Abstract: Small tanks, the ancient man-made rain water storage
systems, support the pheasant life and agriculture of the dry zone of
Sri Lanka. Many small tanks were abandoned with time due to
various reasons. Such tanks, rehabilitated in the recent past, were
found to be less sustainable and most of these rehabilitation
approaches have failed. The objective of this research is to assess the
impact of the rehabilitation approaches in the management of small
tanks in the Kurunegala District of Sri Lanka with respect to eight
small tanks. A Sustainability index was developed using seven
indicators representing the ability and commitment of the villagers to
maintain these tanks. The sustainability index of the eight tanks
varied between 79.2 and 47.2 out of a total score of 100. The
conclusion is that, the approaches used for tank rehabilitation have a
significant effect on the sustainability of the management of these
small tanks.
Abstract: Crucial information barely visible to the human eye is
often embedded in a series of low resolution images taken of the
same scene. Super resolution reconstruction is the process of
combining several low resolution images into a single higher
resolution image. The ideal algorithm should be fast, and should add
sharpness and details, both at edges and in regions without adding
artifacts. In this paper we propose a super resolution blind
reconstruction technique for linearly degraded images. In our
proposed technique the algorithm is divided into three parts an image
registration, wavelets based fusion and an image restoration. In this
paper three low resolution images are considered which may sub
pixels shifted, rotated, blurred or noisy, the sub pixel shifted images
are registered using affine transformation model; A wavelet based
fusion is performed and the noise is removed using soft thresolding.
Our proposed technique reduces blocking artifacts and also
smoothens the edges and it is also able to restore high frequency
details in an image. Our technique is efficient and computationally
fast having clear perspective of real time implementation.
Abstract: In this paper we present a novel approach for human
Body configuration based on the Silhouette. We propose to address
this problem under the Bayesian framework. We use an effective
Model based MCMC (Markov Chain Monte Carlo) method to solve
the configuration problem, in which the best configuration could be
defined as MAP (maximize a posteriori probability) in Bayesian
model. This model based MCMC utilizes the human body model to
drive the MCMC sampling from the solution space. It converses the
original high dimension space into a restricted sub-space constructed
by the human model and uses a hybrid sampling algorithm. We
choose an explicit human model and carefully select the likelihood
functions to represent the best configuration solution. The
experiments show that this method could get an accurate
configuration and timesaving for different human from multi-views.
Abstract: Ethanol is generally used as a therapeutic reagent against Hepatocellular carcinoma (HCC or hepatoma) worldwide, as it can induce Hepatocellular carcinoma cell apoptosis at low concentration through a multifactorial process regulated by several unknown proteins. This paper provides a simple and available proteomic strategy for exploring differentially expressed proteins in the apoptotic pathway. The appropriate concentrations of ethanol required to induce HepG2 cell apoptosis were first assessed by MTT assay, Gisma and fluorescence staining. Next, the central proteins involved in the apoptosis pathway processs were determined using 2D-PAGE, SDS-PAGE, and bio-software analysis. Finally the downregulation of two proteins, AFP and survivin, were determined by immunocytochemistry and reverse transcriptase PCR (RT-PCR) technology. The simple, useful method demonstrated here provides a new approach to proteomic analysis in key bio-regulating process including proliferation, differentiation, apoptosis, immunity and metastasis.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: We have measured the pressure drop and convective
heat transfer coefficient of water – based AL(25nm),AL2O3(30nm)
and CuO(50nm) Nanofluids flowing through a uniform heated
circular tube in the fully developed laminar flow regime. The
experimental results show that the data for Nanofluids friction factor
show a good agreement with analytical prediction from the Darcy's
equation for single-phase flow. After reducing the experimental
results to the form of Reynolds, Rayleigh and Nusselt numbers. The
results show the local Nusselt number and temperature have
distribution with the non-dimensional axial distance from the tube
entry. Study decided that thenNanofluid as Newtonian fluids through
the design of the linear relationship between shear stress and the rate
of stress has been the study of three chains of the Nanofluid with
different concentrations and where the AL, AL2O3 and CuO – water
ranging from (0.25 - 2.5 vol %). In addition to measuring the four
properties of the Nanofluid in practice so as to ensure the validity of
equations of properties developed by the researchers in this area and
these properties is viscosity, specific heat, and density and found that
the difference does not exceed 3.5% for the experimental equations
between them and the practical. The study also demonstrated that the
amount of the increase in heat transfer coefficient for three types of
Nano fluid is AL, AL2O3, and CuO – Water and these ratios are
respectively (45%, 32%, 25%) with insulation and without insulation
(36%, 23%, 19%), and the statement of any of the cases the best
increase in heat transfer has been proven that using insulation is
better than not using it. I have been using three types of Nano
particles and one metallic Nanoparticle and two oxide Nanoparticle
and a statement, whichever gives the best increase in heat transfer.
Abstract: This paper presents an advance in monitoring and
process control of surface roughness in CNC machine for the turning
and milling processes. An integration of the in-process monitoring
and process control of the surface roughness is proposed and
developed during the machining process by using the cutting force
ratio. The previously developed surface roughness models for turning
and milling processes of the author are adopted to predict the inprocess
surface roughness, which consist of the cutting speed, the
feed rate, the tool nose radius, the depth of cut, the rake angle, and
the cutting force ratio. The cutting force ratios obtained from the
turning and the milling are utilized to estimate the in-process surface
roughness. The dynamometers are installed on the tool turret of CNC
turning machine and the table of 5-axis machining center to monitor
the cutting forces. The in-process control of the surface roughness
has been developed and proposed to control the predicted surface
roughness. It has been proved by the cutting tests that the proposed
integration system of the in-process monitoring and the process
control can be used to check the surface roughness during the cutting
by utilizing the cutting force ratio.
Abstract: In this paper, a novel approach for the multidisciplinary design optimization (MDO) of complex mechatronic systems. This approach, which is a part of a global project aiming to include the MDO aspect inside an innovative design process. As a first step, the paper considers the MDO as a redesign approach which is limited to the parametric optimization. After defining and introducing the different keywords, the proposed method which is based on the V-Model which is commonly used in mechatronics.
Abstract: PCCI engines can reduce NOx and PM emissions
simultaneously without sacrificing thermal efficiency, but a low
combustion temperature resulting from early fuel injection, and
ignition occurring prior to TDC, can cause higher THC and CO
emissions and fuel consumption. In conclusion, it was found that the
PCCI combustion achieved by the 2-stage injection strategy with
optimized calibration factors (e.g. EGR rate, injection pressure, swirl
ratio, intake pressure, injection timing) can reduce NOx and PM
emissions simultaneously. This research works are expected to
provide valuable information conducive to a development of an
innovative combustion engine that can fulfill upcoming stringent
emission standards.
Abstract: 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate using NADPH and the enzyme is involved in rate-controlling step of mevalonate. Inhibition of HMGR is considered as effective way to lower cholesterol levels so it is drug target to treat hypercholesterolemia, major risk factor of cardiovascular disease. To discover novel HMGR inhibitor, we performed structure-based pharmacophore modeling combined with molecular dynamics (MD) simulation. Four HMGR inhibitors were used for MD simulation and representative structure of each simulation were selected by clustering analysis. Four structure-based pharmacophore models were generated using the representative structure. The generated models were validated used in virtual screening to find novel scaffolds for inhibiting HMGR. The screened compounds were filtered by applying drug-like properties and used in molecular docking. Finally, four hit compounds were obtained and these complexes were refined using energy minimization. These compounds might be potential leads to design novel HMGR inhibitor.
Abstract: The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.
Abstract: In this paper, we propose novel algorithmic models
based on information fusion and feature transformation in crossmodal
subspace for different types of residue features extracted from
several intra-frame and inter-frame pixel sub-blocks in video
sequences for detecting digital video tampering or forgery. An
evaluation of proposed residue features – the noise residue features
and the quantization features, their transformation in cross-modal
subspace, and their multimodal fusion, for emulated copy-move
tamper scenario shows a significant improvement in tamper detection
accuracy as compared to single mode features without transformation
in cross-modal subspace.
Abstract: The effect of extraction solvent upon properties
of carrageenan from Eucheuma cottonii was studied. The
distilled water and KOH solution (concentration 0.1- 0.5N) were
used as the solvent. Extraction process was carried out in water
bath equipped by stirrer with constant speed of 275 rpm with a
constant ratio of seaweed weight to solvent volume ( 1:50 g/mL)
at 86oC for 45 minutes. The extract was then precipitated in 3
volume of 90% ethanol, oven dried at 60oC. Based on
experimental data, alkali significantly influenced yield and
properties of extracted carrageenan. The extracted carrageenan
was found to have essentially identical FTIR spectra to the
reference samples of kappa-carrageenan. Increasing the KOH
concentration led to carrageenan containing less sulfate content
and intrinsic viscosity. The gel strength increased along with the
increasing of KOH concentration. The decreasing of intrinsic
viscosity value indicates that a polymer degradation occurs
during alkali extraction.
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: In single trial analysis, when using Principal
Component Analysis (PCA) to extract Visual Evoked Potential
(VEP) signals, the selection of principal components (PCs) is an
important issue. We propose a new method here that selects only
the appropriate PCs. We denote the method as selective eigen-rate
(SER). In the method, the VEP is reconstructed based on the rate
of the eigen-values of the PCs. When this technique is applied on
emulated VEP signals added with background
electroencephalogram (EEG), with a focus on extracting the
evoked P3 parameter, it is found to be feasible. The improvement
in signal to noise ratio (SNR) is superior to two other existing
methods of PC selection: Kaiser (KSR) and Residual Power (RP).
Though another PC selection method, Spectral Power Ratio (SPR)
gives a comparable SNR with high noise factors (i.e. EEGs), SER
give more impressive results in such cases. Next, we applied SER
method to real VEP signals to analyse the P3 responses for
matched and non-matched stimuli. The P3 parameters extracted
through our proposed SER method showed higher P3 response for
matched stimulus, which confirms to the existing neuroscience
knowledge. Single trial PCA using KSR and RP methods failed to
indicate any difference for the stimuli.
Abstract: The P-Bigram method is a string comparison methods
base on an internal two characters-based similarity measure. The edit
distance between two strings is the minimal number of elementary
editing operations required to transform one string into the other. The
elementary editing operations include deletion, insertion, substitution
two characters. In this paper, we address the P-Bigram method to
sole the similarity problem in DNA sequence. This method provided
an efficient algorithm that locates all minimum operation in a string.
We have been implemented algorithm and found that our program
calculated that smaller distance than one string. We develop PBigram
edit distance and show that edit distance or the similarity and
implementation using dynamic programming. The performance of
the proposed approach is evaluated using number edit and percentage
similarity measures.
Abstract: In this paper we propose a family of algorithms based
on 3rd and 4th order cumulants for blind single-input single-output
(SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR)
channel estimation driven by non-Gaussian signal. The input signal
represents the signal used in 10GBASE-T (or IEEE 802.3an-2006)
as a Tomlinson-Harashima Precoded (THP) version of random
Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The
proposed algorithms are tested using three non-minimum phase
channel for different Signal-to-Noise Ratios (SNR) and for different
data input length. Numerical simulation results are presented to
illustrate the performance of the proposed algorithms.
Abstract: The flash memory has many advantages such as low power consumption, strong shock resistance, fast I/O and non-volatility. And it is increasingly used in the mobile storage device. The YAFFS, one of the NAND flash file system, is widely used in the embedded device. However, the existing YAFFS takes long time to mount the file system because it scans whole spare areas in all pages of NAND flash memory. In order to solve this problem, we propose a new content-based flash file system using a mounting time reduction technique. The proposed method only scans partial spare areas of some special pages by using content-based block management. The experimental results show that the proposed method reduces the average mounting time by 87.2% comparing with JFFS2 and 69.9% comparing with YAFFS.
Abstract: The separation of dissolved gas including dissolved oxygen can be used in breathing for a human under water. When one is suddenly wrecked or meets a tsunami, one is instantly drowned and cannot breathe under water. To avoid this crisis, when we meet waves, the dissolved gas separated from water by wave is used, while air can be used to breathe when we are about to escape from water. In this thesis, we investigated the separation characteristics of dissolved gas using the pipe type of hollow fiber membrane with polypropylene and the nude type of one with polysulfone. The hollow fiber membranes with good characteristics under water are used to separate the dissolved gas. The hollow fiber membranes with good characteristics in an air are used to transfer air. The combination of membranes with good separation characteristics under water and good transferring one in an air is used to breathe instantly under water to be alive at crisis. These results showed that polypropylene represented better performance than polysulfone under both of air and water conditions.
Abstract: All around the world pulp and paper industries are the
biggest plant production with the environmental pollution as the
biggest challenge facing the pulp manufacturing operations. The
concern among these industries is to produce a high volume of papers
with the high quality standard and of low cost without affecting the
environment. This result obtained from this bleaching study show
that the activation of peroxide was an effective method of reducing
the total applied charge of chlorine dioxide which is harmful to our
environment and also show that softwood and hardwood Kraft pulps
responded linearly to the peroxide treatments. During the bleaching
process the production plant produce chlorines. Under the trial stages
chloride dioxide has been reduced by 3 kg/ton to reduce the
brightness from 65% ISO to 60% ISO of pulp and the dosing point
returned to the E stage charges by pre-treating Kraft pulps with
hydrogen peroxide. The pulp and paper industry has developed
elemental chlorine free (ECF) and totally chlorine free (TCF)
bleaching, in their quest for being environmental friendly, they have
been looking at ways to turn their ECF process into a TCF process
while still being competitive. This prompted the research to
investigate the capability of the hydrogen peroxide as catalyst to
reduce chloride dioxide.