Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ(13)C and δ(2)H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin, i.e., the very subtle differences in isotopic values between the samples.
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.
The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.
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.
Electricity is one of the most important resources and fundamental infrastructure for every nation. Its milestone shows a significant contribution to world development that brought forth new technological breakthroughs throughout the centuries. Electricity demand constantly fluctuates, which affects the supply. Suppliers need to generate more electrical energy when demand is high, and less when demand is low. It is a common practice in power markets to have a reserve margin for unexpected fluctuation of demand. This research paper investigates regression techniques: multiple linear regression (MLR) and vector autoregression (VAR) to forecast demand with predictors of economic growth, population growth, and climate change as well as the demand itself. Auto-Regressive Integrated Moving Average (Auto-ARIMA) was used in benchmarking the forecasting. The results from MLR and VAR (lag-values=20) and Auto-ARIMA are monitored for five months from June to October of 2019. Using the root mean square error (RMSE) as an indicator for accuracy, Auto-ARIMA has the lowest RMSE for four months except in June 2019. VAR (lag-values=20) shows good forecasting capabilities for all five months, considering it uses the same lag values (20) for each month. Three different techniques have been successfully examined in order to find the best model for the prediction of the demand.
The study attempts to use multivariate analysis to evaluate the profile of male player for developments of Long-Term Talent in Sports (LT-TiS) model based on anthropometric and motor fitness components. Data of anthropometric and motor fitness included power, flexibility, coordination and speed were obtained from 2019 respondents aged 7.32±0.52 year. Data interpretations were carried out using multivariate analysis of Principle Components Analysis (PCA) and Discriminant analysis (DA). The adequacy of sampling has been measured using Bartletts tests on sphericity and Kaiser-Meyer-Olkin (KMO) has been used, with this conformance of running the Principal Component Analysis (PCA). Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS model. Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS. As a result, two factors with eigenvalues greater than 1 were extracted which accounted for 55.00% of the variations present in the original variables was found. The two factors were used to obtain the factor score coefficients explained by 27.86% and 27.21% of the variations in player performance respectively. Factor 1 revealed high factor loading on motor fitness compared to factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing; LT−TiS≥65%, 40%≤LT−TiS
The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.
Calligonum polygonoides L. also known as famine food plant, is normally consumed in times of food scarcity in India and Pakistan and also used traditionally in the management of common diseases. The present design aims to provide an insight into the medicinal potential of four solvent extracts of C. polygonoides via an assessment of its phytochemical profile, antioxidant and enzyme inhibitory potential. Phytochemical composition was estimated by deducing total bioactive constituents, UHPLC-MS secondary metabolites profile, and HPLC phenolic quantification. Antioxidant potential was determined via six methods (radical scavenging (DPPH and ABTS), reducing power (FRAP and CUPRAC), phosphomolybdenum total antioxidant capacity and metal chelation activity). Enzyme inhibitory potential was assessed against clinical enzymes (acetylcholinesterase -AChE, butyrylcholinesterase -BChE, tyrosinase, and α-amylase). The highest amounts of phenolic contents were found in chloroform extract (76.59 mg GAE/g extract) which may be attributed to its higher radical scavenging, reducing power and tyrosinase inhibition potential. The n-butanol extract containing the maximum amount of flavonoids (55.84 mg RE/g extract) exhibited highest metal chelating capacity. Similarly, the n-hexane extract was found to be most active against AChE (4.65 mg GALAE/g extract), BChE (6.59 mg GALAE/g extract), and α-amylase (0.70 mmol ACAE/g extract) enzymes. Secondary metabolite assessment of the crude methanol extract as determined by UHPLC-MS analysis revealed the presence of 24 (negative ionization mode) and 15 (positive ionization mode) secondary metabolites, with most of them belonging to phenolic, flavonoids, terpene, and alkaloid groups. Moreover, gallic acid and naringenin were the main phenolics quantified by HPLC-PDA analysis in all the tested extracts (except n-butanol extract). PCA statistical analysis was also conducted to establish any possible relationship amongst bioactive contents and biological activities. Overall, the C. polygonoides extracts could be further considered to isolate bioactive enzyme inhibitory and antioxidant natural phytocompounds.
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.
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.
Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).
In this work; a response surface methodology (RSM) was implemented to investigate the process variables in a hydrogen production system. The effects of five independent variables; namely the temperature (X₁); the flow rate (X₂); the catalyst weight (X₃); the catalyst loading (X₄) and the glycerol-water molar ratio (X₅) on the H₂ yield (Y₁) and the conversion of glycerol to gaseous products (Y₂) were explored. Using multiple regression analysis; the experimental results of the H₂ yield and the glycerol conversion to gases were fit to quadratic polynomial models. The proposed mathematical models have correlated the dependent factors well within the limits that were being examined. The best values of the process variables were a temperature of approximately 600 °C; a feed flow rate of 0.05 mL/min; a catalyst weight of 0.2 g; a catalyst loading of 20% and a glycerol-water molar ratio of approximately 12; where the H₂ yield was predicted to be 57.6% and the conversion of glycerol was predicted to be 75%. To validate the proposed models; statistical analysis using a two-sample t-test was performed; and the results showed that the models could predict the responses satisfactorily within the limits of the variables that were studied.
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.
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.
We aimed to evaluate and determine the effect of diabetes mellitus (DM) on overall survival (OS) and cancer-specific survival (CSS) in early stage cervical cancer (CC) patients. Patients with primary cervical cancer and newly diagnosed were selected from ten different cancer specialist hospitals of Malaysia. Patients' demographic and clinical data were obtained for the prognostic analysis. Kaplan-Meier method was used to estimate patients' survival time (CSS and OS) with DM status and values were compared using the log-rank test. A total of 19,785 newly diagnosed CC patients were registered during 2010-2016, among them only 16,946 (85.6%) with primary CC tumor. There was no difference in treatment modality between DM and non-DM patients. However intergroup assessment showed that type 2DM have significantly higher rate of mortality in both overall mortality (28.3%) and CC-specific (11.7%) as compared to Type 1DM (17.3%; 5.5%) and non DM patients (12.7%; 9.1%) (p
Toxoplasmosis is one of the most common opportunistic parasitic diseases in patients living with HIV/AIDS. This study aimed to determine the seroprevalence of Toxoplasma infection in HIV-infected patients and to identify associated risk factors in Toxoplasma seropositive patients. This study was conducted at a regional public hospital in Hat Yai, southern Thailand during October 2009 to June 2010. Blood samples were collected from 300 HIV-infected patients. Each subject also answered a socio-demographic and risk factors associated with Toxoplasma infection. The prevalence of anti-Toxoplasma IgG antibodies in HIV-infected patients was 109 (36.3%), of which 83 (76.2%) had past infection and 26 (23.9%) had recently acquired Toxoplasma infection as indicated by their IgG avidity. Multivariate analysis using logistic regression showed that gender difference (adjusted OR = 1.69, 95% CI = 1.05-2.72) was the only factor associated with Toxoplasma infection. From the results obtained, these HIV-infected patients could be at high risk of developing clinical evidence of severe toxoplasmosis. Therefore, it is necessary to introduce primary behavioral practices to prevent Toxoplasma infection among HIV-infected patients.
Health-related quality of life (HRQoL) associated factors are vital considerations prior to treatment decision-making for head and neck cancer patients. The study aimed to identify potential socio-demographic and clinical prognostic value of HRQoL in head and neck cancer patients in a developing country. The Functional Assessment of Cancer Therapy-Head and Neck (FACT-H&N)-V4 in Urdu language was administered among 361 head and neck cancer patients. Data were statistically tested through multivariate analysis of variance (MANOVA) and regression modeling to identify the potentially associated factors. Treatment status, tumor stage and tumor site had the strongest negative impact on patients HRQoL, with a statistically significant decrement in FACT summary scales (effect size >0.15). Moderate associated factors of HRQoL included treatment type, marital status, employment status and age (effect size range 0.06-0.15). Weak associated factors of HRQoL with a small effect size (>0.01-0.06) included tumor size and type, gender, education level and ethnicity. This study reports 12 socio-demographic and clinical variables that have a significant impact on HRQoL of head, and neck cancer patients, and that should be considered during treatment decision-making by multidisciplinary teams and also in future HRQoL studies conducted in other developing countries.
To investigate the presence and/or agenesis of third molar (M3) tooth germs in orthodontics patients in Malaysian Malay and Chinese population and evaluate the relationship between presence and/or agenesis of M3 with different skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. Pretreatment records of 300 orthodontic patients (140 males and 160 females, 219 Malaysian Malay and 81 Chinese, average age was 16.27±4.59) were used. Third-molar agenesis was calculated with respect to race, genders, number of missing teeth, jaws, skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. The Pearson chi-square test and ANOVA was performed to determine potential differences. Associations between various factors and M3 presence/agenesis groups were assessed using logistic regression analysis. The percentages of subjects with 1 or more M3 agenesis were 30%, 33% and 31% in the Malaysian Malay, Chinese and total population, respectively. Overall prevalence of M3 agenesis in male and female was equal (P>0.05). The frequency of the agenesis of M3s is greater in maxilla as well in the right side (P>0.05). The prevalence of M3 agenesis in those with a Class III and Class II malocclusion was relatively higher in Malaysian Malay and Malaysian Chinese population respectively. Using stepwise regression analyses, significant associations were found between Mx (P<0.05) and ANB (P<0.05) and M3 agenesis. This multivariate analysis suggested that Mx and ANB were significantly correlated with the M3 presence/agenesis.
Khat chewing is associated with unfavourable health outcomes and family dysfunction. Few studies have addressed the factors associated with khat chewing among Yemeni women. However, the family and husband effects on chewing khat by women have not been addressed. This study aimed to determine the prevalence of khat chewing among Yemeni women and its associated factors, particularly husbands and family factors. A cross-sectional study was conducted among 692 adult Yemeni women in the city of Sana'a in Yemen using structured "face to face" interviews. Mean (±SD) age of women was 27.3 years (±6.10). The prevalence of chewing khat by women was 29.6%. Factors associated with chewing khat among women were chewing khat by husbands (OR = 1.8; 95% CI: 1.26, 2.53), being married (OR = 2.0; 95% CI: 1.20, 3.37), frequent family social gatherings (OR = 1.5; 95% CI: 1.06, 2.10), high family income (OR = 1.57; 95% CI: 1.12, 2.21), larger house (OR = 1.63; 95% CI: 1.16, 2.31), and age of women (OR = 0.64; 95% CI: 0.44, 0.92). It is concluded that khat chewing by women in this study was significantly associated with family factors and with khat chewing by their husbands. Urgent action is needed to control khat chewing particularly among women.