A high-order uniform Cartesian grid compact finite difference scheme for the Goursat problem is developed. The basic idea of high-order compact schemes is to find the compact approximations to the derivatives terms by differentiating centrally the governing equations. Our compact scheme will approximate the derivative terms by involving the higher terms and reducing the number of grid points. The compact finite difference scheme is given for general form of the Goursat problem in uniform domain and illustrates the performance by applying a linear problem. Numerical experiments have been conducted with the new scheme and encouraging results have been obtained. In this paper we present the compact finite difference scheme for the Goursat problem. With the aid of computational software the scheme was programmed for determining the relative errors of linear Goursat problem.
Stock market investing is undoubtedly challenging. Investors have to deal with random, vague and ambiguity stock price volatility before embarking on investment decision. Due to these weaknesses, the conventional model has several limitations; as a result investors are demanding for a new robust model which is able to represent their real situation to solve the uncertainty issues. In this study we developed a new fuzzy portfolio selection model using semi-variance as a risk measure integrates with investor’s judgment on assets’ future performance. Linear programming approach was used to optimize the portfolio risk and return. Empirical data showed that the model were able to derive a resourceful portfolio compared to the naïve portfolio selection.
Primary liver cancer is one of the most common cancer in the world with highest cancer mortality rate. The most common type of primary liver cancer is hepatocellular carcinoma (HCC). There are many risk factors for liver cancer and currently available treatments for HCC are largely inadequate. Gene mutation and dysfunction of p53 are common and is recognized as an important molecular event in hepatocarcinogenesis. Therefore, replacement of the aberrant p53 gene is an attractive approach in the treatment of HCC providing an alternative treatment for primary HCC. In this study, we assessed whether the transfection with wild-type p53 gene is able to restore the pro-apoptotic effects and evaluate the feasibility of gene therapy in fixing a faulty p53 molecule. We established a non-viral cationic lipid-based p53 gene delivery into two human HCC cell lines namely HLF and PLC/PRF/5 cells. Both cell lines have mutations in the p53 gene. We compared the results with the normal liver cell line, WRL68, that constitutively expresses the wild-type p53 gene. In this study, the introduction of wild-type p53 gene into HLF and PLC/PRF/5 cells resulted in an increased of p53 gene expression, protein expression and cells growth inhibition shown in MTS reduction cell viability assay, FITC-Annexin V and PI apoptosis assay, western blot and caspase activity assay. In summary, the study provides a promising therapeutic approach for p53 gene delivery into HCC patients. The p53 gene delivery can be instituted together with chemotherapy as a combination treatment to induce apoptosis.
A hybrid climate model (HCM) is a novel proposed model based on the combination of self-organizing map (SOM) and analog method (AM). The main purpose was to improve the accuracy in rainfall forecasting using HCM. In combination process of HCM, SOM algorithm classifies high dimensional input data to low dimensional of several disjointed clusters in which similar input is grouped. AM searches the future day that has similar property with the day in the past. Consequently, the analog day is mapped to each cluster of SOM to investigate rainfall. In this study, the input data, geopotential height at 850 hPa from the Climate Forecast System Reanalysis (CFSR) are training set data and also the complete rainfall data at 30-meteorological stations from Thai meteorological department (TMD) are observed. To improve capability of rainfall forecasting, three different measures were evaluated. The experimental results showed that the performance of HCM is better than the traditional AM. It is illustrated that the HCM can forecast rainfall proficiently.
A hybrid biofuel cell, a zinc-air cell employing laccase as the oxygen reduction catalyst is investigated. A simple cell design is employed; a membraneless single chamber and a freely suspended laccase in the buffer electrolyte. The cell is characterised based on its open-circuit voltage, power density profile and galvanostatic discharge at 0.5 mA. The activity of laccase as an oxidoreductase is substantiated from the cell discharge profiles. The use of air electrode in the cell design enhanced the energy output by 14%. The zinc-air biofuel cell registered an open-circuit voltage of 1.2 V and is capable to deliver a maximum power density of 1.1 mWcm-2 at 0.4 V. Despite its simple design features, the power output is comparable to that of biocatalytic cell utilising a much more complex system design.
There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic markers and to prove that the prognosis is better with both markers. The proposed hybrid model consisting of two stages, where stage one with ReliefF-GA feature selection method to find an optimal feature of subset and stage two with ANFIS classification to classify either the patients alive or dead after certain years of diagnosis. The proposed prognostic model was experimented on two groups of oral cancer dataset collected locally here in Malaysia, Group 1 with clinicopathologic markers only and Group 2 with both clinicopathologic and genomic markers. The results proved that the proposed model with optimum features selected is more accurate with the use of both clinicopathologic and genomic markers and outperformed the other methods of artificial neural network, support vector machine and logistic regression. This prognostic model is feasible to aid the clinicians in the decision support stage and to identify the high risk markers to better predict the survival rate for each oral cancer patient.
The solid phase synthesis is a method by which organic compound synthesis are performed on a support. With this method, the purification can be carried out easily by simple filtration and washing procedures. Long-chain alcohol (C-100 alcohol) can be used as a support because of its insolubility in organic solvents and its simple structure which enables it to be stable in various reaction conditions. In this study, a 4-aminopyridine derivative has been synthesized from C-100 β-keto ester and a cyano enamine using tin(VI)chloride as catalyst. C-100 β-keto ester was obtained by transesterification of long chain alcohol (the support) with ethyl acetoacetate using boric acid protocol. The cyano enamine was successfully synthesized by Thorpe-Ziegler cyclization initiated by sodium hydride. The 4-aminopyridine derivative was successfully cleaved from the support using sodium isopropoxide in refluxing isopropanol. From the 1H-NMR spectrum at ~ 120oC, it was found that the cleaved support has the same spectrum with the long-chain alcohol used in the beginning of reaction, thus, this long chain alcohol can be reused for other reactions.
Boesenbergia rotunda is a medicinal ginger that has been found to contain several bioactive compounds such as
boesenbergin A, panduratin A, cardamonin, pinostrobin and pinocembrin. These compounds are useful in treating various
ailments, such as oral diseases, inflammation and have also been used as an aphrodisiac. In this study, an efficient
protocol for developing and isolating protoplast cultures for B. rotunda has been established. Rhizome buds of B. rotunda
were used as explants to initiate callus growth and the established cell suspension cultures were used to optimize their
growth conditions. Our results indicated that embryogenic suspension cultures in liquid Murashige and Skoog (MS)
medium supplemented with 3% (w/v) sucrose produced the highest growth rate (µ = 0.1125), whereas no promotive effect
was seen in the presence of 2,4-dichlorophenoxyacetic acid and those that underwent sonication treatment. Amount of
protoplasts isolated ranging from 1-5 × 105
protoplast per mL were isolated using 0.25% (w/v) macerozyme and 1%
(w/v) cellulase for 24 h under continuous agitation (50 rpm) in dark condition. Of the isolated protoplasts, 54.93% were
viable according to fluorescein diacetate staining test. Micro-colonies were recovered in liquid MS medium containing
9 g/L mannitol, 2 mg/L 1-naphthaleneacetic acid and 0.5 mg/L benzylaminopurine (BAP) for 4 weeks and subsequently
transferred to solid MS medium supplemented with 0.5 mg/L BAP for callus initiation. The protoplast system established
in this study would be useful for genetic manipulation and modern breeding program of B. rotund
An investigation on a batch production of palm kernel oil polyol (PKO-p) was conducted via esterification and condensation.
The process design was thoroughly studied as a preliminary step for future upscaling. The process variables included
necessity of vacuum pump, controlling of heating rate, recording the production time, nitrogen gas flow and agitator
speed. About 250 mL PKO-p was successfully synthesized within 3 h. Vacuum pressure was applied to haul out moisture
from the system. The control of heating rate and production time are vital to avoid sudden oxidation.
Outliers in the X-direction or high leverage points are the latest known source of multicollinearity. Multicollinearity is a nonorthogonality of two or more explanatory variables in multiple regression models, which may have important influential impacts on interpreting a fitted regression model. In this paper, we performed Monte Carlo simulation studies to achieve two main objectives. The first objective was to study the effect of certain magnitude and percentage of high leverage points, which are two important issues in tending the high leverage points to be collinearity-enhancing observations, on the multicollinarity pattern of the data. The second objective was to investigate in which situations these points do make different degrees of multicollinearity, such as moderate or severe. According to the simulation results, high leverage points should be in large magnitude for at least two explanatory variables to guarantee that they are the cause of multicollinearity problems. We also proposed some practical Lower Bound (LB) and Upper Bound (UB) for High Leverage Collinearity Influential Measure (HLCIM) which is an essential measure in detecting the degree of multicollinearity. A well-known example is used to confirm the simulation results.
This paper presents the improvement of quality factor (Q) estimation using shift frequency method. A new method was developed based on two previous methods; peak frequency shift (PFS) method and centroid frequency shift (CFS) method. The proposed algorithm has been tested to gauge its performance using three different scenarios; Q variation, travel
time variation, and signal to noise ratio (SNR) variation. The test was performed using the Ricker wavelet with random noise included. Based on the results obtained, it can be concluded that the new proposed method was able to improve Q estimation using shift frequency method. This method can also be implemented in the low and high Q condition, shallow and deep wavelet targets and in the low and high SNR conditions of seismic data. The limitations in the PFS and CFS methods can be reduced by this method.
A copper ion sensor based on a new bis-thiourea compound N1,N3-bis[[3,5-bis(trifluoromethyl)phenyl]carbamothioyl]isophthalamide (or TPC) as neutral carrier was investigated. The immobilization of the TPC into poly(n-butyl acrylate) (pBA) membrane via drop casting and the sensor was characterized by potentiometry. The sensor fabricated from TPC only showed a good Nernstian response towards copper ion with a sensitivity slope of 28.81±0.53mV/decade in the range of 1.0 × 10-6 - 1.0 × 10-4 M. The limit of detection of this sensor was found to be 6.11 × 10-7 M and with short sensor response time (60 - 80 s). This sensor also demonstrated reversibility and reproducibility with 3.69% and 1.84% (Relative Standard Deviation, RSD), respectively. Based on the separate solution method (SSM), the logarithm selectivity coefficients were better than -2.00 for monovalent, divalent and trivalent cations and this confirmed that the sensor exhibited good selectivity towards copper ion. The sensor could attain optimum function without the need in the inclusion of either lipophilic anions as a membrane additive nor plasticizer as a membrane softener. Thus, these are the main advantages. The addition of lipophilic anions into the pBA membrane could cause the sensitivity and selectivity of the copper ion sensor based on ionophore TPC to deteriorate.
Crotalaria pallida Aiton is an herbaceous legume from the family Fabaceae. In the present study, one new cyclopentyliene, crotolidene (1) and seven known compounds, i.e. hydroxydihydrobovolide (2), octacosane (3), trans-phytyl palmitate (4), linoleic acid (5), methyl oleate (6), ethyl palmitate (7), and palmitic acid (8) were isolated from the C. pallida collected from Perak, Malaysia. These compounds were isolated and characterized using extensive chromatographic and spectroscopic methods.
A cylindrical data set consists of circular and linear variables. We focus on developing an outlier detection procedure
for cylindrical regression model proposed by Johnson and Wehrly (1978) based on the k-nearest neighbour approach.
The procedure is applied based on the residuals where the distance between two residuals is measured by the Euclidean
distance. This procedure can be used to detect single or multiple outliers. Cut-off points of the test statistic are generated
and its performance is then evaluated via simulation. For illustration, we apply the test on the wind data set obtained
from the Malaysian Meteorological Department.
This paper proposes a new fuzzy version of Euler’s method for solving differential equations with fuzzy initial values. Our proposed method is based on Zadeh’s extension principle for the reformulation of the classical Euler’s method, which takes into account the dependency problem that arises in fuzzy setting. This problem is often neglected in numerical methods found in the literature for solving differential equations with fuzzy initial values. Several examples are provided to show the advantage of our proposed method compared to the conventional fuzzy version of Euler’s method proposed in the literature.
This research develops a new non-standard scheme based on the Adomian decomposition method (ADM) to solve nonlinear equations. The ADM was adopted to solve the nonlinear differential equation resulting from the discretization of the differential equation. The new scheme does not need to linearize or non-locally linearize the nonlinear term of the differential equation. Two examples are given to demonstrate the efficiency of this scheme.
Based on the hydraulic fracture width gradually narrows along the fracture length, with consideration of the mutual influences of fracture, non-uniform inflow of fractures segments and variable mass flow in the fracture comprehensively, a spatial separation method and time separation method were used to establish fracture horizontal well’s dynamic coupling model of reservoir seepage and fracture flow. The results showed that the calculation productivity of variable width model is higher than that of the fixed width model, while the difference becomes smaller as time increase. Due to mutual interference of the fractures, the production of outer fracture is higher than that of the inner fracture. When the dimensionless fracture conductivity is 0.1, the middle segment of the fracture dominates the productivity and local peak emerges near the horizontal well. The flow in the fracture is with the ‘double U’ type distribution. As the dimensionless fracture conductivity increase, the fractures productivity mainly through the tips and the flow in the fractures with the ‘U’ type distribution. Using the established fracture width variable productivity prediction model, one can achieve the quantitative optimization of fracture shape.
A new species of the genus Boysidia Ancey, 1881, collected from Doi Pha Tang, Chiangrai Province, Northern Thailand, is described as B. phatangensis sp. nov. The new species is characterized by its small size, conical shape with 5 ¼ - 5 ½ whorls, yellowish-brown color, aperture subquadrate consists of 6 barrier teeth, outer lip expanded. B. phatangensis seem limited to the high altitude area in northern part of Thailand.
In the original ElGamal signature scheme and it’s variants, two secret integers-private key and one-time secret key are required to produce a signature on a message, m. The private key of a system will be used throughout the life of the system whereas the one-time secret key only be used once and must be regenerated (different one-time secret key) when signing different message. This paper introduces a new version of ElGamal signature that eliminates the use of one-time secret key. This property will make all attacks, aiming at revealing the one-time secret key irrelevant. The scheme also can be regarded as ‘a right notion of signature scheme’ because we use only one secret key to sign messages.
In competing risks analysis, the primary interest of researchers is the estimation of the net survival probability (NSP) if a cause of failure could be eliminated from a population. The Kaplan-Meier product-limit estimator under the assumption that the eliminated risk is non-informative to the other remaining risks, has been widely used in the estimation of the NSP. The assumption implies that the hazard of the remaining risks before and after the elimination are equal and it could be biased. This paper addressed this possible bias by proposing a non-parametric multistate approach that accounts for an informative eliminated risk in the estimation procedure, whereby the hazard probabilities of the remaining risks before and after the elimination of a risk are not assumed to be equal. When a non-informative eliminated risk was assumed, it was shown that the proposed multistate estimator reduces to the Kaplan-Meier estimator. For illustration purposes, the proposed procedure was implemented on a published dataset and the change in hazard after elimination of a cause is investigated. Comparing the results to those obtained from using the Kaplan-Meier method, it was found that in the presence of (both constant and non-constant) informative eliminated risk, the proposed multistate approach was more sensitive and flexible.