Rainfall data simulation by hidden Markov model and discrete wavelet transformation

In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data. © Springer-Verlag 2008.postprin

Rainfall data simulation by hidden Markov model and discrete wavelet transformation

In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale, the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated simulated data do show some level of agreement with the observed data. © Springer-Verlag 2008.postprin

Human oviductal cells produces three glycoprotein fractions that stimulate mouse embryo development

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Adipocyte fatty acid binding protein potentiates toxic lipids-induced endoplasmic reticulum stress via its inhibition of autophagy

Oral PresentationINTRODUCTION: Chronic inflammation is the key link between obesity and its related cardio-metabolic complications. Endoplasmic reticulum (ER) stress is the potent trigger of inflammation in obese adipose tissue. However, the mechanism that links ER stress with inflammation is unclear. Adipocyte fatty acid binding protein (A-FABP) has been shown to ...published_or_final_versionThe 17th Medicial Research Conference, Department of Medicine, The University of Hong Kong, 14 January 2012. In Hong Kong Medical Journal, 2012, v. 18 n. 1, suppl. 1, p. 25, abstract no. 2

Parametric spectro-temporal analyzer (PASTA) for real-time optical spectrum observation

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Simultaneous demultiplexing of OTDM channels based on swept-pump fiber-optical parametric amplifier

Session - Quantum Information & Parametric Processing OM3B.2We experimentally demonstrate simultaneous demultiplexing of 80-Gb/s OTDM signal by transforming it into WDM idlers (spaced by 1.15 nm), based on a swept-pump fiber-optical parametric amplifier (FOPA), and 10-dB parametric gain is achieved. © 2012 OSApublished_or_final_versio

Ultrafast spectrum observation based on visualized spectro-temporal analyzer (ViSTA)

We present an ultrafast optical spectrum analyzing modality known as visualized spectro-temporal analyzer, which leverages the time-lens focusing mechanism, can realize frame rate as high as 100 MHz, with 0.02-nm resolution and –30-dBm detection sensitivity. © 2013 Optical Society of Americapublished_or_final_versio

In vivo OCT imaging based on la-codoped bismuth-based erbium-doped fiber

We demonstrate a Fourier domain mode-locked laser based on lanthanum-codoped bismuth-based erbium-doped fiber (Bi-EDF) for swept source optical coherence tomography (SS-OCT) imaging. Raman amplification is incorporated to suppress the gain competition and homogenous linewidth broadening effects of Bi-EDF. A wavelength sweeping bandwidth of 81 nm is generated under stable operation. Therefore, in vivo OCT imaging of human finger print and orange slices is enabled and the results are also presented. This scheme paves the way for doped fiber amplifiers to be employed to generate ultra-wideband SSs for OCT applications.published_or_final_versio

Model of growth cone membrane polarization via microtubule length regulation

We present a mathematical model of membrane polarization in growth cones. We proceed by coupling an active transport model of cytosolic proteins along a two-dimensional microtubule (MT) network with a modified Dogterom-Leibler model of MT growth. In particular, we consider a Rac1-stathmin-MT pathway in which the growth and catastrophe rates of MTs are regulated by cytosolic stathmin, while the stathmin is regulated by Rac1 at the membrane. We use regular perturbation theory and numerical simulations to determine the steady-state stathmin concentration, the mean MT length distribution, and the resulting distribution of membrane-bound proteins. We thus show how a nonuniform Rac1 distribution on the membrane generates a polarized distribution of membrane proteins. The mean MT length distribution and hence the degree of membrane polarization are sensitive to the precise form of the Rac1 distribution and parameters such as the catastrophe-promoting constant and tubulin association rate. This is a consequence of the fact that the lateral diffusion of stathmin tends to weaken the effects of Rac1 on the distribution of mean MT lengths

Stochastic active-transport model of cell polarization

We present a stochastic model of active vesicular transport and its role in cell polarization, which takes into account positive feedback between membrane-bound signaling molecules and cytoskeletal filaments. In particular, we consider the cytoplasmic transport of vesicles on a two-dimensional cytoskeletal network, in which a vesicle containing signaling molecules can randomly switch between a diffusing state and a state of directed motion along a filament. Using a quasi-steady-state analysis, we show how the resulting stochastic hybrid system can be reduced to an advection-diffusion equation with anisotropic and space-dependent diffusivity. This equation couples to a reaction-diffusion equation for the membrane-bound transport of signaling molecules. We use linear stability analysis to derive conditions for the growth of a precursor pattern for cell polarization, and we show that the geometry of the cytoskeletal filaments plays a crucial role in determining whether the cell is capable of spontaneous cell polarization or only polarizes in response to an external chemical gradient. As previously found in a simpler deterministic model with uniform and isotropic diffusion, the former occurs if filaments are nucleated at sites on the cell membrane (cortical actin), whereas the latter applies if the filaments nucleate from organizing sites within the cytoplasm (microtubule asters). This is consistent with experimental data on cell polarization in two distinct biological systems, namely, budding yeast and neuronal growth cones. Our more biophysically detailed model of motor transport allows us to determine how the conditions for spontaneous cell polarization depend on motor parameters such as mean speed and the rate of unbinding from filament tracks