium and Tintinnopsis beroidea were the co-dominant microzooplankton. The growth rate and grazing mortality rate were 0.13-3.43 and 0.09-1.92 d(-1) for the different size fractionated phytoplankton. The microzooplankton ingested 73, 171, and 49% of standing stocks, and 95, 70, and 48% of potential primary productivity for total phytoplankton at station CB, DIB, and DB respectively. The carbon flux for total phytoplankton consumed by microzooplankton was 1224.11, 100.76, and 85.85 mu g C 1(-1) d(-1) at station CB, DIB, and DB, respectively. According to the grazing mortality rate, carbon consumption rate and carbon flux turn over rates, microzooplankton in study area mostly preferred to graze on picophytoplankton, which was faster growing but was lowest biomass component of the phytoplankton. The faster grazing on Fast-Growing-Low-Biomass (FGLB) phenomenon in coastal regions is explained as a resource partitioning strategy. This quite likely argues that although microzooplankton grazes strongly on phytoplankton in these regions, these microzooplankton grazers are passive.

Fast formation of NaA zeolite membrane in the microwave field

NaA zeolite membrane was successfully synthesized on the porous alpha-Al2O3 support by microwave heating. The synthesis of NaA zeolite membrane in the microwave field only needs 15 min and the synthesis time is 10 times shorter than that by conventional heating. SEM characterization indicates that the zeolite crystals in the NaA zeolite membrane synthesized by microwave heating are uniform in size; the membrane thickness is about 4 mu m and is thinner than that of the NaA zeolite membrane synthesized by conventional heating. Gas permeation studies indicate that the permeances of the NaA zeolite membrane synthesized by microwave heating are 3-4 times higher than those of the NaA zeolite membrane synthesized by conventional heating, while their permselectivities are comparable.

Fast analysis of capillary electrochromatography with non-porous ODS as the stationary phase

High-speed capillary electrochromatography was developed on both short and long packed columns with 2 mu m non-porous ODS as the stationary phase. Factors that affect the analysis time of samples, such as voltage, electrolyte concentration, pH and organic modifier concentration in the mobile phase, were studied systematically. Fast analysis of aromatic compounds within 13 seconds was realized with column efficiency of 573,000 plates/m and a R.S.D.% of the retention times of all components in 8 consecutive injections below 1.0%. which demonstrated the high efficiency and high reproducibility of such a technique. In addition, DNPH derived aldehydes and ketones in both standards and environmental samples were separated with high speed.

Photoelectron spectroscopy of ammonia via a fast predissociative state

We report a study on resonance enhanced multiphoton ionization photoelectron spectroscopy (REMPI-PES) involving the fast predissociative (A) over tilde state of ammonia, using nano- and femtosecond lasers. The multiphoton scheme involves (1 + 1), (2 + 2), (2 + 2) + 1 and (2 + 2) + 2 photon processes. We have found a progression of stretching vibrations nu(1) in the PE spectrum when pumping NH3 (A) over tilde upsilon(2) = 0, 1 and 3 as intermediate states. The stretching vibration intensity distributions in the photoelectron spectrum are calculated by using the Chebychev method of the wavepacket propagation. The femtosecond spectrum shows a similar feature to the nanosecond spectrum. However, high laser power also causes band broadening and shifting effect as well as above threshold multiphoton ionization.

Fast Perceptual Learning in Visual Hyperacuity

In many different spatial discrimination tasks, such as in determining the sign of the offset in a vernier stimulus, the human visual system exhibits hyperacuity-level performance by evaluating spatial relations with the precision of a fraction of a photoreceptor"s diameter. We propose that this impressive performance depends in part on a fast learning process that uses relatively few examples and occurs at an early processing stage in the visual pathway. We show that this hypothesis is plausible by demonstrating that it is possible to synthesize, from a small number of examples of a given task, a simple (HyperBF) network that attains the required performance level. We then verify with psychophysical experiments some of the key predictions of our conjecture. In particular, we show that fast timulus-specific learning indeed takes place in the human visual system and that this learning does not transfer between two slightly different hyperacuity tasks.

Garbage Collection is Fast, But a Stack is Faster

Prompted by claims that garbage collection can outperform stack allocation when sufficient physical memory is available, we present a careful analysis and set of cross-architecture measurements comparing these two approaches for the implementation of continuation (procedure call) frames. When the frames are allocated on a heap they require additional space, increase the amount of data transferred between memory and registers, and, on current architectures, require more instructions. We find that stack allocation of continuation frames outperforms heap allocation in some cases by almost a factor of three. Thus, stacks remain an important implementation technique for procedure calls, even in the presence of an efficient, compacting garbage collector and large amounts of memory.

Sparse Representations for Fast, One-Shot Learning

Humans rapidly and reliably learn many kinds of regularities and generalizations. We propose a novel model of fast learning that exploits the properties of sparse representations and the constraints imposed by a plausible hardware mechanism. To demonstrate our approach we describe a computational model of acquisition in the domain of morphophonology. We encapsulate phonological information as bidirectional boolean constraint relations operating on the classical linguistic representations of speech sounds in term of distinctive features. The performance model is described as a hardware mechanism that incrementally enforces the constraints. Phonological behavior arises from the action of this mechanism. Constraints are induced from a corpus of common English nouns and verbs. The induction algorithm compiles the corpus into increasingly sophisticated constraints. The algorithm yields one-shot learning from a few examples. Our model has been implemented as a computer program. The program exhibits phonological behavior similar to that of young children. As a bonus the constraints that are acquired can be interpreted as classical linguistic rules.

Fast Pose Estimation with Parameter Sensitive Hashing

Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and high-dimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly becme prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. Our algorithm extends a recently developed method for locality-sensitive hashing, which finds approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice of hash functions; we show how to find the set of hash functions that are optimally relevant to a particular estimation problem. Experiments demonstrate that the resulting algorithm, which we call Parameter-Sensitive Hashing, can rapidly and accurately estimate the articulated pose of human figures from a large database of example images.

Fast Contour Matching Using Approximate Earth Mover's Distance

Weighted graph matching is a good way to align a pair of shapes represented by a set of descriptive local features; the set of correspondences produced by the minimum cost of matching features from one shape to the features of the other often reveals how similar the two shapes are. However, due to the complexity of computing the exact minimum cost matching, previous algorithms could only run efficiently when using a limited number of features per shape, and could not scale to perform retrievals from large databases. We present a contour matching algorithm that quickly computes the minimum weight matching between sets of descriptive local features using a recently introduced low-distortion embedding of the Earth Mover's Distance (EMD) into a normed space. Given a novel embedded contour, the nearest neighbors in a database of embedded contours are retrieved in sublinear time via approximate nearest neighbors search. We demonstrate our shape matching method on databases of 10,000 images of human figures and 60,000 images of handwritten digits.