Postural movements potentially affect aiming stability in archery, thus contributing to chances of inconsistent hits. According to the expertisenovice paradigm, the factor that sets winners apart from ordinary athletes is the former’s ability to control minute changes in their performance. The
present study seeks to determine the relationship between postural sway and shooting performance amongst Malaysian skilled recurve archers. Twenty one skilled Malaysian archers participated in this study, where performance level was measured by rank tournaments International Archery Federation (FITA) score. Postural sway was assessed in terms of anterior deviation (positive value) and posterior deviation (negative value) using ZEPHYR Bio-Harness. Postural sway was analysed at the following three phases; (i) setup, (ii) aiming, and (iii) release. Participants shot 12 arrows to a 30-meter target. Data yielded a significant relationship between postural sway and shooting performance. The correlation coefficients between shooting performance and postural sway value for skilled archers ranged between (r = -0.021 to 0.248) with the highest correlation recorded at the release phase, with the lowest at the aiming phase. The setup phase showed the only anterior deviation throughout the test. During the setup and release phases, correlation between postural sway with shooting performance was significantly noted (p < 0.001). Multiple regression analysis showed that postural sway during the setup and release phases were the significant indicators for shooting performance, accounting approximately 17% and 24% of the variances respectively. In sum, the results indicate that reducing postural sway
during the release phase can increase shooting performance of skilled archery athletes, thus establishing a significant relationship between the postural sway value with shooting performance of skilled archers.
In recent years, many classification models have been developed and applied to increase their accuracy. The concept of distance between two samples or two variables is a fundamental concept in multivariate analysis. This paper proposed a tool that used different similarity distance approaches with ranking method based on Mean Average Precision (MAP). In this study, several similarity distance methods were used, such as Euclidean, Manhattan, Chebyshev, Sorenson and Cosine. The most suitable distance measure was based on the smallest value of distance between the samples. However, the real solution showed that the results were not accurate as and thus, MAP was considered the best approach to overcome current limitations.
Multivariate analyses depend on multivariate normality assumption. Although the analyses are available in SPSS, it is not possible to assess the assumption from the basic package. Statistical assessment of the normality is available in a specialized package, SPSS Amos, in form of Mardia's multivariate kurtosis. However, graphical assessment of the normality by chi-square versus Mahalanobis distance plot is not available in both of the packages. The aim of this article is to present the steps to construct the plot in SPSS in a point-and-click manner as expected by most SPSS users.
Lycopene and total phenolics of pink guava puree industry by-products (refiner, siever and decanter)
were evaluated after steam blanching at selected temperatures and times. Lycopene content was in the order of decanter > siever > refiner (7.3, 6.3 and 1.5 mg/100 g, respectively), and the content of total phenolics was in the order of refiner > siever > decanter (4434.1, 2881.3 and 1529.3 mg GAE/100 g, respectively). Regression coefficients for temperatures (x1) and times (x2) from multiple linear regression models of siever and decanter showed significant (p
The effects of soaking conditions on the quality characteristics of seaweed paste of Kappaphycus alverazii species were studied. Response Surface Methodology (RSM) with a 2-factor, 5-level central composite design (CCD) was conducted to determine the optimum soaking conditions. The interactive effect of dry seaweed: soaking water ratio (X1 = 1: 15-50) and soaking duration (X2 = 30-120 min) on the gel strength (g), whiteness, expansion (%), moisture content (%) and protein content (g/100 g) of the paste were determined. Results showed that the experimental data could be adequately fitted into a second-order polynomial model with multiple regression coefficients (R2) of 0.8141, 0.9245, 0.9118, 0.9113 and 0.9271 for the gel strength, whiteness, expansion, moisture content and protein content, respectively. The gel strength, whiteness, expansion, moisture content and protein content of seaweed paste were dependent on the ratio of dry seaweed to soaking water and also soaking duration. The proposed optimum soaking conditions for the production of seaweed paste is at a ratio of 1:15 (dry seaweed : soaking water) and soaking duration of 117.06 min. Based on the result obtained, the RSM demonstrated a suitable approach for the processing optimization of Kappaphycus alverazii paste.
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.
Forecasting a multiple seasonal data is differ from a usual seasonal data since it contains more than one cycle in a
data. Multiple linear regression (MLR) models have been used widely in load forecasting because of its usefulness in the
forecast a linear relationship with other factors but MLR has a disadvantage of having difficulties in modelling a nonlinear
relationship between the variables and influencing factors. Neural network (NN) model, on the other hand, is a good
model for modelling a nonlinear data. Therefore, in this study, a combination of MLR and NN models has proposed this
combination to overcome the problem. This hybrid model is then compared with MLR and NN models to see the performance
of the hybrid model. RMSE is used as a performance indicator and a proposed graphical error plot is introduce to see the
error graphically. From the result obtained this model gives a better forecast compare to the other two models.
An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods.
Studies have been carried out to determine the chemical (soluble solid content) and physical (firmness) parameters of locally grown Cavendish banana by near infrared (NIR) spectroscopy. NIR spectra in the wavelength region of 680-2500 nm were obtained from a total of 408 Cavendish bananas of different ripeness indices. Chemometrics using multiple linear regression (MLR) was applied to develop calibration models for prediction of firmness and soluble solid content (SSC) of Cavendish banana. Results showed that NIR spectroscopy had the feasibility for non-destructive determination of the quality of Cavendish banana. The coefficient of determination (R2) for firmness and SSC calibration models at different ripeness indices ranged from 0.78 to 0.86 and 0.75 to 0.96, respectively. The calibration models were validated using independent sets of data and prediction models developed with the root mean square error of prediction (RMSEP) ranged from 0.01 to 0.26 kgf and 0.039 to 0.788 Brix for firmness and SSC, respectively. The multi-index models showed considerable robustness but higher prediction error with RMSEP of 0.336 kgf for firmness and 0.937% Brix for SSC compared to index specific model.
Annual air temperature data obtained from twenty-two meteorological stations across Malaysia are modeled using multiple regression. A correlation test was conducted to find statistical relationship between each of the dependent variables: annual maximum and annual average air temperature and predictor variables: longitude, latitude, elevation and wind speed. Regression models using least square estimation method were developed relating the dependent variables to independent variables and the adequacy of the models is determined by the coefficient of determination. The result shows that the longitude and wind speed factors have a significant influence on the annual air temperature in Malaysia.
Previous studies have found that luminance contrast may enhance attention and attention is positively correlated with memory. However, little attention has been given to understand the impact of luminance contrast on memory. The present study attempts to address this gap by examining the effect of luminance contrast on attention and memory. A total of 159 undergraduates were randomly assigned to three luminance contrast conditions (high vs. moderate vs. low) and were administered a modified d2 test and modified words memory test. Multivariate analysis of variance showed significant effect of luminance contrast on memory performance. Participants in the high and moderate luminance contrast groups recalled more words than counterparts in the low contrast group. However, the effect of luminance contrast on attention was not significant, though planned comparison found that high contrast group scored higher than low contrast group. The findings not only shed light on improvement of memory but also have implication for design and marketing and consumer behaviours study.
A field experiment was conducted from June to December, 2013 to study the genetic diversity of 15 modern T. Aman rice
varieties of Bangladesh (Oryza sativa L.) with a view to assess the superior genotype in future hybridization program
for developing new rice varieties that is suitable for the target environment. Analysis of variance for each trait showed
significant differences among the varieties. High heritability associated with high genetic advance in percent of mean
was observed for plant height and thousand seed weight which indicated that selection for these characters would be
effective. Hence, thrust has to be given for these characters in future breeding program to improve the yield trait in rice.
Multivariate analysis based on 10 agronomic characters indicated that the 15 varieties were grouped into four distant
clusters. The inter cluster distance was maximum between cluster II and cluster IV. The highest intra-cluster distance was
found in cluster IV. Based on positive value of vector 1 and vector 2, plant height and 1000-seed weight had maximum
contribution towards genetic divergence. From the results, it can be concluded that the varieties BRRI dhan40, BRRI
dhan44, BRRI dhan46, BRRI dhan49 and BINA dhan7 may be selected for future hybridization program.
This paper reports a computational approach for analysis of FTIR spectra where peaks are detected, assigned and matched across samples to produce a peak table with rows corresponding to samples and columns to variables. The algorithm is applied on a dataset of 103 spectra of a broad range of edible oils for exploratory analysis and variable selection using Self Organising Maps (SOMs) and t-statistics, respectively. Analysis on the resultant peak table allows the underlying patterns and the discriminatory variables to be revealed. The algorithm is user-friendly; it involves a minimal number of tunable parameters and would be useful for analysis of a large and complicated FTIR dataset.
The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate the value of geometric moments technique for producing a set of numerical features, based on which the identification of firearms are made.
In conjunction with a nationwide motorcycle safety program, the provision of exclusive motorcycle lanes has been implemented to overcome link-motorcycle accidents along trunk roads in Malaysia. However, not much work has been done to address accidents at junctions involving motorcycles. This article presents the development of predictive model for motorcycle accidents at three-legged major-minor priority junctions of urban roads in Malaysia. The generalized linear modeling technique was used to develop the model. The final model reveals that motorcycle accidents are proportional to the power of traffic flow. An increase in nonmotorcycle and motorcycle flows entering the junctions is associated with an increase in motorcycle accidents. Nonmotorcycle flow on major roads had the highest effect on the probability of motorcycle accidents. Approach speed, lane width, number of lanes, shoulder width, and land use were found to be significant in explaining motorcycle accidents at the three-legged major-minor priority junctions. These findings should enable traffic engineers to specifically design appropriate junction treatment criteria for nonexclusive motorcycle lane facilities.
Objective: The objectives of this study were to determine the effect of a one and a half year educational intervention on the job dissatisfaction of teachers in 30 Community Based Rehabilitation (CBR) centres in Kelantan, Malaysia, and to identify the factors influencing changes in job dissatisfaction following the intervention. Method: Ten educational modules were administered to the teachers. A validated Malay version of Job Content Questionnaire (JCQ) was used pre intervention, mid intervention and post intervention. Result: Repeated Measure ANOVA revealed there was a statistically significant reduction in the mean of job dissatisfaction (p = 0.048). Multiple Linear Regression revealed that co- worker support (β= 0.034 (95% CI = 0.009, 0.059)), having less decision authority (β: -0.023; 95% CI: -0.036, -0.01) and being single (β: -0.107; 95% CI: -0.176,-0.038) were significantly associated with decreases in job dissatisfaction. Conclusion: The intervention program elicited improvement in job satisfaction. Efforts should be made to sustain the effect of the intervention in reducing job dissatisfaction by continuous support visits to CBR centres.
Phaleria macrocarpa, known as "Mahkota Dewa", is a widely used medicinal plant in Malaysia. This study focused on the characterization of α-glucosidase inhibitory activity of P. macrocarpa extracts using Fourier transform infrared spectroscopy (FTIR)-based metabolomics. P. macrocarpa and its extracts contain thousands of compounds having synergistic effect. Generally, their variability exists, and there are many active components in meager amounts. Thus, the conventional measurement methods of a single component for the quality control are time consuming, laborious, expensive, and unreliable. It is of great interest to develop a rapid prediction method for herbal quality control to investigate the α-glucosidase inhibitory activity of P. macrocarpa by multicomponent analyses. In this study, a rapid and simple analytical method was developed using FTIR spectroscopy-based fingerprinting. A total of 36 extracts of different ethanol concentrations were prepared and tested on inhibitory potential and fingerprinted using FTIR spectroscopy, coupled with chemometrics of orthogonal partial least square (OPLS) at the 4000-400 cm-1 frequency region and resolution of 4 cm-1. The OPLS model generated the highest regression coefficient with R2Y = 0.98 and Q2Y = 0.70, lowest root mean square error estimation = 17.17, and root mean square error of cross validation = 57.29. A five-component (1+4+0) predictive model was build up to correlate FTIR spectra with activity, and the responsible functional groups, such as -CH, -NH, -COOH, and -OH, were identified for the bioactivity. A successful multivariate model was constructed using FTIR-attenuated total reflection as a simple and rapid technique to predict the inhibitory activity.
Aim: Orthodontic treatment duration is variable and associated with many factors Very few studies looks at operator changes influencing treatment duration and outcome. This study aims to evaluate the influence of operator changes on treatment time and quality.
Methodology: This is a 4-year cross-sectional retrospective study of preadjusted Edgewise two-arch appliance cases treated by single/ multiple operators and finished/debonded by the author. 60 singleoperator (Group 1) and 82 multiple-operator (Group 2) cases were selected and the Peer Assessment Rating (PAR) Index was used to measure treatment outcome.
Results: Group 1 (2.31 years, SD.86) had statistically significantly shorter treatment time than Group 2 (3.25 years, SD1.23). Mean % reduction in PAR scores was high (88.7%), although single operators (92%) showed a slightly higher (p=.04) reduction than multiple-operator cases (86.2%). Post-treatment PAR score was slightly higher in Group 2 (4.6, SD5.4) compared with Group 1 (2.6, SD2.9). There was no significant difference in post-treatment PAR scores with operator changes from within and outside the clinic although there was difference in treatment duration (p=.0001). Possible predictors of treatment duration included number of failed/changed appointments, extractions and pre-treatment PAR. Multiple linear regression model showed significant association of treatment time with failed/changed appointments (p=.0001) and number of operators (p=.0001) although this contributed to 57.5% of possible factors (r=.762) .
Conclusion: Change of operator contributes to increased treatment time by 11.3 months. Although standard of treatment was high in both groups there was slightly better outcomes in single operators. The reduction in PAR score can be predicted more accurately in single operators.
Tropical peat swamp forest (PSF) is one of the most endangered ecosystems in the world. However, the impacts of
anthropogenic activities in PSF and its conversion area towards fish biodiversity are less understood. This study
investigates the influences of water physico-chemical parameters on fish occurrences in peat swamp, paddy field and
oil palm plantation in the North Selangor peat swamp forest (NSPSF), Selangor, Malaysia. Fish and water samples were
collected from four sites located in the peat swamps, while two sites were located in the paddy field and oil palm plantation
areas. Multivariate analyses were used to determine the associations between water qualities and fish occurrences in
the three habitats. A total of 1,382 individual fish, belonging to 10 families, 15 genera and 20 species were collected.
The family Cyprinidae had the highest representatives, followed by Bagridae and Osphronemidae. The most abundant
species was Barbonymus schwanefeldii (Bleeker 1854), while the least abundant was Wallago leerii Bleeker, 1851. The
paddy field and oil palm plantation area recorded significantly higher fish diversity and richness relative to peat swamp
(p<0.05). The water physico-chemical parameters, such as pH, DO, NH3
-N, PO4, SO4
, and Cl2 showed no significant
difference between paddy field and oil palm plantation (p>0.05), but was significantly different from the peat swamp
(p<0.05). However, no water quality parameter was consistently observed to be associated with fish occurrences in all
of the three habitats, but water temperature, NH3
-N, Cl2, SO4
, and EC were at least associated with fish occurrences in
two habitats studied. This study confirmed that each habitat possess different water quality parameters associated with
fish occurrences. Understanding all these ecological aspects could help future management and conservation of NSPSF.
In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.