Blood flow through a bifurcated artery with the presence of an overlapping stenosis located at parent’s arterial lumen under the action of a uniform external magnetic field is studied in this paper. Blood is treated as an electrically conducting fluid which exhibits the Magnetohydrodynamics principle and it is characterized by a Newtonian fluid model. The governing equations are discretized using a stabilization technique of finite element known as Galerkin least-squares. The maximum velocity and pressure drop evaluated in this present study are compared with the results found in previous literature and COMSOL Multiphysics. The solutions found in a satisfactory agreement, thus verify the source code is working properly. The effects of dimensionless parameters of Hartmann and Reynolds numbers in the fluid’s velocity and pressure are examined in details with further scientific discussions.
Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. Additionally, by employing partial least square (PLS) regression, the coefficient of determination (R2) of transflectance and transmission were 0.9987 and 0.9994 with root mean square error of calibration (RMSEC) of 0.5931 and 0.6703 respectively. In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. The result of R2 and RMSEC after variable selection for transflectance and transmission were improved significantly. Based on the result of classification and quantification analysis, the transmission mode has yield better prediction model compared to the transflectance mode to distinguish the pure and adulterated palm oil.
The objective of the study is to examine the relationship between environmental indicators and health expenditures in the panel of five selected Asian countries, over the period of 2000-2013. The study used panel cointegration technique for evaluating the nexus between environment and health in the region. The results show that energy demand, forest area, and GDP per unit use of energy have a significant and positive impact on increasing health expenditures in the region. These results have been confirmed by single equation panel cointegration estimators, i.e., fully modified ordinary least squares (FMOLS), dynamic OLS (DOLS), and canonical cointegrating regression (CCR) estimators. In addition, the study used robust least squares regression to confirm the generalizability of the results in Asian context. All these estimators indicate that environmental indicators escalate the health expenditures per capita in a region; therefore, Asian countries should have to upsurge health expenditures for safeguard from environmental evils in a region.
The objective of the study is to establish the link between air pollution, fossil fuel energy consumption, industrialization, alternative and nuclear energy, combustible renewable and wastes, urbanization, and resulting impact on health services in Malaysia. The study employed two-stage least square regression technique on the time series data from 1975 to 2012 to possibly minimize the problem of endogeniety in the health services model. The results in general show that air pollution and environmental indicators act as a strong contributor to influence Malaysian health services. Urbanization and nuclear energy consumption both significantly increases the life expectancy in Malaysia, while fertility rate decreases along with the increasing urbanization in a country. Fossil fuel energy consumption and industrialization both have an indirect relationship with the infant mortality rate, whereas, carbon dioxide emissions have a direct relationship with the sanitation facility in a country. The results conclude that balancing the air pollution, environment, and health services needs strong policy vistas on the end of the government officials.
Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aimed to evaluate the α-glucosidase inhibitory potential of the hydromethanolic extracts of T. scandens leaves and to characterize the metabolites responsible for such activity through gas chromatography-mass spectrometry (GC-MS) metabolomics. Crude hydromethanolic extracts of different strengths were prepared and in vitro assayed for α-glucosidase inhibition. GC-MS analysis was further carried out and the mass spectral data were correlated to the corresponding α-glucosidase inhibitory IC50 values via an orthogonal partial least squares (OPLS) model. The 100%, 80%, 60% and 40% methanol extracts displayed potent α-glucosidase inhibitory potentials. Moreover, the established model identified 16 metabolites to be responsible for the α-glucosidase inhibitory activity of T. scandens. The putative α-glucosidase inhibitory metabolites showed moderate to high affinities (binding energies of -5.9 to -9.8 kcal/mol) upon docking into the active site of Saccharomyces cerevisiae isomaltase. To sum up, an OPLS model was developed as a rapid method to characterize the α-glucosidase inhibitory metabolites existing in the hydromethanolic extracts of T. scandens leaves based on GC-MS metabolite profiling.
This paper considers the problem of outlier detection in bilinear time series data with special focus on BL(1,0,1,1) and BL(1,1,1,1) models. In the previous study, the formulations of effect of innovational outlier on the observations and residuals from the process had been developed and the corresponding least squares estimator of outlier effect had been derived. Consequently, an outlier detection procedure employing bootstrap-based procedure to estimate the variance of the estimator had been proposed. In this paper, we proposed to use the mean absolute deviance and trimmed mean formula to estimate the variance to improve the performances of the procedure. Via simulation, we showed that the procedure based on the trimmed mean formula has successfully improved the performance of the procedure.
Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed.
Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.
A linear quantitative structure activity relationship (QSAR) model is presented for predicting human immunodeficiency virus-1 (HIV-1) reverse transcriptase enzyme inhibition. The 2D QSAR and 3D-QSAR models were developed by stepwise multiple linear regression, partial least square (PLS) regression and k-nearest neighbor-molecular field analysis, PLS regression, respectively using a database consisting of 33 recently discovered benzoxazinones. The primary findings of this study is that the number of hydrogen atoms, number of (-NH2) group connected with solitary single bond alters the inhibition of HIV-1 reverse transcriptase. Further, presence of electrostatic, hydrophobic and steric field descriptors significantly affects the ability of benzoxazinone derivatives to inhibit HIV-1 reverse transcriptase. The selected descriptors could serve as a primer for the design of novel and potent antagonists of HIV-1 reverse transcriptase.
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
This dataset presents the validation process of a survey of factors affecting Indonesian K-12 school teachers' Teachers' Information and Communication Technology Access (TICTA). An initial instrument was developed through the adaptation of instruments from previous studies. Afterward, it was piloted to 120 teachers and tested for its reliability. For the main data collection, the instrument was distributed online and responded by 2775 Indonesian K-12 school teachers. The main data analysis was conducted for the measurement model using four assessments; reflective indicator loadings, internal consistency reliability, convergent, and discriminant validity. The Partial Least Square Structural Equation Model (PLS-SEM) was utilized for the analysis. The dataset is beneficial for educational regulators in providing appropriate access to ICT in K-12 education and for educational researchers for future research on technology access in teaching.
A unit root test based on the modified least squares (MLS) estimator for first-order autoregressive process is proposed and compared with unit root tests based on the ordinary least squares (OLS), the weighted symmetric (WS) and the modified weighted symmetric (MWS) estimators. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of type I error and powers of the unit root tests were estimated via Monte Carlo simulation. The simulation results showed that all unit root tests can control the probability of type I error for all situations. The empirical power of the test is higher than the other unit root tests, and Apart from that, the and tests also provide the highest empirical power. As an illustration, the monthly series of U.S. nominal interest rates on three-month treasury bills is analyzed.
This article aims to uncover novel insights into personality factors and consumer video game engagement modeling. This research empirically validates the role of specific HEXACO personality factors that foster consumer engagement (CE) in electronic sports (eSports) users. Using a survey-based approach, we incorporated the HEXACO 60 items and consumer video game engagement scales for data collection. Data were collected from eSports users, with 250 valid responses. WarpPLS 6.0 was used for partial least squares-structural equation modeling analyses comprising measurement and structural model assessment. The results showed that the reflective measurement model is reliable and sound, whereas the second-order formative measurement model also meets the criteria of indicator weights and collinearity values variance inflation factor (VIF). The results based on the structural model indicate that openness to experience, extraversion, agreeableness, and conscientiousness positively predict CE in eSports. This article is first among others that conceptualizes and validates the HEXACO personality traits as a reflective formative model using the hierarchical component model approach. The research model carries the explanatory capacity for CE in eSports concerning personality dimensions as indicated by the HEXACO model. It highlights the potential benefits of such research especially to marketers who could potentially employ personality modeling to develop tailored strategies to increase CE in video games.
In the title compound, C13H15NO4, the oxopyrrolidin-3-yl ring has an envelope conformation, with the C atom bearing the acetate group being the flap. The acetate and phenyl groups are inclined with respect to the central ring, forming dihedral angles of 50.20 (12) and 87.40 (9)°, respectively, with the least-squares plane through the ring. The dihedral angle between the acetate group and the phenyl ring is 63.22 (8)°, indicating a twisted conformation in the mol-ecule. In the crystal, supra-molecular chains along the b axis are formed by (hy-droxy)O-H⋯O(ring carbon-yl) hydrogen bonds. The chains are consolidated into the three-dimensional architecture by C-H⋯O inter-actions.
In the title compound, C11H12O2S2, two independent but virtually superimposable mol-ecules, A and B, comprise the asymmetric unit. In each mol-ecule, the 1,3-di-thiane ring has a chair conformation with the 1,4-disposed C atoms being above and below the plane through the remaining four atoms. The substituted benzene ring occupies an equatorial position in each case and forms dihedral angles of 85.62 (9) (mol-ecule A) and 85.69 (8)° (mol-ecule B) with the least-squares plane through the 1,3-di-thiane ring. The difference between the mol-ecules rests in the conformation of the five-membered 1,3-dioxole ring which is an envelope in mol-ecule A (the methyl-ene C atom is the flap) and almost planar in mol-ecule B (r.m.s. deviation = 0.046 Å). In the crystal, mol-ecules of A self-associate into supra-molecular zigzag chains (generated by glide symmetry along the c axis) via methyl-ene C-H⋯π inter-actions. Mol-ecules of B form similar chains. The chains pack with no specific directional inter-molecular inter-actions between them.
In the title compound, C10H11BrS2, the 1,3-di-thiane ring has a chair conformation with the 1,4-disposed C atoms being above and below the remaining four atoms. The bromo-benzene ring occupies an equatorial position and forms a dihedral angle of 86.38 (12)° with the least-squares plane through the 1,3-di-thiane ring. Thus, to a first approximation the mol-ecule has mirror symmetry with the mirror containing the bromo-benzene ring and the 1,4-disposed C atoms of the 1,3-di-thiane ring. In the crystal, mol-ecules associate via weak methyl-ene-bromo-benzene C-H⋯π and π-π [Cg⋯Cg = 3.7770 (14) Å for centrosymmetrically related bromo-benzene rings] inter-actions, forming supra-molecular layers parallel to [10-1]; these stack with no specific inter-molecular inter-actions between them.
In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.
This paper discusses the multilevel approach in constructing a model for estimating hierarchically structured data of students' performance. Multilevel models that take into account variation from the clustering of data in different levels are compared to regression models using least squares method. This study also estimates the contributions of gender and ethnic factors on students' performance. Performance data of866 students in a science faculty in an institution of higher learning is obtained and analyzed. This data is hierarchically structured with two levels, namely students and departments. Analysis findings show different parameter estimates for both models. Also, the multilevel model which incorporates variability from different levels and predictors from higher levels is found to provide a better fit for model explaining students' performance.
[Rencana ini membincangkan pendekatan multitahap dalam pembinaan model penganggaran pencapaian pelajar yang mempunyai struktur data hierarki. Model multitahap yang mengambil kira variasi data yang berpunca dari pengelompokan data pada tahap-tahap yang berbeza dibandingkan dengan model regresi linear yang menggunakan kaedah kuasa dua terkecil. Seterusnya kajian ini menganggar sumbangan faktor jantina dan etnik ke atas pencapaian pelajar. Data pencapaian akademik seramai 866 pelajar fakulti sains di sebuah institusi pengajian tinggi telah diperoleh dan dianalisis. Data pelajar ini berstruktur hierarki dengan dua tahap, iaitu pelajar dan jabatan. Hasil kajian menunjukkan kedua-dua kaedah memberikan penganggaran yang berbeza. Malah, didapati model multitahap yang memasukkan variasi dari tahap-tahap berlainan dan pembolehubah peramal dari tahap yang lebih tinggi memberikan padanan model lebih baik bagi menerangkan pencapaian pelajar].
This study assessed the economic value of public urban green spaces (UGSs) in Kuala Lumpur (KL) city by using the hedonic price method (HPM). It involves 1269 house units from eight sub-districts in KL city. Based on the hedonic price method, this study formulates a global and local model. The global model and local model are analyzed using ordinary least square (OLS) regression and geographically weighted regression (GWR). By using the hedonic price method, the house price serves as a proxy for public urban green spaces' economic value. The house price is regressed against the set of three variables which are structural characteristics, neighborhood attributes, and environmental attributes. Measurements of interest in this study are environmental characteristics, including distance to public UGSs and size of public UGSs. The results of the OLS regression illustrated that Taman Rimba Kiara and Taman Tasik Titiwangsa provide the maximum economic value. On average, reducing the distance of the house location to Taman Rimba Kiara by 10 m increased the house price by RM1700. Similarly, increasing the size of the Taman Tasik Titiwangsa by 1000 m2 increases the house price by RM60,000. The advantage of the GWR result is the economic value of public UGSs which can be analyzed by the specific location according to sub-district. From this study, the GWR result exposed that the economic values of Taman Rimba Bukit Kiara and Taman Tasik Titiwangsa were not significant in each of the sub-district within KL city. Taman Rimba Bukit Kiara was negatively significant at all sub-districts except Setapak and certain house locations located at the sub-district of KL. In contrast, Taman Tasik Titiwangsa was positively significant at all sub-districts except certain house locations at the sub-districts of Batu, KL, Setapak, and KL city center. In conclusion, results show that the house price is influenced by the environmental attribute. However, even though both of these public UGSs generate the highest economic value based on distance and size, its significant values with an expected sign are only obtained based on the specific house location as verified by the local model. In terms of model comparison, the local model was better compared with the global model.
Amphetamine-type stimulants (ATS) are a group of chiral amine drugs which are commonly abused for their sympathomimetic and stimulant properties. ATS are extensively metabolised by hepatic cytochrome P450 enzymes. As metabolism of ATS has been shown to be highly stereospecific, stereoselective analytical methods are essential for the quantitative determination of ATS concentrations for both in vivo and in vitro studies of ATS metabolism. This paper describes a new stereoselective method for the simultaneous determination of amphetamine (AM), methamphetamine (MA), 3,4-methylenedioxymethamphetamine (MDMA), 3,4-methylenedioxyamphetamine (MDA), 4-hydroxy-3-methoxymethamphetamine (HMMA), 4-hydroxy-3-methoxyamphetamine (HMA), 3,4-hydroxymethamphetamine (HHMA) and 3,4-hydroxyamphetamine (HHA) in human urine samples validated according to the United States Food and Drug Administration guidelines. In this method, analytes are simultaneously extracted and derivatized with R-(-)-α-methoxy-α-(trifluoromethyl)phenylacetyl chloride (R-MTPCl) as the chiral derivatization reagent. Following this, the analytes were subjected to a second derivatization with N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) which targets the hydroxyl groups present in HMMA, HMA, HHMA and HHA. The derivatized analytes were separated and quantified using gas chromatography-mass spectrometry (GC-MS). The method was evaluated according to the established guidelines for specificity, linearity, precision, accuracy, recovery and stability using a five-day protocol. Intra-day precision ranged from 0.89 to 11.23% RSD whereas inter-day precision was between 1.03 and 12.95% RSD. Accuracy values for the analytes ranged from -5.29% to 13.75%. Limits of quantitation were 10 μg/L for AM, MA, MDMA, HMA and HMMA and 2μg/L for MDA, HMA and HHA. Recoveries and stability values were also within accepted values. The method was applied to authentic ATS-positive samples.